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7252019 PIJPSM-05-2015-0064
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Policing An International Journal of Police Strategies amp ManagementExploring how an areas crime-to-cop ratios impact patrol officer productivity
Luke Bonkiewicz
Ar tic le informationTo cite this documentLuke Bonkiewicz (2016)Exploring how an areas crime-to-cop ratios impact patrol officer productivity Policing AnInternational Journal of Police Strategies amp Management Vol 39 Iss 1 pp -Permanent link to this documenthttpdxdoiorg101108PIJPSM-05-2015-0064
Downloaded on 31 January 2016 At 0921 (PT)
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Introduction
Perhaps no civil servant undertakes the variety of tasks as a municipal police officer As
not only law enforcement agents but also community caretakers and first-responders to every
calamity great and small patrol officers handle a broad array of calls for service during their
daily tour of duty The regular duties of a patrol officer include reactive tasks such as handling
calls for service as well as proactive tasks such as conducting traffic and scofflaw enforcement
searching for suspected perpetrators and patrolling for suspicious individuals or situations (eg
drug deals graffiti vandalisms or individuals lsquocasingrsquo a potential target)
Historically both police researchers and practitioners have grappled with the lack of a
valid and reliable method for evaluating patrol officer activity as well as limited knowledge
about how external factors affect officersrsquo productivity (Crank 1990 Johnson 2011 Shane
2010) Although studies have demonstrated that differences in productivity between patrol
officers may result from biographical factors and daily work experiences (Shane 2011)
operational variables (Amaranto et al 2003) and organizational stressors (Brown amp Campbell
1990) scholars have devoted considerably less attention to how the characteristics of an area
might impact police productivity In other words although individual-level variables impact a
patrol officerrsquos productivity how might the characteristics of a patrol area affect a police
officerrsquos productivity
This paper identifies and analyzes two macro-level correlates of patrol officer
productivity violent crimes per officer and property crimes per officer We first review the
extant literature on patrol officer productivity discussing how an arearsquos violent and property
crime per cop ratios might affect a patrol officerrsquos productivity Next we analyze productivity
data from a mid-sized US police department to examine how these two variables impact the
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number of traffic citations warrants and misdemeanor and felony arrests generated by officers
annually We also examine the relationship between these two variables and a more
sophisticated productivity metric Value Over Replacement Cop (VORC) Finally we discuss
why police scholars and practitioners should pay closer attention to the practical macro-level
correlates of productivity as well as incorporate more advanced patrol productivity metrics
Literature Review
Productivity scholars have generally divided the concept of worker productivity into two
spheres (Hatry 1976) Effectiveness measures a worker or organizationrsquos quality of service
while efficiency gauges how well a worker or organization produces a particular output using the
least amount of resources such as manpower time and money Most police research has
examined productivity in terms of efficiency due mainly to the data available for analysis such
as calls for service and arrests
Police scholars have struggled with how to properly measure patrol officer productivity
There appear to be two main approaches The first approach attempts to identify and quantify
specific types of patrol productivity outputs Some examples include evaluating productivity
using an officerrsquos total arrests (Crank 1990) clearance rates (Marciano amp Heaton 2010)
number of traffic citations (Johnson 2011) and citizen complaints (Lersch 2002) However
researchers have noted that evaluating productivity using these kinds of raw outputs is
ldquoconceptually-simplerdquo shaped primarily by data availability (Chappell MacDonald amp Manz
2006)
The second approach to analyzing patrol officer productivity has incorporated multi-
dimensional indicators of police performance For example Shane (2011) conceptualized
productivity using a z-score summary of multiple outputs including arrests traffic citations
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field contacts official reports sick hours and administrative complaints Similarly Van Meterrsquos
Performance-Based Management or ldquozero-based approachrdquo (Van Meter 2001) uses three planks
(non-scheduled absenteeism cost of preventable error and productive use of time) to evaluate
patrol officers
Notably most existing police productivity research has ignored the concept of productive
time or the amount of time officers have for self-initiated activities after handling calls for
service investigating crimes completing reports and follow-ups and assisting other officers
Using productive time to evaluate officers is critical because two officers could achieve the same
output (in terms of raw arrests or reports) but have done so with considerably different amounts
of productive time due to calls for service follow-ups etc Recent research has proposed the use
of Value Over Replacement Cop or VORC (Bonkiewicz 2015) a metric that incorporates more
than a dozen patrol activities weights those activities evaluates performance in terms of
productive time and compares an officerrsquos performance against a fixed hypothetical estimate
(ie a replacement officer)
In addition to conceptualizing patrol officer productivity researchers have also studied
potential antecedents of productivity including not only biographical variables but also factors
involving workplace environment First certain biographical characteristics might impact
productivity (Hawkins 2001) For instance officers who have children might experience sleep
deprivation and greater fatigue as a result of parenting responsibilities thereby reducing their
workplace energy and motivation and in turn their productivity (Shane 2010) Marital status
may also impact productivity Although the relationship between marriage and worker
productivity is well debated in the literature (Nakosteen amp Zimmer 2001 Becker 1985) it
seems reasonable to believe that married officers might be more productive than unmarried
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officers Married officers have someone in whom to confide a partner who hears about the
challenges and rigors of the job which may lead to stress reduction and improve workplace
motivation An officerrsquos age is another consideration Older officers might be less productive
than younger officers perhaps due to burnout as well as the physical demands of law
enforcement (Goodman 1990) Officersrsquo perceptions of their job may also affect their
proactivity and in turn productivity Brehm and Gates (1993) found that officers who disliked
certain aspects of law enforcement (eg taking people to jail) impacted whether officers engaged
in proactive activities or shirked their duties
Additionally there are also operational features (the ldquoday to dayrdquo rigors) of law
enforcement which can decrease productivity and increase misconduct such as the perceived
danger of the job (Amaranto et al 2003) work schedules and shift work (Vila Morrison amp
Kenney 2002) and fatigue (Vila amp Kenney 2002) Notably in studying the relationship
between daily work experience and performance Shane (2011) found that workload was the
ldquomost important predictor of performancerdquo (p 13)
We believe this last finding merits further study First supervisors evaluate patrol
officers at least in part using productivity outputs If there are factors (ie heavy workload)
beyond an officerrsquos control that are affecting his or her productivity both line-level supervisors
and command staff members should acknowledge and account for these variables during
performance reviews Second if macro-level variables create different workloads thereby
affecting productivity outputs then police researchers should include these measures in future
studies on police activity We suggest that there are at least two macro-level variables important
to future police productivity research
Crime-to-Cop Ratios and Patrol Officer Productivity
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Criminological research has long recognized that crime rates are not uniform across
neighborhoods and cities (Sherman et al 1989 Weisburd et al 1993) Instead of ldquopolicing
peoplerdquo law enforcement agencies frequently identify ldquohot spotsrdquo and devote additional
resources to reducing crime and disorder in specific problem areas With this perspective in
mind it might seem natural to examine the relationship between the crime rate of a patrol area
and an officerrsquos productivity outputs (or vice-versa) For example a high crime area might net
an officer more arrests (and therefore more productivity outputs) than a low crime area On the
other hand a high crime area might also occupy a great deal of an officerrsquos productive time
prohibiting the officer from engaging in proactive activities However crime rate does not fully
capture how a particular area might affect an officerrsquos productivity
Instead we believe macro-level variables related to crime should be estimated in terms of
the number of patrol officers in a patrol area specifically violent crimes per officer and property
crimes per officer For instance some officers may patrol a lower crime area but end up
responding to a greater number of violent crimes than officers in a high crime area simply
because they have fewer officers working the street This ldquocrime per coprdquo ratio can dramatically
affect officersrsquo productivity
For example when an officer investigates a crime he or she might interview the victim
suspect and witnesses as well as take photographs collect evidence and make an arrest There
may be additional follow-up the next day or week such as checking pawnshops for stolen
property or tracking down and interviewing additional witnesses All these activities could
decrease an officerrsquos time for self-initiated activities and in turn an officerrsquos overall
productivity A high crime per cop ratio therefore could decrease productivity for officers even
if these officers work in an area with a low crime rate Furthermore an arearsquos crime per cop
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ratio could also affect officersrsquo productive time even if they are not the primary investigating
officer because secondary officers are often needed during serious incidents such as robberies or
burglaries
The ldquoload sheddingrdquo hypothesis by Maxfield et al (1980 see also Maxfield 1982) also
has important implications for any study examining patrol officer productivity Maxfield et al
suggested that while variations in workload do not affect patrol officersrsquo decisions to record
serious crimes workload does affect officersrsquo decisions to record less serious crimes it may also
affect self-initiated activities as well (Herbert 1989)
Finally we believe that macro-level variables affecting productivity should be divided
between violent and property crimes to account for the time and resources needed to investigate
each incident type When officers respond to assaults they often contact suspects and victims on
scene meaning that officers are able to interview all involved individuals collect evidence and
make an arrest sometimes in less than an hour However when officers respond to burglaries or
larcenies they may be required to dust for fingerprints swab for DNA canvass a neighborhood
for witnesses check pawnshops and engage in other investigative activities that require hours
and even days if the case requires follow-up
With these issues in mind our study addresses the following research questions First
what is the relationship between a patrol arearsquos rate of violent crimes per officer and patrol
officer productivity outputs (ie traffic citations as well as warrant misdemeanor and felony
arrests) Second what is the relationship between a patrol arearsquos rate of property crimes per
officer and patrol officer productivity outputs (again traffic citations and warrant misdemeanor
and felony arrests) Third what is the relationship between the rate of property and violent
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crimes per officer and a patrol officerrsquos productivity when we introduce a more sophisticated
method for analyzing productivity a metric based on productive time and not total time on duty
Before discussing our data and methods there are two important issues meriting brief
discussion First we argue that the crime-to-cop ratio may affect the number of traffic stops
warrants misdemeanors and felonies that a patrol officer makes It can also be argued that the
number of arrests might impact the crime-to-cop ratio Our intent is not to comprehensively
discuss the relationship between the number of officersofficer arrests and an arearsquos crime rate
but it bears noting that the research is inconclusive on this point
Some evidence suggests that the relationship between arrests and crime rates is non-
linear that is crime rates decrease as arrests increase but eventually hit a ldquofloorrdquo and reach
equilibrium (Kane 2006) Second when studies examine the arrest-crime rate relationship they
often find that the relationship only exists for specific crimes in their datasets such as robbery
and motor vehicle thefts (Corman Hope amp Mocan 2005) robbery but not assaults (Kane 2006)
and homicide and auto thefts only (Cloninger amp Sartorius 1979) and so forth Overall the
literature on the arrests-crime rate relationship is mixedmdashsome research finds an effect between
arrests and crime rates some does not (Huizinga and Henry 2008)
The second issue concerns whether a law enforcement agency operates using a generalist
or specialist policing style We analyze data from a generalist municipal police department
meaning that patrol officers investigate low and mid-level crimes but investigators and
detectives are responsible for handling serious crimes cases involving lengthy investigations
(eg narcotics trafficking or white-collar crimes) and incidents in which patrol officers may
need assistance (for example an incident involving numerous victims and witnesses as well as
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cases involving extensive follow-up or knowledge on a specific subject such as gangs or
narcotics)
By contrast when patrol officers in a specialist-style police department respond to any
incident potentially requiring follow-up they might only contact the victim and either hold the
scene until an investigator arrives or forward the information to a detective for follow-up It is
possible indeed likely that macro-level variables might affect generalist patrol officers
differently than specialized officers Due to data limitations however our study focuses on
generalist police officers
Data and Methods
We gathered data on patrol officers and their patrol areas from several sources First we
collected patrol officer productivity data from the Lincoln (Nebraska) Police Departmentrsquos 2013-
2014 productivity reports Using Computer-Aided Dispatch (CAD) and a centralized
productivity database LPD measures numerous patrol officer activities Every month (and
year) LPD generates productivity reports which display each patrol officerrsquos activities and
outputs (see Table 1 for an example of a squadrsquos annual productivity report) Each patrol
activity and output merits further explanation
INSERT TABLE 1 HERE
Calls for Service (CFS) and Officer Assist time When dispatchers send patrol officers on
a call they use CAD to track the minutes the officer spends on the call If the call is a priority
incident dispatchers send both a primary and secondary officer Other unassigned officers might
also respond to the CFS meaning that multiple officers could be secondary officers The annual
productivity record tracks amount of time an officer spent as a primary responder and a
secondary responder
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Report writing time Standard operating procedures dictate that LPD patrol officers
complete specific types of reports for incidents An incident report (IR) details biographical
information of the individuals involved in the incident and summarizes the incident An
Additional Case Information (ACI) is a more detailed report that documents additional
investigative steps or the actions of assisting officers Although the time spent on an IR or ACI
could vary depending on the call for service the average IR and ACI take roughly twenty
minutes to complete1
The Lincoln Police Department also tracks self-initiated activities Most productivity
results from the following proactive activities
Selectives The Lincoln Police Department tracks specific officer patrol activity by
coding its neighborhoods schools parks and business districts When an officer patrols an at-
risk business district or a high-crime neighborhood for example the officer notifies dispatch
using that arearsquos specific code Girded by the principles of ldquohot spotsrdquo policing research officers
use selectives to identify potential candidates for POP projects and heighten the perception of
risk among criminals (Braga amp Bond 2008)
Traffic Warnings and Official Citations Every Lincoln patrol officer drives a police
cruiser and conducts traffic enforcement issuing traffic warnings and official citations The
LPD records department tracks patrol officersrsquo warnings and citations
Misdemeanors felonies and warrants Patrol officers also make misdemeanor and
felony arrests during their productive time Officers might detain a motorist and arrest him or
her for driving under a suspended license or driving while intoxicated or contact a pedestrian
1 983119983142983142983145983139983141983154983155 983156983161983152983141 983137983148983148 983154983141983152983151983154983156983155 983151983150 983139983151983149983152983157983156983141983154983155 983127983141 983151983138983156983137983145983150983141983140 983156983144983145983155 983137983158983141983154983137983143983141 983138983161 983151983138983155983141983154983158983145983150983143 983137 983155983137983149983152983148983141 983151983142 983116983145983150983139983151983148983150 983152983137983156983154983151983148 983151983142983142983145983139983141983154983155
983139983151983149983152983148983141983156983145983150983143 983113983122983155 983137983150983140 983105983107983113983155 983137983150983140 983159983141 983154983141983139983151983143983150983145983162983141 983156983144983137983156 983156983144983145983155 983137983158983141983154983137983143983141 983149983137983161 983140983145983142983142983141983154 983142983154983151983149 983151983156983144983141983154 983152983151983148983145983139983141 983140983141983152983137983154983156983149983141983150983156983155 983117983151983154983141983151983158983141983154983145983142 983140983141983152983137983154983156983149983141983150983156983155 983154983141983153983157983145983154983141 983151983142983142983145983139983141983154983155 983156983151 983139983151983149983152983148983141983156983141 983156983144983141983145983154 983154983141983152983151983154983156983155 983138983141983142983151983154983141 983138983141983139983151983149983145983150983143 983137983158983137983145983148983137983138983148983141 983142983151983154 983137983150983151983156983144983141983154 983139983137983148983148 983142983151983154 983155983141983154983158983145983139983141
983156983144983145983155 983149983141983137983155983157983154983141983149983141983150983156 983145983155 983157983150983150983141983139983141983155983155983137983154983161
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and make an arrest for a sex offender registration violation Officers can also search their
departmentrsquos electronic database for individuals with outstanding warrants in their patrol areas
and attempt to arrest these offenders As with warnings and citations the LPD records
department documents and tracks misdemeanor felony and warrant arrests Moreover the court
system also monitors these types of outputs to process cases thereby adding an additional layer
of reporting accuracy and reliability
Field information reports We also measured the number of field information or
ldquointelligencerdquo reports Officers complete these internal reports to document suspicious or
dangerous individuals or circumstances though these reports are not publicly available
VORC In addition to the raw outputs of traffic citations warrants misdemeanors and
felonies we also include an advanced metric for measuring a patrol officerrsquos productivity Value
Over Replacement Cop (VORC) is a method for evaluating officers that assigns different
weights to productivity outputs and then assesses officer productivity in terms of available time
for self-initiated activities or productive time (Bonkiewicz 2015) We use a variation of this
formula that multiplies an officerrsquos officials misdemeanors and felonies by his or her
prosecution rate Our modified use of VORC can best be explained using the following figure
(Figure 1) and five-step process
INSERT FIGURE 1 HERE
where = total monthly on-duty minutes = CFS minutes Γ = follow-up time and meetings ∆
= officer assist time Ε = of IRs Ζ = of ACIs M = of intelligence reports Η = of
selectives Θ = of warnings times two Κ = of warrants times three Ι = of officials times
three Λ = of misdemeanor arrests times their respective weights Ν = of felonies types their
respective weights Ρ = an officerrsquos prosecution rate Ξ = Officerrsquos Productive time (Α-(Β+Γ+∆+
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20(Ε+Ζ))) Ο= Department average P-score or average (Η+ ((Θ)+(Ι)+(Κ)+(Λ)+(Μ)+(Ν)Ρ))
and Π = Department average Self-Initiated Time
First VORC calculates an officerrsquos total minutes spent on CFS follow-ups officer
assists and reports This amount is an officerrsquos total work time or service minutes Second the
formula subtracts an officerrsquos service minutes from the total on-duty minutes2 This is the
officerrsquos amount of productive time Third VORC calculates an officerrsquos productivity score (or
P-score) using a weighted index Departments can choose different weights for productivity
outputs but for the purpose of this study we used our weights based on the statutes governing
the Lincoln Police Department Citations and arrests are weighted based on whether the offense
was a Traffic Offense a Class III II or I Misdemeanor or a Class V IV III II or I Felony
Accordingly a patrol selective is worth one point a traffic warning two points a traffic citation
three points a warrant arrest three points a Class III misdemeanor arrest four points a Class II
misdemeanor arrest five points a Class I misdemeanor arrest six points a Class V Felony seven
points a Class IV Felony eight points a Class III Felony nine points and Class II and I Felonies
(eg homicides) ten points3 The sum of the weighted productivity outputs is the officerrsquos P-
score The P-score is divided by amount of productive time and then multiplied by 100 (for ease
of understanding) This value the productivity quotient represents the officerrsquos productivity
given the available minutes for proactivity such as searching for wanted individuals or traffic
enforcement
Fourth VORC creates a hypothetical replacement officer against which to measure the
patrol officerrsquos output This replacement player might be imagined as a rookie patrol officer or
983090 983119983150983085983140983157983156983161 983156983145983149983141 983140983151983141983155 983150983151983156 983145983150983139983148983157983140983141 983156983154983137983145983150983145983150983143 983156983145983149983141 983158983137983139983137983156983145983151983150 983151983154 983144983151983148983145983140983137983161 983140983137983161983155 983151983142983142 983151983154 983155983145983139983147 983148983141983137983158983141 983113983150 983151983156983144983141983154 983159983151983154983140983155 983151983150983085983140983157983156983161
983156983145983149983141 983145983155 983156983144983141 983137983149983151983157983150983156 983151983142 983156983145983149983141 983137 983152983137983156983154983151983148 983151983142983142983145983139983141983154 983155983152983141983150983156 983159983151983154983147983145983150983143 983156983144983141 983155983156983154983141983141983156983091 983119983157983154 983159983141983145983143983144983156983145983150983143 983155983161983155983156983141983149 983145983155 983151983150983148983161 983151983150983141 983141983160983137983149983152983148983141 983151983142 983156983144983141 983142983148983141983160983145983138983145983148983145983156983161 983156983144983137983156 983126983119983122983107 983151983142983142983141983154983155 983140983141983152983137983154983156983149983141983150983156983155983099 983141983137983139983144 983137983143983141983150983139983161 983139983137983150
983152983154983145983151983154983145983156983145983162983141 983137983150983140 983137983155983155983145983143983150 983139983151983154983154983141983155983152983151983150983140983145983150983143 983159983141983145983143983144983156983155 983156983151 983156983144983141983145983154 983152983154983141983142983141983154983154983141983140 983152983154983151983140983157983139983156983145983158983145983156983161 983151983157983156983152983157983156983155
7252019 PIJPSM-05-2015-0064
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a recruit who has not completed the academy performing at ninety percent the capacity of fully
trained police officer4 Using LPDrsquos data VORC creates a hypothetical replacement officer by
dividing the departmental average P-score by the departmental average for productive time and
then multiplying it by the officerrsquos productive time to determine what an average officer would
have generated given the officerrsquos amount of productive time This figure is then multiplied by
9 (the capacity of a replacement officer) divided by the officerrsquos productive time and then
multiplied by 100 to generate the replacement officerrsquos productivity quotient Finally VORC
subtracts the replacement officerrsquos score from the patrol officerrsquos score to calculate the officerrsquos
value over the replacement officer This number or VORC indicates the officerrsquos productivity
per time for self-initiated activities compared to the replacement officerrsquos productivity For
example a VORC of 12 means than officer would produce 012 more productivity points (some
combination of warnings citations and arrests) per minute than a replacement officer
Biographical variables We gathered biographical information about the officers in the
sample including gender years of LPD experience maritalcohabiting status and number of
children We also controlled for whether the officer worked first second or third shift because
our data showed significant differences in the number of calls for service each shift handled as
well as the number of reports each shift completed
Geographic variables It is necessary to measure the characteristics of patrol areas to
account for variation across patrol areas For example some areas may feature different crime
rates or levels of social disorganization which command more department resources and police
officers We gathered data by first mapping out every squadrsquos patrol area and then obtaining
tract-level data from the 2010 US Census Our models included measures of established of
983092
983123983139983144983151983148983137983154983155 983137983150983140 983152983151983148983145983139983161 983149983137983147983141983154983155 983149983137983161 983140983145983155983137983143983154983141983141 983159983145983156983144 983141983155983156983145983149983137983156983145983150983143 983137 983154983141983152983148983137983139983141983149983141983150983156 983151983142983142983145983139983141983154991257983155 983139983137983152983137983139983145983156983161 983137983156 983150983145983150983141983156983161 983152983141983154983139983141983150983156 983119983157983154983143983151983137983148 983145983155 983150983151983156 983156983151 983140983141983142983145983150983145983156983145983158983141983148983161 983141983155983156983145983149983137983156983141 983137 983144983161983152983151983156983144983141983156983145983139983137983148 983151983142983142983145983139983141983154991257983155 983152983154983151983140983157983139983156983145983158983145983156983161 983138983157983156 983149983141983154983141983148983161 983156983151 983141983155983156983137983138983148983145983155983144 983137 983154983141983137983155983151983150983137983138983148983141 983142983145983160983141983140
983152983151983145983150983156 983137983143983137983145983150983155983156 983159983144983145983139983144 983156983151 983139983151983149983152983137983154983141 983137983148983148 983152983137983156983154983151983148 983151983142983142983145983139983141983154983155
7252019 PIJPSM-05-2015-0064
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correlates of crime including population density (Hipp amp Roussell 2013) household income
(Gould Weinberg and Mustard 2002) and racial composition (Light amp Harris 2012)
Specifically we use each arearsquos population density median household income and percent
white We also included measures for each arearsquos violent and property crime rate Finally we
gathered information about the number of violent crimes (murders assaults aggravated assaults
sexual assaults and robberies) property crimes (larcenies burglaries vandalisms frauds
forgeries and motor vehicle thefts) and number of police officers in each patrol area thereby
creating variables for violent crime per officer and property crime per officer
Since LPDrsquos patrol officers are grouped with squads it is reasonable to believe that an
officerrsquos productivity might be more similar to an officer within their squad compared to an
officer in a different squad To account for the potential correlations between the individual
units as well as the multi-level nature of the data we used hierarchical linear modeling to
analyze the data
Our primary level variables include an officerrsquos gender marital status number of
children years of law enforcement experience shift calls for service annual number of traffic
stops and citations field information reports and misdemeanor felony and warrant arrests We
also used the officersrsquo productivity information to calculate each patrol officerrsquos VORC
Our secondary level variables include the violent crime per officer rate and property
crime per officer rate for the patrol area of each squad as well several control variables
including population density household median income percent white and the patrol arearsquos
violent and property crime rate Table 2 displays the summary statistics for the first and second-
level variables
INSERT TABLE 2 HERE
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Results
We hypothesized that the violent and property crime-to-cop ratios of an area might affect
the raw productivity outputs of patrol officers as well as a more advanced productivity metric
To examine these questions we constructed five hierarchical linear models estimating the
correlations between reported violent and property crimes per officer and a patrol officerrsquos
annual traffic citations warrant misdemeanor felony arrests and Value Over Replacement Cop
Although not presented here we estimated null models for all dependent variables and found that
significant variation existed between squads when we included no variables included in the
models The chi-square for the LR test for all five null models was statistically significant (p lt
000 in all models)
Table 3 shows that a rise in reported violent crimes per officer increases an officerrsquos
number of warrants and misdemeanors (b= 29 and 76) while arise in reported property crimes
per officer decreases an officerrsquos number of warrants and misdemeanors (b= -15 and -52) We
found no relationship between the crime-to-cop ratios and felony arrests However both the
rates of violent and property crimes per officer decreased the number of traffic citations an
officer writes (b= -168 and -225)
Insert Table 3 here
Table 3 also shows that traffic stops and field information reports are positively
associated with warrant and misdemeanor arrests This supports what police practitioners
already suspect the cops with the most arrests are extremely proactive patrol officers who stop a
large number of vehicles and build investigations through the use of field contacts and
information reports For instance an officer can build felony narcotics cases by contacting
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suspects and informants in the field documenting these efforts through field information reports
and eventually using this information to write search warrants and conduct drug interdiction
Next we investigated whether this relationship between the crime-to-cop ratios and
productivity outputs exists when using an advanced productivity metric specifically an officerrsquos
Value Over Replacement Cop (VORC)
INSERT TABLE 4 HERE
Table 4 shows that there is no correlation between the ratio of property and violent
crimes per officer and VORC Although this stands in contrast to our earlier results we would
not expect to find a relationship between these variables and VORC Advanced metrics such as
VORC evaluate officers based on the amount of time for self-initiated activities (productive
time) and not raw productivity outputs like misdemeanor or felony arrests Even if an officer
took an extremely large number of property crimes thereby decreasing their productive time
VORC would only evaluate that officer based on what the officer produced with the small
amount of productive time This suggests that while a teamrsquos crime-to-cop ratio could affect an
officerrsquos raw productivity outputs ie arrests a teamrsquos crime-to-cop ratio would not affect an
officerrsquos VORC
For example during a given month an officer may take an unusually large number of
burglaries or larcenies thereby leaving the officer with only ten hours for self-initiated activities
With this reduced amount of productive time the officer may generate a below-average number
of officials warrants misdemeanors and felonies which in turn may lead a supervisor to
potentially downgrade the officerrsquos productivity However VORC would recognize and account
for the high number of calls for service minutes and analyze the officerrsquos productivity based on
what the officer generated with only ten hours of productive time It may be that the officer was
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actually very productive but they simply didnrsquot have enough productive time to generate the
same number of traffic citations and arrests as an officer who responded to far fewer property
crimes
Table 4 also shows that traffic stops and field intelligence reports are positively
correlated with VORC These results are consistent with the principles of how VORC evaluates
patrol officer productivity and provides additional support for the validity of this advanced
metric VORC accounts for numerous variables that affect productive time (ie calls for service
minutes) meaning that the key factors which increase or decrease an officerrsquos VORC are largely
related to self-initiated activities (ie traffic stops and information reports) VORC therefore
accounts for the potential effects of macro-level variables and analyzes the activities of the
officer during his or her productive time Nonetheless we recognize that because traffic stops
and field information reports are correlated with VORC an arearsquos crime-to-cop ratio might still
affect an officerrsquos VORC indirectly by impacting the number of stops and information reports
they generate
Discussion
There are several factors which affect a patrol officerrsquos productivity including
biographical characteristics officersrsquo perceptions and attitudes and organizational and
operational features among others This paper identified two macro-level variablesmdashproperty
crimes per officer and violent crimes per officermdashwhich affect the number of traffic citations
warrants misdemeanors and felonies generated annually by an officer Using data from a mid-
sized police department we found that a patrol areas rate of property crimes per officer was
associated with a moderate decrease in an officers annual number of traffic citations warrant
arrests and misdemeanor arrests while a patrol areas rate of violent crimes per officer was also
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associated with a moderate decrease in an officers annual number of traffic citations We also
found that a patrol areas rate of violent crimes per officer is associated with a moderate increase
in an officers annual number of warrant and misdemeanor arrests However when we used a
more advanced productivity metric we found no relationship between property and violent
crimes per officer and an officerrsquos Value Over Replacement Cop or VORC
There are several factors which explain the differing effects of the crime-to-cop ratios on
productivity outputs Let us first address why the violent crime-to-cop ratio would increase a
patrol officerrsquos warrants and misdemeanors while the property crime-to-cop ratio would decrease
these outputs Notably half of all victims of violent crimes know the offender (Truman amp Rand
2010) which aids law enforcement in identifying and locating the perpetrator Moreover both
suspect and victim are frequently on scene when police officers arrive such as during domestic
disturbances Being able to identify and locate the perpetrator helps patrol officers make arrests
thereby increasing their misdemeanor and felony arrests in violent crimes Additionally when
patrol officers arrive and contact individuals during an incident it is standard procedure to check
if the parties involved have outstanding warrants This means that the greater number of violent
crimes that officers respond to the more chances they have to arrest individuals who might have
outstanding warrants It makes sense then that officers who respond to a greater number of
violent crimes have more chances to make warrant and misdemeanor arrests
Conversely property crimes may supply the officer with fewer warrant and misdemeanor
arrests because of low clearance rates We examined the difference in clearance rates between
property and violent crime using incident data from the Lincoln Police Department and we
found that the clearance rate for violent crimes was 418 compared to only 237 for property
crimes Moreover property crimes often also require more investigative time than violent
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crimes Property crime investigations often require considerable follow-up by officers such as
processing a scene contacting financial institutions and checking pawnshops for stolen property
In general property crimes are more time-intensive than violent crimes thereby decreasing
productive time and opportunities for proactive activities while simultaneously netting officers
fewer arrests because of low clearance rates
The impact of a teamrsquos crime-to-cop ratio can also be seen in the relationship between
property and violent crime per officer and the number of traffic citations Table 3 shows that a
rise in both reported violent and property crimes per officer decrease an officerrsquos number of
traffic citations Quite simply if officers respond to a high number of property and violent
crimes then they will have fewer minutes to conduct traffic enforcement and other proactive
enforcement
As noted it is possible that the correlation is bi-directional If an officer makes a greater
number of arrests then a team may witness a decrease in crime because prolific criminals may
have been arrested and placed in jail For example one might interpret Table 3 to mean that an
increase in warrant and misdemeanor warrants led to a decrease in the number of property crimes
per officer However under this hypothesis we should also see that an increase in warrant and
misdemeanor arrests leads to a decrease in the number of violent crimes per officer Instead we
see the opposite hypothesized effect ie more arrests leading to more reported violent crimes 5
983093 983127983141 983139983151983150983155983156983154983157983139983156983141983140 983154983141983143983154983141983155983155983145983151983150 983149983151983140983141983148983155 983157983155983145983150983143 983139983145983156983137983156983145983151983150983155 983159983137983154983154983137983150983156 983137983154983154983141983155983156983155 983149983145983155983140983141983149983141983137983150983151983154 983137983154983154983141983155983156983155 983137983150983140 983142983141983148983151983150983161 983137983154983154983141983155983156983155 983142983154983151983149
983156983144983141 983152983154983141983139983141983140983145983150983143 983149983151983150983156983144 983156983151 983152983154983141983140983145983139983156 983138983151983156983144 983158983145983151983148983141983150983156 983137983150983140 983152983154983151983152983141983154983156983161 983139983154983145983149983141 983154983137983156983141983155 983142983151983154 983156983144983141 983149983151983150983156983144983155 983145983150 983151983157983154 983140983137983156983137983155983141983156 983127983141 983142983151983157983150983140 983150983151
983155983156983137983156983145983155983156983145983139983137983148983148983161 983155983145983143983150983145983142983145983139983137983150983156 983154983141983148983137983156983145983151983150983155983144983145983152983155 983138983141983156983159983141983141983150 983139983145983156983137983156983145983151983150983155 983159983137983154983154983137983150983156 983137983154983154983141983155983156983155 983149983145983155983140983141983149983141983137983150983151983154 983137983154983154983141983155983156983155 983137983150983140 983142983141983148983151983150983161 983137983154983154983141983155983156983155
983137983150983140 983156983144983141 983158983145983151983148983141983150983156 983137983150983140 983152983154983151983152983141983154983156983161 983139983154983145983149983141 983154983137983156983141983155 983151983142 983156983144983141 983138983141983137983156983155 983112983151983159983141983158983141983154 983137983142983156983141983154 983140983145983155983137983143983143983154983141983143983137983156983145983150983143 983156983144983141 983158983145983151983148983141983150983156 983137983150983140 983152983154983151983152983141983154983156983161
983139983154983145983149983141 983154983137983156983141983155 983145983150983156983151 983155983152983141983139983145983142983145983139 983139983154983145983149983141983155 983159983141 983140983145983140 983142983145983150983140 983156983144983137983156 983149983145983155983140983141983149983141983137983150983151983154 983137983150983140 983142983141983148983151983150983161 983137983154983154983141983155983156983155 983155983145983143983150983145983142983145983139983137983150983156983148983161 983152983154983141983140983145983139983156983141983140 983137 983140983141983139983154983141983137983155983141
983145983150 983154983151983138983138983141983154983161 983154983137983156983141983155 983159983144983145983139983144 983145983155 983139983151983150983155983145983155983156983141983150983156 983159983145983156983144 983156983144983141 983148983145983156983141983154983137983156983157983154983141 983105983140983140983145983156983145983151983150983137983148983148983161 983159983141 983145983150983156983141983154983158983145983141983159983141983140 983140983141983152983137983154983156983149983141983150983156 983152983141983154983155983151983150983150983141983148 983137983150983140983148983141983137983154983150983141983140 983156983144983137983156 983156983144983141 983116983145983150983139983151983148983150 983120983151983148983145983139983141 983108983141983152983137983154983156983149983141983150983156 983144983137983140 983155983156983137983142983142983141983140 983137 983156983141983137983149 983140983141983155983145983143983150983141983140 983155983152983141983139983145983142983145983139983137983148983148983161 983156983151 983137983152983152983154983141983144983141983150983140 983154983151983138983138983141983154983161
983155983157983155983152983141983139983156983155 983140983157983154983145983150983143 983156983144983145983155 983156983145983149983141983142983154983137983149983141 983159983144983145983139983144 983155983141983141983149983155 983156983151 983141983160983152983148983137983145983150 983156983144983141 983137983154983154983141983155983156983085983154983151983138983138983141983154983161 983154983137983156983141 983154983141983148983137983156983145983151983150983155983144983145983152
7252019 PIJPSM-05-2015-0064
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We believe our explanation has a stronger theoretical foundation The ratio of property
crimes per officer affects productivity outputs differently than the ratio of violent crimes per
officer for two reasons First violent crimes are different in nature than property crimes
especially with regard to clearance rates Second property crimes often require more
investigative and follow-up time than violent crimes thereby reducing an officerrsquos amount of
productive time and proactive activity
Table 3 also provides a key piece of evidence to support this assertion Table 3 shows
that the number of reported violent crimes per officer increases an officerrsquos number of warrant
and misdemeanor arrests Yet the relationship is the opposite for traffic enforcement the violent
crime-to-cop ratio actually decreases traffic citations This change is due to the fact that while
violent crime investigations often yield warrant and misdemeanor arrests they do not produce
traffic citations Furthermore responding to a greater number of violent crimes decreases the
amount of available time to conduct traffic enforcement in turn reducing the number of traffic
citations In other words the violent crime-to-cop ratio affects traffic stops the same way that
the property crime-to-cop ratio affects all productivity outputs by reducing an officerrsquos
productive time and opportunities for proactivity In fact we found a moderate correlation
between a patrol arearsquos crime-to-cop ratio and the amount of productive time enjoyed by officers
who worked that area The correlation between an arearsquos violent crime-to-cop ratio and
productive time was -21 while the correlation between an arearsquos property crime-to-cop ratio and
productive time was -33 We believe this evidence indicates that officers who work in areas
with a high crime-to-cop ratio have less time for self-initiated activities which drives down
productivity
7252019 PIJPSM-05-2015-0064
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We suggest that the number of police officers and crime rates of an area need to be
analyzed in conjunction with each other Prior theories of police productivity have viewed and
examined these variables separately when in reality we should view these variables in tandem
Combining these variables into crime-to-cop ratios provides a more nuancedmdashand accuratemdash
understanding of patrol activities and their relationship to productivity
Although we found strong evidence that an officerrsquos patrol area affects his or her
productivity (in terms of raw outputs) we also found that a few officer-level variables affected
productivity as well including traffic stops intelligence reports and working third shift Our
results appear to substantiate what many police practitioners already suspect officers who are
proactive in contacting citizens (traffic stops) and building investigations (intelligence reports)
generate more warrant and misdemeanor arrests In addition while some newer police officers
are forced to work third shift because they do not have sufficient seniority to bid for better days
off and shift times many officers work nights because they enjoy the types of calls (eg in-
progress felonies) meaning they may be more motivated to stop vehicles make field contacts
and in turn engage in activities that generate arrests (Brehm amp Gates 1993)
These results have important implications for police researchers and administrators
First our results suggest a need for a deeper understanding of how the staffing levels crime
rates and the other characteristics of a patrol area intersect Second if departments evaluate
their patrol officers using raw productivity outputs such as misdemeanor or felony arrests
administrators must recognize that factors beyond the control of patrol officers might affect
monthly or annual productivity Our research suggests that a patrol arearsquos crime-to-cop ratio can
substantially affect a patrol officerrsquos productivity regardless of the actual crime rate If the
characteristics of a patrol area affect raw productivity outputs police departments should employ
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more advanced productivity analytics (ie VORC) that account for activities which reduce
productive time and evaluate an officerrsquos productivity in terms of available minutes for
proactivity
Nonetheless evaluating both patrol officers and police departments using traffic citations
warrants misdemeanors and felonies is useful because this type of data is readily understood by
citizens and civic officials A neighborhood watch group for instance might care more about
the number of arrests their officers make and less about those officersrsquo productivity scores Our
data suggests that if citizens want patrol officers to write more traffic citations and make more
arrests they should lobby for more police officers on the street It is true that reducing an arearsquos
crime rate impacts the crime-to-cop ratio but our results indicate that putting police officers on
the street may be the most direct way to affect the crime-to-cop ratio
Our study is not without limitations We only examined a police department which uses
the generalist style of policing and it is unknown if our results are applicable to police
departments which use a specialist style of policing Theoretically a departmentrsquos policing
styles can affect factors that increase or decrease a patrol officerrsquos productive time It is possible
that the number of property crimes and violent crimes per officer do not significantly affect a
specialist patrol officerrsquos productivity outputs versus a generalist officerrsquos outputs Future
studies should investigate whether our results can be generalized to all police departments
We have only identified two macro-level variables which affect patrol officer
productivity and we recognize there may be other variables In addition we recommend that
future studies incorporate a wider array of variables and test competing models to determine
whether micro or macro-level variables exhibit stronger effects on productivity In the end
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managing patrol officers and improving productivity requires a greater understanding of both
patrol officers and the areas they work
Acknowledgements and Credits We would like to thank Denver Police Department Crime
Analyst Melissa Ohlhorst and Pennsylvania State University Professor Barry Ruback as well as
the three anonymous reviewers for their insightful comments We also salute Lincoln Police
Sergeant Jake Dilsaver for his logistical support of this project
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References
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Brief Treatment and Crisis Intervention Vol 3 No 1 pp 47-53
Becker G (1985) ldquoHuman capital effort and the sexual division of laborrdquo The Journal of
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Bonkiewicz L (2015) ldquoBobbies and baseball players Evaluating patrol officer productivity
using sabermetricsrdquo Police Quarterly Vol 18 No 1 pp 55-78
Braga A and Bond J (2008) ldquoPolicing crime and disorder hot spots A randomized controlled
trialrdquo Criminology Vol 46 No 3 pp 577-607
Brown J and Campbell E (1990) ldquoSources of occupational stress in the policerdquo Work
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Chappell A MacDonald J and Manz P (2006) ldquoThe organizational determinants
of police arrest decisionsrdquo Crime and Delinquency Vol 52 No 2 pp 287-306
Cloninger D and Sartorius L (1979) ldquoCrime rates clearance rates and enforcement effort
The case of Houston Texasrdquo American Journal of Economics and Sociology Vol 38
No 4 pp 389-402
Corman H and Mocan N (2005) ldquoCarrots sticks and broken windowsrdquo Journal of Law and
Economics Vol 48 No 1 pp 235-266
Crank J (1990) ldquoThe influence of environmental and organizational factors on police style in
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pp 166-189
Goodman A (1990) ldquoA model for police officer burnoutrdquo Journal of Business and
Psychology Vol 5 No 1 pp 85-99
Gould E Weinberg B and Mustard D (2002) Crime rates and local labor marketopportunities in the United States 1979ndash1997rdquo Review of Economics and Statistics Vol
84 No 1 pp 45-61
Hipp J and Roussell A (2013) ldquoMicro-and macro-environment population and the
consequences for crime ratesrdquo Social Forces Vol 92 No 2 pp 563-595
Hatry H (1976) ldquoApproaches to productivity measurement and program evaluationrdquo Public
Productivity Review Vol 1 No 3 pp21-28
Hawkins H (2001) ldquoPolice officer burnout a partial replication of Maslachs Burnout
Inventoryrdquo Police Quarterly Vol 4 No 3 pp 343-360
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Herbert S (1998)ldquoPolice subculture reconsideredrdquo Criminology Vol 36 No 2 pp 343-370
Huizinga D and Henry K (2008) ldquoThe effect of arrest and justice system sanctions on
subsequent behavior Findings from longitudinal and other studiesrdquo In The long view of
crime A synthesis of longitudinal research Springer New York pp 220-254
Johnson R (2011) ldquoOfficer attitudes and management influences on police work productivityrdquo
American Journal of Criminal Justice Vol 36 pp 293-306
Kane R (2006) ldquoOn the limits of social control Structural deterrence and the policing of
lsquosuppressiblersquo crimesrdquo Justice Quarterly Vol 23 No 2 pp 186-213
Kubrin C Messner S Deane G McGeever K and Stucky T (2010) ldquoProactive
policing and robbery rates across US citiesrdquo Criminology Vol 48 No 1 pp 57-97
Lersch K (2002) ldquoAre citizen complaints just another measure of officer productivity An
analysis of citizen complaints and officer activity measuresrdquo Police Practice and
Research Vol 3 pp 135-147
Light M and Harris C (2012) Race space and violence exploring spatial dependence in
structural covariates of white and black violent crime in US countiesrdquo Journal of
Quantitative Criminology Vol 28 No 4 pp 559-586
Maxfield M (1982) ldquoService time dispatch time and demand for police services Helping
more by serving lessrdquo Public Administration Review pp 252-263
Maxfield M Lewis D and Szoc R (1980) ldquoProducing official crimes Verified crime reports
as measures of police outputrdquo Social Science Quarterly pp 221-236
Nakosteen R and Zimmer M (2001) ldquoSpouse selection and earnings Evidence of marital
sortingrdquo Economic InquiryVol 39 No 2 pp 201-2013
Shane J (2010) ldquoOrganizational stressors and police performancerdquo Journal of Criminal
Justice Vol 38 No 4 pp 807-818
Shane J (2011) ldquoDaily work experiences and police performancerdquo Police Practice and
Research Vol 13 No 3 pp 1-19
Sherman L Gartin P and Buerger M (1989) ldquoHot spots of predatory crime Routine
activities and the criminology of placerdquoCriminology Vol 27 No 1 pp27-56
Truman J and Rand M (2010) ldquoBureau of Justice Statistics Bulletin National Crime
Victimization Survey Criminal Victimization 2009rdquo US Department of Justice Office of
Justice programs
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Van Meter D (2001) Evaluating dysfunctional police performance A zero-based approach
Charles C Thomas Publisher Springfield IL
Vila B and Kenney D (2002) ldquoTired cops The prevalence and potential consequences of
police fatiguerdquo National Institute of Justice
Vila B Morrison G and Kenney D (2002) ldquoImproving shift schedule and work-hour
policies and practices to increase police officer performance health and safetyrdquo Police
Quarterly Vol 5 No 1 pp 4-24
Vollaard B and Koning P (2009) ldquoThe effect of police on crime disorder and victim
PrecautionEvidence from a Dutch victimization surveyrdquo International Review of Law
and Economics Vol 29 No 4 pp 336-348
Weisburd D Maher L Sherman L Buerger M Cohn E and Petrisino A (1993)
ldquoContrasting crime general and crime specific theory The case of hot spots of crimerdquo
New Directions in Criminological Theory Advances in Criminological Theory Vol 4
pp 45-70
Author Biography
Luke Bonkiewicz is a patrol officer with the Lincoln Police Department
7252019 PIJPSM-05-2015-0064
httpslidepdfcomreaderfullpijpsm-05-2015-0064 2730
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983101 983089983088983088lowast + + ) + ) + ) + ) lowast )) minus + + + 983090983088 + ) +983089983088)) minus 983089983088983088 lowast
983086 983097) lowast )
7252019 PIJPSM-05-2015-0064
httpslidepdfcomreaderfullpijpsm-05-2015-0064 2830
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983119983142983142983145983139983141983154 983120983154983151983155983141983139983157983156983145983151983150
983122983137983156983141C983110983123
983117983145983150983157983156983141983155983110983125983120
983117983145983150983157983156983141983155
983119983142983142983145983139983141983154
A983155983155983145983155983156983117983145983150983157983156983141983155
983113983150983139983145983140983141983150983156983122983141983152983151983154983156983155
AC983113983122983141983152983151983154983156983155 983123983141983148983141983139983156983145983158983141983155 983127983137983154983150983145983150983143983155 983119983142983142983145983139983145983137983148983155 983127983137983154983154983137983150983156983155 D983125983113983155 983117983145983155983140983141983149983141983137983150983151983154983155 983110983141983148983151983150983145983141983155
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2 95 30072 5424 4116 204 696 228 312 432 84 48 120 8
3 72 32496 6312 6600 144 564 444 720 540 84 36 24 12
4 90 33000 5232 4980 216 192 480 768 240 144 12 72 12
5 91 41724 4824 6048 312 636 132 504 324 108 7 24 24
6 83 23916 7056 6192 144 756 528 996 336 132 36 72 12
7 95 24468 6840 5892 156 624 324 456 480 72 36 120 24
8 90 39972 5196 4920 240 504 384 492 264 36 2 72 24
9 79 25500 4068 5100 336 396 192 684 204 24 36 120 10
10 87 21984 5412 4524 348 552 480 792 312 108 48 84 36
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983120983151983152983157983148983137983156983145983151983150 983140983141 983150983155983145983156983161 20229830856956 1687 4436
983112983151983157983155983141983144983151983148983140 983149983141983140983145983137983150 983145983150983139983151983149983141 ($) 2628198308576020 16535 49756
983159983144983145983156983141 7398308593 007 083
983126983145983151983148983141983150983156 983139983154983145983149983141 983154983137983156983141 (983152983141983154 100000) 8009830854800 1203 2521
983120983154983151983152983141983154983156983161 983139983154983145983149983141 983154983137983156983141 (983152983141983154 100000) 31009830859800 2027 6524
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983110983145983154983155983156983085983116983141 983158983141983148 983126 983137983154983145983137983138983148983141 983155 (983119983142983142983145983139983141983154) 983118=302 983122983137983150983143983141 983123D 983117983141 983137983150
983111983141983150983140983141983154 (983142983141983149983137983148983141=0 983149983137983148983141=1) 09830851 24 94
983117983137983154983154983145983141983140C983151983144983137983138983145983156983145983150983143 09830851 18 79
983151983142 C983144983145983148983140983154983141983150 09830855 170 90
983129983141983137983154983155 983141983160983152983141983154983145983141983150983139983141 198308537 779 1063
C983137983148983148983155 983142983151983154 983155983141983154983158983145983139983141 4819830851011 16121 61365
983124983154983137983142983142983145983139 983155983156983151983152983155 16983085901 14376 20624
983110983145983141983148983140 983145983150983142983151983154983149983137983156983145983151983150 983154983141983152983151983154983156983155 0983085227 2153 2530
983124983154983137983142983142983145983139 983139983145983156983137983156983145983151983150983155 249830851246 14632 18642
983117983145983155983140983141983149983141983137983150983151983154 983137983154983154983141983155983156983155 19983085403 5965 10663
983110983141983148983151983150983161 983137983154983154983141983155983156983155 098308571 1066 852
983126983119983122C 9830851959830851043 151 24
1983155983156 2983150983140 3983154983140
104 96 102983123983144983145983142983156
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983124983137983138983148983141 3 983112983145983141983154983137983154983139983144983145983139983137983148 983116983145983150983141983137983154 983117983151983140983141983148983155 E983155983156983145983149983137983156983145983150983143 983119983142 983142983145983139983141983154983155 A983150983150983157983137983148 983124983154983137983142983142983145983139 C983145983156983137983156983145983151983150983155 983127983137983154983154983137983150983156983155 983117983145983155983140983141983149983141983137983150983151983154983155 983137983150983140 983110983141983148983151983150 983145983141983155
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983120983151983152983157983148983137983156983145983151983150 983140983141983150983155983145983156983161 001 003 001 001 98308501 01 00 00
983112983151983157983155983141983144983151983148983140 983149983141983140983145983137983150 983145983150983139983151983149983141 ($) 002 001 983085000 000 00 00 98308500 00
983159983144983145983156983141 13363 32472 9830858921 5664 9830853274 19211 9830854447 3255
983126983145983151983148983141983150983156 983139983154983145983149983141 983154983137983156983141 (983152983141983154 100000) 006 004 001 001 98308501 02 01 02
983120983154983151983152983141983154983156983161 983139983154983145983149983141 983154983137983156983141 (983152983141983154 100000) 983085003 005 983085002 001 02 03 98308501 00
983120983154983151983152983141983154983156983161 983139983154983145983149983141983155 983152983141983154 983151983142983142983145983139983141983154 983085225 99 98308515 03 98308552 24 98308503 03
983126983145983151983148983141983150983156 983139983154983145983149983141 983152983141983154 983151983142983142983145983139983141983154 983085168 68 29 14 76 31 22 12
983110983145983154983155983156983085983116983141983158983141983148 983126983137983154983145983137983138983148983141983155 (983119983142983142983145983139983141983154) 983118=302
983117983137983148983141 4673 1622 366 147 1613 1051 91 158
983117983137983154983154983145983141983140C983151983144983137983138983145983156983145983150983143 9830857516 3254 201 278 9830851158 1169 98308595 167
983151983142 C983144983145983148983140983154983141983150 183 702 75 77 983085262 217 983085396 207
983129983141983137983154983155 983141983160983152983141983154983145983141983150983139983141 9830851065 492 98308512 13 983085163 33 98308506 05C983137983148983148983155 983142983151983154 983155983141983154983158983145983139983141 24 13 02 02 14 01 98308508 05
983124983154983137983142983142983145983139 983155983156983151983152983155 983085983085 983085983085 03 00 09 02 00 00
983110983145983141983148983140 983145983150983142983151983154983149983137983156983145983151983150 983154983141983152983151983154983156983155 209 33 20 04 79 19 10 03
983123983141983139983151983150983140 983155983144983145983142983156 (983154983141983142983141983154983141983150983139983141 983143983154983151983157983152 983142983145983154983155983156) 2603 1778 187 195 809 730 147 160
983124983144983145 983154983140 983155983144983145 983142983156 (983154983141 983142983141 983154983141 983150983139983141 983143983154983151983157983152 983142983145 983154983155983156) 9872 2155 79 213 2532 952 393 132
983113983150983156983141983154983139983141983152983156 32591 5361 758 339 4147 1912 310 484
983123983145983143983149983137983085983155983153983157983137983154983141983140 1541467 19287 206702 5598
C983144983145983085983155983153983157983137983154983141983140 8294 6133 12348 10663
983152 lt 05
983152lt01
983152lt001
983118983151983156983141 A983148983148 983158983137983154983145983137983138983148983141983155 983137983154983141 983157983150983139983141983150983156983141983154983141983140
983127A983122983122A983118983124 A983122983122E983123983124983123 983117983113983123DE983117EA983118983119983122 A983122983122E983123983124983123 983110E983116983119983118983129 A983122983122E983123983124983123983124983122A983110983110983113C C983113983124A983124983113983119983118983123
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983123983141983139983151983150983140983085983116983141983158983141983148 983126983137983154983145983137983138983148983141983155 (983120983137983156983154983151983148 A983154983141983137) 983118=40 983138 983123E
983120983151983152983157983148983137983156983145983151983150 983140983141983150983155983145983156983161 98308500 00
983112983151983157983155983141983144983151983148983140 983149983141983140983145983137983150 983145983150983139983151983149983141 ($) 00 01
983159983144983145983156983141 318 345
983126983145983151983148983141983150983156 983139983154983145983149983141 983154983137983156983141 (983152983141983154 100000) 98308500 00
983120983154983151983152983141983154983156983161 983139983154983145983149983141 983154983137983156983141 (983152983141983154 100000) 00 00
983120983154983151983152983141983154983156983161 983139983154983145983149983141983155 983152983141983154 983151983142983142983145983139983141983154 98308502 04
983126983145983151983148983141983150983156 983139983154983145983149983141 983152983141983154 983151983142983142983145983139983141983154 02 05
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983117983137983148983141 27 15
983117983137983154983154983145983141983140C983151983144983137983138983145983156983145983150983143 98308532 31
983151983142 C983144983145983148983140983154983141983150 05 03
983129983141983137983154983155 983141983160983152983141983154983145983141983150983139983141 00 01
C983137983148 983148983155 983142983151983154 983155983141983154983158983145983139983141 02 01
983124983154983137983142983142983145983139 983155983156983151983152983155 01 00
983110983145 983141983148983140 983145983150983142983151983154983149983137983156983145 983151983150 983154983141 983152983151983154983156983155 02 00
983123983141983139983151983150983140 983155983144983145983142983156 ( 983154983141983142983141983154983141983150983139983141 983143983154983151983157983152 983142 983145983154983155983156) 23 12
983124983144983145983154983140 983155983144983145983142983156 (983154983141983142983141983154983141983150983139983141 983143983154983151983157983152 983142 983145983154983155983156) 55 14
983113983150983156983141983154983139983141983152983156 165 36
983123983145983143983149983137983085983155983153983157983137983154983141983140 66
C983144983145983085983155983153983157983137983154983141983140 9846
983152 lt 05
983152lt01
983152lt001
983118983151983156983141 A983148983148 983158983137983154983145983137983138983148983141983155 983137983154983141 983157983150983139983141983150983156983141983154983141983140
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Introduction
Perhaps no civil servant undertakes the variety of tasks as a municipal police officer As
not only law enforcement agents but also community caretakers and first-responders to every
calamity great and small patrol officers handle a broad array of calls for service during their
daily tour of duty The regular duties of a patrol officer include reactive tasks such as handling
calls for service as well as proactive tasks such as conducting traffic and scofflaw enforcement
searching for suspected perpetrators and patrolling for suspicious individuals or situations (eg
drug deals graffiti vandalisms or individuals lsquocasingrsquo a potential target)
Historically both police researchers and practitioners have grappled with the lack of a
valid and reliable method for evaluating patrol officer activity as well as limited knowledge
about how external factors affect officersrsquo productivity (Crank 1990 Johnson 2011 Shane
2010) Although studies have demonstrated that differences in productivity between patrol
officers may result from biographical factors and daily work experiences (Shane 2011)
operational variables (Amaranto et al 2003) and organizational stressors (Brown amp Campbell
1990) scholars have devoted considerably less attention to how the characteristics of an area
might impact police productivity In other words although individual-level variables impact a
patrol officerrsquos productivity how might the characteristics of a patrol area affect a police
officerrsquos productivity
This paper identifies and analyzes two macro-level correlates of patrol officer
productivity violent crimes per officer and property crimes per officer We first review the
extant literature on patrol officer productivity discussing how an arearsquos violent and property
crime per cop ratios might affect a patrol officerrsquos productivity Next we analyze productivity
data from a mid-sized US police department to examine how these two variables impact the
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number of traffic citations warrants and misdemeanor and felony arrests generated by officers
annually We also examine the relationship between these two variables and a more
sophisticated productivity metric Value Over Replacement Cop (VORC) Finally we discuss
why police scholars and practitioners should pay closer attention to the practical macro-level
correlates of productivity as well as incorporate more advanced patrol productivity metrics
Literature Review
Productivity scholars have generally divided the concept of worker productivity into two
spheres (Hatry 1976) Effectiveness measures a worker or organizationrsquos quality of service
while efficiency gauges how well a worker or organization produces a particular output using the
least amount of resources such as manpower time and money Most police research has
examined productivity in terms of efficiency due mainly to the data available for analysis such
as calls for service and arrests
Police scholars have struggled with how to properly measure patrol officer productivity
There appear to be two main approaches The first approach attempts to identify and quantify
specific types of patrol productivity outputs Some examples include evaluating productivity
using an officerrsquos total arrests (Crank 1990) clearance rates (Marciano amp Heaton 2010)
number of traffic citations (Johnson 2011) and citizen complaints (Lersch 2002) However
researchers have noted that evaluating productivity using these kinds of raw outputs is
ldquoconceptually-simplerdquo shaped primarily by data availability (Chappell MacDonald amp Manz
2006)
The second approach to analyzing patrol officer productivity has incorporated multi-
dimensional indicators of police performance For example Shane (2011) conceptualized
productivity using a z-score summary of multiple outputs including arrests traffic citations
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field contacts official reports sick hours and administrative complaints Similarly Van Meterrsquos
Performance-Based Management or ldquozero-based approachrdquo (Van Meter 2001) uses three planks
(non-scheduled absenteeism cost of preventable error and productive use of time) to evaluate
patrol officers
Notably most existing police productivity research has ignored the concept of productive
time or the amount of time officers have for self-initiated activities after handling calls for
service investigating crimes completing reports and follow-ups and assisting other officers
Using productive time to evaluate officers is critical because two officers could achieve the same
output (in terms of raw arrests or reports) but have done so with considerably different amounts
of productive time due to calls for service follow-ups etc Recent research has proposed the use
of Value Over Replacement Cop or VORC (Bonkiewicz 2015) a metric that incorporates more
than a dozen patrol activities weights those activities evaluates performance in terms of
productive time and compares an officerrsquos performance against a fixed hypothetical estimate
(ie a replacement officer)
In addition to conceptualizing patrol officer productivity researchers have also studied
potential antecedents of productivity including not only biographical variables but also factors
involving workplace environment First certain biographical characteristics might impact
productivity (Hawkins 2001) For instance officers who have children might experience sleep
deprivation and greater fatigue as a result of parenting responsibilities thereby reducing their
workplace energy and motivation and in turn their productivity (Shane 2010) Marital status
may also impact productivity Although the relationship between marriage and worker
productivity is well debated in the literature (Nakosteen amp Zimmer 2001 Becker 1985) it
seems reasonable to believe that married officers might be more productive than unmarried
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officers Married officers have someone in whom to confide a partner who hears about the
challenges and rigors of the job which may lead to stress reduction and improve workplace
motivation An officerrsquos age is another consideration Older officers might be less productive
than younger officers perhaps due to burnout as well as the physical demands of law
enforcement (Goodman 1990) Officersrsquo perceptions of their job may also affect their
proactivity and in turn productivity Brehm and Gates (1993) found that officers who disliked
certain aspects of law enforcement (eg taking people to jail) impacted whether officers engaged
in proactive activities or shirked their duties
Additionally there are also operational features (the ldquoday to dayrdquo rigors) of law
enforcement which can decrease productivity and increase misconduct such as the perceived
danger of the job (Amaranto et al 2003) work schedules and shift work (Vila Morrison amp
Kenney 2002) and fatigue (Vila amp Kenney 2002) Notably in studying the relationship
between daily work experience and performance Shane (2011) found that workload was the
ldquomost important predictor of performancerdquo (p 13)
We believe this last finding merits further study First supervisors evaluate patrol
officers at least in part using productivity outputs If there are factors (ie heavy workload)
beyond an officerrsquos control that are affecting his or her productivity both line-level supervisors
and command staff members should acknowledge and account for these variables during
performance reviews Second if macro-level variables create different workloads thereby
affecting productivity outputs then police researchers should include these measures in future
studies on police activity We suggest that there are at least two macro-level variables important
to future police productivity research
Crime-to-Cop Ratios and Patrol Officer Productivity
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Criminological research has long recognized that crime rates are not uniform across
neighborhoods and cities (Sherman et al 1989 Weisburd et al 1993) Instead of ldquopolicing
peoplerdquo law enforcement agencies frequently identify ldquohot spotsrdquo and devote additional
resources to reducing crime and disorder in specific problem areas With this perspective in
mind it might seem natural to examine the relationship between the crime rate of a patrol area
and an officerrsquos productivity outputs (or vice-versa) For example a high crime area might net
an officer more arrests (and therefore more productivity outputs) than a low crime area On the
other hand a high crime area might also occupy a great deal of an officerrsquos productive time
prohibiting the officer from engaging in proactive activities However crime rate does not fully
capture how a particular area might affect an officerrsquos productivity
Instead we believe macro-level variables related to crime should be estimated in terms of
the number of patrol officers in a patrol area specifically violent crimes per officer and property
crimes per officer For instance some officers may patrol a lower crime area but end up
responding to a greater number of violent crimes than officers in a high crime area simply
because they have fewer officers working the street This ldquocrime per coprdquo ratio can dramatically
affect officersrsquo productivity
For example when an officer investigates a crime he or she might interview the victim
suspect and witnesses as well as take photographs collect evidence and make an arrest There
may be additional follow-up the next day or week such as checking pawnshops for stolen
property or tracking down and interviewing additional witnesses All these activities could
decrease an officerrsquos time for self-initiated activities and in turn an officerrsquos overall
productivity A high crime per cop ratio therefore could decrease productivity for officers even
if these officers work in an area with a low crime rate Furthermore an arearsquos crime per cop
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ratio could also affect officersrsquo productive time even if they are not the primary investigating
officer because secondary officers are often needed during serious incidents such as robberies or
burglaries
The ldquoload sheddingrdquo hypothesis by Maxfield et al (1980 see also Maxfield 1982) also
has important implications for any study examining patrol officer productivity Maxfield et al
suggested that while variations in workload do not affect patrol officersrsquo decisions to record
serious crimes workload does affect officersrsquo decisions to record less serious crimes it may also
affect self-initiated activities as well (Herbert 1989)
Finally we believe that macro-level variables affecting productivity should be divided
between violent and property crimes to account for the time and resources needed to investigate
each incident type When officers respond to assaults they often contact suspects and victims on
scene meaning that officers are able to interview all involved individuals collect evidence and
make an arrest sometimes in less than an hour However when officers respond to burglaries or
larcenies they may be required to dust for fingerprints swab for DNA canvass a neighborhood
for witnesses check pawnshops and engage in other investigative activities that require hours
and even days if the case requires follow-up
With these issues in mind our study addresses the following research questions First
what is the relationship between a patrol arearsquos rate of violent crimes per officer and patrol
officer productivity outputs (ie traffic citations as well as warrant misdemeanor and felony
arrests) Second what is the relationship between a patrol arearsquos rate of property crimes per
officer and patrol officer productivity outputs (again traffic citations and warrant misdemeanor
and felony arrests) Third what is the relationship between the rate of property and violent
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crimes per officer and a patrol officerrsquos productivity when we introduce a more sophisticated
method for analyzing productivity a metric based on productive time and not total time on duty
Before discussing our data and methods there are two important issues meriting brief
discussion First we argue that the crime-to-cop ratio may affect the number of traffic stops
warrants misdemeanors and felonies that a patrol officer makes It can also be argued that the
number of arrests might impact the crime-to-cop ratio Our intent is not to comprehensively
discuss the relationship between the number of officersofficer arrests and an arearsquos crime rate
but it bears noting that the research is inconclusive on this point
Some evidence suggests that the relationship between arrests and crime rates is non-
linear that is crime rates decrease as arrests increase but eventually hit a ldquofloorrdquo and reach
equilibrium (Kane 2006) Second when studies examine the arrest-crime rate relationship they
often find that the relationship only exists for specific crimes in their datasets such as robbery
and motor vehicle thefts (Corman Hope amp Mocan 2005) robbery but not assaults (Kane 2006)
and homicide and auto thefts only (Cloninger amp Sartorius 1979) and so forth Overall the
literature on the arrests-crime rate relationship is mixedmdashsome research finds an effect between
arrests and crime rates some does not (Huizinga and Henry 2008)
The second issue concerns whether a law enforcement agency operates using a generalist
or specialist policing style We analyze data from a generalist municipal police department
meaning that patrol officers investigate low and mid-level crimes but investigators and
detectives are responsible for handling serious crimes cases involving lengthy investigations
(eg narcotics trafficking or white-collar crimes) and incidents in which patrol officers may
need assistance (for example an incident involving numerous victims and witnesses as well as
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cases involving extensive follow-up or knowledge on a specific subject such as gangs or
narcotics)
By contrast when patrol officers in a specialist-style police department respond to any
incident potentially requiring follow-up they might only contact the victim and either hold the
scene until an investigator arrives or forward the information to a detective for follow-up It is
possible indeed likely that macro-level variables might affect generalist patrol officers
differently than specialized officers Due to data limitations however our study focuses on
generalist police officers
Data and Methods
We gathered data on patrol officers and their patrol areas from several sources First we
collected patrol officer productivity data from the Lincoln (Nebraska) Police Departmentrsquos 2013-
2014 productivity reports Using Computer-Aided Dispatch (CAD) and a centralized
productivity database LPD measures numerous patrol officer activities Every month (and
year) LPD generates productivity reports which display each patrol officerrsquos activities and
outputs (see Table 1 for an example of a squadrsquos annual productivity report) Each patrol
activity and output merits further explanation
INSERT TABLE 1 HERE
Calls for Service (CFS) and Officer Assist time When dispatchers send patrol officers on
a call they use CAD to track the minutes the officer spends on the call If the call is a priority
incident dispatchers send both a primary and secondary officer Other unassigned officers might
also respond to the CFS meaning that multiple officers could be secondary officers The annual
productivity record tracks amount of time an officer spent as a primary responder and a
secondary responder
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Report writing time Standard operating procedures dictate that LPD patrol officers
complete specific types of reports for incidents An incident report (IR) details biographical
information of the individuals involved in the incident and summarizes the incident An
Additional Case Information (ACI) is a more detailed report that documents additional
investigative steps or the actions of assisting officers Although the time spent on an IR or ACI
could vary depending on the call for service the average IR and ACI take roughly twenty
minutes to complete1
The Lincoln Police Department also tracks self-initiated activities Most productivity
results from the following proactive activities
Selectives The Lincoln Police Department tracks specific officer patrol activity by
coding its neighborhoods schools parks and business districts When an officer patrols an at-
risk business district or a high-crime neighborhood for example the officer notifies dispatch
using that arearsquos specific code Girded by the principles of ldquohot spotsrdquo policing research officers
use selectives to identify potential candidates for POP projects and heighten the perception of
risk among criminals (Braga amp Bond 2008)
Traffic Warnings and Official Citations Every Lincoln patrol officer drives a police
cruiser and conducts traffic enforcement issuing traffic warnings and official citations The
LPD records department tracks patrol officersrsquo warnings and citations
Misdemeanors felonies and warrants Patrol officers also make misdemeanor and
felony arrests during their productive time Officers might detain a motorist and arrest him or
her for driving under a suspended license or driving while intoxicated or contact a pedestrian
1 983119983142983142983145983139983141983154983155 983156983161983152983141 983137983148983148 983154983141983152983151983154983156983155 983151983150 983139983151983149983152983157983156983141983154983155 983127983141 983151983138983156983137983145983150983141983140 983156983144983145983155 983137983158983141983154983137983143983141 983138983161 983151983138983155983141983154983158983145983150983143 983137 983155983137983149983152983148983141 983151983142 983116983145983150983139983151983148983150 983152983137983156983154983151983148 983151983142983142983145983139983141983154983155
983139983151983149983152983148983141983156983145983150983143 983113983122983155 983137983150983140 983105983107983113983155 983137983150983140 983159983141 983154983141983139983151983143983150983145983162983141 983156983144983137983156 983156983144983145983155 983137983158983141983154983137983143983141 983149983137983161 983140983145983142983142983141983154 983142983154983151983149 983151983156983144983141983154 983152983151983148983145983139983141 983140983141983152983137983154983156983149983141983150983156983155 983117983151983154983141983151983158983141983154983145983142 983140983141983152983137983154983156983149983141983150983156983155 983154983141983153983157983145983154983141 983151983142983142983145983139983141983154983155 983156983151 983139983151983149983152983148983141983156983141 983156983144983141983145983154 983154983141983152983151983154983156983155 983138983141983142983151983154983141 983138983141983139983151983149983145983150983143 983137983158983137983145983148983137983138983148983141 983142983151983154 983137983150983151983156983144983141983154 983139983137983148983148 983142983151983154 983155983141983154983158983145983139983141
983156983144983145983155 983149983141983137983155983157983154983141983149983141983150983156 983145983155 983157983150983150983141983139983141983155983155983137983154983161
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and make an arrest for a sex offender registration violation Officers can also search their
departmentrsquos electronic database for individuals with outstanding warrants in their patrol areas
and attempt to arrest these offenders As with warnings and citations the LPD records
department documents and tracks misdemeanor felony and warrant arrests Moreover the court
system also monitors these types of outputs to process cases thereby adding an additional layer
of reporting accuracy and reliability
Field information reports We also measured the number of field information or
ldquointelligencerdquo reports Officers complete these internal reports to document suspicious or
dangerous individuals or circumstances though these reports are not publicly available
VORC In addition to the raw outputs of traffic citations warrants misdemeanors and
felonies we also include an advanced metric for measuring a patrol officerrsquos productivity Value
Over Replacement Cop (VORC) is a method for evaluating officers that assigns different
weights to productivity outputs and then assesses officer productivity in terms of available time
for self-initiated activities or productive time (Bonkiewicz 2015) We use a variation of this
formula that multiplies an officerrsquos officials misdemeanors and felonies by his or her
prosecution rate Our modified use of VORC can best be explained using the following figure
(Figure 1) and five-step process
INSERT FIGURE 1 HERE
where = total monthly on-duty minutes = CFS minutes Γ = follow-up time and meetings ∆
= officer assist time Ε = of IRs Ζ = of ACIs M = of intelligence reports Η = of
selectives Θ = of warnings times two Κ = of warrants times three Ι = of officials times
three Λ = of misdemeanor arrests times their respective weights Ν = of felonies types their
respective weights Ρ = an officerrsquos prosecution rate Ξ = Officerrsquos Productive time (Α-(Β+Γ+∆+
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20(Ε+Ζ))) Ο= Department average P-score or average (Η+ ((Θ)+(Ι)+(Κ)+(Λ)+(Μ)+(Ν)Ρ))
and Π = Department average Self-Initiated Time
First VORC calculates an officerrsquos total minutes spent on CFS follow-ups officer
assists and reports This amount is an officerrsquos total work time or service minutes Second the
formula subtracts an officerrsquos service minutes from the total on-duty minutes2 This is the
officerrsquos amount of productive time Third VORC calculates an officerrsquos productivity score (or
P-score) using a weighted index Departments can choose different weights for productivity
outputs but for the purpose of this study we used our weights based on the statutes governing
the Lincoln Police Department Citations and arrests are weighted based on whether the offense
was a Traffic Offense a Class III II or I Misdemeanor or a Class V IV III II or I Felony
Accordingly a patrol selective is worth one point a traffic warning two points a traffic citation
three points a warrant arrest three points a Class III misdemeanor arrest four points a Class II
misdemeanor arrest five points a Class I misdemeanor arrest six points a Class V Felony seven
points a Class IV Felony eight points a Class III Felony nine points and Class II and I Felonies
(eg homicides) ten points3 The sum of the weighted productivity outputs is the officerrsquos P-
score The P-score is divided by amount of productive time and then multiplied by 100 (for ease
of understanding) This value the productivity quotient represents the officerrsquos productivity
given the available minutes for proactivity such as searching for wanted individuals or traffic
enforcement
Fourth VORC creates a hypothetical replacement officer against which to measure the
patrol officerrsquos output This replacement player might be imagined as a rookie patrol officer or
983090 983119983150983085983140983157983156983161 983156983145983149983141 983140983151983141983155 983150983151983156 983145983150983139983148983157983140983141 983156983154983137983145983150983145983150983143 983156983145983149983141 983158983137983139983137983156983145983151983150 983151983154 983144983151983148983145983140983137983161 983140983137983161983155 983151983142983142 983151983154 983155983145983139983147 983148983141983137983158983141 983113983150 983151983156983144983141983154 983159983151983154983140983155 983151983150983085983140983157983156983161
983156983145983149983141 983145983155 983156983144983141 983137983149983151983157983150983156 983151983142 983156983145983149983141 983137 983152983137983156983154983151983148 983151983142983142983145983139983141983154 983155983152983141983150983156 983159983151983154983147983145983150983143 983156983144983141 983155983156983154983141983141983156983091 983119983157983154 983159983141983145983143983144983156983145983150983143 983155983161983155983156983141983149 983145983155 983151983150983148983161 983151983150983141 983141983160983137983149983152983148983141 983151983142 983156983144983141 983142983148983141983160983145983138983145983148983145983156983161 983156983144983137983156 983126983119983122983107 983151983142983142983141983154983155 983140983141983152983137983154983156983149983141983150983156983155983099 983141983137983139983144 983137983143983141983150983139983161 983139983137983150
983152983154983145983151983154983145983156983145983162983141 983137983150983140 983137983155983155983145983143983150 983139983151983154983154983141983155983152983151983150983140983145983150983143 983159983141983145983143983144983156983155 983156983151 983156983144983141983145983154 983152983154983141983142983141983154983154983141983140 983152983154983151983140983157983139983156983145983158983145983156983161 983151983157983156983152983157983156983155
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a recruit who has not completed the academy performing at ninety percent the capacity of fully
trained police officer4 Using LPDrsquos data VORC creates a hypothetical replacement officer by
dividing the departmental average P-score by the departmental average for productive time and
then multiplying it by the officerrsquos productive time to determine what an average officer would
have generated given the officerrsquos amount of productive time This figure is then multiplied by
9 (the capacity of a replacement officer) divided by the officerrsquos productive time and then
multiplied by 100 to generate the replacement officerrsquos productivity quotient Finally VORC
subtracts the replacement officerrsquos score from the patrol officerrsquos score to calculate the officerrsquos
value over the replacement officer This number or VORC indicates the officerrsquos productivity
per time for self-initiated activities compared to the replacement officerrsquos productivity For
example a VORC of 12 means than officer would produce 012 more productivity points (some
combination of warnings citations and arrests) per minute than a replacement officer
Biographical variables We gathered biographical information about the officers in the
sample including gender years of LPD experience maritalcohabiting status and number of
children We also controlled for whether the officer worked first second or third shift because
our data showed significant differences in the number of calls for service each shift handled as
well as the number of reports each shift completed
Geographic variables It is necessary to measure the characteristics of patrol areas to
account for variation across patrol areas For example some areas may feature different crime
rates or levels of social disorganization which command more department resources and police
officers We gathered data by first mapping out every squadrsquos patrol area and then obtaining
tract-level data from the 2010 US Census Our models included measures of established of
983092
983123983139983144983151983148983137983154983155 983137983150983140 983152983151983148983145983139983161 983149983137983147983141983154983155 983149983137983161 983140983145983155983137983143983154983141983141 983159983145983156983144 983141983155983156983145983149983137983156983145983150983143 983137 983154983141983152983148983137983139983141983149983141983150983156 983151983142983142983145983139983141983154991257983155 983139983137983152983137983139983145983156983161 983137983156 983150983145983150983141983156983161 983152983141983154983139983141983150983156 983119983157983154983143983151983137983148 983145983155 983150983151983156 983156983151 983140983141983142983145983150983145983156983145983158983141983148983161 983141983155983156983145983149983137983156983141 983137 983144983161983152983151983156983144983141983156983145983139983137983148 983151983142983142983145983139983141983154991257983155 983152983154983151983140983157983139983156983145983158983145983156983161 983138983157983156 983149983141983154983141983148983161 983156983151 983141983155983156983137983138983148983145983155983144 983137 983154983141983137983155983151983150983137983138983148983141 983142983145983160983141983140
983152983151983145983150983156 983137983143983137983145983150983155983156 983159983144983145983139983144 983156983151 983139983151983149983152983137983154983141 983137983148983148 983152983137983156983154983151983148 983151983142983142983145983139983141983154983155
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correlates of crime including population density (Hipp amp Roussell 2013) household income
(Gould Weinberg and Mustard 2002) and racial composition (Light amp Harris 2012)
Specifically we use each arearsquos population density median household income and percent
white We also included measures for each arearsquos violent and property crime rate Finally we
gathered information about the number of violent crimes (murders assaults aggravated assaults
sexual assaults and robberies) property crimes (larcenies burglaries vandalisms frauds
forgeries and motor vehicle thefts) and number of police officers in each patrol area thereby
creating variables for violent crime per officer and property crime per officer
Since LPDrsquos patrol officers are grouped with squads it is reasonable to believe that an
officerrsquos productivity might be more similar to an officer within their squad compared to an
officer in a different squad To account for the potential correlations between the individual
units as well as the multi-level nature of the data we used hierarchical linear modeling to
analyze the data
Our primary level variables include an officerrsquos gender marital status number of
children years of law enforcement experience shift calls for service annual number of traffic
stops and citations field information reports and misdemeanor felony and warrant arrests We
also used the officersrsquo productivity information to calculate each patrol officerrsquos VORC
Our secondary level variables include the violent crime per officer rate and property
crime per officer rate for the patrol area of each squad as well several control variables
including population density household median income percent white and the patrol arearsquos
violent and property crime rate Table 2 displays the summary statistics for the first and second-
level variables
INSERT TABLE 2 HERE
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Results
We hypothesized that the violent and property crime-to-cop ratios of an area might affect
the raw productivity outputs of patrol officers as well as a more advanced productivity metric
To examine these questions we constructed five hierarchical linear models estimating the
correlations between reported violent and property crimes per officer and a patrol officerrsquos
annual traffic citations warrant misdemeanor felony arrests and Value Over Replacement Cop
Although not presented here we estimated null models for all dependent variables and found that
significant variation existed between squads when we included no variables included in the
models The chi-square for the LR test for all five null models was statistically significant (p lt
000 in all models)
Table 3 shows that a rise in reported violent crimes per officer increases an officerrsquos
number of warrants and misdemeanors (b= 29 and 76) while arise in reported property crimes
per officer decreases an officerrsquos number of warrants and misdemeanors (b= -15 and -52) We
found no relationship between the crime-to-cop ratios and felony arrests However both the
rates of violent and property crimes per officer decreased the number of traffic citations an
officer writes (b= -168 and -225)
Insert Table 3 here
Table 3 also shows that traffic stops and field information reports are positively
associated with warrant and misdemeanor arrests This supports what police practitioners
already suspect the cops with the most arrests are extremely proactive patrol officers who stop a
large number of vehicles and build investigations through the use of field contacts and
information reports For instance an officer can build felony narcotics cases by contacting
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suspects and informants in the field documenting these efforts through field information reports
and eventually using this information to write search warrants and conduct drug interdiction
Next we investigated whether this relationship between the crime-to-cop ratios and
productivity outputs exists when using an advanced productivity metric specifically an officerrsquos
Value Over Replacement Cop (VORC)
INSERT TABLE 4 HERE
Table 4 shows that there is no correlation between the ratio of property and violent
crimes per officer and VORC Although this stands in contrast to our earlier results we would
not expect to find a relationship between these variables and VORC Advanced metrics such as
VORC evaluate officers based on the amount of time for self-initiated activities (productive
time) and not raw productivity outputs like misdemeanor or felony arrests Even if an officer
took an extremely large number of property crimes thereby decreasing their productive time
VORC would only evaluate that officer based on what the officer produced with the small
amount of productive time This suggests that while a teamrsquos crime-to-cop ratio could affect an
officerrsquos raw productivity outputs ie arrests a teamrsquos crime-to-cop ratio would not affect an
officerrsquos VORC
For example during a given month an officer may take an unusually large number of
burglaries or larcenies thereby leaving the officer with only ten hours for self-initiated activities
With this reduced amount of productive time the officer may generate a below-average number
of officials warrants misdemeanors and felonies which in turn may lead a supervisor to
potentially downgrade the officerrsquos productivity However VORC would recognize and account
for the high number of calls for service minutes and analyze the officerrsquos productivity based on
what the officer generated with only ten hours of productive time It may be that the officer was
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actually very productive but they simply didnrsquot have enough productive time to generate the
same number of traffic citations and arrests as an officer who responded to far fewer property
crimes
Table 4 also shows that traffic stops and field intelligence reports are positively
correlated with VORC These results are consistent with the principles of how VORC evaluates
patrol officer productivity and provides additional support for the validity of this advanced
metric VORC accounts for numerous variables that affect productive time (ie calls for service
minutes) meaning that the key factors which increase or decrease an officerrsquos VORC are largely
related to self-initiated activities (ie traffic stops and information reports) VORC therefore
accounts for the potential effects of macro-level variables and analyzes the activities of the
officer during his or her productive time Nonetheless we recognize that because traffic stops
and field information reports are correlated with VORC an arearsquos crime-to-cop ratio might still
affect an officerrsquos VORC indirectly by impacting the number of stops and information reports
they generate
Discussion
There are several factors which affect a patrol officerrsquos productivity including
biographical characteristics officersrsquo perceptions and attitudes and organizational and
operational features among others This paper identified two macro-level variablesmdashproperty
crimes per officer and violent crimes per officermdashwhich affect the number of traffic citations
warrants misdemeanors and felonies generated annually by an officer Using data from a mid-
sized police department we found that a patrol areas rate of property crimes per officer was
associated with a moderate decrease in an officers annual number of traffic citations warrant
arrests and misdemeanor arrests while a patrol areas rate of violent crimes per officer was also
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associated with a moderate decrease in an officers annual number of traffic citations We also
found that a patrol areas rate of violent crimes per officer is associated with a moderate increase
in an officers annual number of warrant and misdemeanor arrests However when we used a
more advanced productivity metric we found no relationship between property and violent
crimes per officer and an officerrsquos Value Over Replacement Cop or VORC
There are several factors which explain the differing effects of the crime-to-cop ratios on
productivity outputs Let us first address why the violent crime-to-cop ratio would increase a
patrol officerrsquos warrants and misdemeanors while the property crime-to-cop ratio would decrease
these outputs Notably half of all victims of violent crimes know the offender (Truman amp Rand
2010) which aids law enforcement in identifying and locating the perpetrator Moreover both
suspect and victim are frequently on scene when police officers arrive such as during domestic
disturbances Being able to identify and locate the perpetrator helps patrol officers make arrests
thereby increasing their misdemeanor and felony arrests in violent crimes Additionally when
patrol officers arrive and contact individuals during an incident it is standard procedure to check
if the parties involved have outstanding warrants This means that the greater number of violent
crimes that officers respond to the more chances they have to arrest individuals who might have
outstanding warrants It makes sense then that officers who respond to a greater number of
violent crimes have more chances to make warrant and misdemeanor arrests
Conversely property crimes may supply the officer with fewer warrant and misdemeanor
arrests because of low clearance rates We examined the difference in clearance rates between
property and violent crime using incident data from the Lincoln Police Department and we
found that the clearance rate for violent crimes was 418 compared to only 237 for property
crimes Moreover property crimes often also require more investigative time than violent
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crimes Property crime investigations often require considerable follow-up by officers such as
processing a scene contacting financial institutions and checking pawnshops for stolen property
In general property crimes are more time-intensive than violent crimes thereby decreasing
productive time and opportunities for proactive activities while simultaneously netting officers
fewer arrests because of low clearance rates
The impact of a teamrsquos crime-to-cop ratio can also be seen in the relationship between
property and violent crime per officer and the number of traffic citations Table 3 shows that a
rise in both reported violent and property crimes per officer decrease an officerrsquos number of
traffic citations Quite simply if officers respond to a high number of property and violent
crimes then they will have fewer minutes to conduct traffic enforcement and other proactive
enforcement
As noted it is possible that the correlation is bi-directional If an officer makes a greater
number of arrests then a team may witness a decrease in crime because prolific criminals may
have been arrested and placed in jail For example one might interpret Table 3 to mean that an
increase in warrant and misdemeanor warrants led to a decrease in the number of property crimes
per officer However under this hypothesis we should also see that an increase in warrant and
misdemeanor arrests leads to a decrease in the number of violent crimes per officer Instead we
see the opposite hypothesized effect ie more arrests leading to more reported violent crimes 5
983093 983127983141 983139983151983150983155983156983154983157983139983156983141983140 983154983141983143983154983141983155983155983145983151983150 983149983151983140983141983148983155 983157983155983145983150983143 983139983145983156983137983156983145983151983150983155 983159983137983154983154983137983150983156 983137983154983154983141983155983156983155 983149983145983155983140983141983149983141983137983150983151983154 983137983154983154983141983155983156983155 983137983150983140 983142983141983148983151983150983161 983137983154983154983141983155983156983155 983142983154983151983149
983156983144983141 983152983154983141983139983141983140983145983150983143 983149983151983150983156983144 983156983151 983152983154983141983140983145983139983156 983138983151983156983144 983158983145983151983148983141983150983156 983137983150983140 983152983154983151983152983141983154983156983161 983139983154983145983149983141 983154983137983156983141983155 983142983151983154 983156983144983141 983149983151983150983156983144983155 983145983150 983151983157983154 983140983137983156983137983155983141983156 983127983141 983142983151983157983150983140 983150983151
983155983156983137983156983145983155983156983145983139983137983148983148983161 983155983145983143983150983145983142983145983139983137983150983156 983154983141983148983137983156983145983151983150983155983144983145983152983155 983138983141983156983159983141983141983150 983139983145983156983137983156983145983151983150983155 983159983137983154983154983137983150983156 983137983154983154983141983155983156983155 983149983145983155983140983141983149983141983137983150983151983154 983137983154983154983141983155983156983155 983137983150983140 983142983141983148983151983150983161 983137983154983154983141983155983156983155
983137983150983140 983156983144983141 983158983145983151983148983141983150983156 983137983150983140 983152983154983151983152983141983154983156983161 983139983154983145983149983141 983154983137983156983141983155 983151983142 983156983144983141 983138983141983137983156983155 983112983151983159983141983158983141983154 983137983142983156983141983154 983140983145983155983137983143983143983154983141983143983137983156983145983150983143 983156983144983141 983158983145983151983148983141983150983156 983137983150983140 983152983154983151983152983141983154983156983161
983139983154983145983149983141 983154983137983156983141983155 983145983150983156983151 983155983152983141983139983145983142983145983139 983139983154983145983149983141983155 983159983141 983140983145983140 983142983145983150983140 983156983144983137983156 983149983145983155983140983141983149983141983137983150983151983154 983137983150983140 983142983141983148983151983150983161 983137983154983154983141983155983156983155 983155983145983143983150983145983142983145983139983137983150983156983148983161 983152983154983141983140983145983139983156983141983140 983137 983140983141983139983154983141983137983155983141
983145983150 983154983151983138983138983141983154983161 983154983137983156983141983155 983159983144983145983139983144 983145983155 983139983151983150983155983145983155983156983141983150983156 983159983145983156983144 983156983144983141 983148983145983156983141983154983137983156983157983154983141 983105983140983140983145983156983145983151983150983137983148983148983161 983159983141 983145983150983156983141983154983158983145983141983159983141983140 983140983141983152983137983154983156983149983141983150983156 983152983141983154983155983151983150983150983141983148 983137983150983140983148983141983137983154983150983141983140 983156983144983137983156 983156983144983141 983116983145983150983139983151983148983150 983120983151983148983145983139983141 983108983141983152983137983154983156983149983141983150983156 983144983137983140 983155983156983137983142983142983141983140 983137 983156983141983137983149 983140983141983155983145983143983150983141983140 983155983152983141983139983145983142983145983139983137983148983148983161 983156983151 983137983152983152983154983141983144983141983150983140 983154983151983138983138983141983154983161
983155983157983155983152983141983139983156983155 983140983157983154983145983150983143 983156983144983145983155 983156983145983149983141983142983154983137983149983141 983159983144983145983139983144 983155983141983141983149983155 983156983151 983141983160983152983148983137983145983150 983156983144983141 983137983154983154983141983155983156983085983154983151983138983138983141983154983161 983154983137983156983141 983154983141983148983137983156983145983151983150983155983144983145983152
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We believe our explanation has a stronger theoretical foundation The ratio of property
crimes per officer affects productivity outputs differently than the ratio of violent crimes per
officer for two reasons First violent crimes are different in nature than property crimes
especially with regard to clearance rates Second property crimes often require more
investigative and follow-up time than violent crimes thereby reducing an officerrsquos amount of
productive time and proactive activity
Table 3 also provides a key piece of evidence to support this assertion Table 3 shows
that the number of reported violent crimes per officer increases an officerrsquos number of warrant
and misdemeanor arrests Yet the relationship is the opposite for traffic enforcement the violent
crime-to-cop ratio actually decreases traffic citations This change is due to the fact that while
violent crime investigations often yield warrant and misdemeanor arrests they do not produce
traffic citations Furthermore responding to a greater number of violent crimes decreases the
amount of available time to conduct traffic enforcement in turn reducing the number of traffic
citations In other words the violent crime-to-cop ratio affects traffic stops the same way that
the property crime-to-cop ratio affects all productivity outputs by reducing an officerrsquos
productive time and opportunities for proactivity In fact we found a moderate correlation
between a patrol arearsquos crime-to-cop ratio and the amount of productive time enjoyed by officers
who worked that area The correlation between an arearsquos violent crime-to-cop ratio and
productive time was -21 while the correlation between an arearsquos property crime-to-cop ratio and
productive time was -33 We believe this evidence indicates that officers who work in areas
with a high crime-to-cop ratio have less time for self-initiated activities which drives down
productivity
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We suggest that the number of police officers and crime rates of an area need to be
analyzed in conjunction with each other Prior theories of police productivity have viewed and
examined these variables separately when in reality we should view these variables in tandem
Combining these variables into crime-to-cop ratios provides a more nuancedmdashand accuratemdash
understanding of patrol activities and their relationship to productivity
Although we found strong evidence that an officerrsquos patrol area affects his or her
productivity (in terms of raw outputs) we also found that a few officer-level variables affected
productivity as well including traffic stops intelligence reports and working third shift Our
results appear to substantiate what many police practitioners already suspect officers who are
proactive in contacting citizens (traffic stops) and building investigations (intelligence reports)
generate more warrant and misdemeanor arrests In addition while some newer police officers
are forced to work third shift because they do not have sufficient seniority to bid for better days
off and shift times many officers work nights because they enjoy the types of calls (eg in-
progress felonies) meaning they may be more motivated to stop vehicles make field contacts
and in turn engage in activities that generate arrests (Brehm amp Gates 1993)
These results have important implications for police researchers and administrators
First our results suggest a need for a deeper understanding of how the staffing levels crime
rates and the other characteristics of a patrol area intersect Second if departments evaluate
their patrol officers using raw productivity outputs such as misdemeanor or felony arrests
administrators must recognize that factors beyond the control of patrol officers might affect
monthly or annual productivity Our research suggests that a patrol arearsquos crime-to-cop ratio can
substantially affect a patrol officerrsquos productivity regardless of the actual crime rate If the
characteristics of a patrol area affect raw productivity outputs police departments should employ
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more advanced productivity analytics (ie VORC) that account for activities which reduce
productive time and evaluate an officerrsquos productivity in terms of available minutes for
proactivity
Nonetheless evaluating both patrol officers and police departments using traffic citations
warrants misdemeanors and felonies is useful because this type of data is readily understood by
citizens and civic officials A neighborhood watch group for instance might care more about
the number of arrests their officers make and less about those officersrsquo productivity scores Our
data suggests that if citizens want patrol officers to write more traffic citations and make more
arrests they should lobby for more police officers on the street It is true that reducing an arearsquos
crime rate impacts the crime-to-cop ratio but our results indicate that putting police officers on
the street may be the most direct way to affect the crime-to-cop ratio
Our study is not without limitations We only examined a police department which uses
the generalist style of policing and it is unknown if our results are applicable to police
departments which use a specialist style of policing Theoretically a departmentrsquos policing
styles can affect factors that increase or decrease a patrol officerrsquos productive time It is possible
that the number of property crimes and violent crimes per officer do not significantly affect a
specialist patrol officerrsquos productivity outputs versus a generalist officerrsquos outputs Future
studies should investigate whether our results can be generalized to all police departments
We have only identified two macro-level variables which affect patrol officer
productivity and we recognize there may be other variables In addition we recommend that
future studies incorporate a wider array of variables and test competing models to determine
whether micro or macro-level variables exhibit stronger effects on productivity In the end
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managing patrol officers and improving productivity requires a greater understanding of both
patrol officers and the areas they work
Acknowledgements and Credits We would like to thank Denver Police Department Crime
Analyst Melissa Ohlhorst and Pennsylvania State University Professor Barry Ruback as well as
the three anonymous reviewers for their insightful comments We also salute Lincoln Police
Sergeant Jake Dilsaver for his logistical support of this project
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References
Amaranto E Steinberg J Castellano C and Mitchell R (2003) ldquoPolice stress interventionsrdquo
Brief Treatment and Crisis Intervention Vol 3 No 1 pp 47-53
Becker G (1985) ldquoHuman capital effort and the sexual division of laborrdquo The Journal of
Labor EconomicsVol 3 No 1 pp 33-58
Bonkiewicz L (2015) ldquoBobbies and baseball players Evaluating patrol officer productivity
using sabermetricsrdquo Police Quarterly Vol 18 No 1 pp 55-78
Braga A and Bond J (2008) ldquoPolicing crime and disorder hot spots A randomized controlled
trialrdquo Criminology Vol 46 No 3 pp 577-607
Brown J and Campbell E (1990) ldquoSources of occupational stress in the policerdquo Work
and Stress Vol 4 No 4 pp 305-318
Chappell A MacDonald J and Manz P (2006) ldquoThe organizational determinants
of police arrest decisionsrdquo Crime and Delinquency Vol 52 No 2 pp 287-306
Cloninger D and Sartorius L (1979) ldquoCrime rates clearance rates and enforcement effort
The case of Houston Texasrdquo American Journal of Economics and Sociology Vol 38
No 4 pp 389-402
Corman H and Mocan N (2005) ldquoCarrots sticks and broken windowsrdquo Journal of Law and
Economics Vol 48 No 1 pp 235-266
Crank J (1990) ldquoThe influence of environmental and organizational factors on police style in
urban and rural environmentsrdquo Journal of Research in Crime and Delinquency Vol 27
pp 166-189
Goodman A (1990) ldquoA model for police officer burnoutrdquo Journal of Business and
Psychology Vol 5 No 1 pp 85-99
Gould E Weinberg B and Mustard D (2002) Crime rates and local labor marketopportunities in the United States 1979ndash1997rdquo Review of Economics and Statistics Vol
84 No 1 pp 45-61
Hipp J and Roussell A (2013) ldquoMicro-and macro-environment population and the
consequences for crime ratesrdquo Social Forces Vol 92 No 2 pp 563-595
Hatry H (1976) ldquoApproaches to productivity measurement and program evaluationrdquo Public
Productivity Review Vol 1 No 3 pp21-28
Hawkins H (2001) ldquoPolice officer burnout a partial replication of Maslachs Burnout
Inventoryrdquo Police Quarterly Vol 4 No 3 pp 343-360
7252019 PIJPSM-05-2015-0064
httpslidepdfcomreaderfullpijpsm-05-2015-0064 2530
Herbert S (1998)ldquoPolice subculture reconsideredrdquo Criminology Vol 36 No 2 pp 343-370
Huizinga D and Henry K (2008) ldquoThe effect of arrest and justice system sanctions on
subsequent behavior Findings from longitudinal and other studiesrdquo In The long view of
crime A synthesis of longitudinal research Springer New York pp 220-254
Johnson R (2011) ldquoOfficer attitudes and management influences on police work productivityrdquo
American Journal of Criminal Justice Vol 36 pp 293-306
Kane R (2006) ldquoOn the limits of social control Structural deterrence and the policing of
lsquosuppressiblersquo crimesrdquo Justice Quarterly Vol 23 No 2 pp 186-213
Kubrin C Messner S Deane G McGeever K and Stucky T (2010) ldquoProactive
policing and robbery rates across US citiesrdquo Criminology Vol 48 No 1 pp 57-97
Lersch K (2002) ldquoAre citizen complaints just another measure of officer productivity An
analysis of citizen complaints and officer activity measuresrdquo Police Practice and
Research Vol 3 pp 135-147
Light M and Harris C (2012) Race space and violence exploring spatial dependence in
structural covariates of white and black violent crime in US countiesrdquo Journal of
Quantitative Criminology Vol 28 No 4 pp 559-586
Maxfield M (1982) ldquoService time dispatch time and demand for police services Helping
more by serving lessrdquo Public Administration Review pp 252-263
Maxfield M Lewis D and Szoc R (1980) ldquoProducing official crimes Verified crime reports
as measures of police outputrdquo Social Science Quarterly pp 221-236
Nakosteen R and Zimmer M (2001) ldquoSpouse selection and earnings Evidence of marital
sortingrdquo Economic InquiryVol 39 No 2 pp 201-2013
Shane J (2010) ldquoOrganizational stressors and police performancerdquo Journal of Criminal
Justice Vol 38 No 4 pp 807-818
Shane J (2011) ldquoDaily work experiences and police performancerdquo Police Practice and
Research Vol 13 No 3 pp 1-19
Sherman L Gartin P and Buerger M (1989) ldquoHot spots of predatory crime Routine
activities and the criminology of placerdquoCriminology Vol 27 No 1 pp27-56
Truman J and Rand M (2010) ldquoBureau of Justice Statistics Bulletin National Crime
Victimization Survey Criminal Victimization 2009rdquo US Department of Justice Office of
Justice programs
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Van Meter D (2001) Evaluating dysfunctional police performance A zero-based approach
Charles C Thomas Publisher Springfield IL
Vila B and Kenney D (2002) ldquoTired cops The prevalence and potential consequences of
police fatiguerdquo National Institute of Justice
Vila B Morrison G and Kenney D (2002) ldquoImproving shift schedule and work-hour
policies and practices to increase police officer performance health and safetyrdquo Police
Quarterly Vol 5 No 1 pp 4-24
Vollaard B and Koning P (2009) ldquoThe effect of police on crime disorder and victim
PrecautionEvidence from a Dutch victimization surveyrdquo International Review of Law
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Weisburd D Maher L Sherman L Buerger M Cohn E and Petrisino A (1993)
ldquoContrasting crime general and crime specific theory The case of hot spots of crimerdquo
New Directions in Criminological Theory Advances in Criminological Theory Vol 4
pp 45-70
Author Biography
Luke Bonkiewicz is a patrol officer with the Lincoln Police Department
7252019 PIJPSM-05-2015-0064
httpslidepdfcomreaderfullpijpsm-05-2015-0064 2730
983110983145983143983157983154983141 983089983086 983126983119983122983107 983110983151983154983149983157983148983137
983101 983089983088983088lowast + + ) + ) + ) + ) lowast )) minus + + + 983090983088 + ) +983089983088)) minus 983089983088983088 lowast
983086 983097) lowast )
7252019 PIJPSM-05-2015-0064
httpslidepdfcomreaderfullpijpsm-05-2015-0064 2830
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983119983142983142983145983139983141983154 983120983154983151983155983141983139983157983156983145983151983150
983122983137983156983141C983110983123
983117983145983150983157983156983141983155983110983125983120
983117983145983150983157983156983141983155
983119983142983142983145983139983141983154
A983155983155983145983155983156983117983145983150983157983156983141983155
983113983150983139983145983140983141983150983156983122983141983152983151983154983156983155
AC983113983122983141983152983151983154983156983155 983123983141983148983141983139983156983145983158983141983155 983127983137983154983150983145983150983143983155 983119983142983142983145983139983145983137983148983155 983127983137983154983154983137983150983156983155 D983125983113983155 983117983145983155983140983141983149983141983137983150983151983154983155 983110983141983148983151983150983145983141983155
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3 72 32496 6312 6600 144 564 444 720 540 84 36 24 12
4 90 33000 5232 4980 216 192 480 768 240 144 12 72 12
5 91 41724 4824 6048 312 636 132 504 324 108 7 24 24
6 83 23916 7056 6192 144 756 528 996 336 132 36 72 12
7 95 24468 6840 5892 156 624 324 456 480 72 36 120 24
8 90 39972 5196 4920 240 504 384 492 264 36 2 72 24
9 79 25500 4068 5100 336 396 192 684 204 24 36 120 10
10 87 21984 5412 4524 348 552 480 792 312 108 48 84 36
983124983137983138983148983141 2 983123983157983149983149983137983154983161 983123983156983137983156983145983155983156983145983139983155 983142983151983154 983126983137983154983145983137983138983148983141983155
983123983141983139983151983150983140983085983116983141983158983141983148 983126983137983154983145983137983138983148983141983155 (983120983137983156983154983151983148 A983154983141983137) 983118=40 983122983137983150983143983141 983123D 983117983141983137983150
983120983151983152983157983148983137983156983145983151983150 983140983141 983150983155983145983156983161 20229830856956 1687 4436
983112983151983157983155983141983144983151983148983140 983149983141983140983145983137983150 983145983150983139983151983149983141 ($) 2628198308576020 16535 49756
983159983144983145983156983141 7398308593 007 083
983126983145983151983148983141983150983156 983139983154983145983149983141 983154983137983156983141 (983152983141983154 100000) 8009830854800 1203 2521
983120983154983151983152983141983154983156983161 983139983154983145983149983141 983154983137983156983141 (983152983141983154 100000) 31009830859800 2027 6524
983120983154983151983152983141983154983156983161 983139983154983145983149983141983155 983152983141983154 983151983142983142983145983139983141983154 5329830851097 1663 8167
983126983145983151983148983141983150983156 983139983154983145983149983141 983152983141983154 983151983142983142983145983139983141983154 194983085372 559 2929
983110983145983154983155983156983085983116983141 983158983141983148 983126 983137983154983145983137983138983148983141 983155 (983119983142983142983145983139983141983154) 983118=302 983122983137983150983143983141 983123D 983117983141 983137983150
983111983141983150983140983141983154 (983142983141983149983137983148983141=0 983149983137983148983141=1) 09830851 24 94
983117983137983154983154983145983141983140C983151983144983137983138983145983156983145983150983143 09830851 18 79
983151983142 C983144983145983148983140983154983141983150 09830855 170 90
983129983141983137983154983155 983141983160983152983141983154983145983141983150983139983141 198308537 779 1063
C983137983148983148983155 983142983151983154 983155983141983154983158983145983139983141 4819830851011 16121 61365
983124983154983137983142983142983145983139 983155983156983151983152983155 16983085901 14376 20624
983110983145983141983148983140 983145983150983142983151983154983149983137983156983145983151983150 983154983141983152983151983154983156983155 0983085227 2153 2530
983124983154983137983142983142983145983139 983139983145983156983137983156983145983151983150983155 249830851246 14632 18642
983117983145983155983140983141983149983141983137983150983151983154 983137983154983154983141983155983156983155 19983085403 5965 10663
983110983141983148983151983150983161 983137983154983154983141983155983156983155 098308571 1066 852
983126983119983122C 9830851959830851043 151 24
1983155983156 2983150983140 3983154983140
104 96 102983123983144983145983142983156
7252019 PIJPSM-05-2015-0064
httpslidepdfcomreaderfullpijpsm-05-2015-0064 2930
983124983137983138983148983141 3 983112983145983141983154983137983154983139983144983145983139983137983148 983116983145983150983141983137983154 983117983151983140983141983148983155 E983155983156983145983149983137983156983145983150983143 983119983142 983142983145983139983141983154983155 A983150983150983157983137983148 983124983154983137983142983142983145983139 C983145983156983137983156983145983151983150983155 983127983137983154983154983137983150983156983155 983117983145983155983140983141983149983141983137983150983151983154983155 983137983150983140 983110983141983148983151983150 983145983141983155
983123983141983139983151983150983140983085983116983141983158983141983148 983126983137983154983145983137983138983148983141983155 (983120983137983156983154983151983148 A983154983141983137) 983118=40 983138 983123E 983138 983123E 983138 983123E 983138 983123E
983120983151983152983157983148983137983156983145983151983150 983140983141983150983155983145983156983161 001 003 001 001 98308501 01 00 00
983112983151983157983155983141983144983151983148983140 983149983141983140983145983137983150 983145983150983139983151983149983141 ($) 002 001 983085000 000 00 00 98308500 00
983159983144983145983156983141 13363 32472 9830858921 5664 9830853274 19211 9830854447 3255
983126983145983151983148983141983150983156 983139983154983145983149983141 983154983137983156983141 (983152983141983154 100000) 006 004 001 001 98308501 02 01 02
983120983154983151983152983141983154983156983161 983139983154983145983149983141 983154983137983156983141 (983152983141983154 100000) 983085003 005 983085002 001 02 03 98308501 00
983120983154983151983152983141983154983156983161 983139983154983145983149983141983155 983152983141983154 983151983142983142983145983139983141983154 983085225 99 98308515 03 98308552 24 98308503 03
983126983145983151983148983141983150983156 983139983154983145983149983141 983152983141983154 983151983142983142983145983139983141983154 983085168 68 29 14 76 31 22 12
983110983145983154983155983156983085983116983141983158983141983148 983126983137983154983145983137983138983148983141983155 (983119983142983142983145983139983141983154) 983118=302
983117983137983148983141 4673 1622 366 147 1613 1051 91 158
983117983137983154983154983145983141983140C983151983144983137983138983145983156983145983150983143 9830857516 3254 201 278 9830851158 1169 98308595 167
983151983142 C983144983145983148983140983154983141983150 183 702 75 77 983085262 217 983085396 207
983129983141983137983154983155 983141983160983152983141983154983145983141983150983139983141 9830851065 492 98308512 13 983085163 33 98308506 05C983137983148983148983155 983142983151983154 983155983141983154983158983145983139983141 24 13 02 02 14 01 98308508 05
983124983154983137983142983142983145983139 983155983156983151983152983155 983085983085 983085983085 03 00 09 02 00 00
983110983145983141983148983140 983145983150983142983151983154983149983137983156983145983151983150 983154983141983152983151983154983156983155 209 33 20 04 79 19 10 03
983123983141983139983151983150983140 983155983144983145983142983156 (983154983141983142983141983154983141983150983139983141 983143983154983151983157983152 983142983145983154983155983156) 2603 1778 187 195 809 730 147 160
983124983144983145 983154983140 983155983144983145 983142983156 (983154983141 983142983141 983154983141 983150983139983141 983143983154983151983157983152 983142983145 983154983155983156) 9872 2155 79 213 2532 952 393 132
983113983150983156983141983154983139983141983152983156 32591 5361 758 339 4147 1912 310 484
983123983145983143983149983137983085983155983153983157983137983154983141983140 1541467 19287 206702 5598
C983144983145983085983155983153983157983137983154983141983140 8294 6133 12348 10663
983152 lt 05
983152lt01
983152lt001
983118983151983156983141 A983148983148 983158983137983154983145983137983138983148983141983155 983137983154983141 983157983150983139983141983150983156983141983154983141983140
983127A983122983122A983118983124 A983122983122E983123983124983123 983117983113983123DE983117EA983118983119983122 A983122983122E983123983124983123 983110E983116983119983118983129 A983122983122E983123983124983123983124983122A983110983110983113C C983113983124A983124983113983119983118983123
7252019 PIJPSM-05-2015-0064
httpslidepdfcomreaderfullpijpsm-05-2015-0064 3030
983124983137983138983148983141 4 983112983145983141983154983137983154983139983144983145983139983137983148 983116983145983150983141983137983154 983117983151983140983141983148983155 E983155983156983145983149983137983156983145983150983143 983119983142983142983145983139983141983154983155 A983150983150983157983137983148 983126983137983148983157983141 983119983158983141983154 983122983141983152983148983137983139983141983149983141983150983156 C983151983152 (983126983119983122C)
983123983141983139983151983150983140983085983116983141983158983141983148 983126983137983154983145983137983138983148983141983155 (983120983137983156983154983151983148 A983154983141983137) 983118=40 983138 983123E
983120983151983152983157983148983137983156983145983151983150 983140983141983150983155983145983156983161 98308500 00
983112983151983157983155983141983144983151983148983140 983149983141983140983145983137983150 983145983150983139983151983149983141 ($) 00 01
983159983144983145983156983141 318 345
983126983145983151983148983141983150983156 983139983154983145983149983141 983154983137983156983141 (983152983141983154 100000) 98308500 00
983120983154983151983152983141983154983156983161 983139983154983145983149983141 983154983137983156983141 (983152983141983154 100000) 00 00
983120983154983151983152983141983154983156983161 983139983154983145983149983141983155 983152983141983154 983151983142983142983145983139983141983154 98308502 04
983126983145983151983148983141983150983156 983139983154983145983149983141 983152983141983154 983151983142983142983145983139983141983154 02 05
983110983145983154983155983156983085983116983141983158983141983148 983126983137983154983145983137983138983148983141983155 (983119983142983142983145983139983141983154) 983118=302
983117983137983148983141 27 15
983117983137983154983154983145983141983140C983151983144983137983138983145983156983145983150983143 98308532 31
983151983142 C983144983145983148983140983154983141983150 05 03
983129983141983137983154983155 983141983160983152983141983154983145983141983150983139983141 00 01
C983137983148 983148983155 983142983151983154 983155983141983154983158983145983139983141 02 01
983124983154983137983142983142983145983139 983155983156983151983152983155 01 00
983110983145 983141983148983140 983145983150983142983151983154983149983137983156983145 983151983150 983154983141 983152983151983154983156983155 02 00
983123983141983139983151983150983140 983155983144983145983142983156 ( 983154983141983142983141983154983141983150983139983141 983143983154983151983157983152 983142 983145983154983155983156) 23 12
983124983144983145983154983140 983155983144983145983142983156 (983154983141983142983141983154983141983150983139983141 983143983154983151983157983152 983142 983145983154983155983156) 55 14
983113983150983156983141983154983139983141983152983156 165 36
983123983145983143983149983137983085983155983153983157983137983154983141983140 66
C983144983145983085983155983153983157983137983154983141983140 9846
983152 lt 05
983152lt01
983152lt001
983118983151983156983141 A983148983148 983158983137983154983145983137983138983148983141983155 983137983154983141 983157983150983139983141983150983156983141983154983141983140
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number of traffic citations warrants and misdemeanor and felony arrests generated by officers
annually We also examine the relationship between these two variables and a more
sophisticated productivity metric Value Over Replacement Cop (VORC) Finally we discuss
why police scholars and practitioners should pay closer attention to the practical macro-level
correlates of productivity as well as incorporate more advanced patrol productivity metrics
Literature Review
Productivity scholars have generally divided the concept of worker productivity into two
spheres (Hatry 1976) Effectiveness measures a worker or organizationrsquos quality of service
while efficiency gauges how well a worker or organization produces a particular output using the
least amount of resources such as manpower time and money Most police research has
examined productivity in terms of efficiency due mainly to the data available for analysis such
as calls for service and arrests
Police scholars have struggled with how to properly measure patrol officer productivity
There appear to be two main approaches The first approach attempts to identify and quantify
specific types of patrol productivity outputs Some examples include evaluating productivity
using an officerrsquos total arrests (Crank 1990) clearance rates (Marciano amp Heaton 2010)
number of traffic citations (Johnson 2011) and citizen complaints (Lersch 2002) However
researchers have noted that evaluating productivity using these kinds of raw outputs is
ldquoconceptually-simplerdquo shaped primarily by data availability (Chappell MacDonald amp Manz
2006)
The second approach to analyzing patrol officer productivity has incorporated multi-
dimensional indicators of police performance For example Shane (2011) conceptualized
productivity using a z-score summary of multiple outputs including arrests traffic citations
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field contacts official reports sick hours and administrative complaints Similarly Van Meterrsquos
Performance-Based Management or ldquozero-based approachrdquo (Van Meter 2001) uses three planks
(non-scheduled absenteeism cost of preventable error and productive use of time) to evaluate
patrol officers
Notably most existing police productivity research has ignored the concept of productive
time or the amount of time officers have for self-initiated activities after handling calls for
service investigating crimes completing reports and follow-ups and assisting other officers
Using productive time to evaluate officers is critical because two officers could achieve the same
output (in terms of raw arrests or reports) but have done so with considerably different amounts
of productive time due to calls for service follow-ups etc Recent research has proposed the use
of Value Over Replacement Cop or VORC (Bonkiewicz 2015) a metric that incorporates more
than a dozen patrol activities weights those activities evaluates performance in terms of
productive time and compares an officerrsquos performance against a fixed hypothetical estimate
(ie a replacement officer)
In addition to conceptualizing patrol officer productivity researchers have also studied
potential antecedents of productivity including not only biographical variables but also factors
involving workplace environment First certain biographical characteristics might impact
productivity (Hawkins 2001) For instance officers who have children might experience sleep
deprivation and greater fatigue as a result of parenting responsibilities thereby reducing their
workplace energy and motivation and in turn their productivity (Shane 2010) Marital status
may also impact productivity Although the relationship between marriage and worker
productivity is well debated in the literature (Nakosteen amp Zimmer 2001 Becker 1985) it
seems reasonable to believe that married officers might be more productive than unmarried
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officers Married officers have someone in whom to confide a partner who hears about the
challenges and rigors of the job which may lead to stress reduction and improve workplace
motivation An officerrsquos age is another consideration Older officers might be less productive
than younger officers perhaps due to burnout as well as the physical demands of law
enforcement (Goodman 1990) Officersrsquo perceptions of their job may also affect their
proactivity and in turn productivity Brehm and Gates (1993) found that officers who disliked
certain aspects of law enforcement (eg taking people to jail) impacted whether officers engaged
in proactive activities or shirked their duties
Additionally there are also operational features (the ldquoday to dayrdquo rigors) of law
enforcement which can decrease productivity and increase misconduct such as the perceived
danger of the job (Amaranto et al 2003) work schedules and shift work (Vila Morrison amp
Kenney 2002) and fatigue (Vila amp Kenney 2002) Notably in studying the relationship
between daily work experience and performance Shane (2011) found that workload was the
ldquomost important predictor of performancerdquo (p 13)
We believe this last finding merits further study First supervisors evaluate patrol
officers at least in part using productivity outputs If there are factors (ie heavy workload)
beyond an officerrsquos control that are affecting his or her productivity both line-level supervisors
and command staff members should acknowledge and account for these variables during
performance reviews Second if macro-level variables create different workloads thereby
affecting productivity outputs then police researchers should include these measures in future
studies on police activity We suggest that there are at least two macro-level variables important
to future police productivity research
Crime-to-Cop Ratios and Patrol Officer Productivity
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Criminological research has long recognized that crime rates are not uniform across
neighborhoods and cities (Sherman et al 1989 Weisburd et al 1993) Instead of ldquopolicing
peoplerdquo law enforcement agencies frequently identify ldquohot spotsrdquo and devote additional
resources to reducing crime and disorder in specific problem areas With this perspective in
mind it might seem natural to examine the relationship between the crime rate of a patrol area
and an officerrsquos productivity outputs (or vice-versa) For example a high crime area might net
an officer more arrests (and therefore more productivity outputs) than a low crime area On the
other hand a high crime area might also occupy a great deal of an officerrsquos productive time
prohibiting the officer from engaging in proactive activities However crime rate does not fully
capture how a particular area might affect an officerrsquos productivity
Instead we believe macro-level variables related to crime should be estimated in terms of
the number of patrol officers in a patrol area specifically violent crimes per officer and property
crimes per officer For instance some officers may patrol a lower crime area but end up
responding to a greater number of violent crimes than officers in a high crime area simply
because they have fewer officers working the street This ldquocrime per coprdquo ratio can dramatically
affect officersrsquo productivity
For example when an officer investigates a crime he or she might interview the victim
suspect and witnesses as well as take photographs collect evidence and make an arrest There
may be additional follow-up the next day or week such as checking pawnshops for stolen
property or tracking down and interviewing additional witnesses All these activities could
decrease an officerrsquos time for self-initiated activities and in turn an officerrsquos overall
productivity A high crime per cop ratio therefore could decrease productivity for officers even
if these officers work in an area with a low crime rate Furthermore an arearsquos crime per cop
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ratio could also affect officersrsquo productive time even if they are not the primary investigating
officer because secondary officers are often needed during serious incidents such as robberies or
burglaries
The ldquoload sheddingrdquo hypothesis by Maxfield et al (1980 see also Maxfield 1982) also
has important implications for any study examining patrol officer productivity Maxfield et al
suggested that while variations in workload do not affect patrol officersrsquo decisions to record
serious crimes workload does affect officersrsquo decisions to record less serious crimes it may also
affect self-initiated activities as well (Herbert 1989)
Finally we believe that macro-level variables affecting productivity should be divided
between violent and property crimes to account for the time and resources needed to investigate
each incident type When officers respond to assaults they often contact suspects and victims on
scene meaning that officers are able to interview all involved individuals collect evidence and
make an arrest sometimes in less than an hour However when officers respond to burglaries or
larcenies they may be required to dust for fingerprints swab for DNA canvass a neighborhood
for witnesses check pawnshops and engage in other investigative activities that require hours
and even days if the case requires follow-up
With these issues in mind our study addresses the following research questions First
what is the relationship between a patrol arearsquos rate of violent crimes per officer and patrol
officer productivity outputs (ie traffic citations as well as warrant misdemeanor and felony
arrests) Second what is the relationship between a patrol arearsquos rate of property crimes per
officer and patrol officer productivity outputs (again traffic citations and warrant misdemeanor
and felony arrests) Third what is the relationship between the rate of property and violent
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crimes per officer and a patrol officerrsquos productivity when we introduce a more sophisticated
method for analyzing productivity a metric based on productive time and not total time on duty
Before discussing our data and methods there are two important issues meriting brief
discussion First we argue that the crime-to-cop ratio may affect the number of traffic stops
warrants misdemeanors and felonies that a patrol officer makes It can also be argued that the
number of arrests might impact the crime-to-cop ratio Our intent is not to comprehensively
discuss the relationship between the number of officersofficer arrests and an arearsquos crime rate
but it bears noting that the research is inconclusive on this point
Some evidence suggests that the relationship between arrests and crime rates is non-
linear that is crime rates decrease as arrests increase but eventually hit a ldquofloorrdquo and reach
equilibrium (Kane 2006) Second when studies examine the arrest-crime rate relationship they
often find that the relationship only exists for specific crimes in their datasets such as robbery
and motor vehicle thefts (Corman Hope amp Mocan 2005) robbery but not assaults (Kane 2006)
and homicide and auto thefts only (Cloninger amp Sartorius 1979) and so forth Overall the
literature on the arrests-crime rate relationship is mixedmdashsome research finds an effect between
arrests and crime rates some does not (Huizinga and Henry 2008)
The second issue concerns whether a law enforcement agency operates using a generalist
or specialist policing style We analyze data from a generalist municipal police department
meaning that patrol officers investigate low and mid-level crimes but investigators and
detectives are responsible for handling serious crimes cases involving lengthy investigations
(eg narcotics trafficking or white-collar crimes) and incidents in which patrol officers may
need assistance (for example an incident involving numerous victims and witnesses as well as
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cases involving extensive follow-up or knowledge on a specific subject such as gangs or
narcotics)
By contrast when patrol officers in a specialist-style police department respond to any
incident potentially requiring follow-up they might only contact the victim and either hold the
scene until an investigator arrives or forward the information to a detective for follow-up It is
possible indeed likely that macro-level variables might affect generalist patrol officers
differently than specialized officers Due to data limitations however our study focuses on
generalist police officers
Data and Methods
We gathered data on patrol officers and their patrol areas from several sources First we
collected patrol officer productivity data from the Lincoln (Nebraska) Police Departmentrsquos 2013-
2014 productivity reports Using Computer-Aided Dispatch (CAD) and a centralized
productivity database LPD measures numerous patrol officer activities Every month (and
year) LPD generates productivity reports which display each patrol officerrsquos activities and
outputs (see Table 1 for an example of a squadrsquos annual productivity report) Each patrol
activity and output merits further explanation
INSERT TABLE 1 HERE
Calls for Service (CFS) and Officer Assist time When dispatchers send patrol officers on
a call they use CAD to track the minutes the officer spends on the call If the call is a priority
incident dispatchers send both a primary and secondary officer Other unassigned officers might
also respond to the CFS meaning that multiple officers could be secondary officers The annual
productivity record tracks amount of time an officer spent as a primary responder and a
secondary responder
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Report writing time Standard operating procedures dictate that LPD patrol officers
complete specific types of reports for incidents An incident report (IR) details biographical
information of the individuals involved in the incident and summarizes the incident An
Additional Case Information (ACI) is a more detailed report that documents additional
investigative steps or the actions of assisting officers Although the time spent on an IR or ACI
could vary depending on the call for service the average IR and ACI take roughly twenty
minutes to complete1
The Lincoln Police Department also tracks self-initiated activities Most productivity
results from the following proactive activities
Selectives The Lincoln Police Department tracks specific officer patrol activity by
coding its neighborhoods schools parks and business districts When an officer patrols an at-
risk business district or a high-crime neighborhood for example the officer notifies dispatch
using that arearsquos specific code Girded by the principles of ldquohot spotsrdquo policing research officers
use selectives to identify potential candidates for POP projects and heighten the perception of
risk among criminals (Braga amp Bond 2008)
Traffic Warnings and Official Citations Every Lincoln patrol officer drives a police
cruiser and conducts traffic enforcement issuing traffic warnings and official citations The
LPD records department tracks patrol officersrsquo warnings and citations
Misdemeanors felonies and warrants Patrol officers also make misdemeanor and
felony arrests during their productive time Officers might detain a motorist and arrest him or
her for driving under a suspended license or driving while intoxicated or contact a pedestrian
1 983119983142983142983145983139983141983154983155 983156983161983152983141 983137983148983148 983154983141983152983151983154983156983155 983151983150 983139983151983149983152983157983156983141983154983155 983127983141 983151983138983156983137983145983150983141983140 983156983144983145983155 983137983158983141983154983137983143983141 983138983161 983151983138983155983141983154983158983145983150983143 983137 983155983137983149983152983148983141 983151983142 983116983145983150983139983151983148983150 983152983137983156983154983151983148 983151983142983142983145983139983141983154983155
983139983151983149983152983148983141983156983145983150983143 983113983122983155 983137983150983140 983105983107983113983155 983137983150983140 983159983141 983154983141983139983151983143983150983145983162983141 983156983144983137983156 983156983144983145983155 983137983158983141983154983137983143983141 983149983137983161 983140983145983142983142983141983154 983142983154983151983149 983151983156983144983141983154 983152983151983148983145983139983141 983140983141983152983137983154983156983149983141983150983156983155 983117983151983154983141983151983158983141983154983145983142 983140983141983152983137983154983156983149983141983150983156983155 983154983141983153983157983145983154983141 983151983142983142983145983139983141983154983155 983156983151 983139983151983149983152983148983141983156983141 983156983144983141983145983154 983154983141983152983151983154983156983155 983138983141983142983151983154983141 983138983141983139983151983149983145983150983143 983137983158983137983145983148983137983138983148983141 983142983151983154 983137983150983151983156983144983141983154 983139983137983148983148 983142983151983154 983155983141983154983158983145983139983141
983156983144983145983155 983149983141983137983155983157983154983141983149983141983150983156 983145983155 983157983150983150983141983139983141983155983155983137983154983161
7252019 PIJPSM-05-2015-0064
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and make an arrest for a sex offender registration violation Officers can also search their
departmentrsquos electronic database for individuals with outstanding warrants in their patrol areas
and attempt to arrest these offenders As with warnings and citations the LPD records
department documents and tracks misdemeanor felony and warrant arrests Moreover the court
system also monitors these types of outputs to process cases thereby adding an additional layer
of reporting accuracy and reliability
Field information reports We also measured the number of field information or
ldquointelligencerdquo reports Officers complete these internal reports to document suspicious or
dangerous individuals or circumstances though these reports are not publicly available
VORC In addition to the raw outputs of traffic citations warrants misdemeanors and
felonies we also include an advanced metric for measuring a patrol officerrsquos productivity Value
Over Replacement Cop (VORC) is a method for evaluating officers that assigns different
weights to productivity outputs and then assesses officer productivity in terms of available time
for self-initiated activities or productive time (Bonkiewicz 2015) We use a variation of this
formula that multiplies an officerrsquos officials misdemeanors and felonies by his or her
prosecution rate Our modified use of VORC can best be explained using the following figure
(Figure 1) and five-step process
INSERT FIGURE 1 HERE
where = total monthly on-duty minutes = CFS minutes Γ = follow-up time and meetings ∆
= officer assist time Ε = of IRs Ζ = of ACIs M = of intelligence reports Η = of
selectives Θ = of warnings times two Κ = of warrants times three Ι = of officials times
three Λ = of misdemeanor arrests times their respective weights Ν = of felonies types their
respective weights Ρ = an officerrsquos prosecution rate Ξ = Officerrsquos Productive time (Α-(Β+Γ+∆+
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20(Ε+Ζ))) Ο= Department average P-score or average (Η+ ((Θ)+(Ι)+(Κ)+(Λ)+(Μ)+(Ν)Ρ))
and Π = Department average Self-Initiated Time
First VORC calculates an officerrsquos total minutes spent on CFS follow-ups officer
assists and reports This amount is an officerrsquos total work time or service minutes Second the
formula subtracts an officerrsquos service minutes from the total on-duty minutes2 This is the
officerrsquos amount of productive time Third VORC calculates an officerrsquos productivity score (or
P-score) using a weighted index Departments can choose different weights for productivity
outputs but for the purpose of this study we used our weights based on the statutes governing
the Lincoln Police Department Citations and arrests are weighted based on whether the offense
was a Traffic Offense a Class III II or I Misdemeanor or a Class V IV III II or I Felony
Accordingly a patrol selective is worth one point a traffic warning two points a traffic citation
three points a warrant arrest three points a Class III misdemeanor arrest four points a Class II
misdemeanor arrest five points a Class I misdemeanor arrest six points a Class V Felony seven
points a Class IV Felony eight points a Class III Felony nine points and Class II and I Felonies
(eg homicides) ten points3 The sum of the weighted productivity outputs is the officerrsquos P-
score The P-score is divided by amount of productive time and then multiplied by 100 (for ease
of understanding) This value the productivity quotient represents the officerrsquos productivity
given the available minutes for proactivity such as searching for wanted individuals or traffic
enforcement
Fourth VORC creates a hypothetical replacement officer against which to measure the
patrol officerrsquos output This replacement player might be imagined as a rookie patrol officer or
983090 983119983150983085983140983157983156983161 983156983145983149983141 983140983151983141983155 983150983151983156 983145983150983139983148983157983140983141 983156983154983137983145983150983145983150983143 983156983145983149983141 983158983137983139983137983156983145983151983150 983151983154 983144983151983148983145983140983137983161 983140983137983161983155 983151983142983142 983151983154 983155983145983139983147 983148983141983137983158983141 983113983150 983151983156983144983141983154 983159983151983154983140983155 983151983150983085983140983157983156983161
983156983145983149983141 983145983155 983156983144983141 983137983149983151983157983150983156 983151983142 983156983145983149983141 983137 983152983137983156983154983151983148 983151983142983142983145983139983141983154 983155983152983141983150983156 983159983151983154983147983145983150983143 983156983144983141 983155983156983154983141983141983156983091 983119983157983154 983159983141983145983143983144983156983145983150983143 983155983161983155983156983141983149 983145983155 983151983150983148983161 983151983150983141 983141983160983137983149983152983148983141 983151983142 983156983144983141 983142983148983141983160983145983138983145983148983145983156983161 983156983144983137983156 983126983119983122983107 983151983142983142983141983154983155 983140983141983152983137983154983156983149983141983150983156983155983099 983141983137983139983144 983137983143983141983150983139983161 983139983137983150
983152983154983145983151983154983145983156983145983162983141 983137983150983140 983137983155983155983145983143983150 983139983151983154983154983141983155983152983151983150983140983145983150983143 983159983141983145983143983144983156983155 983156983151 983156983144983141983145983154 983152983154983141983142983141983154983154983141983140 983152983154983151983140983157983139983156983145983158983145983156983161 983151983157983156983152983157983156983155
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a recruit who has not completed the academy performing at ninety percent the capacity of fully
trained police officer4 Using LPDrsquos data VORC creates a hypothetical replacement officer by
dividing the departmental average P-score by the departmental average for productive time and
then multiplying it by the officerrsquos productive time to determine what an average officer would
have generated given the officerrsquos amount of productive time This figure is then multiplied by
9 (the capacity of a replacement officer) divided by the officerrsquos productive time and then
multiplied by 100 to generate the replacement officerrsquos productivity quotient Finally VORC
subtracts the replacement officerrsquos score from the patrol officerrsquos score to calculate the officerrsquos
value over the replacement officer This number or VORC indicates the officerrsquos productivity
per time for self-initiated activities compared to the replacement officerrsquos productivity For
example a VORC of 12 means than officer would produce 012 more productivity points (some
combination of warnings citations and arrests) per minute than a replacement officer
Biographical variables We gathered biographical information about the officers in the
sample including gender years of LPD experience maritalcohabiting status and number of
children We also controlled for whether the officer worked first second or third shift because
our data showed significant differences in the number of calls for service each shift handled as
well as the number of reports each shift completed
Geographic variables It is necessary to measure the characteristics of patrol areas to
account for variation across patrol areas For example some areas may feature different crime
rates or levels of social disorganization which command more department resources and police
officers We gathered data by first mapping out every squadrsquos patrol area and then obtaining
tract-level data from the 2010 US Census Our models included measures of established of
983092
983123983139983144983151983148983137983154983155 983137983150983140 983152983151983148983145983139983161 983149983137983147983141983154983155 983149983137983161 983140983145983155983137983143983154983141983141 983159983145983156983144 983141983155983156983145983149983137983156983145983150983143 983137 983154983141983152983148983137983139983141983149983141983150983156 983151983142983142983145983139983141983154991257983155 983139983137983152983137983139983145983156983161 983137983156 983150983145983150983141983156983161 983152983141983154983139983141983150983156 983119983157983154983143983151983137983148 983145983155 983150983151983156 983156983151 983140983141983142983145983150983145983156983145983158983141983148983161 983141983155983156983145983149983137983156983141 983137 983144983161983152983151983156983144983141983156983145983139983137983148 983151983142983142983145983139983141983154991257983155 983152983154983151983140983157983139983156983145983158983145983156983161 983138983157983156 983149983141983154983141983148983161 983156983151 983141983155983156983137983138983148983145983155983144 983137 983154983141983137983155983151983150983137983138983148983141 983142983145983160983141983140
983152983151983145983150983156 983137983143983137983145983150983155983156 983159983144983145983139983144 983156983151 983139983151983149983152983137983154983141 983137983148983148 983152983137983156983154983151983148 983151983142983142983145983139983141983154983155
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correlates of crime including population density (Hipp amp Roussell 2013) household income
(Gould Weinberg and Mustard 2002) and racial composition (Light amp Harris 2012)
Specifically we use each arearsquos population density median household income and percent
white We also included measures for each arearsquos violent and property crime rate Finally we
gathered information about the number of violent crimes (murders assaults aggravated assaults
sexual assaults and robberies) property crimes (larcenies burglaries vandalisms frauds
forgeries and motor vehicle thefts) and number of police officers in each patrol area thereby
creating variables for violent crime per officer and property crime per officer
Since LPDrsquos patrol officers are grouped with squads it is reasonable to believe that an
officerrsquos productivity might be more similar to an officer within their squad compared to an
officer in a different squad To account for the potential correlations between the individual
units as well as the multi-level nature of the data we used hierarchical linear modeling to
analyze the data
Our primary level variables include an officerrsquos gender marital status number of
children years of law enforcement experience shift calls for service annual number of traffic
stops and citations field information reports and misdemeanor felony and warrant arrests We
also used the officersrsquo productivity information to calculate each patrol officerrsquos VORC
Our secondary level variables include the violent crime per officer rate and property
crime per officer rate for the patrol area of each squad as well several control variables
including population density household median income percent white and the patrol arearsquos
violent and property crime rate Table 2 displays the summary statistics for the first and second-
level variables
INSERT TABLE 2 HERE
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Results
We hypothesized that the violent and property crime-to-cop ratios of an area might affect
the raw productivity outputs of patrol officers as well as a more advanced productivity metric
To examine these questions we constructed five hierarchical linear models estimating the
correlations between reported violent and property crimes per officer and a patrol officerrsquos
annual traffic citations warrant misdemeanor felony arrests and Value Over Replacement Cop
Although not presented here we estimated null models for all dependent variables and found that
significant variation existed between squads when we included no variables included in the
models The chi-square for the LR test for all five null models was statistically significant (p lt
000 in all models)
Table 3 shows that a rise in reported violent crimes per officer increases an officerrsquos
number of warrants and misdemeanors (b= 29 and 76) while arise in reported property crimes
per officer decreases an officerrsquos number of warrants and misdemeanors (b= -15 and -52) We
found no relationship between the crime-to-cop ratios and felony arrests However both the
rates of violent and property crimes per officer decreased the number of traffic citations an
officer writes (b= -168 and -225)
Insert Table 3 here
Table 3 also shows that traffic stops and field information reports are positively
associated with warrant and misdemeanor arrests This supports what police practitioners
already suspect the cops with the most arrests are extremely proactive patrol officers who stop a
large number of vehicles and build investigations through the use of field contacts and
information reports For instance an officer can build felony narcotics cases by contacting
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suspects and informants in the field documenting these efforts through field information reports
and eventually using this information to write search warrants and conduct drug interdiction
Next we investigated whether this relationship between the crime-to-cop ratios and
productivity outputs exists when using an advanced productivity metric specifically an officerrsquos
Value Over Replacement Cop (VORC)
INSERT TABLE 4 HERE
Table 4 shows that there is no correlation between the ratio of property and violent
crimes per officer and VORC Although this stands in contrast to our earlier results we would
not expect to find a relationship between these variables and VORC Advanced metrics such as
VORC evaluate officers based on the amount of time for self-initiated activities (productive
time) and not raw productivity outputs like misdemeanor or felony arrests Even if an officer
took an extremely large number of property crimes thereby decreasing their productive time
VORC would only evaluate that officer based on what the officer produced with the small
amount of productive time This suggests that while a teamrsquos crime-to-cop ratio could affect an
officerrsquos raw productivity outputs ie arrests a teamrsquos crime-to-cop ratio would not affect an
officerrsquos VORC
For example during a given month an officer may take an unusually large number of
burglaries or larcenies thereby leaving the officer with only ten hours for self-initiated activities
With this reduced amount of productive time the officer may generate a below-average number
of officials warrants misdemeanors and felonies which in turn may lead a supervisor to
potentially downgrade the officerrsquos productivity However VORC would recognize and account
for the high number of calls for service minutes and analyze the officerrsquos productivity based on
what the officer generated with only ten hours of productive time It may be that the officer was
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actually very productive but they simply didnrsquot have enough productive time to generate the
same number of traffic citations and arrests as an officer who responded to far fewer property
crimes
Table 4 also shows that traffic stops and field intelligence reports are positively
correlated with VORC These results are consistent with the principles of how VORC evaluates
patrol officer productivity and provides additional support for the validity of this advanced
metric VORC accounts for numerous variables that affect productive time (ie calls for service
minutes) meaning that the key factors which increase or decrease an officerrsquos VORC are largely
related to self-initiated activities (ie traffic stops and information reports) VORC therefore
accounts for the potential effects of macro-level variables and analyzes the activities of the
officer during his or her productive time Nonetheless we recognize that because traffic stops
and field information reports are correlated with VORC an arearsquos crime-to-cop ratio might still
affect an officerrsquos VORC indirectly by impacting the number of stops and information reports
they generate
Discussion
There are several factors which affect a patrol officerrsquos productivity including
biographical characteristics officersrsquo perceptions and attitudes and organizational and
operational features among others This paper identified two macro-level variablesmdashproperty
crimes per officer and violent crimes per officermdashwhich affect the number of traffic citations
warrants misdemeanors and felonies generated annually by an officer Using data from a mid-
sized police department we found that a patrol areas rate of property crimes per officer was
associated with a moderate decrease in an officers annual number of traffic citations warrant
arrests and misdemeanor arrests while a patrol areas rate of violent crimes per officer was also
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associated with a moderate decrease in an officers annual number of traffic citations We also
found that a patrol areas rate of violent crimes per officer is associated with a moderate increase
in an officers annual number of warrant and misdemeanor arrests However when we used a
more advanced productivity metric we found no relationship between property and violent
crimes per officer and an officerrsquos Value Over Replacement Cop or VORC
There are several factors which explain the differing effects of the crime-to-cop ratios on
productivity outputs Let us first address why the violent crime-to-cop ratio would increase a
patrol officerrsquos warrants and misdemeanors while the property crime-to-cop ratio would decrease
these outputs Notably half of all victims of violent crimes know the offender (Truman amp Rand
2010) which aids law enforcement in identifying and locating the perpetrator Moreover both
suspect and victim are frequently on scene when police officers arrive such as during domestic
disturbances Being able to identify and locate the perpetrator helps patrol officers make arrests
thereby increasing their misdemeanor and felony arrests in violent crimes Additionally when
patrol officers arrive and contact individuals during an incident it is standard procedure to check
if the parties involved have outstanding warrants This means that the greater number of violent
crimes that officers respond to the more chances they have to arrest individuals who might have
outstanding warrants It makes sense then that officers who respond to a greater number of
violent crimes have more chances to make warrant and misdemeanor arrests
Conversely property crimes may supply the officer with fewer warrant and misdemeanor
arrests because of low clearance rates We examined the difference in clearance rates between
property and violent crime using incident data from the Lincoln Police Department and we
found that the clearance rate for violent crimes was 418 compared to only 237 for property
crimes Moreover property crimes often also require more investigative time than violent
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crimes Property crime investigations often require considerable follow-up by officers such as
processing a scene contacting financial institutions and checking pawnshops for stolen property
In general property crimes are more time-intensive than violent crimes thereby decreasing
productive time and opportunities for proactive activities while simultaneously netting officers
fewer arrests because of low clearance rates
The impact of a teamrsquos crime-to-cop ratio can also be seen in the relationship between
property and violent crime per officer and the number of traffic citations Table 3 shows that a
rise in both reported violent and property crimes per officer decrease an officerrsquos number of
traffic citations Quite simply if officers respond to a high number of property and violent
crimes then they will have fewer minutes to conduct traffic enforcement and other proactive
enforcement
As noted it is possible that the correlation is bi-directional If an officer makes a greater
number of arrests then a team may witness a decrease in crime because prolific criminals may
have been arrested and placed in jail For example one might interpret Table 3 to mean that an
increase in warrant and misdemeanor warrants led to a decrease in the number of property crimes
per officer However under this hypothesis we should also see that an increase in warrant and
misdemeanor arrests leads to a decrease in the number of violent crimes per officer Instead we
see the opposite hypothesized effect ie more arrests leading to more reported violent crimes 5
983093 983127983141 983139983151983150983155983156983154983157983139983156983141983140 983154983141983143983154983141983155983155983145983151983150 983149983151983140983141983148983155 983157983155983145983150983143 983139983145983156983137983156983145983151983150983155 983159983137983154983154983137983150983156 983137983154983154983141983155983156983155 983149983145983155983140983141983149983141983137983150983151983154 983137983154983154983141983155983156983155 983137983150983140 983142983141983148983151983150983161 983137983154983154983141983155983156983155 983142983154983151983149
983156983144983141 983152983154983141983139983141983140983145983150983143 983149983151983150983156983144 983156983151 983152983154983141983140983145983139983156 983138983151983156983144 983158983145983151983148983141983150983156 983137983150983140 983152983154983151983152983141983154983156983161 983139983154983145983149983141 983154983137983156983141983155 983142983151983154 983156983144983141 983149983151983150983156983144983155 983145983150 983151983157983154 983140983137983156983137983155983141983156 983127983141 983142983151983157983150983140 983150983151
983155983156983137983156983145983155983156983145983139983137983148983148983161 983155983145983143983150983145983142983145983139983137983150983156 983154983141983148983137983156983145983151983150983155983144983145983152983155 983138983141983156983159983141983141983150 983139983145983156983137983156983145983151983150983155 983159983137983154983154983137983150983156 983137983154983154983141983155983156983155 983149983145983155983140983141983149983141983137983150983151983154 983137983154983154983141983155983156983155 983137983150983140 983142983141983148983151983150983161 983137983154983154983141983155983156983155
983137983150983140 983156983144983141 983158983145983151983148983141983150983156 983137983150983140 983152983154983151983152983141983154983156983161 983139983154983145983149983141 983154983137983156983141983155 983151983142 983156983144983141 983138983141983137983156983155 983112983151983159983141983158983141983154 983137983142983156983141983154 983140983145983155983137983143983143983154983141983143983137983156983145983150983143 983156983144983141 983158983145983151983148983141983150983156 983137983150983140 983152983154983151983152983141983154983156983161
983139983154983145983149983141 983154983137983156983141983155 983145983150983156983151 983155983152983141983139983145983142983145983139 983139983154983145983149983141983155 983159983141 983140983145983140 983142983145983150983140 983156983144983137983156 983149983145983155983140983141983149983141983137983150983151983154 983137983150983140 983142983141983148983151983150983161 983137983154983154983141983155983156983155 983155983145983143983150983145983142983145983139983137983150983156983148983161 983152983154983141983140983145983139983156983141983140 983137 983140983141983139983154983141983137983155983141
983145983150 983154983151983138983138983141983154983161 983154983137983156983141983155 983159983144983145983139983144 983145983155 983139983151983150983155983145983155983156983141983150983156 983159983145983156983144 983156983144983141 983148983145983156983141983154983137983156983157983154983141 983105983140983140983145983156983145983151983150983137983148983148983161 983159983141 983145983150983156983141983154983158983145983141983159983141983140 983140983141983152983137983154983156983149983141983150983156 983152983141983154983155983151983150983150983141983148 983137983150983140983148983141983137983154983150983141983140 983156983144983137983156 983156983144983141 983116983145983150983139983151983148983150 983120983151983148983145983139983141 983108983141983152983137983154983156983149983141983150983156 983144983137983140 983155983156983137983142983142983141983140 983137 983156983141983137983149 983140983141983155983145983143983150983141983140 983155983152983141983139983145983142983145983139983137983148983148983161 983156983151 983137983152983152983154983141983144983141983150983140 983154983151983138983138983141983154983161
983155983157983155983152983141983139983156983155 983140983157983154983145983150983143 983156983144983145983155 983156983145983149983141983142983154983137983149983141 983159983144983145983139983144 983155983141983141983149983155 983156983151 983141983160983152983148983137983145983150 983156983144983141 983137983154983154983141983155983156983085983154983151983138983138983141983154983161 983154983137983156983141 983154983141983148983137983156983145983151983150983155983144983145983152
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We believe our explanation has a stronger theoretical foundation The ratio of property
crimes per officer affects productivity outputs differently than the ratio of violent crimes per
officer for two reasons First violent crimes are different in nature than property crimes
especially with regard to clearance rates Second property crimes often require more
investigative and follow-up time than violent crimes thereby reducing an officerrsquos amount of
productive time and proactive activity
Table 3 also provides a key piece of evidence to support this assertion Table 3 shows
that the number of reported violent crimes per officer increases an officerrsquos number of warrant
and misdemeanor arrests Yet the relationship is the opposite for traffic enforcement the violent
crime-to-cop ratio actually decreases traffic citations This change is due to the fact that while
violent crime investigations often yield warrant and misdemeanor arrests they do not produce
traffic citations Furthermore responding to a greater number of violent crimes decreases the
amount of available time to conduct traffic enforcement in turn reducing the number of traffic
citations In other words the violent crime-to-cop ratio affects traffic stops the same way that
the property crime-to-cop ratio affects all productivity outputs by reducing an officerrsquos
productive time and opportunities for proactivity In fact we found a moderate correlation
between a patrol arearsquos crime-to-cop ratio and the amount of productive time enjoyed by officers
who worked that area The correlation between an arearsquos violent crime-to-cop ratio and
productive time was -21 while the correlation between an arearsquos property crime-to-cop ratio and
productive time was -33 We believe this evidence indicates that officers who work in areas
with a high crime-to-cop ratio have less time for self-initiated activities which drives down
productivity
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We suggest that the number of police officers and crime rates of an area need to be
analyzed in conjunction with each other Prior theories of police productivity have viewed and
examined these variables separately when in reality we should view these variables in tandem
Combining these variables into crime-to-cop ratios provides a more nuancedmdashand accuratemdash
understanding of patrol activities and their relationship to productivity
Although we found strong evidence that an officerrsquos patrol area affects his or her
productivity (in terms of raw outputs) we also found that a few officer-level variables affected
productivity as well including traffic stops intelligence reports and working third shift Our
results appear to substantiate what many police practitioners already suspect officers who are
proactive in contacting citizens (traffic stops) and building investigations (intelligence reports)
generate more warrant and misdemeanor arrests In addition while some newer police officers
are forced to work third shift because they do not have sufficient seniority to bid for better days
off and shift times many officers work nights because they enjoy the types of calls (eg in-
progress felonies) meaning they may be more motivated to stop vehicles make field contacts
and in turn engage in activities that generate arrests (Brehm amp Gates 1993)
These results have important implications for police researchers and administrators
First our results suggest a need for a deeper understanding of how the staffing levels crime
rates and the other characteristics of a patrol area intersect Second if departments evaluate
their patrol officers using raw productivity outputs such as misdemeanor or felony arrests
administrators must recognize that factors beyond the control of patrol officers might affect
monthly or annual productivity Our research suggests that a patrol arearsquos crime-to-cop ratio can
substantially affect a patrol officerrsquos productivity regardless of the actual crime rate If the
characteristics of a patrol area affect raw productivity outputs police departments should employ
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more advanced productivity analytics (ie VORC) that account for activities which reduce
productive time and evaluate an officerrsquos productivity in terms of available minutes for
proactivity
Nonetheless evaluating both patrol officers and police departments using traffic citations
warrants misdemeanors and felonies is useful because this type of data is readily understood by
citizens and civic officials A neighborhood watch group for instance might care more about
the number of arrests their officers make and less about those officersrsquo productivity scores Our
data suggests that if citizens want patrol officers to write more traffic citations and make more
arrests they should lobby for more police officers on the street It is true that reducing an arearsquos
crime rate impacts the crime-to-cop ratio but our results indicate that putting police officers on
the street may be the most direct way to affect the crime-to-cop ratio
Our study is not without limitations We only examined a police department which uses
the generalist style of policing and it is unknown if our results are applicable to police
departments which use a specialist style of policing Theoretically a departmentrsquos policing
styles can affect factors that increase or decrease a patrol officerrsquos productive time It is possible
that the number of property crimes and violent crimes per officer do not significantly affect a
specialist patrol officerrsquos productivity outputs versus a generalist officerrsquos outputs Future
studies should investigate whether our results can be generalized to all police departments
We have only identified two macro-level variables which affect patrol officer
productivity and we recognize there may be other variables In addition we recommend that
future studies incorporate a wider array of variables and test competing models to determine
whether micro or macro-level variables exhibit stronger effects on productivity In the end
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managing patrol officers and improving productivity requires a greater understanding of both
patrol officers and the areas they work
Acknowledgements and Credits We would like to thank Denver Police Department Crime
Analyst Melissa Ohlhorst and Pennsylvania State University Professor Barry Ruback as well as
the three anonymous reviewers for their insightful comments We also salute Lincoln Police
Sergeant Jake Dilsaver for his logistical support of this project
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References
Amaranto E Steinberg J Castellano C and Mitchell R (2003) ldquoPolice stress interventionsrdquo
Brief Treatment and Crisis Intervention Vol 3 No 1 pp 47-53
Becker G (1985) ldquoHuman capital effort and the sexual division of laborrdquo The Journal of
Labor EconomicsVol 3 No 1 pp 33-58
Bonkiewicz L (2015) ldquoBobbies and baseball players Evaluating patrol officer productivity
using sabermetricsrdquo Police Quarterly Vol 18 No 1 pp 55-78
Braga A and Bond J (2008) ldquoPolicing crime and disorder hot spots A randomized controlled
trialrdquo Criminology Vol 46 No 3 pp 577-607
Brown J and Campbell E (1990) ldquoSources of occupational stress in the policerdquo Work
and Stress Vol 4 No 4 pp 305-318
Chappell A MacDonald J and Manz P (2006) ldquoThe organizational determinants
of police arrest decisionsrdquo Crime and Delinquency Vol 52 No 2 pp 287-306
Cloninger D and Sartorius L (1979) ldquoCrime rates clearance rates and enforcement effort
The case of Houston Texasrdquo American Journal of Economics and Sociology Vol 38
No 4 pp 389-402
Corman H and Mocan N (2005) ldquoCarrots sticks and broken windowsrdquo Journal of Law and
Economics Vol 48 No 1 pp 235-266
Crank J (1990) ldquoThe influence of environmental and organizational factors on police style in
urban and rural environmentsrdquo Journal of Research in Crime and Delinquency Vol 27
pp 166-189
Goodman A (1990) ldquoA model for police officer burnoutrdquo Journal of Business and
Psychology Vol 5 No 1 pp 85-99
Gould E Weinberg B and Mustard D (2002) Crime rates and local labor marketopportunities in the United States 1979ndash1997rdquo Review of Economics and Statistics Vol
84 No 1 pp 45-61
Hipp J and Roussell A (2013) ldquoMicro-and macro-environment population and the
consequences for crime ratesrdquo Social Forces Vol 92 No 2 pp 563-595
Hatry H (1976) ldquoApproaches to productivity measurement and program evaluationrdquo Public
Productivity Review Vol 1 No 3 pp21-28
Hawkins H (2001) ldquoPolice officer burnout a partial replication of Maslachs Burnout
Inventoryrdquo Police Quarterly Vol 4 No 3 pp 343-360
7252019 PIJPSM-05-2015-0064
httpslidepdfcomreaderfullpijpsm-05-2015-0064 2530
Herbert S (1998)ldquoPolice subculture reconsideredrdquo Criminology Vol 36 No 2 pp 343-370
Huizinga D and Henry K (2008) ldquoThe effect of arrest and justice system sanctions on
subsequent behavior Findings from longitudinal and other studiesrdquo In The long view of
crime A synthesis of longitudinal research Springer New York pp 220-254
Johnson R (2011) ldquoOfficer attitudes and management influences on police work productivityrdquo
American Journal of Criminal Justice Vol 36 pp 293-306
Kane R (2006) ldquoOn the limits of social control Structural deterrence and the policing of
lsquosuppressiblersquo crimesrdquo Justice Quarterly Vol 23 No 2 pp 186-213
Kubrin C Messner S Deane G McGeever K and Stucky T (2010) ldquoProactive
policing and robbery rates across US citiesrdquo Criminology Vol 48 No 1 pp 57-97
Lersch K (2002) ldquoAre citizen complaints just another measure of officer productivity An
analysis of citizen complaints and officer activity measuresrdquo Police Practice and
Research Vol 3 pp 135-147
Light M and Harris C (2012) Race space and violence exploring spatial dependence in
structural covariates of white and black violent crime in US countiesrdquo Journal of
Quantitative Criminology Vol 28 No 4 pp 559-586
Maxfield M (1982) ldquoService time dispatch time and demand for police services Helping
more by serving lessrdquo Public Administration Review pp 252-263
Maxfield M Lewis D and Szoc R (1980) ldquoProducing official crimes Verified crime reports
as measures of police outputrdquo Social Science Quarterly pp 221-236
Nakosteen R and Zimmer M (2001) ldquoSpouse selection and earnings Evidence of marital
sortingrdquo Economic InquiryVol 39 No 2 pp 201-2013
Shane J (2010) ldquoOrganizational stressors and police performancerdquo Journal of Criminal
Justice Vol 38 No 4 pp 807-818
Shane J (2011) ldquoDaily work experiences and police performancerdquo Police Practice and
Research Vol 13 No 3 pp 1-19
Sherman L Gartin P and Buerger M (1989) ldquoHot spots of predatory crime Routine
activities and the criminology of placerdquoCriminology Vol 27 No 1 pp27-56
Truman J and Rand M (2010) ldquoBureau of Justice Statistics Bulletin National Crime
Victimization Survey Criminal Victimization 2009rdquo US Department of Justice Office of
Justice programs
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httpslidepdfcomreaderfullpijpsm-05-2015-0064 2630
Van Meter D (2001) Evaluating dysfunctional police performance A zero-based approach
Charles C Thomas Publisher Springfield IL
Vila B and Kenney D (2002) ldquoTired cops The prevalence and potential consequences of
police fatiguerdquo National Institute of Justice
Vila B Morrison G and Kenney D (2002) ldquoImproving shift schedule and work-hour
policies and practices to increase police officer performance health and safetyrdquo Police
Quarterly Vol 5 No 1 pp 4-24
Vollaard B and Koning P (2009) ldquoThe effect of police on crime disorder and victim
PrecautionEvidence from a Dutch victimization surveyrdquo International Review of Law
and Economics Vol 29 No 4 pp 336-348
Weisburd D Maher L Sherman L Buerger M Cohn E and Petrisino A (1993)
ldquoContrasting crime general and crime specific theory The case of hot spots of crimerdquo
New Directions in Criminological Theory Advances in Criminological Theory Vol 4
pp 45-70
Author Biography
Luke Bonkiewicz is a patrol officer with the Lincoln Police Department
7252019 PIJPSM-05-2015-0064
httpslidepdfcomreaderfullpijpsm-05-2015-0064 2730
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983101 983089983088983088lowast + + ) + ) + ) + ) lowast )) minus + + + 983090983088 + ) +983089983088)) minus 983089983088983088 lowast
983086 983097) lowast )
7252019 PIJPSM-05-2015-0064
httpslidepdfcomreaderfullpijpsm-05-2015-0064 2830
983124983137983138983148983141 1 E983160983137983149983152983148983141 983151983142 983156983144983141 983116983145983150983139983151983148983150 983120983151983148983145983139983141 D983141983152983137983154983156983149983141983150983156983155 A983150983150983157983137983148 983120983154983151983140983157983139983156983145983158983145983156983161 983122983141983152983151983154983156
983119983142983142983145983139983141983154 983120983154983151983155983141983139983157983156983145983151983150
983122983137983156983141C983110983123
983117983145983150983157983156983141983155983110983125983120
983117983145983150983157983156983141983155
983119983142983142983145983139983141983154
A983155983155983145983155983156983117983145983150983157983156983141983155
983113983150983139983145983140983141983150983156983122983141983152983151983154983156983155
AC983113983122983141983152983151983154983156983155 983123983141983148983141983139983156983145983158983141983155 983127983137983154983150983145983150983143983155 983119983142983142983145983139983145983137983148983155 983127983137983154983154983137983150983156983155 D983125983113983155 983117983145983155983140983141983149983141983137983150983151983154983155 983110983141983148983151983150983145983141983155
1 88 22812 5208 6132 216 348 396 396 264 60 24 72 7
2 95 30072 5424 4116 204 696 228 312 432 84 48 120 8
3 72 32496 6312 6600 144 564 444 720 540 84 36 24 12
4 90 33000 5232 4980 216 192 480 768 240 144 12 72 12
5 91 41724 4824 6048 312 636 132 504 324 108 7 24 24
6 83 23916 7056 6192 144 756 528 996 336 132 36 72 12
7 95 24468 6840 5892 156 624 324 456 480 72 36 120 24
8 90 39972 5196 4920 240 504 384 492 264 36 2 72 24
9 79 25500 4068 5100 336 396 192 684 204 24 36 120 10
10 87 21984 5412 4524 348 552 480 792 312 108 48 84 36
983124983137983138983148983141 2 983123983157983149983149983137983154983161 983123983156983137983156983145983155983156983145983139983155 983142983151983154 983126983137983154983145983137983138983148983141983155
983123983141983139983151983150983140983085983116983141983158983141983148 983126983137983154983145983137983138983148983141983155 (983120983137983156983154983151983148 A983154983141983137) 983118=40 983122983137983150983143983141 983123D 983117983141983137983150
983120983151983152983157983148983137983156983145983151983150 983140983141 983150983155983145983156983161 20229830856956 1687 4436
983112983151983157983155983141983144983151983148983140 983149983141983140983145983137983150 983145983150983139983151983149983141 ($) 2628198308576020 16535 49756
983159983144983145983156983141 7398308593 007 083
983126983145983151983148983141983150983156 983139983154983145983149983141 983154983137983156983141 (983152983141983154 100000) 8009830854800 1203 2521
983120983154983151983152983141983154983156983161 983139983154983145983149983141 983154983137983156983141 (983152983141983154 100000) 31009830859800 2027 6524
983120983154983151983152983141983154983156983161 983139983154983145983149983141983155 983152983141983154 983151983142983142983145983139983141983154 5329830851097 1663 8167
983126983145983151983148983141983150983156 983139983154983145983149983141 983152983141983154 983151983142983142983145983139983141983154 194983085372 559 2929
983110983145983154983155983156983085983116983141 983158983141983148 983126 983137983154983145983137983138983148983141 983155 (983119983142983142983145983139983141983154) 983118=302 983122983137983150983143983141 983123D 983117983141 983137983150
983111983141983150983140983141983154 (983142983141983149983137983148983141=0 983149983137983148983141=1) 09830851 24 94
983117983137983154983154983145983141983140C983151983144983137983138983145983156983145983150983143 09830851 18 79
983151983142 C983144983145983148983140983154983141983150 09830855 170 90
983129983141983137983154983155 983141983160983152983141983154983145983141983150983139983141 198308537 779 1063
C983137983148983148983155 983142983151983154 983155983141983154983158983145983139983141 4819830851011 16121 61365
983124983154983137983142983142983145983139 983155983156983151983152983155 16983085901 14376 20624
983110983145983141983148983140 983145983150983142983151983154983149983137983156983145983151983150 983154983141983152983151983154983156983155 0983085227 2153 2530
983124983154983137983142983142983145983139 983139983145983156983137983156983145983151983150983155 249830851246 14632 18642
983117983145983155983140983141983149983141983137983150983151983154 983137983154983154983141983155983156983155 19983085403 5965 10663
983110983141983148983151983150983161 983137983154983154983141983155983156983155 098308571 1066 852
983126983119983122C 9830851959830851043 151 24
1983155983156 2983150983140 3983154983140
104 96 102983123983144983145983142983156
7252019 PIJPSM-05-2015-0064
httpslidepdfcomreaderfullpijpsm-05-2015-0064 2930
983124983137983138983148983141 3 983112983145983141983154983137983154983139983144983145983139983137983148 983116983145983150983141983137983154 983117983151983140983141983148983155 E983155983156983145983149983137983156983145983150983143 983119983142 983142983145983139983141983154983155 A983150983150983157983137983148 983124983154983137983142983142983145983139 C983145983156983137983156983145983151983150983155 983127983137983154983154983137983150983156983155 983117983145983155983140983141983149983141983137983150983151983154983155 983137983150983140 983110983141983148983151983150 983145983141983155
983123983141983139983151983150983140983085983116983141983158983141983148 983126983137983154983145983137983138983148983141983155 (983120983137983156983154983151983148 A983154983141983137) 983118=40 983138 983123E 983138 983123E 983138 983123E 983138 983123E
983120983151983152983157983148983137983156983145983151983150 983140983141983150983155983145983156983161 001 003 001 001 98308501 01 00 00
983112983151983157983155983141983144983151983148983140 983149983141983140983145983137983150 983145983150983139983151983149983141 ($) 002 001 983085000 000 00 00 98308500 00
983159983144983145983156983141 13363 32472 9830858921 5664 9830853274 19211 9830854447 3255
983126983145983151983148983141983150983156 983139983154983145983149983141 983154983137983156983141 (983152983141983154 100000) 006 004 001 001 98308501 02 01 02
983120983154983151983152983141983154983156983161 983139983154983145983149983141 983154983137983156983141 (983152983141983154 100000) 983085003 005 983085002 001 02 03 98308501 00
983120983154983151983152983141983154983156983161 983139983154983145983149983141983155 983152983141983154 983151983142983142983145983139983141983154 983085225 99 98308515 03 98308552 24 98308503 03
983126983145983151983148983141983150983156 983139983154983145983149983141 983152983141983154 983151983142983142983145983139983141983154 983085168 68 29 14 76 31 22 12
983110983145983154983155983156983085983116983141983158983141983148 983126983137983154983145983137983138983148983141983155 (983119983142983142983145983139983141983154) 983118=302
983117983137983148983141 4673 1622 366 147 1613 1051 91 158
983117983137983154983154983145983141983140C983151983144983137983138983145983156983145983150983143 9830857516 3254 201 278 9830851158 1169 98308595 167
983151983142 C983144983145983148983140983154983141983150 183 702 75 77 983085262 217 983085396 207
983129983141983137983154983155 983141983160983152983141983154983145983141983150983139983141 9830851065 492 98308512 13 983085163 33 98308506 05C983137983148983148983155 983142983151983154 983155983141983154983158983145983139983141 24 13 02 02 14 01 98308508 05
983124983154983137983142983142983145983139 983155983156983151983152983155 983085983085 983085983085 03 00 09 02 00 00
983110983145983141983148983140 983145983150983142983151983154983149983137983156983145983151983150 983154983141983152983151983154983156983155 209 33 20 04 79 19 10 03
983123983141983139983151983150983140 983155983144983145983142983156 (983154983141983142983141983154983141983150983139983141 983143983154983151983157983152 983142983145983154983155983156) 2603 1778 187 195 809 730 147 160
983124983144983145 983154983140 983155983144983145 983142983156 (983154983141 983142983141 983154983141 983150983139983141 983143983154983151983157983152 983142983145 983154983155983156) 9872 2155 79 213 2532 952 393 132
983113983150983156983141983154983139983141983152983156 32591 5361 758 339 4147 1912 310 484
983123983145983143983149983137983085983155983153983157983137983154983141983140 1541467 19287 206702 5598
C983144983145983085983155983153983157983137983154983141983140 8294 6133 12348 10663
983152 lt 05
983152lt01
983152lt001
983118983151983156983141 A983148983148 983158983137983154983145983137983138983148983141983155 983137983154983141 983157983150983139983141983150983156983141983154983141983140
983127A983122983122A983118983124 A983122983122E983123983124983123 983117983113983123DE983117EA983118983119983122 A983122983122E983123983124983123 983110E983116983119983118983129 A983122983122E983123983124983123983124983122A983110983110983113C C983113983124A983124983113983119983118983123
7252019 PIJPSM-05-2015-0064
httpslidepdfcomreaderfullpijpsm-05-2015-0064 3030
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983123983141983139983151983150983140983085983116983141983158983141983148 983126983137983154983145983137983138983148983141983155 (983120983137983156983154983151983148 A983154983141983137) 983118=40 983138 983123E
983120983151983152983157983148983137983156983145983151983150 983140983141983150983155983145983156983161 98308500 00
983112983151983157983155983141983144983151983148983140 983149983141983140983145983137983150 983145983150983139983151983149983141 ($) 00 01
983159983144983145983156983141 318 345
983126983145983151983148983141983150983156 983139983154983145983149983141 983154983137983156983141 (983152983141983154 100000) 98308500 00
983120983154983151983152983141983154983156983161 983139983154983145983149983141 983154983137983156983141 (983152983141983154 100000) 00 00
983120983154983151983152983141983154983156983161 983139983154983145983149983141983155 983152983141983154 983151983142983142983145983139983141983154 98308502 04
983126983145983151983148983141983150983156 983139983154983145983149983141 983152983141983154 983151983142983142983145983139983141983154 02 05
983110983145983154983155983156983085983116983141983158983141983148 983126983137983154983145983137983138983148983141983155 (983119983142983142983145983139983141983154) 983118=302
983117983137983148983141 27 15
983117983137983154983154983145983141983140C983151983144983137983138983145983156983145983150983143 98308532 31
983151983142 C983144983145983148983140983154983141983150 05 03
983129983141983137983154983155 983141983160983152983141983154983145983141983150983139983141 00 01
C983137983148 983148983155 983142983151983154 983155983141983154983158983145983139983141 02 01
983124983154983137983142983142983145983139 983155983156983151983152983155 01 00
983110983145 983141983148983140 983145983150983142983151983154983149983137983156983145 983151983150 983154983141 983152983151983154983156983155 02 00
983123983141983139983151983150983140 983155983144983145983142983156 ( 983154983141983142983141983154983141983150983139983141 983143983154983151983157983152 983142 983145983154983155983156) 23 12
983124983144983145983154983140 983155983144983145983142983156 (983154983141983142983141983154983141983150983139983141 983143983154983151983157983152 983142 983145983154983155983156) 55 14
983113983150983156983141983154983139983141983152983156 165 36
983123983145983143983149983137983085983155983153983157983137983154983141983140 66
C983144983145983085983155983153983157983137983154983141983140 9846
983152 lt 05
983152lt01
983152lt001
983118983151983156983141 A983148983148 983158983137983154983145983137983138983148983141983155 983137983154983141 983157983150983139983141983150983156983141983154983141983140
7252019 PIJPSM-05-2015-0064
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field contacts official reports sick hours and administrative complaints Similarly Van Meterrsquos
Performance-Based Management or ldquozero-based approachrdquo (Van Meter 2001) uses three planks
(non-scheduled absenteeism cost of preventable error and productive use of time) to evaluate
patrol officers
Notably most existing police productivity research has ignored the concept of productive
time or the amount of time officers have for self-initiated activities after handling calls for
service investigating crimes completing reports and follow-ups and assisting other officers
Using productive time to evaluate officers is critical because two officers could achieve the same
output (in terms of raw arrests or reports) but have done so with considerably different amounts
of productive time due to calls for service follow-ups etc Recent research has proposed the use
of Value Over Replacement Cop or VORC (Bonkiewicz 2015) a metric that incorporates more
than a dozen patrol activities weights those activities evaluates performance in terms of
productive time and compares an officerrsquos performance against a fixed hypothetical estimate
(ie a replacement officer)
In addition to conceptualizing patrol officer productivity researchers have also studied
potential antecedents of productivity including not only biographical variables but also factors
involving workplace environment First certain biographical characteristics might impact
productivity (Hawkins 2001) For instance officers who have children might experience sleep
deprivation and greater fatigue as a result of parenting responsibilities thereby reducing their
workplace energy and motivation and in turn their productivity (Shane 2010) Marital status
may also impact productivity Although the relationship between marriage and worker
productivity is well debated in the literature (Nakosteen amp Zimmer 2001 Becker 1985) it
seems reasonable to believe that married officers might be more productive than unmarried
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officers Married officers have someone in whom to confide a partner who hears about the
challenges and rigors of the job which may lead to stress reduction and improve workplace
motivation An officerrsquos age is another consideration Older officers might be less productive
than younger officers perhaps due to burnout as well as the physical demands of law
enforcement (Goodman 1990) Officersrsquo perceptions of their job may also affect their
proactivity and in turn productivity Brehm and Gates (1993) found that officers who disliked
certain aspects of law enforcement (eg taking people to jail) impacted whether officers engaged
in proactive activities or shirked their duties
Additionally there are also operational features (the ldquoday to dayrdquo rigors) of law
enforcement which can decrease productivity and increase misconduct such as the perceived
danger of the job (Amaranto et al 2003) work schedules and shift work (Vila Morrison amp
Kenney 2002) and fatigue (Vila amp Kenney 2002) Notably in studying the relationship
between daily work experience and performance Shane (2011) found that workload was the
ldquomost important predictor of performancerdquo (p 13)
We believe this last finding merits further study First supervisors evaluate patrol
officers at least in part using productivity outputs If there are factors (ie heavy workload)
beyond an officerrsquos control that are affecting his or her productivity both line-level supervisors
and command staff members should acknowledge and account for these variables during
performance reviews Second if macro-level variables create different workloads thereby
affecting productivity outputs then police researchers should include these measures in future
studies on police activity We suggest that there are at least two macro-level variables important
to future police productivity research
Crime-to-Cop Ratios and Patrol Officer Productivity
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Criminological research has long recognized that crime rates are not uniform across
neighborhoods and cities (Sherman et al 1989 Weisburd et al 1993) Instead of ldquopolicing
peoplerdquo law enforcement agencies frequently identify ldquohot spotsrdquo and devote additional
resources to reducing crime and disorder in specific problem areas With this perspective in
mind it might seem natural to examine the relationship between the crime rate of a patrol area
and an officerrsquos productivity outputs (or vice-versa) For example a high crime area might net
an officer more arrests (and therefore more productivity outputs) than a low crime area On the
other hand a high crime area might also occupy a great deal of an officerrsquos productive time
prohibiting the officer from engaging in proactive activities However crime rate does not fully
capture how a particular area might affect an officerrsquos productivity
Instead we believe macro-level variables related to crime should be estimated in terms of
the number of patrol officers in a patrol area specifically violent crimes per officer and property
crimes per officer For instance some officers may patrol a lower crime area but end up
responding to a greater number of violent crimes than officers in a high crime area simply
because they have fewer officers working the street This ldquocrime per coprdquo ratio can dramatically
affect officersrsquo productivity
For example when an officer investigates a crime he or she might interview the victim
suspect and witnesses as well as take photographs collect evidence and make an arrest There
may be additional follow-up the next day or week such as checking pawnshops for stolen
property or tracking down and interviewing additional witnesses All these activities could
decrease an officerrsquos time for self-initiated activities and in turn an officerrsquos overall
productivity A high crime per cop ratio therefore could decrease productivity for officers even
if these officers work in an area with a low crime rate Furthermore an arearsquos crime per cop
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ratio could also affect officersrsquo productive time even if they are not the primary investigating
officer because secondary officers are often needed during serious incidents such as robberies or
burglaries
The ldquoload sheddingrdquo hypothesis by Maxfield et al (1980 see also Maxfield 1982) also
has important implications for any study examining patrol officer productivity Maxfield et al
suggested that while variations in workload do not affect patrol officersrsquo decisions to record
serious crimes workload does affect officersrsquo decisions to record less serious crimes it may also
affect self-initiated activities as well (Herbert 1989)
Finally we believe that macro-level variables affecting productivity should be divided
between violent and property crimes to account for the time and resources needed to investigate
each incident type When officers respond to assaults they often contact suspects and victims on
scene meaning that officers are able to interview all involved individuals collect evidence and
make an arrest sometimes in less than an hour However when officers respond to burglaries or
larcenies they may be required to dust for fingerprints swab for DNA canvass a neighborhood
for witnesses check pawnshops and engage in other investigative activities that require hours
and even days if the case requires follow-up
With these issues in mind our study addresses the following research questions First
what is the relationship between a patrol arearsquos rate of violent crimes per officer and patrol
officer productivity outputs (ie traffic citations as well as warrant misdemeanor and felony
arrests) Second what is the relationship between a patrol arearsquos rate of property crimes per
officer and patrol officer productivity outputs (again traffic citations and warrant misdemeanor
and felony arrests) Third what is the relationship between the rate of property and violent
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crimes per officer and a patrol officerrsquos productivity when we introduce a more sophisticated
method for analyzing productivity a metric based on productive time and not total time on duty
Before discussing our data and methods there are two important issues meriting brief
discussion First we argue that the crime-to-cop ratio may affect the number of traffic stops
warrants misdemeanors and felonies that a patrol officer makes It can also be argued that the
number of arrests might impact the crime-to-cop ratio Our intent is not to comprehensively
discuss the relationship between the number of officersofficer arrests and an arearsquos crime rate
but it bears noting that the research is inconclusive on this point
Some evidence suggests that the relationship between arrests and crime rates is non-
linear that is crime rates decrease as arrests increase but eventually hit a ldquofloorrdquo and reach
equilibrium (Kane 2006) Second when studies examine the arrest-crime rate relationship they
often find that the relationship only exists for specific crimes in their datasets such as robbery
and motor vehicle thefts (Corman Hope amp Mocan 2005) robbery but not assaults (Kane 2006)
and homicide and auto thefts only (Cloninger amp Sartorius 1979) and so forth Overall the
literature on the arrests-crime rate relationship is mixedmdashsome research finds an effect between
arrests and crime rates some does not (Huizinga and Henry 2008)
The second issue concerns whether a law enforcement agency operates using a generalist
or specialist policing style We analyze data from a generalist municipal police department
meaning that patrol officers investigate low and mid-level crimes but investigators and
detectives are responsible for handling serious crimes cases involving lengthy investigations
(eg narcotics trafficking or white-collar crimes) and incidents in which patrol officers may
need assistance (for example an incident involving numerous victims and witnesses as well as
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cases involving extensive follow-up or knowledge on a specific subject such as gangs or
narcotics)
By contrast when patrol officers in a specialist-style police department respond to any
incident potentially requiring follow-up they might only contact the victim and either hold the
scene until an investigator arrives or forward the information to a detective for follow-up It is
possible indeed likely that macro-level variables might affect generalist patrol officers
differently than specialized officers Due to data limitations however our study focuses on
generalist police officers
Data and Methods
We gathered data on patrol officers and their patrol areas from several sources First we
collected patrol officer productivity data from the Lincoln (Nebraska) Police Departmentrsquos 2013-
2014 productivity reports Using Computer-Aided Dispatch (CAD) and a centralized
productivity database LPD measures numerous patrol officer activities Every month (and
year) LPD generates productivity reports which display each patrol officerrsquos activities and
outputs (see Table 1 for an example of a squadrsquos annual productivity report) Each patrol
activity and output merits further explanation
INSERT TABLE 1 HERE
Calls for Service (CFS) and Officer Assist time When dispatchers send patrol officers on
a call they use CAD to track the minutes the officer spends on the call If the call is a priority
incident dispatchers send both a primary and secondary officer Other unassigned officers might
also respond to the CFS meaning that multiple officers could be secondary officers The annual
productivity record tracks amount of time an officer spent as a primary responder and a
secondary responder
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Report writing time Standard operating procedures dictate that LPD patrol officers
complete specific types of reports for incidents An incident report (IR) details biographical
information of the individuals involved in the incident and summarizes the incident An
Additional Case Information (ACI) is a more detailed report that documents additional
investigative steps or the actions of assisting officers Although the time spent on an IR or ACI
could vary depending on the call for service the average IR and ACI take roughly twenty
minutes to complete1
The Lincoln Police Department also tracks self-initiated activities Most productivity
results from the following proactive activities
Selectives The Lincoln Police Department tracks specific officer patrol activity by
coding its neighborhoods schools parks and business districts When an officer patrols an at-
risk business district or a high-crime neighborhood for example the officer notifies dispatch
using that arearsquos specific code Girded by the principles of ldquohot spotsrdquo policing research officers
use selectives to identify potential candidates for POP projects and heighten the perception of
risk among criminals (Braga amp Bond 2008)
Traffic Warnings and Official Citations Every Lincoln patrol officer drives a police
cruiser and conducts traffic enforcement issuing traffic warnings and official citations The
LPD records department tracks patrol officersrsquo warnings and citations
Misdemeanors felonies and warrants Patrol officers also make misdemeanor and
felony arrests during their productive time Officers might detain a motorist and arrest him or
her for driving under a suspended license or driving while intoxicated or contact a pedestrian
1 983119983142983142983145983139983141983154983155 983156983161983152983141 983137983148983148 983154983141983152983151983154983156983155 983151983150 983139983151983149983152983157983156983141983154983155 983127983141 983151983138983156983137983145983150983141983140 983156983144983145983155 983137983158983141983154983137983143983141 983138983161 983151983138983155983141983154983158983145983150983143 983137 983155983137983149983152983148983141 983151983142 983116983145983150983139983151983148983150 983152983137983156983154983151983148 983151983142983142983145983139983141983154983155
983139983151983149983152983148983141983156983145983150983143 983113983122983155 983137983150983140 983105983107983113983155 983137983150983140 983159983141 983154983141983139983151983143983150983145983162983141 983156983144983137983156 983156983144983145983155 983137983158983141983154983137983143983141 983149983137983161 983140983145983142983142983141983154 983142983154983151983149 983151983156983144983141983154 983152983151983148983145983139983141 983140983141983152983137983154983156983149983141983150983156983155 983117983151983154983141983151983158983141983154983145983142 983140983141983152983137983154983156983149983141983150983156983155 983154983141983153983157983145983154983141 983151983142983142983145983139983141983154983155 983156983151 983139983151983149983152983148983141983156983141 983156983144983141983145983154 983154983141983152983151983154983156983155 983138983141983142983151983154983141 983138983141983139983151983149983145983150983143 983137983158983137983145983148983137983138983148983141 983142983151983154 983137983150983151983156983144983141983154 983139983137983148983148 983142983151983154 983155983141983154983158983145983139983141
983156983144983145983155 983149983141983137983155983157983154983141983149983141983150983156 983145983155 983157983150983150983141983139983141983155983155983137983154983161
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and make an arrest for a sex offender registration violation Officers can also search their
departmentrsquos electronic database for individuals with outstanding warrants in their patrol areas
and attempt to arrest these offenders As with warnings and citations the LPD records
department documents and tracks misdemeanor felony and warrant arrests Moreover the court
system also monitors these types of outputs to process cases thereby adding an additional layer
of reporting accuracy and reliability
Field information reports We also measured the number of field information or
ldquointelligencerdquo reports Officers complete these internal reports to document suspicious or
dangerous individuals or circumstances though these reports are not publicly available
VORC In addition to the raw outputs of traffic citations warrants misdemeanors and
felonies we also include an advanced metric for measuring a patrol officerrsquos productivity Value
Over Replacement Cop (VORC) is a method for evaluating officers that assigns different
weights to productivity outputs and then assesses officer productivity in terms of available time
for self-initiated activities or productive time (Bonkiewicz 2015) We use a variation of this
formula that multiplies an officerrsquos officials misdemeanors and felonies by his or her
prosecution rate Our modified use of VORC can best be explained using the following figure
(Figure 1) and five-step process
INSERT FIGURE 1 HERE
where = total monthly on-duty minutes = CFS minutes Γ = follow-up time and meetings ∆
= officer assist time Ε = of IRs Ζ = of ACIs M = of intelligence reports Η = of
selectives Θ = of warnings times two Κ = of warrants times three Ι = of officials times
three Λ = of misdemeanor arrests times their respective weights Ν = of felonies types their
respective weights Ρ = an officerrsquos prosecution rate Ξ = Officerrsquos Productive time (Α-(Β+Γ+∆+
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20(Ε+Ζ))) Ο= Department average P-score or average (Η+ ((Θ)+(Ι)+(Κ)+(Λ)+(Μ)+(Ν)Ρ))
and Π = Department average Self-Initiated Time
First VORC calculates an officerrsquos total minutes spent on CFS follow-ups officer
assists and reports This amount is an officerrsquos total work time or service minutes Second the
formula subtracts an officerrsquos service minutes from the total on-duty minutes2 This is the
officerrsquos amount of productive time Third VORC calculates an officerrsquos productivity score (or
P-score) using a weighted index Departments can choose different weights for productivity
outputs but for the purpose of this study we used our weights based on the statutes governing
the Lincoln Police Department Citations and arrests are weighted based on whether the offense
was a Traffic Offense a Class III II or I Misdemeanor or a Class V IV III II or I Felony
Accordingly a patrol selective is worth one point a traffic warning two points a traffic citation
three points a warrant arrest three points a Class III misdemeanor arrest four points a Class II
misdemeanor arrest five points a Class I misdemeanor arrest six points a Class V Felony seven
points a Class IV Felony eight points a Class III Felony nine points and Class II and I Felonies
(eg homicides) ten points3 The sum of the weighted productivity outputs is the officerrsquos P-
score The P-score is divided by amount of productive time and then multiplied by 100 (for ease
of understanding) This value the productivity quotient represents the officerrsquos productivity
given the available minutes for proactivity such as searching for wanted individuals or traffic
enforcement
Fourth VORC creates a hypothetical replacement officer against which to measure the
patrol officerrsquos output This replacement player might be imagined as a rookie patrol officer or
983090 983119983150983085983140983157983156983161 983156983145983149983141 983140983151983141983155 983150983151983156 983145983150983139983148983157983140983141 983156983154983137983145983150983145983150983143 983156983145983149983141 983158983137983139983137983156983145983151983150 983151983154 983144983151983148983145983140983137983161 983140983137983161983155 983151983142983142 983151983154 983155983145983139983147 983148983141983137983158983141 983113983150 983151983156983144983141983154 983159983151983154983140983155 983151983150983085983140983157983156983161
983156983145983149983141 983145983155 983156983144983141 983137983149983151983157983150983156 983151983142 983156983145983149983141 983137 983152983137983156983154983151983148 983151983142983142983145983139983141983154 983155983152983141983150983156 983159983151983154983147983145983150983143 983156983144983141 983155983156983154983141983141983156983091 983119983157983154 983159983141983145983143983144983156983145983150983143 983155983161983155983156983141983149 983145983155 983151983150983148983161 983151983150983141 983141983160983137983149983152983148983141 983151983142 983156983144983141 983142983148983141983160983145983138983145983148983145983156983161 983156983144983137983156 983126983119983122983107 983151983142983142983141983154983155 983140983141983152983137983154983156983149983141983150983156983155983099 983141983137983139983144 983137983143983141983150983139983161 983139983137983150
983152983154983145983151983154983145983156983145983162983141 983137983150983140 983137983155983155983145983143983150 983139983151983154983154983141983155983152983151983150983140983145983150983143 983159983141983145983143983144983156983155 983156983151 983156983144983141983145983154 983152983154983141983142983141983154983154983141983140 983152983154983151983140983157983139983156983145983158983145983156983161 983151983157983156983152983157983156983155
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a recruit who has not completed the academy performing at ninety percent the capacity of fully
trained police officer4 Using LPDrsquos data VORC creates a hypothetical replacement officer by
dividing the departmental average P-score by the departmental average for productive time and
then multiplying it by the officerrsquos productive time to determine what an average officer would
have generated given the officerrsquos amount of productive time This figure is then multiplied by
9 (the capacity of a replacement officer) divided by the officerrsquos productive time and then
multiplied by 100 to generate the replacement officerrsquos productivity quotient Finally VORC
subtracts the replacement officerrsquos score from the patrol officerrsquos score to calculate the officerrsquos
value over the replacement officer This number or VORC indicates the officerrsquos productivity
per time for self-initiated activities compared to the replacement officerrsquos productivity For
example a VORC of 12 means than officer would produce 012 more productivity points (some
combination of warnings citations and arrests) per minute than a replacement officer
Biographical variables We gathered biographical information about the officers in the
sample including gender years of LPD experience maritalcohabiting status and number of
children We also controlled for whether the officer worked first second or third shift because
our data showed significant differences in the number of calls for service each shift handled as
well as the number of reports each shift completed
Geographic variables It is necessary to measure the characteristics of patrol areas to
account for variation across patrol areas For example some areas may feature different crime
rates or levels of social disorganization which command more department resources and police
officers We gathered data by first mapping out every squadrsquos patrol area and then obtaining
tract-level data from the 2010 US Census Our models included measures of established of
983092
983123983139983144983151983148983137983154983155 983137983150983140 983152983151983148983145983139983161 983149983137983147983141983154983155 983149983137983161 983140983145983155983137983143983154983141983141 983159983145983156983144 983141983155983156983145983149983137983156983145983150983143 983137 983154983141983152983148983137983139983141983149983141983150983156 983151983142983142983145983139983141983154991257983155 983139983137983152983137983139983145983156983161 983137983156 983150983145983150983141983156983161 983152983141983154983139983141983150983156 983119983157983154983143983151983137983148 983145983155 983150983151983156 983156983151 983140983141983142983145983150983145983156983145983158983141983148983161 983141983155983156983145983149983137983156983141 983137 983144983161983152983151983156983144983141983156983145983139983137983148 983151983142983142983145983139983141983154991257983155 983152983154983151983140983157983139983156983145983158983145983156983161 983138983157983156 983149983141983154983141983148983161 983156983151 983141983155983156983137983138983148983145983155983144 983137 983154983141983137983155983151983150983137983138983148983141 983142983145983160983141983140
983152983151983145983150983156 983137983143983137983145983150983155983156 983159983144983145983139983144 983156983151 983139983151983149983152983137983154983141 983137983148983148 983152983137983156983154983151983148 983151983142983142983145983139983141983154983155
7252019 PIJPSM-05-2015-0064
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correlates of crime including population density (Hipp amp Roussell 2013) household income
(Gould Weinberg and Mustard 2002) and racial composition (Light amp Harris 2012)
Specifically we use each arearsquos population density median household income and percent
white We also included measures for each arearsquos violent and property crime rate Finally we
gathered information about the number of violent crimes (murders assaults aggravated assaults
sexual assaults and robberies) property crimes (larcenies burglaries vandalisms frauds
forgeries and motor vehicle thefts) and number of police officers in each patrol area thereby
creating variables for violent crime per officer and property crime per officer
Since LPDrsquos patrol officers are grouped with squads it is reasonable to believe that an
officerrsquos productivity might be more similar to an officer within their squad compared to an
officer in a different squad To account for the potential correlations between the individual
units as well as the multi-level nature of the data we used hierarchical linear modeling to
analyze the data
Our primary level variables include an officerrsquos gender marital status number of
children years of law enforcement experience shift calls for service annual number of traffic
stops and citations field information reports and misdemeanor felony and warrant arrests We
also used the officersrsquo productivity information to calculate each patrol officerrsquos VORC
Our secondary level variables include the violent crime per officer rate and property
crime per officer rate for the patrol area of each squad as well several control variables
including population density household median income percent white and the patrol arearsquos
violent and property crime rate Table 2 displays the summary statistics for the first and second-
level variables
INSERT TABLE 2 HERE
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Results
We hypothesized that the violent and property crime-to-cop ratios of an area might affect
the raw productivity outputs of patrol officers as well as a more advanced productivity metric
To examine these questions we constructed five hierarchical linear models estimating the
correlations between reported violent and property crimes per officer and a patrol officerrsquos
annual traffic citations warrant misdemeanor felony arrests and Value Over Replacement Cop
Although not presented here we estimated null models for all dependent variables and found that
significant variation existed between squads when we included no variables included in the
models The chi-square for the LR test for all five null models was statistically significant (p lt
000 in all models)
Table 3 shows that a rise in reported violent crimes per officer increases an officerrsquos
number of warrants and misdemeanors (b= 29 and 76) while arise in reported property crimes
per officer decreases an officerrsquos number of warrants and misdemeanors (b= -15 and -52) We
found no relationship between the crime-to-cop ratios and felony arrests However both the
rates of violent and property crimes per officer decreased the number of traffic citations an
officer writes (b= -168 and -225)
Insert Table 3 here
Table 3 also shows that traffic stops and field information reports are positively
associated with warrant and misdemeanor arrests This supports what police practitioners
already suspect the cops with the most arrests are extremely proactive patrol officers who stop a
large number of vehicles and build investigations through the use of field contacts and
information reports For instance an officer can build felony narcotics cases by contacting
7252019 PIJPSM-05-2015-0064
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suspects and informants in the field documenting these efforts through field information reports
and eventually using this information to write search warrants and conduct drug interdiction
Next we investigated whether this relationship between the crime-to-cop ratios and
productivity outputs exists when using an advanced productivity metric specifically an officerrsquos
Value Over Replacement Cop (VORC)
INSERT TABLE 4 HERE
Table 4 shows that there is no correlation between the ratio of property and violent
crimes per officer and VORC Although this stands in contrast to our earlier results we would
not expect to find a relationship between these variables and VORC Advanced metrics such as
VORC evaluate officers based on the amount of time for self-initiated activities (productive
time) and not raw productivity outputs like misdemeanor or felony arrests Even if an officer
took an extremely large number of property crimes thereby decreasing their productive time
VORC would only evaluate that officer based on what the officer produced with the small
amount of productive time This suggests that while a teamrsquos crime-to-cop ratio could affect an
officerrsquos raw productivity outputs ie arrests a teamrsquos crime-to-cop ratio would not affect an
officerrsquos VORC
For example during a given month an officer may take an unusually large number of
burglaries or larcenies thereby leaving the officer with only ten hours for self-initiated activities
With this reduced amount of productive time the officer may generate a below-average number
of officials warrants misdemeanors and felonies which in turn may lead a supervisor to
potentially downgrade the officerrsquos productivity However VORC would recognize and account
for the high number of calls for service minutes and analyze the officerrsquos productivity based on
what the officer generated with only ten hours of productive time It may be that the officer was
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actually very productive but they simply didnrsquot have enough productive time to generate the
same number of traffic citations and arrests as an officer who responded to far fewer property
crimes
Table 4 also shows that traffic stops and field intelligence reports are positively
correlated with VORC These results are consistent with the principles of how VORC evaluates
patrol officer productivity and provides additional support for the validity of this advanced
metric VORC accounts for numerous variables that affect productive time (ie calls for service
minutes) meaning that the key factors which increase or decrease an officerrsquos VORC are largely
related to self-initiated activities (ie traffic stops and information reports) VORC therefore
accounts for the potential effects of macro-level variables and analyzes the activities of the
officer during his or her productive time Nonetheless we recognize that because traffic stops
and field information reports are correlated with VORC an arearsquos crime-to-cop ratio might still
affect an officerrsquos VORC indirectly by impacting the number of stops and information reports
they generate
Discussion
There are several factors which affect a patrol officerrsquos productivity including
biographical characteristics officersrsquo perceptions and attitudes and organizational and
operational features among others This paper identified two macro-level variablesmdashproperty
crimes per officer and violent crimes per officermdashwhich affect the number of traffic citations
warrants misdemeanors and felonies generated annually by an officer Using data from a mid-
sized police department we found that a patrol areas rate of property crimes per officer was
associated with a moderate decrease in an officers annual number of traffic citations warrant
arrests and misdemeanor arrests while a patrol areas rate of violent crimes per officer was also
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associated with a moderate decrease in an officers annual number of traffic citations We also
found that a patrol areas rate of violent crimes per officer is associated with a moderate increase
in an officers annual number of warrant and misdemeanor arrests However when we used a
more advanced productivity metric we found no relationship between property and violent
crimes per officer and an officerrsquos Value Over Replacement Cop or VORC
There are several factors which explain the differing effects of the crime-to-cop ratios on
productivity outputs Let us first address why the violent crime-to-cop ratio would increase a
patrol officerrsquos warrants and misdemeanors while the property crime-to-cop ratio would decrease
these outputs Notably half of all victims of violent crimes know the offender (Truman amp Rand
2010) which aids law enforcement in identifying and locating the perpetrator Moreover both
suspect and victim are frequently on scene when police officers arrive such as during domestic
disturbances Being able to identify and locate the perpetrator helps patrol officers make arrests
thereby increasing their misdemeanor and felony arrests in violent crimes Additionally when
patrol officers arrive and contact individuals during an incident it is standard procedure to check
if the parties involved have outstanding warrants This means that the greater number of violent
crimes that officers respond to the more chances they have to arrest individuals who might have
outstanding warrants It makes sense then that officers who respond to a greater number of
violent crimes have more chances to make warrant and misdemeanor arrests
Conversely property crimes may supply the officer with fewer warrant and misdemeanor
arrests because of low clearance rates We examined the difference in clearance rates between
property and violent crime using incident data from the Lincoln Police Department and we
found that the clearance rate for violent crimes was 418 compared to only 237 for property
crimes Moreover property crimes often also require more investigative time than violent
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crimes Property crime investigations often require considerable follow-up by officers such as
processing a scene contacting financial institutions and checking pawnshops for stolen property
In general property crimes are more time-intensive than violent crimes thereby decreasing
productive time and opportunities for proactive activities while simultaneously netting officers
fewer arrests because of low clearance rates
The impact of a teamrsquos crime-to-cop ratio can also be seen in the relationship between
property and violent crime per officer and the number of traffic citations Table 3 shows that a
rise in both reported violent and property crimes per officer decrease an officerrsquos number of
traffic citations Quite simply if officers respond to a high number of property and violent
crimes then they will have fewer minutes to conduct traffic enforcement and other proactive
enforcement
As noted it is possible that the correlation is bi-directional If an officer makes a greater
number of arrests then a team may witness a decrease in crime because prolific criminals may
have been arrested and placed in jail For example one might interpret Table 3 to mean that an
increase in warrant and misdemeanor warrants led to a decrease in the number of property crimes
per officer However under this hypothesis we should also see that an increase in warrant and
misdemeanor arrests leads to a decrease in the number of violent crimes per officer Instead we
see the opposite hypothesized effect ie more arrests leading to more reported violent crimes 5
983093 983127983141 983139983151983150983155983156983154983157983139983156983141983140 983154983141983143983154983141983155983155983145983151983150 983149983151983140983141983148983155 983157983155983145983150983143 983139983145983156983137983156983145983151983150983155 983159983137983154983154983137983150983156 983137983154983154983141983155983156983155 983149983145983155983140983141983149983141983137983150983151983154 983137983154983154983141983155983156983155 983137983150983140 983142983141983148983151983150983161 983137983154983154983141983155983156983155 983142983154983151983149
983156983144983141 983152983154983141983139983141983140983145983150983143 983149983151983150983156983144 983156983151 983152983154983141983140983145983139983156 983138983151983156983144 983158983145983151983148983141983150983156 983137983150983140 983152983154983151983152983141983154983156983161 983139983154983145983149983141 983154983137983156983141983155 983142983151983154 983156983144983141 983149983151983150983156983144983155 983145983150 983151983157983154 983140983137983156983137983155983141983156 983127983141 983142983151983157983150983140 983150983151
983155983156983137983156983145983155983156983145983139983137983148983148983161 983155983145983143983150983145983142983145983139983137983150983156 983154983141983148983137983156983145983151983150983155983144983145983152983155 983138983141983156983159983141983141983150 983139983145983156983137983156983145983151983150983155 983159983137983154983154983137983150983156 983137983154983154983141983155983156983155 983149983145983155983140983141983149983141983137983150983151983154 983137983154983154983141983155983156983155 983137983150983140 983142983141983148983151983150983161 983137983154983154983141983155983156983155
983137983150983140 983156983144983141 983158983145983151983148983141983150983156 983137983150983140 983152983154983151983152983141983154983156983161 983139983154983145983149983141 983154983137983156983141983155 983151983142 983156983144983141 983138983141983137983156983155 983112983151983159983141983158983141983154 983137983142983156983141983154 983140983145983155983137983143983143983154983141983143983137983156983145983150983143 983156983144983141 983158983145983151983148983141983150983156 983137983150983140 983152983154983151983152983141983154983156983161
983139983154983145983149983141 983154983137983156983141983155 983145983150983156983151 983155983152983141983139983145983142983145983139 983139983154983145983149983141983155 983159983141 983140983145983140 983142983145983150983140 983156983144983137983156 983149983145983155983140983141983149983141983137983150983151983154 983137983150983140 983142983141983148983151983150983161 983137983154983154983141983155983156983155 983155983145983143983150983145983142983145983139983137983150983156983148983161 983152983154983141983140983145983139983156983141983140 983137 983140983141983139983154983141983137983155983141
983145983150 983154983151983138983138983141983154983161 983154983137983156983141983155 983159983144983145983139983144 983145983155 983139983151983150983155983145983155983156983141983150983156 983159983145983156983144 983156983144983141 983148983145983156983141983154983137983156983157983154983141 983105983140983140983145983156983145983151983150983137983148983148983161 983159983141 983145983150983156983141983154983158983145983141983159983141983140 983140983141983152983137983154983156983149983141983150983156 983152983141983154983155983151983150983150983141983148 983137983150983140983148983141983137983154983150983141983140 983156983144983137983156 983156983144983141 983116983145983150983139983151983148983150 983120983151983148983145983139983141 983108983141983152983137983154983156983149983141983150983156 983144983137983140 983155983156983137983142983142983141983140 983137 983156983141983137983149 983140983141983155983145983143983150983141983140 983155983152983141983139983145983142983145983139983137983148983148983161 983156983151 983137983152983152983154983141983144983141983150983140 983154983151983138983138983141983154983161
983155983157983155983152983141983139983156983155 983140983157983154983145983150983143 983156983144983145983155 983156983145983149983141983142983154983137983149983141 983159983144983145983139983144 983155983141983141983149983155 983156983151 983141983160983152983148983137983145983150 983156983144983141 983137983154983154983141983155983156983085983154983151983138983138983141983154983161 983154983137983156983141 983154983141983148983137983156983145983151983150983155983144983145983152
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We believe our explanation has a stronger theoretical foundation The ratio of property
crimes per officer affects productivity outputs differently than the ratio of violent crimes per
officer for two reasons First violent crimes are different in nature than property crimes
especially with regard to clearance rates Second property crimes often require more
investigative and follow-up time than violent crimes thereby reducing an officerrsquos amount of
productive time and proactive activity
Table 3 also provides a key piece of evidence to support this assertion Table 3 shows
that the number of reported violent crimes per officer increases an officerrsquos number of warrant
and misdemeanor arrests Yet the relationship is the opposite for traffic enforcement the violent
crime-to-cop ratio actually decreases traffic citations This change is due to the fact that while
violent crime investigations often yield warrant and misdemeanor arrests they do not produce
traffic citations Furthermore responding to a greater number of violent crimes decreases the
amount of available time to conduct traffic enforcement in turn reducing the number of traffic
citations In other words the violent crime-to-cop ratio affects traffic stops the same way that
the property crime-to-cop ratio affects all productivity outputs by reducing an officerrsquos
productive time and opportunities for proactivity In fact we found a moderate correlation
between a patrol arearsquos crime-to-cop ratio and the amount of productive time enjoyed by officers
who worked that area The correlation between an arearsquos violent crime-to-cop ratio and
productive time was -21 while the correlation between an arearsquos property crime-to-cop ratio and
productive time was -33 We believe this evidence indicates that officers who work in areas
with a high crime-to-cop ratio have less time for self-initiated activities which drives down
productivity
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We suggest that the number of police officers and crime rates of an area need to be
analyzed in conjunction with each other Prior theories of police productivity have viewed and
examined these variables separately when in reality we should view these variables in tandem
Combining these variables into crime-to-cop ratios provides a more nuancedmdashand accuratemdash
understanding of patrol activities and their relationship to productivity
Although we found strong evidence that an officerrsquos patrol area affects his or her
productivity (in terms of raw outputs) we also found that a few officer-level variables affected
productivity as well including traffic stops intelligence reports and working third shift Our
results appear to substantiate what many police practitioners already suspect officers who are
proactive in contacting citizens (traffic stops) and building investigations (intelligence reports)
generate more warrant and misdemeanor arrests In addition while some newer police officers
are forced to work third shift because they do not have sufficient seniority to bid for better days
off and shift times many officers work nights because they enjoy the types of calls (eg in-
progress felonies) meaning they may be more motivated to stop vehicles make field contacts
and in turn engage in activities that generate arrests (Brehm amp Gates 1993)
These results have important implications for police researchers and administrators
First our results suggest a need for a deeper understanding of how the staffing levels crime
rates and the other characteristics of a patrol area intersect Second if departments evaluate
their patrol officers using raw productivity outputs such as misdemeanor or felony arrests
administrators must recognize that factors beyond the control of patrol officers might affect
monthly or annual productivity Our research suggests that a patrol arearsquos crime-to-cop ratio can
substantially affect a patrol officerrsquos productivity regardless of the actual crime rate If the
characteristics of a patrol area affect raw productivity outputs police departments should employ
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more advanced productivity analytics (ie VORC) that account for activities which reduce
productive time and evaluate an officerrsquos productivity in terms of available minutes for
proactivity
Nonetheless evaluating both patrol officers and police departments using traffic citations
warrants misdemeanors and felonies is useful because this type of data is readily understood by
citizens and civic officials A neighborhood watch group for instance might care more about
the number of arrests their officers make and less about those officersrsquo productivity scores Our
data suggests that if citizens want patrol officers to write more traffic citations and make more
arrests they should lobby for more police officers on the street It is true that reducing an arearsquos
crime rate impacts the crime-to-cop ratio but our results indicate that putting police officers on
the street may be the most direct way to affect the crime-to-cop ratio
Our study is not without limitations We only examined a police department which uses
the generalist style of policing and it is unknown if our results are applicable to police
departments which use a specialist style of policing Theoretically a departmentrsquos policing
styles can affect factors that increase or decrease a patrol officerrsquos productive time It is possible
that the number of property crimes and violent crimes per officer do not significantly affect a
specialist patrol officerrsquos productivity outputs versus a generalist officerrsquos outputs Future
studies should investigate whether our results can be generalized to all police departments
We have only identified two macro-level variables which affect patrol officer
productivity and we recognize there may be other variables In addition we recommend that
future studies incorporate a wider array of variables and test competing models to determine
whether micro or macro-level variables exhibit stronger effects on productivity In the end
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managing patrol officers and improving productivity requires a greater understanding of both
patrol officers and the areas they work
Acknowledgements and Credits We would like to thank Denver Police Department Crime
Analyst Melissa Ohlhorst and Pennsylvania State University Professor Barry Ruback as well as
the three anonymous reviewers for their insightful comments We also salute Lincoln Police
Sergeant Jake Dilsaver for his logistical support of this project
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References
Amaranto E Steinberg J Castellano C and Mitchell R (2003) ldquoPolice stress interventionsrdquo
Brief Treatment and Crisis Intervention Vol 3 No 1 pp 47-53
Becker G (1985) ldquoHuman capital effort and the sexual division of laborrdquo The Journal of
Labor EconomicsVol 3 No 1 pp 33-58
Bonkiewicz L (2015) ldquoBobbies and baseball players Evaluating patrol officer productivity
using sabermetricsrdquo Police Quarterly Vol 18 No 1 pp 55-78
Braga A and Bond J (2008) ldquoPolicing crime and disorder hot spots A randomized controlled
trialrdquo Criminology Vol 46 No 3 pp 577-607
Brown J and Campbell E (1990) ldquoSources of occupational stress in the policerdquo Work
and Stress Vol 4 No 4 pp 305-318
Chappell A MacDonald J and Manz P (2006) ldquoThe organizational determinants
of police arrest decisionsrdquo Crime and Delinquency Vol 52 No 2 pp 287-306
Cloninger D and Sartorius L (1979) ldquoCrime rates clearance rates and enforcement effort
The case of Houston Texasrdquo American Journal of Economics and Sociology Vol 38
No 4 pp 389-402
Corman H and Mocan N (2005) ldquoCarrots sticks and broken windowsrdquo Journal of Law and
Economics Vol 48 No 1 pp 235-266
Crank J (1990) ldquoThe influence of environmental and organizational factors on police style in
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pp 166-189
Goodman A (1990) ldquoA model for police officer burnoutrdquo Journal of Business and
Psychology Vol 5 No 1 pp 85-99
Gould E Weinberg B and Mustard D (2002) Crime rates and local labor marketopportunities in the United States 1979ndash1997rdquo Review of Economics and Statistics Vol
84 No 1 pp 45-61
Hipp J and Roussell A (2013) ldquoMicro-and macro-environment population and the
consequences for crime ratesrdquo Social Forces Vol 92 No 2 pp 563-595
Hatry H (1976) ldquoApproaches to productivity measurement and program evaluationrdquo Public
Productivity Review Vol 1 No 3 pp21-28
Hawkins H (2001) ldquoPolice officer burnout a partial replication of Maslachs Burnout
Inventoryrdquo Police Quarterly Vol 4 No 3 pp 343-360
7252019 PIJPSM-05-2015-0064
httpslidepdfcomreaderfullpijpsm-05-2015-0064 2530
Herbert S (1998)ldquoPolice subculture reconsideredrdquo Criminology Vol 36 No 2 pp 343-370
Huizinga D and Henry K (2008) ldquoThe effect of arrest and justice system sanctions on
subsequent behavior Findings from longitudinal and other studiesrdquo In The long view of
crime A synthesis of longitudinal research Springer New York pp 220-254
Johnson R (2011) ldquoOfficer attitudes and management influences on police work productivityrdquo
American Journal of Criminal Justice Vol 36 pp 293-306
Kane R (2006) ldquoOn the limits of social control Structural deterrence and the policing of
lsquosuppressiblersquo crimesrdquo Justice Quarterly Vol 23 No 2 pp 186-213
Kubrin C Messner S Deane G McGeever K and Stucky T (2010) ldquoProactive
policing and robbery rates across US citiesrdquo Criminology Vol 48 No 1 pp 57-97
Lersch K (2002) ldquoAre citizen complaints just another measure of officer productivity An
analysis of citizen complaints and officer activity measuresrdquo Police Practice and
Research Vol 3 pp 135-147
Light M and Harris C (2012) Race space and violence exploring spatial dependence in
structural covariates of white and black violent crime in US countiesrdquo Journal of
Quantitative Criminology Vol 28 No 4 pp 559-586
Maxfield M (1982) ldquoService time dispatch time and demand for police services Helping
more by serving lessrdquo Public Administration Review pp 252-263
Maxfield M Lewis D and Szoc R (1980) ldquoProducing official crimes Verified crime reports
as measures of police outputrdquo Social Science Quarterly pp 221-236
Nakosteen R and Zimmer M (2001) ldquoSpouse selection and earnings Evidence of marital
sortingrdquo Economic InquiryVol 39 No 2 pp 201-2013
Shane J (2010) ldquoOrganizational stressors and police performancerdquo Journal of Criminal
Justice Vol 38 No 4 pp 807-818
Shane J (2011) ldquoDaily work experiences and police performancerdquo Police Practice and
Research Vol 13 No 3 pp 1-19
Sherman L Gartin P and Buerger M (1989) ldquoHot spots of predatory crime Routine
activities and the criminology of placerdquoCriminology Vol 27 No 1 pp27-56
Truman J and Rand M (2010) ldquoBureau of Justice Statistics Bulletin National Crime
Victimization Survey Criminal Victimization 2009rdquo US Department of Justice Office of
Justice programs
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httpslidepdfcomreaderfullpijpsm-05-2015-0064 2630
Van Meter D (2001) Evaluating dysfunctional police performance A zero-based approach
Charles C Thomas Publisher Springfield IL
Vila B and Kenney D (2002) ldquoTired cops The prevalence and potential consequences of
police fatiguerdquo National Institute of Justice
Vila B Morrison G and Kenney D (2002) ldquoImproving shift schedule and work-hour
policies and practices to increase police officer performance health and safetyrdquo Police
Quarterly Vol 5 No 1 pp 4-24
Vollaard B and Koning P (2009) ldquoThe effect of police on crime disorder and victim
PrecautionEvidence from a Dutch victimization surveyrdquo International Review of Law
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Weisburd D Maher L Sherman L Buerger M Cohn E and Petrisino A (1993)
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New Directions in Criminological Theory Advances in Criminological Theory Vol 4
pp 45-70
Author Biography
Luke Bonkiewicz is a patrol officer with the Lincoln Police Department
7252019 PIJPSM-05-2015-0064
httpslidepdfcomreaderfullpijpsm-05-2015-0064 2730
983110983145983143983157983154983141 983089983086 983126983119983122983107 983110983151983154983149983157983148983137
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983086 983097) lowast )
7252019 PIJPSM-05-2015-0064
httpslidepdfcomreaderfullpijpsm-05-2015-0064 2830
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983119983142983142983145983139983141983154 983120983154983151983155983141983139983157983156983145983151983150
983122983137983156983141C983110983123
983117983145983150983157983156983141983155983110983125983120
983117983145983150983157983156983141983155
983119983142983142983145983139983141983154
A983155983155983145983155983156983117983145983150983157983156983141983155
983113983150983139983145983140983141983150983156983122983141983152983151983154983156983155
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6 83 23916 7056 6192 144 756 528 996 336 132 36 72 12
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983124983137983138983148983141 2 983123983157983149983149983137983154983161 983123983156983137983156983145983155983156983145983139983155 983142983151983154 983126983137983154983145983137983138983148983141983155
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983120983151983152983157983148983137983156983145983151983150 983140983141 983150983155983145983156983161 20229830856956 1687 4436
983112983151983157983155983141983144983151983148983140 983149983141983140983145983137983150 983145983150983139983151983149983141 ($) 2628198308576020 16535 49756
983159983144983145983156983141 7398308593 007 083
983126983145983151983148983141983150983156 983139983154983145983149983141 983154983137983156983141 (983152983141983154 100000) 8009830854800 1203 2521
983120983154983151983152983141983154983156983161 983139983154983145983149983141 983154983137983156983141 (983152983141983154 100000) 31009830859800 2027 6524
983120983154983151983152983141983154983156983161 983139983154983145983149983141983155 983152983141983154 983151983142983142983145983139983141983154 5329830851097 1663 8167
983126983145983151983148983141983150983156 983139983154983145983149983141 983152983141983154 983151983142983142983145983139983141983154 194983085372 559 2929
983110983145983154983155983156983085983116983141 983158983141983148 983126 983137983154983145983137983138983148983141 983155 (983119983142983142983145983139983141983154) 983118=302 983122983137983150983143983141 983123D 983117983141 983137983150
983111983141983150983140983141983154 (983142983141983149983137983148983141=0 983149983137983148983141=1) 09830851 24 94
983117983137983154983154983145983141983140C983151983144983137983138983145983156983145983150983143 09830851 18 79
983151983142 C983144983145983148983140983154983141983150 09830855 170 90
983129983141983137983154983155 983141983160983152983141983154983145983141983150983139983141 198308537 779 1063
C983137983148983148983155 983142983151983154 983155983141983154983158983145983139983141 4819830851011 16121 61365
983124983154983137983142983142983145983139 983155983156983151983152983155 16983085901 14376 20624
983110983145983141983148983140 983145983150983142983151983154983149983137983156983145983151983150 983154983141983152983151983154983156983155 0983085227 2153 2530
983124983154983137983142983142983145983139 983139983145983156983137983156983145983151983150983155 249830851246 14632 18642
983117983145983155983140983141983149983141983137983150983151983154 983137983154983154983141983155983156983155 19983085403 5965 10663
983110983141983148983151983150983161 983137983154983154983141983155983156983155 098308571 1066 852
983126983119983122C 9830851959830851043 151 24
1983155983156 2983150983140 3983154983140
104 96 102983123983144983145983142983156
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983124983137983138983148983141 3 983112983145983141983154983137983154983139983144983145983139983137983148 983116983145983150983141983137983154 983117983151983140983141983148983155 E983155983156983145983149983137983156983145983150983143 983119983142 983142983145983139983141983154983155 A983150983150983157983137983148 983124983154983137983142983142983145983139 C983145983156983137983156983145983151983150983155 983127983137983154983154983137983150983156983155 983117983145983155983140983141983149983141983137983150983151983154983155 983137983150983140 983110983141983148983151983150 983145983141983155
983123983141983139983151983150983140983085983116983141983158983141983148 983126983137983154983145983137983138983148983141983155 (983120983137983156983154983151983148 A983154983141983137) 983118=40 983138 983123E 983138 983123E 983138 983123E 983138 983123E
983120983151983152983157983148983137983156983145983151983150 983140983141983150983155983145983156983161 001 003 001 001 98308501 01 00 00
983112983151983157983155983141983144983151983148983140 983149983141983140983145983137983150 983145983150983139983151983149983141 ($) 002 001 983085000 000 00 00 98308500 00
983159983144983145983156983141 13363 32472 9830858921 5664 9830853274 19211 9830854447 3255
983126983145983151983148983141983150983156 983139983154983145983149983141 983154983137983156983141 (983152983141983154 100000) 006 004 001 001 98308501 02 01 02
983120983154983151983152983141983154983156983161 983139983154983145983149983141 983154983137983156983141 (983152983141983154 100000) 983085003 005 983085002 001 02 03 98308501 00
983120983154983151983152983141983154983156983161 983139983154983145983149983141983155 983152983141983154 983151983142983142983145983139983141983154 983085225 99 98308515 03 98308552 24 98308503 03
983126983145983151983148983141983150983156 983139983154983145983149983141 983152983141983154 983151983142983142983145983139983141983154 983085168 68 29 14 76 31 22 12
983110983145983154983155983156983085983116983141983158983141983148 983126983137983154983145983137983138983148983141983155 (983119983142983142983145983139983141983154) 983118=302
983117983137983148983141 4673 1622 366 147 1613 1051 91 158
983117983137983154983154983145983141983140C983151983144983137983138983145983156983145983150983143 9830857516 3254 201 278 9830851158 1169 98308595 167
983151983142 C983144983145983148983140983154983141983150 183 702 75 77 983085262 217 983085396 207
983129983141983137983154983155 983141983160983152983141983154983145983141983150983139983141 9830851065 492 98308512 13 983085163 33 98308506 05C983137983148983148983155 983142983151983154 983155983141983154983158983145983139983141 24 13 02 02 14 01 98308508 05
983124983154983137983142983142983145983139 983155983156983151983152983155 983085983085 983085983085 03 00 09 02 00 00
983110983145983141983148983140 983145983150983142983151983154983149983137983156983145983151983150 983154983141983152983151983154983156983155 209 33 20 04 79 19 10 03
983123983141983139983151983150983140 983155983144983145983142983156 (983154983141983142983141983154983141983150983139983141 983143983154983151983157983152 983142983145983154983155983156) 2603 1778 187 195 809 730 147 160
983124983144983145 983154983140 983155983144983145 983142983156 (983154983141 983142983141 983154983141 983150983139983141 983143983154983151983157983152 983142983145 983154983155983156) 9872 2155 79 213 2532 952 393 132
983113983150983156983141983154983139983141983152983156 32591 5361 758 339 4147 1912 310 484
983123983145983143983149983137983085983155983153983157983137983154983141983140 1541467 19287 206702 5598
C983144983145983085983155983153983157983137983154983141983140 8294 6133 12348 10663
983152 lt 05
983152lt01
983152lt001
983118983151983156983141 A983148983148 983158983137983154983145983137983138983148983141983155 983137983154983141 983157983150983139983141983150983156983141983154983141983140
983127A983122983122A983118983124 A983122983122E983123983124983123 983117983113983123DE983117EA983118983119983122 A983122983122E983123983124983123 983110E983116983119983118983129 A983122983122E983123983124983123983124983122A983110983110983113C C983113983124A983124983113983119983118983123
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983124983137983138983148983141 4 983112983145983141983154983137983154983139983144983145983139983137983148 983116983145983150983141983137983154 983117983151983140983141983148983155 E983155983156983145983149983137983156983145983150983143 983119983142983142983145983139983141983154983155 A983150983150983157983137983148 983126983137983148983157983141 983119983158983141983154 983122983141983152983148983137983139983141983149983141983150983156 C983151983152 (983126983119983122C)
983123983141983139983151983150983140983085983116983141983158983141983148 983126983137983154983145983137983138983148983141983155 (983120983137983156983154983151983148 A983154983141983137) 983118=40 983138 983123E
983120983151983152983157983148983137983156983145983151983150 983140983141983150983155983145983156983161 98308500 00
983112983151983157983155983141983144983151983148983140 983149983141983140983145983137983150 983145983150983139983151983149983141 ($) 00 01
983159983144983145983156983141 318 345
983126983145983151983148983141983150983156 983139983154983145983149983141 983154983137983156983141 (983152983141983154 100000) 98308500 00
983120983154983151983152983141983154983156983161 983139983154983145983149983141 983154983137983156983141 (983152983141983154 100000) 00 00
983120983154983151983152983141983154983156983161 983139983154983145983149983141983155 983152983141983154 983151983142983142983145983139983141983154 98308502 04
983126983145983151983148983141983150983156 983139983154983145983149983141 983152983141983154 983151983142983142983145983139983141983154 02 05
983110983145983154983155983156983085983116983141983158983141983148 983126983137983154983145983137983138983148983141983155 (983119983142983142983145983139983141983154) 983118=302
983117983137983148983141 27 15
983117983137983154983154983145983141983140C983151983144983137983138983145983156983145983150983143 98308532 31
983151983142 C983144983145983148983140983154983141983150 05 03
983129983141983137983154983155 983141983160983152983141983154983145983141983150983139983141 00 01
C983137983148 983148983155 983142983151983154 983155983141983154983158983145983139983141 02 01
983124983154983137983142983142983145983139 983155983156983151983152983155 01 00
983110983145 983141983148983140 983145983150983142983151983154983149983137983156983145 983151983150 983154983141 983152983151983154983156983155 02 00
983123983141983139983151983150983140 983155983144983145983142983156 ( 983154983141983142983141983154983141983150983139983141 983143983154983151983157983152 983142 983145983154983155983156) 23 12
983124983144983145983154983140 983155983144983145983142983156 (983154983141983142983141983154983141983150983139983141 983143983154983151983157983152 983142 983145983154983155983156) 55 14
983113983150983156983141983154983139983141983152983156 165 36
983123983145983143983149983137983085983155983153983157983137983154983141983140 66
C983144983145983085983155983153983157983137983154983141983140 9846
983152 lt 05
983152lt01
983152lt001
983118983151983156983141 A983148983148 983158983137983154983145983137983138983148983141983155 983137983154983141 983157983150983139983141983150983156983141983154983141983140
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officers Married officers have someone in whom to confide a partner who hears about the
challenges and rigors of the job which may lead to stress reduction and improve workplace
motivation An officerrsquos age is another consideration Older officers might be less productive
than younger officers perhaps due to burnout as well as the physical demands of law
enforcement (Goodman 1990) Officersrsquo perceptions of their job may also affect their
proactivity and in turn productivity Brehm and Gates (1993) found that officers who disliked
certain aspects of law enforcement (eg taking people to jail) impacted whether officers engaged
in proactive activities or shirked their duties
Additionally there are also operational features (the ldquoday to dayrdquo rigors) of law
enforcement which can decrease productivity and increase misconduct such as the perceived
danger of the job (Amaranto et al 2003) work schedules and shift work (Vila Morrison amp
Kenney 2002) and fatigue (Vila amp Kenney 2002) Notably in studying the relationship
between daily work experience and performance Shane (2011) found that workload was the
ldquomost important predictor of performancerdquo (p 13)
We believe this last finding merits further study First supervisors evaluate patrol
officers at least in part using productivity outputs If there are factors (ie heavy workload)
beyond an officerrsquos control that are affecting his or her productivity both line-level supervisors
and command staff members should acknowledge and account for these variables during
performance reviews Second if macro-level variables create different workloads thereby
affecting productivity outputs then police researchers should include these measures in future
studies on police activity We suggest that there are at least two macro-level variables important
to future police productivity research
Crime-to-Cop Ratios and Patrol Officer Productivity
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Criminological research has long recognized that crime rates are not uniform across
neighborhoods and cities (Sherman et al 1989 Weisburd et al 1993) Instead of ldquopolicing
peoplerdquo law enforcement agencies frequently identify ldquohot spotsrdquo and devote additional
resources to reducing crime and disorder in specific problem areas With this perspective in
mind it might seem natural to examine the relationship between the crime rate of a patrol area
and an officerrsquos productivity outputs (or vice-versa) For example a high crime area might net
an officer more arrests (and therefore more productivity outputs) than a low crime area On the
other hand a high crime area might also occupy a great deal of an officerrsquos productive time
prohibiting the officer from engaging in proactive activities However crime rate does not fully
capture how a particular area might affect an officerrsquos productivity
Instead we believe macro-level variables related to crime should be estimated in terms of
the number of patrol officers in a patrol area specifically violent crimes per officer and property
crimes per officer For instance some officers may patrol a lower crime area but end up
responding to a greater number of violent crimes than officers in a high crime area simply
because they have fewer officers working the street This ldquocrime per coprdquo ratio can dramatically
affect officersrsquo productivity
For example when an officer investigates a crime he or she might interview the victim
suspect and witnesses as well as take photographs collect evidence and make an arrest There
may be additional follow-up the next day or week such as checking pawnshops for stolen
property or tracking down and interviewing additional witnesses All these activities could
decrease an officerrsquos time for self-initiated activities and in turn an officerrsquos overall
productivity A high crime per cop ratio therefore could decrease productivity for officers even
if these officers work in an area with a low crime rate Furthermore an arearsquos crime per cop
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ratio could also affect officersrsquo productive time even if they are not the primary investigating
officer because secondary officers are often needed during serious incidents such as robberies or
burglaries
The ldquoload sheddingrdquo hypothesis by Maxfield et al (1980 see also Maxfield 1982) also
has important implications for any study examining patrol officer productivity Maxfield et al
suggested that while variations in workload do not affect patrol officersrsquo decisions to record
serious crimes workload does affect officersrsquo decisions to record less serious crimes it may also
affect self-initiated activities as well (Herbert 1989)
Finally we believe that macro-level variables affecting productivity should be divided
between violent and property crimes to account for the time and resources needed to investigate
each incident type When officers respond to assaults they often contact suspects and victims on
scene meaning that officers are able to interview all involved individuals collect evidence and
make an arrest sometimes in less than an hour However when officers respond to burglaries or
larcenies they may be required to dust for fingerprints swab for DNA canvass a neighborhood
for witnesses check pawnshops and engage in other investigative activities that require hours
and even days if the case requires follow-up
With these issues in mind our study addresses the following research questions First
what is the relationship between a patrol arearsquos rate of violent crimes per officer and patrol
officer productivity outputs (ie traffic citations as well as warrant misdemeanor and felony
arrests) Second what is the relationship between a patrol arearsquos rate of property crimes per
officer and patrol officer productivity outputs (again traffic citations and warrant misdemeanor
and felony arrests) Third what is the relationship between the rate of property and violent
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crimes per officer and a patrol officerrsquos productivity when we introduce a more sophisticated
method for analyzing productivity a metric based on productive time and not total time on duty
Before discussing our data and methods there are two important issues meriting brief
discussion First we argue that the crime-to-cop ratio may affect the number of traffic stops
warrants misdemeanors and felonies that a patrol officer makes It can also be argued that the
number of arrests might impact the crime-to-cop ratio Our intent is not to comprehensively
discuss the relationship between the number of officersofficer arrests and an arearsquos crime rate
but it bears noting that the research is inconclusive on this point
Some evidence suggests that the relationship between arrests and crime rates is non-
linear that is crime rates decrease as arrests increase but eventually hit a ldquofloorrdquo and reach
equilibrium (Kane 2006) Second when studies examine the arrest-crime rate relationship they
often find that the relationship only exists for specific crimes in their datasets such as robbery
and motor vehicle thefts (Corman Hope amp Mocan 2005) robbery but not assaults (Kane 2006)
and homicide and auto thefts only (Cloninger amp Sartorius 1979) and so forth Overall the
literature on the arrests-crime rate relationship is mixedmdashsome research finds an effect between
arrests and crime rates some does not (Huizinga and Henry 2008)
The second issue concerns whether a law enforcement agency operates using a generalist
or specialist policing style We analyze data from a generalist municipal police department
meaning that patrol officers investigate low and mid-level crimes but investigators and
detectives are responsible for handling serious crimes cases involving lengthy investigations
(eg narcotics trafficking or white-collar crimes) and incidents in which patrol officers may
need assistance (for example an incident involving numerous victims and witnesses as well as
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cases involving extensive follow-up or knowledge on a specific subject such as gangs or
narcotics)
By contrast when patrol officers in a specialist-style police department respond to any
incident potentially requiring follow-up they might only contact the victim and either hold the
scene until an investigator arrives or forward the information to a detective for follow-up It is
possible indeed likely that macro-level variables might affect generalist patrol officers
differently than specialized officers Due to data limitations however our study focuses on
generalist police officers
Data and Methods
We gathered data on patrol officers and their patrol areas from several sources First we
collected patrol officer productivity data from the Lincoln (Nebraska) Police Departmentrsquos 2013-
2014 productivity reports Using Computer-Aided Dispatch (CAD) and a centralized
productivity database LPD measures numerous patrol officer activities Every month (and
year) LPD generates productivity reports which display each patrol officerrsquos activities and
outputs (see Table 1 for an example of a squadrsquos annual productivity report) Each patrol
activity and output merits further explanation
INSERT TABLE 1 HERE
Calls for Service (CFS) and Officer Assist time When dispatchers send patrol officers on
a call they use CAD to track the minutes the officer spends on the call If the call is a priority
incident dispatchers send both a primary and secondary officer Other unassigned officers might
also respond to the CFS meaning that multiple officers could be secondary officers The annual
productivity record tracks amount of time an officer spent as a primary responder and a
secondary responder
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Report writing time Standard operating procedures dictate that LPD patrol officers
complete specific types of reports for incidents An incident report (IR) details biographical
information of the individuals involved in the incident and summarizes the incident An
Additional Case Information (ACI) is a more detailed report that documents additional
investigative steps or the actions of assisting officers Although the time spent on an IR or ACI
could vary depending on the call for service the average IR and ACI take roughly twenty
minutes to complete1
The Lincoln Police Department also tracks self-initiated activities Most productivity
results from the following proactive activities
Selectives The Lincoln Police Department tracks specific officer patrol activity by
coding its neighborhoods schools parks and business districts When an officer patrols an at-
risk business district or a high-crime neighborhood for example the officer notifies dispatch
using that arearsquos specific code Girded by the principles of ldquohot spotsrdquo policing research officers
use selectives to identify potential candidates for POP projects and heighten the perception of
risk among criminals (Braga amp Bond 2008)
Traffic Warnings and Official Citations Every Lincoln patrol officer drives a police
cruiser and conducts traffic enforcement issuing traffic warnings and official citations The
LPD records department tracks patrol officersrsquo warnings and citations
Misdemeanors felonies and warrants Patrol officers also make misdemeanor and
felony arrests during their productive time Officers might detain a motorist and arrest him or
her for driving under a suspended license or driving while intoxicated or contact a pedestrian
1 983119983142983142983145983139983141983154983155 983156983161983152983141 983137983148983148 983154983141983152983151983154983156983155 983151983150 983139983151983149983152983157983156983141983154983155 983127983141 983151983138983156983137983145983150983141983140 983156983144983145983155 983137983158983141983154983137983143983141 983138983161 983151983138983155983141983154983158983145983150983143 983137 983155983137983149983152983148983141 983151983142 983116983145983150983139983151983148983150 983152983137983156983154983151983148 983151983142983142983145983139983141983154983155
983139983151983149983152983148983141983156983145983150983143 983113983122983155 983137983150983140 983105983107983113983155 983137983150983140 983159983141 983154983141983139983151983143983150983145983162983141 983156983144983137983156 983156983144983145983155 983137983158983141983154983137983143983141 983149983137983161 983140983145983142983142983141983154 983142983154983151983149 983151983156983144983141983154 983152983151983148983145983139983141 983140983141983152983137983154983156983149983141983150983156983155 983117983151983154983141983151983158983141983154983145983142 983140983141983152983137983154983156983149983141983150983156983155 983154983141983153983157983145983154983141 983151983142983142983145983139983141983154983155 983156983151 983139983151983149983152983148983141983156983141 983156983144983141983145983154 983154983141983152983151983154983156983155 983138983141983142983151983154983141 983138983141983139983151983149983145983150983143 983137983158983137983145983148983137983138983148983141 983142983151983154 983137983150983151983156983144983141983154 983139983137983148983148 983142983151983154 983155983141983154983158983145983139983141
983156983144983145983155 983149983141983137983155983157983154983141983149983141983150983156 983145983155 983157983150983150983141983139983141983155983155983137983154983161
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and make an arrest for a sex offender registration violation Officers can also search their
departmentrsquos electronic database for individuals with outstanding warrants in their patrol areas
and attempt to arrest these offenders As with warnings and citations the LPD records
department documents and tracks misdemeanor felony and warrant arrests Moreover the court
system also monitors these types of outputs to process cases thereby adding an additional layer
of reporting accuracy and reliability
Field information reports We also measured the number of field information or
ldquointelligencerdquo reports Officers complete these internal reports to document suspicious or
dangerous individuals or circumstances though these reports are not publicly available
VORC In addition to the raw outputs of traffic citations warrants misdemeanors and
felonies we also include an advanced metric for measuring a patrol officerrsquos productivity Value
Over Replacement Cop (VORC) is a method for evaluating officers that assigns different
weights to productivity outputs and then assesses officer productivity in terms of available time
for self-initiated activities or productive time (Bonkiewicz 2015) We use a variation of this
formula that multiplies an officerrsquos officials misdemeanors and felonies by his or her
prosecution rate Our modified use of VORC can best be explained using the following figure
(Figure 1) and five-step process
INSERT FIGURE 1 HERE
where = total monthly on-duty minutes = CFS minutes Γ = follow-up time and meetings ∆
= officer assist time Ε = of IRs Ζ = of ACIs M = of intelligence reports Η = of
selectives Θ = of warnings times two Κ = of warrants times three Ι = of officials times
three Λ = of misdemeanor arrests times their respective weights Ν = of felonies types their
respective weights Ρ = an officerrsquos prosecution rate Ξ = Officerrsquos Productive time (Α-(Β+Γ+∆+
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20(Ε+Ζ))) Ο= Department average P-score or average (Η+ ((Θ)+(Ι)+(Κ)+(Λ)+(Μ)+(Ν)Ρ))
and Π = Department average Self-Initiated Time
First VORC calculates an officerrsquos total minutes spent on CFS follow-ups officer
assists and reports This amount is an officerrsquos total work time or service minutes Second the
formula subtracts an officerrsquos service minutes from the total on-duty minutes2 This is the
officerrsquos amount of productive time Third VORC calculates an officerrsquos productivity score (or
P-score) using a weighted index Departments can choose different weights for productivity
outputs but for the purpose of this study we used our weights based on the statutes governing
the Lincoln Police Department Citations and arrests are weighted based on whether the offense
was a Traffic Offense a Class III II or I Misdemeanor or a Class V IV III II or I Felony
Accordingly a patrol selective is worth one point a traffic warning two points a traffic citation
three points a warrant arrest three points a Class III misdemeanor arrest four points a Class II
misdemeanor arrest five points a Class I misdemeanor arrest six points a Class V Felony seven
points a Class IV Felony eight points a Class III Felony nine points and Class II and I Felonies
(eg homicides) ten points3 The sum of the weighted productivity outputs is the officerrsquos P-
score The P-score is divided by amount of productive time and then multiplied by 100 (for ease
of understanding) This value the productivity quotient represents the officerrsquos productivity
given the available minutes for proactivity such as searching for wanted individuals or traffic
enforcement
Fourth VORC creates a hypothetical replacement officer against which to measure the
patrol officerrsquos output This replacement player might be imagined as a rookie patrol officer or
983090 983119983150983085983140983157983156983161 983156983145983149983141 983140983151983141983155 983150983151983156 983145983150983139983148983157983140983141 983156983154983137983145983150983145983150983143 983156983145983149983141 983158983137983139983137983156983145983151983150 983151983154 983144983151983148983145983140983137983161 983140983137983161983155 983151983142983142 983151983154 983155983145983139983147 983148983141983137983158983141 983113983150 983151983156983144983141983154 983159983151983154983140983155 983151983150983085983140983157983156983161
983156983145983149983141 983145983155 983156983144983141 983137983149983151983157983150983156 983151983142 983156983145983149983141 983137 983152983137983156983154983151983148 983151983142983142983145983139983141983154 983155983152983141983150983156 983159983151983154983147983145983150983143 983156983144983141 983155983156983154983141983141983156983091 983119983157983154 983159983141983145983143983144983156983145983150983143 983155983161983155983156983141983149 983145983155 983151983150983148983161 983151983150983141 983141983160983137983149983152983148983141 983151983142 983156983144983141 983142983148983141983160983145983138983145983148983145983156983161 983156983144983137983156 983126983119983122983107 983151983142983142983141983154983155 983140983141983152983137983154983156983149983141983150983156983155983099 983141983137983139983144 983137983143983141983150983139983161 983139983137983150
983152983154983145983151983154983145983156983145983162983141 983137983150983140 983137983155983155983145983143983150 983139983151983154983154983141983155983152983151983150983140983145983150983143 983159983141983145983143983144983156983155 983156983151 983156983144983141983145983154 983152983154983141983142983141983154983154983141983140 983152983154983151983140983157983139983156983145983158983145983156983161 983151983157983156983152983157983156983155
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a recruit who has not completed the academy performing at ninety percent the capacity of fully
trained police officer4 Using LPDrsquos data VORC creates a hypothetical replacement officer by
dividing the departmental average P-score by the departmental average for productive time and
then multiplying it by the officerrsquos productive time to determine what an average officer would
have generated given the officerrsquos amount of productive time This figure is then multiplied by
9 (the capacity of a replacement officer) divided by the officerrsquos productive time and then
multiplied by 100 to generate the replacement officerrsquos productivity quotient Finally VORC
subtracts the replacement officerrsquos score from the patrol officerrsquos score to calculate the officerrsquos
value over the replacement officer This number or VORC indicates the officerrsquos productivity
per time for self-initiated activities compared to the replacement officerrsquos productivity For
example a VORC of 12 means than officer would produce 012 more productivity points (some
combination of warnings citations and arrests) per minute than a replacement officer
Biographical variables We gathered biographical information about the officers in the
sample including gender years of LPD experience maritalcohabiting status and number of
children We also controlled for whether the officer worked first second or third shift because
our data showed significant differences in the number of calls for service each shift handled as
well as the number of reports each shift completed
Geographic variables It is necessary to measure the characteristics of patrol areas to
account for variation across patrol areas For example some areas may feature different crime
rates or levels of social disorganization which command more department resources and police
officers We gathered data by first mapping out every squadrsquos patrol area and then obtaining
tract-level data from the 2010 US Census Our models included measures of established of
983092
983123983139983144983151983148983137983154983155 983137983150983140 983152983151983148983145983139983161 983149983137983147983141983154983155 983149983137983161 983140983145983155983137983143983154983141983141 983159983145983156983144 983141983155983156983145983149983137983156983145983150983143 983137 983154983141983152983148983137983139983141983149983141983150983156 983151983142983142983145983139983141983154991257983155 983139983137983152983137983139983145983156983161 983137983156 983150983145983150983141983156983161 983152983141983154983139983141983150983156 983119983157983154983143983151983137983148 983145983155 983150983151983156 983156983151 983140983141983142983145983150983145983156983145983158983141983148983161 983141983155983156983145983149983137983156983141 983137 983144983161983152983151983156983144983141983156983145983139983137983148 983151983142983142983145983139983141983154991257983155 983152983154983151983140983157983139983156983145983158983145983156983161 983138983157983156 983149983141983154983141983148983161 983156983151 983141983155983156983137983138983148983145983155983144 983137 983154983141983137983155983151983150983137983138983148983141 983142983145983160983141983140
983152983151983145983150983156 983137983143983137983145983150983155983156 983159983144983145983139983144 983156983151 983139983151983149983152983137983154983141 983137983148983148 983152983137983156983154983151983148 983151983142983142983145983139983141983154983155
7252019 PIJPSM-05-2015-0064
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correlates of crime including population density (Hipp amp Roussell 2013) household income
(Gould Weinberg and Mustard 2002) and racial composition (Light amp Harris 2012)
Specifically we use each arearsquos population density median household income and percent
white We also included measures for each arearsquos violent and property crime rate Finally we
gathered information about the number of violent crimes (murders assaults aggravated assaults
sexual assaults and robberies) property crimes (larcenies burglaries vandalisms frauds
forgeries and motor vehicle thefts) and number of police officers in each patrol area thereby
creating variables for violent crime per officer and property crime per officer
Since LPDrsquos patrol officers are grouped with squads it is reasonable to believe that an
officerrsquos productivity might be more similar to an officer within their squad compared to an
officer in a different squad To account for the potential correlations between the individual
units as well as the multi-level nature of the data we used hierarchical linear modeling to
analyze the data
Our primary level variables include an officerrsquos gender marital status number of
children years of law enforcement experience shift calls for service annual number of traffic
stops and citations field information reports and misdemeanor felony and warrant arrests We
also used the officersrsquo productivity information to calculate each patrol officerrsquos VORC
Our secondary level variables include the violent crime per officer rate and property
crime per officer rate for the patrol area of each squad as well several control variables
including population density household median income percent white and the patrol arearsquos
violent and property crime rate Table 2 displays the summary statistics for the first and second-
level variables
INSERT TABLE 2 HERE
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Results
We hypothesized that the violent and property crime-to-cop ratios of an area might affect
the raw productivity outputs of patrol officers as well as a more advanced productivity metric
To examine these questions we constructed five hierarchical linear models estimating the
correlations between reported violent and property crimes per officer and a patrol officerrsquos
annual traffic citations warrant misdemeanor felony arrests and Value Over Replacement Cop
Although not presented here we estimated null models for all dependent variables and found that
significant variation existed between squads when we included no variables included in the
models The chi-square for the LR test for all five null models was statistically significant (p lt
000 in all models)
Table 3 shows that a rise in reported violent crimes per officer increases an officerrsquos
number of warrants and misdemeanors (b= 29 and 76) while arise in reported property crimes
per officer decreases an officerrsquos number of warrants and misdemeanors (b= -15 and -52) We
found no relationship between the crime-to-cop ratios and felony arrests However both the
rates of violent and property crimes per officer decreased the number of traffic citations an
officer writes (b= -168 and -225)
Insert Table 3 here
Table 3 also shows that traffic stops and field information reports are positively
associated with warrant and misdemeanor arrests This supports what police practitioners
already suspect the cops with the most arrests are extremely proactive patrol officers who stop a
large number of vehicles and build investigations through the use of field contacts and
information reports For instance an officer can build felony narcotics cases by contacting
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suspects and informants in the field documenting these efforts through field information reports
and eventually using this information to write search warrants and conduct drug interdiction
Next we investigated whether this relationship between the crime-to-cop ratios and
productivity outputs exists when using an advanced productivity metric specifically an officerrsquos
Value Over Replacement Cop (VORC)
INSERT TABLE 4 HERE
Table 4 shows that there is no correlation between the ratio of property and violent
crimes per officer and VORC Although this stands in contrast to our earlier results we would
not expect to find a relationship between these variables and VORC Advanced metrics such as
VORC evaluate officers based on the amount of time for self-initiated activities (productive
time) and not raw productivity outputs like misdemeanor or felony arrests Even if an officer
took an extremely large number of property crimes thereby decreasing their productive time
VORC would only evaluate that officer based on what the officer produced with the small
amount of productive time This suggests that while a teamrsquos crime-to-cop ratio could affect an
officerrsquos raw productivity outputs ie arrests a teamrsquos crime-to-cop ratio would not affect an
officerrsquos VORC
For example during a given month an officer may take an unusually large number of
burglaries or larcenies thereby leaving the officer with only ten hours for self-initiated activities
With this reduced amount of productive time the officer may generate a below-average number
of officials warrants misdemeanors and felonies which in turn may lead a supervisor to
potentially downgrade the officerrsquos productivity However VORC would recognize and account
for the high number of calls for service minutes and analyze the officerrsquos productivity based on
what the officer generated with only ten hours of productive time It may be that the officer was
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actually very productive but they simply didnrsquot have enough productive time to generate the
same number of traffic citations and arrests as an officer who responded to far fewer property
crimes
Table 4 also shows that traffic stops and field intelligence reports are positively
correlated with VORC These results are consistent with the principles of how VORC evaluates
patrol officer productivity and provides additional support for the validity of this advanced
metric VORC accounts for numerous variables that affect productive time (ie calls for service
minutes) meaning that the key factors which increase or decrease an officerrsquos VORC are largely
related to self-initiated activities (ie traffic stops and information reports) VORC therefore
accounts for the potential effects of macro-level variables and analyzes the activities of the
officer during his or her productive time Nonetheless we recognize that because traffic stops
and field information reports are correlated with VORC an arearsquos crime-to-cop ratio might still
affect an officerrsquos VORC indirectly by impacting the number of stops and information reports
they generate
Discussion
There are several factors which affect a patrol officerrsquos productivity including
biographical characteristics officersrsquo perceptions and attitudes and organizational and
operational features among others This paper identified two macro-level variablesmdashproperty
crimes per officer and violent crimes per officermdashwhich affect the number of traffic citations
warrants misdemeanors and felonies generated annually by an officer Using data from a mid-
sized police department we found that a patrol areas rate of property crimes per officer was
associated with a moderate decrease in an officers annual number of traffic citations warrant
arrests and misdemeanor arrests while a patrol areas rate of violent crimes per officer was also
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associated with a moderate decrease in an officers annual number of traffic citations We also
found that a patrol areas rate of violent crimes per officer is associated with a moderate increase
in an officers annual number of warrant and misdemeanor arrests However when we used a
more advanced productivity metric we found no relationship between property and violent
crimes per officer and an officerrsquos Value Over Replacement Cop or VORC
There are several factors which explain the differing effects of the crime-to-cop ratios on
productivity outputs Let us first address why the violent crime-to-cop ratio would increase a
patrol officerrsquos warrants and misdemeanors while the property crime-to-cop ratio would decrease
these outputs Notably half of all victims of violent crimes know the offender (Truman amp Rand
2010) which aids law enforcement in identifying and locating the perpetrator Moreover both
suspect and victim are frequently on scene when police officers arrive such as during domestic
disturbances Being able to identify and locate the perpetrator helps patrol officers make arrests
thereby increasing their misdemeanor and felony arrests in violent crimes Additionally when
patrol officers arrive and contact individuals during an incident it is standard procedure to check
if the parties involved have outstanding warrants This means that the greater number of violent
crimes that officers respond to the more chances they have to arrest individuals who might have
outstanding warrants It makes sense then that officers who respond to a greater number of
violent crimes have more chances to make warrant and misdemeanor arrests
Conversely property crimes may supply the officer with fewer warrant and misdemeanor
arrests because of low clearance rates We examined the difference in clearance rates between
property and violent crime using incident data from the Lincoln Police Department and we
found that the clearance rate for violent crimes was 418 compared to only 237 for property
crimes Moreover property crimes often also require more investigative time than violent
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crimes Property crime investigations often require considerable follow-up by officers such as
processing a scene contacting financial institutions and checking pawnshops for stolen property
In general property crimes are more time-intensive than violent crimes thereby decreasing
productive time and opportunities for proactive activities while simultaneously netting officers
fewer arrests because of low clearance rates
The impact of a teamrsquos crime-to-cop ratio can also be seen in the relationship between
property and violent crime per officer and the number of traffic citations Table 3 shows that a
rise in both reported violent and property crimes per officer decrease an officerrsquos number of
traffic citations Quite simply if officers respond to a high number of property and violent
crimes then they will have fewer minutes to conduct traffic enforcement and other proactive
enforcement
As noted it is possible that the correlation is bi-directional If an officer makes a greater
number of arrests then a team may witness a decrease in crime because prolific criminals may
have been arrested and placed in jail For example one might interpret Table 3 to mean that an
increase in warrant and misdemeanor warrants led to a decrease in the number of property crimes
per officer However under this hypothesis we should also see that an increase in warrant and
misdemeanor arrests leads to a decrease in the number of violent crimes per officer Instead we
see the opposite hypothesized effect ie more arrests leading to more reported violent crimes 5
983093 983127983141 983139983151983150983155983156983154983157983139983156983141983140 983154983141983143983154983141983155983155983145983151983150 983149983151983140983141983148983155 983157983155983145983150983143 983139983145983156983137983156983145983151983150983155 983159983137983154983154983137983150983156 983137983154983154983141983155983156983155 983149983145983155983140983141983149983141983137983150983151983154 983137983154983154983141983155983156983155 983137983150983140 983142983141983148983151983150983161 983137983154983154983141983155983156983155 983142983154983151983149
983156983144983141 983152983154983141983139983141983140983145983150983143 983149983151983150983156983144 983156983151 983152983154983141983140983145983139983156 983138983151983156983144 983158983145983151983148983141983150983156 983137983150983140 983152983154983151983152983141983154983156983161 983139983154983145983149983141 983154983137983156983141983155 983142983151983154 983156983144983141 983149983151983150983156983144983155 983145983150 983151983157983154 983140983137983156983137983155983141983156 983127983141 983142983151983157983150983140 983150983151
983155983156983137983156983145983155983156983145983139983137983148983148983161 983155983145983143983150983145983142983145983139983137983150983156 983154983141983148983137983156983145983151983150983155983144983145983152983155 983138983141983156983159983141983141983150 983139983145983156983137983156983145983151983150983155 983159983137983154983154983137983150983156 983137983154983154983141983155983156983155 983149983145983155983140983141983149983141983137983150983151983154 983137983154983154983141983155983156983155 983137983150983140 983142983141983148983151983150983161 983137983154983154983141983155983156983155
983137983150983140 983156983144983141 983158983145983151983148983141983150983156 983137983150983140 983152983154983151983152983141983154983156983161 983139983154983145983149983141 983154983137983156983141983155 983151983142 983156983144983141 983138983141983137983156983155 983112983151983159983141983158983141983154 983137983142983156983141983154 983140983145983155983137983143983143983154983141983143983137983156983145983150983143 983156983144983141 983158983145983151983148983141983150983156 983137983150983140 983152983154983151983152983141983154983156983161
983139983154983145983149983141 983154983137983156983141983155 983145983150983156983151 983155983152983141983139983145983142983145983139 983139983154983145983149983141983155 983159983141 983140983145983140 983142983145983150983140 983156983144983137983156 983149983145983155983140983141983149983141983137983150983151983154 983137983150983140 983142983141983148983151983150983161 983137983154983154983141983155983156983155 983155983145983143983150983145983142983145983139983137983150983156983148983161 983152983154983141983140983145983139983156983141983140 983137 983140983141983139983154983141983137983155983141
983145983150 983154983151983138983138983141983154983161 983154983137983156983141983155 983159983144983145983139983144 983145983155 983139983151983150983155983145983155983156983141983150983156 983159983145983156983144 983156983144983141 983148983145983156983141983154983137983156983157983154983141 983105983140983140983145983156983145983151983150983137983148983148983161 983159983141 983145983150983156983141983154983158983145983141983159983141983140 983140983141983152983137983154983156983149983141983150983156 983152983141983154983155983151983150983150983141983148 983137983150983140983148983141983137983154983150983141983140 983156983144983137983156 983156983144983141 983116983145983150983139983151983148983150 983120983151983148983145983139983141 983108983141983152983137983154983156983149983141983150983156 983144983137983140 983155983156983137983142983142983141983140 983137 983156983141983137983149 983140983141983155983145983143983150983141983140 983155983152983141983139983145983142983145983139983137983148983148983161 983156983151 983137983152983152983154983141983144983141983150983140 983154983151983138983138983141983154983161
983155983157983155983152983141983139983156983155 983140983157983154983145983150983143 983156983144983145983155 983156983145983149983141983142983154983137983149983141 983159983144983145983139983144 983155983141983141983149983155 983156983151 983141983160983152983148983137983145983150 983156983144983141 983137983154983154983141983155983156983085983154983151983138983138983141983154983161 983154983137983156983141 983154983141983148983137983156983145983151983150983155983144983145983152
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We believe our explanation has a stronger theoretical foundation The ratio of property
crimes per officer affects productivity outputs differently than the ratio of violent crimes per
officer for two reasons First violent crimes are different in nature than property crimes
especially with regard to clearance rates Second property crimes often require more
investigative and follow-up time than violent crimes thereby reducing an officerrsquos amount of
productive time and proactive activity
Table 3 also provides a key piece of evidence to support this assertion Table 3 shows
that the number of reported violent crimes per officer increases an officerrsquos number of warrant
and misdemeanor arrests Yet the relationship is the opposite for traffic enforcement the violent
crime-to-cop ratio actually decreases traffic citations This change is due to the fact that while
violent crime investigations often yield warrant and misdemeanor arrests they do not produce
traffic citations Furthermore responding to a greater number of violent crimes decreases the
amount of available time to conduct traffic enforcement in turn reducing the number of traffic
citations In other words the violent crime-to-cop ratio affects traffic stops the same way that
the property crime-to-cop ratio affects all productivity outputs by reducing an officerrsquos
productive time and opportunities for proactivity In fact we found a moderate correlation
between a patrol arearsquos crime-to-cop ratio and the amount of productive time enjoyed by officers
who worked that area The correlation between an arearsquos violent crime-to-cop ratio and
productive time was -21 while the correlation between an arearsquos property crime-to-cop ratio and
productive time was -33 We believe this evidence indicates that officers who work in areas
with a high crime-to-cop ratio have less time for self-initiated activities which drives down
productivity
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We suggest that the number of police officers and crime rates of an area need to be
analyzed in conjunction with each other Prior theories of police productivity have viewed and
examined these variables separately when in reality we should view these variables in tandem
Combining these variables into crime-to-cop ratios provides a more nuancedmdashand accuratemdash
understanding of patrol activities and their relationship to productivity
Although we found strong evidence that an officerrsquos patrol area affects his or her
productivity (in terms of raw outputs) we also found that a few officer-level variables affected
productivity as well including traffic stops intelligence reports and working third shift Our
results appear to substantiate what many police practitioners already suspect officers who are
proactive in contacting citizens (traffic stops) and building investigations (intelligence reports)
generate more warrant and misdemeanor arrests In addition while some newer police officers
are forced to work third shift because they do not have sufficient seniority to bid for better days
off and shift times many officers work nights because they enjoy the types of calls (eg in-
progress felonies) meaning they may be more motivated to stop vehicles make field contacts
and in turn engage in activities that generate arrests (Brehm amp Gates 1993)
These results have important implications for police researchers and administrators
First our results suggest a need for a deeper understanding of how the staffing levels crime
rates and the other characteristics of a patrol area intersect Second if departments evaluate
their patrol officers using raw productivity outputs such as misdemeanor or felony arrests
administrators must recognize that factors beyond the control of patrol officers might affect
monthly or annual productivity Our research suggests that a patrol arearsquos crime-to-cop ratio can
substantially affect a patrol officerrsquos productivity regardless of the actual crime rate If the
characteristics of a patrol area affect raw productivity outputs police departments should employ
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more advanced productivity analytics (ie VORC) that account for activities which reduce
productive time and evaluate an officerrsquos productivity in terms of available minutes for
proactivity
Nonetheless evaluating both patrol officers and police departments using traffic citations
warrants misdemeanors and felonies is useful because this type of data is readily understood by
citizens and civic officials A neighborhood watch group for instance might care more about
the number of arrests their officers make and less about those officersrsquo productivity scores Our
data suggests that if citizens want patrol officers to write more traffic citations and make more
arrests they should lobby for more police officers on the street It is true that reducing an arearsquos
crime rate impacts the crime-to-cop ratio but our results indicate that putting police officers on
the street may be the most direct way to affect the crime-to-cop ratio
Our study is not without limitations We only examined a police department which uses
the generalist style of policing and it is unknown if our results are applicable to police
departments which use a specialist style of policing Theoretically a departmentrsquos policing
styles can affect factors that increase or decrease a patrol officerrsquos productive time It is possible
that the number of property crimes and violent crimes per officer do not significantly affect a
specialist patrol officerrsquos productivity outputs versus a generalist officerrsquos outputs Future
studies should investigate whether our results can be generalized to all police departments
We have only identified two macro-level variables which affect patrol officer
productivity and we recognize there may be other variables In addition we recommend that
future studies incorporate a wider array of variables and test competing models to determine
whether micro or macro-level variables exhibit stronger effects on productivity In the end
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managing patrol officers and improving productivity requires a greater understanding of both
patrol officers and the areas they work
Acknowledgements and Credits We would like to thank Denver Police Department Crime
Analyst Melissa Ohlhorst and Pennsylvania State University Professor Barry Ruback as well as
the three anonymous reviewers for their insightful comments We also salute Lincoln Police
Sergeant Jake Dilsaver for his logistical support of this project
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References
Amaranto E Steinberg J Castellano C and Mitchell R (2003) ldquoPolice stress interventionsrdquo
Brief Treatment and Crisis Intervention Vol 3 No 1 pp 47-53
Becker G (1985) ldquoHuman capital effort and the sexual division of laborrdquo The Journal of
Labor EconomicsVol 3 No 1 pp 33-58
Bonkiewicz L (2015) ldquoBobbies and baseball players Evaluating patrol officer productivity
using sabermetricsrdquo Police Quarterly Vol 18 No 1 pp 55-78
Braga A and Bond J (2008) ldquoPolicing crime and disorder hot spots A randomized controlled
trialrdquo Criminology Vol 46 No 3 pp 577-607
Brown J and Campbell E (1990) ldquoSources of occupational stress in the policerdquo Work
and Stress Vol 4 No 4 pp 305-318
Chappell A MacDonald J and Manz P (2006) ldquoThe organizational determinants
of police arrest decisionsrdquo Crime and Delinquency Vol 52 No 2 pp 287-306
Cloninger D and Sartorius L (1979) ldquoCrime rates clearance rates and enforcement effort
The case of Houston Texasrdquo American Journal of Economics and Sociology Vol 38
No 4 pp 389-402
Corman H and Mocan N (2005) ldquoCarrots sticks and broken windowsrdquo Journal of Law and
Economics Vol 48 No 1 pp 235-266
Crank J (1990) ldquoThe influence of environmental and organizational factors on police style in
urban and rural environmentsrdquo Journal of Research in Crime and Delinquency Vol 27
pp 166-189
Goodman A (1990) ldquoA model for police officer burnoutrdquo Journal of Business and
Psychology Vol 5 No 1 pp 85-99
Gould E Weinberg B and Mustard D (2002) Crime rates and local labor marketopportunities in the United States 1979ndash1997rdquo Review of Economics and Statistics Vol
84 No 1 pp 45-61
Hipp J and Roussell A (2013) ldquoMicro-and macro-environment population and the
consequences for crime ratesrdquo Social Forces Vol 92 No 2 pp 563-595
Hatry H (1976) ldquoApproaches to productivity measurement and program evaluationrdquo Public
Productivity Review Vol 1 No 3 pp21-28
Hawkins H (2001) ldquoPolice officer burnout a partial replication of Maslachs Burnout
Inventoryrdquo Police Quarterly Vol 4 No 3 pp 343-360
7252019 PIJPSM-05-2015-0064
httpslidepdfcomreaderfullpijpsm-05-2015-0064 2530
Herbert S (1998)ldquoPolice subculture reconsideredrdquo Criminology Vol 36 No 2 pp 343-370
Huizinga D and Henry K (2008) ldquoThe effect of arrest and justice system sanctions on
subsequent behavior Findings from longitudinal and other studiesrdquo In The long view of
crime A synthesis of longitudinal research Springer New York pp 220-254
Johnson R (2011) ldquoOfficer attitudes and management influences on police work productivityrdquo
American Journal of Criminal Justice Vol 36 pp 293-306
Kane R (2006) ldquoOn the limits of social control Structural deterrence and the policing of
lsquosuppressiblersquo crimesrdquo Justice Quarterly Vol 23 No 2 pp 186-213
Kubrin C Messner S Deane G McGeever K and Stucky T (2010) ldquoProactive
policing and robbery rates across US citiesrdquo Criminology Vol 48 No 1 pp 57-97
Lersch K (2002) ldquoAre citizen complaints just another measure of officer productivity An
analysis of citizen complaints and officer activity measuresrdquo Police Practice and
Research Vol 3 pp 135-147
Light M and Harris C (2012) Race space and violence exploring spatial dependence in
structural covariates of white and black violent crime in US countiesrdquo Journal of
Quantitative Criminology Vol 28 No 4 pp 559-586
Maxfield M (1982) ldquoService time dispatch time and demand for police services Helping
more by serving lessrdquo Public Administration Review pp 252-263
Maxfield M Lewis D and Szoc R (1980) ldquoProducing official crimes Verified crime reports
as measures of police outputrdquo Social Science Quarterly pp 221-236
Nakosteen R and Zimmer M (2001) ldquoSpouse selection and earnings Evidence of marital
sortingrdquo Economic InquiryVol 39 No 2 pp 201-2013
Shane J (2010) ldquoOrganizational stressors and police performancerdquo Journal of Criminal
Justice Vol 38 No 4 pp 807-818
Shane J (2011) ldquoDaily work experiences and police performancerdquo Police Practice and
Research Vol 13 No 3 pp 1-19
Sherman L Gartin P and Buerger M (1989) ldquoHot spots of predatory crime Routine
activities and the criminology of placerdquoCriminology Vol 27 No 1 pp27-56
Truman J and Rand M (2010) ldquoBureau of Justice Statistics Bulletin National Crime
Victimization Survey Criminal Victimization 2009rdquo US Department of Justice Office of
Justice programs
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Van Meter D (2001) Evaluating dysfunctional police performance A zero-based approach
Charles C Thomas Publisher Springfield IL
Vila B and Kenney D (2002) ldquoTired cops The prevalence and potential consequences of
police fatiguerdquo National Institute of Justice
Vila B Morrison G and Kenney D (2002) ldquoImproving shift schedule and work-hour
policies and practices to increase police officer performance health and safetyrdquo Police
Quarterly Vol 5 No 1 pp 4-24
Vollaard B and Koning P (2009) ldquoThe effect of police on crime disorder and victim
PrecautionEvidence from a Dutch victimization surveyrdquo International Review of Law
and Economics Vol 29 No 4 pp 336-348
Weisburd D Maher L Sherman L Buerger M Cohn E and Petrisino A (1993)
ldquoContrasting crime general and crime specific theory The case of hot spots of crimerdquo
New Directions in Criminological Theory Advances in Criminological Theory Vol 4
pp 45-70
Author Biography
Luke Bonkiewicz is a patrol officer with the Lincoln Police Department
7252019 PIJPSM-05-2015-0064
httpslidepdfcomreaderfullpijpsm-05-2015-0064 2730
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983101 983089983088983088lowast + + ) + ) + ) + ) lowast )) minus + + + 983090983088 + ) +983089983088)) minus 983089983088983088 lowast
983086 983097) lowast )
7252019 PIJPSM-05-2015-0064
httpslidepdfcomreaderfullpijpsm-05-2015-0064 2830
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983119983142983142983145983139983141983154 983120983154983151983155983141983139983157983156983145983151983150
983122983137983156983141C983110983123
983117983145983150983157983156983141983155983110983125983120
983117983145983150983157983156983141983155
983119983142983142983145983139983141983154
A983155983155983145983155983156983117983145983150983157983156983141983155
983113983150983139983145983140983141983150983156983122983141983152983151983154983156983155
AC983113983122983141983152983151983154983156983155 983123983141983148983141983139983156983145983158983141983155 983127983137983154983150983145983150983143983155 983119983142983142983145983139983145983137983148983155 983127983137983154983154983137983150983156983155 D983125983113983155 983117983145983155983140983141983149983141983137983150983151983154983155 983110983141983148983151983150983145983141983155
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2 95 30072 5424 4116 204 696 228 312 432 84 48 120 8
3 72 32496 6312 6600 144 564 444 720 540 84 36 24 12
4 90 33000 5232 4980 216 192 480 768 240 144 12 72 12
5 91 41724 4824 6048 312 636 132 504 324 108 7 24 24
6 83 23916 7056 6192 144 756 528 996 336 132 36 72 12
7 95 24468 6840 5892 156 624 324 456 480 72 36 120 24
8 90 39972 5196 4920 240 504 384 492 264 36 2 72 24
9 79 25500 4068 5100 336 396 192 684 204 24 36 120 10
10 87 21984 5412 4524 348 552 480 792 312 108 48 84 36
983124983137983138983148983141 2 983123983157983149983149983137983154983161 983123983156983137983156983145983155983156983145983139983155 983142983151983154 983126983137983154983145983137983138983148983141983155
983123983141983139983151983150983140983085983116983141983158983141983148 983126983137983154983145983137983138983148983141983155 (983120983137983156983154983151983148 A983154983141983137) 983118=40 983122983137983150983143983141 983123D 983117983141983137983150
983120983151983152983157983148983137983156983145983151983150 983140983141 983150983155983145983156983161 20229830856956 1687 4436
983112983151983157983155983141983144983151983148983140 983149983141983140983145983137983150 983145983150983139983151983149983141 ($) 2628198308576020 16535 49756
983159983144983145983156983141 7398308593 007 083
983126983145983151983148983141983150983156 983139983154983145983149983141 983154983137983156983141 (983152983141983154 100000) 8009830854800 1203 2521
983120983154983151983152983141983154983156983161 983139983154983145983149983141 983154983137983156983141 (983152983141983154 100000) 31009830859800 2027 6524
983120983154983151983152983141983154983156983161 983139983154983145983149983141983155 983152983141983154 983151983142983142983145983139983141983154 5329830851097 1663 8167
983126983145983151983148983141983150983156 983139983154983145983149983141 983152983141983154 983151983142983142983145983139983141983154 194983085372 559 2929
983110983145983154983155983156983085983116983141 983158983141983148 983126 983137983154983145983137983138983148983141 983155 (983119983142983142983145983139983141983154) 983118=302 983122983137983150983143983141 983123D 983117983141 983137983150
983111983141983150983140983141983154 (983142983141983149983137983148983141=0 983149983137983148983141=1) 09830851 24 94
983117983137983154983154983145983141983140C983151983144983137983138983145983156983145983150983143 09830851 18 79
983151983142 C983144983145983148983140983154983141983150 09830855 170 90
983129983141983137983154983155 983141983160983152983141983154983145983141983150983139983141 198308537 779 1063
C983137983148983148983155 983142983151983154 983155983141983154983158983145983139983141 4819830851011 16121 61365
983124983154983137983142983142983145983139 983155983156983151983152983155 16983085901 14376 20624
983110983145983141983148983140 983145983150983142983151983154983149983137983156983145983151983150 983154983141983152983151983154983156983155 0983085227 2153 2530
983124983154983137983142983142983145983139 983139983145983156983137983156983145983151983150983155 249830851246 14632 18642
983117983145983155983140983141983149983141983137983150983151983154 983137983154983154983141983155983156983155 19983085403 5965 10663
983110983141983148983151983150983161 983137983154983154983141983155983156983155 098308571 1066 852
983126983119983122C 9830851959830851043 151 24
1983155983156 2983150983140 3983154983140
104 96 102983123983144983145983142983156
7252019 PIJPSM-05-2015-0064
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983124983137983138983148983141 3 983112983145983141983154983137983154983139983144983145983139983137983148 983116983145983150983141983137983154 983117983151983140983141983148983155 E983155983156983145983149983137983156983145983150983143 983119983142 983142983145983139983141983154983155 A983150983150983157983137983148 983124983154983137983142983142983145983139 C983145983156983137983156983145983151983150983155 983127983137983154983154983137983150983156983155 983117983145983155983140983141983149983141983137983150983151983154983155 983137983150983140 983110983141983148983151983150 983145983141983155
983123983141983139983151983150983140983085983116983141983158983141983148 983126983137983154983145983137983138983148983141983155 (983120983137983156983154983151983148 A983154983141983137) 983118=40 983138 983123E 983138 983123E 983138 983123E 983138 983123E
983120983151983152983157983148983137983156983145983151983150 983140983141983150983155983145983156983161 001 003 001 001 98308501 01 00 00
983112983151983157983155983141983144983151983148983140 983149983141983140983145983137983150 983145983150983139983151983149983141 ($) 002 001 983085000 000 00 00 98308500 00
983159983144983145983156983141 13363 32472 9830858921 5664 9830853274 19211 9830854447 3255
983126983145983151983148983141983150983156 983139983154983145983149983141 983154983137983156983141 (983152983141983154 100000) 006 004 001 001 98308501 02 01 02
983120983154983151983152983141983154983156983161 983139983154983145983149983141 983154983137983156983141 (983152983141983154 100000) 983085003 005 983085002 001 02 03 98308501 00
983120983154983151983152983141983154983156983161 983139983154983145983149983141983155 983152983141983154 983151983142983142983145983139983141983154 983085225 99 98308515 03 98308552 24 98308503 03
983126983145983151983148983141983150983156 983139983154983145983149983141 983152983141983154 983151983142983142983145983139983141983154 983085168 68 29 14 76 31 22 12
983110983145983154983155983156983085983116983141983158983141983148 983126983137983154983145983137983138983148983141983155 (983119983142983142983145983139983141983154) 983118=302
983117983137983148983141 4673 1622 366 147 1613 1051 91 158
983117983137983154983154983145983141983140C983151983144983137983138983145983156983145983150983143 9830857516 3254 201 278 9830851158 1169 98308595 167
983151983142 C983144983145983148983140983154983141983150 183 702 75 77 983085262 217 983085396 207
983129983141983137983154983155 983141983160983152983141983154983145983141983150983139983141 9830851065 492 98308512 13 983085163 33 98308506 05C983137983148983148983155 983142983151983154 983155983141983154983158983145983139983141 24 13 02 02 14 01 98308508 05
983124983154983137983142983142983145983139 983155983156983151983152983155 983085983085 983085983085 03 00 09 02 00 00
983110983145983141983148983140 983145983150983142983151983154983149983137983156983145983151983150 983154983141983152983151983154983156983155 209 33 20 04 79 19 10 03
983123983141983139983151983150983140 983155983144983145983142983156 (983154983141983142983141983154983141983150983139983141 983143983154983151983157983152 983142983145983154983155983156) 2603 1778 187 195 809 730 147 160
983124983144983145 983154983140 983155983144983145 983142983156 (983154983141 983142983141 983154983141 983150983139983141 983143983154983151983157983152 983142983145 983154983155983156) 9872 2155 79 213 2532 952 393 132
983113983150983156983141983154983139983141983152983156 32591 5361 758 339 4147 1912 310 484
983123983145983143983149983137983085983155983153983157983137983154983141983140 1541467 19287 206702 5598
C983144983145983085983155983153983157983137983154983141983140 8294 6133 12348 10663
983152 lt 05
983152lt01
983152lt001
983118983151983156983141 A983148983148 983158983137983154983145983137983138983148983141983155 983137983154983141 983157983150983139983141983150983156983141983154983141983140
983127A983122983122A983118983124 A983122983122E983123983124983123 983117983113983123DE983117EA983118983119983122 A983122983122E983123983124983123 983110E983116983119983118983129 A983122983122E983123983124983123983124983122A983110983110983113C C983113983124A983124983113983119983118983123
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983123983141983139983151983150983140983085983116983141983158983141983148 983126983137983154983145983137983138983148983141983155 (983120983137983156983154983151983148 A983154983141983137) 983118=40 983138 983123E
983120983151983152983157983148983137983156983145983151983150 983140983141983150983155983145983156983161 98308500 00
983112983151983157983155983141983144983151983148983140 983149983141983140983145983137983150 983145983150983139983151983149983141 ($) 00 01
983159983144983145983156983141 318 345
983126983145983151983148983141983150983156 983139983154983145983149983141 983154983137983156983141 (983152983141983154 100000) 98308500 00
983120983154983151983152983141983154983156983161 983139983154983145983149983141 983154983137983156983141 (983152983141983154 100000) 00 00
983120983154983151983152983141983154983156983161 983139983154983145983149983141983155 983152983141983154 983151983142983142983145983139983141983154 98308502 04
983126983145983151983148983141983150983156 983139983154983145983149983141 983152983141983154 983151983142983142983145983139983141983154 02 05
983110983145983154983155983156983085983116983141983158983141983148 983126983137983154983145983137983138983148983141983155 (983119983142983142983145983139983141983154) 983118=302
983117983137983148983141 27 15
983117983137983154983154983145983141983140C983151983144983137983138983145983156983145983150983143 98308532 31
983151983142 C983144983145983148983140983154983141983150 05 03
983129983141983137983154983155 983141983160983152983141983154983145983141983150983139983141 00 01
C983137983148 983148983155 983142983151983154 983155983141983154983158983145983139983141 02 01
983124983154983137983142983142983145983139 983155983156983151983152983155 01 00
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983123983141983139983151983150983140 983155983144983145983142983156 ( 983154983141983142983141983154983141983150983139983141 983143983154983151983157983152 983142 983145983154983155983156) 23 12
983124983144983145983154983140 983155983144983145983142983156 (983154983141983142983141983154983141983150983139983141 983143983154983151983157983152 983142 983145983154983155983156) 55 14
983113983150983156983141983154983139983141983152983156 165 36
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C983144983145983085983155983153983157983137983154983141983140 9846
983152 lt 05
983152lt01
983152lt001
983118983151983156983141 A983148983148 983158983137983154983145983137983138983148983141983155 983137983154983141 983157983150983139983141983150983156983141983154983141983140
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Criminological research has long recognized that crime rates are not uniform across
neighborhoods and cities (Sherman et al 1989 Weisburd et al 1993) Instead of ldquopolicing
peoplerdquo law enforcement agencies frequently identify ldquohot spotsrdquo and devote additional
resources to reducing crime and disorder in specific problem areas With this perspective in
mind it might seem natural to examine the relationship between the crime rate of a patrol area
and an officerrsquos productivity outputs (or vice-versa) For example a high crime area might net
an officer more arrests (and therefore more productivity outputs) than a low crime area On the
other hand a high crime area might also occupy a great deal of an officerrsquos productive time
prohibiting the officer from engaging in proactive activities However crime rate does not fully
capture how a particular area might affect an officerrsquos productivity
Instead we believe macro-level variables related to crime should be estimated in terms of
the number of patrol officers in a patrol area specifically violent crimes per officer and property
crimes per officer For instance some officers may patrol a lower crime area but end up
responding to a greater number of violent crimes than officers in a high crime area simply
because they have fewer officers working the street This ldquocrime per coprdquo ratio can dramatically
affect officersrsquo productivity
For example when an officer investigates a crime he or she might interview the victim
suspect and witnesses as well as take photographs collect evidence and make an arrest There
may be additional follow-up the next day or week such as checking pawnshops for stolen
property or tracking down and interviewing additional witnesses All these activities could
decrease an officerrsquos time for self-initiated activities and in turn an officerrsquos overall
productivity A high crime per cop ratio therefore could decrease productivity for officers even
if these officers work in an area with a low crime rate Furthermore an arearsquos crime per cop
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ratio could also affect officersrsquo productive time even if they are not the primary investigating
officer because secondary officers are often needed during serious incidents such as robberies or
burglaries
The ldquoload sheddingrdquo hypothesis by Maxfield et al (1980 see also Maxfield 1982) also
has important implications for any study examining patrol officer productivity Maxfield et al
suggested that while variations in workload do not affect patrol officersrsquo decisions to record
serious crimes workload does affect officersrsquo decisions to record less serious crimes it may also
affect self-initiated activities as well (Herbert 1989)
Finally we believe that macro-level variables affecting productivity should be divided
between violent and property crimes to account for the time and resources needed to investigate
each incident type When officers respond to assaults they often contact suspects and victims on
scene meaning that officers are able to interview all involved individuals collect evidence and
make an arrest sometimes in less than an hour However when officers respond to burglaries or
larcenies they may be required to dust for fingerprints swab for DNA canvass a neighborhood
for witnesses check pawnshops and engage in other investigative activities that require hours
and even days if the case requires follow-up
With these issues in mind our study addresses the following research questions First
what is the relationship between a patrol arearsquos rate of violent crimes per officer and patrol
officer productivity outputs (ie traffic citations as well as warrant misdemeanor and felony
arrests) Second what is the relationship between a patrol arearsquos rate of property crimes per
officer and patrol officer productivity outputs (again traffic citations and warrant misdemeanor
and felony arrests) Third what is the relationship between the rate of property and violent
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crimes per officer and a patrol officerrsquos productivity when we introduce a more sophisticated
method for analyzing productivity a metric based on productive time and not total time on duty
Before discussing our data and methods there are two important issues meriting brief
discussion First we argue that the crime-to-cop ratio may affect the number of traffic stops
warrants misdemeanors and felonies that a patrol officer makes It can also be argued that the
number of arrests might impact the crime-to-cop ratio Our intent is not to comprehensively
discuss the relationship between the number of officersofficer arrests and an arearsquos crime rate
but it bears noting that the research is inconclusive on this point
Some evidence suggests that the relationship between arrests and crime rates is non-
linear that is crime rates decrease as arrests increase but eventually hit a ldquofloorrdquo and reach
equilibrium (Kane 2006) Second when studies examine the arrest-crime rate relationship they
often find that the relationship only exists for specific crimes in their datasets such as robbery
and motor vehicle thefts (Corman Hope amp Mocan 2005) robbery but not assaults (Kane 2006)
and homicide and auto thefts only (Cloninger amp Sartorius 1979) and so forth Overall the
literature on the arrests-crime rate relationship is mixedmdashsome research finds an effect between
arrests and crime rates some does not (Huizinga and Henry 2008)
The second issue concerns whether a law enforcement agency operates using a generalist
or specialist policing style We analyze data from a generalist municipal police department
meaning that patrol officers investigate low and mid-level crimes but investigators and
detectives are responsible for handling serious crimes cases involving lengthy investigations
(eg narcotics trafficking or white-collar crimes) and incidents in which patrol officers may
need assistance (for example an incident involving numerous victims and witnesses as well as
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cases involving extensive follow-up or knowledge on a specific subject such as gangs or
narcotics)
By contrast when patrol officers in a specialist-style police department respond to any
incident potentially requiring follow-up they might only contact the victim and either hold the
scene until an investigator arrives or forward the information to a detective for follow-up It is
possible indeed likely that macro-level variables might affect generalist patrol officers
differently than specialized officers Due to data limitations however our study focuses on
generalist police officers
Data and Methods
We gathered data on patrol officers and their patrol areas from several sources First we
collected patrol officer productivity data from the Lincoln (Nebraska) Police Departmentrsquos 2013-
2014 productivity reports Using Computer-Aided Dispatch (CAD) and a centralized
productivity database LPD measures numerous patrol officer activities Every month (and
year) LPD generates productivity reports which display each patrol officerrsquos activities and
outputs (see Table 1 for an example of a squadrsquos annual productivity report) Each patrol
activity and output merits further explanation
INSERT TABLE 1 HERE
Calls for Service (CFS) and Officer Assist time When dispatchers send patrol officers on
a call they use CAD to track the minutes the officer spends on the call If the call is a priority
incident dispatchers send both a primary and secondary officer Other unassigned officers might
also respond to the CFS meaning that multiple officers could be secondary officers The annual
productivity record tracks amount of time an officer spent as a primary responder and a
secondary responder
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Report writing time Standard operating procedures dictate that LPD patrol officers
complete specific types of reports for incidents An incident report (IR) details biographical
information of the individuals involved in the incident and summarizes the incident An
Additional Case Information (ACI) is a more detailed report that documents additional
investigative steps or the actions of assisting officers Although the time spent on an IR or ACI
could vary depending on the call for service the average IR and ACI take roughly twenty
minutes to complete1
The Lincoln Police Department also tracks self-initiated activities Most productivity
results from the following proactive activities
Selectives The Lincoln Police Department tracks specific officer patrol activity by
coding its neighborhoods schools parks and business districts When an officer patrols an at-
risk business district or a high-crime neighborhood for example the officer notifies dispatch
using that arearsquos specific code Girded by the principles of ldquohot spotsrdquo policing research officers
use selectives to identify potential candidates for POP projects and heighten the perception of
risk among criminals (Braga amp Bond 2008)
Traffic Warnings and Official Citations Every Lincoln patrol officer drives a police
cruiser and conducts traffic enforcement issuing traffic warnings and official citations The
LPD records department tracks patrol officersrsquo warnings and citations
Misdemeanors felonies and warrants Patrol officers also make misdemeanor and
felony arrests during their productive time Officers might detain a motorist and arrest him or
her for driving under a suspended license or driving while intoxicated or contact a pedestrian
1 983119983142983142983145983139983141983154983155 983156983161983152983141 983137983148983148 983154983141983152983151983154983156983155 983151983150 983139983151983149983152983157983156983141983154983155 983127983141 983151983138983156983137983145983150983141983140 983156983144983145983155 983137983158983141983154983137983143983141 983138983161 983151983138983155983141983154983158983145983150983143 983137 983155983137983149983152983148983141 983151983142 983116983145983150983139983151983148983150 983152983137983156983154983151983148 983151983142983142983145983139983141983154983155
983139983151983149983152983148983141983156983145983150983143 983113983122983155 983137983150983140 983105983107983113983155 983137983150983140 983159983141 983154983141983139983151983143983150983145983162983141 983156983144983137983156 983156983144983145983155 983137983158983141983154983137983143983141 983149983137983161 983140983145983142983142983141983154 983142983154983151983149 983151983156983144983141983154 983152983151983148983145983139983141 983140983141983152983137983154983156983149983141983150983156983155 983117983151983154983141983151983158983141983154983145983142 983140983141983152983137983154983156983149983141983150983156983155 983154983141983153983157983145983154983141 983151983142983142983145983139983141983154983155 983156983151 983139983151983149983152983148983141983156983141 983156983144983141983145983154 983154983141983152983151983154983156983155 983138983141983142983151983154983141 983138983141983139983151983149983145983150983143 983137983158983137983145983148983137983138983148983141 983142983151983154 983137983150983151983156983144983141983154 983139983137983148983148 983142983151983154 983155983141983154983158983145983139983141
983156983144983145983155 983149983141983137983155983157983154983141983149983141983150983156 983145983155 983157983150983150983141983139983141983155983155983137983154983161
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and make an arrest for a sex offender registration violation Officers can also search their
departmentrsquos electronic database for individuals with outstanding warrants in their patrol areas
and attempt to arrest these offenders As with warnings and citations the LPD records
department documents and tracks misdemeanor felony and warrant arrests Moreover the court
system also monitors these types of outputs to process cases thereby adding an additional layer
of reporting accuracy and reliability
Field information reports We also measured the number of field information or
ldquointelligencerdquo reports Officers complete these internal reports to document suspicious or
dangerous individuals or circumstances though these reports are not publicly available
VORC In addition to the raw outputs of traffic citations warrants misdemeanors and
felonies we also include an advanced metric for measuring a patrol officerrsquos productivity Value
Over Replacement Cop (VORC) is a method for evaluating officers that assigns different
weights to productivity outputs and then assesses officer productivity in terms of available time
for self-initiated activities or productive time (Bonkiewicz 2015) We use a variation of this
formula that multiplies an officerrsquos officials misdemeanors and felonies by his or her
prosecution rate Our modified use of VORC can best be explained using the following figure
(Figure 1) and five-step process
INSERT FIGURE 1 HERE
where = total monthly on-duty minutes = CFS minutes Γ = follow-up time and meetings ∆
= officer assist time Ε = of IRs Ζ = of ACIs M = of intelligence reports Η = of
selectives Θ = of warnings times two Κ = of warrants times three Ι = of officials times
three Λ = of misdemeanor arrests times their respective weights Ν = of felonies types their
respective weights Ρ = an officerrsquos prosecution rate Ξ = Officerrsquos Productive time (Α-(Β+Γ+∆+
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20(Ε+Ζ))) Ο= Department average P-score or average (Η+ ((Θ)+(Ι)+(Κ)+(Λ)+(Μ)+(Ν)Ρ))
and Π = Department average Self-Initiated Time
First VORC calculates an officerrsquos total minutes spent on CFS follow-ups officer
assists and reports This amount is an officerrsquos total work time or service minutes Second the
formula subtracts an officerrsquos service minutes from the total on-duty minutes2 This is the
officerrsquos amount of productive time Third VORC calculates an officerrsquos productivity score (or
P-score) using a weighted index Departments can choose different weights for productivity
outputs but for the purpose of this study we used our weights based on the statutes governing
the Lincoln Police Department Citations and arrests are weighted based on whether the offense
was a Traffic Offense a Class III II or I Misdemeanor or a Class V IV III II or I Felony
Accordingly a patrol selective is worth one point a traffic warning two points a traffic citation
three points a warrant arrest three points a Class III misdemeanor arrest four points a Class II
misdemeanor arrest five points a Class I misdemeanor arrest six points a Class V Felony seven
points a Class IV Felony eight points a Class III Felony nine points and Class II and I Felonies
(eg homicides) ten points3 The sum of the weighted productivity outputs is the officerrsquos P-
score The P-score is divided by amount of productive time and then multiplied by 100 (for ease
of understanding) This value the productivity quotient represents the officerrsquos productivity
given the available minutes for proactivity such as searching for wanted individuals or traffic
enforcement
Fourth VORC creates a hypothetical replacement officer against which to measure the
patrol officerrsquos output This replacement player might be imagined as a rookie patrol officer or
983090 983119983150983085983140983157983156983161 983156983145983149983141 983140983151983141983155 983150983151983156 983145983150983139983148983157983140983141 983156983154983137983145983150983145983150983143 983156983145983149983141 983158983137983139983137983156983145983151983150 983151983154 983144983151983148983145983140983137983161 983140983137983161983155 983151983142983142 983151983154 983155983145983139983147 983148983141983137983158983141 983113983150 983151983156983144983141983154 983159983151983154983140983155 983151983150983085983140983157983156983161
983156983145983149983141 983145983155 983156983144983141 983137983149983151983157983150983156 983151983142 983156983145983149983141 983137 983152983137983156983154983151983148 983151983142983142983145983139983141983154 983155983152983141983150983156 983159983151983154983147983145983150983143 983156983144983141 983155983156983154983141983141983156983091 983119983157983154 983159983141983145983143983144983156983145983150983143 983155983161983155983156983141983149 983145983155 983151983150983148983161 983151983150983141 983141983160983137983149983152983148983141 983151983142 983156983144983141 983142983148983141983160983145983138983145983148983145983156983161 983156983144983137983156 983126983119983122983107 983151983142983142983141983154983155 983140983141983152983137983154983156983149983141983150983156983155983099 983141983137983139983144 983137983143983141983150983139983161 983139983137983150
983152983154983145983151983154983145983156983145983162983141 983137983150983140 983137983155983155983145983143983150 983139983151983154983154983141983155983152983151983150983140983145983150983143 983159983141983145983143983144983156983155 983156983151 983156983144983141983145983154 983152983154983141983142983141983154983154983141983140 983152983154983151983140983157983139983156983145983158983145983156983161 983151983157983156983152983157983156983155
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a recruit who has not completed the academy performing at ninety percent the capacity of fully
trained police officer4 Using LPDrsquos data VORC creates a hypothetical replacement officer by
dividing the departmental average P-score by the departmental average for productive time and
then multiplying it by the officerrsquos productive time to determine what an average officer would
have generated given the officerrsquos amount of productive time This figure is then multiplied by
9 (the capacity of a replacement officer) divided by the officerrsquos productive time and then
multiplied by 100 to generate the replacement officerrsquos productivity quotient Finally VORC
subtracts the replacement officerrsquos score from the patrol officerrsquos score to calculate the officerrsquos
value over the replacement officer This number or VORC indicates the officerrsquos productivity
per time for self-initiated activities compared to the replacement officerrsquos productivity For
example a VORC of 12 means than officer would produce 012 more productivity points (some
combination of warnings citations and arrests) per minute than a replacement officer
Biographical variables We gathered biographical information about the officers in the
sample including gender years of LPD experience maritalcohabiting status and number of
children We also controlled for whether the officer worked first second or third shift because
our data showed significant differences in the number of calls for service each shift handled as
well as the number of reports each shift completed
Geographic variables It is necessary to measure the characteristics of patrol areas to
account for variation across patrol areas For example some areas may feature different crime
rates or levels of social disorganization which command more department resources and police
officers We gathered data by first mapping out every squadrsquos patrol area and then obtaining
tract-level data from the 2010 US Census Our models included measures of established of
983092
983123983139983144983151983148983137983154983155 983137983150983140 983152983151983148983145983139983161 983149983137983147983141983154983155 983149983137983161 983140983145983155983137983143983154983141983141 983159983145983156983144 983141983155983156983145983149983137983156983145983150983143 983137 983154983141983152983148983137983139983141983149983141983150983156 983151983142983142983145983139983141983154991257983155 983139983137983152983137983139983145983156983161 983137983156 983150983145983150983141983156983161 983152983141983154983139983141983150983156 983119983157983154983143983151983137983148 983145983155 983150983151983156 983156983151 983140983141983142983145983150983145983156983145983158983141983148983161 983141983155983156983145983149983137983156983141 983137 983144983161983152983151983156983144983141983156983145983139983137983148 983151983142983142983145983139983141983154991257983155 983152983154983151983140983157983139983156983145983158983145983156983161 983138983157983156 983149983141983154983141983148983161 983156983151 983141983155983156983137983138983148983145983155983144 983137 983154983141983137983155983151983150983137983138983148983141 983142983145983160983141983140
983152983151983145983150983156 983137983143983137983145983150983155983156 983159983144983145983139983144 983156983151 983139983151983149983152983137983154983141 983137983148983148 983152983137983156983154983151983148 983151983142983142983145983139983141983154983155
7252019 PIJPSM-05-2015-0064
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correlates of crime including population density (Hipp amp Roussell 2013) household income
(Gould Weinberg and Mustard 2002) and racial composition (Light amp Harris 2012)
Specifically we use each arearsquos population density median household income and percent
white We also included measures for each arearsquos violent and property crime rate Finally we
gathered information about the number of violent crimes (murders assaults aggravated assaults
sexual assaults and robberies) property crimes (larcenies burglaries vandalisms frauds
forgeries and motor vehicle thefts) and number of police officers in each patrol area thereby
creating variables for violent crime per officer and property crime per officer
Since LPDrsquos patrol officers are grouped with squads it is reasonable to believe that an
officerrsquos productivity might be more similar to an officer within their squad compared to an
officer in a different squad To account for the potential correlations between the individual
units as well as the multi-level nature of the data we used hierarchical linear modeling to
analyze the data
Our primary level variables include an officerrsquos gender marital status number of
children years of law enforcement experience shift calls for service annual number of traffic
stops and citations field information reports and misdemeanor felony and warrant arrests We
also used the officersrsquo productivity information to calculate each patrol officerrsquos VORC
Our secondary level variables include the violent crime per officer rate and property
crime per officer rate for the patrol area of each squad as well several control variables
including population density household median income percent white and the patrol arearsquos
violent and property crime rate Table 2 displays the summary statistics for the first and second-
level variables
INSERT TABLE 2 HERE
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Results
We hypothesized that the violent and property crime-to-cop ratios of an area might affect
the raw productivity outputs of patrol officers as well as a more advanced productivity metric
To examine these questions we constructed five hierarchical linear models estimating the
correlations between reported violent and property crimes per officer and a patrol officerrsquos
annual traffic citations warrant misdemeanor felony arrests and Value Over Replacement Cop
Although not presented here we estimated null models for all dependent variables and found that
significant variation existed between squads when we included no variables included in the
models The chi-square for the LR test for all five null models was statistically significant (p lt
000 in all models)
Table 3 shows that a rise in reported violent crimes per officer increases an officerrsquos
number of warrants and misdemeanors (b= 29 and 76) while arise in reported property crimes
per officer decreases an officerrsquos number of warrants and misdemeanors (b= -15 and -52) We
found no relationship between the crime-to-cop ratios and felony arrests However both the
rates of violent and property crimes per officer decreased the number of traffic citations an
officer writes (b= -168 and -225)
Insert Table 3 here
Table 3 also shows that traffic stops and field information reports are positively
associated with warrant and misdemeanor arrests This supports what police practitioners
already suspect the cops with the most arrests are extremely proactive patrol officers who stop a
large number of vehicles and build investigations through the use of field contacts and
information reports For instance an officer can build felony narcotics cases by contacting
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suspects and informants in the field documenting these efforts through field information reports
and eventually using this information to write search warrants and conduct drug interdiction
Next we investigated whether this relationship between the crime-to-cop ratios and
productivity outputs exists when using an advanced productivity metric specifically an officerrsquos
Value Over Replacement Cop (VORC)
INSERT TABLE 4 HERE
Table 4 shows that there is no correlation between the ratio of property and violent
crimes per officer and VORC Although this stands in contrast to our earlier results we would
not expect to find a relationship between these variables and VORC Advanced metrics such as
VORC evaluate officers based on the amount of time for self-initiated activities (productive
time) and not raw productivity outputs like misdemeanor or felony arrests Even if an officer
took an extremely large number of property crimes thereby decreasing their productive time
VORC would only evaluate that officer based on what the officer produced with the small
amount of productive time This suggests that while a teamrsquos crime-to-cop ratio could affect an
officerrsquos raw productivity outputs ie arrests a teamrsquos crime-to-cop ratio would not affect an
officerrsquos VORC
For example during a given month an officer may take an unusually large number of
burglaries or larcenies thereby leaving the officer with only ten hours for self-initiated activities
With this reduced amount of productive time the officer may generate a below-average number
of officials warrants misdemeanors and felonies which in turn may lead a supervisor to
potentially downgrade the officerrsquos productivity However VORC would recognize and account
for the high number of calls for service minutes and analyze the officerrsquos productivity based on
what the officer generated with only ten hours of productive time It may be that the officer was
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actually very productive but they simply didnrsquot have enough productive time to generate the
same number of traffic citations and arrests as an officer who responded to far fewer property
crimes
Table 4 also shows that traffic stops and field intelligence reports are positively
correlated with VORC These results are consistent with the principles of how VORC evaluates
patrol officer productivity and provides additional support for the validity of this advanced
metric VORC accounts for numerous variables that affect productive time (ie calls for service
minutes) meaning that the key factors which increase or decrease an officerrsquos VORC are largely
related to self-initiated activities (ie traffic stops and information reports) VORC therefore
accounts for the potential effects of macro-level variables and analyzes the activities of the
officer during his or her productive time Nonetheless we recognize that because traffic stops
and field information reports are correlated with VORC an arearsquos crime-to-cop ratio might still
affect an officerrsquos VORC indirectly by impacting the number of stops and information reports
they generate
Discussion
There are several factors which affect a patrol officerrsquos productivity including
biographical characteristics officersrsquo perceptions and attitudes and organizational and
operational features among others This paper identified two macro-level variablesmdashproperty
crimes per officer and violent crimes per officermdashwhich affect the number of traffic citations
warrants misdemeanors and felonies generated annually by an officer Using data from a mid-
sized police department we found that a patrol areas rate of property crimes per officer was
associated with a moderate decrease in an officers annual number of traffic citations warrant
arrests and misdemeanor arrests while a patrol areas rate of violent crimes per officer was also
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associated with a moderate decrease in an officers annual number of traffic citations We also
found that a patrol areas rate of violent crimes per officer is associated with a moderate increase
in an officers annual number of warrant and misdemeanor arrests However when we used a
more advanced productivity metric we found no relationship between property and violent
crimes per officer and an officerrsquos Value Over Replacement Cop or VORC
There are several factors which explain the differing effects of the crime-to-cop ratios on
productivity outputs Let us first address why the violent crime-to-cop ratio would increase a
patrol officerrsquos warrants and misdemeanors while the property crime-to-cop ratio would decrease
these outputs Notably half of all victims of violent crimes know the offender (Truman amp Rand
2010) which aids law enforcement in identifying and locating the perpetrator Moreover both
suspect and victim are frequently on scene when police officers arrive such as during domestic
disturbances Being able to identify and locate the perpetrator helps patrol officers make arrests
thereby increasing their misdemeanor and felony arrests in violent crimes Additionally when
patrol officers arrive and contact individuals during an incident it is standard procedure to check
if the parties involved have outstanding warrants This means that the greater number of violent
crimes that officers respond to the more chances they have to arrest individuals who might have
outstanding warrants It makes sense then that officers who respond to a greater number of
violent crimes have more chances to make warrant and misdemeanor arrests
Conversely property crimes may supply the officer with fewer warrant and misdemeanor
arrests because of low clearance rates We examined the difference in clearance rates between
property and violent crime using incident data from the Lincoln Police Department and we
found that the clearance rate for violent crimes was 418 compared to only 237 for property
crimes Moreover property crimes often also require more investigative time than violent
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crimes Property crime investigations often require considerable follow-up by officers such as
processing a scene contacting financial institutions and checking pawnshops for stolen property
In general property crimes are more time-intensive than violent crimes thereby decreasing
productive time and opportunities for proactive activities while simultaneously netting officers
fewer arrests because of low clearance rates
The impact of a teamrsquos crime-to-cop ratio can also be seen in the relationship between
property and violent crime per officer and the number of traffic citations Table 3 shows that a
rise in both reported violent and property crimes per officer decrease an officerrsquos number of
traffic citations Quite simply if officers respond to a high number of property and violent
crimes then they will have fewer minutes to conduct traffic enforcement and other proactive
enforcement
As noted it is possible that the correlation is bi-directional If an officer makes a greater
number of arrests then a team may witness a decrease in crime because prolific criminals may
have been arrested and placed in jail For example one might interpret Table 3 to mean that an
increase in warrant and misdemeanor warrants led to a decrease in the number of property crimes
per officer However under this hypothesis we should also see that an increase in warrant and
misdemeanor arrests leads to a decrease in the number of violent crimes per officer Instead we
see the opposite hypothesized effect ie more arrests leading to more reported violent crimes 5
983093 983127983141 983139983151983150983155983156983154983157983139983156983141983140 983154983141983143983154983141983155983155983145983151983150 983149983151983140983141983148983155 983157983155983145983150983143 983139983145983156983137983156983145983151983150983155 983159983137983154983154983137983150983156 983137983154983154983141983155983156983155 983149983145983155983140983141983149983141983137983150983151983154 983137983154983154983141983155983156983155 983137983150983140 983142983141983148983151983150983161 983137983154983154983141983155983156983155 983142983154983151983149
983156983144983141 983152983154983141983139983141983140983145983150983143 983149983151983150983156983144 983156983151 983152983154983141983140983145983139983156 983138983151983156983144 983158983145983151983148983141983150983156 983137983150983140 983152983154983151983152983141983154983156983161 983139983154983145983149983141 983154983137983156983141983155 983142983151983154 983156983144983141 983149983151983150983156983144983155 983145983150 983151983157983154 983140983137983156983137983155983141983156 983127983141 983142983151983157983150983140 983150983151
983155983156983137983156983145983155983156983145983139983137983148983148983161 983155983145983143983150983145983142983145983139983137983150983156 983154983141983148983137983156983145983151983150983155983144983145983152983155 983138983141983156983159983141983141983150 983139983145983156983137983156983145983151983150983155 983159983137983154983154983137983150983156 983137983154983154983141983155983156983155 983149983145983155983140983141983149983141983137983150983151983154 983137983154983154983141983155983156983155 983137983150983140 983142983141983148983151983150983161 983137983154983154983141983155983156983155
983137983150983140 983156983144983141 983158983145983151983148983141983150983156 983137983150983140 983152983154983151983152983141983154983156983161 983139983154983145983149983141 983154983137983156983141983155 983151983142 983156983144983141 983138983141983137983156983155 983112983151983159983141983158983141983154 983137983142983156983141983154 983140983145983155983137983143983143983154983141983143983137983156983145983150983143 983156983144983141 983158983145983151983148983141983150983156 983137983150983140 983152983154983151983152983141983154983156983161
983139983154983145983149983141 983154983137983156983141983155 983145983150983156983151 983155983152983141983139983145983142983145983139 983139983154983145983149983141983155 983159983141 983140983145983140 983142983145983150983140 983156983144983137983156 983149983145983155983140983141983149983141983137983150983151983154 983137983150983140 983142983141983148983151983150983161 983137983154983154983141983155983156983155 983155983145983143983150983145983142983145983139983137983150983156983148983161 983152983154983141983140983145983139983156983141983140 983137 983140983141983139983154983141983137983155983141
983145983150 983154983151983138983138983141983154983161 983154983137983156983141983155 983159983144983145983139983144 983145983155 983139983151983150983155983145983155983156983141983150983156 983159983145983156983144 983156983144983141 983148983145983156983141983154983137983156983157983154983141 983105983140983140983145983156983145983151983150983137983148983148983161 983159983141 983145983150983156983141983154983158983145983141983159983141983140 983140983141983152983137983154983156983149983141983150983156 983152983141983154983155983151983150983150983141983148 983137983150983140983148983141983137983154983150983141983140 983156983144983137983156 983156983144983141 983116983145983150983139983151983148983150 983120983151983148983145983139983141 983108983141983152983137983154983156983149983141983150983156 983144983137983140 983155983156983137983142983142983141983140 983137 983156983141983137983149 983140983141983155983145983143983150983141983140 983155983152983141983139983145983142983145983139983137983148983148983161 983156983151 983137983152983152983154983141983144983141983150983140 983154983151983138983138983141983154983161
983155983157983155983152983141983139983156983155 983140983157983154983145983150983143 983156983144983145983155 983156983145983149983141983142983154983137983149983141 983159983144983145983139983144 983155983141983141983149983155 983156983151 983141983160983152983148983137983145983150 983156983144983141 983137983154983154983141983155983156983085983154983151983138983138983141983154983161 983154983137983156983141 983154983141983148983137983156983145983151983150983155983144983145983152
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We believe our explanation has a stronger theoretical foundation The ratio of property
crimes per officer affects productivity outputs differently than the ratio of violent crimes per
officer for two reasons First violent crimes are different in nature than property crimes
especially with regard to clearance rates Second property crimes often require more
investigative and follow-up time than violent crimes thereby reducing an officerrsquos amount of
productive time and proactive activity
Table 3 also provides a key piece of evidence to support this assertion Table 3 shows
that the number of reported violent crimes per officer increases an officerrsquos number of warrant
and misdemeanor arrests Yet the relationship is the opposite for traffic enforcement the violent
crime-to-cop ratio actually decreases traffic citations This change is due to the fact that while
violent crime investigations often yield warrant and misdemeanor arrests they do not produce
traffic citations Furthermore responding to a greater number of violent crimes decreases the
amount of available time to conduct traffic enforcement in turn reducing the number of traffic
citations In other words the violent crime-to-cop ratio affects traffic stops the same way that
the property crime-to-cop ratio affects all productivity outputs by reducing an officerrsquos
productive time and opportunities for proactivity In fact we found a moderate correlation
between a patrol arearsquos crime-to-cop ratio and the amount of productive time enjoyed by officers
who worked that area The correlation between an arearsquos violent crime-to-cop ratio and
productive time was -21 while the correlation between an arearsquos property crime-to-cop ratio and
productive time was -33 We believe this evidence indicates that officers who work in areas
with a high crime-to-cop ratio have less time for self-initiated activities which drives down
productivity
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We suggest that the number of police officers and crime rates of an area need to be
analyzed in conjunction with each other Prior theories of police productivity have viewed and
examined these variables separately when in reality we should view these variables in tandem
Combining these variables into crime-to-cop ratios provides a more nuancedmdashand accuratemdash
understanding of patrol activities and their relationship to productivity
Although we found strong evidence that an officerrsquos patrol area affects his or her
productivity (in terms of raw outputs) we also found that a few officer-level variables affected
productivity as well including traffic stops intelligence reports and working third shift Our
results appear to substantiate what many police practitioners already suspect officers who are
proactive in contacting citizens (traffic stops) and building investigations (intelligence reports)
generate more warrant and misdemeanor arrests In addition while some newer police officers
are forced to work third shift because they do not have sufficient seniority to bid for better days
off and shift times many officers work nights because they enjoy the types of calls (eg in-
progress felonies) meaning they may be more motivated to stop vehicles make field contacts
and in turn engage in activities that generate arrests (Brehm amp Gates 1993)
These results have important implications for police researchers and administrators
First our results suggest a need for a deeper understanding of how the staffing levels crime
rates and the other characteristics of a patrol area intersect Second if departments evaluate
their patrol officers using raw productivity outputs such as misdemeanor or felony arrests
administrators must recognize that factors beyond the control of patrol officers might affect
monthly or annual productivity Our research suggests that a patrol arearsquos crime-to-cop ratio can
substantially affect a patrol officerrsquos productivity regardless of the actual crime rate If the
characteristics of a patrol area affect raw productivity outputs police departments should employ
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more advanced productivity analytics (ie VORC) that account for activities which reduce
productive time and evaluate an officerrsquos productivity in terms of available minutes for
proactivity
Nonetheless evaluating both patrol officers and police departments using traffic citations
warrants misdemeanors and felonies is useful because this type of data is readily understood by
citizens and civic officials A neighborhood watch group for instance might care more about
the number of arrests their officers make and less about those officersrsquo productivity scores Our
data suggests that if citizens want patrol officers to write more traffic citations and make more
arrests they should lobby for more police officers on the street It is true that reducing an arearsquos
crime rate impacts the crime-to-cop ratio but our results indicate that putting police officers on
the street may be the most direct way to affect the crime-to-cop ratio
Our study is not without limitations We only examined a police department which uses
the generalist style of policing and it is unknown if our results are applicable to police
departments which use a specialist style of policing Theoretically a departmentrsquos policing
styles can affect factors that increase or decrease a patrol officerrsquos productive time It is possible
that the number of property crimes and violent crimes per officer do not significantly affect a
specialist patrol officerrsquos productivity outputs versus a generalist officerrsquos outputs Future
studies should investigate whether our results can be generalized to all police departments
We have only identified two macro-level variables which affect patrol officer
productivity and we recognize there may be other variables In addition we recommend that
future studies incorporate a wider array of variables and test competing models to determine
whether micro or macro-level variables exhibit stronger effects on productivity In the end
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managing patrol officers and improving productivity requires a greater understanding of both
patrol officers and the areas they work
Acknowledgements and Credits We would like to thank Denver Police Department Crime
Analyst Melissa Ohlhorst and Pennsylvania State University Professor Barry Ruback as well as
the three anonymous reviewers for their insightful comments We also salute Lincoln Police
Sergeant Jake Dilsaver for his logistical support of this project
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References
Amaranto E Steinberg J Castellano C and Mitchell R (2003) ldquoPolice stress interventionsrdquo
Brief Treatment and Crisis Intervention Vol 3 No 1 pp 47-53
Becker G (1985) ldquoHuman capital effort and the sexual division of laborrdquo The Journal of
Labor EconomicsVol 3 No 1 pp 33-58
Bonkiewicz L (2015) ldquoBobbies and baseball players Evaluating patrol officer productivity
using sabermetricsrdquo Police Quarterly Vol 18 No 1 pp 55-78
Braga A and Bond J (2008) ldquoPolicing crime and disorder hot spots A randomized controlled
trialrdquo Criminology Vol 46 No 3 pp 577-607
Brown J and Campbell E (1990) ldquoSources of occupational stress in the policerdquo Work
and Stress Vol 4 No 4 pp 305-318
Chappell A MacDonald J and Manz P (2006) ldquoThe organizational determinants
of police arrest decisionsrdquo Crime and Delinquency Vol 52 No 2 pp 287-306
Cloninger D and Sartorius L (1979) ldquoCrime rates clearance rates and enforcement effort
The case of Houston Texasrdquo American Journal of Economics and Sociology Vol 38
No 4 pp 389-402
Corman H and Mocan N (2005) ldquoCarrots sticks and broken windowsrdquo Journal of Law and
Economics Vol 48 No 1 pp 235-266
Crank J (1990) ldquoThe influence of environmental and organizational factors on police style in
urban and rural environmentsrdquo Journal of Research in Crime and Delinquency Vol 27
pp 166-189
Goodman A (1990) ldquoA model for police officer burnoutrdquo Journal of Business and
Psychology Vol 5 No 1 pp 85-99
Gould E Weinberg B and Mustard D (2002) Crime rates and local labor marketopportunities in the United States 1979ndash1997rdquo Review of Economics and Statistics Vol
84 No 1 pp 45-61
Hipp J and Roussell A (2013) ldquoMicro-and macro-environment population and the
consequences for crime ratesrdquo Social Forces Vol 92 No 2 pp 563-595
Hatry H (1976) ldquoApproaches to productivity measurement and program evaluationrdquo Public
Productivity Review Vol 1 No 3 pp21-28
Hawkins H (2001) ldquoPolice officer burnout a partial replication of Maslachs Burnout
Inventoryrdquo Police Quarterly Vol 4 No 3 pp 343-360
7252019 PIJPSM-05-2015-0064
httpslidepdfcomreaderfullpijpsm-05-2015-0064 2530
Herbert S (1998)ldquoPolice subculture reconsideredrdquo Criminology Vol 36 No 2 pp 343-370
Huizinga D and Henry K (2008) ldquoThe effect of arrest and justice system sanctions on
subsequent behavior Findings from longitudinal and other studiesrdquo In The long view of
crime A synthesis of longitudinal research Springer New York pp 220-254
Johnson R (2011) ldquoOfficer attitudes and management influences on police work productivityrdquo
American Journal of Criminal Justice Vol 36 pp 293-306
Kane R (2006) ldquoOn the limits of social control Structural deterrence and the policing of
lsquosuppressiblersquo crimesrdquo Justice Quarterly Vol 23 No 2 pp 186-213
Kubrin C Messner S Deane G McGeever K and Stucky T (2010) ldquoProactive
policing and robbery rates across US citiesrdquo Criminology Vol 48 No 1 pp 57-97
Lersch K (2002) ldquoAre citizen complaints just another measure of officer productivity An
analysis of citizen complaints and officer activity measuresrdquo Police Practice and
Research Vol 3 pp 135-147
Light M and Harris C (2012) Race space and violence exploring spatial dependence in
structural covariates of white and black violent crime in US countiesrdquo Journal of
Quantitative Criminology Vol 28 No 4 pp 559-586
Maxfield M (1982) ldquoService time dispatch time and demand for police services Helping
more by serving lessrdquo Public Administration Review pp 252-263
Maxfield M Lewis D and Szoc R (1980) ldquoProducing official crimes Verified crime reports
as measures of police outputrdquo Social Science Quarterly pp 221-236
Nakosteen R and Zimmer M (2001) ldquoSpouse selection and earnings Evidence of marital
sortingrdquo Economic InquiryVol 39 No 2 pp 201-2013
Shane J (2010) ldquoOrganizational stressors and police performancerdquo Journal of Criminal
Justice Vol 38 No 4 pp 807-818
Shane J (2011) ldquoDaily work experiences and police performancerdquo Police Practice and
Research Vol 13 No 3 pp 1-19
Sherman L Gartin P and Buerger M (1989) ldquoHot spots of predatory crime Routine
activities and the criminology of placerdquoCriminology Vol 27 No 1 pp27-56
Truman J and Rand M (2010) ldquoBureau of Justice Statistics Bulletin National Crime
Victimization Survey Criminal Victimization 2009rdquo US Department of Justice Office of
Justice programs
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httpslidepdfcomreaderfullpijpsm-05-2015-0064 2630
Van Meter D (2001) Evaluating dysfunctional police performance A zero-based approach
Charles C Thomas Publisher Springfield IL
Vila B and Kenney D (2002) ldquoTired cops The prevalence and potential consequences of
police fatiguerdquo National Institute of Justice
Vila B Morrison G and Kenney D (2002) ldquoImproving shift schedule and work-hour
policies and practices to increase police officer performance health and safetyrdquo Police
Quarterly Vol 5 No 1 pp 4-24
Vollaard B and Koning P (2009) ldquoThe effect of police on crime disorder and victim
PrecautionEvidence from a Dutch victimization surveyrdquo International Review of Law
and Economics Vol 29 No 4 pp 336-348
Weisburd D Maher L Sherman L Buerger M Cohn E and Petrisino A (1993)
ldquoContrasting crime general and crime specific theory The case of hot spots of crimerdquo
New Directions in Criminological Theory Advances in Criminological Theory Vol 4
pp 45-70
Author Biography
Luke Bonkiewicz is a patrol officer with the Lincoln Police Department
7252019 PIJPSM-05-2015-0064
httpslidepdfcomreaderfullpijpsm-05-2015-0064 2730
983110983145983143983157983154983141 983089983086 983126983119983122983107 983110983151983154983149983157983148983137
983101 983089983088983088lowast + + ) + ) + ) + ) lowast )) minus + + + 983090983088 + ) +983089983088)) minus 983089983088983088 lowast
983086 983097) lowast )
7252019 PIJPSM-05-2015-0064
httpslidepdfcomreaderfullpijpsm-05-2015-0064 2830
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983119983142983142983145983139983141983154 983120983154983151983155983141983139983157983156983145983151983150
983122983137983156983141C983110983123
983117983145983150983157983156983141983155983110983125983120
983117983145983150983157983156983141983155
983119983142983142983145983139983141983154
A983155983155983145983155983156983117983145983150983157983156983141983155
983113983150983139983145983140983141983150983156983122983141983152983151983154983156983155
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5 91 41724 4824 6048 312 636 132 504 324 108 7 24 24
6 83 23916 7056 6192 144 756 528 996 336 132 36 72 12
7 95 24468 6840 5892 156 624 324 456 480 72 36 120 24
8 90 39972 5196 4920 240 504 384 492 264 36 2 72 24
9 79 25500 4068 5100 336 396 192 684 204 24 36 120 10
10 87 21984 5412 4524 348 552 480 792 312 108 48 84 36
983124983137983138983148983141 2 983123983157983149983149983137983154983161 983123983156983137983156983145983155983156983145983139983155 983142983151983154 983126983137983154983145983137983138983148983141983155
983123983141983139983151983150983140983085983116983141983158983141983148 983126983137983154983145983137983138983148983141983155 (983120983137983156983154983151983148 A983154983141983137) 983118=40 983122983137983150983143983141 983123D 983117983141983137983150
983120983151983152983157983148983137983156983145983151983150 983140983141 983150983155983145983156983161 20229830856956 1687 4436
983112983151983157983155983141983144983151983148983140 983149983141983140983145983137983150 983145983150983139983151983149983141 ($) 2628198308576020 16535 49756
983159983144983145983156983141 7398308593 007 083
983126983145983151983148983141983150983156 983139983154983145983149983141 983154983137983156983141 (983152983141983154 100000) 8009830854800 1203 2521
983120983154983151983152983141983154983156983161 983139983154983145983149983141 983154983137983156983141 (983152983141983154 100000) 31009830859800 2027 6524
983120983154983151983152983141983154983156983161 983139983154983145983149983141983155 983152983141983154 983151983142983142983145983139983141983154 5329830851097 1663 8167
983126983145983151983148983141983150983156 983139983154983145983149983141 983152983141983154 983151983142983142983145983139983141983154 194983085372 559 2929
983110983145983154983155983156983085983116983141 983158983141983148 983126 983137983154983145983137983138983148983141 983155 (983119983142983142983145983139983141983154) 983118=302 983122983137983150983143983141 983123D 983117983141 983137983150
983111983141983150983140983141983154 (983142983141983149983137983148983141=0 983149983137983148983141=1) 09830851 24 94
983117983137983154983154983145983141983140C983151983144983137983138983145983156983145983150983143 09830851 18 79
983151983142 C983144983145983148983140983154983141983150 09830855 170 90
983129983141983137983154983155 983141983160983152983141983154983145983141983150983139983141 198308537 779 1063
C983137983148983148983155 983142983151983154 983155983141983154983158983145983139983141 4819830851011 16121 61365
983124983154983137983142983142983145983139 983155983156983151983152983155 16983085901 14376 20624
983110983145983141983148983140 983145983150983142983151983154983149983137983156983145983151983150 983154983141983152983151983154983156983155 0983085227 2153 2530
983124983154983137983142983142983145983139 983139983145983156983137983156983145983151983150983155 249830851246 14632 18642
983117983145983155983140983141983149983141983137983150983151983154 983137983154983154983141983155983156983155 19983085403 5965 10663
983110983141983148983151983150983161 983137983154983154983141983155983156983155 098308571 1066 852
983126983119983122C 9830851959830851043 151 24
1983155983156 2983150983140 3983154983140
104 96 102983123983144983145983142983156
7252019 PIJPSM-05-2015-0064
httpslidepdfcomreaderfullpijpsm-05-2015-0064 2930
983124983137983138983148983141 3 983112983145983141983154983137983154983139983144983145983139983137983148 983116983145983150983141983137983154 983117983151983140983141983148983155 E983155983156983145983149983137983156983145983150983143 983119983142 983142983145983139983141983154983155 A983150983150983157983137983148 983124983154983137983142983142983145983139 C983145983156983137983156983145983151983150983155 983127983137983154983154983137983150983156983155 983117983145983155983140983141983149983141983137983150983151983154983155 983137983150983140 983110983141983148983151983150 983145983141983155
983123983141983139983151983150983140983085983116983141983158983141983148 983126983137983154983145983137983138983148983141983155 (983120983137983156983154983151983148 A983154983141983137) 983118=40 983138 983123E 983138 983123E 983138 983123E 983138 983123E
983120983151983152983157983148983137983156983145983151983150 983140983141983150983155983145983156983161 001 003 001 001 98308501 01 00 00
983112983151983157983155983141983144983151983148983140 983149983141983140983145983137983150 983145983150983139983151983149983141 ($) 002 001 983085000 000 00 00 98308500 00
983159983144983145983156983141 13363 32472 9830858921 5664 9830853274 19211 9830854447 3255
983126983145983151983148983141983150983156 983139983154983145983149983141 983154983137983156983141 (983152983141983154 100000) 006 004 001 001 98308501 02 01 02
983120983154983151983152983141983154983156983161 983139983154983145983149983141 983154983137983156983141 (983152983141983154 100000) 983085003 005 983085002 001 02 03 98308501 00
983120983154983151983152983141983154983156983161 983139983154983145983149983141983155 983152983141983154 983151983142983142983145983139983141983154 983085225 99 98308515 03 98308552 24 98308503 03
983126983145983151983148983141983150983156 983139983154983145983149983141 983152983141983154 983151983142983142983145983139983141983154 983085168 68 29 14 76 31 22 12
983110983145983154983155983156983085983116983141983158983141983148 983126983137983154983145983137983138983148983141983155 (983119983142983142983145983139983141983154) 983118=302
983117983137983148983141 4673 1622 366 147 1613 1051 91 158
983117983137983154983154983145983141983140C983151983144983137983138983145983156983145983150983143 9830857516 3254 201 278 9830851158 1169 98308595 167
983151983142 C983144983145983148983140983154983141983150 183 702 75 77 983085262 217 983085396 207
983129983141983137983154983155 983141983160983152983141983154983145983141983150983139983141 9830851065 492 98308512 13 983085163 33 98308506 05C983137983148983148983155 983142983151983154 983155983141983154983158983145983139983141 24 13 02 02 14 01 98308508 05
983124983154983137983142983142983145983139 983155983156983151983152983155 983085983085 983085983085 03 00 09 02 00 00
983110983145983141983148983140 983145983150983142983151983154983149983137983156983145983151983150 983154983141983152983151983154983156983155 209 33 20 04 79 19 10 03
983123983141983139983151983150983140 983155983144983145983142983156 (983154983141983142983141983154983141983150983139983141 983143983154983151983157983152 983142983145983154983155983156) 2603 1778 187 195 809 730 147 160
983124983144983145 983154983140 983155983144983145 983142983156 (983154983141 983142983141 983154983141 983150983139983141 983143983154983151983157983152 983142983145 983154983155983156) 9872 2155 79 213 2532 952 393 132
983113983150983156983141983154983139983141983152983156 32591 5361 758 339 4147 1912 310 484
983123983145983143983149983137983085983155983153983157983137983154983141983140 1541467 19287 206702 5598
C983144983145983085983155983153983157983137983154983141983140 8294 6133 12348 10663
983152 lt 05
983152lt01
983152lt001
983118983151983156983141 A983148983148 983158983137983154983145983137983138983148983141983155 983137983154983141 983157983150983139983141983150983156983141983154983141983140
983127A983122983122A983118983124 A983122983122E983123983124983123 983117983113983123DE983117EA983118983119983122 A983122983122E983123983124983123 983110E983116983119983118983129 A983122983122E983123983124983123983124983122A983110983110983113C C983113983124A983124983113983119983118983123
7252019 PIJPSM-05-2015-0064
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983124983137983138983148983141 4 983112983145983141983154983137983154983139983144983145983139983137983148 983116983145983150983141983137983154 983117983151983140983141983148983155 E983155983156983145983149983137983156983145983150983143 983119983142983142983145983139983141983154983155 A983150983150983157983137983148 983126983137983148983157983141 983119983158983141983154 983122983141983152983148983137983139983141983149983141983150983156 C983151983152 (983126983119983122C)
983123983141983139983151983150983140983085983116983141983158983141983148 983126983137983154983145983137983138983148983141983155 (983120983137983156983154983151983148 A983154983141983137) 983118=40 983138 983123E
983120983151983152983157983148983137983156983145983151983150 983140983141983150983155983145983156983161 98308500 00
983112983151983157983155983141983144983151983148983140 983149983141983140983145983137983150 983145983150983139983151983149983141 ($) 00 01
983159983144983145983156983141 318 345
983126983145983151983148983141983150983156 983139983154983145983149983141 983154983137983156983141 (983152983141983154 100000) 98308500 00
983120983154983151983152983141983154983156983161 983139983154983145983149983141 983154983137983156983141 (983152983141983154 100000) 00 00
983120983154983151983152983141983154983156983161 983139983154983145983149983141983155 983152983141983154 983151983142983142983145983139983141983154 98308502 04
983126983145983151983148983141983150983156 983139983154983145983149983141 983152983141983154 983151983142983142983145983139983141983154 02 05
983110983145983154983155983156983085983116983141983158983141983148 983126983137983154983145983137983138983148983141983155 (983119983142983142983145983139983141983154) 983118=302
983117983137983148983141 27 15
983117983137983154983154983145983141983140C983151983144983137983138983145983156983145983150983143 98308532 31
983151983142 C983144983145983148983140983154983141983150 05 03
983129983141983137983154983155 983141983160983152983141983154983145983141983150983139983141 00 01
C983137983148 983148983155 983142983151983154 983155983141983154983158983145983139983141 02 01
983124983154983137983142983142983145983139 983155983156983151983152983155 01 00
983110983145 983141983148983140 983145983150983142983151983154983149983137983156983145 983151983150 983154983141 983152983151983154983156983155 02 00
983123983141983139983151983150983140 983155983144983145983142983156 ( 983154983141983142983141983154983141983150983139983141 983143983154983151983157983152 983142 983145983154983155983156) 23 12
983124983144983145983154983140 983155983144983145983142983156 (983154983141983142983141983154983141983150983139983141 983143983154983151983157983152 983142 983145983154983155983156) 55 14
983113983150983156983141983154983139983141983152983156 165 36
983123983145983143983149983137983085983155983153983157983137983154983141983140 66
C983144983145983085983155983153983157983137983154983141983140 9846
983152 lt 05
983152lt01
983152lt001
983118983151983156983141 A983148983148 983158983137983154983145983137983138983148983141983155 983137983154983141 983157983150983139983141983150983156983141983154983141983140
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ratio could also affect officersrsquo productive time even if they are not the primary investigating
officer because secondary officers are often needed during serious incidents such as robberies or
burglaries
The ldquoload sheddingrdquo hypothesis by Maxfield et al (1980 see also Maxfield 1982) also
has important implications for any study examining patrol officer productivity Maxfield et al
suggested that while variations in workload do not affect patrol officersrsquo decisions to record
serious crimes workload does affect officersrsquo decisions to record less serious crimes it may also
affect self-initiated activities as well (Herbert 1989)
Finally we believe that macro-level variables affecting productivity should be divided
between violent and property crimes to account for the time and resources needed to investigate
each incident type When officers respond to assaults they often contact suspects and victims on
scene meaning that officers are able to interview all involved individuals collect evidence and
make an arrest sometimes in less than an hour However when officers respond to burglaries or
larcenies they may be required to dust for fingerprints swab for DNA canvass a neighborhood
for witnesses check pawnshops and engage in other investigative activities that require hours
and even days if the case requires follow-up
With these issues in mind our study addresses the following research questions First
what is the relationship between a patrol arearsquos rate of violent crimes per officer and patrol
officer productivity outputs (ie traffic citations as well as warrant misdemeanor and felony
arrests) Second what is the relationship between a patrol arearsquos rate of property crimes per
officer and patrol officer productivity outputs (again traffic citations and warrant misdemeanor
and felony arrests) Third what is the relationship between the rate of property and violent
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crimes per officer and a patrol officerrsquos productivity when we introduce a more sophisticated
method for analyzing productivity a metric based on productive time and not total time on duty
Before discussing our data and methods there are two important issues meriting brief
discussion First we argue that the crime-to-cop ratio may affect the number of traffic stops
warrants misdemeanors and felonies that a patrol officer makes It can also be argued that the
number of arrests might impact the crime-to-cop ratio Our intent is not to comprehensively
discuss the relationship between the number of officersofficer arrests and an arearsquos crime rate
but it bears noting that the research is inconclusive on this point
Some evidence suggests that the relationship between arrests and crime rates is non-
linear that is crime rates decrease as arrests increase but eventually hit a ldquofloorrdquo and reach
equilibrium (Kane 2006) Second when studies examine the arrest-crime rate relationship they
often find that the relationship only exists for specific crimes in their datasets such as robbery
and motor vehicle thefts (Corman Hope amp Mocan 2005) robbery but not assaults (Kane 2006)
and homicide and auto thefts only (Cloninger amp Sartorius 1979) and so forth Overall the
literature on the arrests-crime rate relationship is mixedmdashsome research finds an effect between
arrests and crime rates some does not (Huizinga and Henry 2008)
The second issue concerns whether a law enforcement agency operates using a generalist
or specialist policing style We analyze data from a generalist municipal police department
meaning that patrol officers investigate low and mid-level crimes but investigators and
detectives are responsible for handling serious crimes cases involving lengthy investigations
(eg narcotics trafficking or white-collar crimes) and incidents in which patrol officers may
need assistance (for example an incident involving numerous victims and witnesses as well as
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cases involving extensive follow-up or knowledge on a specific subject such as gangs or
narcotics)
By contrast when patrol officers in a specialist-style police department respond to any
incident potentially requiring follow-up they might only contact the victim and either hold the
scene until an investigator arrives or forward the information to a detective for follow-up It is
possible indeed likely that macro-level variables might affect generalist patrol officers
differently than specialized officers Due to data limitations however our study focuses on
generalist police officers
Data and Methods
We gathered data on patrol officers and their patrol areas from several sources First we
collected patrol officer productivity data from the Lincoln (Nebraska) Police Departmentrsquos 2013-
2014 productivity reports Using Computer-Aided Dispatch (CAD) and a centralized
productivity database LPD measures numerous patrol officer activities Every month (and
year) LPD generates productivity reports which display each patrol officerrsquos activities and
outputs (see Table 1 for an example of a squadrsquos annual productivity report) Each patrol
activity and output merits further explanation
INSERT TABLE 1 HERE
Calls for Service (CFS) and Officer Assist time When dispatchers send patrol officers on
a call they use CAD to track the minutes the officer spends on the call If the call is a priority
incident dispatchers send both a primary and secondary officer Other unassigned officers might
also respond to the CFS meaning that multiple officers could be secondary officers The annual
productivity record tracks amount of time an officer spent as a primary responder and a
secondary responder
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Report writing time Standard operating procedures dictate that LPD patrol officers
complete specific types of reports for incidents An incident report (IR) details biographical
information of the individuals involved in the incident and summarizes the incident An
Additional Case Information (ACI) is a more detailed report that documents additional
investigative steps or the actions of assisting officers Although the time spent on an IR or ACI
could vary depending on the call for service the average IR and ACI take roughly twenty
minutes to complete1
The Lincoln Police Department also tracks self-initiated activities Most productivity
results from the following proactive activities
Selectives The Lincoln Police Department tracks specific officer patrol activity by
coding its neighborhoods schools parks and business districts When an officer patrols an at-
risk business district or a high-crime neighborhood for example the officer notifies dispatch
using that arearsquos specific code Girded by the principles of ldquohot spotsrdquo policing research officers
use selectives to identify potential candidates for POP projects and heighten the perception of
risk among criminals (Braga amp Bond 2008)
Traffic Warnings and Official Citations Every Lincoln patrol officer drives a police
cruiser and conducts traffic enforcement issuing traffic warnings and official citations The
LPD records department tracks patrol officersrsquo warnings and citations
Misdemeanors felonies and warrants Patrol officers also make misdemeanor and
felony arrests during their productive time Officers might detain a motorist and arrest him or
her for driving under a suspended license or driving while intoxicated or contact a pedestrian
1 983119983142983142983145983139983141983154983155 983156983161983152983141 983137983148983148 983154983141983152983151983154983156983155 983151983150 983139983151983149983152983157983156983141983154983155 983127983141 983151983138983156983137983145983150983141983140 983156983144983145983155 983137983158983141983154983137983143983141 983138983161 983151983138983155983141983154983158983145983150983143 983137 983155983137983149983152983148983141 983151983142 983116983145983150983139983151983148983150 983152983137983156983154983151983148 983151983142983142983145983139983141983154983155
983139983151983149983152983148983141983156983145983150983143 983113983122983155 983137983150983140 983105983107983113983155 983137983150983140 983159983141 983154983141983139983151983143983150983145983162983141 983156983144983137983156 983156983144983145983155 983137983158983141983154983137983143983141 983149983137983161 983140983145983142983142983141983154 983142983154983151983149 983151983156983144983141983154 983152983151983148983145983139983141 983140983141983152983137983154983156983149983141983150983156983155 983117983151983154983141983151983158983141983154983145983142 983140983141983152983137983154983156983149983141983150983156983155 983154983141983153983157983145983154983141 983151983142983142983145983139983141983154983155 983156983151 983139983151983149983152983148983141983156983141 983156983144983141983145983154 983154983141983152983151983154983156983155 983138983141983142983151983154983141 983138983141983139983151983149983145983150983143 983137983158983137983145983148983137983138983148983141 983142983151983154 983137983150983151983156983144983141983154 983139983137983148983148 983142983151983154 983155983141983154983158983145983139983141
983156983144983145983155 983149983141983137983155983157983154983141983149983141983150983156 983145983155 983157983150983150983141983139983141983155983155983137983154983161
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and make an arrest for a sex offender registration violation Officers can also search their
departmentrsquos electronic database for individuals with outstanding warrants in their patrol areas
and attempt to arrest these offenders As with warnings and citations the LPD records
department documents and tracks misdemeanor felony and warrant arrests Moreover the court
system also monitors these types of outputs to process cases thereby adding an additional layer
of reporting accuracy and reliability
Field information reports We also measured the number of field information or
ldquointelligencerdquo reports Officers complete these internal reports to document suspicious or
dangerous individuals or circumstances though these reports are not publicly available
VORC In addition to the raw outputs of traffic citations warrants misdemeanors and
felonies we also include an advanced metric for measuring a patrol officerrsquos productivity Value
Over Replacement Cop (VORC) is a method for evaluating officers that assigns different
weights to productivity outputs and then assesses officer productivity in terms of available time
for self-initiated activities or productive time (Bonkiewicz 2015) We use a variation of this
formula that multiplies an officerrsquos officials misdemeanors and felonies by his or her
prosecution rate Our modified use of VORC can best be explained using the following figure
(Figure 1) and five-step process
INSERT FIGURE 1 HERE
where = total monthly on-duty minutes = CFS minutes Γ = follow-up time and meetings ∆
= officer assist time Ε = of IRs Ζ = of ACIs M = of intelligence reports Η = of
selectives Θ = of warnings times two Κ = of warrants times three Ι = of officials times
three Λ = of misdemeanor arrests times their respective weights Ν = of felonies types their
respective weights Ρ = an officerrsquos prosecution rate Ξ = Officerrsquos Productive time (Α-(Β+Γ+∆+
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20(Ε+Ζ))) Ο= Department average P-score or average (Η+ ((Θ)+(Ι)+(Κ)+(Λ)+(Μ)+(Ν)Ρ))
and Π = Department average Self-Initiated Time
First VORC calculates an officerrsquos total minutes spent on CFS follow-ups officer
assists and reports This amount is an officerrsquos total work time or service minutes Second the
formula subtracts an officerrsquos service minutes from the total on-duty minutes2 This is the
officerrsquos amount of productive time Third VORC calculates an officerrsquos productivity score (or
P-score) using a weighted index Departments can choose different weights for productivity
outputs but for the purpose of this study we used our weights based on the statutes governing
the Lincoln Police Department Citations and arrests are weighted based on whether the offense
was a Traffic Offense a Class III II or I Misdemeanor or a Class V IV III II or I Felony
Accordingly a patrol selective is worth one point a traffic warning two points a traffic citation
three points a warrant arrest three points a Class III misdemeanor arrest four points a Class II
misdemeanor arrest five points a Class I misdemeanor arrest six points a Class V Felony seven
points a Class IV Felony eight points a Class III Felony nine points and Class II and I Felonies
(eg homicides) ten points3 The sum of the weighted productivity outputs is the officerrsquos P-
score The P-score is divided by amount of productive time and then multiplied by 100 (for ease
of understanding) This value the productivity quotient represents the officerrsquos productivity
given the available minutes for proactivity such as searching for wanted individuals or traffic
enforcement
Fourth VORC creates a hypothetical replacement officer against which to measure the
patrol officerrsquos output This replacement player might be imagined as a rookie patrol officer or
983090 983119983150983085983140983157983156983161 983156983145983149983141 983140983151983141983155 983150983151983156 983145983150983139983148983157983140983141 983156983154983137983145983150983145983150983143 983156983145983149983141 983158983137983139983137983156983145983151983150 983151983154 983144983151983148983145983140983137983161 983140983137983161983155 983151983142983142 983151983154 983155983145983139983147 983148983141983137983158983141 983113983150 983151983156983144983141983154 983159983151983154983140983155 983151983150983085983140983157983156983161
983156983145983149983141 983145983155 983156983144983141 983137983149983151983157983150983156 983151983142 983156983145983149983141 983137 983152983137983156983154983151983148 983151983142983142983145983139983141983154 983155983152983141983150983156 983159983151983154983147983145983150983143 983156983144983141 983155983156983154983141983141983156983091 983119983157983154 983159983141983145983143983144983156983145983150983143 983155983161983155983156983141983149 983145983155 983151983150983148983161 983151983150983141 983141983160983137983149983152983148983141 983151983142 983156983144983141 983142983148983141983160983145983138983145983148983145983156983161 983156983144983137983156 983126983119983122983107 983151983142983142983141983154983155 983140983141983152983137983154983156983149983141983150983156983155983099 983141983137983139983144 983137983143983141983150983139983161 983139983137983150
983152983154983145983151983154983145983156983145983162983141 983137983150983140 983137983155983155983145983143983150 983139983151983154983154983141983155983152983151983150983140983145983150983143 983159983141983145983143983144983156983155 983156983151 983156983144983141983145983154 983152983154983141983142983141983154983154983141983140 983152983154983151983140983157983139983156983145983158983145983156983161 983151983157983156983152983157983156983155
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a recruit who has not completed the academy performing at ninety percent the capacity of fully
trained police officer4 Using LPDrsquos data VORC creates a hypothetical replacement officer by
dividing the departmental average P-score by the departmental average for productive time and
then multiplying it by the officerrsquos productive time to determine what an average officer would
have generated given the officerrsquos amount of productive time This figure is then multiplied by
9 (the capacity of a replacement officer) divided by the officerrsquos productive time and then
multiplied by 100 to generate the replacement officerrsquos productivity quotient Finally VORC
subtracts the replacement officerrsquos score from the patrol officerrsquos score to calculate the officerrsquos
value over the replacement officer This number or VORC indicates the officerrsquos productivity
per time for self-initiated activities compared to the replacement officerrsquos productivity For
example a VORC of 12 means than officer would produce 012 more productivity points (some
combination of warnings citations and arrests) per minute than a replacement officer
Biographical variables We gathered biographical information about the officers in the
sample including gender years of LPD experience maritalcohabiting status and number of
children We also controlled for whether the officer worked first second or third shift because
our data showed significant differences in the number of calls for service each shift handled as
well as the number of reports each shift completed
Geographic variables It is necessary to measure the characteristics of patrol areas to
account for variation across patrol areas For example some areas may feature different crime
rates or levels of social disorganization which command more department resources and police
officers We gathered data by first mapping out every squadrsquos patrol area and then obtaining
tract-level data from the 2010 US Census Our models included measures of established of
983092
983123983139983144983151983148983137983154983155 983137983150983140 983152983151983148983145983139983161 983149983137983147983141983154983155 983149983137983161 983140983145983155983137983143983154983141983141 983159983145983156983144 983141983155983156983145983149983137983156983145983150983143 983137 983154983141983152983148983137983139983141983149983141983150983156 983151983142983142983145983139983141983154991257983155 983139983137983152983137983139983145983156983161 983137983156 983150983145983150983141983156983161 983152983141983154983139983141983150983156 983119983157983154983143983151983137983148 983145983155 983150983151983156 983156983151 983140983141983142983145983150983145983156983145983158983141983148983161 983141983155983156983145983149983137983156983141 983137 983144983161983152983151983156983144983141983156983145983139983137983148 983151983142983142983145983139983141983154991257983155 983152983154983151983140983157983139983156983145983158983145983156983161 983138983157983156 983149983141983154983141983148983161 983156983151 983141983155983156983137983138983148983145983155983144 983137 983154983141983137983155983151983150983137983138983148983141 983142983145983160983141983140
983152983151983145983150983156 983137983143983137983145983150983155983156 983159983144983145983139983144 983156983151 983139983151983149983152983137983154983141 983137983148983148 983152983137983156983154983151983148 983151983142983142983145983139983141983154983155
7252019 PIJPSM-05-2015-0064
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correlates of crime including population density (Hipp amp Roussell 2013) household income
(Gould Weinberg and Mustard 2002) and racial composition (Light amp Harris 2012)
Specifically we use each arearsquos population density median household income and percent
white We also included measures for each arearsquos violent and property crime rate Finally we
gathered information about the number of violent crimes (murders assaults aggravated assaults
sexual assaults and robberies) property crimes (larcenies burglaries vandalisms frauds
forgeries and motor vehicle thefts) and number of police officers in each patrol area thereby
creating variables for violent crime per officer and property crime per officer
Since LPDrsquos patrol officers are grouped with squads it is reasonable to believe that an
officerrsquos productivity might be more similar to an officer within their squad compared to an
officer in a different squad To account for the potential correlations between the individual
units as well as the multi-level nature of the data we used hierarchical linear modeling to
analyze the data
Our primary level variables include an officerrsquos gender marital status number of
children years of law enforcement experience shift calls for service annual number of traffic
stops and citations field information reports and misdemeanor felony and warrant arrests We
also used the officersrsquo productivity information to calculate each patrol officerrsquos VORC
Our secondary level variables include the violent crime per officer rate and property
crime per officer rate for the patrol area of each squad as well several control variables
including population density household median income percent white and the patrol arearsquos
violent and property crime rate Table 2 displays the summary statistics for the first and second-
level variables
INSERT TABLE 2 HERE
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Results
We hypothesized that the violent and property crime-to-cop ratios of an area might affect
the raw productivity outputs of patrol officers as well as a more advanced productivity metric
To examine these questions we constructed five hierarchical linear models estimating the
correlations between reported violent and property crimes per officer and a patrol officerrsquos
annual traffic citations warrant misdemeanor felony arrests and Value Over Replacement Cop
Although not presented here we estimated null models for all dependent variables and found that
significant variation existed between squads when we included no variables included in the
models The chi-square for the LR test for all five null models was statistically significant (p lt
000 in all models)
Table 3 shows that a rise in reported violent crimes per officer increases an officerrsquos
number of warrants and misdemeanors (b= 29 and 76) while arise in reported property crimes
per officer decreases an officerrsquos number of warrants and misdemeanors (b= -15 and -52) We
found no relationship between the crime-to-cop ratios and felony arrests However both the
rates of violent and property crimes per officer decreased the number of traffic citations an
officer writes (b= -168 and -225)
Insert Table 3 here
Table 3 also shows that traffic stops and field information reports are positively
associated with warrant and misdemeanor arrests This supports what police practitioners
already suspect the cops with the most arrests are extremely proactive patrol officers who stop a
large number of vehicles and build investigations through the use of field contacts and
information reports For instance an officer can build felony narcotics cases by contacting
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suspects and informants in the field documenting these efforts through field information reports
and eventually using this information to write search warrants and conduct drug interdiction
Next we investigated whether this relationship between the crime-to-cop ratios and
productivity outputs exists when using an advanced productivity metric specifically an officerrsquos
Value Over Replacement Cop (VORC)
INSERT TABLE 4 HERE
Table 4 shows that there is no correlation between the ratio of property and violent
crimes per officer and VORC Although this stands in contrast to our earlier results we would
not expect to find a relationship between these variables and VORC Advanced metrics such as
VORC evaluate officers based on the amount of time for self-initiated activities (productive
time) and not raw productivity outputs like misdemeanor or felony arrests Even if an officer
took an extremely large number of property crimes thereby decreasing their productive time
VORC would only evaluate that officer based on what the officer produced with the small
amount of productive time This suggests that while a teamrsquos crime-to-cop ratio could affect an
officerrsquos raw productivity outputs ie arrests a teamrsquos crime-to-cop ratio would not affect an
officerrsquos VORC
For example during a given month an officer may take an unusually large number of
burglaries or larcenies thereby leaving the officer with only ten hours for self-initiated activities
With this reduced amount of productive time the officer may generate a below-average number
of officials warrants misdemeanors and felonies which in turn may lead a supervisor to
potentially downgrade the officerrsquos productivity However VORC would recognize and account
for the high number of calls for service minutes and analyze the officerrsquos productivity based on
what the officer generated with only ten hours of productive time It may be that the officer was
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actually very productive but they simply didnrsquot have enough productive time to generate the
same number of traffic citations and arrests as an officer who responded to far fewer property
crimes
Table 4 also shows that traffic stops and field intelligence reports are positively
correlated with VORC These results are consistent with the principles of how VORC evaluates
patrol officer productivity and provides additional support for the validity of this advanced
metric VORC accounts for numerous variables that affect productive time (ie calls for service
minutes) meaning that the key factors which increase or decrease an officerrsquos VORC are largely
related to self-initiated activities (ie traffic stops and information reports) VORC therefore
accounts for the potential effects of macro-level variables and analyzes the activities of the
officer during his or her productive time Nonetheless we recognize that because traffic stops
and field information reports are correlated with VORC an arearsquos crime-to-cop ratio might still
affect an officerrsquos VORC indirectly by impacting the number of stops and information reports
they generate
Discussion
There are several factors which affect a patrol officerrsquos productivity including
biographical characteristics officersrsquo perceptions and attitudes and organizational and
operational features among others This paper identified two macro-level variablesmdashproperty
crimes per officer and violent crimes per officermdashwhich affect the number of traffic citations
warrants misdemeanors and felonies generated annually by an officer Using data from a mid-
sized police department we found that a patrol areas rate of property crimes per officer was
associated with a moderate decrease in an officers annual number of traffic citations warrant
arrests and misdemeanor arrests while a patrol areas rate of violent crimes per officer was also
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associated with a moderate decrease in an officers annual number of traffic citations We also
found that a patrol areas rate of violent crimes per officer is associated with a moderate increase
in an officers annual number of warrant and misdemeanor arrests However when we used a
more advanced productivity metric we found no relationship between property and violent
crimes per officer and an officerrsquos Value Over Replacement Cop or VORC
There are several factors which explain the differing effects of the crime-to-cop ratios on
productivity outputs Let us first address why the violent crime-to-cop ratio would increase a
patrol officerrsquos warrants and misdemeanors while the property crime-to-cop ratio would decrease
these outputs Notably half of all victims of violent crimes know the offender (Truman amp Rand
2010) which aids law enforcement in identifying and locating the perpetrator Moreover both
suspect and victim are frequently on scene when police officers arrive such as during domestic
disturbances Being able to identify and locate the perpetrator helps patrol officers make arrests
thereby increasing their misdemeanor and felony arrests in violent crimes Additionally when
patrol officers arrive and contact individuals during an incident it is standard procedure to check
if the parties involved have outstanding warrants This means that the greater number of violent
crimes that officers respond to the more chances they have to arrest individuals who might have
outstanding warrants It makes sense then that officers who respond to a greater number of
violent crimes have more chances to make warrant and misdemeanor arrests
Conversely property crimes may supply the officer with fewer warrant and misdemeanor
arrests because of low clearance rates We examined the difference in clearance rates between
property and violent crime using incident data from the Lincoln Police Department and we
found that the clearance rate for violent crimes was 418 compared to only 237 for property
crimes Moreover property crimes often also require more investigative time than violent
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crimes Property crime investigations often require considerable follow-up by officers such as
processing a scene contacting financial institutions and checking pawnshops for stolen property
In general property crimes are more time-intensive than violent crimes thereby decreasing
productive time and opportunities for proactive activities while simultaneously netting officers
fewer arrests because of low clearance rates
The impact of a teamrsquos crime-to-cop ratio can also be seen in the relationship between
property and violent crime per officer and the number of traffic citations Table 3 shows that a
rise in both reported violent and property crimes per officer decrease an officerrsquos number of
traffic citations Quite simply if officers respond to a high number of property and violent
crimes then they will have fewer minutes to conduct traffic enforcement and other proactive
enforcement
As noted it is possible that the correlation is bi-directional If an officer makes a greater
number of arrests then a team may witness a decrease in crime because prolific criminals may
have been arrested and placed in jail For example one might interpret Table 3 to mean that an
increase in warrant and misdemeanor warrants led to a decrease in the number of property crimes
per officer However under this hypothesis we should also see that an increase in warrant and
misdemeanor arrests leads to a decrease in the number of violent crimes per officer Instead we
see the opposite hypothesized effect ie more arrests leading to more reported violent crimes 5
983093 983127983141 983139983151983150983155983156983154983157983139983156983141983140 983154983141983143983154983141983155983155983145983151983150 983149983151983140983141983148983155 983157983155983145983150983143 983139983145983156983137983156983145983151983150983155 983159983137983154983154983137983150983156 983137983154983154983141983155983156983155 983149983145983155983140983141983149983141983137983150983151983154 983137983154983154983141983155983156983155 983137983150983140 983142983141983148983151983150983161 983137983154983154983141983155983156983155 983142983154983151983149
983156983144983141 983152983154983141983139983141983140983145983150983143 983149983151983150983156983144 983156983151 983152983154983141983140983145983139983156 983138983151983156983144 983158983145983151983148983141983150983156 983137983150983140 983152983154983151983152983141983154983156983161 983139983154983145983149983141 983154983137983156983141983155 983142983151983154 983156983144983141 983149983151983150983156983144983155 983145983150 983151983157983154 983140983137983156983137983155983141983156 983127983141 983142983151983157983150983140 983150983151
983155983156983137983156983145983155983156983145983139983137983148983148983161 983155983145983143983150983145983142983145983139983137983150983156 983154983141983148983137983156983145983151983150983155983144983145983152983155 983138983141983156983159983141983141983150 983139983145983156983137983156983145983151983150983155 983159983137983154983154983137983150983156 983137983154983154983141983155983156983155 983149983145983155983140983141983149983141983137983150983151983154 983137983154983154983141983155983156983155 983137983150983140 983142983141983148983151983150983161 983137983154983154983141983155983156983155
983137983150983140 983156983144983141 983158983145983151983148983141983150983156 983137983150983140 983152983154983151983152983141983154983156983161 983139983154983145983149983141 983154983137983156983141983155 983151983142 983156983144983141 983138983141983137983156983155 983112983151983159983141983158983141983154 983137983142983156983141983154 983140983145983155983137983143983143983154983141983143983137983156983145983150983143 983156983144983141 983158983145983151983148983141983150983156 983137983150983140 983152983154983151983152983141983154983156983161
983139983154983145983149983141 983154983137983156983141983155 983145983150983156983151 983155983152983141983139983145983142983145983139 983139983154983145983149983141983155 983159983141 983140983145983140 983142983145983150983140 983156983144983137983156 983149983145983155983140983141983149983141983137983150983151983154 983137983150983140 983142983141983148983151983150983161 983137983154983154983141983155983156983155 983155983145983143983150983145983142983145983139983137983150983156983148983161 983152983154983141983140983145983139983156983141983140 983137 983140983141983139983154983141983137983155983141
983145983150 983154983151983138983138983141983154983161 983154983137983156983141983155 983159983144983145983139983144 983145983155 983139983151983150983155983145983155983156983141983150983156 983159983145983156983144 983156983144983141 983148983145983156983141983154983137983156983157983154983141 983105983140983140983145983156983145983151983150983137983148983148983161 983159983141 983145983150983156983141983154983158983145983141983159983141983140 983140983141983152983137983154983156983149983141983150983156 983152983141983154983155983151983150983150983141983148 983137983150983140983148983141983137983154983150983141983140 983156983144983137983156 983156983144983141 983116983145983150983139983151983148983150 983120983151983148983145983139983141 983108983141983152983137983154983156983149983141983150983156 983144983137983140 983155983156983137983142983142983141983140 983137 983156983141983137983149 983140983141983155983145983143983150983141983140 983155983152983141983139983145983142983145983139983137983148983148983161 983156983151 983137983152983152983154983141983144983141983150983140 983154983151983138983138983141983154983161
983155983157983155983152983141983139983156983155 983140983157983154983145983150983143 983156983144983145983155 983156983145983149983141983142983154983137983149983141 983159983144983145983139983144 983155983141983141983149983155 983156983151 983141983160983152983148983137983145983150 983156983144983141 983137983154983154983141983155983156983085983154983151983138983138983141983154983161 983154983137983156983141 983154983141983148983137983156983145983151983150983155983144983145983152
7252019 PIJPSM-05-2015-0064
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We believe our explanation has a stronger theoretical foundation The ratio of property
crimes per officer affects productivity outputs differently than the ratio of violent crimes per
officer for two reasons First violent crimes are different in nature than property crimes
especially with regard to clearance rates Second property crimes often require more
investigative and follow-up time than violent crimes thereby reducing an officerrsquos amount of
productive time and proactive activity
Table 3 also provides a key piece of evidence to support this assertion Table 3 shows
that the number of reported violent crimes per officer increases an officerrsquos number of warrant
and misdemeanor arrests Yet the relationship is the opposite for traffic enforcement the violent
crime-to-cop ratio actually decreases traffic citations This change is due to the fact that while
violent crime investigations often yield warrant and misdemeanor arrests they do not produce
traffic citations Furthermore responding to a greater number of violent crimes decreases the
amount of available time to conduct traffic enforcement in turn reducing the number of traffic
citations In other words the violent crime-to-cop ratio affects traffic stops the same way that
the property crime-to-cop ratio affects all productivity outputs by reducing an officerrsquos
productive time and opportunities for proactivity In fact we found a moderate correlation
between a patrol arearsquos crime-to-cop ratio and the amount of productive time enjoyed by officers
who worked that area The correlation between an arearsquos violent crime-to-cop ratio and
productive time was -21 while the correlation between an arearsquos property crime-to-cop ratio and
productive time was -33 We believe this evidence indicates that officers who work in areas
with a high crime-to-cop ratio have less time for self-initiated activities which drives down
productivity
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We suggest that the number of police officers and crime rates of an area need to be
analyzed in conjunction with each other Prior theories of police productivity have viewed and
examined these variables separately when in reality we should view these variables in tandem
Combining these variables into crime-to-cop ratios provides a more nuancedmdashand accuratemdash
understanding of patrol activities and their relationship to productivity
Although we found strong evidence that an officerrsquos patrol area affects his or her
productivity (in terms of raw outputs) we also found that a few officer-level variables affected
productivity as well including traffic stops intelligence reports and working third shift Our
results appear to substantiate what many police practitioners already suspect officers who are
proactive in contacting citizens (traffic stops) and building investigations (intelligence reports)
generate more warrant and misdemeanor arrests In addition while some newer police officers
are forced to work third shift because they do not have sufficient seniority to bid for better days
off and shift times many officers work nights because they enjoy the types of calls (eg in-
progress felonies) meaning they may be more motivated to stop vehicles make field contacts
and in turn engage in activities that generate arrests (Brehm amp Gates 1993)
These results have important implications for police researchers and administrators
First our results suggest a need for a deeper understanding of how the staffing levels crime
rates and the other characteristics of a patrol area intersect Second if departments evaluate
their patrol officers using raw productivity outputs such as misdemeanor or felony arrests
administrators must recognize that factors beyond the control of patrol officers might affect
monthly or annual productivity Our research suggests that a patrol arearsquos crime-to-cop ratio can
substantially affect a patrol officerrsquos productivity regardless of the actual crime rate If the
characteristics of a patrol area affect raw productivity outputs police departments should employ
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more advanced productivity analytics (ie VORC) that account for activities which reduce
productive time and evaluate an officerrsquos productivity in terms of available minutes for
proactivity
Nonetheless evaluating both patrol officers and police departments using traffic citations
warrants misdemeanors and felonies is useful because this type of data is readily understood by
citizens and civic officials A neighborhood watch group for instance might care more about
the number of arrests their officers make and less about those officersrsquo productivity scores Our
data suggests that if citizens want patrol officers to write more traffic citations and make more
arrests they should lobby for more police officers on the street It is true that reducing an arearsquos
crime rate impacts the crime-to-cop ratio but our results indicate that putting police officers on
the street may be the most direct way to affect the crime-to-cop ratio
Our study is not without limitations We only examined a police department which uses
the generalist style of policing and it is unknown if our results are applicable to police
departments which use a specialist style of policing Theoretically a departmentrsquos policing
styles can affect factors that increase or decrease a patrol officerrsquos productive time It is possible
that the number of property crimes and violent crimes per officer do not significantly affect a
specialist patrol officerrsquos productivity outputs versus a generalist officerrsquos outputs Future
studies should investigate whether our results can be generalized to all police departments
We have only identified two macro-level variables which affect patrol officer
productivity and we recognize there may be other variables In addition we recommend that
future studies incorporate a wider array of variables and test competing models to determine
whether micro or macro-level variables exhibit stronger effects on productivity In the end
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managing patrol officers and improving productivity requires a greater understanding of both
patrol officers and the areas they work
Acknowledgements and Credits We would like to thank Denver Police Department Crime
Analyst Melissa Ohlhorst and Pennsylvania State University Professor Barry Ruback as well as
the three anonymous reviewers for their insightful comments We also salute Lincoln Police
Sergeant Jake Dilsaver for his logistical support of this project
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Brief Treatment and Crisis Intervention Vol 3 No 1 pp 47-53
Becker G (1985) ldquoHuman capital effort and the sexual division of laborrdquo The Journal of
Labor EconomicsVol 3 No 1 pp 33-58
Bonkiewicz L (2015) ldquoBobbies and baseball players Evaluating patrol officer productivity
using sabermetricsrdquo Police Quarterly Vol 18 No 1 pp 55-78
Braga A and Bond J (2008) ldquoPolicing crime and disorder hot spots A randomized controlled
trialrdquo Criminology Vol 46 No 3 pp 577-607
Brown J and Campbell E (1990) ldquoSources of occupational stress in the policerdquo Work
and Stress Vol 4 No 4 pp 305-318
Chappell A MacDonald J and Manz P (2006) ldquoThe organizational determinants
of police arrest decisionsrdquo Crime and Delinquency Vol 52 No 2 pp 287-306
Cloninger D and Sartorius L (1979) ldquoCrime rates clearance rates and enforcement effort
The case of Houston Texasrdquo American Journal of Economics and Sociology Vol 38
No 4 pp 389-402
Corman H and Mocan N (2005) ldquoCarrots sticks and broken windowsrdquo Journal of Law and
Economics Vol 48 No 1 pp 235-266
Crank J (1990) ldquoThe influence of environmental and organizational factors on police style in
urban and rural environmentsrdquo Journal of Research in Crime and Delinquency Vol 27
pp 166-189
Goodman A (1990) ldquoA model for police officer burnoutrdquo Journal of Business and
Psychology Vol 5 No 1 pp 85-99
Gould E Weinberg B and Mustard D (2002) Crime rates and local labor marketopportunities in the United States 1979ndash1997rdquo Review of Economics and Statistics Vol
84 No 1 pp 45-61
Hipp J and Roussell A (2013) ldquoMicro-and macro-environment population and the
consequences for crime ratesrdquo Social Forces Vol 92 No 2 pp 563-595
Hatry H (1976) ldquoApproaches to productivity measurement and program evaluationrdquo Public
Productivity Review Vol 1 No 3 pp21-28
Hawkins H (2001) ldquoPolice officer burnout a partial replication of Maslachs Burnout
Inventoryrdquo Police Quarterly Vol 4 No 3 pp 343-360
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Herbert S (1998)ldquoPolice subculture reconsideredrdquo Criminology Vol 36 No 2 pp 343-370
Huizinga D and Henry K (2008) ldquoThe effect of arrest and justice system sanctions on
subsequent behavior Findings from longitudinal and other studiesrdquo In The long view of
crime A synthesis of longitudinal research Springer New York pp 220-254
Johnson R (2011) ldquoOfficer attitudes and management influences on police work productivityrdquo
American Journal of Criminal Justice Vol 36 pp 293-306
Kane R (2006) ldquoOn the limits of social control Structural deterrence and the policing of
lsquosuppressiblersquo crimesrdquo Justice Quarterly Vol 23 No 2 pp 186-213
Kubrin C Messner S Deane G McGeever K and Stucky T (2010) ldquoProactive
policing and robbery rates across US citiesrdquo Criminology Vol 48 No 1 pp 57-97
Lersch K (2002) ldquoAre citizen complaints just another measure of officer productivity An
analysis of citizen complaints and officer activity measuresrdquo Police Practice and
Research Vol 3 pp 135-147
Light M and Harris C (2012) Race space and violence exploring spatial dependence in
structural covariates of white and black violent crime in US countiesrdquo Journal of
Quantitative Criminology Vol 28 No 4 pp 559-586
Maxfield M (1982) ldquoService time dispatch time and demand for police services Helping
more by serving lessrdquo Public Administration Review pp 252-263
Maxfield M Lewis D and Szoc R (1980) ldquoProducing official crimes Verified crime reports
as measures of police outputrdquo Social Science Quarterly pp 221-236
Nakosteen R and Zimmer M (2001) ldquoSpouse selection and earnings Evidence of marital
sortingrdquo Economic InquiryVol 39 No 2 pp 201-2013
Shane J (2010) ldquoOrganizational stressors and police performancerdquo Journal of Criminal
Justice Vol 38 No 4 pp 807-818
Shane J (2011) ldquoDaily work experiences and police performancerdquo Police Practice and
Research Vol 13 No 3 pp 1-19
Sherman L Gartin P and Buerger M (1989) ldquoHot spots of predatory crime Routine
activities and the criminology of placerdquoCriminology Vol 27 No 1 pp27-56
Truman J and Rand M (2010) ldquoBureau of Justice Statistics Bulletin National Crime
Victimization Survey Criminal Victimization 2009rdquo US Department of Justice Office of
Justice programs
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Van Meter D (2001) Evaluating dysfunctional police performance A zero-based approach
Charles C Thomas Publisher Springfield IL
Vila B and Kenney D (2002) ldquoTired cops The prevalence and potential consequences of
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Vila B Morrison G and Kenney D (2002) ldquoImproving shift schedule and work-hour
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Vollaard B and Koning P (2009) ldquoThe effect of police on crime disorder and victim
PrecautionEvidence from a Dutch victimization surveyrdquo International Review of Law
and Economics Vol 29 No 4 pp 336-348
Weisburd D Maher L Sherman L Buerger M Cohn E and Petrisino A (1993)
ldquoContrasting crime general and crime specific theory The case of hot spots of crimerdquo
New Directions in Criminological Theory Advances in Criminological Theory Vol 4
pp 45-70
Author Biography
Luke Bonkiewicz is a patrol officer with the Lincoln Police Department
7252019 PIJPSM-05-2015-0064
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983086 983097) lowast )
7252019 PIJPSM-05-2015-0064
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983119983142983142983145983139983141983154 983120983154983151983155983141983139983157983156983145983151983150
983122983137983156983141C983110983123
983117983145983150983157983156983141983155983110983125983120
983117983145983150983157983156983141983155
983119983142983142983145983139983141983154
A983155983155983145983155983156983117983145983150983157983156983141983155
983113983150983139983145983140983141983150983156983122983141983152983151983154983156983155
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2 95 30072 5424 4116 204 696 228 312 432 84 48 120 8
3 72 32496 6312 6600 144 564 444 720 540 84 36 24 12
4 90 33000 5232 4980 216 192 480 768 240 144 12 72 12
5 91 41724 4824 6048 312 636 132 504 324 108 7 24 24
6 83 23916 7056 6192 144 756 528 996 336 132 36 72 12
7 95 24468 6840 5892 156 624 324 456 480 72 36 120 24
8 90 39972 5196 4920 240 504 384 492 264 36 2 72 24
9 79 25500 4068 5100 336 396 192 684 204 24 36 120 10
10 87 21984 5412 4524 348 552 480 792 312 108 48 84 36
983124983137983138983148983141 2 983123983157983149983149983137983154983161 983123983156983137983156983145983155983156983145983139983155 983142983151983154 983126983137983154983145983137983138983148983141983155
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983120983151983152983157983148983137983156983145983151983150 983140983141 983150983155983145983156983161 20229830856956 1687 4436
983112983151983157983155983141983144983151983148983140 983149983141983140983145983137983150 983145983150983139983151983149983141 ($) 2628198308576020 16535 49756
983159983144983145983156983141 7398308593 007 083
983126983145983151983148983141983150983156 983139983154983145983149983141 983154983137983156983141 (983152983141983154 100000) 8009830854800 1203 2521
983120983154983151983152983141983154983156983161 983139983154983145983149983141 983154983137983156983141 (983152983141983154 100000) 31009830859800 2027 6524
983120983154983151983152983141983154983156983161 983139983154983145983149983141983155 983152983141983154 983151983142983142983145983139983141983154 5329830851097 1663 8167
983126983145983151983148983141983150983156 983139983154983145983149983141 983152983141983154 983151983142983142983145983139983141983154 194983085372 559 2929
983110983145983154983155983156983085983116983141 983158983141983148 983126 983137983154983145983137983138983148983141 983155 (983119983142983142983145983139983141983154) 983118=302 983122983137983150983143983141 983123D 983117983141 983137983150
983111983141983150983140983141983154 (983142983141983149983137983148983141=0 983149983137983148983141=1) 09830851 24 94
983117983137983154983154983145983141983140C983151983144983137983138983145983156983145983150983143 09830851 18 79
983151983142 C983144983145983148983140983154983141983150 09830855 170 90
983129983141983137983154983155 983141983160983152983141983154983145983141983150983139983141 198308537 779 1063
C983137983148983148983155 983142983151983154 983155983141983154983158983145983139983141 4819830851011 16121 61365
983124983154983137983142983142983145983139 983155983156983151983152983155 16983085901 14376 20624
983110983145983141983148983140 983145983150983142983151983154983149983137983156983145983151983150 983154983141983152983151983154983156983155 0983085227 2153 2530
983124983154983137983142983142983145983139 983139983145983156983137983156983145983151983150983155 249830851246 14632 18642
983117983145983155983140983141983149983141983137983150983151983154 983137983154983154983141983155983156983155 19983085403 5965 10663
983110983141983148983151983150983161 983137983154983154983141983155983156983155 098308571 1066 852
983126983119983122C 9830851959830851043 151 24
1983155983156 2983150983140 3983154983140
104 96 102983123983144983145983142983156
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983123983141983139983151983150983140983085983116983141983158983141983148 983126983137983154983145983137983138983148983141983155 (983120983137983156983154983151983148 A983154983141983137) 983118=40 983138 983123E 983138 983123E 983138 983123E 983138 983123E
983120983151983152983157983148983137983156983145983151983150 983140983141983150983155983145983156983161 001 003 001 001 98308501 01 00 00
983112983151983157983155983141983144983151983148983140 983149983141983140983145983137983150 983145983150983139983151983149983141 ($) 002 001 983085000 000 00 00 98308500 00
983159983144983145983156983141 13363 32472 9830858921 5664 9830853274 19211 9830854447 3255
983126983145983151983148983141983150983156 983139983154983145983149983141 983154983137983156983141 (983152983141983154 100000) 006 004 001 001 98308501 02 01 02
983120983154983151983152983141983154983156983161 983139983154983145983149983141 983154983137983156983141 (983152983141983154 100000) 983085003 005 983085002 001 02 03 98308501 00
983120983154983151983152983141983154983156983161 983139983154983145983149983141983155 983152983141983154 983151983142983142983145983139983141983154 983085225 99 98308515 03 98308552 24 98308503 03
983126983145983151983148983141983150983156 983139983154983145983149983141 983152983141983154 983151983142983142983145983139983141983154 983085168 68 29 14 76 31 22 12
983110983145983154983155983156983085983116983141983158983141983148 983126983137983154983145983137983138983148983141983155 (983119983142983142983145983139983141983154) 983118=302
983117983137983148983141 4673 1622 366 147 1613 1051 91 158
983117983137983154983154983145983141983140C983151983144983137983138983145983156983145983150983143 9830857516 3254 201 278 9830851158 1169 98308595 167
983151983142 C983144983145983148983140983154983141983150 183 702 75 77 983085262 217 983085396 207
983129983141983137983154983155 983141983160983152983141983154983145983141983150983139983141 9830851065 492 98308512 13 983085163 33 98308506 05C983137983148983148983155 983142983151983154 983155983141983154983158983145983139983141 24 13 02 02 14 01 98308508 05
983124983154983137983142983142983145983139 983155983156983151983152983155 983085983085 983085983085 03 00 09 02 00 00
983110983145983141983148983140 983145983150983142983151983154983149983137983156983145983151983150 983154983141983152983151983154983156983155 209 33 20 04 79 19 10 03
983123983141983139983151983150983140 983155983144983145983142983156 (983154983141983142983141983154983141983150983139983141 983143983154983151983157983152 983142983145983154983155983156) 2603 1778 187 195 809 730 147 160
983124983144983145 983154983140 983155983144983145 983142983156 (983154983141 983142983141 983154983141 983150983139983141 983143983154983151983157983152 983142983145 983154983155983156) 9872 2155 79 213 2532 952 393 132
983113983150983156983141983154983139983141983152983156 32591 5361 758 339 4147 1912 310 484
983123983145983143983149983137983085983155983153983157983137983154983141983140 1541467 19287 206702 5598
C983144983145983085983155983153983157983137983154983141983140 8294 6133 12348 10663
983152 lt 05
983152lt01
983152lt001
983118983151983156983141 A983148983148 983158983137983154983145983137983138983148983141983155 983137983154983141 983157983150983139983141983150983156983141983154983141983140
983127A983122983122A983118983124 A983122983122E983123983124983123 983117983113983123DE983117EA983118983119983122 A983122983122E983123983124983123 983110E983116983119983118983129 A983122983122E983123983124983123983124983122A983110983110983113C C983113983124A983124983113983119983118983123
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983124983137983138983148983141 4 983112983145983141983154983137983154983139983144983145983139983137983148 983116983145983150983141983137983154 983117983151983140983141983148983155 E983155983156983145983149983137983156983145983150983143 983119983142983142983145983139983141983154983155 A983150983150983157983137983148 983126983137983148983157983141 983119983158983141983154 983122983141983152983148983137983139983141983149983141983150983156 C983151983152 (983126983119983122C)
983123983141983139983151983150983140983085983116983141983158983141983148 983126983137983154983145983137983138983148983141983155 (983120983137983156983154983151983148 A983154983141983137) 983118=40 983138 983123E
983120983151983152983157983148983137983156983145983151983150 983140983141983150983155983145983156983161 98308500 00
983112983151983157983155983141983144983151983148983140 983149983141983140983145983137983150 983145983150983139983151983149983141 ($) 00 01
983159983144983145983156983141 318 345
983126983145983151983148983141983150983156 983139983154983145983149983141 983154983137983156983141 (983152983141983154 100000) 98308500 00
983120983154983151983152983141983154983156983161 983139983154983145983149983141 983154983137983156983141 (983152983141983154 100000) 00 00
983120983154983151983152983141983154983156983161 983139983154983145983149983141983155 983152983141983154 983151983142983142983145983139983141983154 98308502 04
983126983145983151983148983141983150983156 983139983154983145983149983141 983152983141983154 983151983142983142983145983139983141983154 02 05
983110983145983154983155983156983085983116983141983158983141983148 983126983137983154983145983137983138983148983141983155 (983119983142983142983145983139983141983154) 983118=302
983117983137983148983141 27 15
983117983137983154983154983145983141983140C983151983144983137983138983145983156983145983150983143 98308532 31
983151983142 C983144983145983148983140983154983141983150 05 03
983129983141983137983154983155 983141983160983152983141983154983145983141983150983139983141 00 01
C983137983148 983148983155 983142983151983154 983155983141983154983158983145983139983141 02 01
983124983154983137983142983142983145983139 983155983156983151983152983155 01 00
983110983145 983141983148983140 983145983150983142983151983154983149983137983156983145 983151983150 983154983141 983152983151983154983156983155 02 00
983123983141983139983151983150983140 983155983144983145983142983156 ( 983154983141983142983141983154983141983150983139983141 983143983154983151983157983152 983142 983145983154983155983156) 23 12
983124983144983145983154983140 983155983144983145983142983156 (983154983141983142983141983154983141983150983139983141 983143983154983151983157983152 983142 983145983154983155983156) 55 14
983113983150983156983141983154983139983141983152983156 165 36
983123983145983143983149983137983085983155983153983157983137983154983141983140 66
C983144983145983085983155983153983157983137983154983141983140 9846
983152 lt 05
983152lt01
983152lt001
983118983151983156983141 A983148983148 983158983137983154983145983137983138983148983141983155 983137983154983141 983157983150983139983141983150983156983141983154983141983140
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crimes per officer and a patrol officerrsquos productivity when we introduce a more sophisticated
method for analyzing productivity a metric based on productive time and not total time on duty
Before discussing our data and methods there are two important issues meriting brief
discussion First we argue that the crime-to-cop ratio may affect the number of traffic stops
warrants misdemeanors and felonies that a patrol officer makes It can also be argued that the
number of arrests might impact the crime-to-cop ratio Our intent is not to comprehensively
discuss the relationship between the number of officersofficer arrests and an arearsquos crime rate
but it bears noting that the research is inconclusive on this point
Some evidence suggests that the relationship between arrests and crime rates is non-
linear that is crime rates decrease as arrests increase but eventually hit a ldquofloorrdquo and reach
equilibrium (Kane 2006) Second when studies examine the arrest-crime rate relationship they
often find that the relationship only exists for specific crimes in their datasets such as robbery
and motor vehicle thefts (Corman Hope amp Mocan 2005) robbery but not assaults (Kane 2006)
and homicide and auto thefts only (Cloninger amp Sartorius 1979) and so forth Overall the
literature on the arrests-crime rate relationship is mixedmdashsome research finds an effect between
arrests and crime rates some does not (Huizinga and Henry 2008)
The second issue concerns whether a law enforcement agency operates using a generalist
or specialist policing style We analyze data from a generalist municipal police department
meaning that patrol officers investigate low and mid-level crimes but investigators and
detectives are responsible for handling serious crimes cases involving lengthy investigations
(eg narcotics trafficking or white-collar crimes) and incidents in which patrol officers may
need assistance (for example an incident involving numerous victims and witnesses as well as
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cases involving extensive follow-up or knowledge on a specific subject such as gangs or
narcotics)
By contrast when patrol officers in a specialist-style police department respond to any
incident potentially requiring follow-up they might only contact the victim and either hold the
scene until an investigator arrives or forward the information to a detective for follow-up It is
possible indeed likely that macro-level variables might affect generalist patrol officers
differently than specialized officers Due to data limitations however our study focuses on
generalist police officers
Data and Methods
We gathered data on patrol officers and their patrol areas from several sources First we
collected patrol officer productivity data from the Lincoln (Nebraska) Police Departmentrsquos 2013-
2014 productivity reports Using Computer-Aided Dispatch (CAD) and a centralized
productivity database LPD measures numerous patrol officer activities Every month (and
year) LPD generates productivity reports which display each patrol officerrsquos activities and
outputs (see Table 1 for an example of a squadrsquos annual productivity report) Each patrol
activity and output merits further explanation
INSERT TABLE 1 HERE
Calls for Service (CFS) and Officer Assist time When dispatchers send patrol officers on
a call they use CAD to track the minutes the officer spends on the call If the call is a priority
incident dispatchers send both a primary and secondary officer Other unassigned officers might
also respond to the CFS meaning that multiple officers could be secondary officers The annual
productivity record tracks amount of time an officer spent as a primary responder and a
secondary responder
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Report writing time Standard operating procedures dictate that LPD patrol officers
complete specific types of reports for incidents An incident report (IR) details biographical
information of the individuals involved in the incident and summarizes the incident An
Additional Case Information (ACI) is a more detailed report that documents additional
investigative steps or the actions of assisting officers Although the time spent on an IR or ACI
could vary depending on the call for service the average IR and ACI take roughly twenty
minutes to complete1
The Lincoln Police Department also tracks self-initiated activities Most productivity
results from the following proactive activities
Selectives The Lincoln Police Department tracks specific officer patrol activity by
coding its neighborhoods schools parks and business districts When an officer patrols an at-
risk business district or a high-crime neighborhood for example the officer notifies dispatch
using that arearsquos specific code Girded by the principles of ldquohot spotsrdquo policing research officers
use selectives to identify potential candidates for POP projects and heighten the perception of
risk among criminals (Braga amp Bond 2008)
Traffic Warnings and Official Citations Every Lincoln patrol officer drives a police
cruiser and conducts traffic enforcement issuing traffic warnings and official citations The
LPD records department tracks patrol officersrsquo warnings and citations
Misdemeanors felonies and warrants Patrol officers also make misdemeanor and
felony arrests during their productive time Officers might detain a motorist and arrest him or
her for driving under a suspended license or driving while intoxicated or contact a pedestrian
1 983119983142983142983145983139983141983154983155 983156983161983152983141 983137983148983148 983154983141983152983151983154983156983155 983151983150 983139983151983149983152983157983156983141983154983155 983127983141 983151983138983156983137983145983150983141983140 983156983144983145983155 983137983158983141983154983137983143983141 983138983161 983151983138983155983141983154983158983145983150983143 983137 983155983137983149983152983148983141 983151983142 983116983145983150983139983151983148983150 983152983137983156983154983151983148 983151983142983142983145983139983141983154983155
983139983151983149983152983148983141983156983145983150983143 983113983122983155 983137983150983140 983105983107983113983155 983137983150983140 983159983141 983154983141983139983151983143983150983145983162983141 983156983144983137983156 983156983144983145983155 983137983158983141983154983137983143983141 983149983137983161 983140983145983142983142983141983154 983142983154983151983149 983151983156983144983141983154 983152983151983148983145983139983141 983140983141983152983137983154983156983149983141983150983156983155 983117983151983154983141983151983158983141983154983145983142 983140983141983152983137983154983156983149983141983150983156983155 983154983141983153983157983145983154983141 983151983142983142983145983139983141983154983155 983156983151 983139983151983149983152983148983141983156983141 983156983144983141983145983154 983154983141983152983151983154983156983155 983138983141983142983151983154983141 983138983141983139983151983149983145983150983143 983137983158983137983145983148983137983138983148983141 983142983151983154 983137983150983151983156983144983141983154 983139983137983148983148 983142983151983154 983155983141983154983158983145983139983141
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and make an arrest for a sex offender registration violation Officers can also search their
departmentrsquos electronic database for individuals with outstanding warrants in their patrol areas
and attempt to arrest these offenders As with warnings and citations the LPD records
department documents and tracks misdemeanor felony and warrant arrests Moreover the court
system also monitors these types of outputs to process cases thereby adding an additional layer
of reporting accuracy and reliability
Field information reports We also measured the number of field information or
ldquointelligencerdquo reports Officers complete these internal reports to document suspicious or
dangerous individuals or circumstances though these reports are not publicly available
VORC In addition to the raw outputs of traffic citations warrants misdemeanors and
felonies we also include an advanced metric for measuring a patrol officerrsquos productivity Value
Over Replacement Cop (VORC) is a method for evaluating officers that assigns different
weights to productivity outputs and then assesses officer productivity in terms of available time
for self-initiated activities or productive time (Bonkiewicz 2015) We use a variation of this
formula that multiplies an officerrsquos officials misdemeanors and felonies by his or her
prosecution rate Our modified use of VORC can best be explained using the following figure
(Figure 1) and five-step process
INSERT FIGURE 1 HERE
where = total monthly on-duty minutes = CFS minutes Γ = follow-up time and meetings ∆
= officer assist time Ε = of IRs Ζ = of ACIs M = of intelligence reports Η = of
selectives Θ = of warnings times two Κ = of warrants times three Ι = of officials times
three Λ = of misdemeanor arrests times their respective weights Ν = of felonies types their
respective weights Ρ = an officerrsquos prosecution rate Ξ = Officerrsquos Productive time (Α-(Β+Γ+∆+
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20(Ε+Ζ))) Ο= Department average P-score or average (Η+ ((Θ)+(Ι)+(Κ)+(Λ)+(Μ)+(Ν)Ρ))
and Π = Department average Self-Initiated Time
First VORC calculates an officerrsquos total minutes spent on CFS follow-ups officer
assists and reports This amount is an officerrsquos total work time or service minutes Second the
formula subtracts an officerrsquos service minutes from the total on-duty minutes2 This is the
officerrsquos amount of productive time Third VORC calculates an officerrsquos productivity score (or
P-score) using a weighted index Departments can choose different weights for productivity
outputs but for the purpose of this study we used our weights based on the statutes governing
the Lincoln Police Department Citations and arrests are weighted based on whether the offense
was a Traffic Offense a Class III II or I Misdemeanor or a Class V IV III II or I Felony
Accordingly a patrol selective is worth one point a traffic warning two points a traffic citation
three points a warrant arrest three points a Class III misdemeanor arrest four points a Class II
misdemeanor arrest five points a Class I misdemeanor arrest six points a Class V Felony seven
points a Class IV Felony eight points a Class III Felony nine points and Class II and I Felonies
(eg homicides) ten points3 The sum of the weighted productivity outputs is the officerrsquos P-
score The P-score is divided by amount of productive time and then multiplied by 100 (for ease
of understanding) This value the productivity quotient represents the officerrsquos productivity
given the available minutes for proactivity such as searching for wanted individuals or traffic
enforcement
Fourth VORC creates a hypothetical replacement officer against which to measure the
patrol officerrsquos output This replacement player might be imagined as a rookie patrol officer or
983090 983119983150983085983140983157983156983161 983156983145983149983141 983140983151983141983155 983150983151983156 983145983150983139983148983157983140983141 983156983154983137983145983150983145983150983143 983156983145983149983141 983158983137983139983137983156983145983151983150 983151983154 983144983151983148983145983140983137983161 983140983137983161983155 983151983142983142 983151983154 983155983145983139983147 983148983141983137983158983141 983113983150 983151983156983144983141983154 983159983151983154983140983155 983151983150983085983140983157983156983161
983156983145983149983141 983145983155 983156983144983141 983137983149983151983157983150983156 983151983142 983156983145983149983141 983137 983152983137983156983154983151983148 983151983142983142983145983139983141983154 983155983152983141983150983156 983159983151983154983147983145983150983143 983156983144983141 983155983156983154983141983141983156983091 983119983157983154 983159983141983145983143983144983156983145983150983143 983155983161983155983156983141983149 983145983155 983151983150983148983161 983151983150983141 983141983160983137983149983152983148983141 983151983142 983156983144983141 983142983148983141983160983145983138983145983148983145983156983161 983156983144983137983156 983126983119983122983107 983151983142983142983141983154983155 983140983141983152983137983154983156983149983141983150983156983155983099 983141983137983139983144 983137983143983141983150983139983161 983139983137983150
983152983154983145983151983154983145983156983145983162983141 983137983150983140 983137983155983155983145983143983150 983139983151983154983154983141983155983152983151983150983140983145983150983143 983159983141983145983143983144983156983155 983156983151 983156983144983141983145983154 983152983154983141983142983141983154983154983141983140 983152983154983151983140983157983139983156983145983158983145983156983161 983151983157983156983152983157983156983155
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a recruit who has not completed the academy performing at ninety percent the capacity of fully
trained police officer4 Using LPDrsquos data VORC creates a hypothetical replacement officer by
dividing the departmental average P-score by the departmental average for productive time and
then multiplying it by the officerrsquos productive time to determine what an average officer would
have generated given the officerrsquos amount of productive time This figure is then multiplied by
9 (the capacity of a replacement officer) divided by the officerrsquos productive time and then
multiplied by 100 to generate the replacement officerrsquos productivity quotient Finally VORC
subtracts the replacement officerrsquos score from the patrol officerrsquos score to calculate the officerrsquos
value over the replacement officer This number or VORC indicates the officerrsquos productivity
per time for self-initiated activities compared to the replacement officerrsquos productivity For
example a VORC of 12 means than officer would produce 012 more productivity points (some
combination of warnings citations and arrests) per minute than a replacement officer
Biographical variables We gathered biographical information about the officers in the
sample including gender years of LPD experience maritalcohabiting status and number of
children We also controlled for whether the officer worked first second or third shift because
our data showed significant differences in the number of calls for service each shift handled as
well as the number of reports each shift completed
Geographic variables It is necessary to measure the characteristics of patrol areas to
account for variation across patrol areas For example some areas may feature different crime
rates or levels of social disorganization which command more department resources and police
officers We gathered data by first mapping out every squadrsquos patrol area and then obtaining
tract-level data from the 2010 US Census Our models included measures of established of
983092
983123983139983144983151983148983137983154983155 983137983150983140 983152983151983148983145983139983161 983149983137983147983141983154983155 983149983137983161 983140983145983155983137983143983154983141983141 983159983145983156983144 983141983155983156983145983149983137983156983145983150983143 983137 983154983141983152983148983137983139983141983149983141983150983156 983151983142983142983145983139983141983154991257983155 983139983137983152983137983139983145983156983161 983137983156 983150983145983150983141983156983161 983152983141983154983139983141983150983156 983119983157983154983143983151983137983148 983145983155 983150983151983156 983156983151 983140983141983142983145983150983145983156983145983158983141983148983161 983141983155983156983145983149983137983156983141 983137 983144983161983152983151983156983144983141983156983145983139983137983148 983151983142983142983145983139983141983154991257983155 983152983154983151983140983157983139983156983145983158983145983156983161 983138983157983156 983149983141983154983141983148983161 983156983151 983141983155983156983137983138983148983145983155983144 983137 983154983141983137983155983151983150983137983138983148983141 983142983145983160983141983140
983152983151983145983150983156 983137983143983137983145983150983155983156 983159983144983145983139983144 983156983151 983139983151983149983152983137983154983141 983137983148983148 983152983137983156983154983151983148 983151983142983142983145983139983141983154983155
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correlates of crime including population density (Hipp amp Roussell 2013) household income
(Gould Weinberg and Mustard 2002) and racial composition (Light amp Harris 2012)
Specifically we use each arearsquos population density median household income and percent
white We also included measures for each arearsquos violent and property crime rate Finally we
gathered information about the number of violent crimes (murders assaults aggravated assaults
sexual assaults and robberies) property crimes (larcenies burglaries vandalisms frauds
forgeries and motor vehicle thefts) and number of police officers in each patrol area thereby
creating variables for violent crime per officer and property crime per officer
Since LPDrsquos patrol officers are grouped with squads it is reasonable to believe that an
officerrsquos productivity might be more similar to an officer within their squad compared to an
officer in a different squad To account for the potential correlations between the individual
units as well as the multi-level nature of the data we used hierarchical linear modeling to
analyze the data
Our primary level variables include an officerrsquos gender marital status number of
children years of law enforcement experience shift calls for service annual number of traffic
stops and citations field information reports and misdemeanor felony and warrant arrests We
also used the officersrsquo productivity information to calculate each patrol officerrsquos VORC
Our secondary level variables include the violent crime per officer rate and property
crime per officer rate for the patrol area of each squad as well several control variables
including population density household median income percent white and the patrol arearsquos
violent and property crime rate Table 2 displays the summary statistics for the first and second-
level variables
INSERT TABLE 2 HERE
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Results
We hypothesized that the violent and property crime-to-cop ratios of an area might affect
the raw productivity outputs of patrol officers as well as a more advanced productivity metric
To examine these questions we constructed five hierarchical linear models estimating the
correlations between reported violent and property crimes per officer and a patrol officerrsquos
annual traffic citations warrant misdemeanor felony arrests and Value Over Replacement Cop
Although not presented here we estimated null models for all dependent variables and found that
significant variation existed between squads when we included no variables included in the
models The chi-square for the LR test for all five null models was statistically significant (p lt
000 in all models)
Table 3 shows that a rise in reported violent crimes per officer increases an officerrsquos
number of warrants and misdemeanors (b= 29 and 76) while arise in reported property crimes
per officer decreases an officerrsquos number of warrants and misdemeanors (b= -15 and -52) We
found no relationship between the crime-to-cop ratios and felony arrests However both the
rates of violent and property crimes per officer decreased the number of traffic citations an
officer writes (b= -168 and -225)
Insert Table 3 here
Table 3 also shows that traffic stops and field information reports are positively
associated with warrant and misdemeanor arrests This supports what police practitioners
already suspect the cops with the most arrests are extremely proactive patrol officers who stop a
large number of vehicles and build investigations through the use of field contacts and
information reports For instance an officer can build felony narcotics cases by contacting
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suspects and informants in the field documenting these efforts through field information reports
and eventually using this information to write search warrants and conduct drug interdiction
Next we investigated whether this relationship between the crime-to-cop ratios and
productivity outputs exists when using an advanced productivity metric specifically an officerrsquos
Value Over Replacement Cop (VORC)
INSERT TABLE 4 HERE
Table 4 shows that there is no correlation between the ratio of property and violent
crimes per officer and VORC Although this stands in contrast to our earlier results we would
not expect to find a relationship between these variables and VORC Advanced metrics such as
VORC evaluate officers based on the amount of time for self-initiated activities (productive
time) and not raw productivity outputs like misdemeanor or felony arrests Even if an officer
took an extremely large number of property crimes thereby decreasing their productive time
VORC would only evaluate that officer based on what the officer produced with the small
amount of productive time This suggests that while a teamrsquos crime-to-cop ratio could affect an
officerrsquos raw productivity outputs ie arrests a teamrsquos crime-to-cop ratio would not affect an
officerrsquos VORC
For example during a given month an officer may take an unusually large number of
burglaries or larcenies thereby leaving the officer with only ten hours for self-initiated activities
With this reduced amount of productive time the officer may generate a below-average number
of officials warrants misdemeanors and felonies which in turn may lead a supervisor to
potentially downgrade the officerrsquos productivity However VORC would recognize and account
for the high number of calls for service minutes and analyze the officerrsquos productivity based on
what the officer generated with only ten hours of productive time It may be that the officer was
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actually very productive but they simply didnrsquot have enough productive time to generate the
same number of traffic citations and arrests as an officer who responded to far fewer property
crimes
Table 4 also shows that traffic stops and field intelligence reports are positively
correlated with VORC These results are consistent with the principles of how VORC evaluates
patrol officer productivity and provides additional support for the validity of this advanced
metric VORC accounts for numerous variables that affect productive time (ie calls for service
minutes) meaning that the key factors which increase or decrease an officerrsquos VORC are largely
related to self-initiated activities (ie traffic stops and information reports) VORC therefore
accounts for the potential effects of macro-level variables and analyzes the activities of the
officer during his or her productive time Nonetheless we recognize that because traffic stops
and field information reports are correlated with VORC an arearsquos crime-to-cop ratio might still
affect an officerrsquos VORC indirectly by impacting the number of stops and information reports
they generate
Discussion
There are several factors which affect a patrol officerrsquos productivity including
biographical characteristics officersrsquo perceptions and attitudes and organizational and
operational features among others This paper identified two macro-level variablesmdashproperty
crimes per officer and violent crimes per officermdashwhich affect the number of traffic citations
warrants misdemeanors and felonies generated annually by an officer Using data from a mid-
sized police department we found that a patrol areas rate of property crimes per officer was
associated with a moderate decrease in an officers annual number of traffic citations warrant
arrests and misdemeanor arrests while a patrol areas rate of violent crimes per officer was also
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associated with a moderate decrease in an officers annual number of traffic citations We also
found that a patrol areas rate of violent crimes per officer is associated with a moderate increase
in an officers annual number of warrant and misdemeanor arrests However when we used a
more advanced productivity metric we found no relationship between property and violent
crimes per officer and an officerrsquos Value Over Replacement Cop or VORC
There are several factors which explain the differing effects of the crime-to-cop ratios on
productivity outputs Let us first address why the violent crime-to-cop ratio would increase a
patrol officerrsquos warrants and misdemeanors while the property crime-to-cop ratio would decrease
these outputs Notably half of all victims of violent crimes know the offender (Truman amp Rand
2010) which aids law enforcement in identifying and locating the perpetrator Moreover both
suspect and victim are frequently on scene when police officers arrive such as during domestic
disturbances Being able to identify and locate the perpetrator helps patrol officers make arrests
thereby increasing their misdemeanor and felony arrests in violent crimes Additionally when
patrol officers arrive and contact individuals during an incident it is standard procedure to check
if the parties involved have outstanding warrants This means that the greater number of violent
crimes that officers respond to the more chances they have to arrest individuals who might have
outstanding warrants It makes sense then that officers who respond to a greater number of
violent crimes have more chances to make warrant and misdemeanor arrests
Conversely property crimes may supply the officer with fewer warrant and misdemeanor
arrests because of low clearance rates We examined the difference in clearance rates between
property and violent crime using incident data from the Lincoln Police Department and we
found that the clearance rate for violent crimes was 418 compared to only 237 for property
crimes Moreover property crimes often also require more investigative time than violent
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crimes Property crime investigations often require considerable follow-up by officers such as
processing a scene contacting financial institutions and checking pawnshops for stolen property
In general property crimes are more time-intensive than violent crimes thereby decreasing
productive time and opportunities for proactive activities while simultaneously netting officers
fewer arrests because of low clearance rates
The impact of a teamrsquos crime-to-cop ratio can also be seen in the relationship between
property and violent crime per officer and the number of traffic citations Table 3 shows that a
rise in both reported violent and property crimes per officer decrease an officerrsquos number of
traffic citations Quite simply if officers respond to a high number of property and violent
crimes then they will have fewer minutes to conduct traffic enforcement and other proactive
enforcement
As noted it is possible that the correlation is bi-directional If an officer makes a greater
number of arrests then a team may witness a decrease in crime because prolific criminals may
have been arrested and placed in jail For example one might interpret Table 3 to mean that an
increase in warrant and misdemeanor warrants led to a decrease in the number of property crimes
per officer However under this hypothesis we should also see that an increase in warrant and
misdemeanor arrests leads to a decrease in the number of violent crimes per officer Instead we
see the opposite hypothesized effect ie more arrests leading to more reported violent crimes 5
983093 983127983141 983139983151983150983155983156983154983157983139983156983141983140 983154983141983143983154983141983155983155983145983151983150 983149983151983140983141983148983155 983157983155983145983150983143 983139983145983156983137983156983145983151983150983155 983159983137983154983154983137983150983156 983137983154983154983141983155983156983155 983149983145983155983140983141983149983141983137983150983151983154 983137983154983154983141983155983156983155 983137983150983140 983142983141983148983151983150983161 983137983154983154983141983155983156983155 983142983154983151983149
983156983144983141 983152983154983141983139983141983140983145983150983143 983149983151983150983156983144 983156983151 983152983154983141983140983145983139983156 983138983151983156983144 983158983145983151983148983141983150983156 983137983150983140 983152983154983151983152983141983154983156983161 983139983154983145983149983141 983154983137983156983141983155 983142983151983154 983156983144983141 983149983151983150983156983144983155 983145983150 983151983157983154 983140983137983156983137983155983141983156 983127983141 983142983151983157983150983140 983150983151
983155983156983137983156983145983155983156983145983139983137983148983148983161 983155983145983143983150983145983142983145983139983137983150983156 983154983141983148983137983156983145983151983150983155983144983145983152983155 983138983141983156983159983141983141983150 983139983145983156983137983156983145983151983150983155 983159983137983154983154983137983150983156 983137983154983154983141983155983156983155 983149983145983155983140983141983149983141983137983150983151983154 983137983154983154983141983155983156983155 983137983150983140 983142983141983148983151983150983161 983137983154983154983141983155983156983155
983137983150983140 983156983144983141 983158983145983151983148983141983150983156 983137983150983140 983152983154983151983152983141983154983156983161 983139983154983145983149983141 983154983137983156983141983155 983151983142 983156983144983141 983138983141983137983156983155 983112983151983159983141983158983141983154 983137983142983156983141983154 983140983145983155983137983143983143983154983141983143983137983156983145983150983143 983156983144983141 983158983145983151983148983141983150983156 983137983150983140 983152983154983151983152983141983154983156983161
983139983154983145983149983141 983154983137983156983141983155 983145983150983156983151 983155983152983141983139983145983142983145983139 983139983154983145983149983141983155 983159983141 983140983145983140 983142983145983150983140 983156983144983137983156 983149983145983155983140983141983149983141983137983150983151983154 983137983150983140 983142983141983148983151983150983161 983137983154983154983141983155983156983155 983155983145983143983150983145983142983145983139983137983150983156983148983161 983152983154983141983140983145983139983156983141983140 983137 983140983141983139983154983141983137983155983141
983145983150 983154983151983138983138983141983154983161 983154983137983156983141983155 983159983144983145983139983144 983145983155 983139983151983150983155983145983155983156983141983150983156 983159983145983156983144 983156983144983141 983148983145983156983141983154983137983156983157983154983141 983105983140983140983145983156983145983151983150983137983148983148983161 983159983141 983145983150983156983141983154983158983145983141983159983141983140 983140983141983152983137983154983156983149983141983150983156 983152983141983154983155983151983150983150983141983148 983137983150983140983148983141983137983154983150983141983140 983156983144983137983156 983156983144983141 983116983145983150983139983151983148983150 983120983151983148983145983139983141 983108983141983152983137983154983156983149983141983150983156 983144983137983140 983155983156983137983142983142983141983140 983137 983156983141983137983149 983140983141983155983145983143983150983141983140 983155983152983141983139983145983142983145983139983137983148983148983161 983156983151 983137983152983152983154983141983144983141983150983140 983154983151983138983138983141983154983161
983155983157983155983152983141983139983156983155 983140983157983154983145983150983143 983156983144983145983155 983156983145983149983141983142983154983137983149983141 983159983144983145983139983144 983155983141983141983149983155 983156983151 983141983160983152983148983137983145983150 983156983144983141 983137983154983154983141983155983156983085983154983151983138983138983141983154983161 983154983137983156983141 983154983141983148983137983156983145983151983150983155983144983145983152
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We believe our explanation has a stronger theoretical foundation The ratio of property
crimes per officer affects productivity outputs differently than the ratio of violent crimes per
officer for two reasons First violent crimes are different in nature than property crimes
especially with regard to clearance rates Second property crimes often require more
investigative and follow-up time than violent crimes thereby reducing an officerrsquos amount of
productive time and proactive activity
Table 3 also provides a key piece of evidence to support this assertion Table 3 shows
that the number of reported violent crimes per officer increases an officerrsquos number of warrant
and misdemeanor arrests Yet the relationship is the opposite for traffic enforcement the violent
crime-to-cop ratio actually decreases traffic citations This change is due to the fact that while
violent crime investigations often yield warrant and misdemeanor arrests they do not produce
traffic citations Furthermore responding to a greater number of violent crimes decreases the
amount of available time to conduct traffic enforcement in turn reducing the number of traffic
citations In other words the violent crime-to-cop ratio affects traffic stops the same way that
the property crime-to-cop ratio affects all productivity outputs by reducing an officerrsquos
productive time and opportunities for proactivity In fact we found a moderate correlation
between a patrol arearsquos crime-to-cop ratio and the amount of productive time enjoyed by officers
who worked that area The correlation between an arearsquos violent crime-to-cop ratio and
productive time was -21 while the correlation between an arearsquos property crime-to-cop ratio and
productive time was -33 We believe this evidence indicates that officers who work in areas
with a high crime-to-cop ratio have less time for self-initiated activities which drives down
productivity
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We suggest that the number of police officers and crime rates of an area need to be
analyzed in conjunction with each other Prior theories of police productivity have viewed and
examined these variables separately when in reality we should view these variables in tandem
Combining these variables into crime-to-cop ratios provides a more nuancedmdashand accuratemdash
understanding of patrol activities and their relationship to productivity
Although we found strong evidence that an officerrsquos patrol area affects his or her
productivity (in terms of raw outputs) we also found that a few officer-level variables affected
productivity as well including traffic stops intelligence reports and working third shift Our
results appear to substantiate what many police practitioners already suspect officers who are
proactive in contacting citizens (traffic stops) and building investigations (intelligence reports)
generate more warrant and misdemeanor arrests In addition while some newer police officers
are forced to work third shift because they do not have sufficient seniority to bid for better days
off and shift times many officers work nights because they enjoy the types of calls (eg in-
progress felonies) meaning they may be more motivated to stop vehicles make field contacts
and in turn engage in activities that generate arrests (Brehm amp Gates 1993)
These results have important implications for police researchers and administrators
First our results suggest a need for a deeper understanding of how the staffing levels crime
rates and the other characteristics of a patrol area intersect Second if departments evaluate
their patrol officers using raw productivity outputs such as misdemeanor or felony arrests
administrators must recognize that factors beyond the control of patrol officers might affect
monthly or annual productivity Our research suggests that a patrol arearsquos crime-to-cop ratio can
substantially affect a patrol officerrsquos productivity regardless of the actual crime rate If the
characteristics of a patrol area affect raw productivity outputs police departments should employ
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more advanced productivity analytics (ie VORC) that account for activities which reduce
productive time and evaluate an officerrsquos productivity in terms of available minutes for
proactivity
Nonetheless evaluating both patrol officers and police departments using traffic citations
warrants misdemeanors and felonies is useful because this type of data is readily understood by
citizens and civic officials A neighborhood watch group for instance might care more about
the number of arrests their officers make and less about those officersrsquo productivity scores Our
data suggests that if citizens want patrol officers to write more traffic citations and make more
arrests they should lobby for more police officers on the street It is true that reducing an arearsquos
crime rate impacts the crime-to-cop ratio but our results indicate that putting police officers on
the street may be the most direct way to affect the crime-to-cop ratio
Our study is not without limitations We only examined a police department which uses
the generalist style of policing and it is unknown if our results are applicable to police
departments which use a specialist style of policing Theoretically a departmentrsquos policing
styles can affect factors that increase or decrease a patrol officerrsquos productive time It is possible
that the number of property crimes and violent crimes per officer do not significantly affect a
specialist patrol officerrsquos productivity outputs versus a generalist officerrsquos outputs Future
studies should investigate whether our results can be generalized to all police departments
We have only identified two macro-level variables which affect patrol officer
productivity and we recognize there may be other variables In addition we recommend that
future studies incorporate a wider array of variables and test competing models to determine
whether micro or macro-level variables exhibit stronger effects on productivity In the end
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managing patrol officers and improving productivity requires a greater understanding of both
patrol officers and the areas they work
Acknowledgements and Credits We would like to thank Denver Police Department Crime
Analyst Melissa Ohlhorst and Pennsylvania State University Professor Barry Ruback as well as
the three anonymous reviewers for their insightful comments We also salute Lincoln Police
Sergeant Jake Dilsaver for his logistical support of this project
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References
Amaranto E Steinberg J Castellano C and Mitchell R (2003) ldquoPolice stress interventionsrdquo
Brief Treatment and Crisis Intervention Vol 3 No 1 pp 47-53
Becker G (1985) ldquoHuman capital effort and the sexual division of laborrdquo The Journal of
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Bonkiewicz L (2015) ldquoBobbies and baseball players Evaluating patrol officer productivity
using sabermetricsrdquo Police Quarterly Vol 18 No 1 pp 55-78
Braga A and Bond J (2008) ldquoPolicing crime and disorder hot spots A randomized controlled
trialrdquo Criminology Vol 46 No 3 pp 577-607
Brown J and Campbell E (1990) ldquoSources of occupational stress in the policerdquo Work
and Stress Vol 4 No 4 pp 305-318
Chappell A MacDonald J and Manz P (2006) ldquoThe organizational determinants
of police arrest decisionsrdquo Crime and Delinquency Vol 52 No 2 pp 287-306
Cloninger D and Sartorius L (1979) ldquoCrime rates clearance rates and enforcement effort
The case of Houston Texasrdquo American Journal of Economics and Sociology Vol 38
No 4 pp 389-402
Corman H and Mocan N (2005) ldquoCarrots sticks and broken windowsrdquo Journal of Law and
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Crank J (1990) ldquoThe influence of environmental and organizational factors on police style in
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pp 166-189
Goodman A (1990) ldquoA model for police officer burnoutrdquo Journal of Business and
Psychology Vol 5 No 1 pp 85-99
Gould E Weinberg B and Mustard D (2002) Crime rates and local labor marketopportunities in the United States 1979ndash1997rdquo Review of Economics and Statistics Vol
84 No 1 pp 45-61
Hipp J and Roussell A (2013) ldquoMicro-and macro-environment population and the
consequences for crime ratesrdquo Social Forces Vol 92 No 2 pp 563-595
Hatry H (1976) ldquoApproaches to productivity measurement and program evaluationrdquo Public
Productivity Review Vol 1 No 3 pp21-28
Hawkins H (2001) ldquoPolice officer burnout a partial replication of Maslachs Burnout
Inventoryrdquo Police Quarterly Vol 4 No 3 pp 343-360
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Herbert S (1998)ldquoPolice subculture reconsideredrdquo Criminology Vol 36 No 2 pp 343-370
Huizinga D and Henry K (2008) ldquoThe effect of arrest and justice system sanctions on
subsequent behavior Findings from longitudinal and other studiesrdquo In The long view of
crime A synthesis of longitudinal research Springer New York pp 220-254
Johnson R (2011) ldquoOfficer attitudes and management influences on police work productivityrdquo
American Journal of Criminal Justice Vol 36 pp 293-306
Kane R (2006) ldquoOn the limits of social control Structural deterrence and the policing of
lsquosuppressiblersquo crimesrdquo Justice Quarterly Vol 23 No 2 pp 186-213
Kubrin C Messner S Deane G McGeever K and Stucky T (2010) ldquoProactive
policing and robbery rates across US citiesrdquo Criminology Vol 48 No 1 pp 57-97
Lersch K (2002) ldquoAre citizen complaints just another measure of officer productivity An
analysis of citizen complaints and officer activity measuresrdquo Police Practice and
Research Vol 3 pp 135-147
Light M and Harris C (2012) Race space and violence exploring spatial dependence in
structural covariates of white and black violent crime in US countiesrdquo Journal of
Quantitative Criminology Vol 28 No 4 pp 559-586
Maxfield M (1982) ldquoService time dispatch time and demand for police services Helping
more by serving lessrdquo Public Administration Review pp 252-263
Maxfield M Lewis D and Szoc R (1980) ldquoProducing official crimes Verified crime reports
as measures of police outputrdquo Social Science Quarterly pp 221-236
Nakosteen R and Zimmer M (2001) ldquoSpouse selection and earnings Evidence of marital
sortingrdquo Economic InquiryVol 39 No 2 pp 201-2013
Shane J (2010) ldquoOrganizational stressors and police performancerdquo Journal of Criminal
Justice Vol 38 No 4 pp 807-818
Shane J (2011) ldquoDaily work experiences and police performancerdquo Police Practice and
Research Vol 13 No 3 pp 1-19
Sherman L Gartin P and Buerger M (1989) ldquoHot spots of predatory crime Routine
activities and the criminology of placerdquoCriminology Vol 27 No 1 pp27-56
Truman J and Rand M (2010) ldquoBureau of Justice Statistics Bulletin National Crime
Victimization Survey Criminal Victimization 2009rdquo US Department of Justice Office of
Justice programs
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Van Meter D (2001) Evaluating dysfunctional police performance A zero-based approach
Charles C Thomas Publisher Springfield IL
Vila B and Kenney D (2002) ldquoTired cops The prevalence and potential consequences of
police fatiguerdquo National Institute of Justice
Vila B Morrison G and Kenney D (2002) ldquoImproving shift schedule and work-hour
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Vollaard B and Koning P (2009) ldquoThe effect of police on crime disorder and victim
PrecautionEvidence from a Dutch victimization surveyrdquo International Review of Law
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Weisburd D Maher L Sherman L Buerger M Cohn E and Petrisino A (1993)
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New Directions in Criminological Theory Advances in Criminological Theory Vol 4
pp 45-70
Author Biography
Luke Bonkiewicz is a patrol officer with the Lincoln Police Department
7252019 PIJPSM-05-2015-0064
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983101 983089983088983088lowast + + ) + ) + ) + ) lowast )) minus + + + 983090983088 + ) +983089983088)) minus 983089983088983088 lowast
983086 983097) lowast )
7252019 PIJPSM-05-2015-0064
httpslidepdfcomreaderfullpijpsm-05-2015-0064 2830
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983119983142983142983145983139983141983154 983120983154983151983155983141983139983157983156983145983151983150
983122983137983156983141C983110983123
983117983145983150983157983156983141983155983110983125983120
983117983145983150983157983156983141983155
983119983142983142983145983139983141983154
A983155983155983145983155983156983117983145983150983157983156983141983155
983113983150983139983145983140983141983150983156983122983141983152983151983154983156983155
AC983113983122983141983152983151983154983156983155 983123983141983148983141983139983156983145983158983141983155 983127983137983154983150983145983150983143983155 983119983142983142983145983139983145983137983148983155 983127983137983154983154983137983150983156983155 D983125983113983155 983117983145983155983140983141983149983141983137983150983151983154983155 983110983141983148983151983150983145983141983155
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3 72 32496 6312 6600 144 564 444 720 540 84 36 24 12
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5 91 41724 4824 6048 312 636 132 504 324 108 7 24 24
6 83 23916 7056 6192 144 756 528 996 336 132 36 72 12
7 95 24468 6840 5892 156 624 324 456 480 72 36 120 24
8 90 39972 5196 4920 240 504 384 492 264 36 2 72 24
9 79 25500 4068 5100 336 396 192 684 204 24 36 120 10
10 87 21984 5412 4524 348 552 480 792 312 108 48 84 36
983124983137983138983148983141 2 983123983157983149983149983137983154983161 983123983156983137983156983145983155983156983145983139983155 983142983151983154 983126983137983154983145983137983138983148983141983155
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983120983151983152983157983148983137983156983145983151983150 983140983141 983150983155983145983156983161 20229830856956 1687 4436
983112983151983157983155983141983144983151983148983140 983149983141983140983145983137983150 983145983150983139983151983149983141 ($) 2628198308576020 16535 49756
983159983144983145983156983141 7398308593 007 083
983126983145983151983148983141983150983156 983139983154983145983149983141 983154983137983156983141 (983152983141983154 100000) 8009830854800 1203 2521
983120983154983151983152983141983154983156983161 983139983154983145983149983141 983154983137983156983141 (983152983141983154 100000) 31009830859800 2027 6524
983120983154983151983152983141983154983156983161 983139983154983145983149983141983155 983152983141983154 983151983142983142983145983139983141983154 5329830851097 1663 8167
983126983145983151983148983141983150983156 983139983154983145983149983141 983152983141983154 983151983142983142983145983139983141983154 194983085372 559 2929
983110983145983154983155983156983085983116983141 983158983141983148 983126 983137983154983145983137983138983148983141 983155 (983119983142983142983145983139983141983154) 983118=302 983122983137983150983143983141 983123D 983117983141 983137983150
983111983141983150983140983141983154 (983142983141983149983137983148983141=0 983149983137983148983141=1) 09830851 24 94
983117983137983154983154983145983141983140C983151983144983137983138983145983156983145983150983143 09830851 18 79
983151983142 C983144983145983148983140983154983141983150 09830855 170 90
983129983141983137983154983155 983141983160983152983141983154983145983141983150983139983141 198308537 779 1063
C983137983148983148983155 983142983151983154 983155983141983154983158983145983139983141 4819830851011 16121 61365
983124983154983137983142983142983145983139 983155983156983151983152983155 16983085901 14376 20624
983110983145983141983148983140 983145983150983142983151983154983149983137983156983145983151983150 983154983141983152983151983154983156983155 0983085227 2153 2530
983124983154983137983142983142983145983139 983139983145983156983137983156983145983151983150983155 249830851246 14632 18642
983117983145983155983140983141983149983141983137983150983151983154 983137983154983154983141983155983156983155 19983085403 5965 10663
983110983141983148983151983150983161 983137983154983154983141983155983156983155 098308571 1066 852
983126983119983122C 9830851959830851043 151 24
1983155983156 2983150983140 3983154983140
104 96 102983123983144983145983142983156
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983123983141983139983151983150983140983085983116983141983158983141983148 983126983137983154983145983137983138983148983141983155 (983120983137983156983154983151983148 A983154983141983137) 983118=40 983138 983123E 983138 983123E 983138 983123E 983138 983123E
983120983151983152983157983148983137983156983145983151983150 983140983141983150983155983145983156983161 001 003 001 001 98308501 01 00 00
983112983151983157983155983141983144983151983148983140 983149983141983140983145983137983150 983145983150983139983151983149983141 ($) 002 001 983085000 000 00 00 98308500 00
983159983144983145983156983141 13363 32472 9830858921 5664 9830853274 19211 9830854447 3255
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983126983145983151983148983141983150983156 983139983154983145983149983141 983152983141983154 983151983142983142983145983139983141983154 983085168 68 29 14 76 31 22 12
983110983145983154983155983156983085983116983141983158983141983148 983126983137983154983145983137983138983148983141983155 (983119983142983142983145983139983141983154) 983118=302
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983129983141983137983154983155 983141983160983152983141983154983145983141983150983139983141 9830851065 492 98308512 13 983085163 33 98308506 05C983137983148983148983155 983142983151983154 983155983141983154983158983145983139983141 24 13 02 02 14 01 98308508 05
983124983154983137983142983142983145983139 983155983156983151983152983155 983085983085 983085983085 03 00 09 02 00 00
983110983145983141983148983140 983145983150983142983151983154983149983137983156983145983151983150 983154983141983152983151983154983156983155 209 33 20 04 79 19 10 03
983123983141983139983151983150983140 983155983144983145983142983156 (983154983141983142983141983154983141983150983139983141 983143983154983151983157983152 983142983145983154983155983156) 2603 1778 187 195 809 730 147 160
983124983144983145 983154983140 983155983144983145 983142983156 (983154983141 983142983141 983154983141 983150983139983141 983143983154983151983157983152 983142983145 983154983155983156) 9872 2155 79 213 2532 952 393 132
983113983150983156983141983154983139983141983152983156 32591 5361 758 339 4147 1912 310 484
983123983145983143983149983137983085983155983153983157983137983154983141983140 1541467 19287 206702 5598
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983118983151983156983141 A983148983148 983158983137983154983145983137983138983148983141983155 983137983154983141 983157983150983139983141983150983156983141983154983141983140
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7252019 PIJPSM-05-2015-0064
httpslidepdfcomreaderfullpijpsm-05-2015-0064 3030
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983123983141983139983151983150983140983085983116983141983158983141983148 983126983137983154983145983137983138983148983141983155 (983120983137983156983154983151983148 A983154983141983137) 983118=40 983138 983123E
983120983151983152983157983148983137983156983145983151983150 983140983141983150983155983145983156983161 98308500 00
983112983151983157983155983141983144983151983148983140 983149983141983140983145983137983150 983145983150983139983151983149983141 ($) 00 01
983159983144983145983156983141 318 345
983126983145983151983148983141983150983156 983139983154983145983149983141 983154983137983156983141 (983152983141983154 100000) 98308500 00
983120983154983151983152983141983154983156983161 983139983154983145983149983141 983154983137983156983141 (983152983141983154 100000) 00 00
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983126983145983151983148983141983150983156 983139983154983145983149983141 983152983141983154 983151983142983142983145983139983141983154 02 05
983110983145983154983155983156983085983116983141983158983141983148 983126983137983154983145983137983138983148983141983155 (983119983142983142983145983139983141983154) 983118=302
983117983137983148983141 27 15
983117983137983154983154983145983141983140C983151983144983137983138983145983156983145983150983143 98308532 31
983151983142 C983144983145983148983140983154983141983150 05 03
983129983141983137983154983155 983141983160983152983141983154983145983141983150983139983141 00 01
C983137983148 983148983155 983142983151983154 983155983141983154983158983145983139983141 02 01
983124983154983137983142983142983145983139 983155983156983151983152983155 01 00
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983123983145983143983149983137983085983155983153983157983137983154983141983140 66
C983144983145983085983155983153983157983137983154983141983140 9846
983152 lt 05
983152lt01
983152lt001
983118983151983156983141 A983148983148 983158983137983154983145983137983138983148983141983155 983137983154983141 983157983150983139983141983150983156983141983154983141983140
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cases involving extensive follow-up or knowledge on a specific subject such as gangs or
narcotics)
By contrast when patrol officers in a specialist-style police department respond to any
incident potentially requiring follow-up they might only contact the victim and either hold the
scene until an investigator arrives or forward the information to a detective for follow-up It is
possible indeed likely that macro-level variables might affect generalist patrol officers
differently than specialized officers Due to data limitations however our study focuses on
generalist police officers
Data and Methods
We gathered data on patrol officers and their patrol areas from several sources First we
collected patrol officer productivity data from the Lincoln (Nebraska) Police Departmentrsquos 2013-
2014 productivity reports Using Computer-Aided Dispatch (CAD) and a centralized
productivity database LPD measures numerous patrol officer activities Every month (and
year) LPD generates productivity reports which display each patrol officerrsquos activities and
outputs (see Table 1 for an example of a squadrsquos annual productivity report) Each patrol
activity and output merits further explanation
INSERT TABLE 1 HERE
Calls for Service (CFS) and Officer Assist time When dispatchers send patrol officers on
a call they use CAD to track the minutes the officer spends on the call If the call is a priority
incident dispatchers send both a primary and secondary officer Other unassigned officers might
also respond to the CFS meaning that multiple officers could be secondary officers The annual
productivity record tracks amount of time an officer spent as a primary responder and a
secondary responder
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Report writing time Standard operating procedures dictate that LPD patrol officers
complete specific types of reports for incidents An incident report (IR) details biographical
information of the individuals involved in the incident and summarizes the incident An
Additional Case Information (ACI) is a more detailed report that documents additional
investigative steps or the actions of assisting officers Although the time spent on an IR or ACI
could vary depending on the call for service the average IR and ACI take roughly twenty
minutes to complete1
The Lincoln Police Department also tracks self-initiated activities Most productivity
results from the following proactive activities
Selectives The Lincoln Police Department tracks specific officer patrol activity by
coding its neighborhoods schools parks and business districts When an officer patrols an at-
risk business district or a high-crime neighborhood for example the officer notifies dispatch
using that arearsquos specific code Girded by the principles of ldquohot spotsrdquo policing research officers
use selectives to identify potential candidates for POP projects and heighten the perception of
risk among criminals (Braga amp Bond 2008)
Traffic Warnings and Official Citations Every Lincoln patrol officer drives a police
cruiser and conducts traffic enforcement issuing traffic warnings and official citations The
LPD records department tracks patrol officersrsquo warnings and citations
Misdemeanors felonies and warrants Patrol officers also make misdemeanor and
felony arrests during their productive time Officers might detain a motorist and arrest him or
her for driving under a suspended license or driving while intoxicated or contact a pedestrian
1 983119983142983142983145983139983141983154983155 983156983161983152983141 983137983148983148 983154983141983152983151983154983156983155 983151983150 983139983151983149983152983157983156983141983154983155 983127983141 983151983138983156983137983145983150983141983140 983156983144983145983155 983137983158983141983154983137983143983141 983138983161 983151983138983155983141983154983158983145983150983143 983137 983155983137983149983152983148983141 983151983142 983116983145983150983139983151983148983150 983152983137983156983154983151983148 983151983142983142983145983139983141983154983155
983139983151983149983152983148983141983156983145983150983143 983113983122983155 983137983150983140 983105983107983113983155 983137983150983140 983159983141 983154983141983139983151983143983150983145983162983141 983156983144983137983156 983156983144983145983155 983137983158983141983154983137983143983141 983149983137983161 983140983145983142983142983141983154 983142983154983151983149 983151983156983144983141983154 983152983151983148983145983139983141 983140983141983152983137983154983156983149983141983150983156983155 983117983151983154983141983151983158983141983154983145983142 983140983141983152983137983154983156983149983141983150983156983155 983154983141983153983157983145983154983141 983151983142983142983145983139983141983154983155 983156983151 983139983151983149983152983148983141983156983141 983156983144983141983145983154 983154983141983152983151983154983156983155 983138983141983142983151983154983141 983138983141983139983151983149983145983150983143 983137983158983137983145983148983137983138983148983141 983142983151983154 983137983150983151983156983144983141983154 983139983137983148983148 983142983151983154 983155983141983154983158983145983139983141
983156983144983145983155 983149983141983137983155983157983154983141983149983141983150983156 983145983155 983157983150983150983141983139983141983155983155983137983154983161
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and make an arrest for a sex offender registration violation Officers can also search their
departmentrsquos electronic database for individuals with outstanding warrants in their patrol areas
and attempt to arrest these offenders As with warnings and citations the LPD records
department documents and tracks misdemeanor felony and warrant arrests Moreover the court
system also monitors these types of outputs to process cases thereby adding an additional layer
of reporting accuracy and reliability
Field information reports We also measured the number of field information or
ldquointelligencerdquo reports Officers complete these internal reports to document suspicious or
dangerous individuals or circumstances though these reports are not publicly available
VORC In addition to the raw outputs of traffic citations warrants misdemeanors and
felonies we also include an advanced metric for measuring a patrol officerrsquos productivity Value
Over Replacement Cop (VORC) is a method for evaluating officers that assigns different
weights to productivity outputs and then assesses officer productivity in terms of available time
for self-initiated activities or productive time (Bonkiewicz 2015) We use a variation of this
formula that multiplies an officerrsquos officials misdemeanors and felonies by his or her
prosecution rate Our modified use of VORC can best be explained using the following figure
(Figure 1) and five-step process
INSERT FIGURE 1 HERE
where = total monthly on-duty minutes = CFS minutes Γ = follow-up time and meetings ∆
= officer assist time Ε = of IRs Ζ = of ACIs M = of intelligence reports Η = of
selectives Θ = of warnings times two Κ = of warrants times three Ι = of officials times
three Λ = of misdemeanor arrests times their respective weights Ν = of felonies types their
respective weights Ρ = an officerrsquos prosecution rate Ξ = Officerrsquos Productive time (Α-(Β+Γ+∆+
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20(Ε+Ζ))) Ο= Department average P-score or average (Η+ ((Θ)+(Ι)+(Κ)+(Λ)+(Μ)+(Ν)Ρ))
and Π = Department average Self-Initiated Time
First VORC calculates an officerrsquos total minutes spent on CFS follow-ups officer
assists and reports This amount is an officerrsquos total work time or service minutes Second the
formula subtracts an officerrsquos service minutes from the total on-duty minutes2 This is the
officerrsquos amount of productive time Third VORC calculates an officerrsquos productivity score (or
P-score) using a weighted index Departments can choose different weights for productivity
outputs but for the purpose of this study we used our weights based on the statutes governing
the Lincoln Police Department Citations and arrests are weighted based on whether the offense
was a Traffic Offense a Class III II or I Misdemeanor or a Class V IV III II or I Felony
Accordingly a patrol selective is worth one point a traffic warning two points a traffic citation
three points a warrant arrest three points a Class III misdemeanor arrest four points a Class II
misdemeanor arrest five points a Class I misdemeanor arrest six points a Class V Felony seven
points a Class IV Felony eight points a Class III Felony nine points and Class II and I Felonies
(eg homicides) ten points3 The sum of the weighted productivity outputs is the officerrsquos P-
score The P-score is divided by amount of productive time and then multiplied by 100 (for ease
of understanding) This value the productivity quotient represents the officerrsquos productivity
given the available minutes for proactivity such as searching for wanted individuals or traffic
enforcement
Fourth VORC creates a hypothetical replacement officer against which to measure the
patrol officerrsquos output This replacement player might be imagined as a rookie patrol officer or
983090 983119983150983085983140983157983156983161 983156983145983149983141 983140983151983141983155 983150983151983156 983145983150983139983148983157983140983141 983156983154983137983145983150983145983150983143 983156983145983149983141 983158983137983139983137983156983145983151983150 983151983154 983144983151983148983145983140983137983161 983140983137983161983155 983151983142983142 983151983154 983155983145983139983147 983148983141983137983158983141 983113983150 983151983156983144983141983154 983159983151983154983140983155 983151983150983085983140983157983156983161
983156983145983149983141 983145983155 983156983144983141 983137983149983151983157983150983156 983151983142 983156983145983149983141 983137 983152983137983156983154983151983148 983151983142983142983145983139983141983154 983155983152983141983150983156 983159983151983154983147983145983150983143 983156983144983141 983155983156983154983141983141983156983091 983119983157983154 983159983141983145983143983144983156983145983150983143 983155983161983155983156983141983149 983145983155 983151983150983148983161 983151983150983141 983141983160983137983149983152983148983141 983151983142 983156983144983141 983142983148983141983160983145983138983145983148983145983156983161 983156983144983137983156 983126983119983122983107 983151983142983142983141983154983155 983140983141983152983137983154983156983149983141983150983156983155983099 983141983137983139983144 983137983143983141983150983139983161 983139983137983150
983152983154983145983151983154983145983156983145983162983141 983137983150983140 983137983155983155983145983143983150 983139983151983154983154983141983155983152983151983150983140983145983150983143 983159983141983145983143983144983156983155 983156983151 983156983144983141983145983154 983152983154983141983142983141983154983154983141983140 983152983154983151983140983157983139983156983145983158983145983156983161 983151983157983156983152983157983156983155
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a recruit who has not completed the academy performing at ninety percent the capacity of fully
trained police officer4 Using LPDrsquos data VORC creates a hypothetical replacement officer by
dividing the departmental average P-score by the departmental average for productive time and
then multiplying it by the officerrsquos productive time to determine what an average officer would
have generated given the officerrsquos amount of productive time This figure is then multiplied by
9 (the capacity of a replacement officer) divided by the officerrsquos productive time and then
multiplied by 100 to generate the replacement officerrsquos productivity quotient Finally VORC
subtracts the replacement officerrsquos score from the patrol officerrsquos score to calculate the officerrsquos
value over the replacement officer This number or VORC indicates the officerrsquos productivity
per time for self-initiated activities compared to the replacement officerrsquos productivity For
example a VORC of 12 means than officer would produce 012 more productivity points (some
combination of warnings citations and arrests) per minute than a replacement officer
Biographical variables We gathered biographical information about the officers in the
sample including gender years of LPD experience maritalcohabiting status and number of
children We also controlled for whether the officer worked first second or third shift because
our data showed significant differences in the number of calls for service each shift handled as
well as the number of reports each shift completed
Geographic variables It is necessary to measure the characteristics of patrol areas to
account for variation across patrol areas For example some areas may feature different crime
rates or levels of social disorganization which command more department resources and police
officers We gathered data by first mapping out every squadrsquos patrol area and then obtaining
tract-level data from the 2010 US Census Our models included measures of established of
983092
983123983139983144983151983148983137983154983155 983137983150983140 983152983151983148983145983139983161 983149983137983147983141983154983155 983149983137983161 983140983145983155983137983143983154983141983141 983159983145983156983144 983141983155983156983145983149983137983156983145983150983143 983137 983154983141983152983148983137983139983141983149983141983150983156 983151983142983142983145983139983141983154991257983155 983139983137983152983137983139983145983156983161 983137983156 983150983145983150983141983156983161 983152983141983154983139983141983150983156 983119983157983154983143983151983137983148 983145983155 983150983151983156 983156983151 983140983141983142983145983150983145983156983145983158983141983148983161 983141983155983156983145983149983137983156983141 983137 983144983161983152983151983156983144983141983156983145983139983137983148 983151983142983142983145983139983141983154991257983155 983152983154983151983140983157983139983156983145983158983145983156983161 983138983157983156 983149983141983154983141983148983161 983156983151 983141983155983156983137983138983148983145983155983144 983137 983154983141983137983155983151983150983137983138983148983141 983142983145983160983141983140
983152983151983145983150983156 983137983143983137983145983150983155983156 983159983144983145983139983144 983156983151 983139983151983149983152983137983154983141 983137983148983148 983152983137983156983154983151983148 983151983142983142983145983139983141983154983155
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correlates of crime including population density (Hipp amp Roussell 2013) household income
(Gould Weinberg and Mustard 2002) and racial composition (Light amp Harris 2012)
Specifically we use each arearsquos population density median household income and percent
white We also included measures for each arearsquos violent and property crime rate Finally we
gathered information about the number of violent crimes (murders assaults aggravated assaults
sexual assaults and robberies) property crimes (larcenies burglaries vandalisms frauds
forgeries and motor vehicle thefts) and number of police officers in each patrol area thereby
creating variables for violent crime per officer and property crime per officer
Since LPDrsquos patrol officers are grouped with squads it is reasonable to believe that an
officerrsquos productivity might be more similar to an officer within their squad compared to an
officer in a different squad To account for the potential correlations between the individual
units as well as the multi-level nature of the data we used hierarchical linear modeling to
analyze the data
Our primary level variables include an officerrsquos gender marital status number of
children years of law enforcement experience shift calls for service annual number of traffic
stops and citations field information reports and misdemeanor felony and warrant arrests We
also used the officersrsquo productivity information to calculate each patrol officerrsquos VORC
Our secondary level variables include the violent crime per officer rate and property
crime per officer rate for the patrol area of each squad as well several control variables
including population density household median income percent white and the patrol arearsquos
violent and property crime rate Table 2 displays the summary statistics for the first and second-
level variables
INSERT TABLE 2 HERE
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Results
We hypothesized that the violent and property crime-to-cop ratios of an area might affect
the raw productivity outputs of patrol officers as well as a more advanced productivity metric
To examine these questions we constructed five hierarchical linear models estimating the
correlations between reported violent and property crimes per officer and a patrol officerrsquos
annual traffic citations warrant misdemeanor felony arrests and Value Over Replacement Cop
Although not presented here we estimated null models for all dependent variables and found that
significant variation existed between squads when we included no variables included in the
models The chi-square for the LR test for all five null models was statistically significant (p lt
000 in all models)
Table 3 shows that a rise in reported violent crimes per officer increases an officerrsquos
number of warrants and misdemeanors (b= 29 and 76) while arise in reported property crimes
per officer decreases an officerrsquos number of warrants and misdemeanors (b= -15 and -52) We
found no relationship between the crime-to-cop ratios and felony arrests However both the
rates of violent and property crimes per officer decreased the number of traffic citations an
officer writes (b= -168 and -225)
Insert Table 3 here
Table 3 also shows that traffic stops and field information reports are positively
associated with warrant and misdemeanor arrests This supports what police practitioners
already suspect the cops with the most arrests are extremely proactive patrol officers who stop a
large number of vehicles and build investigations through the use of field contacts and
information reports For instance an officer can build felony narcotics cases by contacting
7252019 PIJPSM-05-2015-0064
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suspects and informants in the field documenting these efforts through field information reports
and eventually using this information to write search warrants and conduct drug interdiction
Next we investigated whether this relationship between the crime-to-cop ratios and
productivity outputs exists when using an advanced productivity metric specifically an officerrsquos
Value Over Replacement Cop (VORC)
INSERT TABLE 4 HERE
Table 4 shows that there is no correlation between the ratio of property and violent
crimes per officer and VORC Although this stands in contrast to our earlier results we would
not expect to find a relationship between these variables and VORC Advanced metrics such as
VORC evaluate officers based on the amount of time for self-initiated activities (productive
time) and not raw productivity outputs like misdemeanor or felony arrests Even if an officer
took an extremely large number of property crimes thereby decreasing their productive time
VORC would only evaluate that officer based on what the officer produced with the small
amount of productive time This suggests that while a teamrsquos crime-to-cop ratio could affect an
officerrsquos raw productivity outputs ie arrests a teamrsquos crime-to-cop ratio would not affect an
officerrsquos VORC
For example during a given month an officer may take an unusually large number of
burglaries or larcenies thereby leaving the officer with only ten hours for self-initiated activities
With this reduced amount of productive time the officer may generate a below-average number
of officials warrants misdemeanors and felonies which in turn may lead a supervisor to
potentially downgrade the officerrsquos productivity However VORC would recognize and account
for the high number of calls for service minutes and analyze the officerrsquos productivity based on
what the officer generated with only ten hours of productive time It may be that the officer was
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actually very productive but they simply didnrsquot have enough productive time to generate the
same number of traffic citations and arrests as an officer who responded to far fewer property
crimes
Table 4 also shows that traffic stops and field intelligence reports are positively
correlated with VORC These results are consistent with the principles of how VORC evaluates
patrol officer productivity and provides additional support for the validity of this advanced
metric VORC accounts for numerous variables that affect productive time (ie calls for service
minutes) meaning that the key factors which increase or decrease an officerrsquos VORC are largely
related to self-initiated activities (ie traffic stops and information reports) VORC therefore
accounts for the potential effects of macro-level variables and analyzes the activities of the
officer during his or her productive time Nonetheless we recognize that because traffic stops
and field information reports are correlated with VORC an arearsquos crime-to-cop ratio might still
affect an officerrsquos VORC indirectly by impacting the number of stops and information reports
they generate
Discussion
There are several factors which affect a patrol officerrsquos productivity including
biographical characteristics officersrsquo perceptions and attitudes and organizational and
operational features among others This paper identified two macro-level variablesmdashproperty
crimes per officer and violent crimes per officermdashwhich affect the number of traffic citations
warrants misdemeanors and felonies generated annually by an officer Using data from a mid-
sized police department we found that a patrol areas rate of property crimes per officer was
associated with a moderate decrease in an officers annual number of traffic citations warrant
arrests and misdemeanor arrests while a patrol areas rate of violent crimes per officer was also
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associated with a moderate decrease in an officers annual number of traffic citations We also
found that a patrol areas rate of violent crimes per officer is associated with a moderate increase
in an officers annual number of warrant and misdemeanor arrests However when we used a
more advanced productivity metric we found no relationship between property and violent
crimes per officer and an officerrsquos Value Over Replacement Cop or VORC
There are several factors which explain the differing effects of the crime-to-cop ratios on
productivity outputs Let us first address why the violent crime-to-cop ratio would increase a
patrol officerrsquos warrants and misdemeanors while the property crime-to-cop ratio would decrease
these outputs Notably half of all victims of violent crimes know the offender (Truman amp Rand
2010) which aids law enforcement in identifying and locating the perpetrator Moreover both
suspect and victim are frequently on scene when police officers arrive such as during domestic
disturbances Being able to identify and locate the perpetrator helps patrol officers make arrests
thereby increasing their misdemeanor and felony arrests in violent crimes Additionally when
patrol officers arrive and contact individuals during an incident it is standard procedure to check
if the parties involved have outstanding warrants This means that the greater number of violent
crimes that officers respond to the more chances they have to arrest individuals who might have
outstanding warrants It makes sense then that officers who respond to a greater number of
violent crimes have more chances to make warrant and misdemeanor arrests
Conversely property crimes may supply the officer with fewer warrant and misdemeanor
arrests because of low clearance rates We examined the difference in clearance rates between
property and violent crime using incident data from the Lincoln Police Department and we
found that the clearance rate for violent crimes was 418 compared to only 237 for property
crimes Moreover property crimes often also require more investigative time than violent
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crimes Property crime investigations often require considerable follow-up by officers such as
processing a scene contacting financial institutions and checking pawnshops for stolen property
In general property crimes are more time-intensive than violent crimes thereby decreasing
productive time and opportunities for proactive activities while simultaneously netting officers
fewer arrests because of low clearance rates
The impact of a teamrsquos crime-to-cop ratio can also be seen in the relationship between
property and violent crime per officer and the number of traffic citations Table 3 shows that a
rise in both reported violent and property crimes per officer decrease an officerrsquos number of
traffic citations Quite simply if officers respond to a high number of property and violent
crimes then they will have fewer minutes to conduct traffic enforcement and other proactive
enforcement
As noted it is possible that the correlation is bi-directional If an officer makes a greater
number of arrests then a team may witness a decrease in crime because prolific criminals may
have been arrested and placed in jail For example one might interpret Table 3 to mean that an
increase in warrant and misdemeanor warrants led to a decrease in the number of property crimes
per officer However under this hypothesis we should also see that an increase in warrant and
misdemeanor arrests leads to a decrease in the number of violent crimes per officer Instead we
see the opposite hypothesized effect ie more arrests leading to more reported violent crimes 5
983093 983127983141 983139983151983150983155983156983154983157983139983156983141983140 983154983141983143983154983141983155983155983145983151983150 983149983151983140983141983148983155 983157983155983145983150983143 983139983145983156983137983156983145983151983150983155 983159983137983154983154983137983150983156 983137983154983154983141983155983156983155 983149983145983155983140983141983149983141983137983150983151983154 983137983154983154983141983155983156983155 983137983150983140 983142983141983148983151983150983161 983137983154983154983141983155983156983155 983142983154983151983149
983156983144983141 983152983154983141983139983141983140983145983150983143 983149983151983150983156983144 983156983151 983152983154983141983140983145983139983156 983138983151983156983144 983158983145983151983148983141983150983156 983137983150983140 983152983154983151983152983141983154983156983161 983139983154983145983149983141 983154983137983156983141983155 983142983151983154 983156983144983141 983149983151983150983156983144983155 983145983150 983151983157983154 983140983137983156983137983155983141983156 983127983141 983142983151983157983150983140 983150983151
983155983156983137983156983145983155983156983145983139983137983148983148983161 983155983145983143983150983145983142983145983139983137983150983156 983154983141983148983137983156983145983151983150983155983144983145983152983155 983138983141983156983159983141983141983150 983139983145983156983137983156983145983151983150983155 983159983137983154983154983137983150983156 983137983154983154983141983155983156983155 983149983145983155983140983141983149983141983137983150983151983154 983137983154983154983141983155983156983155 983137983150983140 983142983141983148983151983150983161 983137983154983154983141983155983156983155
983137983150983140 983156983144983141 983158983145983151983148983141983150983156 983137983150983140 983152983154983151983152983141983154983156983161 983139983154983145983149983141 983154983137983156983141983155 983151983142 983156983144983141 983138983141983137983156983155 983112983151983159983141983158983141983154 983137983142983156983141983154 983140983145983155983137983143983143983154983141983143983137983156983145983150983143 983156983144983141 983158983145983151983148983141983150983156 983137983150983140 983152983154983151983152983141983154983156983161
983139983154983145983149983141 983154983137983156983141983155 983145983150983156983151 983155983152983141983139983145983142983145983139 983139983154983145983149983141983155 983159983141 983140983145983140 983142983145983150983140 983156983144983137983156 983149983145983155983140983141983149983141983137983150983151983154 983137983150983140 983142983141983148983151983150983161 983137983154983154983141983155983156983155 983155983145983143983150983145983142983145983139983137983150983156983148983161 983152983154983141983140983145983139983156983141983140 983137 983140983141983139983154983141983137983155983141
983145983150 983154983151983138983138983141983154983161 983154983137983156983141983155 983159983144983145983139983144 983145983155 983139983151983150983155983145983155983156983141983150983156 983159983145983156983144 983156983144983141 983148983145983156983141983154983137983156983157983154983141 983105983140983140983145983156983145983151983150983137983148983148983161 983159983141 983145983150983156983141983154983158983145983141983159983141983140 983140983141983152983137983154983156983149983141983150983156 983152983141983154983155983151983150983150983141983148 983137983150983140983148983141983137983154983150983141983140 983156983144983137983156 983156983144983141 983116983145983150983139983151983148983150 983120983151983148983145983139983141 983108983141983152983137983154983156983149983141983150983156 983144983137983140 983155983156983137983142983142983141983140 983137 983156983141983137983149 983140983141983155983145983143983150983141983140 983155983152983141983139983145983142983145983139983137983148983148983161 983156983151 983137983152983152983154983141983144983141983150983140 983154983151983138983138983141983154983161
983155983157983155983152983141983139983156983155 983140983157983154983145983150983143 983156983144983145983155 983156983145983149983141983142983154983137983149983141 983159983144983145983139983144 983155983141983141983149983155 983156983151 983141983160983152983148983137983145983150 983156983144983141 983137983154983154983141983155983156983085983154983151983138983138983141983154983161 983154983137983156983141 983154983141983148983137983156983145983151983150983155983144983145983152
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We believe our explanation has a stronger theoretical foundation The ratio of property
crimes per officer affects productivity outputs differently than the ratio of violent crimes per
officer for two reasons First violent crimes are different in nature than property crimes
especially with regard to clearance rates Second property crimes often require more
investigative and follow-up time than violent crimes thereby reducing an officerrsquos amount of
productive time and proactive activity
Table 3 also provides a key piece of evidence to support this assertion Table 3 shows
that the number of reported violent crimes per officer increases an officerrsquos number of warrant
and misdemeanor arrests Yet the relationship is the opposite for traffic enforcement the violent
crime-to-cop ratio actually decreases traffic citations This change is due to the fact that while
violent crime investigations often yield warrant and misdemeanor arrests they do not produce
traffic citations Furthermore responding to a greater number of violent crimes decreases the
amount of available time to conduct traffic enforcement in turn reducing the number of traffic
citations In other words the violent crime-to-cop ratio affects traffic stops the same way that
the property crime-to-cop ratio affects all productivity outputs by reducing an officerrsquos
productive time and opportunities for proactivity In fact we found a moderate correlation
between a patrol arearsquos crime-to-cop ratio and the amount of productive time enjoyed by officers
who worked that area The correlation between an arearsquos violent crime-to-cop ratio and
productive time was -21 while the correlation between an arearsquos property crime-to-cop ratio and
productive time was -33 We believe this evidence indicates that officers who work in areas
with a high crime-to-cop ratio have less time for self-initiated activities which drives down
productivity
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We suggest that the number of police officers and crime rates of an area need to be
analyzed in conjunction with each other Prior theories of police productivity have viewed and
examined these variables separately when in reality we should view these variables in tandem
Combining these variables into crime-to-cop ratios provides a more nuancedmdashand accuratemdash
understanding of patrol activities and their relationship to productivity
Although we found strong evidence that an officerrsquos patrol area affects his or her
productivity (in terms of raw outputs) we also found that a few officer-level variables affected
productivity as well including traffic stops intelligence reports and working third shift Our
results appear to substantiate what many police practitioners already suspect officers who are
proactive in contacting citizens (traffic stops) and building investigations (intelligence reports)
generate more warrant and misdemeanor arrests In addition while some newer police officers
are forced to work third shift because they do not have sufficient seniority to bid for better days
off and shift times many officers work nights because they enjoy the types of calls (eg in-
progress felonies) meaning they may be more motivated to stop vehicles make field contacts
and in turn engage in activities that generate arrests (Brehm amp Gates 1993)
These results have important implications for police researchers and administrators
First our results suggest a need for a deeper understanding of how the staffing levels crime
rates and the other characteristics of a patrol area intersect Second if departments evaluate
their patrol officers using raw productivity outputs such as misdemeanor or felony arrests
administrators must recognize that factors beyond the control of patrol officers might affect
monthly or annual productivity Our research suggests that a patrol arearsquos crime-to-cop ratio can
substantially affect a patrol officerrsquos productivity regardless of the actual crime rate If the
characteristics of a patrol area affect raw productivity outputs police departments should employ
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more advanced productivity analytics (ie VORC) that account for activities which reduce
productive time and evaluate an officerrsquos productivity in terms of available minutes for
proactivity
Nonetheless evaluating both patrol officers and police departments using traffic citations
warrants misdemeanors and felonies is useful because this type of data is readily understood by
citizens and civic officials A neighborhood watch group for instance might care more about
the number of arrests their officers make and less about those officersrsquo productivity scores Our
data suggests that if citizens want patrol officers to write more traffic citations and make more
arrests they should lobby for more police officers on the street It is true that reducing an arearsquos
crime rate impacts the crime-to-cop ratio but our results indicate that putting police officers on
the street may be the most direct way to affect the crime-to-cop ratio
Our study is not without limitations We only examined a police department which uses
the generalist style of policing and it is unknown if our results are applicable to police
departments which use a specialist style of policing Theoretically a departmentrsquos policing
styles can affect factors that increase or decrease a patrol officerrsquos productive time It is possible
that the number of property crimes and violent crimes per officer do not significantly affect a
specialist patrol officerrsquos productivity outputs versus a generalist officerrsquos outputs Future
studies should investigate whether our results can be generalized to all police departments
We have only identified two macro-level variables which affect patrol officer
productivity and we recognize there may be other variables In addition we recommend that
future studies incorporate a wider array of variables and test competing models to determine
whether micro or macro-level variables exhibit stronger effects on productivity In the end
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managing patrol officers and improving productivity requires a greater understanding of both
patrol officers and the areas they work
Acknowledgements and Credits We would like to thank Denver Police Department Crime
Analyst Melissa Ohlhorst and Pennsylvania State University Professor Barry Ruback as well as
the three anonymous reviewers for their insightful comments We also salute Lincoln Police
Sergeant Jake Dilsaver for his logistical support of this project
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References
Amaranto E Steinberg J Castellano C and Mitchell R (2003) ldquoPolice stress interventionsrdquo
Brief Treatment and Crisis Intervention Vol 3 No 1 pp 47-53
Becker G (1985) ldquoHuman capital effort and the sexual division of laborrdquo The Journal of
Labor EconomicsVol 3 No 1 pp 33-58
Bonkiewicz L (2015) ldquoBobbies and baseball players Evaluating patrol officer productivity
using sabermetricsrdquo Police Quarterly Vol 18 No 1 pp 55-78
Braga A and Bond J (2008) ldquoPolicing crime and disorder hot spots A randomized controlled
trialrdquo Criminology Vol 46 No 3 pp 577-607
Brown J and Campbell E (1990) ldquoSources of occupational stress in the policerdquo Work
and Stress Vol 4 No 4 pp 305-318
Chappell A MacDonald J and Manz P (2006) ldquoThe organizational determinants
of police arrest decisionsrdquo Crime and Delinquency Vol 52 No 2 pp 287-306
Cloninger D and Sartorius L (1979) ldquoCrime rates clearance rates and enforcement effort
The case of Houston Texasrdquo American Journal of Economics and Sociology Vol 38
No 4 pp 389-402
Corman H and Mocan N (2005) ldquoCarrots sticks and broken windowsrdquo Journal of Law and
Economics Vol 48 No 1 pp 235-266
Crank J (1990) ldquoThe influence of environmental and organizational factors on police style in
urban and rural environmentsrdquo Journal of Research in Crime and Delinquency Vol 27
pp 166-189
Goodman A (1990) ldquoA model for police officer burnoutrdquo Journal of Business and
Psychology Vol 5 No 1 pp 85-99
Gould E Weinberg B and Mustard D (2002) Crime rates and local labor marketopportunities in the United States 1979ndash1997rdquo Review of Economics and Statistics Vol
84 No 1 pp 45-61
Hipp J and Roussell A (2013) ldquoMicro-and macro-environment population and the
consequences for crime ratesrdquo Social Forces Vol 92 No 2 pp 563-595
Hatry H (1976) ldquoApproaches to productivity measurement and program evaluationrdquo Public
Productivity Review Vol 1 No 3 pp21-28
Hawkins H (2001) ldquoPolice officer burnout a partial replication of Maslachs Burnout
Inventoryrdquo Police Quarterly Vol 4 No 3 pp 343-360
7252019 PIJPSM-05-2015-0064
httpslidepdfcomreaderfullpijpsm-05-2015-0064 2530
Herbert S (1998)ldquoPolice subculture reconsideredrdquo Criminology Vol 36 No 2 pp 343-370
Huizinga D and Henry K (2008) ldquoThe effect of arrest and justice system sanctions on
subsequent behavior Findings from longitudinal and other studiesrdquo In The long view of
crime A synthesis of longitudinal research Springer New York pp 220-254
Johnson R (2011) ldquoOfficer attitudes and management influences on police work productivityrdquo
American Journal of Criminal Justice Vol 36 pp 293-306
Kane R (2006) ldquoOn the limits of social control Structural deterrence and the policing of
lsquosuppressiblersquo crimesrdquo Justice Quarterly Vol 23 No 2 pp 186-213
Kubrin C Messner S Deane G McGeever K and Stucky T (2010) ldquoProactive
policing and robbery rates across US citiesrdquo Criminology Vol 48 No 1 pp 57-97
Lersch K (2002) ldquoAre citizen complaints just another measure of officer productivity An
analysis of citizen complaints and officer activity measuresrdquo Police Practice and
Research Vol 3 pp 135-147
Light M and Harris C (2012) Race space and violence exploring spatial dependence in
structural covariates of white and black violent crime in US countiesrdquo Journal of
Quantitative Criminology Vol 28 No 4 pp 559-586
Maxfield M (1982) ldquoService time dispatch time and demand for police services Helping
more by serving lessrdquo Public Administration Review pp 252-263
Maxfield M Lewis D and Szoc R (1980) ldquoProducing official crimes Verified crime reports
as measures of police outputrdquo Social Science Quarterly pp 221-236
Nakosteen R and Zimmer M (2001) ldquoSpouse selection and earnings Evidence of marital
sortingrdquo Economic InquiryVol 39 No 2 pp 201-2013
Shane J (2010) ldquoOrganizational stressors and police performancerdquo Journal of Criminal
Justice Vol 38 No 4 pp 807-818
Shane J (2011) ldquoDaily work experiences and police performancerdquo Police Practice and
Research Vol 13 No 3 pp 1-19
Sherman L Gartin P and Buerger M (1989) ldquoHot spots of predatory crime Routine
activities and the criminology of placerdquoCriminology Vol 27 No 1 pp27-56
Truman J and Rand M (2010) ldquoBureau of Justice Statistics Bulletin National Crime
Victimization Survey Criminal Victimization 2009rdquo US Department of Justice Office of
Justice programs
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httpslidepdfcomreaderfullpijpsm-05-2015-0064 2630
Van Meter D (2001) Evaluating dysfunctional police performance A zero-based approach
Charles C Thomas Publisher Springfield IL
Vila B and Kenney D (2002) ldquoTired cops The prevalence and potential consequences of
police fatiguerdquo National Institute of Justice
Vila B Morrison G and Kenney D (2002) ldquoImproving shift schedule and work-hour
policies and practices to increase police officer performance health and safetyrdquo Police
Quarterly Vol 5 No 1 pp 4-24
Vollaard B and Koning P (2009) ldquoThe effect of police on crime disorder and victim
PrecautionEvidence from a Dutch victimization surveyrdquo International Review of Law
and Economics Vol 29 No 4 pp 336-348
Weisburd D Maher L Sherman L Buerger M Cohn E and Petrisino A (1993)
ldquoContrasting crime general and crime specific theory The case of hot spots of crimerdquo
New Directions in Criminological Theory Advances in Criminological Theory Vol 4
pp 45-70
Author Biography
Luke Bonkiewicz is a patrol officer with the Lincoln Police Department
7252019 PIJPSM-05-2015-0064
httpslidepdfcomreaderfullpijpsm-05-2015-0064 2730
983110983145983143983157983154983141 983089983086 983126983119983122983107 983110983151983154983149983157983148983137
983101 983089983088983088lowast + + ) + ) + ) + ) lowast )) minus + + + 983090983088 + ) +983089983088)) minus 983089983088983088 lowast
983086 983097) lowast )
7252019 PIJPSM-05-2015-0064
httpslidepdfcomreaderfullpijpsm-05-2015-0064 2830
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983119983142983142983145983139983141983154 983120983154983151983155983141983139983157983156983145983151983150
983122983137983156983141C983110983123
983117983145983150983157983156983141983155983110983125983120
983117983145983150983157983156983141983155
983119983142983142983145983139983141983154
A983155983155983145983155983156983117983145983150983157983156983141983155
983113983150983139983145983140983141983150983156983122983141983152983151983154983156983155
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3 72 32496 6312 6600 144 564 444 720 540 84 36 24 12
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5 91 41724 4824 6048 312 636 132 504 324 108 7 24 24
6 83 23916 7056 6192 144 756 528 996 336 132 36 72 12
7 95 24468 6840 5892 156 624 324 456 480 72 36 120 24
8 90 39972 5196 4920 240 504 384 492 264 36 2 72 24
9 79 25500 4068 5100 336 396 192 684 204 24 36 120 10
10 87 21984 5412 4524 348 552 480 792 312 108 48 84 36
983124983137983138983148983141 2 983123983157983149983149983137983154983161 983123983156983137983156983145983155983156983145983139983155 983142983151983154 983126983137983154983145983137983138983148983141983155
983123983141983139983151983150983140983085983116983141983158983141983148 983126983137983154983145983137983138983148983141983155 (983120983137983156983154983151983148 A983154983141983137) 983118=40 983122983137983150983143983141 983123D 983117983141983137983150
983120983151983152983157983148983137983156983145983151983150 983140983141 983150983155983145983156983161 20229830856956 1687 4436
983112983151983157983155983141983144983151983148983140 983149983141983140983145983137983150 983145983150983139983151983149983141 ($) 2628198308576020 16535 49756
983159983144983145983156983141 7398308593 007 083
983126983145983151983148983141983150983156 983139983154983145983149983141 983154983137983156983141 (983152983141983154 100000) 8009830854800 1203 2521
983120983154983151983152983141983154983156983161 983139983154983145983149983141 983154983137983156983141 (983152983141983154 100000) 31009830859800 2027 6524
983120983154983151983152983141983154983156983161 983139983154983145983149983141983155 983152983141983154 983151983142983142983145983139983141983154 5329830851097 1663 8167
983126983145983151983148983141983150983156 983139983154983145983149983141 983152983141983154 983151983142983142983145983139983141983154 194983085372 559 2929
983110983145983154983155983156983085983116983141 983158983141983148 983126 983137983154983145983137983138983148983141 983155 (983119983142983142983145983139983141983154) 983118=302 983122983137983150983143983141 983123D 983117983141 983137983150
983111983141983150983140983141983154 (983142983141983149983137983148983141=0 983149983137983148983141=1) 09830851 24 94
983117983137983154983154983145983141983140C983151983144983137983138983145983156983145983150983143 09830851 18 79
983151983142 C983144983145983148983140983154983141983150 09830855 170 90
983129983141983137983154983155 983141983160983152983141983154983145983141983150983139983141 198308537 779 1063
C983137983148983148983155 983142983151983154 983155983141983154983158983145983139983141 4819830851011 16121 61365
983124983154983137983142983142983145983139 983155983156983151983152983155 16983085901 14376 20624
983110983145983141983148983140 983145983150983142983151983154983149983137983156983145983151983150 983154983141983152983151983154983156983155 0983085227 2153 2530
983124983154983137983142983142983145983139 983139983145983156983137983156983145983151983150983155 249830851246 14632 18642
983117983145983155983140983141983149983141983137983150983151983154 983137983154983154983141983155983156983155 19983085403 5965 10663
983110983141983148983151983150983161 983137983154983154983141983155983156983155 098308571 1066 852
983126983119983122C 9830851959830851043 151 24
1983155983156 2983150983140 3983154983140
104 96 102983123983144983145983142983156
7252019 PIJPSM-05-2015-0064
httpslidepdfcomreaderfullpijpsm-05-2015-0064 2930
983124983137983138983148983141 3 983112983145983141983154983137983154983139983144983145983139983137983148 983116983145983150983141983137983154 983117983151983140983141983148983155 E983155983156983145983149983137983156983145983150983143 983119983142 983142983145983139983141983154983155 A983150983150983157983137983148 983124983154983137983142983142983145983139 C983145983156983137983156983145983151983150983155 983127983137983154983154983137983150983156983155 983117983145983155983140983141983149983141983137983150983151983154983155 983137983150983140 983110983141983148983151983150 983145983141983155
983123983141983139983151983150983140983085983116983141983158983141983148 983126983137983154983145983137983138983148983141983155 (983120983137983156983154983151983148 A983154983141983137) 983118=40 983138 983123E 983138 983123E 983138 983123E 983138 983123E
983120983151983152983157983148983137983156983145983151983150 983140983141983150983155983145983156983161 001 003 001 001 98308501 01 00 00
983112983151983157983155983141983144983151983148983140 983149983141983140983145983137983150 983145983150983139983151983149983141 ($) 002 001 983085000 000 00 00 98308500 00
983159983144983145983156983141 13363 32472 9830858921 5664 9830853274 19211 9830854447 3255
983126983145983151983148983141983150983156 983139983154983145983149983141 983154983137983156983141 (983152983141983154 100000) 006 004 001 001 98308501 02 01 02
983120983154983151983152983141983154983156983161 983139983154983145983149983141 983154983137983156983141 (983152983141983154 100000) 983085003 005 983085002 001 02 03 98308501 00
983120983154983151983152983141983154983156983161 983139983154983145983149983141983155 983152983141983154 983151983142983142983145983139983141983154 983085225 99 98308515 03 98308552 24 98308503 03
983126983145983151983148983141983150983156 983139983154983145983149983141 983152983141983154 983151983142983142983145983139983141983154 983085168 68 29 14 76 31 22 12
983110983145983154983155983156983085983116983141983158983141983148 983126983137983154983145983137983138983148983141983155 (983119983142983142983145983139983141983154) 983118=302
983117983137983148983141 4673 1622 366 147 1613 1051 91 158
983117983137983154983154983145983141983140C983151983144983137983138983145983156983145983150983143 9830857516 3254 201 278 9830851158 1169 98308595 167
983151983142 C983144983145983148983140983154983141983150 183 702 75 77 983085262 217 983085396 207
983129983141983137983154983155 983141983160983152983141983154983145983141983150983139983141 9830851065 492 98308512 13 983085163 33 98308506 05C983137983148983148983155 983142983151983154 983155983141983154983158983145983139983141 24 13 02 02 14 01 98308508 05
983124983154983137983142983142983145983139 983155983156983151983152983155 983085983085 983085983085 03 00 09 02 00 00
983110983145983141983148983140 983145983150983142983151983154983149983137983156983145983151983150 983154983141983152983151983154983156983155 209 33 20 04 79 19 10 03
983123983141983139983151983150983140 983155983144983145983142983156 (983154983141983142983141983154983141983150983139983141 983143983154983151983157983152 983142983145983154983155983156) 2603 1778 187 195 809 730 147 160
983124983144983145 983154983140 983155983144983145 983142983156 (983154983141 983142983141 983154983141 983150983139983141 983143983154983151983157983152 983142983145 983154983155983156) 9872 2155 79 213 2532 952 393 132
983113983150983156983141983154983139983141983152983156 32591 5361 758 339 4147 1912 310 484
983123983145983143983149983137983085983155983153983157983137983154983141983140 1541467 19287 206702 5598
C983144983145983085983155983153983157983137983154983141983140 8294 6133 12348 10663
983152 lt 05
983152lt01
983152lt001
983118983151983156983141 A983148983148 983158983137983154983145983137983138983148983141983155 983137983154983141 983157983150983139983141983150983156983141983154983141983140
983127A983122983122A983118983124 A983122983122E983123983124983123 983117983113983123DE983117EA983118983119983122 A983122983122E983123983124983123 983110E983116983119983118983129 A983122983122E983123983124983123983124983122A983110983110983113C C983113983124A983124983113983119983118983123
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983124983137983138983148983141 4 983112983145983141983154983137983154983139983144983145983139983137983148 983116983145983150983141983137983154 983117983151983140983141983148983155 E983155983156983145983149983137983156983145983150983143 983119983142983142983145983139983141983154983155 A983150983150983157983137983148 983126983137983148983157983141 983119983158983141983154 983122983141983152983148983137983139983141983149983141983150983156 C983151983152 (983126983119983122C)
983123983141983139983151983150983140983085983116983141983158983141983148 983126983137983154983145983137983138983148983141983155 (983120983137983156983154983151983148 A983154983141983137) 983118=40 983138 983123E
983120983151983152983157983148983137983156983145983151983150 983140983141983150983155983145983156983161 98308500 00
983112983151983157983155983141983144983151983148983140 983149983141983140983145983137983150 983145983150983139983151983149983141 ($) 00 01
983159983144983145983156983141 318 345
983126983145983151983148983141983150983156 983139983154983145983149983141 983154983137983156983141 (983152983141983154 100000) 98308500 00
983120983154983151983152983141983154983156983161 983139983154983145983149983141 983154983137983156983141 (983152983141983154 100000) 00 00
983120983154983151983152983141983154983156983161 983139983154983145983149983141983155 983152983141983154 983151983142983142983145983139983141983154 98308502 04
983126983145983151983148983141983150983156 983139983154983145983149983141 983152983141983154 983151983142983142983145983139983141983154 02 05
983110983145983154983155983156983085983116983141983158983141983148 983126983137983154983145983137983138983148983141983155 (983119983142983142983145983139983141983154) 983118=302
983117983137983148983141 27 15
983117983137983154983154983145983141983140C983151983144983137983138983145983156983145983150983143 98308532 31
983151983142 C983144983145983148983140983154983141983150 05 03
983129983141983137983154983155 983141983160983152983141983154983145983141983150983139983141 00 01
C983137983148 983148983155 983142983151983154 983155983141983154983158983145983139983141 02 01
983124983154983137983142983142983145983139 983155983156983151983152983155 01 00
983110983145 983141983148983140 983145983150983142983151983154983149983137983156983145 983151983150 983154983141 983152983151983154983156983155 02 00
983123983141983139983151983150983140 983155983144983145983142983156 ( 983154983141983142983141983154983141983150983139983141 983143983154983151983157983152 983142 983145983154983155983156) 23 12
983124983144983145983154983140 983155983144983145983142983156 (983154983141983142983141983154983141983150983139983141 983143983154983151983157983152 983142 983145983154983155983156) 55 14
983113983150983156983141983154983139983141983152983156 165 36
983123983145983143983149983137983085983155983153983157983137983154983141983140 66
C983144983145983085983155983153983157983137983154983141983140 9846
983152 lt 05
983152lt01
983152lt001
983118983151983156983141 A983148983148 983158983137983154983145983137983138983148983141983155 983137983154983141 983157983150983139983141983150983156983141983154983141983140
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Report writing time Standard operating procedures dictate that LPD patrol officers
complete specific types of reports for incidents An incident report (IR) details biographical
information of the individuals involved in the incident and summarizes the incident An
Additional Case Information (ACI) is a more detailed report that documents additional
investigative steps or the actions of assisting officers Although the time spent on an IR or ACI
could vary depending on the call for service the average IR and ACI take roughly twenty
minutes to complete1
The Lincoln Police Department also tracks self-initiated activities Most productivity
results from the following proactive activities
Selectives The Lincoln Police Department tracks specific officer patrol activity by
coding its neighborhoods schools parks and business districts When an officer patrols an at-
risk business district or a high-crime neighborhood for example the officer notifies dispatch
using that arearsquos specific code Girded by the principles of ldquohot spotsrdquo policing research officers
use selectives to identify potential candidates for POP projects and heighten the perception of
risk among criminals (Braga amp Bond 2008)
Traffic Warnings and Official Citations Every Lincoln patrol officer drives a police
cruiser and conducts traffic enforcement issuing traffic warnings and official citations The
LPD records department tracks patrol officersrsquo warnings and citations
Misdemeanors felonies and warrants Patrol officers also make misdemeanor and
felony arrests during their productive time Officers might detain a motorist and arrest him or
her for driving under a suspended license or driving while intoxicated or contact a pedestrian
1 983119983142983142983145983139983141983154983155 983156983161983152983141 983137983148983148 983154983141983152983151983154983156983155 983151983150 983139983151983149983152983157983156983141983154983155 983127983141 983151983138983156983137983145983150983141983140 983156983144983145983155 983137983158983141983154983137983143983141 983138983161 983151983138983155983141983154983158983145983150983143 983137 983155983137983149983152983148983141 983151983142 983116983145983150983139983151983148983150 983152983137983156983154983151983148 983151983142983142983145983139983141983154983155
983139983151983149983152983148983141983156983145983150983143 983113983122983155 983137983150983140 983105983107983113983155 983137983150983140 983159983141 983154983141983139983151983143983150983145983162983141 983156983144983137983156 983156983144983145983155 983137983158983141983154983137983143983141 983149983137983161 983140983145983142983142983141983154 983142983154983151983149 983151983156983144983141983154 983152983151983148983145983139983141 983140983141983152983137983154983156983149983141983150983156983155 983117983151983154983141983151983158983141983154983145983142 983140983141983152983137983154983156983149983141983150983156983155 983154983141983153983157983145983154983141 983151983142983142983145983139983141983154983155 983156983151 983139983151983149983152983148983141983156983141 983156983144983141983145983154 983154983141983152983151983154983156983155 983138983141983142983151983154983141 983138983141983139983151983149983145983150983143 983137983158983137983145983148983137983138983148983141 983142983151983154 983137983150983151983156983144983141983154 983139983137983148983148 983142983151983154 983155983141983154983158983145983139983141
983156983144983145983155 983149983141983137983155983157983154983141983149983141983150983156 983145983155 983157983150983150983141983139983141983155983155983137983154983161
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and make an arrest for a sex offender registration violation Officers can also search their
departmentrsquos electronic database for individuals with outstanding warrants in their patrol areas
and attempt to arrest these offenders As with warnings and citations the LPD records
department documents and tracks misdemeanor felony and warrant arrests Moreover the court
system also monitors these types of outputs to process cases thereby adding an additional layer
of reporting accuracy and reliability
Field information reports We also measured the number of field information or
ldquointelligencerdquo reports Officers complete these internal reports to document suspicious or
dangerous individuals or circumstances though these reports are not publicly available
VORC In addition to the raw outputs of traffic citations warrants misdemeanors and
felonies we also include an advanced metric for measuring a patrol officerrsquos productivity Value
Over Replacement Cop (VORC) is a method for evaluating officers that assigns different
weights to productivity outputs and then assesses officer productivity in terms of available time
for self-initiated activities or productive time (Bonkiewicz 2015) We use a variation of this
formula that multiplies an officerrsquos officials misdemeanors and felonies by his or her
prosecution rate Our modified use of VORC can best be explained using the following figure
(Figure 1) and five-step process
INSERT FIGURE 1 HERE
where = total monthly on-duty minutes = CFS minutes Γ = follow-up time and meetings ∆
= officer assist time Ε = of IRs Ζ = of ACIs M = of intelligence reports Η = of
selectives Θ = of warnings times two Κ = of warrants times three Ι = of officials times
three Λ = of misdemeanor arrests times their respective weights Ν = of felonies types their
respective weights Ρ = an officerrsquos prosecution rate Ξ = Officerrsquos Productive time (Α-(Β+Γ+∆+
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20(Ε+Ζ))) Ο= Department average P-score or average (Η+ ((Θ)+(Ι)+(Κ)+(Λ)+(Μ)+(Ν)Ρ))
and Π = Department average Self-Initiated Time
First VORC calculates an officerrsquos total minutes spent on CFS follow-ups officer
assists and reports This amount is an officerrsquos total work time or service minutes Second the
formula subtracts an officerrsquos service minutes from the total on-duty minutes2 This is the
officerrsquos amount of productive time Third VORC calculates an officerrsquos productivity score (or
P-score) using a weighted index Departments can choose different weights for productivity
outputs but for the purpose of this study we used our weights based on the statutes governing
the Lincoln Police Department Citations and arrests are weighted based on whether the offense
was a Traffic Offense a Class III II or I Misdemeanor or a Class V IV III II or I Felony
Accordingly a patrol selective is worth one point a traffic warning two points a traffic citation
three points a warrant arrest three points a Class III misdemeanor arrest four points a Class II
misdemeanor arrest five points a Class I misdemeanor arrest six points a Class V Felony seven
points a Class IV Felony eight points a Class III Felony nine points and Class II and I Felonies
(eg homicides) ten points3 The sum of the weighted productivity outputs is the officerrsquos P-
score The P-score is divided by amount of productive time and then multiplied by 100 (for ease
of understanding) This value the productivity quotient represents the officerrsquos productivity
given the available minutes for proactivity such as searching for wanted individuals or traffic
enforcement
Fourth VORC creates a hypothetical replacement officer against which to measure the
patrol officerrsquos output This replacement player might be imagined as a rookie patrol officer or
983090 983119983150983085983140983157983156983161 983156983145983149983141 983140983151983141983155 983150983151983156 983145983150983139983148983157983140983141 983156983154983137983145983150983145983150983143 983156983145983149983141 983158983137983139983137983156983145983151983150 983151983154 983144983151983148983145983140983137983161 983140983137983161983155 983151983142983142 983151983154 983155983145983139983147 983148983141983137983158983141 983113983150 983151983156983144983141983154 983159983151983154983140983155 983151983150983085983140983157983156983161
983156983145983149983141 983145983155 983156983144983141 983137983149983151983157983150983156 983151983142 983156983145983149983141 983137 983152983137983156983154983151983148 983151983142983142983145983139983141983154 983155983152983141983150983156 983159983151983154983147983145983150983143 983156983144983141 983155983156983154983141983141983156983091 983119983157983154 983159983141983145983143983144983156983145983150983143 983155983161983155983156983141983149 983145983155 983151983150983148983161 983151983150983141 983141983160983137983149983152983148983141 983151983142 983156983144983141 983142983148983141983160983145983138983145983148983145983156983161 983156983144983137983156 983126983119983122983107 983151983142983142983141983154983155 983140983141983152983137983154983156983149983141983150983156983155983099 983141983137983139983144 983137983143983141983150983139983161 983139983137983150
983152983154983145983151983154983145983156983145983162983141 983137983150983140 983137983155983155983145983143983150 983139983151983154983154983141983155983152983151983150983140983145983150983143 983159983141983145983143983144983156983155 983156983151 983156983144983141983145983154 983152983154983141983142983141983154983154983141983140 983152983154983151983140983157983139983156983145983158983145983156983161 983151983157983156983152983157983156983155
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a recruit who has not completed the academy performing at ninety percent the capacity of fully
trained police officer4 Using LPDrsquos data VORC creates a hypothetical replacement officer by
dividing the departmental average P-score by the departmental average for productive time and
then multiplying it by the officerrsquos productive time to determine what an average officer would
have generated given the officerrsquos amount of productive time This figure is then multiplied by
9 (the capacity of a replacement officer) divided by the officerrsquos productive time and then
multiplied by 100 to generate the replacement officerrsquos productivity quotient Finally VORC
subtracts the replacement officerrsquos score from the patrol officerrsquos score to calculate the officerrsquos
value over the replacement officer This number or VORC indicates the officerrsquos productivity
per time for self-initiated activities compared to the replacement officerrsquos productivity For
example a VORC of 12 means than officer would produce 012 more productivity points (some
combination of warnings citations and arrests) per minute than a replacement officer
Biographical variables We gathered biographical information about the officers in the
sample including gender years of LPD experience maritalcohabiting status and number of
children We also controlled for whether the officer worked first second or third shift because
our data showed significant differences in the number of calls for service each shift handled as
well as the number of reports each shift completed
Geographic variables It is necessary to measure the characteristics of patrol areas to
account for variation across patrol areas For example some areas may feature different crime
rates or levels of social disorganization which command more department resources and police
officers We gathered data by first mapping out every squadrsquos patrol area and then obtaining
tract-level data from the 2010 US Census Our models included measures of established of
983092
983123983139983144983151983148983137983154983155 983137983150983140 983152983151983148983145983139983161 983149983137983147983141983154983155 983149983137983161 983140983145983155983137983143983154983141983141 983159983145983156983144 983141983155983156983145983149983137983156983145983150983143 983137 983154983141983152983148983137983139983141983149983141983150983156 983151983142983142983145983139983141983154991257983155 983139983137983152983137983139983145983156983161 983137983156 983150983145983150983141983156983161 983152983141983154983139983141983150983156 983119983157983154983143983151983137983148 983145983155 983150983151983156 983156983151 983140983141983142983145983150983145983156983145983158983141983148983161 983141983155983156983145983149983137983156983141 983137 983144983161983152983151983156983144983141983156983145983139983137983148 983151983142983142983145983139983141983154991257983155 983152983154983151983140983157983139983156983145983158983145983156983161 983138983157983156 983149983141983154983141983148983161 983156983151 983141983155983156983137983138983148983145983155983144 983137 983154983141983137983155983151983150983137983138983148983141 983142983145983160983141983140
983152983151983145983150983156 983137983143983137983145983150983155983156 983159983144983145983139983144 983156983151 983139983151983149983152983137983154983141 983137983148983148 983152983137983156983154983151983148 983151983142983142983145983139983141983154983155
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correlates of crime including population density (Hipp amp Roussell 2013) household income
(Gould Weinberg and Mustard 2002) and racial composition (Light amp Harris 2012)
Specifically we use each arearsquos population density median household income and percent
white We also included measures for each arearsquos violent and property crime rate Finally we
gathered information about the number of violent crimes (murders assaults aggravated assaults
sexual assaults and robberies) property crimes (larcenies burglaries vandalisms frauds
forgeries and motor vehicle thefts) and number of police officers in each patrol area thereby
creating variables for violent crime per officer and property crime per officer
Since LPDrsquos patrol officers are grouped with squads it is reasonable to believe that an
officerrsquos productivity might be more similar to an officer within their squad compared to an
officer in a different squad To account for the potential correlations between the individual
units as well as the multi-level nature of the data we used hierarchical linear modeling to
analyze the data
Our primary level variables include an officerrsquos gender marital status number of
children years of law enforcement experience shift calls for service annual number of traffic
stops and citations field information reports and misdemeanor felony and warrant arrests We
also used the officersrsquo productivity information to calculate each patrol officerrsquos VORC
Our secondary level variables include the violent crime per officer rate and property
crime per officer rate for the patrol area of each squad as well several control variables
including population density household median income percent white and the patrol arearsquos
violent and property crime rate Table 2 displays the summary statistics for the first and second-
level variables
INSERT TABLE 2 HERE
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Results
We hypothesized that the violent and property crime-to-cop ratios of an area might affect
the raw productivity outputs of patrol officers as well as a more advanced productivity metric
To examine these questions we constructed five hierarchical linear models estimating the
correlations between reported violent and property crimes per officer and a patrol officerrsquos
annual traffic citations warrant misdemeanor felony arrests and Value Over Replacement Cop
Although not presented here we estimated null models for all dependent variables and found that
significant variation existed between squads when we included no variables included in the
models The chi-square for the LR test for all five null models was statistically significant (p lt
000 in all models)
Table 3 shows that a rise in reported violent crimes per officer increases an officerrsquos
number of warrants and misdemeanors (b= 29 and 76) while arise in reported property crimes
per officer decreases an officerrsquos number of warrants and misdemeanors (b= -15 and -52) We
found no relationship between the crime-to-cop ratios and felony arrests However both the
rates of violent and property crimes per officer decreased the number of traffic citations an
officer writes (b= -168 and -225)
Insert Table 3 here
Table 3 also shows that traffic stops and field information reports are positively
associated with warrant and misdemeanor arrests This supports what police practitioners
already suspect the cops with the most arrests are extremely proactive patrol officers who stop a
large number of vehicles and build investigations through the use of field contacts and
information reports For instance an officer can build felony narcotics cases by contacting
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suspects and informants in the field documenting these efforts through field information reports
and eventually using this information to write search warrants and conduct drug interdiction
Next we investigated whether this relationship between the crime-to-cop ratios and
productivity outputs exists when using an advanced productivity metric specifically an officerrsquos
Value Over Replacement Cop (VORC)
INSERT TABLE 4 HERE
Table 4 shows that there is no correlation between the ratio of property and violent
crimes per officer and VORC Although this stands in contrast to our earlier results we would
not expect to find a relationship between these variables and VORC Advanced metrics such as
VORC evaluate officers based on the amount of time for self-initiated activities (productive
time) and not raw productivity outputs like misdemeanor or felony arrests Even if an officer
took an extremely large number of property crimes thereby decreasing their productive time
VORC would only evaluate that officer based on what the officer produced with the small
amount of productive time This suggests that while a teamrsquos crime-to-cop ratio could affect an
officerrsquos raw productivity outputs ie arrests a teamrsquos crime-to-cop ratio would not affect an
officerrsquos VORC
For example during a given month an officer may take an unusually large number of
burglaries or larcenies thereby leaving the officer with only ten hours for self-initiated activities
With this reduced amount of productive time the officer may generate a below-average number
of officials warrants misdemeanors and felonies which in turn may lead a supervisor to
potentially downgrade the officerrsquos productivity However VORC would recognize and account
for the high number of calls for service minutes and analyze the officerrsquos productivity based on
what the officer generated with only ten hours of productive time It may be that the officer was
7252019 PIJPSM-05-2015-0064
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actually very productive but they simply didnrsquot have enough productive time to generate the
same number of traffic citations and arrests as an officer who responded to far fewer property
crimes
Table 4 also shows that traffic stops and field intelligence reports are positively
correlated with VORC These results are consistent with the principles of how VORC evaluates
patrol officer productivity and provides additional support for the validity of this advanced
metric VORC accounts for numerous variables that affect productive time (ie calls for service
minutes) meaning that the key factors which increase or decrease an officerrsquos VORC are largely
related to self-initiated activities (ie traffic stops and information reports) VORC therefore
accounts for the potential effects of macro-level variables and analyzes the activities of the
officer during his or her productive time Nonetheless we recognize that because traffic stops
and field information reports are correlated with VORC an arearsquos crime-to-cop ratio might still
affect an officerrsquos VORC indirectly by impacting the number of stops and information reports
they generate
Discussion
There are several factors which affect a patrol officerrsquos productivity including
biographical characteristics officersrsquo perceptions and attitudes and organizational and
operational features among others This paper identified two macro-level variablesmdashproperty
crimes per officer and violent crimes per officermdashwhich affect the number of traffic citations
warrants misdemeanors and felonies generated annually by an officer Using data from a mid-
sized police department we found that a patrol areas rate of property crimes per officer was
associated with a moderate decrease in an officers annual number of traffic citations warrant
arrests and misdemeanor arrests while a patrol areas rate of violent crimes per officer was also
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associated with a moderate decrease in an officers annual number of traffic citations We also
found that a patrol areas rate of violent crimes per officer is associated with a moderate increase
in an officers annual number of warrant and misdemeanor arrests However when we used a
more advanced productivity metric we found no relationship between property and violent
crimes per officer and an officerrsquos Value Over Replacement Cop or VORC
There are several factors which explain the differing effects of the crime-to-cop ratios on
productivity outputs Let us first address why the violent crime-to-cop ratio would increase a
patrol officerrsquos warrants and misdemeanors while the property crime-to-cop ratio would decrease
these outputs Notably half of all victims of violent crimes know the offender (Truman amp Rand
2010) which aids law enforcement in identifying and locating the perpetrator Moreover both
suspect and victim are frequently on scene when police officers arrive such as during domestic
disturbances Being able to identify and locate the perpetrator helps patrol officers make arrests
thereby increasing their misdemeanor and felony arrests in violent crimes Additionally when
patrol officers arrive and contact individuals during an incident it is standard procedure to check
if the parties involved have outstanding warrants This means that the greater number of violent
crimes that officers respond to the more chances they have to arrest individuals who might have
outstanding warrants It makes sense then that officers who respond to a greater number of
violent crimes have more chances to make warrant and misdemeanor arrests
Conversely property crimes may supply the officer with fewer warrant and misdemeanor
arrests because of low clearance rates We examined the difference in clearance rates between
property and violent crime using incident data from the Lincoln Police Department and we
found that the clearance rate for violent crimes was 418 compared to only 237 for property
crimes Moreover property crimes often also require more investigative time than violent
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crimes Property crime investigations often require considerable follow-up by officers such as
processing a scene contacting financial institutions and checking pawnshops for stolen property
In general property crimes are more time-intensive than violent crimes thereby decreasing
productive time and opportunities for proactive activities while simultaneously netting officers
fewer arrests because of low clearance rates
The impact of a teamrsquos crime-to-cop ratio can also be seen in the relationship between
property and violent crime per officer and the number of traffic citations Table 3 shows that a
rise in both reported violent and property crimes per officer decrease an officerrsquos number of
traffic citations Quite simply if officers respond to a high number of property and violent
crimes then they will have fewer minutes to conduct traffic enforcement and other proactive
enforcement
As noted it is possible that the correlation is bi-directional If an officer makes a greater
number of arrests then a team may witness a decrease in crime because prolific criminals may
have been arrested and placed in jail For example one might interpret Table 3 to mean that an
increase in warrant and misdemeanor warrants led to a decrease in the number of property crimes
per officer However under this hypothesis we should also see that an increase in warrant and
misdemeanor arrests leads to a decrease in the number of violent crimes per officer Instead we
see the opposite hypothesized effect ie more arrests leading to more reported violent crimes 5
983093 983127983141 983139983151983150983155983156983154983157983139983156983141983140 983154983141983143983154983141983155983155983145983151983150 983149983151983140983141983148983155 983157983155983145983150983143 983139983145983156983137983156983145983151983150983155 983159983137983154983154983137983150983156 983137983154983154983141983155983156983155 983149983145983155983140983141983149983141983137983150983151983154 983137983154983154983141983155983156983155 983137983150983140 983142983141983148983151983150983161 983137983154983154983141983155983156983155 983142983154983151983149
983156983144983141 983152983154983141983139983141983140983145983150983143 983149983151983150983156983144 983156983151 983152983154983141983140983145983139983156 983138983151983156983144 983158983145983151983148983141983150983156 983137983150983140 983152983154983151983152983141983154983156983161 983139983154983145983149983141 983154983137983156983141983155 983142983151983154 983156983144983141 983149983151983150983156983144983155 983145983150 983151983157983154 983140983137983156983137983155983141983156 983127983141 983142983151983157983150983140 983150983151
983155983156983137983156983145983155983156983145983139983137983148983148983161 983155983145983143983150983145983142983145983139983137983150983156 983154983141983148983137983156983145983151983150983155983144983145983152983155 983138983141983156983159983141983141983150 983139983145983156983137983156983145983151983150983155 983159983137983154983154983137983150983156 983137983154983154983141983155983156983155 983149983145983155983140983141983149983141983137983150983151983154 983137983154983154983141983155983156983155 983137983150983140 983142983141983148983151983150983161 983137983154983154983141983155983156983155
983137983150983140 983156983144983141 983158983145983151983148983141983150983156 983137983150983140 983152983154983151983152983141983154983156983161 983139983154983145983149983141 983154983137983156983141983155 983151983142 983156983144983141 983138983141983137983156983155 983112983151983159983141983158983141983154 983137983142983156983141983154 983140983145983155983137983143983143983154983141983143983137983156983145983150983143 983156983144983141 983158983145983151983148983141983150983156 983137983150983140 983152983154983151983152983141983154983156983161
983139983154983145983149983141 983154983137983156983141983155 983145983150983156983151 983155983152983141983139983145983142983145983139 983139983154983145983149983141983155 983159983141 983140983145983140 983142983145983150983140 983156983144983137983156 983149983145983155983140983141983149983141983137983150983151983154 983137983150983140 983142983141983148983151983150983161 983137983154983154983141983155983156983155 983155983145983143983150983145983142983145983139983137983150983156983148983161 983152983154983141983140983145983139983156983141983140 983137 983140983141983139983154983141983137983155983141
983145983150 983154983151983138983138983141983154983161 983154983137983156983141983155 983159983144983145983139983144 983145983155 983139983151983150983155983145983155983156983141983150983156 983159983145983156983144 983156983144983141 983148983145983156983141983154983137983156983157983154983141 983105983140983140983145983156983145983151983150983137983148983148983161 983159983141 983145983150983156983141983154983158983145983141983159983141983140 983140983141983152983137983154983156983149983141983150983156 983152983141983154983155983151983150983150983141983148 983137983150983140983148983141983137983154983150983141983140 983156983144983137983156 983156983144983141 983116983145983150983139983151983148983150 983120983151983148983145983139983141 983108983141983152983137983154983156983149983141983150983156 983144983137983140 983155983156983137983142983142983141983140 983137 983156983141983137983149 983140983141983155983145983143983150983141983140 983155983152983141983139983145983142983145983139983137983148983148983161 983156983151 983137983152983152983154983141983144983141983150983140 983154983151983138983138983141983154983161
983155983157983155983152983141983139983156983155 983140983157983154983145983150983143 983156983144983145983155 983156983145983149983141983142983154983137983149983141 983159983144983145983139983144 983155983141983141983149983155 983156983151 983141983160983152983148983137983145983150 983156983144983141 983137983154983154983141983155983156983085983154983151983138983138983141983154983161 983154983137983156983141 983154983141983148983137983156983145983151983150983155983144983145983152
7252019 PIJPSM-05-2015-0064
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We believe our explanation has a stronger theoretical foundation The ratio of property
crimes per officer affects productivity outputs differently than the ratio of violent crimes per
officer for two reasons First violent crimes are different in nature than property crimes
especially with regard to clearance rates Second property crimes often require more
investigative and follow-up time than violent crimes thereby reducing an officerrsquos amount of
productive time and proactive activity
Table 3 also provides a key piece of evidence to support this assertion Table 3 shows
that the number of reported violent crimes per officer increases an officerrsquos number of warrant
and misdemeanor arrests Yet the relationship is the opposite for traffic enforcement the violent
crime-to-cop ratio actually decreases traffic citations This change is due to the fact that while
violent crime investigations often yield warrant and misdemeanor arrests they do not produce
traffic citations Furthermore responding to a greater number of violent crimes decreases the
amount of available time to conduct traffic enforcement in turn reducing the number of traffic
citations In other words the violent crime-to-cop ratio affects traffic stops the same way that
the property crime-to-cop ratio affects all productivity outputs by reducing an officerrsquos
productive time and opportunities for proactivity In fact we found a moderate correlation
between a patrol arearsquos crime-to-cop ratio and the amount of productive time enjoyed by officers
who worked that area The correlation between an arearsquos violent crime-to-cop ratio and
productive time was -21 while the correlation between an arearsquos property crime-to-cop ratio and
productive time was -33 We believe this evidence indicates that officers who work in areas
with a high crime-to-cop ratio have less time for self-initiated activities which drives down
productivity
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We suggest that the number of police officers and crime rates of an area need to be
analyzed in conjunction with each other Prior theories of police productivity have viewed and
examined these variables separately when in reality we should view these variables in tandem
Combining these variables into crime-to-cop ratios provides a more nuancedmdashand accuratemdash
understanding of patrol activities and their relationship to productivity
Although we found strong evidence that an officerrsquos patrol area affects his or her
productivity (in terms of raw outputs) we also found that a few officer-level variables affected
productivity as well including traffic stops intelligence reports and working third shift Our
results appear to substantiate what many police practitioners already suspect officers who are
proactive in contacting citizens (traffic stops) and building investigations (intelligence reports)
generate more warrant and misdemeanor arrests In addition while some newer police officers
are forced to work third shift because they do not have sufficient seniority to bid for better days
off and shift times many officers work nights because they enjoy the types of calls (eg in-
progress felonies) meaning they may be more motivated to stop vehicles make field contacts
and in turn engage in activities that generate arrests (Brehm amp Gates 1993)
These results have important implications for police researchers and administrators
First our results suggest a need for a deeper understanding of how the staffing levels crime
rates and the other characteristics of a patrol area intersect Second if departments evaluate
their patrol officers using raw productivity outputs such as misdemeanor or felony arrests
administrators must recognize that factors beyond the control of patrol officers might affect
monthly or annual productivity Our research suggests that a patrol arearsquos crime-to-cop ratio can
substantially affect a patrol officerrsquos productivity regardless of the actual crime rate If the
characteristics of a patrol area affect raw productivity outputs police departments should employ
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more advanced productivity analytics (ie VORC) that account for activities which reduce
productive time and evaluate an officerrsquos productivity in terms of available minutes for
proactivity
Nonetheless evaluating both patrol officers and police departments using traffic citations
warrants misdemeanors and felonies is useful because this type of data is readily understood by
citizens and civic officials A neighborhood watch group for instance might care more about
the number of arrests their officers make and less about those officersrsquo productivity scores Our
data suggests that if citizens want patrol officers to write more traffic citations and make more
arrests they should lobby for more police officers on the street It is true that reducing an arearsquos
crime rate impacts the crime-to-cop ratio but our results indicate that putting police officers on
the street may be the most direct way to affect the crime-to-cop ratio
Our study is not without limitations We only examined a police department which uses
the generalist style of policing and it is unknown if our results are applicable to police
departments which use a specialist style of policing Theoretically a departmentrsquos policing
styles can affect factors that increase or decrease a patrol officerrsquos productive time It is possible
that the number of property crimes and violent crimes per officer do not significantly affect a
specialist patrol officerrsquos productivity outputs versus a generalist officerrsquos outputs Future
studies should investigate whether our results can be generalized to all police departments
We have only identified two macro-level variables which affect patrol officer
productivity and we recognize there may be other variables In addition we recommend that
future studies incorporate a wider array of variables and test competing models to determine
whether micro or macro-level variables exhibit stronger effects on productivity In the end
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managing patrol officers and improving productivity requires a greater understanding of both
patrol officers and the areas they work
Acknowledgements and Credits We would like to thank Denver Police Department Crime
Analyst Melissa Ohlhorst and Pennsylvania State University Professor Barry Ruback as well as
the three anonymous reviewers for their insightful comments We also salute Lincoln Police
Sergeant Jake Dilsaver for his logistical support of this project
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References
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Brief Treatment and Crisis Intervention Vol 3 No 1 pp 47-53
Becker G (1985) ldquoHuman capital effort and the sexual division of laborrdquo The Journal of
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Bonkiewicz L (2015) ldquoBobbies and baseball players Evaluating patrol officer productivity
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Braga A and Bond J (2008) ldquoPolicing crime and disorder hot spots A randomized controlled
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Brown J and Campbell E (1990) ldquoSources of occupational stress in the policerdquo Work
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Chappell A MacDonald J and Manz P (2006) ldquoThe organizational determinants
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Cloninger D and Sartorius L (1979) ldquoCrime rates clearance rates and enforcement effort
The case of Houston Texasrdquo American Journal of Economics and Sociology Vol 38
No 4 pp 389-402
Corman H and Mocan N (2005) ldquoCarrots sticks and broken windowsrdquo Journal of Law and
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Crank J (1990) ldquoThe influence of environmental and organizational factors on police style in
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Goodman A (1990) ldquoA model for police officer burnoutrdquo Journal of Business and
Psychology Vol 5 No 1 pp 85-99
Gould E Weinberg B and Mustard D (2002) Crime rates and local labor marketopportunities in the United States 1979ndash1997rdquo Review of Economics and Statistics Vol
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Hipp J and Roussell A (2013) ldquoMicro-and macro-environment population and the
consequences for crime ratesrdquo Social Forces Vol 92 No 2 pp 563-595
Hatry H (1976) ldquoApproaches to productivity measurement and program evaluationrdquo Public
Productivity Review Vol 1 No 3 pp21-28
Hawkins H (2001) ldquoPolice officer burnout a partial replication of Maslachs Burnout
Inventoryrdquo Police Quarterly Vol 4 No 3 pp 343-360
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Herbert S (1998)ldquoPolice subculture reconsideredrdquo Criminology Vol 36 No 2 pp 343-370
Huizinga D and Henry K (2008) ldquoThe effect of arrest and justice system sanctions on
subsequent behavior Findings from longitudinal and other studiesrdquo In The long view of
crime A synthesis of longitudinal research Springer New York pp 220-254
Johnson R (2011) ldquoOfficer attitudes and management influences on police work productivityrdquo
American Journal of Criminal Justice Vol 36 pp 293-306
Kane R (2006) ldquoOn the limits of social control Structural deterrence and the policing of
lsquosuppressiblersquo crimesrdquo Justice Quarterly Vol 23 No 2 pp 186-213
Kubrin C Messner S Deane G McGeever K and Stucky T (2010) ldquoProactive
policing and robbery rates across US citiesrdquo Criminology Vol 48 No 1 pp 57-97
Lersch K (2002) ldquoAre citizen complaints just another measure of officer productivity An
analysis of citizen complaints and officer activity measuresrdquo Police Practice and
Research Vol 3 pp 135-147
Light M and Harris C (2012) Race space and violence exploring spatial dependence in
structural covariates of white and black violent crime in US countiesrdquo Journal of
Quantitative Criminology Vol 28 No 4 pp 559-586
Maxfield M (1982) ldquoService time dispatch time and demand for police services Helping
more by serving lessrdquo Public Administration Review pp 252-263
Maxfield M Lewis D and Szoc R (1980) ldquoProducing official crimes Verified crime reports
as measures of police outputrdquo Social Science Quarterly pp 221-236
Nakosteen R and Zimmer M (2001) ldquoSpouse selection and earnings Evidence of marital
sortingrdquo Economic InquiryVol 39 No 2 pp 201-2013
Shane J (2010) ldquoOrganizational stressors and police performancerdquo Journal of Criminal
Justice Vol 38 No 4 pp 807-818
Shane J (2011) ldquoDaily work experiences and police performancerdquo Police Practice and
Research Vol 13 No 3 pp 1-19
Sherman L Gartin P and Buerger M (1989) ldquoHot spots of predatory crime Routine
activities and the criminology of placerdquoCriminology Vol 27 No 1 pp27-56
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Van Meter D (2001) Evaluating dysfunctional police performance A zero-based approach
Charles C Thomas Publisher Springfield IL
Vila B and Kenney D (2002) ldquoTired cops The prevalence and potential consequences of
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Vila B Morrison G and Kenney D (2002) ldquoImproving shift schedule and work-hour
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Quarterly Vol 5 No 1 pp 4-24
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Weisburd D Maher L Sherman L Buerger M Cohn E and Petrisino A (1993)
ldquoContrasting crime general and crime specific theory The case of hot spots of crimerdquo
New Directions in Criminological Theory Advances in Criminological Theory Vol 4
pp 45-70
Author Biography
Luke Bonkiewicz is a patrol officer with the Lincoln Police Department
7252019 PIJPSM-05-2015-0064
httpslidepdfcomreaderfullpijpsm-05-2015-0064 2730
983110983145983143983157983154983141 983089983086 983126983119983122983107 983110983151983154983149983157983148983137
983101 983089983088983088lowast + + ) + ) + ) + ) lowast )) minus + + + 983090983088 + ) +983089983088)) minus 983089983088983088 lowast
983086 983097) lowast )
7252019 PIJPSM-05-2015-0064
httpslidepdfcomreaderfullpijpsm-05-2015-0064 2830
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983119983142983142983145983139983141983154 983120983154983151983155983141983139983157983156983145983151983150
983122983137983156983141C983110983123
983117983145983150983157983156983141983155983110983125983120
983117983145983150983157983156983141983155
983119983142983142983145983139983141983154
A983155983155983145983155983156983117983145983150983157983156983141983155
983113983150983139983145983140983141983150983156983122983141983152983151983154983156983155
AC983113983122983141983152983151983154983156983155 983123983141983148983141983139983156983145983158983141983155 983127983137983154983150983145983150983143983155 983119983142983142983145983139983145983137983148983155 983127983137983154983154983137983150983156983155 D983125983113983155 983117983145983155983140983141983149983141983137983150983151983154983155 983110983141983148983151983150983145983141983155
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3 72 32496 6312 6600 144 564 444 720 540 84 36 24 12
4 90 33000 5232 4980 216 192 480 768 240 144 12 72 12
5 91 41724 4824 6048 312 636 132 504 324 108 7 24 24
6 83 23916 7056 6192 144 756 528 996 336 132 36 72 12
7 95 24468 6840 5892 156 624 324 456 480 72 36 120 24
8 90 39972 5196 4920 240 504 384 492 264 36 2 72 24
9 79 25500 4068 5100 336 396 192 684 204 24 36 120 10
10 87 21984 5412 4524 348 552 480 792 312 108 48 84 36
983124983137983138983148983141 2 983123983157983149983149983137983154983161 983123983156983137983156983145983155983156983145983139983155 983142983151983154 983126983137983154983145983137983138983148983141983155
983123983141983139983151983150983140983085983116983141983158983141983148 983126983137983154983145983137983138983148983141983155 (983120983137983156983154983151983148 A983154983141983137) 983118=40 983122983137983150983143983141 983123D 983117983141983137983150
983120983151983152983157983148983137983156983145983151983150 983140983141 983150983155983145983156983161 20229830856956 1687 4436
983112983151983157983155983141983144983151983148983140 983149983141983140983145983137983150 983145983150983139983151983149983141 ($) 2628198308576020 16535 49756
983159983144983145983156983141 7398308593 007 083
983126983145983151983148983141983150983156 983139983154983145983149983141 983154983137983156983141 (983152983141983154 100000) 8009830854800 1203 2521
983120983154983151983152983141983154983156983161 983139983154983145983149983141 983154983137983156983141 (983152983141983154 100000) 31009830859800 2027 6524
983120983154983151983152983141983154983156983161 983139983154983145983149983141983155 983152983141983154 983151983142983142983145983139983141983154 5329830851097 1663 8167
983126983145983151983148983141983150983156 983139983154983145983149983141 983152983141983154 983151983142983142983145983139983141983154 194983085372 559 2929
983110983145983154983155983156983085983116983141 983158983141983148 983126 983137983154983145983137983138983148983141 983155 (983119983142983142983145983139983141983154) 983118=302 983122983137983150983143983141 983123D 983117983141 983137983150
983111983141983150983140983141983154 (983142983141983149983137983148983141=0 983149983137983148983141=1) 09830851 24 94
983117983137983154983154983145983141983140C983151983144983137983138983145983156983145983150983143 09830851 18 79
983151983142 C983144983145983148983140983154983141983150 09830855 170 90
983129983141983137983154983155 983141983160983152983141983154983145983141983150983139983141 198308537 779 1063
C983137983148983148983155 983142983151983154 983155983141983154983158983145983139983141 4819830851011 16121 61365
983124983154983137983142983142983145983139 983155983156983151983152983155 16983085901 14376 20624
983110983145983141983148983140 983145983150983142983151983154983149983137983156983145983151983150 983154983141983152983151983154983156983155 0983085227 2153 2530
983124983154983137983142983142983145983139 983139983145983156983137983156983145983151983150983155 249830851246 14632 18642
983117983145983155983140983141983149983141983137983150983151983154 983137983154983154983141983155983156983155 19983085403 5965 10663
983110983141983148983151983150983161 983137983154983154983141983155983156983155 098308571 1066 852
983126983119983122C 9830851959830851043 151 24
1983155983156 2983150983140 3983154983140
104 96 102983123983144983145983142983156
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983123983141983139983151983150983140983085983116983141983158983141983148 983126983137983154983145983137983138983148983141983155 (983120983137983156983154983151983148 A983154983141983137) 983118=40 983138 983123E 983138 983123E 983138 983123E 983138 983123E
983120983151983152983157983148983137983156983145983151983150 983140983141983150983155983145983156983161 001 003 001 001 98308501 01 00 00
983112983151983157983155983141983144983151983148983140 983149983141983140983145983137983150 983145983150983139983151983149983141 ($) 002 001 983085000 000 00 00 98308500 00
983159983144983145983156983141 13363 32472 9830858921 5664 9830853274 19211 9830854447 3255
983126983145983151983148983141983150983156 983139983154983145983149983141 983154983137983156983141 (983152983141983154 100000) 006 004 001 001 98308501 02 01 02
983120983154983151983152983141983154983156983161 983139983154983145983149983141 983154983137983156983141 (983152983141983154 100000) 983085003 005 983085002 001 02 03 98308501 00
983120983154983151983152983141983154983156983161 983139983154983145983149983141983155 983152983141983154 983151983142983142983145983139983141983154 983085225 99 98308515 03 98308552 24 98308503 03
983126983145983151983148983141983150983156 983139983154983145983149983141 983152983141983154 983151983142983142983145983139983141983154 983085168 68 29 14 76 31 22 12
983110983145983154983155983156983085983116983141983158983141983148 983126983137983154983145983137983138983148983141983155 (983119983142983142983145983139983141983154) 983118=302
983117983137983148983141 4673 1622 366 147 1613 1051 91 158
983117983137983154983154983145983141983140C983151983144983137983138983145983156983145983150983143 9830857516 3254 201 278 9830851158 1169 98308595 167
983151983142 C983144983145983148983140983154983141983150 183 702 75 77 983085262 217 983085396 207
983129983141983137983154983155 983141983160983152983141983154983145983141983150983139983141 9830851065 492 98308512 13 983085163 33 98308506 05C983137983148983148983155 983142983151983154 983155983141983154983158983145983139983141 24 13 02 02 14 01 98308508 05
983124983154983137983142983142983145983139 983155983156983151983152983155 983085983085 983085983085 03 00 09 02 00 00
983110983145983141983148983140 983145983150983142983151983154983149983137983156983145983151983150 983154983141983152983151983154983156983155 209 33 20 04 79 19 10 03
983123983141983139983151983150983140 983155983144983145983142983156 (983154983141983142983141983154983141983150983139983141 983143983154983151983157983152 983142983145983154983155983156) 2603 1778 187 195 809 730 147 160
983124983144983145 983154983140 983155983144983145 983142983156 (983154983141 983142983141 983154983141 983150983139983141 983143983154983151983157983152 983142983145 983154983155983156) 9872 2155 79 213 2532 952 393 132
983113983150983156983141983154983139983141983152983156 32591 5361 758 339 4147 1912 310 484
983123983145983143983149983137983085983155983153983157983137983154983141983140 1541467 19287 206702 5598
C983144983145983085983155983153983157983137983154983141983140 8294 6133 12348 10663
983152 lt 05
983152lt01
983152lt001
983118983151983156983141 A983148983148 983158983137983154983145983137983138983148983141983155 983137983154983141 983157983150983139983141983150983156983141983154983141983140
983127A983122983122A983118983124 A983122983122E983123983124983123 983117983113983123DE983117EA983118983119983122 A983122983122E983123983124983123 983110E983116983119983118983129 A983122983122E983123983124983123983124983122A983110983110983113C C983113983124A983124983113983119983118983123
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983123983141983139983151983150983140983085983116983141983158983141983148 983126983137983154983145983137983138983148983141983155 (983120983137983156983154983151983148 A983154983141983137) 983118=40 983138 983123E
983120983151983152983157983148983137983156983145983151983150 983140983141983150983155983145983156983161 98308500 00
983112983151983157983155983141983144983151983148983140 983149983141983140983145983137983150 983145983150983139983151983149983141 ($) 00 01
983159983144983145983156983141 318 345
983126983145983151983148983141983150983156 983139983154983145983149983141 983154983137983156983141 (983152983141983154 100000) 98308500 00
983120983154983151983152983141983154983156983161 983139983154983145983149983141 983154983137983156983141 (983152983141983154 100000) 00 00
983120983154983151983152983141983154983156983161 983139983154983145983149983141983155 983152983141983154 983151983142983142983145983139983141983154 98308502 04
983126983145983151983148983141983150983156 983139983154983145983149983141 983152983141983154 983151983142983142983145983139983141983154 02 05
983110983145983154983155983156983085983116983141983158983141983148 983126983137983154983145983137983138983148983141983155 (983119983142983142983145983139983141983154) 983118=302
983117983137983148983141 27 15
983117983137983154983154983145983141983140C983151983144983137983138983145983156983145983150983143 98308532 31
983151983142 C983144983145983148983140983154983141983150 05 03
983129983141983137983154983155 983141983160983152983141983154983145983141983150983139983141 00 01
C983137983148 983148983155 983142983151983154 983155983141983154983158983145983139983141 02 01
983124983154983137983142983142983145983139 983155983156983151983152983155 01 00
983110983145 983141983148983140 983145983150983142983151983154983149983137983156983145 983151983150 983154983141 983152983151983154983156983155 02 00
983123983141983139983151983150983140 983155983144983145983142983156 ( 983154983141983142983141983154983141983150983139983141 983143983154983151983157983152 983142 983145983154983155983156) 23 12
983124983144983145983154983140 983155983144983145983142983156 (983154983141983142983141983154983141983150983139983141 983143983154983151983157983152 983142 983145983154983155983156) 55 14
983113983150983156983141983154983139983141983152983156 165 36
983123983145983143983149983137983085983155983153983157983137983154983141983140 66
C983144983145983085983155983153983157983137983154983141983140 9846
983152 lt 05
983152lt01
983152lt001
983118983151983156983141 A983148983148 983158983137983154983145983137983138983148983141983155 983137983154983141 983157983150983139983141983150983156983141983154983141983140
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and make an arrest for a sex offender registration violation Officers can also search their
departmentrsquos electronic database for individuals with outstanding warrants in their patrol areas
and attempt to arrest these offenders As with warnings and citations the LPD records
department documents and tracks misdemeanor felony and warrant arrests Moreover the court
system also monitors these types of outputs to process cases thereby adding an additional layer
of reporting accuracy and reliability
Field information reports We also measured the number of field information or
ldquointelligencerdquo reports Officers complete these internal reports to document suspicious or
dangerous individuals or circumstances though these reports are not publicly available
VORC In addition to the raw outputs of traffic citations warrants misdemeanors and
felonies we also include an advanced metric for measuring a patrol officerrsquos productivity Value
Over Replacement Cop (VORC) is a method for evaluating officers that assigns different
weights to productivity outputs and then assesses officer productivity in terms of available time
for self-initiated activities or productive time (Bonkiewicz 2015) We use a variation of this
formula that multiplies an officerrsquos officials misdemeanors and felonies by his or her
prosecution rate Our modified use of VORC can best be explained using the following figure
(Figure 1) and five-step process
INSERT FIGURE 1 HERE
where = total monthly on-duty minutes = CFS minutes Γ = follow-up time and meetings ∆
= officer assist time Ε = of IRs Ζ = of ACIs M = of intelligence reports Η = of
selectives Θ = of warnings times two Κ = of warrants times three Ι = of officials times
three Λ = of misdemeanor arrests times their respective weights Ν = of felonies types their
respective weights Ρ = an officerrsquos prosecution rate Ξ = Officerrsquos Productive time (Α-(Β+Γ+∆+
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20(Ε+Ζ))) Ο= Department average P-score or average (Η+ ((Θ)+(Ι)+(Κ)+(Λ)+(Μ)+(Ν)Ρ))
and Π = Department average Self-Initiated Time
First VORC calculates an officerrsquos total minutes spent on CFS follow-ups officer
assists and reports This amount is an officerrsquos total work time or service minutes Second the
formula subtracts an officerrsquos service minutes from the total on-duty minutes2 This is the
officerrsquos amount of productive time Third VORC calculates an officerrsquos productivity score (or
P-score) using a weighted index Departments can choose different weights for productivity
outputs but for the purpose of this study we used our weights based on the statutes governing
the Lincoln Police Department Citations and arrests are weighted based on whether the offense
was a Traffic Offense a Class III II or I Misdemeanor or a Class V IV III II or I Felony
Accordingly a patrol selective is worth one point a traffic warning two points a traffic citation
three points a warrant arrest three points a Class III misdemeanor arrest four points a Class II
misdemeanor arrest five points a Class I misdemeanor arrest six points a Class V Felony seven
points a Class IV Felony eight points a Class III Felony nine points and Class II and I Felonies
(eg homicides) ten points3 The sum of the weighted productivity outputs is the officerrsquos P-
score The P-score is divided by amount of productive time and then multiplied by 100 (for ease
of understanding) This value the productivity quotient represents the officerrsquos productivity
given the available minutes for proactivity such as searching for wanted individuals or traffic
enforcement
Fourth VORC creates a hypothetical replacement officer against which to measure the
patrol officerrsquos output This replacement player might be imagined as a rookie patrol officer or
983090 983119983150983085983140983157983156983161 983156983145983149983141 983140983151983141983155 983150983151983156 983145983150983139983148983157983140983141 983156983154983137983145983150983145983150983143 983156983145983149983141 983158983137983139983137983156983145983151983150 983151983154 983144983151983148983145983140983137983161 983140983137983161983155 983151983142983142 983151983154 983155983145983139983147 983148983141983137983158983141 983113983150 983151983156983144983141983154 983159983151983154983140983155 983151983150983085983140983157983156983161
983156983145983149983141 983145983155 983156983144983141 983137983149983151983157983150983156 983151983142 983156983145983149983141 983137 983152983137983156983154983151983148 983151983142983142983145983139983141983154 983155983152983141983150983156 983159983151983154983147983145983150983143 983156983144983141 983155983156983154983141983141983156983091 983119983157983154 983159983141983145983143983144983156983145983150983143 983155983161983155983156983141983149 983145983155 983151983150983148983161 983151983150983141 983141983160983137983149983152983148983141 983151983142 983156983144983141 983142983148983141983160983145983138983145983148983145983156983161 983156983144983137983156 983126983119983122983107 983151983142983142983141983154983155 983140983141983152983137983154983156983149983141983150983156983155983099 983141983137983139983144 983137983143983141983150983139983161 983139983137983150
983152983154983145983151983154983145983156983145983162983141 983137983150983140 983137983155983155983145983143983150 983139983151983154983154983141983155983152983151983150983140983145983150983143 983159983141983145983143983144983156983155 983156983151 983156983144983141983145983154 983152983154983141983142983141983154983154983141983140 983152983154983151983140983157983139983156983145983158983145983156983161 983151983157983156983152983157983156983155
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a recruit who has not completed the academy performing at ninety percent the capacity of fully
trained police officer4 Using LPDrsquos data VORC creates a hypothetical replacement officer by
dividing the departmental average P-score by the departmental average for productive time and
then multiplying it by the officerrsquos productive time to determine what an average officer would
have generated given the officerrsquos amount of productive time This figure is then multiplied by
9 (the capacity of a replacement officer) divided by the officerrsquos productive time and then
multiplied by 100 to generate the replacement officerrsquos productivity quotient Finally VORC
subtracts the replacement officerrsquos score from the patrol officerrsquos score to calculate the officerrsquos
value over the replacement officer This number or VORC indicates the officerrsquos productivity
per time for self-initiated activities compared to the replacement officerrsquos productivity For
example a VORC of 12 means than officer would produce 012 more productivity points (some
combination of warnings citations and arrests) per minute than a replacement officer
Biographical variables We gathered biographical information about the officers in the
sample including gender years of LPD experience maritalcohabiting status and number of
children We also controlled for whether the officer worked first second or third shift because
our data showed significant differences in the number of calls for service each shift handled as
well as the number of reports each shift completed
Geographic variables It is necessary to measure the characteristics of patrol areas to
account for variation across patrol areas For example some areas may feature different crime
rates or levels of social disorganization which command more department resources and police
officers We gathered data by first mapping out every squadrsquos patrol area and then obtaining
tract-level data from the 2010 US Census Our models included measures of established of
983092
983123983139983144983151983148983137983154983155 983137983150983140 983152983151983148983145983139983161 983149983137983147983141983154983155 983149983137983161 983140983145983155983137983143983154983141983141 983159983145983156983144 983141983155983156983145983149983137983156983145983150983143 983137 983154983141983152983148983137983139983141983149983141983150983156 983151983142983142983145983139983141983154991257983155 983139983137983152983137983139983145983156983161 983137983156 983150983145983150983141983156983161 983152983141983154983139983141983150983156 983119983157983154983143983151983137983148 983145983155 983150983151983156 983156983151 983140983141983142983145983150983145983156983145983158983141983148983161 983141983155983156983145983149983137983156983141 983137 983144983161983152983151983156983144983141983156983145983139983137983148 983151983142983142983145983139983141983154991257983155 983152983154983151983140983157983139983156983145983158983145983156983161 983138983157983156 983149983141983154983141983148983161 983156983151 983141983155983156983137983138983148983145983155983144 983137 983154983141983137983155983151983150983137983138983148983141 983142983145983160983141983140
983152983151983145983150983156 983137983143983137983145983150983155983156 983159983144983145983139983144 983156983151 983139983151983149983152983137983154983141 983137983148983148 983152983137983156983154983151983148 983151983142983142983145983139983141983154983155
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correlates of crime including population density (Hipp amp Roussell 2013) household income
(Gould Weinberg and Mustard 2002) and racial composition (Light amp Harris 2012)
Specifically we use each arearsquos population density median household income and percent
white We also included measures for each arearsquos violent and property crime rate Finally we
gathered information about the number of violent crimes (murders assaults aggravated assaults
sexual assaults and robberies) property crimes (larcenies burglaries vandalisms frauds
forgeries and motor vehicle thefts) and number of police officers in each patrol area thereby
creating variables for violent crime per officer and property crime per officer
Since LPDrsquos patrol officers are grouped with squads it is reasonable to believe that an
officerrsquos productivity might be more similar to an officer within their squad compared to an
officer in a different squad To account for the potential correlations between the individual
units as well as the multi-level nature of the data we used hierarchical linear modeling to
analyze the data
Our primary level variables include an officerrsquos gender marital status number of
children years of law enforcement experience shift calls for service annual number of traffic
stops and citations field information reports and misdemeanor felony and warrant arrests We
also used the officersrsquo productivity information to calculate each patrol officerrsquos VORC
Our secondary level variables include the violent crime per officer rate and property
crime per officer rate for the patrol area of each squad as well several control variables
including population density household median income percent white and the patrol arearsquos
violent and property crime rate Table 2 displays the summary statistics for the first and second-
level variables
INSERT TABLE 2 HERE
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Results
We hypothesized that the violent and property crime-to-cop ratios of an area might affect
the raw productivity outputs of patrol officers as well as a more advanced productivity metric
To examine these questions we constructed five hierarchical linear models estimating the
correlations between reported violent and property crimes per officer and a patrol officerrsquos
annual traffic citations warrant misdemeanor felony arrests and Value Over Replacement Cop
Although not presented here we estimated null models for all dependent variables and found that
significant variation existed between squads when we included no variables included in the
models The chi-square for the LR test for all five null models was statistically significant (p lt
000 in all models)
Table 3 shows that a rise in reported violent crimes per officer increases an officerrsquos
number of warrants and misdemeanors (b= 29 and 76) while arise in reported property crimes
per officer decreases an officerrsquos number of warrants and misdemeanors (b= -15 and -52) We
found no relationship between the crime-to-cop ratios and felony arrests However both the
rates of violent and property crimes per officer decreased the number of traffic citations an
officer writes (b= -168 and -225)
Insert Table 3 here
Table 3 also shows that traffic stops and field information reports are positively
associated with warrant and misdemeanor arrests This supports what police practitioners
already suspect the cops with the most arrests are extremely proactive patrol officers who stop a
large number of vehicles and build investigations through the use of field contacts and
information reports For instance an officer can build felony narcotics cases by contacting
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suspects and informants in the field documenting these efforts through field information reports
and eventually using this information to write search warrants and conduct drug interdiction
Next we investigated whether this relationship between the crime-to-cop ratios and
productivity outputs exists when using an advanced productivity metric specifically an officerrsquos
Value Over Replacement Cop (VORC)
INSERT TABLE 4 HERE
Table 4 shows that there is no correlation between the ratio of property and violent
crimes per officer and VORC Although this stands in contrast to our earlier results we would
not expect to find a relationship between these variables and VORC Advanced metrics such as
VORC evaluate officers based on the amount of time for self-initiated activities (productive
time) and not raw productivity outputs like misdemeanor or felony arrests Even if an officer
took an extremely large number of property crimes thereby decreasing their productive time
VORC would only evaluate that officer based on what the officer produced with the small
amount of productive time This suggests that while a teamrsquos crime-to-cop ratio could affect an
officerrsquos raw productivity outputs ie arrests a teamrsquos crime-to-cop ratio would not affect an
officerrsquos VORC
For example during a given month an officer may take an unusually large number of
burglaries or larcenies thereby leaving the officer with only ten hours for self-initiated activities
With this reduced amount of productive time the officer may generate a below-average number
of officials warrants misdemeanors and felonies which in turn may lead a supervisor to
potentially downgrade the officerrsquos productivity However VORC would recognize and account
for the high number of calls for service minutes and analyze the officerrsquos productivity based on
what the officer generated with only ten hours of productive time It may be that the officer was
7252019 PIJPSM-05-2015-0064
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actually very productive but they simply didnrsquot have enough productive time to generate the
same number of traffic citations and arrests as an officer who responded to far fewer property
crimes
Table 4 also shows that traffic stops and field intelligence reports are positively
correlated with VORC These results are consistent with the principles of how VORC evaluates
patrol officer productivity and provides additional support for the validity of this advanced
metric VORC accounts for numerous variables that affect productive time (ie calls for service
minutes) meaning that the key factors which increase or decrease an officerrsquos VORC are largely
related to self-initiated activities (ie traffic stops and information reports) VORC therefore
accounts for the potential effects of macro-level variables and analyzes the activities of the
officer during his or her productive time Nonetheless we recognize that because traffic stops
and field information reports are correlated with VORC an arearsquos crime-to-cop ratio might still
affect an officerrsquos VORC indirectly by impacting the number of stops and information reports
they generate
Discussion
There are several factors which affect a patrol officerrsquos productivity including
biographical characteristics officersrsquo perceptions and attitudes and organizational and
operational features among others This paper identified two macro-level variablesmdashproperty
crimes per officer and violent crimes per officermdashwhich affect the number of traffic citations
warrants misdemeanors and felonies generated annually by an officer Using data from a mid-
sized police department we found that a patrol areas rate of property crimes per officer was
associated with a moderate decrease in an officers annual number of traffic citations warrant
arrests and misdemeanor arrests while a patrol areas rate of violent crimes per officer was also
7252019 PIJPSM-05-2015-0064
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associated with a moderate decrease in an officers annual number of traffic citations We also
found that a patrol areas rate of violent crimes per officer is associated with a moderate increase
in an officers annual number of warrant and misdemeanor arrests However when we used a
more advanced productivity metric we found no relationship between property and violent
crimes per officer and an officerrsquos Value Over Replacement Cop or VORC
There are several factors which explain the differing effects of the crime-to-cop ratios on
productivity outputs Let us first address why the violent crime-to-cop ratio would increase a
patrol officerrsquos warrants and misdemeanors while the property crime-to-cop ratio would decrease
these outputs Notably half of all victims of violent crimes know the offender (Truman amp Rand
2010) which aids law enforcement in identifying and locating the perpetrator Moreover both
suspect and victim are frequently on scene when police officers arrive such as during domestic
disturbances Being able to identify and locate the perpetrator helps patrol officers make arrests
thereby increasing their misdemeanor and felony arrests in violent crimes Additionally when
patrol officers arrive and contact individuals during an incident it is standard procedure to check
if the parties involved have outstanding warrants This means that the greater number of violent
crimes that officers respond to the more chances they have to arrest individuals who might have
outstanding warrants It makes sense then that officers who respond to a greater number of
violent crimes have more chances to make warrant and misdemeanor arrests
Conversely property crimes may supply the officer with fewer warrant and misdemeanor
arrests because of low clearance rates We examined the difference in clearance rates between
property and violent crime using incident data from the Lincoln Police Department and we
found that the clearance rate for violent crimes was 418 compared to only 237 for property
crimes Moreover property crimes often also require more investigative time than violent
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crimes Property crime investigations often require considerable follow-up by officers such as
processing a scene contacting financial institutions and checking pawnshops for stolen property
In general property crimes are more time-intensive than violent crimes thereby decreasing
productive time and opportunities for proactive activities while simultaneously netting officers
fewer arrests because of low clearance rates
The impact of a teamrsquos crime-to-cop ratio can also be seen in the relationship between
property and violent crime per officer and the number of traffic citations Table 3 shows that a
rise in both reported violent and property crimes per officer decrease an officerrsquos number of
traffic citations Quite simply if officers respond to a high number of property and violent
crimes then they will have fewer minutes to conduct traffic enforcement and other proactive
enforcement
As noted it is possible that the correlation is bi-directional If an officer makes a greater
number of arrests then a team may witness a decrease in crime because prolific criminals may
have been arrested and placed in jail For example one might interpret Table 3 to mean that an
increase in warrant and misdemeanor warrants led to a decrease in the number of property crimes
per officer However under this hypothesis we should also see that an increase in warrant and
misdemeanor arrests leads to a decrease in the number of violent crimes per officer Instead we
see the opposite hypothesized effect ie more arrests leading to more reported violent crimes 5
983093 983127983141 983139983151983150983155983156983154983157983139983156983141983140 983154983141983143983154983141983155983155983145983151983150 983149983151983140983141983148983155 983157983155983145983150983143 983139983145983156983137983156983145983151983150983155 983159983137983154983154983137983150983156 983137983154983154983141983155983156983155 983149983145983155983140983141983149983141983137983150983151983154 983137983154983154983141983155983156983155 983137983150983140 983142983141983148983151983150983161 983137983154983154983141983155983156983155 983142983154983151983149
983156983144983141 983152983154983141983139983141983140983145983150983143 983149983151983150983156983144 983156983151 983152983154983141983140983145983139983156 983138983151983156983144 983158983145983151983148983141983150983156 983137983150983140 983152983154983151983152983141983154983156983161 983139983154983145983149983141 983154983137983156983141983155 983142983151983154 983156983144983141 983149983151983150983156983144983155 983145983150 983151983157983154 983140983137983156983137983155983141983156 983127983141 983142983151983157983150983140 983150983151
983155983156983137983156983145983155983156983145983139983137983148983148983161 983155983145983143983150983145983142983145983139983137983150983156 983154983141983148983137983156983145983151983150983155983144983145983152983155 983138983141983156983159983141983141983150 983139983145983156983137983156983145983151983150983155 983159983137983154983154983137983150983156 983137983154983154983141983155983156983155 983149983145983155983140983141983149983141983137983150983151983154 983137983154983154983141983155983156983155 983137983150983140 983142983141983148983151983150983161 983137983154983154983141983155983156983155
983137983150983140 983156983144983141 983158983145983151983148983141983150983156 983137983150983140 983152983154983151983152983141983154983156983161 983139983154983145983149983141 983154983137983156983141983155 983151983142 983156983144983141 983138983141983137983156983155 983112983151983159983141983158983141983154 983137983142983156983141983154 983140983145983155983137983143983143983154983141983143983137983156983145983150983143 983156983144983141 983158983145983151983148983141983150983156 983137983150983140 983152983154983151983152983141983154983156983161
983139983154983145983149983141 983154983137983156983141983155 983145983150983156983151 983155983152983141983139983145983142983145983139 983139983154983145983149983141983155 983159983141 983140983145983140 983142983145983150983140 983156983144983137983156 983149983145983155983140983141983149983141983137983150983151983154 983137983150983140 983142983141983148983151983150983161 983137983154983154983141983155983156983155 983155983145983143983150983145983142983145983139983137983150983156983148983161 983152983154983141983140983145983139983156983141983140 983137 983140983141983139983154983141983137983155983141
983145983150 983154983151983138983138983141983154983161 983154983137983156983141983155 983159983144983145983139983144 983145983155 983139983151983150983155983145983155983156983141983150983156 983159983145983156983144 983156983144983141 983148983145983156983141983154983137983156983157983154983141 983105983140983140983145983156983145983151983150983137983148983148983161 983159983141 983145983150983156983141983154983158983145983141983159983141983140 983140983141983152983137983154983156983149983141983150983156 983152983141983154983155983151983150983150983141983148 983137983150983140983148983141983137983154983150983141983140 983156983144983137983156 983156983144983141 983116983145983150983139983151983148983150 983120983151983148983145983139983141 983108983141983152983137983154983156983149983141983150983156 983144983137983140 983155983156983137983142983142983141983140 983137 983156983141983137983149 983140983141983155983145983143983150983141983140 983155983152983141983139983145983142983145983139983137983148983148983161 983156983151 983137983152983152983154983141983144983141983150983140 983154983151983138983138983141983154983161
983155983157983155983152983141983139983156983155 983140983157983154983145983150983143 983156983144983145983155 983156983145983149983141983142983154983137983149983141 983159983144983145983139983144 983155983141983141983149983155 983156983151 983141983160983152983148983137983145983150 983156983144983141 983137983154983154983141983155983156983085983154983151983138983138983141983154983161 983154983137983156983141 983154983141983148983137983156983145983151983150983155983144983145983152
7252019 PIJPSM-05-2015-0064
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We believe our explanation has a stronger theoretical foundation The ratio of property
crimes per officer affects productivity outputs differently than the ratio of violent crimes per
officer for two reasons First violent crimes are different in nature than property crimes
especially with regard to clearance rates Second property crimes often require more
investigative and follow-up time than violent crimes thereby reducing an officerrsquos amount of
productive time and proactive activity
Table 3 also provides a key piece of evidence to support this assertion Table 3 shows
that the number of reported violent crimes per officer increases an officerrsquos number of warrant
and misdemeanor arrests Yet the relationship is the opposite for traffic enforcement the violent
crime-to-cop ratio actually decreases traffic citations This change is due to the fact that while
violent crime investigations often yield warrant and misdemeanor arrests they do not produce
traffic citations Furthermore responding to a greater number of violent crimes decreases the
amount of available time to conduct traffic enforcement in turn reducing the number of traffic
citations In other words the violent crime-to-cop ratio affects traffic stops the same way that
the property crime-to-cop ratio affects all productivity outputs by reducing an officerrsquos
productive time and opportunities for proactivity In fact we found a moderate correlation
between a patrol arearsquos crime-to-cop ratio and the amount of productive time enjoyed by officers
who worked that area The correlation between an arearsquos violent crime-to-cop ratio and
productive time was -21 while the correlation between an arearsquos property crime-to-cop ratio and
productive time was -33 We believe this evidence indicates that officers who work in areas
with a high crime-to-cop ratio have less time for self-initiated activities which drives down
productivity
7252019 PIJPSM-05-2015-0064
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We suggest that the number of police officers and crime rates of an area need to be
analyzed in conjunction with each other Prior theories of police productivity have viewed and
examined these variables separately when in reality we should view these variables in tandem
Combining these variables into crime-to-cop ratios provides a more nuancedmdashand accuratemdash
understanding of patrol activities and their relationship to productivity
Although we found strong evidence that an officerrsquos patrol area affects his or her
productivity (in terms of raw outputs) we also found that a few officer-level variables affected
productivity as well including traffic stops intelligence reports and working third shift Our
results appear to substantiate what many police practitioners already suspect officers who are
proactive in contacting citizens (traffic stops) and building investigations (intelligence reports)
generate more warrant and misdemeanor arrests In addition while some newer police officers
are forced to work third shift because they do not have sufficient seniority to bid for better days
off and shift times many officers work nights because they enjoy the types of calls (eg in-
progress felonies) meaning they may be more motivated to stop vehicles make field contacts
and in turn engage in activities that generate arrests (Brehm amp Gates 1993)
These results have important implications for police researchers and administrators
First our results suggest a need for a deeper understanding of how the staffing levels crime
rates and the other characteristics of a patrol area intersect Second if departments evaluate
their patrol officers using raw productivity outputs such as misdemeanor or felony arrests
administrators must recognize that factors beyond the control of patrol officers might affect
monthly or annual productivity Our research suggests that a patrol arearsquos crime-to-cop ratio can
substantially affect a patrol officerrsquos productivity regardless of the actual crime rate If the
characteristics of a patrol area affect raw productivity outputs police departments should employ
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more advanced productivity analytics (ie VORC) that account for activities which reduce
productive time and evaluate an officerrsquos productivity in terms of available minutes for
proactivity
Nonetheless evaluating both patrol officers and police departments using traffic citations
warrants misdemeanors and felonies is useful because this type of data is readily understood by
citizens and civic officials A neighborhood watch group for instance might care more about
the number of arrests their officers make and less about those officersrsquo productivity scores Our
data suggests that if citizens want patrol officers to write more traffic citations and make more
arrests they should lobby for more police officers on the street It is true that reducing an arearsquos
crime rate impacts the crime-to-cop ratio but our results indicate that putting police officers on
the street may be the most direct way to affect the crime-to-cop ratio
Our study is not without limitations We only examined a police department which uses
the generalist style of policing and it is unknown if our results are applicable to police
departments which use a specialist style of policing Theoretically a departmentrsquos policing
styles can affect factors that increase or decrease a patrol officerrsquos productive time It is possible
that the number of property crimes and violent crimes per officer do not significantly affect a
specialist patrol officerrsquos productivity outputs versus a generalist officerrsquos outputs Future
studies should investigate whether our results can be generalized to all police departments
We have only identified two macro-level variables which affect patrol officer
productivity and we recognize there may be other variables In addition we recommend that
future studies incorporate a wider array of variables and test competing models to determine
whether micro or macro-level variables exhibit stronger effects on productivity In the end
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managing patrol officers and improving productivity requires a greater understanding of both
patrol officers and the areas they work
Acknowledgements and Credits We would like to thank Denver Police Department Crime
Analyst Melissa Ohlhorst and Pennsylvania State University Professor Barry Ruback as well as
the three anonymous reviewers for their insightful comments We also salute Lincoln Police
Sergeant Jake Dilsaver for his logistical support of this project
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References
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Becker G (1985) ldquoHuman capital effort and the sexual division of laborrdquo The Journal of
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Bonkiewicz L (2015) ldquoBobbies and baseball players Evaluating patrol officer productivity
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Braga A and Bond J (2008) ldquoPolicing crime and disorder hot spots A randomized controlled
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Brown J and Campbell E (1990) ldquoSources of occupational stress in the policerdquo Work
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Chappell A MacDonald J and Manz P (2006) ldquoThe organizational determinants
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Cloninger D and Sartorius L (1979) ldquoCrime rates clearance rates and enforcement effort
The case of Houston Texasrdquo American Journal of Economics and Sociology Vol 38
No 4 pp 389-402
Corman H and Mocan N (2005) ldquoCarrots sticks and broken windowsrdquo Journal of Law and
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Crank J (1990) ldquoThe influence of environmental and organizational factors on police style in
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Goodman A (1990) ldquoA model for police officer burnoutrdquo Journal of Business and
Psychology Vol 5 No 1 pp 85-99
Gould E Weinberg B and Mustard D (2002) Crime rates and local labor marketopportunities in the United States 1979ndash1997rdquo Review of Economics and Statistics Vol
84 No 1 pp 45-61
Hipp J and Roussell A (2013) ldquoMicro-and macro-environment population and the
consequences for crime ratesrdquo Social Forces Vol 92 No 2 pp 563-595
Hatry H (1976) ldquoApproaches to productivity measurement and program evaluationrdquo Public
Productivity Review Vol 1 No 3 pp21-28
Hawkins H (2001) ldquoPolice officer burnout a partial replication of Maslachs Burnout
Inventoryrdquo Police Quarterly Vol 4 No 3 pp 343-360
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Herbert S (1998)ldquoPolice subculture reconsideredrdquo Criminology Vol 36 No 2 pp 343-370
Huizinga D and Henry K (2008) ldquoThe effect of arrest and justice system sanctions on
subsequent behavior Findings from longitudinal and other studiesrdquo In The long view of
crime A synthesis of longitudinal research Springer New York pp 220-254
Johnson R (2011) ldquoOfficer attitudes and management influences on police work productivityrdquo
American Journal of Criminal Justice Vol 36 pp 293-306
Kane R (2006) ldquoOn the limits of social control Structural deterrence and the policing of
lsquosuppressiblersquo crimesrdquo Justice Quarterly Vol 23 No 2 pp 186-213
Kubrin C Messner S Deane G McGeever K and Stucky T (2010) ldquoProactive
policing and robbery rates across US citiesrdquo Criminology Vol 48 No 1 pp 57-97
Lersch K (2002) ldquoAre citizen complaints just another measure of officer productivity An
analysis of citizen complaints and officer activity measuresrdquo Police Practice and
Research Vol 3 pp 135-147
Light M and Harris C (2012) Race space and violence exploring spatial dependence in
structural covariates of white and black violent crime in US countiesrdquo Journal of
Quantitative Criminology Vol 28 No 4 pp 559-586
Maxfield M (1982) ldquoService time dispatch time and demand for police services Helping
more by serving lessrdquo Public Administration Review pp 252-263
Maxfield M Lewis D and Szoc R (1980) ldquoProducing official crimes Verified crime reports
as measures of police outputrdquo Social Science Quarterly pp 221-236
Nakosteen R and Zimmer M (2001) ldquoSpouse selection and earnings Evidence of marital
sortingrdquo Economic InquiryVol 39 No 2 pp 201-2013
Shane J (2010) ldquoOrganizational stressors and police performancerdquo Journal of Criminal
Justice Vol 38 No 4 pp 807-818
Shane J (2011) ldquoDaily work experiences and police performancerdquo Police Practice and
Research Vol 13 No 3 pp 1-19
Sherman L Gartin P and Buerger M (1989) ldquoHot spots of predatory crime Routine
activities and the criminology of placerdquoCriminology Vol 27 No 1 pp27-56
Truman J and Rand M (2010) ldquoBureau of Justice Statistics Bulletin National Crime
Victimization Survey Criminal Victimization 2009rdquo US Department of Justice Office of
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Van Meter D (2001) Evaluating dysfunctional police performance A zero-based approach
Charles C Thomas Publisher Springfield IL
Vila B and Kenney D (2002) ldquoTired cops The prevalence and potential consequences of
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Vila B Morrison G and Kenney D (2002) ldquoImproving shift schedule and work-hour
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Quarterly Vol 5 No 1 pp 4-24
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Weisburd D Maher L Sherman L Buerger M Cohn E and Petrisino A (1993)
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New Directions in Criminological Theory Advances in Criminological Theory Vol 4
pp 45-70
Author Biography
Luke Bonkiewicz is a patrol officer with the Lincoln Police Department
7252019 PIJPSM-05-2015-0064
httpslidepdfcomreaderfullpijpsm-05-2015-0064 2730
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983086 983097) lowast )
7252019 PIJPSM-05-2015-0064
httpslidepdfcomreaderfullpijpsm-05-2015-0064 2830
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983119983142983142983145983139983141983154 983120983154983151983155983141983139983157983156983145983151983150
983122983137983156983141C983110983123
983117983145983150983157983156983141983155983110983125983120
983117983145983150983157983156983141983155
983119983142983142983145983139983141983154
A983155983155983145983155983156983117983145983150983157983156983141983155
983113983150983139983145983140983141983150983156983122983141983152983151983154983156983155
AC983113983122983141983152983151983154983156983155 983123983141983148983141983139983156983145983158983141983155 983127983137983154983150983145983150983143983155 983119983142983142983145983139983145983137983148983155 983127983137983154983154983137983150983156983155 D983125983113983155 983117983145983155983140983141983149983141983137983150983151983154983155 983110983141983148983151983150983145983141983155
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2 95 30072 5424 4116 204 696 228 312 432 84 48 120 8
3 72 32496 6312 6600 144 564 444 720 540 84 36 24 12
4 90 33000 5232 4980 216 192 480 768 240 144 12 72 12
5 91 41724 4824 6048 312 636 132 504 324 108 7 24 24
6 83 23916 7056 6192 144 756 528 996 336 132 36 72 12
7 95 24468 6840 5892 156 624 324 456 480 72 36 120 24
8 90 39972 5196 4920 240 504 384 492 264 36 2 72 24
9 79 25500 4068 5100 336 396 192 684 204 24 36 120 10
10 87 21984 5412 4524 348 552 480 792 312 108 48 84 36
983124983137983138983148983141 2 983123983157983149983149983137983154983161 983123983156983137983156983145983155983156983145983139983155 983142983151983154 983126983137983154983145983137983138983148983141983155
983123983141983139983151983150983140983085983116983141983158983141983148 983126983137983154983145983137983138983148983141983155 (983120983137983156983154983151983148 A983154983141983137) 983118=40 983122983137983150983143983141 983123D 983117983141983137983150
983120983151983152983157983148983137983156983145983151983150 983140983141 983150983155983145983156983161 20229830856956 1687 4436
983112983151983157983155983141983144983151983148983140 983149983141983140983145983137983150 983145983150983139983151983149983141 ($) 2628198308576020 16535 49756
983159983144983145983156983141 7398308593 007 083
983126983145983151983148983141983150983156 983139983154983145983149983141 983154983137983156983141 (983152983141983154 100000) 8009830854800 1203 2521
983120983154983151983152983141983154983156983161 983139983154983145983149983141 983154983137983156983141 (983152983141983154 100000) 31009830859800 2027 6524
983120983154983151983152983141983154983156983161 983139983154983145983149983141983155 983152983141983154 983151983142983142983145983139983141983154 5329830851097 1663 8167
983126983145983151983148983141983150983156 983139983154983145983149983141 983152983141983154 983151983142983142983145983139983141983154 194983085372 559 2929
983110983145983154983155983156983085983116983141 983158983141983148 983126 983137983154983145983137983138983148983141 983155 (983119983142983142983145983139983141983154) 983118=302 983122983137983150983143983141 983123D 983117983141 983137983150
983111983141983150983140983141983154 (983142983141983149983137983148983141=0 983149983137983148983141=1) 09830851 24 94
983117983137983154983154983145983141983140C983151983144983137983138983145983156983145983150983143 09830851 18 79
983151983142 C983144983145983148983140983154983141983150 09830855 170 90
983129983141983137983154983155 983141983160983152983141983154983145983141983150983139983141 198308537 779 1063
C983137983148983148983155 983142983151983154 983155983141983154983158983145983139983141 4819830851011 16121 61365
983124983154983137983142983142983145983139 983155983156983151983152983155 16983085901 14376 20624
983110983145983141983148983140 983145983150983142983151983154983149983137983156983145983151983150 983154983141983152983151983154983156983155 0983085227 2153 2530
983124983154983137983142983142983145983139 983139983145983156983137983156983145983151983150983155 249830851246 14632 18642
983117983145983155983140983141983149983141983137983150983151983154 983137983154983154983141983155983156983155 19983085403 5965 10663
983110983141983148983151983150983161 983137983154983154983141983155983156983155 098308571 1066 852
983126983119983122C 9830851959830851043 151 24
1983155983156 2983150983140 3983154983140
104 96 102983123983144983145983142983156
7252019 PIJPSM-05-2015-0064
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983123983141983139983151983150983140983085983116983141983158983141983148 983126983137983154983145983137983138983148983141983155 (983120983137983156983154983151983148 A983154983141983137) 983118=40 983138 983123E 983138 983123E 983138 983123E 983138 983123E
983120983151983152983157983148983137983156983145983151983150 983140983141983150983155983145983156983161 001 003 001 001 98308501 01 00 00
983112983151983157983155983141983144983151983148983140 983149983141983140983145983137983150 983145983150983139983151983149983141 ($) 002 001 983085000 000 00 00 98308500 00
983159983144983145983156983141 13363 32472 9830858921 5664 9830853274 19211 9830854447 3255
983126983145983151983148983141983150983156 983139983154983145983149983141 983154983137983156983141 (983152983141983154 100000) 006 004 001 001 98308501 02 01 02
983120983154983151983152983141983154983156983161 983139983154983145983149983141 983154983137983156983141 (983152983141983154 100000) 983085003 005 983085002 001 02 03 98308501 00
983120983154983151983152983141983154983156983161 983139983154983145983149983141983155 983152983141983154 983151983142983142983145983139983141983154 983085225 99 98308515 03 98308552 24 98308503 03
983126983145983151983148983141983150983156 983139983154983145983149983141 983152983141983154 983151983142983142983145983139983141983154 983085168 68 29 14 76 31 22 12
983110983145983154983155983156983085983116983141983158983141983148 983126983137983154983145983137983138983148983141983155 (983119983142983142983145983139983141983154) 983118=302
983117983137983148983141 4673 1622 366 147 1613 1051 91 158
983117983137983154983154983145983141983140C983151983144983137983138983145983156983145983150983143 9830857516 3254 201 278 9830851158 1169 98308595 167
983151983142 C983144983145983148983140983154983141983150 183 702 75 77 983085262 217 983085396 207
983129983141983137983154983155 983141983160983152983141983154983145983141983150983139983141 9830851065 492 98308512 13 983085163 33 98308506 05C983137983148983148983155 983142983151983154 983155983141983154983158983145983139983141 24 13 02 02 14 01 98308508 05
983124983154983137983142983142983145983139 983155983156983151983152983155 983085983085 983085983085 03 00 09 02 00 00
983110983145983141983148983140 983145983150983142983151983154983149983137983156983145983151983150 983154983141983152983151983154983156983155 209 33 20 04 79 19 10 03
983123983141983139983151983150983140 983155983144983145983142983156 (983154983141983142983141983154983141983150983139983141 983143983154983151983157983152 983142983145983154983155983156) 2603 1778 187 195 809 730 147 160
983124983144983145 983154983140 983155983144983145 983142983156 (983154983141 983142983141 983154983141 983150983139983141 983143983154983151983157983152 983142983145 983154983155983156) 9872 2155 79 213 2532 952 393 132
983113983150983156983141983154983139983141983152983156 32591 5361 758 339 4147 1912 310 484
983123983145983143983149983137983085983155983153983157983137983154983141983140 1541467 19287 206702 5598
C983144983145983085983155983153983157983137983154983141983140 8294 6133 12348 10663
983152 lt 05
983152lt01
983152lt001
983118983151983156983141 A983148983148 983158983137983154983145983137983138983148983141983155 983137983154983141 983157983150983139983141983150983156983141983154983141983140
983127A983122983122A983118983124 A983122983122E983123983124983123 983117983113983123DE983117EA983118983119983122 A983122983122E983123983124983123 983110E983116983119983118983129 A983122983122E983123983124983123983124983122A983110983110983113C C983113983124A983124983113983119983118983123
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983124983137983138983148983141 4 983112983145983141983154983137983154983139983144983145983139983137983148 983116983145983150983141983137983154 983117983151983140983141983148983155 E983155983156983145983149983137983156983145983150983143 983119983142983142983145983139983141983154983155 A983150983150983157983137983148 983126983137983148983157983141 983119983158983141983154 983122983141983152983148983137983139983141983149983141983150983156 C983151983152 (983126983119983122C)
983123983141983139983151983150983140983085983116983141983158983141983148 983126983137983154983145983137983138983148983141983155 (983120983137983156983154983151983148 A983154983141983137) 983118=40 983138 983123E
983120983151983152983157983148983137983156983145983151983150 983140983141983150983155983145983156983161 98308500 00
983112983151983157983155983141983144983151983148983140 983149983141983140983145983137983150 983145983150983139983151983149983141 ($) 00 01
983159983144983145983156983141 318 345
983126983145983151983148983141983150983156 983139983154983145983149983141 983154983137983156983141 (983152983141983154 100000) 98308500 00
983120983154983151983152983141983154983156983161 983139983154983145983149983141 983154983137983156983141 (983152983141983154 100000) 00 00
983120983154983151983152983141983154983156983161 983139983154983145983149983141983155 983152983141983154 983151983142983142983145983139983141983154 98308502 04
983126983145983151983148983141983150983156 983139983154983145983149983141 983152983141983154 983151983142983142983145983139983141983154 02 05
983110983145983154983155983156983085983116983141983158983141983148 983126983137983154983145983137983138983148983141983155 (983119983142983142983145983139983141983154) 983118=302
983117983137983148983141 27 15
983117983137983154983154983145983141983140C983151983144983137983138983145983156983145983150983143 98308532 31
983151983142 C983144983145983148983140983154983141983150 05 03
983129983141983137983154983155 983141983160983152983141983154983145983141983150983139983141 00 01
C983137983148 983148983155 983142983151983154 983155983141983154983158983145983139983141 02 01
983124983154983137983142983142983145983139 983155983156983151983152983155 01 00
983110983145 983141983148983140 983145983150983142983151983154983149983137983156983145 983151983150 983154983141 983152983151983154983156983155 02 00
983123983141983139983151983150983140 983155983144983145983142983156 ( 983154983141983142983141983154983141983150983139983141 983143983154983151983157983152 983142 983145983154983155983156) 23 12
983124983144983145983154983140 983155983144983145983142983156 (983154983141983142983141983154983141983150983139983141 983143983154983151983157983152 983142 983145983154983155983156) 55 14
983113983150983156983141983154983139983141983152983156 165 36
983123983145983143983149983137983085983155983153983157983137983154983141983140 66
C983144983145983085983155983153983157983137983154983141983140 9846
983152 lt 05
983152lt01
983152lt001
983118983151983156983141 A983148983148 983158983137983154983145983137983138983148983141983155 983137983154983141 983157983150983139983141983150983156983141983154983141983140
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20(Ε+Ζ))) Ο= Department average P-score or average (Η+ ((Θ)+(Ι)+(Κ)+(Λ)+(Μ)+(Ν)Ρ))
and Π = Department average Self-Initiated Time
First VORC calculates an officerrsquos total minutes spent on CFS follow-ups officer
assists and reports This amount is an officerrsquos total work time or service minutes Second the
formula subtracts an officerrsquos service minutes from the total on-duty minutes2 This is the
officerrsquos amount of productive time Third VORC calculates an officerrsquos productivity score (or
P-score) using a weighted index Departments can choose different weights for productivity
outputs but for the purpose of this study we used our weights based on the statutes governing
the Lincoln Police Department Citations and arrests are weighted based on whether the offense
was a Traffic Offense a Class III II or I Misdemeanor or a Class V IV III II or I Felony
Accordingly a patrol selective is worth one point a traffic warning two points a traffic citation
three points a warrant arrest three points a Class III misdemeanor arrest four points a Class II
misdemeanor arrest five points a Class I misdemeanor arrest six points a Class V Felony seven
points a Class IV Felony eight points a Class III Felony nine points and Class II and I Felonies
(eg homicides) ten points3 The sum of the weighted productivity outputs is the officerrsquos P-
score The P-score is divided by amount of productive time and then multiplied by 100 (for ease
of understanding) This value the productivity quotient represents the officerrsquos productivity
given the available minutes for proactivity such as searching for wanted individuals or traffic
enforcement
Fourth VORC creates a hypothetical replacement officer against which to measure the
patrol officerrsquos output This replacement player might be imagined as a rookie patrol officer or
983090 983119983150983085983140983157983156983161 983156983145983149983141 983140983151983141983155 983150983151983156 983145983150983139983148983157983140983141 983156983154983137983145983150983145983150983143 983156983145983149983141 983158983137983139983137983156983145983151983150 983151983154 983144983151983148983145983140983137983161 983140983137983161983155 983151983142983142 983151983154 983155983145983139983147 983148983141983137983158983141 983113983150 983151983156983144983141983154 983159983151983154983140983155 983151983150983085983140983157983156983161
983156983145983149983141 983145983155 983156983144983141 983137983149983151983157983150983156 983151983142 983156983145983149983141 983137 983152983137983156983154983151983148 983151983142983142983145983139983141983154 983155983152983141983150983156 983159983151983154983147983145983150983143 983156983144983141 983155983156983154983141983141983156983091 983119983157983154 983159983141983145983143983144983156983145983150983143 983155983161983155983156983141983149 983145983155 983151983150983148983161 983151983150983141 983141983160983137983149983152983148983141 983151983142 983156983144983141 983142983148983141983160983145983138983145983148983145983156983161 983156983144983137983156 983126983119983122983107 983151983142983142983141983154983155 983140983141983152983137983154983156983149983141983150983156983155983099 983141983137983139983144 983137983143983141983150983139983161 983139983137983150
983152983154983145983151983154983145983156983145983162983141 983137983150983140 983137983155983155983145983143983150 983139983151983154983154983141983155983152983151983150983140983145983150983143 983159983141983145983143983144983156983155 983156983151 983156983144983141983145983154 983152983154983141983142983141983154983154983141983140 983152983154983151983140983157983139983156983145983158983145983156983161 983151983157983156983152983157983156983155
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a recruit who has not completed the academy performing at ninety percent the capacity of fully
trained police officer4 Using LPDrsquos data VORC creates a hypothetical replacement officer by
dividing the departmental average P-score by the departmental average for productive time and
then multiplying it by the officerrsquos productive time to determine what an average officer would
have generated given the officerrsquos amount of productive time This figure is then multiplied by
9 (the capacity of a replacement officer) divided by the officerrsquos productive time and then
multiplied by 100 to generate the replacement officerrsquos productivity quotient Finally VORC
subtracts the replacement officerrsquos score from the patrol officerrsquos score to calculate the officerrsquos
value over the replacement officer This number or VORC indicates the officerrsquos productivity
per time for self-initiated activities compared to the replacement officerrsquos productivity For
example a VORC of 12 means than officer would produce 012 more productivity points (some
combination of warnings citations and arrests) per minute than a replacement officer
Biographical variables We gathered biographical information about the officers in the
sample including gender years of LPD experience maritalcohabiting status and number of
children We also controlled for whether the officer worked first second or third shift because
our data showed significant differences in the number of calls for service each shift handled as
well as the number of reports each shift completed
Geographic variables It is necessary to measure the characteristics of patrol areas to
account for variation across patrol areas For example some areas may feature different crime
rates or levels of social disorganization which command more department resources and police
officers We gathered data by first mapping out every squadrsquos patrol area and then obtaining
tract-level data from the 2010 US Census Our models included measures of established of
983092
983123983139983144983151983148983137983154983155 983137983150983140 983152983151983148983145983139983161 983149983137983147983141983154983155 983149983137983161 983140983145983155983137983143983154983141983141 983159983145983156983144 983141983155983156983145983149983137983156983145983150983143 983137 983154983141983152983148983137983139983141983149983141983150983156 983151983142983142983145983139983141983154991257983155 983139983137983152983137983139983145983156983161 983137983156 983150983145983150983141983156983161 983152983141983154983139983141983150983156 983119983157983154983143983151983137983148 983145983155 983150983151983156 983156983151 983140983141983142983145983150983145983156983145983158983141983148983161 983141983155983156983145983149983137983156983141 983137 983144983161983152983151983156983144983141983156983145983139983137983148 983151983142983142983145983139983141983154991257983155 983152983154983151983140983157983139983156983145983158983145983156983161 983138983157983156 983149983141983154983141983148983161 983156983151 983141983155983156983137983138983148983145983155983144 983137 983154983141983137983155983151983150983137983138983148983141 983142983145983160983141983140
983152983151983145983150983156 983137983143983137983145983150983155983156 983159983144983145983139983144 983156983151 983139983151983149983152983137983154983141 983137983148983148 983152983137983156983154983151983148 983151983142983142983145983139983141983154983155
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correlates of crime including population density (Hipp amp Roussell 2013) household income
(Gould Weinberg and Mustard 2002) and racial composition (Light amp Harris 2012)
Specifically we use each arearsquos population density median household income and percent
white We also included measures for each arearsquos violent and property crime rate Finally we
gathered information about the number of violent crimes (murders assaults aggravated assaults
sexual assaults and robberies) property crimes (larcenies burglaries vandalisms frauds
forgeries and motor vehicle thefts) and number of police officers in each patrol area thereby
creating variables for violent crime per officer and property crime per officer
Since LPDrsquos patrol officers are grouped with squads it is reasonable to believe that an
officerrsquos productivity might be more similar to an officer within their squad compared to an
officer in a different squad To account for the potential correlations between the individual
units as well as the multi-level nature of the data we used hierarchical linear modeling to
analyze the data
Our primary level variables include an officerrsquos gender marital status number of
children years of law enforcement experience shift calls for service annual number of traffic
stops and citations field information reports and misdemeanor felony and warrant arrests We
also used the officersrsquo productivity information to calculate each patrol officerrsquos VORC
Our secondary level variables include the violent crime per officer rate and property
crime per officer rate for the patrol area of each squad as well several control variables
including population density household median income percent white and the patrol arearsquos
violent and property crime rate Table 2 displays the summary statistics for the first and second-
level variables
INSERT TABLE 2 HERE
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Results
We hypothesized that the violent and property crime-to-cop ratios of an area might affect
the raw productivity outputs of patrol officers as well as a more advanced productivity metric
To examine these questions we constructed five hierarchical linear models estimating the
correlations between reported violent and property crimes per officer and a patrol officerrsquos
annual traffic citations warrant misdemeanor felony arrests and Value Over Replacement Cop
Although not presented here we estimated null models for all dependent variables and found that
significant variation existed between squads when we included no variables included in the
models The chi-square for the LR test for all five null models was statistically significant (p lt
000 in all models)
Table 3 shows that a rise in reported violent crimes per officer increases an officerrsquos
number of warrants and misdemeanors (b= 29 and 76) while arise in reported property crimes
per officer decreases an officerrsquos number of warrants and misdemeanors (b= -15 and -52) We
found no relationship between the crime-to-cop ratios and felony arrests However both the
rates of violent and property crimes per officer decreased the number of traffic citations an
officer writes (b= -168 and -225)
Insert Table 3 here
Table 3 also shows that traffic stops and field information reports are positively
associated with warrant and misdemeanor arrests This supports what police practitioners
already suspect the cops with the most arrests are extremely proactive patrol officers who stop a
large number of vehicles and build investigations through the use of field contacts and
information reports For instance an officer can build felony narcotics cases by contacting
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suspects and informants in the field documenting these efforts through field information reports
and eventually using this information to write search warrants and conduct drug interdiction
Next we investigated whether this relationship between the crime-to-cop ratios and
productivity outputs exists when using an advanced productivity metric specifically an officerrsquos
Value Over Replacement Cop (VORC)
INSERT TABLE 4 HERE
Table 4 shows that there is no correlation between the ratio of property and violent
crimes per officer and VORC Although this stands in contrast to our earlier results we would
not expect to find a relationship between these variables and VORC Advanced metrics such as
VORC evaluate officers based on the amount of time for self-initiated activities (productive
time) and not raw productivity outputs like misdemeanor or felony arrests Even if an officer
took an extremely large number of property crimes thereby decreasing their productive time
VORC would only evaluate that officer based on what the officer produced with the small
amount of productive time This suggests that while a teamrsquos crime-to-cop ratio could affect an
officerrsquos raw productivity outputs ie arrests a teamrsquos crime-to-cop ratio would not affect an
officerrsquos VORC
For example during a given month an officer may take an unusually large number of
burglaries or larcenies thereby leaving the officer with only ten hours for self-initiated activities
With this reduced amount of productive time the officer may generate a below-average number
of officials warrants misdemeanors and felonies which in turn may lead a supervisor to
potentially downgrade the officerrsquos productivity However VORC would recognize and account
for the high number of calls for service minutes and analyze the officerrsquos productivity based on
what the officer generated with only ten hours of productive time It may be that the officer was
7252019 PIJPSM-05-2015-0064
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actually very productive but they simply didnrsquot have enough productive time to generate the
same number of traffic citations and arrests as an officer who responded to far fewer property
crimes
Table 4 also shows that traffic stops and field intelligence reports are positively
correlated with VORC These results are consistent with the principles of how VORC evaluates
patrol officer productivity and provides additional support for the validity of this advanced
metric VORC accounts for numerous variables that affect productive time (ie calls for service
minutes) meaning that the key factors which increase or decrease an officerrsquos VORC are largely
related to self-initiated activities (ie traffic stops and information reports) VORC therefore
accounts for the potential effects of macro-level variables and analyzes the activities of the
officer during his or her productive time Nonetheless we recognize that because traffic stops
and field information reports are correlated with VORC an arearsquos crime-to-cop ratio might still
affect an officerrsquos VORC indirectly by impacting the number of stops and information reports
they generate
Discussion
There are several factors which affect a patrol officerrsquos productivity including
biographical characteristics officersrsquo perceptions and attitudes and organizational and
operational features among others This paper identified two macro-level variablesmdashproperty
crimes per officer and violent crimes per officermdashwhich affect the number of traffic citations
warrants misdemeanors and felonies generated annually by an officer Using data from a mid-
sized police department we found that a patrol areas rate of property crimes per officer was
associated with a moderate decrease in an officers annual number of traffic citations warrant
arrests and misdemeanor arrests while a patrol areas rate of violent crimes per officer was also
7252019 PIJPSM-05-2015-0064
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associated with a moderate decrease in an officers annual number of traffic citations We also
found that a patrol areas rate of violent crimes per officer is associated with a moderate increase
in an officers annual number of warrant and misdemeanor arrests However when we used a
more advanced productivity metric we found no relationship between property and violent
crimes per officer and an officerrsquos Value Over Replacement Cop or VORC
There are several factors which explain the differing effects of the crime-to-cop ratios on
productivity outputs Let us first address why the violent crime-to-cop ratio would increase a
patrol officerrsquos warrants and misdemeanors while the property crime-to-cop ratio would decrease
these outputs Notably half of all victims of violent crimes know the offender (Truman amp Rand
2010) which aids law enforcement in identifying and locating the perpetrator Moreover both
suspect and victim are frequently on scene when police officers arrive such as during domestic
disturbances Being able to identify and locate the perpetrator helps patrol officers make arrests
thereby increasing their misdemeanor and felony arrests in violent crimes Additionally when
patrol officers arrive and contact individuals during an incident it is standard procedure to check
if the parties involved have outstanding warrants This means that the greater number of violent
crimes that officers respond to the more chances they have to arrest individuals who might have
outstanding warrants It makes sense then that officers who respond to a greater number of
violent crimes have more chances to make warrant and misdemeanor arrests
Conversely property crimes may supply the officer with fewer warrant and misdemeanor
arrests because of low clearance rates We examined the difference in clearance rates between
property and violent crime using incident data from the Lincoln Police Department and we
found that the clearance rate for violent crimes was 418 compared to only 237 for property
crimes Moreover property crimes often also require more investigative time than violent
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crimes Property crime investigations often require considerable follow-up by officers such as
processing a scene contacting financial institutions and checking pawnshops for stolen property
In general property crimes are more time-intensive than violent crimes thereby decreasing
productive time and opportunities for proactive activities while simultaneously netting officers
fewer arrests because of low clearance rates
The impact of a teamrsquos crime-to-cop ratio can also be seen in the relationship between
property and violent crime per officer and the number of traffic citations Table 3 shows that a
rise in both reported violent and property crimes per officer decrease an officerrsquos number of
traffic citations Quite simply if officers respond to a high number of property and violent
crimes then they will have fewer minutes to conduct traffic enforcement and other proactive
enforcement
As noted it is possible that the correlation is bi-directional If an officer makes a greater
number of arrests then a team may witness a decrease in crime because prolific criminals may
have been arrested and placed in jail For example one might interpret Table 3 to mean that an
increase in warrant and misdemeanor warrants led to a decrease in the number of property crimes
per officer However under this hypothesis we should also see that an increase in warrant and
misdemeanor arrests leads to a decrease in the number of violent crimes per officer Instead we
see the opposite hypothesized effect ie more arrests leading to more reported violent crimes 5
983093 983127983141 983139983151983150983155983156983154983157983139983156983141983140 983154983141983143983154983141983155983155983145983151983150 983149983151983140983141983148983155 983157983155983145983150983143 983139983145983156983137983156983145983151983150983155 983159983137983154983154983137983150983156 983137983154983154983141983155983156983155 983149983145983155983140983141983149983141983137983150983151983154 983137983154983154983141983155983156983155 983137983150983140 983142983141983148983151983150983161 983137983154983154983141983155983156983155 983142983154983151983149
983156983144983141 983152983154983141983139983141983140983145983150983143 983149983151983150983156983144 983156983151 983152983154983141983140983145983139983156 983138983151983156983144 983158983145983151983148983141983150983156 983137983150983140 983152983154983151983152983141983154983156983161 983139983154983145983149983141 983154983137983156983141983155 983142983151983154 983156983144983141 983149983151983150983156983144983155 983145983150 983151983157983154 983140983137983156983137983155983141983156 983127983141 983142983151983157983150983140 983150983151
983155983156983137983156983145983155983156983145983139983137983148983148983161 983155983145983143983150983145983142983145983139983137983150983156 983154983141983148983137983156983145983151983150983155983144983145983152983155 983138983141983156983159983141983141983150 983139983145983156983137983156983145983151983150983155 983159983137983154983154983137983150983156 983137983154983154983141983155983156983155 983149983145983155983140983141983149983141983137983150983151983154 983137983154983154983141983155983156983155 983137983150983140 983142983141983148983151983150983161 983137983154983154983141983155983156983155
983137983150983140 983156983144983141 983158983145983151983148983141983150983156 983137983150983140 983152983154983151983152983141983154983156983161 983139983154983145983149983141 983154983137983156983141983155 983151983142 983156983144983141 983138983141983137983156983155 983112983151983159983141983158983141983154 983137983142983156983141983154 983140983145983155983137983143983143983154983141983143983137983156983145983150983143 983156983144983141 983158983145983151983148983141983150983156 983137983150983140 983152983154983151983152983141983154983156983161
983139983154983145983149983141 983154983137983156983141983155 983145983150983156983151 983155983152983141983139983145983142983145983139 983139983154983145983149983141983155 983159983141 983140983145983140 983142983145983150983140 983156983144983137983156 983149983145983155983140983141983149983141983137983150983151983154 983137983150983140 983142983141983148983151983150983161 983137983154983154983141983155983156983155 983155983145983143983150983145983142983145983139983137983150983156983148983161 983152983154983141983140983145983139983156983141983140 983137 983140983141983139983154983141983137983155983141
983145983150 983154983151983138983138983141983154983161 983154983137983156983141983155 983159983144983145983139983144 983145983155 983139983151983150983155983145983155983156983141983150983156 983159983145983156983144 983156983144983141 983148983145983156983141983154983137983156983157983154983141 983105983140983140983145983156983145983151983150983137983148983148983161 983159983141 983145983150983156983141983154983158983145983141983159983141983140 983140983141983152983137983154983156983149983141983150983156 983152983141983154983155983151983150983150983141983148 983137983150983140983148983141983137983154983150983141983140 983156983144983137983156 983156983144983141 983116983145983150983139983151983148983150 983120983151983148983145983139983141 983108983141983152983137983154983156983149983141983150983156 983144983137983140 983155983156983137983142983142983141983140 983137 983156983141983137983149 983140983141983155983145983143983150983141983140 983155983152983141983139983145983142983145983139983137983148983148983161 983156983151 983137983152983152983154983141983144983141983150983140 983154983151983138983138983141983154983161
983155983157983155983152983141983139983156983155 983140983157983154983145983150983143 983156983144983145983155 983156983145983149983141983142983154983137983149983141 983159983144983145983139983144 983155983141983141983149983155 983156983151 983141983160983152983148983137983145983150 983156983144983141 983137983154983154983141983155983156983085983154983151983138983138983141983154983161 983154983137983156983141 983154983141983148983137983156983145983151983150983155983144983145983152
7252019 PIJPSM-05-2015-0064
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We believe our explanation has a stronger theoretical foundation The ratio of property
crimes per officer affects productivity outputs differently than the ratio of violent crimes per
officer for two reasons First violent crimes are different in nature than property crimes
especially with regard to clearance rates Second property crimes often require more
investigative and follow-up time than violent crimes thereby reducing an officerrsquos amount of
productive time and proactive activity
Table 3 also provides a key piece of evidence to support this assertion Table 3 shows
that the number of reported violent crimes per officer increases an officerrsquos number of warrant
and misdemeanor arrests Yet the relationship is the opposite for traffic enforcement the violent
crime-to-cop ratio actually decreases traffic citations This change is due to the fact that while
violent crime investigations often yield warrant and misdemeanor arrests they do not produce
traffic citations Furthermore responding to a greater number of violent crimes decreases the
amount of available time to conduct traffic enforcement in turn reducing the number of traffic
citations In other words the violent crime-to-cop ratio affects traffic stops the same way that
the property crime-to-cop ratio affects all productivity outputs by reducing an officerrsquos
productive time and opportunities for proactivity In fact we found a moderate correlation
between a patrol arearsquos crime-to-cop ratio and the amount of productive time enjoyed by officers
who worked that area The correlation between an arearsquos violent crime-to-cop ratio and
productive time was -21 while the correlation between an arearsquos property crime-to-cop ratio and
productive time was -33 We believe this evidence indicates that officers who work in areas
with a high crime-to-cop ratio have less time for self-initiated activities which drives down
productivity
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We suggest that the number of police officers and crime rates of an area need to be
analyzed in conjunction with each other Prior theories of police productivity have viewed and
examined these variables separately when in reality we should view these variables in tandem
Combining these variables into crime-to-cop ratios provides a more nuancedmdashand accuratemdash
understanding of patrol activities and their relationship to productivity
Although we found strong evidence that an officerrsquos patrol area affects his or her
productivity (in terms of raw outputs) we also found that a few officer-level variables affected
productivity as well including traffic stops intelligence reports and working third shift Our
results appear to substantiate what many police practitioners already suspect officers who are
proactive in contacting citizens (traffic stops) and building investigations (intelligence reports)
generate more warrant and misdemeanor arrests In addition while some newer police officers
are forced to work third shift because they do not have sufficient seniority to bid for better days
off and shift times many officers work nights because they enjoy the types of calls (eg in-
progress felonies) meaning they may be more motivated to stop vehicles make field contacts
and in turn engage in activities that generate arrests (Brehm amp Gates 1993)
These results have important implications for police researchers and administrators
First our results suggest a need for a deeper understanding of how the staffing levels crime
rates and the other characteristics of a patrol area intersect Second if departments evaluate
their patrol officers using raw productivity outputs such as misdemeanor or felony arrests
administrators must recognize that factors beyond the control of patrol officers might affect
monthly or annual productivity Our research suggests that a patrol arearsquos crime-to-cop ratio can
substantially affect a patrol officerrsquos productivity regardless of the actual crime rate If the
characteristics of a patrol area affect raw productivity outputs police departments should employ
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more advanced productivity analytics (ie VORC) that account for activities which reduce
productive time and evaluate an officerrsquos productivity in terms of available minutes for
proactivity
Nonetheless evaluating both patrol officers and police departments using traffic citations
warrants misdemeanors and felonies is useful because this type of data is readily understood by
citizens and civic officials A neighborhood watch group for instance might care more about
the number of arrests their officers make and less about those officersrsquo productivity scores Our
data suggests that if citizens want patrol officers to write more traffic citations and make more
arrests they should lobby for more police officers on the street It is true that reducing an arearsquos
crime rate impacts the crime-to-cop ratio but our results indicate that putting police officers on
the street may be the most direct way to affect the crime-to-cop ratio
Our study is not without limitations We only examined a police department which uses
the generalist style of policing and it is unknown if our results are applicable to police
departments which use a specialist style of policing Theoretically a departmentrsquos policing
styles can affect factors that increase or decrease a patrol officerrsquos productive time It is possible
that the number of property crimes and violent crimes per officer do not significantly affect a
specialist patrol officerrsquos productivity outputs versus a generalist officerrsquos outputs Future
studies should investigate whether our results can be generalized to all police departments
We have only identified two macro-level variables which affect patrol officer
productivity and we recognize there may be other variables In addition we recommend that
future studies incorporate a wider array of variables and test competing models to determine
whether micro or macro-level variables exhibit stronger effects on productivity In the end
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managing patrol officers and improving productivity requires a greater understanding of both
patrol officers and the areas they work
Acknowledgements and Credits We would like to thank Denver Police Department Crime
Analyst Melissa Ohlhorst and Pennsylvania State University Professor Barry Ruback as well as
the three anonymous reviewers for their insightful comments We also salute Lincoln Police
Sergeant Jake Dilsaver for his logistical support of this project
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References
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Becker G (1985) ldquoHuman capital effort and the sexual division of laborrdquo The Journal of
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Bonkiewicz L (2015) ldquoBobbies and baseball players Evaluating patrol officer productivity
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Braga A and Bond J (2008) ldquoPolicing crime and disorder hot spots A randomized controlled
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Brown J and Campbell E (1990) ldquoSources of occupational stress in the policerdquo Work
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Chappell A MacDonald J and Manz P (2006) ldquoThe organizational determinants
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Cloninger D and Sartorius L (1979) ldquoCrime rates clearance rates and enforcement effort
The case of Houston Texasrdquo American Journal of Economics and Sociology Vol 38
No 4 pp 389-402
Corman H and Mocan N (2005) ldquoCarrots sticks and broken windowsrdquo Journal of Law and
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Crank J (1990) ldquoThe influence of environmental and organizational factors on police style in
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Goodman A (1990) ldquoA model for police officer burnoutrdquo Journal of Business and
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Gould E Weinberg B and Mustard D (2002) Crime rates and local labor marketopportunities in the United States 1979ndash1997rdquo Review of Economics and Statistics Vol
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Hipp J and Roussell A (2013) ldquoMicro-and macro-environment population and the
consequences for crime ratesrdquo Social Forces Vol 92 No 2 pp 563-595
Hatry H (1976) ldquoApproaches to productivity measurement and program evaluationrdquo Public
Productivity Review Vol 1 No 3 pp21-28
Hawkins H (2001) ldquoPolice officer burnout a partial replication of Maslachs Burnout
Inventoryrdquo Police Quarterly Vol 4 No 3 pp 343-360
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Herbert S (1998)ldquoPolice subculture reconsideredrdquo Criminology Vol 36 No 2 pp 343-370
Huizinga D and Henry K (2008) ldquoThe effect of arrest and justice system sanctions on
subsequent behavior Findings from longitudinal and other studiesrdquo In The long view of
crime A synthesis of longitudinal research Springer New York pp 220-254
Johnson R (2011) ldquoOfficer attitudes and management influences on police work productivityrdquo
American Journal of Criminal Justice Vol 36 pp 293-306
Kane R (2006) ldquoOn the limits of social control Structural deterrence and the policing of
lsquosuppressiblersquo crimesrdquo Justice Quarterly Vol 23 No 2 pp 186-213
Kubrin C Messner S Deane G McGeever K and Stucky T (2010) ldquoProactive
policing and robbery rates across US citiesrdquo Criminology Vol 48 No 1 pp 57-97
Lersch K (2002) ldquoAre citizen complaints just another measure of officer productivity An
analysis of citizen complaints and officer activity measuresrdquo Police Practice and
Research Vol 3 pp 135-147
Light M and Harris C (2012) Race space and violence exploring spatial dependence in
structural covariates of white and black violent crime in US countiesrdquo Journal of
Quantitative Criminology Vol 28 No 4 pp 559-586
Maxfield M (1982) ldquoService time dispatch time and demand for police services Helping
more by serving lessrdquo Public Administration Review pp 252-263
Maxfield M Lewis D and Szoc R (1980) ldquoProducing official crimes Verified crime reports
as measures of police outputrdquo Social Science Quarterly pp 221-236
Nakosteen R and Zimmer M (2001) ldquoSpouse selection and earnings Evidence of marital
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Shane J (2010) ldquoOrganizational stressors and police performancerdquo Journal of Criminal
Justice Vol 38 No 4 pp 807-818
Shane J (2011) ldquoDaily work experiences and police performancerdquo Police Practice and
Research Vol 13 No 3 pp 1-19
Sherman L Gartin P and Buerger M (1989) ldquoHot spots of predatory crime Routine
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Van Meter D (2001) Evaluating dysfunctional police performance A zero-based approach
Charles C Thomas Publisher Springfield IL
Vila B and Kenney D (2002) ldquoTired cops The prevalence and potential consequences of
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Vila B Morrison G and Kenney D (2002) ldquoImproving shift schedule and work-hour
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Weisburd D Maher L Sherman L Buerger M Cohn E and Petrisino A (1993)
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New Directions in Criminological Theory Advances in Criminological Theory Vol 4
pp 45-70
Author Biography
Luke Bonkiewicz is a patrol officer with the Lincoln Police Department
7252019 PIJPSM-05-2015-0064
httpslidepdfcomreaderfullpijpsm-05-2015-0064 2730
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983086 983097) lowast )
7252019 PIJPSM-05-2015-0064
httpslidepdfcomreaderfullpijpsm-05-2015-0064 2830
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983119983142983142983145983139983141983154 983120983154983151983155983141983139983157983156983145983151983150
983122983137983156983141C983110983123
983117983145983150983157983156983141983155983110983125983120
983117983145983150983157983156983141983155
983119983142983142983145983139983141983154
A983155983155983145983155983156983117983145983150983157983156983141983155
983113983150983139983145983140983141983150983156983122983141983152983151983154983156983155
AC983113983122983141983152983151983154983156983155 983123983141983148983141983139983156983145983158983141983155 983127983137983154983150983145983150983143983155 983119983142983142983145983139983145983137983148983155 983127983137983154983154983137983150983156983155 D983125983113983155 983117983145983155983140983141983149983141983137983150983151983154983155 983110983141983148983151983150983145983141983155
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3 72 32496 6312 6600 144 564 444 720 540 84 36 24 12
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5 91 41724 4824 6048 312 636 132 504 324 108 7 24 24
6 83 23916 7056 6192 144 756 528 996 336 132 36 72 12
7 95 24468 6840 5892 156 624 324 456 480 72 36 120 24
8 90 39972 5196 4920 240 504 384 492 264 36 2 72 24
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10 87 21984 5412 4524 348 552 480 792 312 108 48 84 36
983124983137983138983148983141 2 983123983157983149983149983137983154983161 983123983156983137983156983145983155983156983145983139983155 983142983151983154 983126983137983154983145983137983138983148983141983155
983123983141983139983151983150983140983085983116983141983158983141983148 983126983137983154983145983137983138983148983141983155 (983120983137983156983154983151983148 A983154983141983137) 983118=40 983122983137983150983143983141 983123D 983117983141983137983150
983120983151983152983157983148983137983156983145983151983150 983140983141 983150983155983145983156983161 20229830856956 1687 4436
983112983151983157983155983141983144983151983148983140 983149983141983140983145983137983150 983145983150983139983151983149983141 ($) 2628198308576020 16535 49756
983159983144983145983156983141 7398308593 007 083
983126983145983151983148983141983150983156 983139983154983145983149983141 983154983137983156983141 (983152983141983154 100000) 8009830854800 1203 2521
983120983154983151983152983141983154983156983161 983139983154983145983149983141 983154983137983156983141 (983152983141983154 100000) 31009830859800 2027 6524
983120983154983151983152983141983154983156983161 983139983154983145983149983141983155 983152983141983154 983151983142983142983145983139983141983154 5329830851097 1663 8167
983126983145983151983148983141983150983156 983139983154983145983149983141 983152983141983154 983151983142983142983145983139983141983154 194983085372 559 2929
983110983145983154983155983156983085983116983141 983158983141983148 983126 983137983154983145983137983138983148983141 983155 (983119983142983142983145983139983141983154) 983118=302 983122983137983150983143983141 983123D 983117983141 983137983150
983111983141983150983140983141983154 (983142983141983149983137983148983141=0 983149983137983148983141=1) 09830851 24 94
983117983137983154983154983145983141983140C983151983144983137983138983145983156983145983150983143 09830851 18 79
983151983142 C983144983145983148983140983154983141983150 09830855 170 90
983129983141983137983154983155 983141983160983152983141983154983145983141983150983139983141 198308537 779 1063
C983137983148983148983155 983142983151983154 983155983141983154983158983145983139983141 4819830851011 16121 61365
983124983154983137983142983142983145983139 983155983156983151983152983155 16983085901 14376 20624
983110983145983141983148983140 983145983150983142983151983154983149983137983156983145983151983150 983154983141983152983151983154983156983155 0983085227 2153 2530
983124983154983137983142983142983145983139 983139983145983156983137983156983145983151983150983155 249830851246 14632 18642
983117983145983155983140983141983149983141983137983150983151983154 983137983154983154983141983155983156983155 19983085403 5965 10663
983110983141983148983151983150983161 983137983154983154983141983155983156983155 098308571 1066 852
983126983119983122C 9830851959830851043 151 24
1983155983156 2983150983140 3983154983140
104 96 102983123983144983145983142983156
7252019 PIJPSM-05-2015-0064
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983123983141983139983151983150983140983085983116983141983158983141983148 983126983137983154983145983137983138983148983141983155 (983120983137983156983154983151983148 A983154983141983137) 983118=40 983138 983123E 983138 983123E 983138 983123E 983138 983123E
983120983151983152983157983148983137983156983145983151983150 983140983141983150983155983145983156983161 001 003 001 001 98308501 01 00 00
983112983151983157983155983141983144983151983148983140 983149983141983140983145983137983150 983145983150983139983151983149983141 ($) 002 001 983085000 000 00 00 98308500 00
983159983144983145983156983141 13363 32472 9830858921 5664 9830853274 19211 9830854447 3255
983126983145983151983148983141983150983156 983139983154983145983149983141 983154983137983156983141 (983152983141983154 100000) 006 004 001 001 98308501 02 01 02
983120983154983151983152983141983154983156983161 983139983154983145983149983141 983154983137983156983141 (983152983141983154 100000) 983085003 005 983085002 001 02 03 98308501 00
983120983154983151983152983141983154983156983161 983139983154983145983149983141983155 983152983141983154 983151983142983142983145983139983141983154 983085225 99 98308515 03 98308552 24 98308503 03
983126983145983151983148983141983150983156 983139983154983145983149983141 983152983141983154 983151983142983142983145983139983141983154 983085168 68 29 14 76 31 22 12
983110983145983154983155983156983085983116983141983158983141983148 983126983137983154983145983137983138983148983141983155 (983119983142983142983145983139983141983154) 983118=302
983117983137983148983141 4673 1622 366 147 1613 1051 91 158
983117983137983154983154983145983141983140C983151983144983137983138983145983156983145983150983143 9830857516 3254 201 278 9830851158 1169 98308595 167
983151983142 C983144983145983148983140983154983141983150 183 702 75 77 983085262 217 983085396 207
983129983141983137983154983155 983141983160983152983141983154983145983141983150983139983141 9830851065 492 98308512 13 983085163 33 98308506 05C983137983148983148983155 983142983151983154 983155983141983154983158983145983139983141 24 13 02 02 14 01 98308508 05
983124983154983137983142983142983145983139 983155983156983151983152983155 983085983085 983085983085 03 00 09 02 00 00
983110983145983141983148983140 983145983150983142983151983154983149983137983156983145983151983150 983154983141983152983151983154983156983155 209 33 20 04 79 19 10 03
983123983141983139983151983150983140 983155983144983145983142983156 (983154983141983142983141983154983141983150983139983141 983143983154983151983157983152 983142983145983154983155983156) 2603 1778 187 195 809 730 147 160
983124983144983145 983154983140 983155983144983145 983142983156 (983154983141 983142983141 983154983141 983150983139983141 983143983154983151983157983152 983142983145 983154983155983156) 9872 2155 79 213 2532 952 393 132
983113983150983156983141983154983139983141983152983156 32591 5361 758 339 4147 1912 310 484
983123983145983143983149983137983085983155983153983157983137983154983141983140 1541467 19287 206702 5598
C983144983145983085983155983153983157983137983154983141983140 8294 6133 12348 10663
983152 lt 05
983152lt01
983152lt001
983118983151983156983141 A983148983148 983158983137983154983145983137983138983148983141983155 983137983154983141 983157983150983139983141983150983156983141983154983141983140
983127A983122983122A983118983124 A983122983122E983123983124983123 983117983113983123DE983117EA983118983119983122 A983122983122E983123983124983123 983110E983116983119983118983129 A983122983122E983123983124983123983124983122A983110983110983113C C983113983124A983124983113983119983118983123
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983124983137983138983148983141 4 983112983145983141983154983137983154983139983144983145983139983137983148 983116983145983150983141983137983154 983117983151983140983141983148983155 E983155983156983145983149983137983156983145983150983143 983119983142983142983145983139983141983154983155 A983150983150983157983137983148 983126983137983148983157983141 983119983158983141983154 983122983141983152983148983137983139983141983149983141983150983156 C983151983152 (983126983119983122C)
983123983141983139983151983150983140983085983116983141983158983141983148 983126983137983154983145983137983138983148983141983155 (983120983137983156983154983151983148 A983154983141983137) 983118=40 983138 983123E
983120983151983152983157983148983137983156983145983151983150 983140983141983150983155983145983156983161 98308500 00
983112983151983157983155983141983144983151983148983140 983149983141983140983145983137983150 983145983150983139983151983149983141 ($) 00 01
983159983144983145983156983141 318 345
983126983145983151983148983141983150983156 983139983154983145983149983141 983154983137983156983141 (983152983141983154 100000) 98308500 00
983120983154983151983152983141983154983156983161 983139983154983145983149983141 983154983137983156983141 (983152983141983154 100000) 00 00
983120983154983151983152983141983154983156983161 983139983154983145983149983141983155 983152983141983154 983151983142983142983145983139983141983154 98308502 04
983126983145983151983148983141983150983156 983139983154983145983149983141 983152983141983154 983151983142983142983145983139983141983154 02 05
983110983145983154983155983156983085983116983141983158983141983148 983126983137983154983145983137983138983148983141983155 (983119983142983142983145983139983141983154) 983118=302
983117983137983148983141 27 15
983117983137983154983154983145983141983140C983151983144983137983138983145983156983145983150983143 98308532 31
983151983142 C983144983145983148983140983154983141983150 05 03
983129983141983137983154983155 983141983160983152983141983154983145983141983150983139983141 00 01
C983137983148 983148983155 983142983151983154 983155983141983154983158983145983139983141 02 01
983124983154983137983142983142983145983139 983155983156983151983152983155 01 00
983110983145 983141983148983140 983145983150983142983151983154983149983137983156983145 983151983150 983154983141 983152983151983154983156983155 02 00
983123983141983139983151983150983140 983155983144983145983142983156 ( 983154983141983142983141983154983141983150983139983141 983143983154983151983157983152 983142 983145983154983155983156) 23 12
983124983144983145983154983140 983155983144983145983142983156 (983154983141983142983141983154983141983150983139983141 983143983154983151983157983152 983142 983145983154983155983156) 55 14
983113983150983156983141983154983139983141983152983156 165 36
983123983145983143983149983137983085983155983153983157983137983154983141983140 66
C983144983145983085983155983153983157983137983154983141983140 9846
983152 lt 05
983152lt01
983152lt001
983118983151983156983141 A983148983148 983158983137983154983145983137983138983148983141983155 983137983154983141 983157983150983139983141983150983156983141983154983141983140
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a recruit who has not completed the academy performing at ninety percent the capacity of fully
trained police officer4 Using LPDrsquos data VORC creates a hypothetical replacement officer by
dividing the departmental average P-score by the departmental average for productive time and
then multiplying it by the officerrsquos productive time to determine what an average officer would
have generated given the officerrsquos amount of productive time This figure is then multiplied by
9 (the capacity of a replacement officer) divided by the officerrsquos productive time and then
multiplied by 100 to generate the replacement officerrsquos productivity quotient Finally VORC
subtracts the replacement officerrsquos score from the patrol officerrsquos score to calculate the officerrsquos
value over the replacement officer This number or VORC indicates the officerrsquos productivity
per time for self-initiated activities compared to the replacement officerrsquos productivity For
example a VORC of 12 means than officer would produce 012 more productivity points (some
combination of warnings citations and arrests) per minute than a replacement officer
Biographical variables We gathered biographical information about the officers in the
sample including gender years of LPD experience maritalcohabiting status and number of
children We also controlled for whether the officer worked first second or third shift because
our data showed significant differences in the number of calls for service each shift handled as
well as the number of reports each shift completed
Geographic variables It is necessary to measure the characteristics of patrol areas to
account for variation across patrol areas For example some areas may feature different crime
rates or levels of social disorganization which command more department resources and police
officers We gathered data by first mapping out every squadrsquos patrol area and then obtaining
tract-level data from the 2010 US Census Our models included measures of established of
983092
983123983139983144983151983148983137983154983155 983137983150983140 983152983151983148983145983139983161 983149983137983147983141983154983155 983149983137983161 983140983145983155983137983143983154983141983141 983159983145983156983144 983141983155983156983145983149983137983156983145983150983143 983137 983154983141983152983148983137983139983141983149983141983150983156 983151983142983142983145983139983141983154991257983155 983139983137983152983137983139983145983156983161 983137983156 983150983145983150983141983156983161 983152983141983154983139983141983150983156 983119983157983154983143983151983137983148 983145983155 983150983151983156 983156983151 983140983141983142983145983150983145983156983145983158983141983148983161 983141983155983156983145983149983137983156983141 983137 983144983161983152983151983156983144983141983156983145983139983137983148 983151983142983142983145983139983141983154991257983155 983152983154983151983140983157983139983156983145983158983145983156983161 983138983157983156 983149983141983154983141983148983161 983156983151 983141983155983156983137983138983148983145983155983144 983137 983154983141983137983155983151983150983137983138983148983141 983142983145983160983141983140
983152983151983145983150983156 983137983143983137983145983150983155983156 983159983144983145983139983144 983156983151 983139983151983149983152983137983154983141 983137983148983148 983152983137983156983154983151983148 983151983142983142983145983139983141983154983155
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correlates of crime including population density (Hipp amp Roussell 2013) household income
(Gould Weinberg and Mustard 2002) and racial composition (Light amp Harris 2012)
Specifically we use each arearsquos population density median household income and percent
white We also included measures for each arearsquos violent and property crime rate Finally we
gathered information about the number of violent crimes (murders assaults aggravated assaults
sexual assaults and robberies) property crimes (larcenies burglaries vandalisms frauds
forgeries and motor vehicle thefts) and number of police officers in each patrol area thereby
creating variables for violent crime per officer and property crime per officer
Since LPDrsquos patrol officers are grouped with squads it is reasonable to believe that an
officerrsquos productivity might be more similar to an officer within their squad compared to an
officer in a different squad To account for the potential correlations between the individual
units as well as the multi-level nature of the data we used hierarchical linear modeling to
analyze the data
Our primary level variables include an officerrsquos gender marital status number of
children years of law enforcement experience shift calls for service annual number of traffic
stops and citations field information reports and misdemeanor felony and warrant arrests We
also used the officersrsquo productivity information to calculate each patrol officerrsquos VORC
Our secondary level variables include the violent crime per officer rate and property
crime per officer rate for the patrol area of each squad as well several control variables
including population density household median income percent white and the patrol arearsquos
violent and property crime rate Table 2 displays the summary statistics for the first and second-
level variables
INSERT TABLE 2 HERE
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Results
We hypothesized that the violent and property crime-to-cop ratios of an area might affect
the raw productivity outputs of patrol officers as well as a more advanced productivity metric
To examine these questions we constructed five hierarchical linear models estimating the
correlations between reported violent and property crimes per officer and a patrol officerrsquos
annual traffic citations warrant misdemeanor felony arrests and Value Over Replacement Cop
Although not presented here we estimated null models for all dependent variables and found that
significant variation existed between squads when we included no variables included in the
models The chi-square for the LR test for all five null models was statistically significant (p lt
000 in all models)
Table 3 shows that a rise in reported violent crimes per officer increases an officerrsquos
number of warrants and misdemeanors (b= 29 and 76) while arise in reported property crimes
per officer decreases an officerrsquos number of warrants and misdemeanors (b= -15 and -52) We
found no relationship between the crime-to-cop ratios and felony arrests However both the
rates of violent and property crimes per officer decreased the number of traffic citations an
officer writes (b= -168 and -225)
Insert Table 3 here
Table 3 also shows that traffic stops and field information reports are positively
associated with warrant and misdemeanor arrests This supports what police practitioners
already suspect the cops with the most arrests are extremely proactive patrol officers who stop a
large number of vehicles and build investigations through the use of field contacts and
information reports For instance an officer can build felony narcotics cases by contacting
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suspects and informants in the field documenting these efforts through field information reports
and eventually using this information to write search warrants and conduct drug interdiction
Next we investigated whether this relationship between the crime-to-cop ratios and
productivity outputs exists when using an advanced productivity metric specifically an officerrsquos
Value Over Replacement Cop (VORC)
INSERT TABLE 4 HERE
Table 4 shows that there is no correlation between the ratio of property and violent
crimes per officer and VORC Although this stands in contrast to our earlier results we would
not expect to find a relationship between these variables and VORC Advanced metrics such as
VORC evaluate officers based on the amount of time for self-initiated activities (productive
time) and not raw productivity outputs like misdemeanor or felony arrests Even if an officer
took an extremely large number of property crimes thereby decreasing their productive time
VORC would only evaluate that officer based on what the officer produced with the small
amount of productive time This suggests that while a teamrsquos crime-to-cop ratio could affect an
officerrsquos raw productivity outputs ie arrests a teamrsquos crime-to-cop ratio would not affect an
officerrsquos VORC
For example during a given month an officer may take an unusually large number of
burglaries or larcenies thereby leaving the officer with only ten hours for self-initiated activities
With this reduced amount of productive time the officer may generate a below-average number
of officials warrants misdemeanors and felonies which in turn may lead a supervisor to
potentially downgrade the officerrsquos productivity However VORC would recognize and account
for the high number of calls for service minutes and analyze the officerrsquos productivity based on
what the officer generated with only ten hours of productive time It may be that the officer was
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actually very productive but they simply didnrsquot have enough productive time to generate the
same number of traffic citations and arrests as an officer who responded to far fewer property
crimes
Table 4 also shows that traffic stops and field intelligence reports are positively
correlated with VORC These results are consistent with the principles of how VORC evaluates
patrol officer productivity and provides additional support for the validity of this advanced
metric VORC accounts for numerous variables that affect productive time (ie calls for service
minutes) meaning that the key factors which increase or decrease an officerrsquos VORC are largely
related to self-initiated activities (ie traffic stops and information reports) VORC therefore
accounts for the potential effects of macro-level variables and analyzes the activities of the
officer during his or her productive time Nonetheless we recognize that because traffic stops
and field information reports are correlated with VORC an arearsquos crime-to-cop ratio might still
affect an officerrsquos VORC indirectly by impacting the number of stops and information reports
they generate
Discussion
There are several factors which affect a patrol officerrsquos productivity including
biographical characteristics officersrsquo perceptions and attitudes and organizational and
operational features among others This paper identified two macro-level variablesmdashproperty
crimes per officer and violent crimes per officermdashwhich affect the number of traffic citations
warrants misdemeanors and felonies generated annually by an officer Using data from a mid-
sized police department we found that a patrol areas rate of property crimes per officer was
associated with a moderate decrease in an officers annual number of traffic citations warrant
arrests and misdemeanor arrests while a patrol areas rate of violent crimes per officer was also
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associated with a moderate decrease in an officers annual number of traffic citations We also
found that a patrol areas rate of violent crimes per officer is associated with a moderate increase
in an officers annual number of warrant and misdemeanor arrests However when we used a
more advanced productivity metric we found no relationship between property and violent
crimes per officer and an officerrsquos Value Over Replacement Cop or VORC
There are several factors which explain the differing effects of the crime-to-cop ratios on
productivity outputs Let us first address why the violent crime-to-cop ratio would increase a
patrol officerrsquos warrants and misdemeanors while the property crime-to-cop ratio would decrease
these outputs Notably half of all victims of violent crimes know the offender (Truman amp Rand
2010) which aids law enforcement in identifying and locating the perpetrator Moreover both
suspect and victim are frequently on scene when police officers arrive such as during domestic
disturbances Being able to identify and locate the perpetrator helps patrol officers make arrests
thereby increasing their misdemeanor and felony arrests in violent crimes Additionally when
patrol officers arrive and contact individuals during an incident it is standard procedure to check
if the parties involved have outstanding warrants This means that the greater number of violent
crimes that officers respond to the more chances they have to arrest individuals who might have
outstanding warrants It makes sense then that officers who respond to a greater number of
violent crimes have more chances to make warrant and misdemeanor arrests
Conversely property crimes may supply the officer with fewer warrant and misdemeanor
arrests because of low clearance rates We examined the difference in clearance rates between
property and violent crime using incident data from the Lincoln Police Department and we
found that the clearance rate for violent crimes was 418 compared to only 237 for property
crimes Moreover property crimes often also require more investigative time than violent
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crimes Property crime investigations often require considerable follow-up by officers such as
processing a scene contacting financial institutions and checking pawnshops for stolen property
In general property crimes are more time-intensive than violent crimes thereby decreasing
productive time and opportunities for proactive activities while simultaneously netting officers
fewer arrests because of low clearance rates
The impact of a teamrsquos crime-to-cop ratio can also be seen in the relationship between
property and violent crime per officer and the number of traffic citations Table 3 shows that a
rise in both reported violent and property crimes per officer decrease an officerrsquos number of
traffic citations Quite simply if officers respond to a high number of property and violent
crimes then they will have fewer minutes to conduct traffic enforcement and other proactive
enforcement
As noted it is possible that the correlation is bi-directional If an officer makes a greater
number of arrests then a team may witness a decrease in crime because prolific criminals may
have been arrested and placed in jail For example one might interpret Table 3 to mean that an
increase in warrant and misdemeanor warrants led to a decrease in the number of property crimes
per officer However under this hypothesis we should also see that an increase in warrant and
misdemeanor arrests leads to a decrease in the number of violent crimes per officer Instead we
see the opposite hypothesized effect ie more arrests leading to more reported violent crimes 5
983093 983127983141 983139983151983150983155983156983154983157983139983156983141983140 983154983141983143983154983141983155983155983145983151983150 983149983151983140983141983148983155 983157983155983145983150983143 983139983145983156983137983156983145983151983150983155 983159983137983154983154983137983150983156 983137983154983154983141983155983156983155 983149983145983155983140983141983149983141983137983150983151983154 983137983154983154983141983155983156983155 983137983150983140 983142983141983148983151983150983161 983137983154983154983141983155983156983155 983142983154983151983149
983156983144983141 983152983154983141983139983141983140983145983150983143 983149983151983150983156983144 983156983151 983152983154983141983140983145983139983156 983138983151983156983144 983158983145983151983148983141983150983156 983137983150983140 983152983154983151983152983141983154983156983161 983139983154983145983149983141 983154983137983156983141983155 983142983151983154 983156983144983141 983149983151983150983156983144983155 983145983150 983151983157983154 983140983137983156983137983155983141983156 983127983141 983142983151983157983150983140 983150983151
983155983156983137983156983145983155983156983145983139983137983148983148983161 983155983145983143983150983145983142983145983139983137983150983156 983154983141983148983137983156983145983151983150983155983144983145983152983155 983138983141983156983159983141983141983150 983139983145983156983137983156983145983151983150983155 983159983137983154983154983137983150983156 983137983154983154983141983155983156983155 983149983145983155983140983141983149983141983137983150983151983154 983137983154983154983141983155983156983155 983137983150983140 983142983141983148983151983150983161 983137983154983154983141983155983156983155
983137983150983140 983156983144983141 983158983145983151983148983141983150983156 983137983150983140 983152983154983151983152983141983154983156983161 983139983154983145983149983141 983154983137983156983141983155 983151983142 983156983144983141 983138983141983137983156983155 983112983151983159983141983158983141983154 983137983142983156983141983154 983140983145983155983137983143983143983154983141983143983137983156983145983150983143 983156983144983141 983158983145983151983148983141983150983156 983137983150983140 983152983154983151983152983141983154983156983161
983139983154983145983149983141 983154983137983156983141983155 983145983150983156983151 983155983152983141983139983145983142983145983139 983139983154983145983149983141983155 983159983141 983140983145983140 983142983145983150983140 983156983144983137983156 983149983145983155983140983141983149983141983137983150983151983154 983137983150983140 983142983141983148983151983150983161 983137983154983154983141983155983156983155 983155983145983143983150983145983142983145983139983137983150983156983148983161 983152983154983141983140983145983139983156983141983140 983137 983140983141983139983154983141983137983155983141
983145983150 983154983151983138983138983141983154983161 983154983137983156983141983155 983159983144983145983139983144 983145983155 983139983151983150983155983145983155983156983141983150983156 983159983145983156983144 983156983144983141 983148983145983156983141983154983137983156983157983154983141 983105983140983140983145983156983145983151983150983137983148983148983161 983159983141 983145983150983156983141983154983158983145983141983159983141983140 983140983141983152983137983154983156983149983141983150983156 983152983141983154983155983151983150983150983141983148 983137983150983140983148983141983137983154983150983141983140 983156983144983137983156 983156983144983141 983116983145983150983139983151983148983150 983120983151983148983145983139983141 983108983141983152983137983154983156983149983141983150983156 983144983137983140 983155983156983137983142983142983141983140 983137 983156983141983137983149 983140983141983155983145983143983150983141983140 983155983152983141983139983145983142983145983139983137983148983148983161 983156983151 983137983152983152983154983141983144983141983150983140 983154983151983138983138983141983154983161
983155983157983155983152983141983139983156983155 983140983157983154983145983150983143 983156983144983145983155 983156983145983149983141983142983154983137983149983141 983159983144983145983139983144 983155983141983141983149983155 983156983151 983141983160983152983148983137983145983150 983156983144983141 983137983154983154983141983155983156983085983154983151983138983138983141983154983161 983154983137983156983141 983154983141983148983137983156983145983151983150983155983144983145983152
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We believe our explanation has a stronger theoretical foundation The ratio of property
crimes per officer affects productivity outputs differently than the ratio of violent crimes per
officer for two reasons First violent crimes are different in nature than property crimes
especially with regard to clearance rates Second property crimes often require more
investigative and follow-up time than violent crimes thereby reducing an officerrsquos amount of
productive time and proactive activity
Table 3 also provides a key piece of evidence to support this assertion Table 3 shows
that the number of reported violent crimes per officer increases an officerrsquos number of warrant
and misdemeanor arrests Yet the relationship is the opposite for traffic enforcement the violent
crime-to-cop ratio actually decreases traffic citations This change is due to the fact that while
violent crime investigations often yield warrant and misdemeanor arrests they do not produce
traffic citations Furthermore responding to a greater number of violent crimes decreases the
amount of available time to conduct traffic enforcement in turn reducing the number of traffic
citations In other words the violent crime-to-cop ratio affects traffic stops the same way that
the property crime-to-cop ratio affects all productivity outputs by reducing an officerrsquos
productive time and opportunities for proactivity In fact we found a moderate correlation
between a patrol arearsquos crime-to-cop ratio and the amount of productive time enjoyed by officers
who worked that area The correlation between an arearsquos violent crime-to-cop ratio and
productive time was -21 while the correlation between an arearsquos property crime-to-cop ratio and
productive time was -33 We believe this evidence indicates that officers who work in areas
with a high crime-to-cop ratio have less time for self-initiated activities which drives down
productivity
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We suggest that the number of police officers and crime rates of an area need to be
analyzed in conjunction with each other Prior theories of police productivity have viewed and
examined these variables separately when in reality we should view these variables in tandem
Combining these variables into crime-to-cop ratios provides a more nuancedmdashand accuratemdash
understanding of patrol activities and their relationship to productivity
Although we found strong evidence that an officerrsquos patrol area affects his or her
productivity (in terms of raw outputs) we also found that a few officer-level variables affected
productivity as well including traffic stops intelligence reports and working third shift Our
results appear to substantiate what many police practitioners already suspect officers who are
proactive in contacting citizens (traffic stops) and building investigations (intelligence reports)
generate more warrant and misdemeanor arrests In addition while some newer police officers
are forced to work third shift because they do not have sufficient seniority to bid for better days
off and shift times many officers work nights because they enjoy the types of calls (eg in-
progress felonies) meaning they may be more motivated to stop vehicles make field contacts
and in turn engage in activities that generate arrests (Brehm amp Gates 1993)
These results have important implications for police researchers and administrators
First our results suggest a need for a deeper understanding of how the staffing levels crime
rates and the other characteristics of a patrol area intersect Second if departments evaluate
their patrol officers using raw productivity outputs such as misdemeanor or felony arrests
administrators must recognize that factors beyond the control of patrol officers might affect
monthly or annual productivity Our research suggests that a patrol arearsquos crime-to-cop ratio can
substantially affect a patrol officerrsquos productivity regardless of the actual crime rate If the
characteristics of a patrol area affect raw productivity outputs police departments should employ
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more advanced productivity analytics (ie VORC) that account for activities which reduce
productive time and evaluate an officerrsquos productivity in terms of available minutes for
proactivity
Nonetheless evaluating both patrol officers and police departments using traffic citations
warrants misdemeanors and felonies is useful because this type of data is readily understood by
citizens and civic officials A neighborhood watch group for instance might care more about
the number of arrests their officers make and less about those officersrsquo productivity scores Our
data suggests that if citizens want patrol officers to write more traffic citations and make more
arrests they should lobby for more police officers on the street It is true that reducing an arearsquos
crime rate impacts the crime-to-cop ratio but our results indicate that putting police officers on
the street may be the most direct way to affect the crime-to-cop ratio
Our study is not without limitations We only examined a police department which uses
the generalist style of policing and it is unknown if our results are applicable to police
departments which use a specialist style of policing Theoretically a departmentrsquos policing
styles can affect factors that increase or decrease a patrol officerrsquos productive time It is possible
that the number of property crimes and violent crimes per officer do not significantly affect a
specialist patrol officerrsquos productivity outputs versus a generalist officerrsquos outputs Future
studies should investigate whether our results can be generalized to all police departments
We have only identified two macro-level variables which affect patrol officer
productivity and we recognize there may be other variables In addition we recommend that
future studies incorporate a wider array of variables and test competing models to determine
whether micro or macro-level variables exhibit stronger effects on productivity In the end
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managing patrol officers and improving productivity requires a greater understanding of both
patrol officers and the areas they work
Acknowledgements and Credits We would like to thank Denver Police Department Crime
Analyst Melissa Ohlhorst and Pennsylvania State University Professor Barry Ruback as well as
the three anonymous reviewers for their insightful comments We also salute Lincoln Police
Sergeant Jake Dilsaver for his logistical support of this project
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References
Amaranto E Steinberg J Castellano C and Mitchell R (2003) ldquoPolice stress interventionsrdquo
Brief Treatment and Crisis Intervention Vol 3 No 1 pp 47-53
Becker G (1985) ldquoHuman capital effort and the sexual division of laborrdquo The Journal of
Labor EconomicsVol 3 No 1 pp 33-58
Bonkiewicz L (2015) ldquoBobbies and baseball players Evaluating patrol officer productivity
using sabermetricsrdquo Police Quarterly Vol 18 No 1 pp 55-78
Braga A and Bond J (2008) ldquoPolicing crime and disorder hot spots A randomized controlled
trialrdquo Criminology Vol 46 No 3 pp 577-607
Brown J and Campbell E (1990) ldquoSources of occupational stress in the policerdquo Work
and Stress Vol 4 No 4 pp 305-318
Chappell A MacDonald J and Manz P (2006) ldquoThe organizational determinants
of police arrest decisionsrdquo Crime and Delinquency Vol 52 No 2 pp 287-306
Cloninger D and Sartorius L (1979) ldquoCrime rates clearance rates and enforcement effort
The case of Houston Texasrdquo American Journal of Economics and Sociology Vol 38
No 4 pp 389-402
Corman H and Mocan N (2005) ldquoCarrots sticks and broken windowsrdquo Journal of Law and
Economics Vol 48 No 1 pp 235-266
Crank J (1990) ldquoThe influence of environmental and organizational factors on police style in
urban and rural environmentsrdquo Journal of Research in Crime and Delinquency Vol 27
pp 166-189
Goodman A (1990) ldquoA model for police officer burnoutrdquo Journal of Business and
Psychology Vol 5 No 1 pp 85-99
Gould E Weinberg B and Mustard D (2002) Crime rates and local labor marketopportunities in the United States 1979ndash1997rdquo Review of Economics and Statistics Vol
84 No 1 pp 45-61
Hipp J and Roussell A (2013) ldquoMicro-and macro-environment population and the
consequences for crime ratesrdquo Social Forces Vol 92 No 2 pp 563-595
Hatry H (1976) ldquoApproaches to productivity measurement and program evaluationrdquo Public
Productivity Review Vol 1 No 3 pp21-28
Hawkins H (2001) ldquoPolice officer burnout a partial replication of Maslachs Burnout
Inventoryrdquo Police Quarterly Vol 4 No 3 pp 343-360
7252019 PIJPSM-05-2015-0064
httpslidepdfcomreaderfullpijpsm-05-2015-0064 2530
Herbert S (1998)ldquoPolice subculture reconsideredrdquo Criminology Vol 36 No 2 pp 343-370
Huizinga D and Henry K (2008) ldquoThe effect of arrest and justice system sanctions on
subsequent behavior Findings from longitudinal and other studiesrdquo In The long view of
crime A synthesis of longitudinal research Springer New York pp 220-254
Johnson R (2011) ldquoOfficer attitudes and management influences on police work productivityrdquo
American Journal of Criminal Justice Vol 36 pp 293-306
Kane R (2006) ldquoOn the limits of social control Structural deterrence and the policing of
lsquosuppressiblersquo crimesrdquo Justice Quarterly Vol 23 No 2 pp 186-213
Kubrin C Messner S Deane G McGeever K and Stucky T (2010) ldquoProactive
policing and robbery rates across US citiesrdquo Criminology Vol 48 No 1 pp 57-97
Lersch K (2002) ldquoAre citizen complaints just another measure of officer productivity An
analysis of citizen complaints and officer activity measuresrdquo Police Practice and
Research Vol 3 pp 135-147
Light M and Harris C (2012) Race space and violence exploring spatial dependence in
structural covariates of white and black violent crime in US countiesrdquo Journal of
Quantitative Criminology Vol 28 No 4 pp 559-586
Maxfield M (1982) ldquoService time dispatch time and demand for police services Helping
more by serving lessrdquo Public Administration Review pp 252-263
Maxfield M Lewis D and Szoc R (1980) ldquoProducing official crimes Verified crime reports
as measures of police outputrdquo Social Science Quarterly pp 221-236
Nakosteen R and Zimmer M (2001) ldquoSpouse selection and earnings Evidence of marital
sortingrdquo Economic InquiryVol 39 No 2 pp 201-2013
Shane J (2010) ldquoOrganizational stressors and police performancerdquo Journal of Criminal
Justice Vol 38 No 4 pp 807-818
Shane J (2011) ldquoDaily work experiences and police performancerdquo Police Practice and
Research Vol 13 No 3 pp 1-19
Sherman L Gartin P and Buerger M (1989) ldquoHot spots of predatory crime Routine
activities and the criminology of placerdquoCriminology Vol 27 No 1 pp27-56
Truman J and Rand M (2010) ldquoBureau of Justice Statistics Bulletin National Crime
Victimization Survey Criminal Victimization 2009rdquo US Department of Justice Office of
Justice programs
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Van Meter D (2001) Evaluating dysfunctional police performance A zero-based approach
Charles C Thomas Publisher Springfield IL
Vila B and Kenney D (2002) ldquoTired cops The prevalence and potential consequences of
police fatiguerdquo National Institute of Justice
Vila B Morrison G and Kenney D (2002) ldquoImproving shift schedule and work-hour
policies and practices to increase police officer performance health and safetyrdquo Police
Quarterly Vol 5 No 1 pp 4-24
Vollaard B and Koning P (2009) ldquoThe effect of police on crime disorder and victim
PrecautionEvidence from a Dutch victimization surveyrdquo International Review of Law
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Weisburd D Maher L Sherman L Buerger M Cohn E and Petrisino A (1993)
ldquoContrasting crime general and crime specific theory The case of hot spots of crimerdquo
New Directions in Criminological Theory Advances in Criminological Theory Vol 4
pp 45-70
Author Biography
Luke Bonkiewicz is a patrol officer with the Lincoln Police Department
7252019 PIJPSM-05-2015-0064
httpslidepdfcomreaderfullpijpsm-05-2015-0064 2730
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983101 983089983088983088lowast + + ) + ) + ) + ) lowast )) minus + + + 983090983088 + ) +983089983088)) minus 983089983088983088 lowast
983086 983097) lowast )
7252019 PIJPSM-05-2015-0064
httpslidepdfcomreaderfullpijpsm-05-2015-0064 2830
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983119983142983142983145983139983141983154 983120983154983151983155983141983139983157983156983145983151983150
983122983137983156983141C983110983123
983117983145983150983157983156983141983155983110983125983120
983117983145983150983157983156983141983155
983119983142983142983145983139983141983154
A983155983155983145983155983156983117983145983150983157983156983141983155
983113983150983139983145983140983141983150983156983122983141983152983151983154983156983155
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5 91 41724 4824 6048 312 636 132 504 324 108 7 24 24
6 83 23916 7056 6192 144 756 528 996 336 132 36 72 12
7 95 24468 6840 5892 156 624 324 456 480 72 36 120 24
8 90 39972 5196 4920 240 504 384 492 264 36 2 72 24
9 79 25500 4068 5100 336 396 192 684 204 24 36 120 10
10 87 21984 5412 4524 348 552 480 792 312 108 48 84 36
983124983137983138983148983141 2 983123983157983149983149983137983154983161 983123983156983137983156983145983155983156983145983139983155 983142983151983154 983126983137983154983145983137983138983148983141983155
983123983141983139983151983150983140983085983116983141983158983141983148 983126983137983154983145983137983138983148983141983155 (983120983137983156983154983151983148 A983154983141983137) 983118=40 983122983137983150983143983141 983123D 983117983141983137983150
983120983151983152983157983148983137983156983145983151983150 983140983141 983150983155983145983156983161 20229830856956 1687 4436
983112983151983157983155983141983144983151983148983140 983149983141983140983145983137983150 983145983150983139983151983149983141 ($) 2628198308576020 16535 49756
983159983144983145983156983141 7398308593 007 083
983126983145983151983148983141983150983156 983139983154983145983149983141 983154983137983156983141 (983152983141983154 100000) 8009830854800 1203 2521
983120983154983151983152983141983154983156983161 983139983154983145983149983141 983154983137983156983141 (983152983141983154 100000) 31009830859800 2027 6524
983120983154983151983152983141983154983156983161 983139983154983145983149983141983155 983152983141983154 983151983142983142983145983139983141983154 5329830851097 1663 8167
983126983145983151983148983141983150983156 983139983154983145983149983141 983152983141983154 983151983142983142983145983139983141983154 194983085372 559 2929
983110983145983154983155983156983085983116983141 983158983141983148 983126 983137983154983145983137983138983148983141 983155 (983119983142983142983145983139983141983154) 983118=302 983122983137983150983143983141 983123D 983117983141 983137983150
983111983141983150983140983141983154 (983142983141983149983137983148983141=0 983149983137983148983141=1) 09830851 24 94
983117983137983154983154983145983141983140C983151983144983137983138983145983156983145983150983143 09830851 18 79
983151983142 C983144983145983148983140983154983141983150 09830855 170 90
983129983141983137983154983155 983141983160983152983141983154983145983141983150983139983141 198308537 779 1063
C983137983148983148983155 983142983151983154 983155983141983154983158983145983139983141 4819830851011 16121 61365
983124983154983137983142983142983145983139 983155983156983151983152983155 16983085901 14376 20624
983110983145983141983148983140 983145983150983142983151983154983149983137983156983145983151983150 983154983141983152983151983154983156983155 0983085227 2153 2530
983124983154983137983142983142983145983139 983139983145983156983137983156983145983151983150983155 249830851246 14632 18642
983117983145983155983140983141983149983141983137983150983151983154 983137983154983154983141983155983156983155 19983085403 5965 10663
983110983141983148983151983150983161 983137983154983154983141983155983156983155 098308571 1066 852
983126983119983122C 9830851959830851043 151 24
1983155983156 2983150983140 3983154983140
104 96 102983123983144983145983142983156
7252019 PIJPSM-05-2015-0064
httpslidepdfcomreaderfullpijpsm-05-2015-0064 2930
983124983137983138983148983141 3 983112983145983141983154983137983154983139983144983145983139983137983148 983116983145983150983141983137983154 983117983151983140983141983148983155 E983155983156983145983149983137983156983145983150983143 983119983142 983142983145983139983141983154983155 A983150983150983157983137983148 983124983154983137983142983142983145983139 C983145983156983137983156983145983151983150983155 983127983137983154983154983137983150983156983155 983117983145983155983140983141983149983141983137983150983151983154983155 983137983150983140 983110983141983148983151983150 983145983141983155
983123983141983139983151983150983140983085983116983141983158983141983148 983126983137983154983145983137983138983148983141983155 (983120983137983156983154983151983148 A983154983141983137) 983118=40 983138 983123E 983138 983123E 983138 983123E 983138 983123E
983120983151983152983157983148983137983156983145983151983150 983140983141983150983155983145983156983161 001 003 001 001 98308501 01 00 00
983112983151983157983155983141983144983151983148983140 983149983141983140983145983137983150 983145983150983139983151983149983141 ($) 002 001 983085000 000 00 00 98308500 00
983159983144983145983156983141 13363 32472 9830858921 5664 9830853274 19211 9830854447 3255
983126983145983151983148983141983150983156 983139983154983145983149983141 983154983137983156983141 (983152983141983154 100000) 006 004 001 001 98308501 02 01 02
983120983154983151983152983141983154983156983161 983139983154983145983149983141 983154983137983156983141 (983152983141983154 100000) 983085003 005 983085002 001 02 03 98308501 00
983120983154983151983152983141983154983156983161 983139983154983145983149983141983155 983152983141983154 983151983142983142983145983139983141983154 983085225 99 98308515 03 98308552 24 98308503 03
983126983145983151983148983141983150983156 983139983154983145983149983141 983152983141983154 983151983142983142983145983139983141983154 983085168 68 29 14 76 31 22 12
983110983145983154983155983156983085983116983141983158983141983148 983126983137983154983145983137983138983148983141983155 (983119983142983142983145983139983141983154) 983118=302
983117983137983148983141 4673 1622 366 147 1613 1051 91 158
983117983137983154983154983145983141983140C983151983144983137983138983145983156983145983150983143 9830857516 3254 201 278 9830851158 1169 98308595 167
983151983142 C983144983145983148983140983154983141983150 183 702 75 77 983085262 217 983085396 207
983129983141983137983154983155 983141983160983152983141983154983145983141983150983139983141 9830851065 492 98308512 13 983085163 33 98308506 05C983137983148983148983155 983142983151983154 983155983141983154983158983145983139983141 24 13 02 02 14 01 98308508 05
983124983154983137983142983142983145983139 983155983156983151983152983155 983085983085 983085983085 03 00 09 02 00 00
983110983145983141983148983140 983145983150983142983151983154983149983137983156983145983151983150 983154983141983152983151983154983156983155 209 33 20 04 79 19 10 03
983123983141983139983151983150983140 983155983144983145983142983156 (983154983141983142983141983154983141983150983139983141 983143983154983151983157983152 983142983145983154983155983156) 2603 1778 187 195 809 730 147 160
983124983144983145 983154983140 983155983144983145 983142983156 (983154983141 983142983141 983154983141 983150983139983141 983143983154983151983157983152 983142983145 983154983155983156) 9872 2155 79 213 2532 952 393 132
983113983150983156983141983154983139983141983152983156 32591 5361 758 339 4147 1912 310 484
983123983145983143983149983137983085983155983153983157983137983154983141983140 1541467 19287 206702 5598
C983144983145983085983155983153983157983137983154983141983140 8294 6133 12348 10663
983152 lt 05
983152lt01
983152lt001
983118983151983156983141 A983148983148 983158983137983154983145983137983138983148983141983155 983137983154983141 983157983150983139983141983150983156983141983154983141983140
983127A983122983122A983118983124 A983122983122E983123983124983123 983117983113983123DE983117EA983118983119983122 A983122983122E983123983124983123 983110E983116983119983118983129 A983122983122E983123983124983123983124983122A983110983110983113C C983113983124A983124983113983119983118983123
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983124983137983138983148983141 4 983112983145983141983154983137983154983139983144983145983139983137983148 983116983145983150983141983137983154 983117983151983140983141983148983155 E983155983156983145983149983137983156983145983150983143 983119983142983142983145983139983141983154983155 A983150983150983157983137983148 983126983137983148983157983141 983119983158983141983154 983122983141983152983148983137983139983141983149983141983150983156 C983151983152 (983126983119983122C)
983123983141983139983151983150983140983085983116983141983158983141983148 983126983137983154983145983137983138983148983141983155 (983120983137983156983154983151983148 A983154983141983137) 983118=40 983138 983123E
983120983151983152983157983148983137983156983145983151983150 983140983141983150983155983145983156983161 98308500 00
983112983151983157983155983141983144983151983148983140 983149983141983140983145983137983150 983145983150983139983151983149983141 ($) 00 01
983159983144983145983156983141 318 345
983126983145983151983148983141983150983156 983139983154983145983149983141 983154983137983156983141 (983152983141983154 100000) 98308500 00
983120983154983151983152983141983154983156983161 983139983154983145983149983141 983154983137983156983141 (983152983141983154 100000) 00 00
983120983154983151983152983141983154983156983161 983139983154983145983149983141983155 983152983141983154 983151983142983142983145983139983141983154 98308502 04
983126983145983151983148983141983150983156 983139983154983145983149983141 983152983141983154 983151983142983142983145983139983141983154 02 05
983110983145983154983155983156983085983116983141983158983141983148 983126983137983154983145983137983138983148983141983155 (983119983142983142983145983139983141983154) 983118=302
983117983137983148983141 27 15
983117983137983154983154983145983141983140C983151983144983137983138983145983156983145983150983143 98308532 31
983151983142 C983144983145983148983140983154983141983150 05 03
983129983141983137983154983155 983141983160983152983141983154983145983141983150983139983141 00 01
C983137983148 983148983155 983142983151983154 983155983141983154983158983145983139983141 02 01
983124983154983137983142983142983145983139 983155983156983151983152983155 01 00
983110983145 983141983148983140 983145983150983142983151983154983149983137983156983145 983151983150 983154983141 983152983151983154983156983155 02 00
983123983141983139983151983150983140 983155983144983145983142983156 ( 983154983141983142983141983154983141983150983139983141 983143983154983151983157983152 983142 983145983154983155983156) 23 12
983124983144983145983154983140 983155983144983145983142983156 (983154983141983142983141983154983141983150983139983141 983143983154983151983157983152 983142 983145983154983155983156) 55 14
983113983150983156983141983154983139983141983152983156 165 36
983123983145983143983149983137983085983155983153983157983137983154983141983140 66
C983144983145983085983155983153983157983137983154983141983140 9846
983152 lt 05
983152lt01
983152lt001
983118983151983156983141 A983148983148 983158983137983154983145983137983138983148983141983155 983137983154983141 983157983150983139983141983150983156983141983154983141983140
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correlates of crime including population density (Hipp amp Roussell 2013) household income
(Gould Weinberg and Mustard 2002) and racial composition (Light amp Harris 2012)
Specifically we use each arearsquos population density median household income and percent
white We also included measures for each arearsquos violent and property crime rate Finally we
gathered information about the number of violent crimes (murders assaults aggravated assaults
sexual assaults and robberies) property crimes (larcenies burglaries vandalisms frauds
forgeries and motor vehicle thefts) and number of police officers in each patrol area thereby
creating variables for violent crime per officer and property crime per officer
Since LPDrsquos patrol officers are grouped with squads it is reasonable to believe that an
officerrsquos productivity might be more similar to an officer within their squad compared to an
officer in a different squad To account for the potential correlations between the individual
units as well as the multi-level nature of the data we used hierarchical linear modeling to
analyze the data
Our primary level variables include an officerrsquos gender marital status number of
children years of law enforcement experience shift calls for service annual number of traffic
stops and citations field information reports and misdemeanor felony and warrant arrests We
also used the officersrsquo productivity information to calculate each patrol officerrsquos VORC
Our secondary level variables include the violent crime per officer rate and property
crime per officer rate for the patrol area of each squad as well several control variables
including population density household median income percent white and the patrol arearsquos
violent and property crime rate Table 2 displays the summary statistics for the first and second-
level variables
INSERT TABLE 2 HERE
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Results
We hypothesized that the violent and property crime-to-cop ratios of an area might affect
the raw productivity outputs of patrol officers as well as a more advanced productivity metric
To examine these questions we constructed five hierarchical linear models estimating the
correlations between reported violent and property crimes per officer and a patrol officerrsquos
annual traffic citations warrant misdemeanor felony arrests and Value Over Replacement Cop
Although not presented here we estimated null models for all dependent variables and found that
significant variation existed between squads when we included no variables included in the
models The chi-square for the LR test for all five null models was statistically significant (p lt
000 in all models)
Table 3 shows that a rise in reported violent crimes per officer increases an officerrsquos
number of warrants and misdemeanors (b= 29 and 76) while arise in reported property crimes
per officer decreases an officerrsquos number of warrants and misdemeanors (b= -15 and -52) We
found no relationship between the crime-to-cop ratios and felony arrests However both the
rates of violent and property crimes per officer decreased the number of traffic citations an
officer writes (b= -168 and -225)
Insert Table 3 here
Table 3 also shows that traffic stops and field information reports are positively
associated with warrant and misdemeanor arrests This supports what police practitioners
already suspect the cops with the most arrests are extremely proactive patrol officers who stop a
large number of vehicles and build investigations through the use of field contacts and
information reports For instance an officer can build felony narcotics cases by contacting
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suspects and informants in the field documenting these efforts through field information reports
and eventually using this information to write search warrants and conduct drug interdiction
Next we investigated whether this relationship between the crime-to-cop ratios and
productivity outputs exists when using an advanced productivity metric specifically an officerrsquos
Value Over Replacement Cop (VORC)
INSERT TABLE 4 HERE
Table 4 shows that there is no correlation between the ratio of property and violent
crimes per officer and VORC Although this stands in contrast to our earlier results we would
not expect to find a relationship between these variables and VORC Advanced metrics such as
VORC evaluate officers based on the amount of time for self-initiated activities (productive
time) and not raw productivity outputs like misdemeanor or felony arrests Even if an officer
took an extremely large number of property crimes thereby decreasing their productive time
VORC would only evaluate that officer based on what the officer produced with the small
amount of productive time This suggests that while a teamrsquos crime-to-cop ratio could affect an
officerrsquos raw productivity outputs ie arrests a teamrsquos crime-to-cop ratio would not affect an
officerrsquos VORC
For example during a given month an officer may take an unusually large number of
burglaries or larcenies thereby leaving the officer with only ten hours for self-initiated activities
With this reduced amount of productive time the officer may generate a below-average number
of officials warrants misdemeanors and felonies which in turn may lead a supervisor to
potentially downgrade the officerrsquos productivity However VORC would recognize and account
for the high number of calls for service minutes and analyze the officerrsquos productivity based on
what the officer generated with only ten hours of productive time It may be that the officer was
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actually very productive but they simply didnrsquot have enough productive time to generate the
same number of traffic citations and arrests as an officer who responded to far fewer property
crimes
Table 4 also shows that traffic stops and field intelligence reports are positively
correlated with VORC These results are consistent with the principles of how VORC evaluates
patrol officer productivity and provides additional support for the validity of this advanced
metric VORC accounts for numerous variables that affect productive time (ie calls for service
minutes) meaning that the key factors which increase or decrease an officerrsquos VORC are largely
related to self-initiated activities (ie traffic stops and information reports) VORC therefore
accounts for the potential effects of macro-level variables and analyzes the activities of the
officer during his or her productive time Nonetheless we recognize that because traffic stops
and field information reports are correlated with VORC an arearsquos crime-to-cop ratio might still
affect an officerrsquos VORC indirectly by impacting the number of stops and information reports
they generate
Discussion
There are several factors which affect a patrol officerrsquos productivity including
biographical characteristics officersrsquo perceptions and attitudes and organizational and
operational features among others This paper identified two macro-level variablesmdashproperty
crimes per officer and violent crimes per officermdashwhich affect the number of traffic citations
warrants misdemeanors and felonies generated annually by an officer Using data from a mid-
sized police department we found that a patrol areas rate of property crimes per officer was
associated with a moderate decrease in an officers annual number of traffic citations warrant
arrests and misdemeanor arrests while a patrol areas rate of violent crimes per officer was also
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associated with a moderate decrease in an officers annual number of traffic citations We also
found that a patrol areas rate of violent crimes per officer is associated with a moderate increase
in an officers annual number of warrant and misdemeanor arrests However when we used a
more advanced productivity metric we found no relationship between property and violent
crimes per officer and an officerrsquos Value Over Replacement Cop or VORC
There are several factors which explain the differing effects of the crime-to-cop ratios on
productivity outputs Let us first address why the violent crime-to-cop ratio would increase a
patrol officerrsquos warrants and misdemeanors while the property crime-to-cop ratio would decrease
these outputs Notably half of all victims of violent crimes know the offender (Truman amp Rand
2010) which aids law enforcement in identifying and locating the perpetrator Moreover both
suspect and victim are frequently on scene when police officers arrive such as during domestic
disturbances Being able to identify and locate the perpetrator helps patrol officers make arrests
thereby increasing their misdemeanor and felony arrests in violent crimes Additionally when
patrol officers arrive and contact individuals during an incident it is standard procedure to check
if the parties involved have outstanding warrants This means that the greater number of violent
crimes that officers respond to the more chances they have to arrest individuals who might have
outstanding warrants It makes sense then that officers who respond to a greater number of
violent crimes have more chances to make warrant and misdemeanor arrests
Conversely property crimes may supply the officer with fewer warrant and misdemeanor
arrests because of low clearance rates We examined the difference in clearance rates between
property and violent crime using incident data from the Lincoln Police Department and we
found that the clearance rate for violent crimes was 418 compared to only 237 for property
crimes Moreover property crimes often also require more investigative time than violent
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crimes Property crime investigations often require considerable follow-up by officers such as
processing a scene contacting financial institutions and checking pawnshops for stolen property
In general property crimes are more time-intensive than violent crimes thereby decreasing
productive time and opportunities for proactive activities while simultaneously netting officers
fewer arrests because of low clearance rates
The impact of a teamrsquos crime-to-cop ratio can also be seen in the relationship between
property and violent crime per officer and the number of traffic citations Table 3 shows that a
rise in both reported violent and property crimes per officer decrease an officerrsquos number of
traffic citations Quite simply if officers respond to a high number of property and violent
crimes then they will have fewer minutes to conduct traffic enforcement and other proactive
enforcement
As noted it is possible that the correlation is bi-directional If an officer makes a greater
number of arrests then a team may witness a decrease in crime because prolific criminals may
have been arrested and placed in jail For example one might interpret Table 3 to mean that an
increase in warrant and misdemeanor warrants led to a decrease in the number of property crimes
per officer However under this hypothesis we should also see that an increase in warrant and
misdemeanor arrests leads to a decrease in the number of violent crimes per officer Instead we
see the opposite hypothesized effect ie more arrests leading to more reported violent crimes 5
983093 983127983141 983139983151983150983155983156983154983157983139983156983141983140 983154983141983143983154983141983155983155983145983151983150 983149983151983140983141983148983155 983157983155983145983150983143 983139983145983156983137983156983145983151983150983155 983159983137983154983154983137983150983156 983137983154983154983141983155983156983155 983149983145983155983140983141983149983141983137983150983151983154 983137983154983154983141983155983156983155 983137983150983140 983142983141983148983151983150983161 983137983154983154983141983155983156983155 983142983154983151983149
983156983144983141 983152983154983141983139983141983140983145983150983143 983149983151983150983156983144 983156983151 983152983154983141983140983145983139983156 983138983151983156983144 983158983145983151983148983141983150983156 983137983150983140 983152983154983151983152983141983154983156983161 983139983154983145983149983141 983154983137983156983141983155 983142983151983154 983156983144983141 983149983151983150983156983144983155 983145983150 983151983157983154 983140983137983156983137983155983141983156 983127983141 983142983151983157983150983140 983150983151
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983137983150983140 983156983144983141 983158983145983151983148983141983150983156 983137983150983140 983152983154983151983152983141983154983156983161 983139983154983145983149983141 983154983137983156983141983155 983151983142 983156983144983141 983138983141983137983156983155 983112983151983159983141983158983141983154 983137983142983156983141983154 983140983145983155983137983143983143983154983141983143983137983156983145983150983143 983156983144983141 983158983145983151983148983141983150983156 983137983150983140 983152983154983151983152983141983154983156983161
983139983154983145983149983141 983154983137983156983141983155 983145983150983156983151 983155983152983141983139983145983142983145983139 983139983154983145983149983141983155 983159983141 983140983145983140 983142983145983150983140 983156983144983137983156 983149983145983155983140983141983149983141983137983150983151983154 983137983150983140 983142983141983148983151983150983161 983137983154983154983141983155983156983155 983155983145983143983150983145983142983145983139983137983150983156983148983161 983152983154983141983140983145983139983156983141983140 983137 983140983141983139983154983141983137983155983141
983145983150 983154983151983138983138983141983154983161 983154983137983156983141983155 983159983144983145983139983144 983145983155 983139983151983150983155983145983155983156983141983150983156 983159983145983156983144 983156983144983141 983148983145983156983141983154983137983156983157983154983141 983105983140983140983145983156983145983151983150983137983148983148983161 983159983141 983145983150983156983141983154983158983145983141983159983141983140 983140983141983152983137983154983156983149983141983150983156 983152983141983154983155983151983150983150983141983148 983137983150983140983148983141983137983154983150983141983140 983156983144983137983156 983156983144983141 983116983145983150983139983151983148983150 983120983151983148983145983139983141 983108983141983152983137983154983156983149983141983150983156 983144983137983140 983155983156983137983142983142983141983140 983137 983156983141983137983149 983140983141983155983145983143983150983141983140 983155983152983141983139983145983142983145983139983137983148983148983161 983156983151 983137983152983152983154983141983144983141983150983140 983154983151983138983138983141983154983161
983155983157983155983152983141983139983156983155 983140983157983154983145983150983143 983156983144983145983155 983156983145983149983141983142983154983137983149983141 983159983144983145983139983144 983155983141983141983149983155 983156983151 983141983160983152983148983137983145983150 983156983144983141 983137983154983154983141983155983156983085983154983151983138983138983141983154983161 983154983137983156983141 983154983141983148983137983156983145983151983150983155983144983145983152
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We believe our explanation has a stronger theoretical foundation The ratio of property
crimes per officer affects productivity outputs differently than the ratio of violent crimes per
officer for two reasons First violent crimes are different in nature than property crimes
especially with regard to clearance rates Second property crimes often require more
investigative and follow-up time than violent crimes thereby reducing an officerrsquos amount of
productive time and proactive activity
Table 3 also provides a key piece of evidence to support this assertion Table 3 shows
that the number of reported violent crimes per officer increases an officerrsquos number of warrant
and misdemeanor arrests Yet the relationship is the opposite for traffic enforcement the violent
crime-to-cop ratio actually decreases traffic citations This change is due to the fact that while
violent crime investigations often yield warrant and misdemeanor arrests they do not produce
traffic citations Furthermore responding to a greater number of violent crimes decreases the
amount of available time to conduct traffic enforcement in turn reducing the number of traffic
citations In other words the violent crime-to-cop ratio affects traffic stops the same way that
the property crime-to-cop ratio affects all productivity outputs by reducing an officerrsquos
productive time and opportunities for proactivity In fact we found a moderate correlation
between a patrol arearsquos crime-to-cop ratio and the amount of productive time enjoyed by officers
who worked that area The correlation between an arearsquos violent crime-to-cop ratio and
productive time was -21 while the correlation between an arearsquos property crime-to-cop ratio and
productive time was -33 We believe this evidence indicates that officers who work in areas
with a high crime-to-cop ratio have less time for self-initiated activities which drives down
productivity
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We suggest that the number of police officers and crime rates of an area need to be
analyzed in conjunction with each other Prior theories of police productivity have viewed and
examined these variables separately when in reality we should view these variables in tandem
Combining these variables into crime-to-cop ratios provides a more nuancedmdashand accuratemdash
understanding of patrol activities and their relationship to productivity
Although we found strong evidence that an officerrsquos patrol area affects his or her
productivity (in terms of raw outputs) we also found that a few officer-level variables affected
productivity as well including traffic stops intelligence reports and working third shift Our
results appear to substantiate what many police practitioners already suspect officers who are
proactive in contacting citizens (traffic stops) and building investigations (intelligence reports)
generate more warrant and misdemeanor arrests In addition while some newer police officers
are forced to work third shift because they do not have sufficient seniority to bid for better days
off and shift times many officers work nights because they enjoy the types of calls (eg in-
progress felonies) meaning they may be more motivated to stop vehicles make field contacts
and in turn engage in activities that generate arrests (Brehm amp Gates 1993)
These results have important implications for police researchers and administrators
First our results suggest a need for a deeper understanding of how the staffing levels crime
rates and the other characteristics of a patrol area intersect Second if departments evaluate
their patrol officers using raw productivity outputs such as misdemeanor or felony arrests
administrators must recognize that factors beyond the control of patrol officers might affect
monthly or annual productivity Our research suggests that a patrol arearsquos crime-to-cop ratio can
substantially affect a patrol officerrsquos productivity regardless of the actual crime rate If the
characteristics of a patrol area affect raw productivity outputs police departments should employ
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more advanced productivity analytics (ie VORC) that account for activities which reduce
productive time and evaluate an officerrsquos productivity in terms of available minutes for
proactivity
Nonetheless evaluating both patrol officers and police departments using traffic citations
warrants misdemeanors and felonies is useful because this type of data is readily understood by
citizens and civic officials A neighborhood watch group for instance might care more about
the number of arrests their officers make and less about those officersrsquo productivity scores Our
data suggests that if citizens want patrol officers to write more traffic citations and make more
arrests they should lobby for more police officers on the street It is true that reducing an arearsquos
crime rate impacts the crime-to-cop ratio but our results indicate that putting police officers on
the street may be the most direct way to affect the crime-to-cop ratio
Our study is not without limitations We only examined a police department which uses
the generalist style of policing and it is unknown if our results are applicable to police
departments which use a specialist style of policing Theoretically a departmentrsquos policing
styles can affect factors that increase or decrease a patrol officerrsquos productive time It is possible
that the number of property crimes and violent crimes per officer do not significantly affect a
specialist patrol officerrsquos productivity outputs versus a generalist officerrsquos outputs Future
studies should investigate whether our results can be generalized to all police departments
We have only identified two macro-level variables which affect patrol officer
productivity and we recognize there may be other variables In addition we recommend that
future studies incorporate a wider array of variables and test competing models to determine
whether micro or macro-level variables exhibit stronger effects on productivity In the end
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managing patrol officers and improving productivity requires a greater understanding of both
patrol officers and the areas they work
Acknowledgements and Credits We would like to thank Denver Police Department Crime
Analyst Melissa Ohlhorst and Pennsylvania State University Professor Barry Ruback as well as
the three anonymous reviewers for their insightful comments We also salute Lincoln Police
Sergeant Jake Dilsaver for his logistical support of this project
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References
Amaranto E Steinberg J Castellano C and Mitchell R (2003) ldquoPolice stress interventionsrdquo
Brief Treatment and Crisis Intervention Vol 3 No 1 pp 47-53
Becker G (1985) ldquoHuman capital effort and the sexual division of laborrdquo The Journal of
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Bonkiewicz L (2015) ldquoBobbies and baseball players Evaluating patrol officer productivity
using sabermetricsrdquo Police Quarterly Vol 18 No 1 pp 55-78
Braga A and Bond J (2008) ldquoPolicing crime and disorder hot spots A randomized controlled
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Brown J and Campbell E (1990) ldquoSources of occupational stress in the policerdquo Work
and Stress Vol 4 No 4 pp 305-318
Chappell A MacDonald J and Manz P (2006) ldquoThe organizational determinants
of police arrest decisionsrdquo Crime and Delinquency Vol 52 No 2 pp 287-306
Cloninger D and Sartorius L (1979) ldquoCrime rates clearance rates and enforcement effort
The case of Houston Texasrdquo American Journal of Economics and Sociology Vol 38
No 4 pp 389-402
Corman H and Mocan N (2005) ldquoCarrots sticks and broken windowsrdquo Journal of Law and
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Crank J (1990) ldquoThe influence of environmental and organizational factors on police style in
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pp 166-189
Goodman A (1990) ldquoA model for police officer burnoutrdquo Journal of Business and
Psychology Vol 5 No 1 pp 85-99
Gould E Weinberg B and Mustard D (2002) Crime rates and local labor marketopportunities in the United States 1979ndash1997rdquo Review of Economics and Statistics Vol
84 No 1 pp 45-61
Hipp J and Roussell A (2013) ldquoMicro-and macro-environment population and the
consequences for crime ratesrdquo Social Forces Vol 92 No 2 pp 563-595
Hatry H (1976) ldquoApproaches to productivity measurement and program evaluationrdquo Public
Productivity Review Vol 1 No 3 pp21-28
Hawkins H (2001) ldquoPolice officer burnout a partial replication of Maslachs Burnout
Inventoryrdquo Police Quarterly Vol 4 No 3 pp 343-360
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Herbert S (1998)ldquoPolice subculture reconsideredrdquo Criminology Vol 36 No 2 pp 343-370
Huizinga D and Henry K (2008) ldquoThe effect of arrest and justice system sanctions on
subsequent behavior Findings from longitudinal and other studiesrdquo In The long view of
crime A synthesis of longitudinal research Springer New York pp 220-254
Johnson R (2011) ldquoOfficer attitudes and management influences on police work productivityrdquo
American Journal of Criminal Justice Vol 36 pp 293-306
Kane R (2006) ldquoOn the limits of social control Structural deterrence and the policing of
lsquosuppressiblersquo crimesrdquo Justice Quarterly Vol 23 No 2 pp 186-213
Kubrin C Messner S Deane G McGeever K and Stucky T (2010) ldquoProactive
policing and robbery rates across US citiesrdquo Criminology Vol 48 No 1 pp 57-97
Lersch K (2002) ldquoAre citizen complaints just another measure of officer productivity An
analysis of citizen complaints and officer activity measuresrdquo Police Practice and
Research Vol 3 pp 135-147
Light M and Harris C (2012) Race space and violence exploring spatial dependence in
structural covariates of white and black violent crime in US countiesrdquo Journal of
Quantitative Criminology Vol 28 No 4 pp 559-586
Maxfield M (1982) ldquoService time dispatch time and demand for police services Helping
more by serving lessrdquo Public Administration Review pp 252-263
Maxfield M Lewis D and Szoc R (1980) ldquoProducing official crimes Verified crime reports
as measures of police outputrdquo Social Science Quarterly pp 221-236
Nakosteen R and Zimmer M (2001) ldquoSpouse selection and earnings Evidence of marital
sortingrdquo Economic InquiryVol 39 No 2 pp 201-2013
Shane J (2010) ldquoOrganizational stressors and police performancerdquo Journal of Criminal
Justice Vol 38 No 4 pp 807-818
Shane J (2011) ldquoDaily work experiences and police performancerdquo Police Practice and
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Sherman L Gartin P and Buerger M (1989) ldquoHot spots of predatory crime Routine
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Van Meter D (2001) Evaluating dysfunctional police performance A zero-based approach
Charles C Thomas Publisher Springfield IL
Vila B and Kenney D (2002) ldquoTired cops The prevalence and potential consequences of
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Vila B Morrison G and Kenney D (2002) ldquoImproving shift schedule and work-hour
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New Directions in Criminological Theory Advances in Criminological Theory Vol 4
pp 45-70
Author Biography
Luke Bonkiewicz is a patrol officer with the Lincoln Police Department
7252019 PIJPSM-05-2015-0064
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983101 983089983088983088lowast + + ) + ) + ) + ) lowast )) minus + + + 983090983088 + ) +983089983088)) minus 983089983088983088 lowast
983086 983097) lowast )
7252019 PIJPSM-05-2015-0064
httpslidepdfcomreaderfullpijpsm-05-2015-0064 2830
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983119983142983142983145983139983141983154 983120983154983151983155983141983139983157983156983145983151983150
983122983137983156983141C983110983123
983117983145983150983157983156983141983155983110983125983120
983117983145983150983157983156983141983155
983119983142983142983145983139983141983154
A983155983155983145983155983156983117983145983150983157983156983141983155
983113983150983139983145983140983141983150983156983122983141983152983151983154983156983155
AC983113983122983141983152983151983154983156983155 983123983141983148983141983139983156983145983158983141983155 983127983137983154983150983145983150983143983155 983119983142983142983145983139983145983137983148983155 983127983137983154983154983137983150983156983155 D983125983113983155 983117983145983155983140983141983149983141983137983150983151983154983155 983110983141983148983151983150983145983141983155
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3 72 32496 6312 6600 144 564 444 720 540 84 36 24 12
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5 91 41724 4824 6048 312 636 132 504 324 108 7 24 24
6 83 23916 7056 6192 144 756 528 996 336 132 36 72 12
7 95 24468 6840 5892 156 624 324 456 480 72 36 120 24
8 90 39972 5196 4920 240 504 384 492 264 36 2 72 24
9 79 25500 4068 5100 336 396 192 684 204 24 36 120 10
10 87 21984 5412 4524 348 552 480 792 312 108 48 84 36
983124983137983138983148983141 2 983123983157983149983149983137983154983161 983123983156983137983156983145983155983156983145983139983155 983142983151983154 983126983137983154983145983137983138983148983141983155
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983120983151983152983157983148983137983156983145983151983150 983140983141 983150983155983145983156983161 20229830856956 1687 4436
983112983151983157983155983141983144983151983148983140 983149983141983140983145983137983150 983145983150983139983151983149983141 ($) 2628198308576020 16535 49756
983159983144983145983156983141 7398308593 007 083
983126983145983151983148983141983150983156 983139983154983145983149983141 983154983137983156983141 (983152983141983154 100000) 8009830854800 1203 2521
983120983154983151983152983141983154983156983161 983139983154983145983149983141 983154983137983156983141 (983152983141983154 100000) 31009830859800 2027 6524
983120983154983151983152983141983154983156983161 983139983154983145983149983141983155 983152983141983154 983151983142983142983145983139983141983154 5329830851097 1663 8167
983126983145983151983148983141983150983156 983139983154983145983149983141 983152983141983154 983151983142983142983145983139983141983154 194983085372 559 2929
983110983145983154983155983156983085983116983141 983158983141983148 983126 983137983154983145983137983138983148983141 983155 (983119983142983142983145983139983141983154) 983118=302 983122983137983150983143983141 983123D 983117983141 983137983150
983111983141983150983140983141983154 (983142983141983149983137983148983141=0 983149983137983148983141=1) 09830851 24 94
983117983137983154983154983145983141983140C983151983144983137983138983145983156983145983150983143 09830851 18 79
983151983142 C983144983145983148983140983154983141983150 09830855 170 90
983129983141983137983154983155 983141983160983152983141983154983145983141983150983139983141 198308537 779 1063
C983137983148983148983155 983142983151983154 983155983141983154983158983145983139983141 4819830851011 16121 61365
983124983154983137983142983142983145983139 983155983156983151983152983155 16983085901 14376 20624
983110983145983141983148983140 983145983150983142983151983154983149983137983156983145983151983150 983154983141983152983151983154983156983155 0983085227 2153 2530
983124983154983137983142983142983145983139 983139983145983156983137983156983145983151983150983155 249830851246 14632 18642
983117983145983155983140983141983149983141983137983150983151983154 983137983154983154983141983155983156983155 19983085403 5965 10663
983110983141983148983151983150983161 983137983154983154983141983155983156983155 098308571 1066 852
983126983119983122C 9830851959830851043 151 24
1983155983156 2983150983140 3983154983140
104 96 102983123983144983145983142983156
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983123983141983139983151983150983140983085983116983141983158983141983148 983126983137983154983145983137983138983148983141983155 (983120983137983156983154983151983148 A983154983141983137) 983118=40 983138 983123E 983138 983123E 983138 983123E 983138 983123E
983120983151983152983157983148983137983156983145983151983150 983140983141983150983155983145983156983161 001 003 001 001 98308501 01 00 00
983112983151983157983155983141983144983151983148983140 983149983141983140983145983137983150 983145983150983139983151983149983141 ($) 002 001 983085000 000 00 00 98308500 00
983159983144983145983156983141 13363 32472 9830858921 5664 9830853274 19211 9830854447 3255
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983120983154983151983152983141983154983156983161 983139983154983145983149983141 983154983137983156983141 (983152983141983154 100000) 983085003 005 983085002 001 02 03 98308501 00
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983126983145983151983148983141983150983156 983139983154983145983149983141 983152983141983154 983151983142983142983145983139983141983154 983085168 68 29 14 76 31 22 12
983110983145983154983155983156983085983116983141983158983141983148 983126983137983154983145983137983138983148983141983155 (983119983142983142983145983139983141983154) 983118=302
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983129983141983137983154983155 983141983160983152983141983154983145983141983150983139983141 9830851065 492 98308512 13 983085163 33 98308506 05C983137983148983148983155 983142983151983154 983155983141983154983158983145983139983141 24 13 02 02 14 01 98308508 05
983124983154983137983142983142983145983139 983155983156983151983152983155 983085983085 983085983085 03 00 09 02 00 00
983110983145983141983148983140 983145983150983142983151983154983149983137983156983145983151983150 983154983141983152983151983154983156983155 209 33 20 04 79 19 10 03
983123983141983139983151983150983140 983155983144983145983142983156 (983154983141983142983141983154983141983150983139983141 983143983154983151983157983152 983142983145983154983155983156) 2603 1778 187 195 809 730 147 160
983124983144983145 983154983140 983155983144983145 983142983156 (983154983141 983142983141 983154983141 983150983139983141 983143983154983151983157983152 983142983145 983154983155983156) 9872 2155 79 213 2532 952 393 132
983113983150983156983141983154983139983141983152983156 32591 5361 758 339 4147 1912 310 484
983123983145983143983149983137983085983155983153983157983137983154983141983140 1541467 19287 206702 5598
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983118983151983156983141 A983148983148 983158983137983154983145983137983138983148983141983155 983137983154983141 983157983150983139983141983150983156983141983154983141983140
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7252019 PIJPSM-05-2015-0064
httpslidepdfcomreaderfullpijpsm-05-2015-0064 3030
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983123983141983139983151983150983140983085983116983141983158983141983148 983126983137983154983145983137983138983148983141983155 (983120983137983156983154983151983148 A983154983141983137) 983118=40 983138 983123E
983120983151983152983157983148983137983156983145983151983150 983140983141983150983155983145983156983161 98308500 00
983112983151983157983155983141983144983151983148983140 983149983141983140983145983137983150 983145983150983139983151983149983141 ($) 00 01
983159983144983145983156983141 318 345
983126983145983151983148983141983150983156 983139983154983145983149983141 983154983137983156983141 (983152983141983154 100000) 98308500 00
983120983154983151983152983141983154983156983161 983139983154983145983149983141 983154983137983156983141 (983152983141983154 100000) 00 00
983120983154983151983152983141983154983156983161 983139983154983145983149983141983155 983152983141983154 983151983142983142983145983139983141983154 98308502 04
983126983145983151983148983141983150983156 983139983154983145983149983141 983152983141983154 983151983142983142983145983139983141983154 02 05
983110983145983154983155983156983085983116983141983158983141983148 983126983137983154983145983137983138983148983141983155 (983119983142983142983145983139983141983154) 983118=302
983117983137983148983141 27 15
983117983137983154983154983145983141983140C983151983144983137983138983145983156983145983150983143 98308532 31
983151983142 C983144983145983148983140983154983141983150 05 03
983129983141983137983154983155 983141983160983152983141983154983145983141983150983139983141 00 01
C983137983148 983148983155 983142983151983154 983155983141983154983158983145983139983141 02 01
983124983154983137983142983142983145983139 983155983156983151983152983155 01 00
983110983145 983141983148983140 983145983150983142983151983154983149983137983156983145 983151983150 983154983141 983152983151983154983156983155 02 00
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983124983144983145983154983140 983155983144983145983142983156 (983154983141983142983141983154983141983150983139983141 983143983154983151983157983152 983142 983145983154983155983156) 55 14
983113983150983156983141983154983139983141983152983156 165 36
983123983145983143983149983137983085983155983153983157983137983154983141983140 66
C983144983145983085983155983153983157983137983154983141983140 9846
983152 lt 05
983152lt01
983152lt001
983118983151983156983141 A983148983148 983158983137983154983145983137983138983148983141983155 983137983154983141 983157983150983139983141983150983156983141983154983141983140
7252019 PIJPSM-05-2015-0064
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Results
We hypothesized that the violent and property crime-to-cop ratios of an area might affect
the raw productivity outputs of patrol officers as well as a more advanced productivity metric
To examine these questions we constructed five hierarchical linear models estimating the
correlations between reported violent and property crimes per officer and a patrol officerrsquos
annual traffic citations warrant misdemeanor felony arrests and Value Over Replacement Cop
Although not presented here we estimated null models for all dependent variables and found that
significant variation existed between squads when we included no variables included in the
models The chi-square for the LR test for all five null models was statistically significant (p lt
000 in all models)
Table 3 shows that a rise in reported violent crimes per officer increases an officerrsquos
number of warrants and misdemeanors (b= 29 and 76) while arise in reported property crimes
per officer decreases an officerrsquos number of warrants and misdemeanors (b= -15 and -52) We
found no relationship between the crime-to-cop ratios and felony arrests However both the
rates of violent and property crimes per officer decreased the number of traffic citations an
officer writes (b= -168 and -225)
Insert Table 3 here
Table 3 also shows that traffic stops and field information reports are positively
associated with warrant and misdemeanor arrests This supports what police practitioners
already suspect the cops with the most arrests are extremely proactive patrol officers who stop a
large number of vehicles and build investigations through the use of field contacts and
information reports For instance an officer can build felony narcotics cases by contacting
7252019 PIJPSM-05-2015-0064
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suspects and informants in the field documenting these efforts through field information reports
and eventually using this information to write search warrants and conduct drug interdiction
Next we investigated whether this relationship between the crime-to-cop ratios and
productivity outputs exists when using an advanced productivity metric specifically an officerrsquos
Value Over Replacement Cop (VORC)
INSERT TABLE 4 HERE
Table 4 shows that there is no correlation between the ratio of property and violent
crimes per officer and VORC Although this stands in contrast to our earlier results we would
not expect to find a relationship between these variables and VORC Advanced metrics such as
VORC evaluate officers based on the amount of time for self-initiated activities (productive
time) and not raw productivity outputs like misdemeanor or felony arrests Even if an officer
took an extremely large number of property crimes thereby decreasing their productive time
VORC would only evaluate that officer based on what the officer produced with the small
amount of productive time This suggests that while a teamrsquos crime-to-cop ratio could affect an
officerrsquos raw productivity outputs ie arrests a teamrsquos crime-to-cop ratio would not affect an
officerrsquos VORC
For example during a given month an officer may take an unusually large number of
burglaries or larcenies thereby leaving the officer with only ten hours for self-initiated activities
With this reduced amount of productive time the officer may generate a below-average number
of officials warrants misdemeanors and felonies which in turn may lead a supervisor to
potentially downgrade the officerrsquos productivity However VORC would recognize and account
for the high number of calls for service minutes and analyze the officerrsquos productivity based on
what the officer generated with only ten hours of productive time It may be that the officer was
7252019 PIJPSM-05-2015-0064
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actually very productive but they simply didnrsquot have enough productive time to generate the
same number of traffic citations and arrests as an officer who responded to far fewer property
crimes
Table 4 also shows that traffic stops and field intelligence reports are positively
correlated with VORC These results are consistent with the principles of how VORC evaluates
patrol officer productivity and provides additional support for the validity of this advanced
metric VORC accounts for numerous variables that affect productive time (ie calls for service
minutes) meaning that the key factors which increase or decrease an officerrsquos VORC are largely
related to self-initiated activities (ie traffic stops and information reports) VORC therefore
accounts for the potential effects of macro-level variables and analyzes the activities of the
officer during his or her productive time Nonetheless we recognize that because traffic stops
and field information reports are correlated with VORC an arearsquos crime-to-cop ratio might still
affect an officerrsquos VORC indirectly by impacting the number of stops and information reports
they generate
Discussion
There are several factors which affect a patrol officerrsquos productivity including
biographical characteristics officersrsquo perceptions and attitudes and organizational and
operational features among others This paper identified two macro-level variablesmdashproperty
crimes per officer and violent crimes per officermdashwhich affect the number of traffic citations
warrants misdemeanors and felonies generated annually by an officer Using data from a mid-
sized police department we found that a patrol areas rate of property crimes per officer was
associated with a moderate decrease in an officers annual number of traffic citations warrant
arrests and misdemeanor arrests while a patrol areas rate of violent crimes per officer was also
7252019 PIJPSM-05-2015-0064
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associated with a moderate decrease in an officers annual number of traffic citations We also
found that a patrol areas rate of violent crimes per officer is associated with a moderate increase
in an officers annual number of warrant and misdemeanor arrests However when we used a
more advanced productivity metric we found no relationship between property and violent
crimes per officer and an officerrsquos Value Over Replacement Cop or VORC
There are several factors which explain the differing effects of the crime-to-cop ratios on
productivity outputs Let us first address why the violent crime-to-cop ratio would increase a
patrol officerrsquos warrants and misdemeanors while the property crime-to-cop ratio would decrease
these outputs Notably half of all victims of violent crimes know the offender (Truman amp Rand
2010) which aids law enforcement in identifying and locating the perpetrator Moreover both
suspect and victim are frequently on scene when police officers arrive such as during domestic
disturbances Being able to identify and locate the perpetrator helps patrol officers make arrests
thereby increasing their misdemeanor and felony arrests in violent crimes Additionally when
patrol officers arrive and contact individuals during an incident it is standard procedure to check
if the parties involved have outstanding warrants This means that the greater number of violent
crimes that officers respond to the more chances they have to arrest individuals who might have
outstanding warrants It makes sense then that officers who respond to a greater number of
violent crimes have more chances to make warrant and misdemeanor arrests
Conversely property crimes may supply the officer with fewer warrant and misdemeanor
arrests because of low clearance rates We examined the difference in clearance rates between
property and violent crime using incident data from the Lincoln Police Department and we
found that the clearance rate for violent crimes was 418 compared to only 237 for property
crimes Moreover property crimes often also require more investigative time than violent
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crimes Property crime investigations often require considerable follow-up by officers such as
processing a scene contacting financial institutions and checking pawnshops for stolen property
In general property crimes are more time-intensive than violent crimes thereby decreasing
productive time and opportunities for proactive activities while simultaneously netting officers
fewer arrests because of low clearance rates
The impact of a teamrsquos crime-to-cop ratio can also be seen in the relationship between
property and violent crime per officer and the number of traffic citations Table 3 shows that a
rise in both reported violent and property crimes per officer decrease an officerrsquos number of
traffic citations Quite simply if officers respond to a high number of property and violent
crimes then they will have fewer minutes to conduct traffic enforcement and other proactive
enforcement
As noted it is possible that the correlation is bi-directional If an officer makes a greater
number of arrests then a team may witness a decrease in crime because prolific criminals may
have been arrested and placed in jail For example one might interpret Table 3 to mean that an
increase in warrant and misdemeanor warrants led to a decrease in the number of property crimes
per officer However under this hypothesis we should also see that an increase in warrant and
misdemeanor arrests leads to a decrease in the number of violent crimes per officer Instead we
see the opposite hypothesized effect ie more arrests leading to more reported violent crimes 5
983093 983127983141 983139983151983150983155983156983154983157983139983156983141983140 983154983141983143983154983141983155983155983145983151983150 983149983151983140983141983148983155 983157983155983145983150983143 983139983145983156983137983156983145983151983150983155 983159983137983154983154983137983150983156 983137983154983154983141983155983156983155 983149983145983155983140983141983149983141983137983150983151983154 983137983154983154983141983155983156983155 983137983150983140 983142983141983148983151983150983161 983137983154983154983141983155983156983155 983142983154983151983149
983156983144983141 983152983154983141983139983141983140983145983150983143 983149983151983150983156983144 983156983151 983152983154983141983140983145983139983156 983138983151983156983144 983158983145983151983148983141983150983156 983137983150983140 983152983154983151983152983141983154983156983161 983139983154983145983149983141 983154983137983156983141983155 983142983151983154 983156983144983141 983149983151983150983156983144983155 983145983150 983151983157983154 983140983137983156983137983155983141983156 983127983141 983142983151983157983150983140 983150983151
983155983156983137983156983145983155983156983145983139983137983148983148983161 983155983145983143983150983145983142983145983139983137983150983156 983154983141983148983137983156983145983151983150983155983144983145983152983155 983138983141983156983159983141983141983150 983139983145983156983137983156983145983151983150983155 983159983137983154983154983137983150983156 983137983154983154983141983155983156983155 983149983145983155983140983141983149983141983137983150983151983154 983137983154983154983141983155983156983155 983137983150983140 983142983141983148983151983150983161 983137983154983154983141983155983156983155
983137983150983140 983156983144983141 983158983145983151983148983141983150983156 983137983150983140 983152983154983151983152983141983154983156983161 983139983154983145983149983141 983154983137983156983141983155 983151983142 983156983144983141 983138983141983137983156983155 983112983151983159983141983158983141983154 983137983142983156983141983154 983140983145983155983137983143983143983154983141983143983137983156983145983150983143 983156983144983141 983158983145983151983148983141983150983156 983137983150983140 983152983154983151983152983141983154983156983161
983139983154983145983149983141 983154983137983156983141983155 983145983150983156983151 983155983152983141983139983145983142983145983139 983139983154983145983149983141983155 983159983141 983140983145983140 983142983145983150983140 983156983144983137983156 983149983145983155983140983141983149983141983137983150983151983154 983137983150983140 983142983141983148983151983150983161 983137983154983154983141983155983156983155 983155983145983143983150983145983142983145983139983137983150983156983148983161 983152983154983141983140983145983139983156983141983140 983137 983140983141983139983154983141983137983155983141
983145983150 983154983151983138983138983141983154983161 983154983137983156983141983155 983159983144983145983139983144 983145983155 983139983151983150983155983145983155983156983141983150983156 983159983145983156983144 983156983144983141 983148983145983156983141983154983137983156983157983154983141 983105983140983140983145983156983145983151983150983137983148983148983161 983159983141 983145983150983156983141983154983158983145983141983159983141983140 983140983141983152983137983154983156983149983141983150983156 983152983141983154983155983151983150983150983141983148 983137983150983140983148983141983137983154983150983141983140 983156983144983137983156 983156983144983141 983116983145983150983139983151983148983150 983120983151983148983145983139983141 983108983141983152983137983154983156983149983141983150983156 983144983137983140 983155983156983137983142983142983141983140 983137 983156983141983137983149 983140983141983155983145983143983150983141983140 983155983152983141983139983145983142983145983139983137983148983148983161 983156983151 983137983152983152983154983141983144983141983150983140 983154983151983138983138983141983154983161
983155983157983155983152983141983139983156983155 983140983157983154983145983150983143 983156983144983145983155 983156983145983149983141983142983154983137983149983141 983159983144983145983139983144 983155983141983141983149983155 983156983151 983141983160983152983148983137983145983150 983156983144983141 983137983154983154983141983155983156983085983154983151983138983138983141983154983161 983154983137983156983141 983154983141983148983137983156983145983151983150983155983144983145983152
7252019 PIJPSM-05-2015-0064
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We believe our explanation has a stronger theoretical foundation The ratio of property
crimes per officer affects productivity outputs differently than the ratio of violent crimes per
officer for two reasons First violent crimes are different in nature than property crimes
especially with regard to clearance rates Second property crimes often require more
investigative and follow-up time than violent crimes thereby reducing an officerrsquos amount of
productive time and proactive activity
Table 3 also provides a key piece of evidence to support this assertion Table 3 shows
that the number of reported violent crimes per officer increases an officerrsquos number of warrant
and misdemeanor arrests Yet the relationship is the opposite for traffic enforcement the violent
crime-to-cop ratio actually decreases traffic citations This change is due to the fact that while
violent crime investigations often yield warrant and misdemeanor arrests they do not produce
traffic citations Furthermore responding to a greater number of violent crimes decreases the
amount of available time to conduct traffic enforcement in turn reducing the number of traffic
citations In other words the violent crime-to-cop ratio affects traffic stops the same way that
the property crime-to-cop ratio affects all productivity outputs by reducing an officerrsquos
productive time and opportunities for proactivity In fact we found a moderate correlation
between a patrol arearsquos crime-to-cop ratio and the amount of productive time enjoyed by officers
who worked that area The correlation between an arearsquos violent crime-to-cop ratio and
productive time was -21 while the correlation between an arearsquos property crime-to-cop ratio and
productive time was -33 We believe this evidence indicates that officers who work in areas
with a high crime-to-cop ratio have less time for self-initiated activities which drives down
productivity
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We suggest that the number of police officers and crime rates of an area need to be
analyzed in conjunction with each other Prior theories of police productivity have viewed and
examined these variables separately when in reality we should view these variables in tandem
Combining these variables into crime-to-cop ratios provides a more nuancedmdashand accuratemdash
understanding of patrol activities and their relationship to productivity
Although we found strong evidence that an officerrsquos patrol area affects his or her
productivity (in terms of raw outputs) we also found that a few officer-level variables affected
productivity as well including traffic stops intelligence reports and working third shift Our
results appear to substantiate what many police practitioners already suspect officers who are
proactive in contacting citizens (traffic stops) and building investigations (intelligence reports)
generate more warrant and misdemeanor arrests In addition while some newer police officers
are forced to work third shift because they do not have sufficient seniority to bid for better days
off and shift times many officers work nights because they enjoy the types of calls (eg in-
progress felonies) meaning they may be more motivated to stop vehicles make field contacts
and in turn engage in activities that generate arrests (Brehm amp Gates 1993)
These results have important implications for police researchers and administrators
First our results suggest a need for a deeper understanding of how the staffing levels crime
rates and the other characteristics of a patrol area intersect Second if departments evaluate
their patrol officers using raw productivity outputs such as misdemeanor or felony arrests
administrators must recognize that factors beyond the control of patrol officers might affect
monthly or annual productivity Our research suggests that a patrol arearsquos crime-to-cop ratio can
substantially affect a patrol officerrsquos productivity regardless of the actual crime rate If the
characteristics of a patrol area affect raw productivity outputs police departments should employ
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more advanced productivity analytics (ie VORC) that account for activities which reduce
productive time and evaluate an officerrsquos productivity in terms of available minutes for
proactivity
Nonetheless evaluating both patrol officers and police departments using traffic citations
warrants misdemeanors and felonies is useful because this type of data is readily understood by
citizens and civic officials A neighborhood watch group for instance might care more about
the number of arrests their officers make and less about those officersrsquo productivity scores Our
data suggests that if citizens want patrol officers to write more traffic citations and make more
arrests they should lobby for more police officers on the street It is true that reducing an arearsquos
crime rate impacts the crime-to-cop ratio but our results indicate that putting police officers on
the street may be the most direct way to affect the crime-to-cop ratio
Our study is not without limitations We only examined a police department which uses
the generalist style of policing and it is unknown if our results are applicable to police
departments which use a specialist style of policing Theoretically a departmentrsquos policing
styles can affect factors that increase or decrease a patrol officerrsquos productive time It is possible
that the number of property crimes and violent crimes per officer do not significantly affect a
specialist patrol officerrsquos productivity outputs versus a generalist officerrsquos outputs Future
studies should investigate whether our results can be generalized to all police departments
We have only identified two macro-level variables which affect patrol officer
productivity and we recognize there may be other variables In addition we recommend that
future studies incorporate a wider array of variables and test competing models to determine
whether micro or macro-level variables exhibit stronger effects on productivity In the end
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managing patrol officers and improving productivity requires a greater understanding of both
patrol officers and the areas they work
Acknowledgements and Credits We would like to thank Denver Police Department Crime
Analyst Melissa Ohlhorst and Pennsylvania State University Professor Barry Ruback as well as
the three anonymous reviewers for their insightful comments We also salute Lincoln Police
Sergeant Jake Dilsaver for his logistical support of this project
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References
Amaranto E Steinberg J Castellano C and Mitchell R (2003) ldquoPolice stress interventionsrdquo
Brief Treatment and Crisis Intervention Vol 3 No 1 pp 47-53
Becker G (1985) ldquoHuman capital effort and the sexual division of laborrdquo The Journal of
Labor EconomicsVol 3 No 1 pp 33-58
Bonkiewicz L (2015) ldquoBobbies and baseball players Evaluating patrol officer productivity
using sabermetricsrdquo Police Quarterly Vol 18 No 1 pp 55-78
Braga A and Bond J (2008) ldquoPolicing crime and disorder hot spots A randomized controlled
trialrdquo Criminology Vol 46 No 3 pp 577-607
Brown J and Campbell E (1990) ldquoSources of occupational stress in the policerdquo Work
and Stress Vol 4 No 4 pp 305-318
Chappell A MacDonald J and Manz P (2006) ldquoThe organizational determinants
of police arrest decisionsrdquo Crime and Delinquency Vol 52 No 2 pp 287-306
Cloninger D and Sartorius L (1979) ldquoCrime rates clearance rates and enforcement effort
The case of Houston Texasrdquo American Journal of Economics and Sociology Vol 38
No 4 pp 389-402
Corman H and Mocan N (2005) ldquoCarrots sticks and broken windowsrdquo Journal of Law and
Economics Vol 48 No 1 pp 235-266
Crank J (1990) ldquoThe influence of environmental and organizational factors on police style in
urban and rural environmentsrdquo Journal of Research in Crime and Delinquency Vol 27
pp 166-189
Goodman A (1990) ldquoA model for police officer burnoutrdquo Journal of Business and
Psychology Vol 5 No 1 pp 85-99
Gould E Weinberg B and Mustard D (2002) Crime rates and local labor marketopportunities in the United States 1979ndash1997rdquo Review of Economics and Statistics Vol
84 No 1 pp 45-61
Hipp J and Roussell A (2013) ldquoMicro-and macro-environment population and the
consequences for crime ratesrdquo Social Forces Vol 92 No 2 pp 563-595
Hatry H (1976) ldquoApproaches to productivity measurement and program evaluationrdquo Public
Productivity Review Vol 1 No 3 pp21-28
Hawkins H (2001) ldquoPolice officer burnout a partial replication of Maslachs Burnout
Inventoryrdquo Police Quarterly Vol 4 No 3 pp 343-360
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httpslidepdfcomreaderfullpijpsm-05-2015-0064 2530
Herbert S (1998)ldquoPolice subculture reconsideredrdquo Criminology Vol 36 No 2 pp 343-370
Huizinga D and Henry K (2008) ldquoThe effect of arrest and justice system sanctions on
subsequent behavior Findings from longitudinal and other studiesrdquo In The long view of
crime A synthesis of longitudinal research Springer New York pp 220-254
Johnson R (2011) ldquoOfficer attitudes and management influences on police work productivityrdquo
American Journal of Criminal Justice Vol 36 pp 293-306
Kane R (2006) ldquoOn the limits of social control Structural deterrence and the policing of
lsquosuppressiblersquo crimesrdquo Justice Quarterly Vol 23 No 2 pp 186-213
Kubrin C Messner S Deane G McGeever K and Stucky T (2010) ldquoProactive
policing and robbery rates across US citiesrdquo Criminology Vol 48 No 1 pp 57-97
Lersch K (2002) ldquoAre citizen complaints just another measure of officer productivity An
analysis of citizen complaints and officer activity measuresrdquo Police Practice and
Research Vol 3 pp 135-147
Light M and Harris C (2012) Race space and violence exploring spatial dependence in
structural covariates of white and black violent crime in US countiesrdquo Journal of
Quantitative Criminology Vol 28 No 4 pp 559-586
Maxfield M (1982) ldquoService time dispatch time and demand for police services Helping
more by serving lessrdquo Public Administration Review pp 252-263
Maxfield M Lewis D and Szoc R (1980) ldquoProducing official crimes Verified crime reports
as measures of police outputrdquo Social Science Quarterly pp 221-236
Nakosteen R and Zimmer M (2001) ldquoSpouse selection and earnings Evidence of marital
sortingrdquo Economic InquiryVol 39 No 2 pp 201-2013
Shane J (2010) ldquoOrganizational stressors and police performancerdquo Journal of Criminal
Justice Vol 38 No 4 pp 807-818
Shane J (2011) ldquoDaily work experiences and police performancerdquo Police Practice and
Research Vol 13 No 3 pp 1-19
Sherman L Gartin P and Buerger M (1989) ldquoHot spots of predatory crime Routine
activities and the criminology of placerdquoCriminology Vol 27 No 1 pp27-56
Truman J and Rand M (2010) ldquoBureau of Justice Statistics Bulletin National Crime
Victimization Survey Criminal Victimization 2009rdquo US Department of Justice Office of
Justice programs
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httpslidepdfcomreaderfullpijpsm-05-2015-0064 2630
Van Meter D (2001) Evaluating dysfunctional police performance A zero-based approach
Charles C Thomas Publisher Springfield IL
Vila B and Kenney D (2002) ldquoTired cops The prevalence and potential consequences of
police fatiguerdquo National Institute of Justice
Vila B Morrison G and Kenney D (2002) ldquoImproving shift schedule and work-hour
policies and practices to increase police officer performance health and safetyrdquo Police
Quarterly Vol 5 No 1 pp 4-24
Vollaard B and Koning P (2009) ldquoThe effect of police on crime disorder and victim
PrecautionEvidence from a Dutch victimization surveyrdquo International Review of Law
and Economics Vol 29 No 4 pp 336-348
Weisburd D Maher L Sherman L Buerger M Cohn E and Petrisino A (1993)
ldquoContrasting crime general and crime specific theory The case of hot spots of crimerdquo
New Directions in Criminological Theory Advances in Criminological Theory Vol 4
pp 45-70
Author Biography
Luke Bonkiewicz is a patrol officer with the Lincoln Police Department
7252019 PIJPSM-05-2015-0064
httpslidepdfcomreaderfullpijpsm-05-2015-0064 2730
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983101 983089983088983088lowast + + ) + ) + ) + ) lowast )) minus + + + 983090983088 + ) +983089983088)) minus 983089983088983088 lowast
983086 983097) lowast )
7252019 PIJPSM-05-2015-0064
httpslidepdfcomreaderfullpijpsm-05-2015-0064 2830
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983119983142983142983145983139983141983154 983120983154983151983155983141983139983157983156983145983151983150
983122983137983156983141C983110983123
983117983145983150983157983156983141983155983110983125983120
983117983145983150983157983156983141983155
983119983142983142983145983139983141983154
A983155983155983145983155983156983117983145983150983157983156983141983155
983113983150983139983145983140983141983150983156983122983141983152983151983154983156983155
AC983113983122983141983152983151983154983156983155 983123983141983148983141983139983156983145983158983141983155 983127983137983154983150983145983150983143983155 983119983142983142983145983139983145983137983148983155 983127983137983154983154983137983150983156983155 D983125983113983155 983117983145983155983140983141983149983141983137983150983151983154983155 983110983141983148983151983150983145983141983155
1 88 22812 5208 6132 216 348 396 396 264 60 24 72 7
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3 72 32496 6312 6600 144 564 444 720 540 84 36 24 12
4 90 33000 5232 4980 216 192 480 768 240 144 12 72 12
5 91 41724 4824 6048 312 636 132 504 324 108 7 24 24
6 83 23916 7056 6192 144 756 528 996 336 132 36 72 12
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10 87 21984 5412 4524 348 552 480 792 312 108 48 84 36
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983120983151983152983157983148983137983156983145983151983150 983140983141 983150983155983145983156983161 20229830856956 1687 4436
983112983151983157983155983141983144983151983148983140 983149983141983140983145983137983150 983145983150983139983151983149983141 ($) 2628198308576020 16535 49756
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983129983141983137983154983155 983141983160983152983141983154983145983141983150983139983141 198308537 779 1063
C983137983148983148983155 983142983151983154 983155983141983154983158983145983139983141 4819830851011 16121 61365
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1983155983156 2983150983140 3983154983140
104 96 102983123983144983145983142983156
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983112983151983157983155983141983144983151983148983140 983149983141983140983145983137983150 983145983150983139983151983149983141 ($) 002 001 983085000 000 00 00 98308500 00
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983151983142 C983144983145983148983140983154983141983150 05 03
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983152 lt 05
983152lt01
983152lt001
983118983151983156983141 A983148983148 983158983137983154983145983137983138983148983141983155 983137983154983141 983157983150983139983141983150983156983141983154983141983140
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suspects and informants in the field documenting these efforts through field information reports
and eventually using this information to write search warrants and conduct drug interdiction
Next we investigated whether this relationship between the crime-to-cop ratios and
productivity outputs exists when using an advanced productivity metric specifically an officerrsquos
Value Over Replacement Cop (VORC)
INSERT TABLE 4 HERE
Table 4 shows that there is no correlation between the ratio of property and violent
crimes per officer and VORC Although this stands in contrast to our earlier results we would
not expect to find a relationship between these variables and VORC Advanced metrics such as
VORC evaluate officers based on the amount of time for self-initiated activities (productive
time) and not raw productivity outputs like misdemeanor or felony arrests Even if an officer
took an extremely large number of property crimes thereby decreasing their productive time
VORC would only evaluate that officer based on what the officer produced with the small
amount of productive time This suggests that while a teamrsquos crime-to-cop ratio could affect an
officerrsquos raw productivity outputs ie arrests a teamrsquos crime-to-cop ratio would not affect an
officerrsquos VORC
For example during a given month an officer may take an unusually large number of
burglaries or larcenies thereby leaving the officer with only ten hours for self-initiated activities
With this reduced amount of productive time the officer may generate a below-average number
of officials warrants misdemeanors and felonies which in turn may lead a supervisor to
potentially downgrade the officerrsquos productivity However VORC would recognize and account
for the high number of calls for service minutes and analyze the officerrsquos productivity based on
what the officer generated with only ten hours of productive time It may be that the officer was
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actually very productive but they simply didnrsquot have enough productive time to generate the
same number of traffic citations and arrests as an officer who responded to far fewer property
crimes
Table 4 also shows that traffic stops and field intelligence reports are positively
correlated with VORC These results are consistent with the principles of how VORC evaluates
patrol officer productivity and provides additional support for the validity of this advanced
metric VORC accounts for numerous variables that affect productive time (ie calls for service
minutes) meaning that the key factors which increase or decrease an officerrsquos VORC are largely
related to self-initiated activities (ie traffic stops and information reports) VORC therefore
accounts for the potential effects of macro-level variables and analyzes the activities of the
officer during his or her productive time Nonetheless we recognize that because traffic stops
and field information reports are correlated with VORC an arearsquos crime-to-cop ratio might still
affect an officerrsquos VORC indirectly by impacting the number of stops and information reports
they generate
Discussion
There are several factors which affect a patrol officerrsquos productivity including
biographical characteristics officersrsquo perceptions and attitudes and organizational and
operational features among others This paper identified two macro-level variablesmdashproperty
crimes per officer and violent crimes per officermdashwhich affect the number of traffic citations
warrants misdemeanors and felonies generated annually by an officer Using data from a mid-
sized police department we found that a patrol areas rate of property crimes per officer was
associated with a moderate decrease in an officers annual number of traffic citations warrant
arrests and misdemeanor arrests while a patrol areas rate of violent crimes per officer was also
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associated with a moderate decrease in an officers annual number of traffic citations We also
found that a patrol areas rate of violent crimes per officer is associated with a moderate increase
in an officers annual number of warrant and misdemeanor arrests However when we used a
more advanced productivity metric we found no relationship between property and violent
crimes per officer and an officerrsquos Value Over Replacement Cop or VORC
There are several factors which explain the differing effects of the crime-to-cop ratios on
productivity outputs Let us first address why the violent crime-to-cop ratio would increase a
patrol officerrsquos warrants and misdemeanors while the property crime-to-cop ratio would decrease
these outputs Notably half of all victims of violent crimes know the offender (Truman amp Rand
2010) which aids law enforcement in identifying and locating the perpetrator Moreover both
suspect and victim are frequently on scene when police officers arrive such as during domestic
disturbances Being able to identify and locate the perpetrator helps patrol officers make arrests
thereby increasing their misdemeanor and felony arrests in violent crimes Additionally when
patrol officers arrive and contact individuals during an incident it is standard procedure to check
if the parties involved have outstanding warrants This means that the greater number of violent
crimes that officers respond to the more chances they have to arrest individuals who might have
outstanding warrants It makes sense then that officers who respond to a greater number of
violent crimes have more chances to make warrant and misdemeanor arrests
Conversely property crimes may supply the officer with fewer warrant and misdemeanor
arrests because of low clearance rates We examined the difference in clearance rates between
property and violent crime using incident data from the Lincoln Police Department and we
found that the clearance rate for violent crimes was 418 compared to only 237 for property
crimes Moreover property crimes often also require more investigative time than violent
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crimes Property crime investigations often require considerable follow-up by officers such as
processing a scene contacting financial institutions and checking pawnshops for stolen property
In general property crimes are more time-intensive than violent crimes thereby decreasing
productive time and opportunities for proactive activities while simultaneously netting officers
fewer arrests because of low clearance rates
The impact of a teamrsquos crime-to-cop ratio can also be seen in the relationship between
property and violent crime per officer and the number of traffic citations Table 3 shows that a
rise in both reported violent and property crimes per officer decrease an officerrsquos number of
traffic citations Quite simply if officers respond to a high number of property and violent
crimes then they will have fewer minutes to conduct traffic enforcement and other proactive
enforcement
As noted it is possible that the correlation is bi-directional If an officer makes a greater
number of arrests then a team may witness a decrease in crime because prolific criminals may
have been arrested and placed in jail For example one might interpret Table 3 to mean that an
increase in warrant and misdemeanor warrants led to a decrease in the number of property crimes
per officer However under this hypothesis we should also see that an increase in warrant and
misdemeanor arrests leads to a decrease in the number of violent crimes per officer Instead we
see the opposite hypothesized effect ie more arrests leading to more reported violent crimes 5
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983139983154983145983149983141 983154983137983156983141983155 983145983150983156983151 983155983152983141983139983145983142983145983139 983139983154983145983149983141983155 983159983141 983140983145983140 983142983145983150983140 983156983144983137983156 983149983145983155983140983141983149983141983137983150983151983154 983137983150983140 983142983141983148983151983150983161 983137983154983154983141983155983156983155 983155983145983143983150983145983142983145983139983137983150983156983148983161 983152983154983141983140983145983139983156983141983140 983137 983140983141983139983154983141983137983155983141
983145983150 983154983151983138983138983141983154983161 983154983137983156983141983155 983159983144983145983139983144 983145983155 983139983151983150983155983145983155983156983141983150983156 983159983145983156983144 983156983144983141 983148983145983156983141983154983137983156983157983154983141 983105983140983140983145983156983145983151983150983137983148983148983161 983159983141 983145983150983156983141983154983158983145983141983159983141983140 983140983141983152983137983154983156983149983141983150983156 983152983141983154983155983151983150983150983141983148 983137983150983140983148983141983137983154983150983141983140 983156983144983137983156 983156983144983141 983116983145983150983139983151983148983150 983120983151983148983145983139983141 983108983141983152983137983154983156983149983141983150983156 983144983137983140 983155983156983137983142983142983141983140 983137 983156983141983137983149 983140983141983155983145983143983150983141983140 983155983152983141983139983145983142983145983139983137983148983148983161 983156983151 983137983152983152983154983141983144983141983150983140 983154983151983138983138983141983154983161
983155983157983155983152983141983139983156983155 983140983157983154983145983150983143 983156983144983145983155 983156983145983149983141983142983154983137983149983141 983159983144983145983139983144 983155983141983141983149983155 983156983151 983141983160983152983148983137983145983150 983156983144983141 983137983154983154983141983155983156983085983154983151983138983138983141983154983161 983154983137983156983141 983154983141983148983137983156983145983151983150983155983144983145983152
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We believe our explanation has a stronger theoretical foundation The ratio of property
crimes per officer affects productivity outputs differently than the ratio of violent crimes per
officer for two reasons First violent crimes are different in nature than property crimes
especially with regard to clearance rates Second property crimes often require more
investigative and follow-up time than violent crimes thereby reducing an officerrsquos amount of
productive time and proactive activity
Table 3 also provides a key piece of evidence to support this assertion Table 3 shows
that the number of reported violent crimes per officer increases an officerrsquos number of warrant
and misdemeanor arrests Yet the relationship is the opposite for traffic enforcement the violent
crime-to-cop ratio actually decreases traffic citations This change is due to the fact that while
violent crime investigations often yield warrant and misdemeanor arrests they do not produce
traffic citations Furthermore responding to a greater number of violent crimes decreases the
amount of available time to conduct traffic enforcement in turn reducing the number of traffic
citations In other words the violent crime-to-cop ratio affects traffic stops the same way that
the property crime-to-cop ratio affects all productivity outputs by reducing an officerrsquos
productive time and opportunities for proactivity In fact we found a moderate correlation
between a patrol arearsquos crime-to-cop ratio and the amount of productive time enjoyed by officers
who worked that area The correlation between an arearsquos violent crime-to-cop ratio and
productive time was -21 while the correlation between an arearsquos property crime-to-cop ratio and
productive time was -33 We believe this evidence indicates that officers who work in areas
with a high crime-to-cop ratio have less time for self-initiated activities which drives down
productivity
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We suggest that the number of police officers and crime rates of an area need to be
analyzed in conjunction with each other Prior theories of police productivity have viewed and
examined these variables separately when in reality we should view these variables in tandem
Combining these variables into crime-to-cop ratios provides a more nuancedmdashand accuratemdash
understanding of patrol activities and their relationship to productivity
Although we found strong evidence that an officerrsquos patrol area affects his or her
productivity (in terms of raw outputs) we also found that a few officer-level variables affected
productivity as well including traffic stops intelligence reports and working third shift Our
results appear to substantiate what many police practitioners already suspect officers who are
proactive in contacting citizens (traffic stops) and building investigations (intelligence reports)
generate more warrant and misdemeanor arrests In addition while some newer police officers
are forced to work third shift because they do not have sufficient seniority to bid for better days
off and shift times many officers work nights because they enjoy the types of calls (eg in-
progress felonies) meaning they may be more motivated to stop vehicles make field contacts
and in turn engage in activities that generate arrests (Brehm amp Gates 1993)
These results have important implications for police researchers and administrators
First our results suggest a need for a deeper understanding of how the staffing levels crime
rates and the other characteristics of a patrol area intersect Second if departments evaluate
their patrol officers using raw productivity outputs such as misdemeanor or felony arrests
administrators must recognize that factors beyond the control of patrol officers might affect
monthly or annual productivity Our research suggests that a patrol arearsquos crime-to-cop ratio can
substantially affect a patrol officerrsquos productivity regardless of the actual crime rate If the
characteristics of a patrol area affect raw productivity outputs police departments should employ
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more advanced productivity analytics (ie VORC) that account for activities which reduce
productive time and evaluate an officerrsquos productivity in terms of available minutes for
proactivity
Nonetheless evaluating both patrol officers and police departments using traffic citations
warrants misdemeanors and felonies is useful because this type of data is readily understood by
citizens and civic officials A neighborhood watch group for instance might care more about
the number of arrests their officers make and less about those officersrsquo productivity scores Our
data suggests that if citizens want patrol officers to write more traffic citations and make more
arrests they should lobby for more police officers on the street It is true that reducing an arearsquos
crime rate impacts the crime-to-cop ratio but our results indicate that putting police officers on
the street may be the most direct way to affect the crime-to-cop ratio
Our study is not without limitations We only examined a police department which uses
the generalist style of policing and it is unknown if our results are applicable to police
departments which use a specialist style of policing Theoretically a departmentrsquos policing
styles can affect factors that increase or decrease a patrol officerrsquos productive time It is possible
that the number of property crimes and violent crimes per officer do not significantly affect a
specialist patrol officerrsquos productivity outputs versus a generalist officerrsquos outputs Future
studies should investigate whether our results can be generalized to all police departments
We have only identified two macro-level variables which affect patrol officer
productivity and we recognize there may be other variables In addition we recommend that
future studies incorporate a wider array of variables and test competing models to determine
whether micro or macro-level variables exhibit stronger effects on productivity In the end
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managing patrol officers and improving productivity requires a greater understanding of both
patrol officers and the areas they work
Acknowledgements and Credits We would like to thank Denver Police Department Crime
Analyst Melissa Ohlhorst and Pennsylvania State University Professor Barry Ruback as well as
the three anonymous reviewers for their insightful comments We also salute Lincoln Police
Sergeant Jake Dilsaver for his logistical support of this project
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References
Amaranto E Steinberg J Castellano C and Mitchell R (2003) ldquoPolice stress interventionsrdquo
Brief Treatment and Crisis Intervention Vol 3 No 1 pp 47-53
Becker G (1985) ldquoHuman capital effort and the sexual division of laborrdquo The Journal of
Labor EconomicsVol 3 No 1 pp 33-58
Bonkiewicz L (2015) ldquoBobbies and baseball players Evaluating patrol officer productivity
using sabermetricsrdquo Police Quarterly Vol 18 No 1 pp 55-78
Braga A and Bond J (2008) ldquoPolicing crime and disorder hot spots A randomized controlled
trialrdquo Criminology Vol 46 No 3 pp 577-607
Brown J and Campbell E (1990) ldquoSources of occupational stress in the policerdquo Work
and Stress Vol 4 No 4 pp 305-318
Chappell A MacDonald J and Manz P (2006) ldquoThe organizational determinants
of police arrest decisionsrdquo Crime and Delinquency Vol 52 No 2 pp 287-306
Cloninger D and Sartorius L (1979) ldquoCrime rates clearance rates and enforcement effort
The case of Houston Texasrdquo American Journal of Economics and Sociology Vol 38
No 4 pp 389-402
Corman H and Mocan N (2005) ldquoCarrots sticks and broken windowsrdquo Journal of Law and
Economics Vol 48 No 1 pp 235-266
Crank J (1990) ldquoThe influence of environmental and organizational factors on police style in
urban and rural environmentsrdquo Journal of Research in Crime and Delinquency Vol 27
pp 166-189
Goodman A (1990) ldquoA model for police officer burnoutrdquo Journal of Business and
Psychology Vol 5 No 1 pp 85-99
Gould E Weinberg B and Mustard D (2002) Crime rates and local labor marketopportunities in the United States 1979ndash1997rdquo Review of Economics and Statistics Vol
84 No 1 pp 45-61
Hipp J and Roussell A (2013) ldquoMicro-and macro-environment population and the
consequences for crime ratesrdquo Social Forces Vol 92 No 2 pp 563-595
Hatry H (1976) ldquoApproaches to productivity measurement and program evaluationrdquo Public
Productivity Review Vol 1 No 3 pp21-28
Hawkins H (2001) ldquoPolice officer burnout a partial replication of Maslachs Burnout
Inventoryrdquo Police Quarterly Vol 4 No 3 pp 343-360
7252019 PIJPSM-05-2015-0064
httpslidepdfcomreaderfullpijpsm-05-2015-0064 2530
Herbert S (1998)ldquoPolice subculture reconsideredrdquo Criminology Vol 36 No 2 pp 343-370
Huizinga D and Henry K (2008) ldquoThe effect of arrest and justice system sanctions on
subsequent behavior Findings from longitudinal and other studiesrdquo In The long view of
crime A synthesis of longitudinal research Springer New York pp 220-254
Johnson R (2011) ldquoOfficer attitudes and management influences on police work productivityrdquo
American Journal of Criminal Justice Vol 36 pp 293-306
Kane R (2006) ldquoOn the limits of social control Structural deterrence and the policing of
lsquosuppressiblersquo crimesrdquo Justice Quarterly Vol 23 No 2 pp 186-213
Kubrin C Messner S Deane G McGeever K and Stucky T (2010) ldquoProactive
policing and robbery rates across US citiesrdquo Criminology Vol 48 No 1 pp 57-97
Lersch K (2002) ldquoAre citizen complaints just another measure of officer productivity An
analysis of citizen complaints and officer activity measuresrdquo Police Practice and
Research Vol 3 pp 135-147
Light M and Harris C (2012) Race space and violence exploring spatial dependence in
structural covariates of white and black violent crime in US countiesrdquo Journal of
Quantitative Criminology Vol 28 No 4 pp 559-586
Maxfield M (1982) ldquoService time dispatch time and demand for police services Helping
more by serving lessrdquo Public Administration Review pp 252-263
Maxfield M Lewis D and Szoc R (1980) ldquoProducing official crimes Verified crime reports
as measures of police outputrdquo Social Science Quarterly pp 221-236
Nakosteen R and Zimmer M (2001) ldquoSpouse selection and earnings Evidence of marital
sortingrdquo Economic InquiryVol 39 No 2 pp 201-2013
Shane J (2010) ldquoOrganizational stressors and police performancerdquo Journal of Criminal
Justice Vol 38 No 4 pp 807-818
Shane J (2011) ldquoDaily work experiences and police performancerdquo Police Practice and
Research Vol 13 No 3 pp 1-19
Sherman L Gartin P and Buerger M (1989) ldquoHot spots of predatory crime Routine
activities and the criminology of placerdquoCriminology Vol 27 No 1 pp27-56
Truman J and Rand M (2010) ldquoBureau of Justice Statistics Bulletin National Crime
Victimization Survey Criminal Victimization 2009rdquo US Department of Justice Office of
Justice programs
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httpslidepdfcomreaderfullpijpsm-05-2015-0064 2630
Van Meter D (2001) Evaluating dysfunctional police performance A zero-based approach
Charles C Thomas Publisher Springfield IL
Vila B and Kenney D (2002) ldquoTired cops The prevalence and potential consequences of
police fatiguerdquo National Institute of Justice
Vila B Morrison G and Kenney D (2002) ldquoImproving shift schedule and work-hour
policies and practices to increase police officer performance health and safetyrdquo Police
Quarterly Vol 5 No 1 pp 4-24
Vollaard B and Koning P (2009) ldquoThe effect of police on crime disorder and victim
PrecautionEvidence from a Dutch victimization surveyrdquo International Review of Law
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Weisburd D Maher L Sherman L Buerger M Cohn E and Petrisino A (1993)
ldquoContrasting crime general and crime specific theory The case of hot spots of crimerdquo
New Directions in Criminological Theory Advances in Criminological Theory Vol 4
pp 45-70
Author Biography
Luke Bonkiewicz is a patrol officer with the Lincoln Police Department
7252019 PIJPSM-05-2015-0064
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983110983145983143983157983154983141 983089983086 983126983119983122983107 983110983151983154983149983157983148983137
983101 983089983088983088lowast + + ) + ) + ) + ) lowast )) minus + + + 983090983088 + ) +983089983088)) minus 983089983088983088 lowast
983086 983097) lowast )
7252019 PIJPSM-05-2015-0064
httpslidepdfcomreaderfullpijpsm-05-2015-0064 2830
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983119983142983142983145983139983141983154 983120983154983151983155983141983139983157983156983145983151983150
983122983137983156983141C983110983123
983117983145983150983157983156983141983155983110983125983120
983117983145983150983157983156983141983155
983119983142983142983145983139983141983154
A983155983155983145983155983156983117983145983150983157983156983141983155
983113983150983139983145983140983141983150983156983122983141983152983151983154983156983155
AC983113983122983141983152983151983154983156983155 983123983141983148983141983139983156983145983158983141983155 983127983137983154983150983145983150983143983155 983119983142983142983145983139983145983137983148983155 983127983137983154983154983137983150983156983155 D983125983113983155 983117983145983155983140983141983149983141983137983150983151983154983155 983110983141983148983151983150983145983141983155
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2 95 30072 5424 4116 204 696 228 312 432 84 48 120 8
3 72 32496 6312 6600 144 564 444 720 540 84 36 24 12
4 90 33000 5232 4980 216 192 480 768 240 144 12 72 12
5 91 41724 4824 6048 312 636 132 504 324 108 7 24 24
6 83 23916 7056 6192 144 756 528 996 336 132 36 72 12
7 95 24468 6840 5892 156 624 324 456 480 72 36 120 24
8 90 39972 5196 4920 240 504 384 492 264 36 2 72 24
9 79 25500 4068 5100 336 396 192 684 204 24 36 120 10
10 87 21984 5412 4524 348 552 480 792 312 108 48 84 36
983124983137983138983148983141 2 983123983157983149983149983137983154983161 983123983156983137983156983145983155983156983145983139983155 983142983151983154 983126983137983154983145983137983138983148983141983155
983123983141983139983151983150983140983085983116983141983158983141983148 983126983137983154983145983137983138983148983141983155 (983120983137983156983154983151983148 A983154983141983137) 983118=40 983122983137983150983143983141 983123D 983117983141983137983150
983120983151983152983157983148983137983156983145983151983150 983140983141 983150983155983145983156983161 20229830856956 1687 4436
983112983151983157983155983141983144983151983148983140 983149983141983140983145983137983150 983145983150983139983151983149983141 ($) 2628198308576020 16535 49756
983159983144983145983156983141 7398308593 007 083
983126983145983151983148983141983150983156 983139983154983145983149983141 983154983137983156983141 (983152983141983154 100000) 8009830854800 1203 2521
983120983154983151983152983141983154983156983161 983139983154983145983149983141 983154983137983156983141 (983152983141983154 100000) 31009830859800 2027 6524
983120983154983151983152983141983154983156983161 983139983154983145983149983141983155 983152983141983154 983151983142983142983145983139983141983154 5329830851097 1663 8167
983126983145983151983148983141983150983156 983139983154983145983149983141 983152983141983154 983151983142983142983145983139983141983154 194983085372 559 2929
983110983145983154983155983156983085983116983141 983158983141983148 983126 983137983154983145983137983138983148983141 983155 (983119983142983142983145983139983141983154) 983118=302 983122983137983150983143983141 983123D 983117983141 983137983150
983111983141983150983140983141983154 (983142983141983149983137983148983141=0 983149983137983148983141=1) 09830851 24 94
983117983137983154983154983145983141983140C983151983144983137983138983145983156983145983150983143 09830851 18 79
983151983142 C983144983145983148983140983154983141983150 09830855 170 90
983129983141983137983154983155 983141983160983152983141983154983145983141983150983139983141 198308537 779 1063
C983137983148983148983155 983142983151983154 983155983141983154983158983145983139983141 4819830851011 16121 61365
983124983154983137983142983142983145983139 983155983156983151983152983155 16983085901 14376 20624
983110983145983141983148983140 983145983150983142983151983154983149983137983156983145983151983150 983154983141983152983151983154983156983155 0983085227 2153 2530
983124983154983137983142983142983145983139 983139983145983156983137983156983145983151983150983155 249830851246 14632 18642
983117983145983155983140983141983149983141983137983150983151983154 983137983154983154983141983155983156983155 19983085403 5965 10663
983110983141983148983151983150983161 983137983154983154983141983155983156983155 098308571 1066 852
983126983119983122C 9830851959830851043 151 24
1983155983156 2983150983140 3983154983140
104 96 102983123983144983145983142983156
7252019 PIJPSM-05-2015-0064
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983124983137983138983148983141 3 983112983145983141983154983137983154983139983144983145983139983137983148 983116983145983150983141983137983154 983117983151983140983141983148983155 E983155983156983145983149983137983156983145983150983143 983119983142 983142983145983139983141983154983155 A983150983150983157983137983148 983124983154983137983142983142983145983139 C983145983156983137983156983145983151983150983155 983127983137983154983154983137983150983156983155 983117983145983155983140983141983149983141983137983150983151983154983155 983137983150983140 983110983141983148983151983150 983145983141983155
983123983141983139983151983150983140983085983116983141983158983141983148 983126983137983154983145983137983138983148983141983155 (983120983137983156983154983151983148 A983154983141983137) 983118=40 983138 983123E 983138 983123E 983138 983123E 983138 983123E
983120983151983152983157983148983137983156983145983151983150 983140983141983150983155983145983156983161 001 003 001 001 98308501 01 00 00
983112983151983157983155983141983144983151983148983140 983149983141983140983145983137983150 983145983150983139983151983149983141 ($) 002 001 983085000 000 00 00 98308500 00
983159983144983145983156983141 13363 32472 9830858921 5664 9830853274 19211 9830854447 3255
983126983145983151983148983141983150983156 983139983154983145983149983141 983154983137983156983141 (983152983141983154 100000) 006 004 001 001 98308501 02 01 02
983120983154983151983152983141983154983156983161 983139983154983145983149983141 983154983137983156983141 (983152983141983154 100000) 983085003 005 983085002 001 02 03 98308501 00
983120983154983151983152983141983154983156983161 983139983154983145983149983141983155 983152983141983154 983151983142983142983145983139983141983154 983085225 99 98308515 03 98308552 24 98308503 03
983126983145983151983148983141983150983156 983139983154983145983149983141 983152983141983154 983151983142983142983145983139983141983154 983085168 68 29 14 76 31 22 12
983110983145983154983155983156983085983116983141983158983141983148 983126983137983154983145983137983138983148983141983155 (983119983142983142983145983139983141983154) 983118=302
983117983137983148983141 4673 1622 366 147 1613 1051 91 158
983117983137983154983154983145983141983140C983151983144983137983138983145983156983145983150983143 9830857516 3254 201 278 9830851158 1169 98308595 167
983151983142 C983144983145983148983140983154983141983150 183 702 75 77 983085262 217 983085396 207
983129983141983137983154983155 983141983160983152983141983154983145983141983150983139983141 9830851065 492 98308512 13 983085163 33 98308506 05C983137983148983148983155 983142983151983154 983155983141983154983158983145983139983141 24 13 02 02 14 01 98308508 05
983124983154983137983142983142983145983139 983155983156983151983152983155 983085983085 983085983085 03 00 09 02 00 00
983110983145983141983148983140 983145983150983142983151983154983149983137983156983145983151983150 983154983141983152983151983154983156983155 209 33 20 04 79 19 10 03
983123983141983139983151983150983140 983155983144983145983142983156 (983154983141983142983141983154983141983150983139983141 983143983154983151983157983152 983142983145983154983155983156) 2603 1778 187 195 809 730 147 160
983124983144983145 983154983140 983155983144983145 983142983156 (983154983141 983142983141 983154983141 983150983139983141 983143983154983151983157983152 983142983145 983154983155983156) 9872 2155 79 213 2532 952 393 132
983113983150983156983141983154983139983141983152983156 32591 5361 758 339 4147 1912 310 484
983123983145983143983149983137983085983155983153983157983137983154983141983140 1541467 19287 206702 5598
C983144983145983085983155983153983157983137983154983141983140 8294 6133 12348 10663
983152 lt 05
983152lt01
983152lt001
983118983151983156983141 A983148983148 983158983137983154983145983137983138983148983141983155 983137983154983141 983157983150983139983141983150983156983141983154983141983140
983127A983122983122A983118983124 A983122983122E983123983124983123 983117983113983123DE983117EA983118983119983122 A983122983122E983123983124983123 983110E983116983119983118983129 A983122983122E983123983124983123983124983122A983110983110983113C C983113983124A983124983113983119983118983123
7252019 PIJPSM-05-2015-0064
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983124983137983138983148983141 4 983112983145983141983154983137983154983139983144983145983139983137983148 983116983145983150983141983137983154 983117983151983140983141983148983155 E983155983156983145983149983137983156983145983150983143 983119983142983142983145983139983141983154983155 A983150983150983157983137983148 983126983137983148983157983141 983119983158983141983154 983122983141983152983148983137983139983141983149983141983150983156 C983151983152 (983126983119983122C)
983123983141983139983151983150983140983085983116983141983158983141983148 983126983137983154983145983137983138983148983141983155 (983120983137983156983154983151983148 A983154983141983137) 983118=40 983138 983123E
983120983151983152983157983148983137983156983145983151983150 983140983141983150983155983145983156983161 98308500 00
983112983151983157983155983141983144983151983148983140 983149983141983140983145983137983150 983145983150983139983151983149983141 ($) 00 01
983159983144983145983156983141 318 345
983126983145983151983148983141983150983156 983139983154983145983149983141 983154983137983156983141 (983152983141983154 100000) 98308500 00
983120983154983151983152983141983154983156983161 983139983154983145983149983141 983154983137983156983141 (983152983141983154 100000) 00 00
983120983154983151983152983141983154983156983161 983139983154983145983149983141983155 983152983141983154 983151983142983142983145983139983141983154 98308502 04
983126983145983151983148983141983150983156 983139983154983145983149983141 983152983141983154 983151983142983142983145983139983141983154 02 05
983110983145983154983155983156983085983116983141983158983141983148 983126983137983154983145983137983138983148983141983155 (983119983142983142983145983139983141983154) 983118=302
983117983137983148983141 27 15
983117983137983154983154983145983141983140C983151983144983137983138983145983156983145983150983143 98308532 31
983151983142 C983144983145983148983140983154983141983150 05 03
983129983141983137983154983155 983141983160983152983141983154983145983141983150983139983141 00 01
C983137983148 983148983155 983142983151983154 983155983141983154983158983145983139983141 02 01
983124983154983137983142983142983145983139 983155983156983151983152983155 01 00
983110983145 983141983148983140 983145983150983142983151983154983149983137983156983145 983151983150 983154983141 983152983151983154983156983155 02 00
983123983141983139983151983150983140 983155983144983145983142983156 ( 983154983141983142983141983154983141983150983139983141 983143983154983151983157983152 983142 983145983154983155983156) 23 12
983124983144983145983154983140 983155983144983145983142983156 (983154983141983142983141983154983141983150983139983141 983143983154983151983157983152 983142 983145983154983155983156) 55 14
983113983150983156983141983154983139983141983152983156 165 36
983123983145983143983149983137983085983155983153983157983137983154983141983140 66
C983144983145983085983155983153983157983137983154983141983140 9846
983152 lt 05
983152lt01
983152lt001
983118983151983156983141 A983148983148 983158983137983154983145983137983138983148983141983155 983137983154983141 983157983150983139983141983150983156983141983154983141983140
7252019 PIJPSM-05-2015-0064
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actually very productive but they simply didnrsquot have enough productive time to generate the
same number of traffic citations and arrests as an officer who responded to far fewer property
crimes
Table 4 also shows that traffic stops and field intelligence reports are positively
correlated with VORC These results are consistent with the principles of how VORC evaluates
patrol officer productivity and provides additional support for the validity of this advanced
metric VORC accounts for numerous variables that affect productive time (ie calls for service
minutes) meaning that the key factors which increase or decrease an officerrsquos VORC are largely
related to self-initiated activities (ie traffic stops and information reports) VORC therefore
accounts for the potential effects of macro-level variables and analyzes the activities of the
officer during his or her productive time Nonetheless we recognize that because traffic stops
and field information reports are correlated with VORC an arearsquos crime-to-cop ratio might still
affect an officerrsquos VORC indirectly by impacting the number of stops and information reports
they generate
Discussion
There are several factors which affect a patrol officerrsquos productivity including
biographical characteristics officersrsquo perceptions and attitudes and organizational and
operational features among others This paper identified two macro-level variablesmdashproperty
crimes per officer and violent crimes per officermdashwhich affect the number of traffic citations
warrants misdemeanors and felonies generated annually by an officer Using data from a mid-
sized police department we found that a patrol areas rate of property crimes per officer was
associated with a moderate decrease in an officers annual number of traffic citations warrant
arrests and misdemeanor arrests while a patrol areas rate of violent crimes per officer was also
7252019 PIJPSM-05-2015-0064
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associated with a moderate decrease in an officers annual number of traffic citations We also
found that a patrol areas rate of violent crimes per officer is associated with a moderate increase
in an officers annual number of warrant and misdemeanor arrests However when we used a
more advanced productivity metric we found no relationship between property and violent
crimes per officer and an officerrsquos Value Over Replacement Cop or VORC
There are several factors which explain the differing effects of the crime-to-cop ratios on
productivity outputs Let us first address why the violent crime-to-cop ratio would increase a
patrol officerrsquos warrants and misdemeanors while the property crime-to-cop ratio would decrease
these outputs Notably half of all victims of violent crimes know the offender (Truman amp Rand
2010) which aids law enforcement in identifying and locating the perpetrator Moreover both
suspect and victim are frequently on scene when police officers arrive such as during domestic
disturbances Being able to identify and locate the perpetrator helps patrol officers make arrests
thereby increasing their misdemeanor and felony arrests in violent crimes Additionally when
patrol officers arrive and contact individuals during an incident it is standard procedure to check
if the parties involved have outstanding warrants This means that the greater number of violent
crimes that officers respond to the more chances they have to arrest individuals who might have
outstanding warrants It makes sense then that officers who respond to a greater number of
violent crimes have more chances to make warrant and misdemeanor arrests
Conversely property crimes may supply the officer with fewer warrant and misdemeanor
arrests because of low clearance rates We examined the difference in clearance rates between
property and violent crime using incident data from the Lincoln Police Department and we
found that the clearance rate for violent crimes was 418 compared to only 237 for property
crimes Moreover property crimes often also require more investigative time than violent
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crimes Property crime investigations often require considerable follow-up by officers such as
processing a scene contacting financial institutions and checking pawnshops for stolen property
In general property crimes are more time-intensive than violent crimes thereby decreasing
productive time and opportunities for proactive activities while simultaneously netting officers
fewer arrests because of low clearance rates
The impact of a teamrsquos crime-to-cop ratio can also be seen in the relationship between
property and violent crime per officer and the number of traffic citations Table 3 shows that a
rise in both reported violent and property crimes per officer decrease an officerrsquos number of
traffic citations Quite simply if officers respond to a high number of property and violent
crimes then they will have fewer minutes to conduct traffic enforcement and other proactive
enforcement
As noted it is possible that the correlation is bi-directional If an officer makes a greater
number of arrests then a team may witness a decrease in crime because prolific criminals may
have been arrested and placed in jail For example one might interpret Table 3 to mean that an
increase in warrant and misdemeanor warrants led to a decrease in the number of property crimes
per officer However under this hypothesis we should also see that an increase in warrant and
misdemeanor arrests leads to a decrease in the number of violent crimes per officer Instead we
see the opposite hypothesized effect ie more arrests leading to more reported violent crimes 5
983093 983127983141 983139983151983150983155983156983154983157983139983156983141983140 983154983141983143983154983141983155983155983145983151983150 983149983151983140983141983148983155 983157983155983145983150983143 983139983145983156983137983156983145983151983150983155 983159983137983154983154983137983150983156 983137983154983154983141983155983156983155 983149983145983155983140983141983149983141983137983150983151983154 983137983154983154983141983155983156983155 983137983150983140 983142983141983148983151983150983161 983137983154983154983141983155983156983155 983142983154983151983149
983156983144983141 983152983154983141983139983141983140983145983150983143 983149983151983150983156983144 983156983151 983152983154983141983140983145983139983156 983138983151983156983144 983158983145983151983148983141983150983156 983137983150983140 983152983154983151983152983141983154983156983161 983139983154983145983149983141 983154983137983156983141983155 983142983151983154 983156983144983141 983149983151983150983156983144983155 983145983150 983151983157983154 983140983137983156983137983155983141983156 983127983141 983142983151983157983150983140 983150983151
983155983156983137983156983145983155983156983145983139983137983148983148983161 983155983145983143983150983145983142983145983139983137983150983156 983154983141983148983137983156983145983151983150983155983144983145983152983155 983138983141983156983159983141983141983150 983139983145983156983137983156983145983151983150983155 983159983137983154983154983137983150983156 983137983154983154983141983155983156983155 983149983145983155983140983141983149983141983137983150983151983154 983137983154983154983141983155983156983155 983137983150983140 983142983141983148983151983150983161 983137983154983154983141983155983156983155
983137983150983140 983156983144983141 983158983145983151983148983141983150983156 983137983150983140 983152983154983151983152983141983154983156983161 983139983154983145983149983141 983154983137983156983141983155 983151983142 983156983144983141 983138983141983137983156983155 983112983151983159983141983158983141983154 983137983142983156983141983154 983140983145983155983137983143983143983154983141983143983137983156983145983150983143 983156983144983141 983158983145983151983148983141983150983156 983137983150983140 983152983154983151983152983141983154983156983161
983139983154983145983149983141 983154983137983156983141983155 983145983150983156983151 983155983152983141983139983145983142983145983139 983139983154983145983149983141983155 983159983141 983140983145983140 983142983145983150983140 983156983144983137983156 983149983145983155983140983141983149983141983137983150983151983154 983137983150983140 983142983141983148983151983150983161 983137983154983154983141983155983156983155 983155983145983143983150983145983142983145983139983137983150983156983148983161 983152983154983141983140983145983139983156983141983140 983137 983140983141983139983154983141983137983155983141
983145983150 983154983151983138983138983141983154983161 983154983137983156983141983155 983159983144983145983139983144 983145983155 983139983151983150983155983145983155983156983141983150983156 983159983145983156983144 983156983144983141 983148983145983156983141983154983137983156983157983154983141 983105983140983140983145983156983145983151983150983137983148983148983161 983159983141 983145983150983156983141983154983158983145983141983159983141983140 983140983141983152983137983154983156983149983141983150983156 983152983141983154983155983151983150983150983141983148 983137983150983140983148983141983137983154983150983141983140 983156983144983137983156 983156983144983141 983116983145983150983139983151983148983150 983120983151983148983145983139983141 983108983141983152983137983154983156983149983141983150983156 983144983137983140 983155983156983137983142983142983141983140 983137 983156983141983137983149 983140983141983155983145983143983150983141983140 983155983152983141983139983145983142983145983139983137983148983148983161 983156983151 983137983152983152983154983141983144983141983150983140 983154983151983138983138983141983154983161
983155983157983155983152983141983139983156983155 983140983157983154983145983150983143 983156983144983145983155 983156983145983149983141983142983154983137983149983141 983159983144983145983139983144 983155983141983141983149983155 983156983151 983141983160983152983148983137983145983150 983156983144983141 983137983154983154983141983155983156983085983154983151983138983138983141983154983161 983154983137983156983141 983154983141983148983137983156983145983151983150983155983144983145983152
7252019 PIJPSM-05-2015-0064
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We believe our explanation has a stronger theoretical foundation The ratio of property
crimes per officer affects productivity outputs differently than the ratio of violent crimes per
officer for two reasons First violent crimes are different in nature than property crimes
especially with regard to clearance rates Second property crimes often require more
investigative and follow-up time than violent crimes thereby reducing an officerrsquos amount of
productive time and proactive activity
Table 3 also provides a key piece of evidence to support this assertion Table 3 shows
that the number of reported violent crimes per officer increases an officerrsquos number of warrant
and misdemeanor arrests Yet the relationship is the opposite for traffic enforcement the violent
crime-to-cop ratio actually decreases traffic citations This change is due to the fact that while
violent crime investigations often yield warrant and misdemeanor arrests they do not produce
traffic citations Furthermore responding to a greater number of violent crimes decreases the
amount of available time to conduct traffic enforcement in turn reducing the number of traffic
citations In other words the violent crime-to-cop ratio affects traffic stops the same way that
the property crime-to-cop ratio affects all productivity outputs by reducing an officerrsquos
productive time and opportunities for proactivity In fact we found a moderate correlation
between a patrol arearsquos crime-to-cop ratio and the amount of productive time enjoyed by officers
who worked that area The correlation between an arearsquos violent crime-to-cop ratio and
productive time was -21 while the correlation between an arearsquos property crime-to-cop ratio and
productive time was -33 We believe this evidence indicates that officers who work in areas
with a high crime-to-cop ratio have less time for self-initiated activities which drives down
productivity
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We suggest that the number of police officers and crime rates of an area need to be
analyzed in conjunction with each other Prior theories of police productivity have viewed and
examined these variables separately when in reality we should view these variables in tandem
Combining these variables into crime-to-cop ratios provides a more nuancedmdashand accuratemdash
understanding of patrol activities and their relationship to productivity
Although we found strong evidence that an officerrsquos patrol area affects his or her
productivity (in terms of raw outputs) we also found that a few officer-level variables affected
productivity as well including traffic stops intelligence reports and working third shift Our
results appear to substantiate what many police practitioners already suspect officers who are
proactive in contacting citizens (traffic stops) and building investigations (intelligence reports)
generate more warrant and misdemeanor arrests In addition while some newer police officers
are forced to work third shift because they do not have sufficient seniority to bid for better days
off and shift times many officers work nights because they enjoy the types of calls (eg in-
progress felonies) meaning they may be more motivated to stop vehicles make field contacts
and in turn engage in activities that generate arrests (Brehm amp Gates 1993)
These results have important implications for police researchers and administrators
First our results suggest a need for a deeper understanding of how the staffing levels crime
rates and the other characteristics of a patrol area intersect Second if departments evaluate
their patrol officers using raw productivity outputs such as misdemeanor or felony arrests
administrators must recognize that factors beyond the control of patrol officers might affect
monthly or annual productivity Our research suggests that a patrol arearsquos crime-to-cop ratio can
substantially affect a patrol officerrsquos productivity regardless of the actual crime rate If the
characteristics of a patrol area affect raw productivity outputs police departments should employ
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more advanced productivity analytics (ie VORC) that account for activities which reduce
productive time and evaluate an officerrsquos productivity in terms of available minutes for
proactivity
Nonetheless evaluating both patrol officers and police departments using traffic citations
warrants misdemeanors and felonies is useful because this type of data is readily understood by
citizens and civic officials A neighborhood watch group for instance might care more about
the number of arrests their officers make and less about those officersrsquo productivity scores Our
data suggests that if citizens want patrol officers to write more traffic citations and make more
arrests they should lobby for more police officers on the street It is true that reducing an arearsquos
crime rate impacts the crime-to-cop ratio but our results indicate that putting police officers on
the street may be the most direct way to affect the crime-to-cop ratio
Our study is not without limitations We only examined a police department which uses
the generalist style of policing and it is unknown if our results are applicable to police
departments which use a specialist style of policing Theoretically a departmentrsquos policing
styles can affect factors that increase or decrease a patrol officerrsquos productive time It is possible
that the number of property crimes and violent crimes per officer do not significantly affect a
specialist patrol officerrsquos productivity outputs versus a generalist officerrsquos outputs Future
studies should investigate whether our results can be generalized to all police departments
We have only identified two macro-level variables which affect patrol officer
productivity and we recognize there may be other variables In addition we recommend that
future studies incorporate a wider array of variables and test competing models to determine
whether micro or macro-level variables exhibit stronger effects on productivity In the end
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managing patrol officers and improving productivity requires a greater understanding of both
patrol officers and the areas they work
Acknowledgements and Credits We would like to thank Denver Police Department Crime
Analyst Melissa Ohlhorst and Pennsylvania State University Professor Barry Ruback as well as
the three anonymous reviewers for their insightful comments We also salute Lincoln Police
Sergeant Jake Dilsaver for his logistical support of this project
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Amaranto E Steinberg J Castellano C and Mitchell R (2003) ldquoPolice stress interventionsrdquo
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Bonkiewicz L (2015) ldquoBobbies and baseball players Evaluating patrol officer productivity
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Braga A and Bond J (2008) ldquoPolicing crime and disorder hot spots A randomized controlled
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Brown J and Campbell E (1990) ldquoSources of occupational stress in the policerdquo Work
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Chappell A MacDonald J and Manz P (2006) ldquoThe organizational determinants
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Cloninger D and Sartorius L (1979) ldquoCrime rates clearance rates and enforcement effort
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No 4 pp 389-402
Corman H and Mocan N (2005) ldquoCarrots sticks and broken windowsrdquo Journal of Law and
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Crank J (1990) ldquoThe influence of environmental and organizational factors on police style in
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Goodman A (1990) ldquoA model for police officer burnoutrdquo Journal of Business and
Psychology Vol 5 No 1 pp 85-99
Gould E Weinberg B and Mustard D (2002) Crime rates and local labor marketopportunities in the United States 1979ndash1997rdquo Review of Economics and Statistics Vol
84 No 1 pp 45-61
Hipp J and Roussell A (2013) ldquoMicro-and macro-environment population and the
consequences for crime ratesrdquo Social Forces Vol 92 No 2 pp 563-595
Hatry H (1976) ldquoApproaches to productivity measurement and program evaluationrdquo Public
Productivity Review Vol 1 No 3 pp21-28
Hawkins H (2001) ldquoPolice officer burnout a partial replication of Maslachs Burnout
Inventoryrdquo Police Quarterly Vol 4 No 3 pp 343-360
7252019 PIJPSM-05-2015-0064
httpslidepdfcomreaderfullpijpsm-05-2015-0064 2530
Herbert S (1998)ldquoPolice subculture reconsideredrdquo Criminology Vol 36 No 2 pp 343-370
Huizinga D and Henry K (2008) ldquoThe effect of arrest and justice system sanctions on
subsequent behavior Findings from longitudinal and other studiesrdquo In The long view of
crime A synthesis of longitudinal research Springer New York pp 220-254
Johnson R (2011) ldquoOfficer attitudes and management influences on police work productivityrdquo
American Journal of Criminal Justice Vol 36 pp 293-306
Kane R (2006) ldquoOn the limits of social control Structural deterrence and the policing of
lsquosuppressiblersquo crimesrdquo Justice Quarterly Vol 23 No 2 pp 186-213
Kubrin C Messner S Deane G McGeever K and Stucky T (2010) ldquoProactive
policing and robbery rates across US citiesrdquo Criminology Vol 48 No 1 pp 57-97
Lersch K (2002) ldquoAre citizen complaints just another measure of officer productivity An
analysis of citizen complaints and officer activity measuresrdquo Police Practice and
Research Vol 3 pp 135-147
Light M and Harris C (2012) Race space and violence exploring spatial dependence in
structural covariates of white and black violent crime in US countiesrdquo Journal of
Quantitative Criminology Vol 28 No 4 pp 559-586
Maxfield M (1982) ldquoService time dispatch time and demand for police services Helping
more by serving lessrdquo Public Administration Review pp 252-263
Maxfield M Lewis D and Szoc R (1980) ldquoProducing official crimes Verified crime reports
as measures of police outputrdquo Social Science Quarterly pp 221-236
Nakosteen R and Zimmer M (2001) ldquoSpouse selection and earnings Evidence of marital
sortingrdquo Economic InquiryVol 39 No 2 pp 201-2013
Shane J (2010) ldquoOrganizational stressors and police performancerdquo Journal of Criminal
Justice Vol 38 No 4 pp 807-818
Shane J (2011) ldquoDaily work experiences and police performancerdquo Police Practice and
Research Vol 13 No 3 pp 1-19
Sherman L Gartin P and Buerger M (1989) ldquoHot spots of predatory crime Routine
activities and the criminology of placerdquoCriminology Vol 27 No 1 pp27-56
Truman J and Rand M (2010) ldquoBureau of Justice Statistics Bulletin National Crime
Victimization Survey Criminal Victimization 2009rdquo US Department of Justice Office of
Justice programs
7252019 PIJPSM-05-2015-0064
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Van Meter D (2001) Evaluating dysfunctional police performance A zero-based approach
Charles C Thomas Publisher Springfield IL
Vila B and Kenney D (2002) ldquoTired cops The prevalence and potential consequences of
police fatiguerdquo National Institute of Justice
Vila B Morrison G and Kenney D (2002) ldquoImproving shift schedule and work-hour
policies and practices to increase police officer performance health and safetyrdquo Police
Quarterly Vol 5 No 1 pp 4-24
Vollaard B and Koning P (2009) ldquoThe effect of police on crime disorder and victim
PrecautionEvidence from a Dutch victimization surveyrdquo International Review of Law
and Economics Vol 29 No 4 pp 336-348
Weisburd D Maher L Sherman L Buerger M Cohn E and Petrisino A (1993)
ldquoContrasting crime general and crime specific theory The case of hot spots of crimerdquo
New Directions in Criminological Theory Advances in Criminological Theory Vol 4
pp 45-70
Author Biography
Luke Bonkiewicz is a patrol officer with the Lincoln Police Department
7252019 PIJPSM-05-2015-0064
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983101 983089983088983088lowast + + ) + ) + ) + ) lowast )) minus + + + 983090983088 + ) +983089983088)) minus 983089983088983088 lowast
983086 983097) lowast )
7252019 PIJPSM-05-2015-0064
httpslidepdfcomreaderfullpijpsm-05-2015-0064 2830
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983119983142983142983145983139983141983154 983120983154983151983155983141983139983157983156983145983151983150
983122983137983156983141C983110983123
983117983145983150983157983156983141983155983110983125983120
983117983145983150983157983156983141983155
983119983142983142983145983139983141983154
A983155983155983145983155983156983117983145983150983157983156983141983155
983113983150983139983145983140983141983150983156983122983141983152983151983154983156983155
AC983113983122983141983152983151983154983156983155 983123983141983148983141983139983156983145983158983141983155 983127983137983154983150983145983150983143983155 983119983142983142983145983139983145983137983148983155 983127983137983154983154983137983150983156983155 D983125983113983155 983117983145983155983140983141983149983141983137983150983151983154983155 983110983141983148983151983150983145983141983155
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2 95 30072 5424 4116 204 696 228 312 432 84 48 120 8
3 72 32496 6312 6600 144 564 444 720 540 84 36 24 12
4 90 33000 5232 4980 216 192 480 768 240 144 12 72 12
5 91 41724 4824 6048 312 636 132 504 324 108 7 24 24
6 83 23916 7056 6192 144 756 528 996 336 132 36 72 12
7 95 24468 6840 5892 156 624 324 456 480 72 36 120 24
8 90 39972 5196 4920 240 504 384 492 264 36 2 72 24
9 79 25500 4068 5100 336 396 192 684 204 24 36 120 10
10 87 21984 5412 4524 348 552 480 792 312 108 48 84 36
983124983137983138983148983141 2 983123983157983149983149983137983154983161 983123983156983137983156983145983155983156983145983139983155 983142983151983154 983126983137983154983145983137983138983148983141983155
983123983141983139983151983150983140983085983116983141983158983141983148 983126983137983154983145983137983138983148983141983155 (983120983137983156983154983151983148 A983154983141983137) 983118=40 983122983137983150983143983141 983123D 983117983141983137983150
983120983151983152983157983148983137983156983145983151983150 983140983141 983150983155983145983156983161 20229830856956 1687 4436
983112983151983157983155983141983144983151983148983140 983149983141983140983145983137983150 983145983150983139983151983149983141 ($) 2628198308576020 16535 49756
983159983144983145983156983141 7398308593 007 083
983126983145983151983148983141983150983156 983139983154983145983149983141 983154983137983156983141 (983152983141983154 100000) 8009830854800 1203 2521
983120983154983151983152983141983154983156983161 983139983154983145983149983141 983154983137983156983141 (983152983141983154 100000) 31009830859800 2027 6524
983120983154983151983152983141983154983156983161 983139983154983145983149983141983155 983152983141983154 983151983142983142983145983139983141983154 5329830851097 1663 8167
983126983145983151983148983141983150983156 983139983154983145983149983141 983152983141983154 983151983142983142983145983139983141983154 194983085372 559 2929
983110983145983154983155983156983085983116983141 983158983141983148 983126 983137983154983145983137983138983148983141 983155 (983119983142983142983145983139983141983154) 983118=302 983122983137983150983143983141 983123D 983117983141 983137983150
983111983141983150983140983141983154 (983142983141983149983137983148983141=0 983149983137983148983141=1) 09830851 24 94
983117983137983154983154983145983141983140C983151983144983137983138983145983156983145983150983143 09830851 18 79
983151983142 C983144983145983148983140983154983141983150 09830855 170 90
983129983141983137983154983155 983141983160983152983141983154983145983141983150983139983141 198308537 779 1063
C983137983148983148983155 983142983151983154 983155983141983154983158983145983139983141 4819830851011 16121 61365
983124983154983137983142983142983145983139 983155983156983151983152983155 16983085901 14376 20624
983110983145983141983148983140 983145983150983142983151983154983149983137983156983145983151983150 983154983141983152983151983154983156983155 0983085227 2153 2530
983124983154983137983142983142983145983139 983139983145983156983137983156983145983151983150983155 249830851246 14632 18642
983117983145983155983140983141983149983141983137983150983151983154 983137983154983154983141983155983156983155 19983085403 5965 10663
983110983141983148983151983150983161 983137983154983154983141983155983156983155 098308571 1066 852
983126983119983122C 9830851959830851043 151 24
1983155983156 2983150983140 3983154983140
104 96 102983123983144983145983142983156
7252019 PIJPSM-05-2015-0064
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983123983141983139983151983150983140983085983116983141983158983141983148 983126983137983154983145983137983138983148983141983155 (983120983137983156983154983151983148 A983154983141983137) 983118=40 983138 983123E 983138 983123E 983138 983123E 983138 983123E
983120983151983152983157983148983137983156983145983151983150 983140983141983150983155983145983156983161 001 003 001 001 98308501 01 00 00
983112983151983157983155983141983144983151983148983140 983149983141983140983145983137983150 983145983150983139983151983149983141 ($) 002 001 983085000 000 00 00 98308500 00
983159983144983145983156983141 13363 32472 9830858921 5664 9830853274 19211 9830854447 3255
983126983145983151983148983141983150983156 983139983154983145983149983141 983154983137983156983141 (983152983141983154 100000) 006 004 001 001 98308501 02 01 02
983120983154983151983152983141983154983156983161 983139983154983145983149983141 983154983137983156983141 (983152983141983154 100000) 983085003 005 983085002 001 02 03 98308501 00
983120983154983151983152983141983154983156983161 983139983154983145983149983141983155 983152983141983154 983151983142983142983145983139983141983154 983085225 99 98308515 03 98308552 24 98308503 03
983126983145983151983148983141983150983156 983139983154983145983149983141 983152983141983154 983151983142983142983145983139983141983154 983085168 68 29 14 76 31 22 12
983110983145983154983155983156983085983116983141983158983141983148 983126983137983154983145983137983138983148983141983155 (983119983142983142983145983139983141983154) 983118=302
983117983137983148983141 4673 1622 366 147 1613 1051 91 158
983117983137983154983154983145983141983140C983151983144983137983138983145983156983145983150983143 9830857516 3254 201 278 9830851158 1169 98308595 167
983151983142 C983144983145983148983140983154983141983150 183 702 75 77 983085262 217 983085396 207
983129983141983137983154983155 983141983160983152983141983154983145983141983150983139983141 9830851065 492 98308512 13 983085163 33 98308506 05C983137983148983148983155 983142983151983154 983155983141983154983158983145983139983141 24 13 02 02 14 01 98308508 05
983124983154983137983142983142983145983139 983155983156983151983152983155 983085983085 983085983085 03 00 09 02 00 00
983110983145983141983148983140 983145983150983142983151983154983149983137983156983145983151983150 983154983141983152983151983154983156983155 209 33 20 04 79 19 10 03
983123983141983139983151983150983140 983155983144983145983142983156 (983154983141983142983141983154983141983150983139983141 983143983154983151983157983152 983142983145983154983155983156) 2603 1778 187 195 809 730 147 160
983124983144983145 983154983140 983155983144983145 983142983156 (983154983141 983142983141 983154983141 983150983139983141 983143983154983151983157983152 983142983145 983154983155983156) 9872 2155 79 213 2532 952 393 132
983113983150983156983141983154983139983141983152983156 32591 5361 758 339 4147 1912 310 484
983123983145983143983149983137983085983155983153983157983137983154983141983140 1541467 19287 206702 5598
C983144983145983085983155983153983157983137983154983141983140 8294 6133 12348 10663
983152 lt 05
983152lt01
983152lt001
983118983151983156983141 A983148983148 983158983137983154983145983137983138983148983141983155 983137983154983141 983157983150983139983141983150983156983141983154983141983140
983127A983122983122A983118983124 A983122983122E983123983124983123 983117983113983123DE983117EA983118983119983122 A983122983122E983123983124983123 983110E983116983119983118983129 A983122983122E983123983124983123983124983122A983110983110983113C C983113983124A983124983113983119983118983123
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983124983137983138983148983141 4 983112983145983141983154983137983154983139983144983145983139983137983148 983116983145983150983141983137983154 983117983151983140983141983148983155 E983155983156983145983149983137983156983145983150983143 983119983142983142983145983139983141983154983155 A983150983150983157983137983148 983126983137983148983157983141 983119983158983141983154 983122983141983152983148983137983139983141983149983141983150983156 C983151983152 (983126983119983122C)
983123983141983139983151983150983140983085983116983141983158983141983148 983126983137983154983145983137983138983148983141983155 (983120983137983156983154983151983148 A983154983141983137) 983118=40 983138 983123E
983120983151983152983157983148983137983156983145983151983150 983140983141983150983155983145983156983161 98308500 00
983112983151983157983155983141983144983151983148983140 983149983141983140983145983137983150 983145983150983139983151983149983141 ($) 00 01
983159983144983145983156983141 318 345
983126983145983151983148983141983150983156 983139983154983145983149983141 983154983137983156983141 (983152983141983154 100000) 98308500 00
983120983154983151983152983141983154983156983161 983139983154983145983149983141 983154983137983156983141 (983152983141983154 100000) 00 00
983120983154983151983152983141983154983156983161 983139983154983145983149983141983155 983152983141983154 983151983142983142983145983139983141983154 98308502 04
983126983145983151983148983141983150983156 983139983154983145983149983141 983152983141983154 983151983142983142983145983139983141983154 02 05
983110983145983154983155983156983085983116983141983158983141983148 983126983137983154983145983137983138983148983141983155 (983119983142983142983145983139983141983154) 983118=302
983117983137983148983141 27 15
983117983137983154983154983145983141983140C983151983144983137983138983145983156983145983150983143 98308532 31
983151983142 C983144983145983148983140983154983141983150 05 03
983129983141983137983154983155 983141983160983152983141983154983145983141983150983139983141 00 01
C983137983148 983148983155 983142983151983154 983155983141983154983158983145983139983141 02 01
983124983154983137983142983142983145983139 983155983156983151983152983155 01 00
983110983145 983141983148983140 983145983150983142983151983154983149983137983156983145 983151983150 983154983141 983152983151983154983156983155 02 00
983123983141983139983151983150983140 983155983144983145983142983156 ( 983154983141983142983141983154983141983150983139983141 983143983154983151983157983152 983142 983145983154983155983156) 23 12
983124983144983145983154983140 983155983144983145983142983156 (983154983141983142983141983154983141983150983139983141 983143983154983151983157983152 983142 983145983154983155983156) 55 14
983113983150983156983141983154983139983141983152983156 165 36
983123983145983143983149983137983085983155983153983157983137983154983141983140 66
C983144983145983085983155983153983157983137983154983141983140 9846
983152 lt 05
983152lt01
983152lt001
983118983151983156983141 A983148983148 983158983137983154983145983137983138983148983141983155 983137983154983141 983157983150983139983141983150983156983141983154983141983140
7252019 PIJPSM-05-2015-0064
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associated with a moderate decrease in an officers annual number of traffic citations We also
found that a patrol areas rate of violent crimes per officer is associated with a moderate increase
in an officers annual number of warrant and misdemeanor arrests However when we used a
more advanced productivity metric we found no relationship between property and violent
crimes per officer and an officerrsquos Value Over Replacement Cop or VORC
There are several factors which explain the differing effects of the crime-to-cop ratios on
productivity outputs Let us first address why the violent crime-to-cop ratio would increase a
patrol officerrsquos warrants and misdemeanors while the property crime-to-cop ratio would decrease
these outputs Notably half of all victims of violent crimes know the offender (Truman amp Rand
2010) which aids law enforcement in identifying and locating the perpetrator Moreover both
suspect and victim are frequently on scene when police officers arrive such as during domestic
disturbances Being able to identify and locate the perpetrator helps patrol officers make arrests
thereby increasing their misdemeanor and felony arrests in violent crimes Additionally when
patrol officers arrive and contact individuals during an incident it is standard procedure to check
if the parties involved have outstanding warrants This means that the greater number of violent
crimes that officers respond to the more chances they have to arrest individuals who might have
outstanding warrants It makes sense then that officers who respond to a greater number of
violent crimes have more chances to make warrant and misdemeanor arrests
Conversely property crimes may supply the officer with fewer warrant and misdemeanor
arrests because of low clearance rates We examined the difference in clearance rates between
property and violent crime using incident data from the Lincoln Police Department and we
found that the clearance rate for violent crimes was 418 compared to only 237 for property
crimes Moreover property crimes often also require more investigative time than violent
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crimes Property crime investigations often require considerable follow-up by officers such as
processing a scene contacting financial institutions and checking pawnshops for stolen property
In general property crimes are more time-intensive than violent crimes thereby decreasing
productive time and opportunities for proactive activities while simultaneously netting officers
fewer arrests because of low clearance rates
The impact of a teamrsquos crime-to-cop ratio can also be seen in the relationship between
property and violent crime per officer and the number of traffic citations Table 3 shows that a
rise in both reported violent and property crimes per officer decrease an officerrsquos number of
traffic citations Quite simply if officers respond to a high number of property and violent
crimes then they will have fewer minutes to conduct traffic enforcement and other proactive
enforcement
As noted it is possible that the correlation is bi-directional If an officer makes a greater
number of arrests then a team may witness a decrease in crime because prolific criminals may
have been arrested and placed in jail For example one might interpret Table 3 to mean that an
increase in warrant and misdemeanor warrants led to a decrease in the number of property crimes
per officer However under this hypothesis we should also see that an increase in warrant and
misdemeanor arrests leads to a decrease in the number of violent crimes per officer Instead we
see the opposite hypothesized effect ie more arrests leading to more reported violent crimes 5
983093 983127983141 983139983151983150983155983156983154983157983139983156983141983140 983154983141983143983154983141983155983155983145983151983150 983149983151983140983141983148983155 983157983155983145983150983143 983139983145983156983137983156983145983151983150983155 983159983137983154983154983137983150983156 983137983154983154983141983155983156983155 983149983145983155983140983141983149983141983137983150983151983154 983137983154983154983141983155983156983155 983137983150983140 983142983141983148983151983150983161 983137983154983154983141983155983156983155 983142983154983151983149
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983137983150983140 983156983144983141 983158983145983151983148983141983150983156 983137983150983140 983152983154983151983152983141983154983156983161 983139983154983145983149983141 983154983137983156983141983155 983151983142 983156983144983141 983138983141983137983156983155 983112983151983159983141983158983141983154 983137983142983156983141983154 983140983145983155983137983143983143983154983141983143983137983156983145983150983143 983156983144983141 983158983145983151983148983141983150983156 983137983150983140 983152983154983151983152983141983154983156983161
983139983154983145983149983141 983154983137983156983141983155 983145983150983156983151 983155983152983141983139983145983142983145983139 983139983154983145983149983141983155 983159983141 983140983145983140 983142983145983150983140 983156983144983137983156 983149983145983155983140983141983149983141983137983150983151983154 983137983150983140 983142983141983148983151983150983161 983137983154983154983141983155983156983155 983155983145983143983150983145983142983145983139983137983150983156983148983161 983152983154983141983140983145983139983156983141983140 983137 983140983141983139983154983141983137983155983141
983145983150 983154983151983138983138983141983154983161 983154983137983156983141983155 983159983144983145983139983144 983145983155 983139983151983150983155983145983155983156983141983150983156 983159983145983156983144 983156983144983141 983148983145983156983141983154983137983156983157983154983141 983105983140983140983145983156983145983151983150983137983148983148983161 983159983141 983145983150983156983141983154983158983145983141983159983141983140 983140983141983152983137983154983156983149983141983150983156 983152983141983154983155983151983150983150983141983148 983137983150983140983148983141983137983154983150983141983140 983156983144983137983156 983156983144983141 983116983145983150983139983151983148983150 983120983151983148983145983139983141 983108983141983152983137983154983156983149983141983150983156 983144983137983140 983155983156983137983142983142983141983140 983137 983156983141983137983149 983140983141983155983145983143983150983141983140 983155983152983141983139983145983142983145983139983137983148983148983161 983156983151 983137983152983152983154983141983144983141983150983140 983154983151983138983138983141983154983161
983155983157983155983152983141983139983156983155 983140983157983154983145983150983143 983156983144983145983155 983156983145983149983141983142983154983137983149983141 983159983144983145983139983144 983155983141983141983149983155 983156983151 983141983160983152983148983137983145983150 983156983144983141 983137983154983154983141983155983156983085983154983151983138983138983141983154983161 983154983137983156983141 983154983141983148983137983156983145983151983150983155983144983145983152
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We believe our explanation has a stronger theoretical foundation The ratio of property
crimes per officer affects productivity outputs differently than the ratio of violent crimes per
officer for two reasons First violent crimes are different in nature than property crimes
especially with regard to clearance rates Second property crimes often require more
investigative and follow-up time than violent crimes thereby reducing an officerrsquos amount of
productive time and proactive activity
Table 3 also provides a key piece of evidence to support this assertion Table 3 shows
that the number of reported violent crimes per officer increases an officerrsquos number of warrant
and misdemeanor arrests Yet the relationship is the opposite for traffic enforcement the violent
crime-to-cop ratio actually decreases traffic citations This change is due to the fact that while
violent crime investigations often yield warrant and misdemeanor arrests they do not produce
traffic citations Furthermore responding to a greater number of violent crimes decreases the
amount of available time to conduct traffic enforcement in turn reducing the number of traffic
citations In other words the violent crime-to-cop ratio affects traffic stops the same way that
the property crime-to-cop ratio affects all productivity outputs by reducing an officerrsquos
productive time and opportunities for proactivity In fact we found a moderate correlation
between a patrol arearsquos crime-to-cop ratio and the amount of productive time enjoyed by officers
who worked that area The correlation between an arearsquos violent crime-to-cop ratio and
productive time was -21 while the correlation between an arearsquos property crime-to-cop ratio and
productive time was -33 We believe this evidence indicates that officers who work in areas
with a high crime-to-cop ratio have less time for self-initiated activities which drives down
productivity
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We suggest that the number of police officers and crime rates of an area need to be
analyzed in conjunction with each other Prior theories of police productivity have viewed and
examined these variables separately when in reality we should view these variables in tandem
Combining these variables into crime-to-cop ratios provides a more nuancedmdashand accuratemdash
understanding of patrol activities and their relationship to productivity
Although we found strong evidence that an officerrsquos patrol area affects his or her
productivity (in terms of raw outputs) we also found that a few officer-level variables affected
productivity as well including traffic stops intelligence reports and working third shift Our
results appear to substantiate what many police practitioners already suspect officers who are
proactive in contacting citizens (traffic stops) and building investigations (intelligence reports)
generate more warrant and misdemeanor arrests In addition while some newer police officers
are forced to work third shift because they do not have sufficient seniority to bid for better days
off and shift times many officers work nights because they enjoy the types of calls (eg in-
progress felonies) meaning they may be more motivated to stop vehicles make field contacts
and in turn engage in activities that generate arrests (Brehm amp Gates 1993)
These results have important implications for police researchers and administrators
First our results suggest a need for a deeper understanding of how the staffing levels crime
rates and the other characteristics of a patrol area intersect Second if departments evaluate
their patrol officers using raw productivity outputs such as misdemeanor or felony arrests
administrators must recognize that factors beyond the control of patrol officers might affect
monthly or annual productivity Our research suggests that a patrol arearsquos crime-to-cop ratio can
substantially affect a patrol officerrsquos productivity regardless of the actual crime rate If the
characteristics of a patrol area affect raw productivity outputs police departments should employ
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more advanced productivity analytics (ie VORC) that account for activities which reduce
productive time and evaluate an officerrsquos productivity in terms of available minutes for
proactivity
Nonetheless evaluating both patrol officers and police departments using traffic citations
warrants misdemeanors and felonies is useful because this type of data is readily understood by
citizens and civic officials A neighborhood watch group for instance might care more about
the number of arrests their officers make and less about those officersrsquo productivity scores Our
data suggests that if citizens want patrol officers to write more traffic citations and make more
arrests they should lobby for more police officers on the street It is true that reducing an arearsquos
crime rate impacts the crime-to-cop ratio but our results indicate that putting police officers on
the street may be the most direct way to affect the crime-to-cop ratio
Our study is not without limitations We only examined a police department which uses
the generalist style of policing and it is unknown if our results are applicable to police
departments which use a specialist style of policing Theoretically a departmentrsquos policing
styles can affect factors that increase or decrease a patrol officerrsquos productive time It is possible
that the number of property crimes and violent crimes per officer do not significantly affect a
specialist patrol officerrsquos productivity outputs versus a generalist officerrsquos outputs Future
studies should investigate whether our results can be generalized to all police departments
We have only identified two macro-level variables which affect patrol officer
productivity and we recognize there may be other variables In addition we recommend that
future studies incorporate a wider array of variables and test competing models to determine
whether micro or macro-level variables exhibit stronger effects on productivity In the end
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managing patrol officers and improving productivity requires a greater understanding of both
patrol officers and the areas they work
Acknowledgements and Credits We would like to thank Denver Police Department Crime
Analyst Melissa Ohlhorst and Pennsylvania State University Professor Barry Ruback as well as
the three anonymous reviewers for their insightful comments We also salute Lincoln Police
Sergeant Jake Dilsaver for his logistical support of this project
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References
Amaranto E Steinberg J Castellano C and Mitchell R (2003) ldquoPolice stress interventionsrdquo
Brief Treatment and Crisis Intervention Vol 3 No 1 pp 47-53
Becker G (1985) ldquoHuman capital effort and the sexual division of laborrdquo The Journal of
Labor EconomicsVol 3 No 1 pp 33-58
Bonkiewicz L (2015) ldquoBobbies and baseball players Evaluating patrol officer productivity
using sabermetricsrdquo Police Quarterly Vol 18 No 1 pp 55-78
Braga A and Bond J (2008) ldquoPolicing crime and disorder hot spots A randomized controlled
trialrdquo Criminology Vol 46 No 3 pp 577-607
Brown J and Campbell E (1990) ldquoSources of occupational stress in the policerdquo Work
and Stress Vol 4 No 4 pp 305-318
Chappell A MacDonald J and Manz P (2006) ldquoThe organizational determinants
of police arrest decisionsrdquo Crime and Delinquency Vol 52 No 2 pp 287-306
Cloninger D and Sartorius L (1979) ldquoCrime rates clearance rates and enforcement effort
The case of Houston Texasrdquo American Journal of Economics and Sociology Vol 38
No 4 pp 389-402
Corman H and Mocan N (2005) ldquoCarrots sticks and broken windowsrdquo Journal of Law and
Economics Vol 48 No 1 pp 235-266
Crank J (1990) ldquoThe influence of environmental and organizational factors on police style in
urban and rural environmentsrdquo Journal of Research in Crime and Delinquency Vol 27
pp 166-189
Goodman A (1990) ldquoA model for police officer burnoutrdquo Journal of Business and
Psychology Vol 5 No 1 pp 85-99
Gould E Weinberg B and Mustard D (2002) Crime rates and local labor marketopportunities in the United States 1979ndash1997rdquo Review of Economics and Statistics Vol
84 No 1 pp 45-61
Hipp J and Roussell A (2013) ldquoMicro-and macro-environment population and the
consequences for crime ratesrdquo Social Forces Vol 92 No 2 pp 563-595
Hatry H (1976) ldquoApproaches to productivity measurement and program evaluationrdquo Public
Productivity Review Vol 1 No 3 pp21-28
Hawkins H (2001) ldquoPolice officer burnout a partial replication of Maslachs Burnout
Inventoryrdquo Police Quarterly Vol 4 No 3 pp 343-360
7252019 PIJPSM-05-2015-0064
httpslidepdfcomreaderfullpijpsm-05-2015-0064 2530
Herbert S (1998)ldquoPolice subculture reconsideredrdquo Criminology Vol 36 No 2 pp 343-370
Huizinga D and Henry K (2008) ldquoThe effect of arrest and justice system sanctions on
subsequent behavior Findings from longitudinal and other studiesrdquo In The long view of
crime A synthesis of longitudinal research Springer New York pp 220-254
Johnson R (2011) ldquoOfficer attitudes and management influences on police work productivityrdquo
American Journal of Criminal Justice Vol 36 pp 293-306
Kane R (2006) ldquoOn the limits of social control Structural deterrence and the policing of
lsquosuppressiblersquo crimesrdquo Justice Quarterly Vol 23 No 2 pp 186-213
Kubrin C Messner S Deane G McGeever K and Stucky T (2010) ldquoProactive
policing and robbery rates across US citiesrdquo Criminology Vol 48 No 1 pp 57-97
Lersch K (2002) ldquoAre citizen complaints just another measure of officer productivity An
analysis of citizen complaints and officer activity measuresrdquo Police Practice and
Research Vol 3 pp 135-147
Light M and Harris C (2012) Race space and violence exploring spatial dependence in
structural covariates of white and black violent crime in US countiesrdquo Journal of
Quantitative Criminology Vol 28 No 4 pp 559-586
Maxfield M (1982) ldquoService time dispatch time and demand for police services Helping
more by serving lessrdquo Public Administration Review pp 252-263
Maxfield M Lewis D and Szoc R (1980) ldquoProducing official crimes Verified crime reports
as measures of police outputrdquo Social Science Quarterly pp 221-236
Nakosteen R and Zimmer M (2001) ldquoSpouse selection and earnings Evidence of marital
sortingrdquo Economic InquiryVol 39 No 2 pp 201-2013
Shane J (2010) ldquoOrganizational stressors and police performancerdquo Journal of Criminal
Justice Vol 38 No 4 pp 807-818
Shane J (2011) ldquoDaily work experiences and police performancerdquo Police Practice and
Research Vol 13 No 3 pp 1-19
Sherman L Gartin P and Buerger M (1989) ldquoHot spots of predatory crime Routine
activities and the criminology of placerdquoCriminology Vol 27 No 1 pp27-56
Truman J and Rand M (2010) ldquoBureau of Justice Statistics Bulletin National Crime
Victimization Survey Criminal Victimization 2009rdquo US Department of Justice Office of
Justice programs
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httpslidepdfcomreaderfullpijpsm-05-2015-0064 2630
Van Meter D (2001) Evaluating dysfunctional police performance A zero-based approach
Charles C Thomas Publisher Springfield IL
Vila B and Kenney D (2002) ldquoTired cops The prevalence and potential consequences of
police fatiguerdquo National Institute of Justice
Vila B Morrison G and Kenney D (2002) ldquoImproving shift schedule and work-hour
policies and practices to increase police officer performance health and safetyrdquo Police
Quarterly Vol 5 No 1 pp 4-24
Vollaard B and Koning P (2009) ldquoThe effect of police on crime disorder and victim
PrecautionEvidence from a Dutch victimization surveyrdquo International Review of Law
and Economics Vol 29 No 4 pp 336-348
Weisburd D Maher L Sherman L Buerger M Cohn E and Petrisino A (1993)
ldquoContrasting crime general and crime specific theory The case of hot spots of crimerdquo
New Directions in Criminological Theory Advances in Criminological Theory Vol 4
pp 45-70
Author Biography
Luke Bonkiewicz is a patrol officer with the Lincoln Police Department
7252019 PIJPSM-05-2015-0064
httpslidepdfcomreaderfullpijpsm-05-2015-0064 2730
983110983145983143983157983154983141 983089983086 983126983119983122983107 983110983151983154983149983157983148983137
983101 983089983088983088lowast + + ) + ) + ) + ) lowast )) minus + + + 983090983088 + ) +983089983088)) minus 983089983088983088 lowast
983086 983097) lowast )
7252019 PIJPSM-05-2015-0064
httpslidepdfcomreaderfullpijpsm-05-2015-0064 2830
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983119983142983142983145983139983141983154 983120983154983151983155983141983139983157983156983145983151983150
983122983137983156983141C983110983123
983117983145983150983157983156983141983155983110983125983120
983117983145983150983157983156983141983155
983119983142983142983145983139983141983154
A983155983155983145983155983156983117983145983150983157983156983141983155
983113983150983139983145983140983141983150983156983122983141983152983151983154983156983155
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3 72 32496 6312 6600 144 564 444 720 540 84 36 24 12
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5 91 41724 4824 6048 312 636 132 504 324 108 7 24 24
6 83 23916 7056 6192 144 756 528 996 336 132 36 72 12
7 95 24468 6840 5892 156 624 324 456 480 72 36 120 24
8 90 39972 5196 4920 240 504 384 492 264 36 2 72 24
9 79 25500 4068 5100 336 396 192 684 204 24 36 120 10
10 87 21984 5412 4524 348 552 480 792 312 108 48 84 36
983124983137983138983148983141 2 983123983157983149983149983137983154983161 983123983156983137983156983145983155983156983145983139983155 983142983151983154 983126983137983154983145983137983138983148983141983155
983123983141983139983151983150983140983085983116983141983158983141983148 983126983137983154983145983137983138983148983141983155 (983120983137983156983154983151983148 A983154983141983137) 983118=40 983122983137983150983143983141 983123D 983117983141983137983150
983120983151983152983157983148983137983156983145983151983150 983140983141 983150983155983145983156983161 20229830856956 1687 4436
983112983151983157983155983141983144983151983148983140 983149983141983140983145983137983150 983145983150983139983151983149983141 ($) 2628198308576020 16535 49756
983159983144983145983156983141 7398308593 007 083
983126983145983151983148983141983150983156 983139983154983145983149983141 983154983137983156983141 (983152983141983154 100000) 8009830854800 1203 2521
983120983154983151983152983141983154983156983161 983139983154983145983149983141 983154983137983156983141 (983152983141983154 100000) 31009830859800 2027 6524
983120983154983151983152983141983154983156983161 983139983154983145983149983141983155 983152983141983154 983151983142983142983145983139983141983154 5329830851097 1663 8167
983126983145983151983148983141983150983156 983139983154983145983149983141 983152983141983154 983151983142983142983145983139983141983154 194983085372 559 2929
983110983145983154983155983156983085983116983141 983158983141983148 983126 983137983154983145983137983138983148983141 983155 (983119983142983142983145983139983141983154) 983118=302 983122983137983150983143983141 983123D 983117983141 983137983150
983111983141983150983140983141983154 (983142983141983149983137983148983141=0 983149983137983148983141=1) 09830851 24 94
983117983137983154983154983145983141983140C983151983144983137983138983145983156983145983150983143 09830851 18 79
983151983142 C983144983145983148983140983154983141983150 09830855 170 90
983129983141983137983154983155 983141983160983152983141983154983145983141983150983139983141 198308537 779 1063
C983137983148983148983155 983142983151983154 983155983141983154983158983145983139983141 4819830851011 16121 61365
983124983154983137983142983142983145983139 983155983156983151983152983155 16983085901 14376 20624
983110983145983141983148983140 983145983150983142983151983154983149983137983156983145983151983150 983154983141983152983151983154983156983155 0983085227 2153 2530
983124983154983137983142983142983145983139 983139983145983156983137983156983145983151983150983155 249830851246 14632 18642
983117983145983155983140983141983149983141983137983150983151983154 983137983154983154983141983155983156983155 19983085403 5965 10663
983110983141983148983151983150983161 983137983154983154983141983155983156983155 098308571 1066 852
983126983119983122C 9830851959830851043 151 24
1983155983156 2983150983140 3983154983140
104 96 102983123983144983145983142983156
7252019 PIJPSM-05-2015-0064
httpslidepdfcomreaderfullpijpsm-05-2015-0064 2930
983124983137983138983148983141 3 983112983145983141983154983137983154983139983144983145983139983137983148 983116983145983150983141983137983154 983117983151983140983141983148983155 E983155983156983145983149983137983156983145983150983143 983119983142 983142983145983139983141983154983155 A983150983150983157983137983148 983124983154983137983142983142983145983139 C983145983156983137983156983145983151983150983155 983127983137983154983154983137983150983156983155 983117983145983155983140983141983149983141983137983150983151983154983155 983137983150983140 983110983141983148983151983150 983145983141983155
983123983141983139983151983150983140983085983116983141983158983141983148 983126983137983154983145983137983138983148983141983155 (983120983137983156983154983151983148 A983154983141983137) 983118=40 983138 983123E 983138 983123E 983138 983123E 983138 983123E
983120983151983152983157983148983137983156983145983151983150 983140983141983150983155983145983156983161 001 003 001 001 98308501 01 00 00
983112983151983157983155983141983144983151983148983140 983149983141983140983145983137983150 983145983150983139983151983149983141 ($) 002 001 983085000 000 00 00 98308500 00
983159983144983145983156983141 13363 32472 9830858921 5664 9830853274 19211 9830854447 3255
983126983145983151983148983141983150983156 983139983154983145983149983141 983154983137983156983141 (983152983141983154 100000) 006 004 001 001 98308501 02 01 02
983120983154983151983152983141983154983156983161 983139983154983145983149983141 983154983137983156983141 (983152983141983154 100000) 983085003 005 983085002 001 02 03 98308501 00
983120983154983151983152983141983154983156983161 983139983154983145983149983141983155 983152983141983154 983151983142983142983145983139983141983154 983085225 99 98308515 03 98308552 24 98308503 03
983126983145983151983148983141983150983156 983139983154983145983149983141 983152983141983154 983151983142983142983145983139983141983154 983085168 68 29 14 76 31 22 12
983110983145983154983155983156983085983116983141983158983141983148 983126983137983154983145983137983138983148983141983155 (983119983142983142983145983139983141983154) 983118=302
983117983137983148983141 4673 1622 366 147 1613 1051 91 158
983117983137983154983154983145983141983140C983151983144983137983138983145983156983145983150983143 9830857516 3254 201 278 9830851158 1169 98308595 167
983151983142 C983144983145983148983140983154983141983150 183 702 75 77 983085262 217 983085396 207
983129983141983137983154983155 983141983160983152983141983154983145983141983150983139983141 9830851065 492 98308512 13 983085163 33 98308506 05C983137983148983148983155 983142983151983154 983155983141983154983158983145983139983141 24 13 02 02 14 01 98308508 05
983124983154983137983142983142983145983139 983155983156983151983152983155 983085983085 983085983085 03 00 09 02 00 00
983110983145983141983148983140 983145983150983142983151983154983149983137983156983145983151983150 983154983141983152983151983154983156983155 209 33 20 04 79 19 10 03
983123983141983139983151983150983140 983155983144983145983142983156 (983154983141983142983141983154983141983150983139983141 983143983154983151983157983152 983142983145983154983155983156) 2603 1778 187 195 809 730 147 160
983124983144983145 983154983140 983155983144983145 983142983156 (983154983141 983142983141 983154983141 983150983139983141 983143983154983151983157983152 983142983145 983154983155983156) 9872 2155 79 213 2532 952 393 132
983113983150983156983141983154983139983141983152983156 32591 5361 758 339 4147 1912 310 484
983123983145983143983149983137983085983155983153983157983137983154983141983140 1541467 19287 206702 5598
C983144983145983085983155983153983157983137983154983141983140 8294 6133 12348 10663
983152 lt 05
983152lt01
983152lt001
983118983151983156983141 A983148983148 983158983137983154983145983137983138983148983141983155 983137983154983141 983157983150983139983141983150983156983141983154983141983140
983127A983122983122A983118983124 A983122983122E983123983124983123 983117983113983123DE983117EA983118983119983122 A983122983122E983123983124983123 983110E983116983119983118983129 A983122983122E983123983124983123983124983122A983110983110983113C C983113983124A983124983113983119983118983123
7252019 PIJPSM-05-2015-0064
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983124983137983138983148983141 4 983112983145983141983154983137983154983139983144983145983139983137983148 983116983145983150983141983137983154 983117983151983140983141983148983155 E983155983156983145983149983137983156983145983150983143 983119983142983142983145983139983141983154983155 A983150983150983157983137983148 983126983137983148983157983141 983119983158983141983154 983122983141983152983148983137983139983141983149983141983150983156 C983151983152 (983126983119983122C)
983123983141983139983151983150983140983085983116983141983158983141983148 983126983137983154983145983137983138983148983141983155 (983120983137983156983154983151983148 A983154983141983137) 983118=40 983138 983123E
983120983151983152983157983148983137983156983145983151983150 983140983141983150983155983145983156983161 98308500 00
983112983151983157983155983141983144983151983148983140 983149983141983140983145983137983150 983145983150983139983151983149983141 ($) 00 01
983159983144983145983156983141 318 345
983126983145983151983148983141983150983156 983139983154983145983149983141 983154983137983156983141 (983152983141983154 100000) 98308500 00
983120983154983151983152983141983154983156983161 983139983154983145983149983141 983154983137983156983141 (983152983141983154 100000) 00 00
983120983154983151983152983141983154983156983161 983139983154983145983149983141983155 983152983141983154 983151983142983142983145983139983141983154 98308502 04
983126983145983151983148983141983150983156 983139983154983145983149983141 983152983141983154 983151983142983142983145983139983141983154 02 05
983110983145983154983155983156983085983116983141983158983141983148 983126983137983154983145983137983138983148983141983155 (983119983142983142983145983139983141983154) 983118=302
983117983137983148983141 27 15
983117983137983154983154983145983141983140C983151983144983137983138983145983156983145983150983143 98308532 31
983151983142 C983144983145983148983140983154983141983150 05 03
983129983141983137983154983155 983141983160983152983141983154983145983141983150983139983141 00 01
C983137983148 983148983155 983142983151983154 983155983141983154983158983145983139983141 02 01
983124983154983137983142983142983145983139 983155983156983151983152983155 01 00
983110983145 983141983148983140 983145983150983142983151983154983149983137983156983145 983151983150 983154983141 983152983151983154983156983155 02 00
983123983141983139983151983150983140 983155983144983145983142983156 ( 983154983141983142983141983154983141983150983139983141 983143983154983151983157983152 983142 983145983154983155983156) 23 12
983124983144983145983154983140 983155983144983145983142983156 (983154983141983142983141983154983141983150983139983141 983143983154983151983157983152 983142 983145983154983155983156) 55 14
983113983150983156983141983154983139983141983152983156 165 36
983123983145983143983149983137983085983155983153983157983137983154983141983140 66
C983144983145983085983155983153983157983137983154983141983140 9846
983152 lt 05
983152lt01
983152lt001
983118983151983156983141 A983148983148 983158983137983154983145983137983138983148983141983155 983137983154983141 983157983150983139983141983150983156983141983154983141983140
7252019 PIJPSM-05-2015-0064
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crimes Property crime investigations often require considerable follow-up by officers such as
processing a scene contacting financial institutions and checking pawnshops for stolen property
In general property crimes are more time-intensive than violent crimes thereby decreasing
productive time and opportunities for proactive activities while simultaneously netting officers
fewer arrests because of low clearance rates
The impact of a teamrsquos crime-to-cop ratio can also be seen in the relationship between
property and violent crime per officer and the number of traffic citations Table 3 shows that a
rise in both reported violent and property crimes per officer decrease an officerrsquos number of
traffic citations Quite simply if officers respond to a high number of property and violent
crimes then they will have fewer minutes to conduct traffic enforcement and other proactive
enforcement
As noted it is possible that the correlation is bi-directional If an officer makes a greater
number of arrests then a team may witness a decrease in crime because prolific criminals may
have been arrested and placed in jail For example one might interpret Table 3 to mean that an
increase in warrant and misdemeanor warrants led to a decrease in the number of property crimes
per officer However under this hypothesis we should also see that an increase in warrant and
misdemeanor arrests leads to a decrease in the number of violent crimes per officer Instead we
see the opposite hypothesized effect ie more arrests leading to more reported violent crimes 5
983093 983127983141 983139983151983150983155983156983154983157983139983156983141983140 983154983141983143983154983141983155983155983145983151983150 983149983151983140983141983148983155 983157983155983145983150983143 983139983145983156983137983156983145983151983150983155 983159983137983154983154983137983150983156 983137983154983154983141983155983156983155 983149983145983155983140983141983149983141983137983150983151983154 983137983154983154983141983155983156983155 983137983150983140 983142983141983148983151983150983161 983137983154983154983141983155983156983155 983142983154983151983149
983156983144983141 983152983154983141983139983141983140983145983150983143 983149983151983150983156983144 983156983151 983152983154983141983140983145983139983156 983138983151983156983144 983158983145983151983148983141983150983156 983137983150983140 983152983154983151983152983141983154983156983161 983139983154983145983149983141 983154983137983156983141983155 983142983151983154 983156983144983141 983149983151983150983156983144983155 983145983150 983151983157983154 983140983137983156983137983155983141983156 983127983141 983142983151983157983150983140 983150983151
983155983156983137983156983145983155983156983145983139983137983148983148983161 983155983145983143983150983145983142983145983139983137983150983156 983154983141983148983137983156983145983151983150983155983144983145983152983155 983138983141983156983159983141983141983150 983139983145983156983137983156983145983151983150983155 983159983137983154983154983137983150983156 983137983154983154983141983155983156983155 983149983145983155983140983141983149983141983137983150983151983154 983137983154983154983141983155983156983155 983137983150983140 983142983141983148983151983150983161 983137983154983154983141983155983156983155
983137983150983140 983156983144983141 983158983145983151983148983141983150983156 983137983150983140 983152983154983151983152983141983154983156983161 983139983154983145983149983141 983154983137983156983141983155 983151983142 983156983144983141 983138983141983137983156983155 983112983151983159983141983158983141983154 983137983142983156983141983154 983140983145983155983137983143983143983154983141983143983137983156983145983150983143 983156983144983141 983158983145983151983148983141983150983156 983137983150983140 983152983154983151983152983141983154983156983161
983139983154983145983149983141 983154983137983156983141983155 983145983150983156983151 983155983152983141983139983145983142983145983139 983139983154983145983149983141983155 983159983141 983140983145983140 983142983145983150983140 983156983144983137983156 983149983145983155983140983141983149983141983137983150983151983154 983137983150983140 983142983141983148983151983150983161 983137983154983154983141983155983156983155 983155983145983143983150983145983142983145983139983137983150983156983148983161 983152983154983141983140983145983139983156983141983140 983137 983140983141983139983154983141983137983155983141
983145983150 983154983151983138983138983141983154983161 983154983137983156983141983155 983159983144983145983139983144 983145983155 983139983151983150983155983145983155983156983141983150983156 983159983145983156983144 983156983144983141 983148983145983156983141983154983137983156983157983154983141 983105983140983140983145983156983145983151983150983137983148983148983161 983159983141 983145983150983156983141983154983158983145983141983159983141983140 983140983141983152983137983154983156983149983141983150983156 983152983141983154983155983151983150983150983141983148 983137983150983140983148983141983137983154983150983141983140 983156983144983137983156 983156983144983141 983116983145983150983139983151983148983150 983120983151983148983145983139983141 983108983141983152983137983154983156983149983141983150983156 983144983137983140 983155983156983137983142983142983141983140 983137 983156983141983137983149 983140983141983155983145983143983150983141983140 983155983152983141983139983145983142983145983139983137983148983148983161 983156983151 983137983152983152983154983141983144983141983150983140 983154983151983138983138983141983154983161
983155983157983155983152983141983139983156983155 983140983157983154983145983150983143 983156983144983145983155 983156983145983149983141983142983154983137983149983141 983159983144983145983139983144 983155983141983141983149983155 983156983151 983141983160983152983148983137983145983150 983156983144983141 983137983154983154983141983155983156983085983154983151983138983138983141983154983161 983154983137983156983141 983154983141983148983137983156983145983151983150983155983144983145983152
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We believe our explanation has a stronger theoretical foundation The ratio of property
crimes per officer affects productivity outputs differently than the ratio of violent crimes per
officer for two reasons First violent crimes are different in nature than property crimes
especially with regard to clearance rates Second property crimes often require more
investigative and follow-up time than violent crimes thereby reducing an officerrsquos amount of
productive time and proactive activity
Table 3 also provides a key piece of evidence to support this assertion Table 3 shows
that the number of reported violent crimes per officer increases an officerrsquos number of warrant
and misdemeanor arrests Yet the relationship is the opposite for traffic enforcement the violent
crime-to-cop ratio actually decreases traffic citations This change is due to the fact that while
violent crime investigations often yield warrant and misdemeanor arrests they do not produce
traffic citations Furthermore responding to a greater number of violent crimes decreases the
amount of available time to conduct traffic enforcement in turn reducing the number of traffic
citations In other words the violent crime-to-cop ratio affects traffic stops the same way that
the property crime-to-cop ratio affects all productivity outputs by reducing an officerrsquos
productive time and opportunities for proactivity In fact we found a moderate correlation
between a patrol arearsquos crime-to-cop ratio and the amount of productive time enjoyed by officers
who worked that area The correlation between an arearsquos violent crime-to-cop ratio and
productive time was -21 while the correlation between an arearsquos property crime-to-cop ratio and
productive time was -33 We believe this evidence indicates that officers who work in areas
with a high crime-to-cop ratio have less time for self-initiated activities which drives down
productivity
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We suggest that the number of police officers and crime rates of an area need to be
analyzed in conjunction with each other Prior theories of police productivity have viewed and
examined these variables separately when in reality we should view these variables in tandem
Combining these variables into crime-to-cop ratios provides a more nuancedmdashand accuratemdash
understanding of patrol activities and their relationship to productivity
Although we found strong evidence that an officerrsquos patrol area affects his or her
productivity (in terms of raw outputs) we also found that a few officer-level variables affected
productivity as well including traffic stops intelligence reports and working third shift Our
results appear to substantiate what many police practitioners already suspect officers who are
proactive in contacting citizens (traffic stops) and building investigations (intelligence reports)
generate more warrant and misdemeanor arrests In addition while some newer police officers
are forced to work third shift because they do not have sufficient seniority to bid for better days
off and shift times many officers work nights because they enjoy the types of calls (eg in-
progress felonies) meaning they may be more motivated to stop vehicles make field contacts
and in turn engage in activities that generate arrests (Brehm amp Gates 1993)
These results have important implications for police researchers and administrators
First our results suggest a need for a deeper understanding of how the staffing levels crime
rates and the other characteristics of a patrol area intersect Second if departments evaluate
their patrol officers using raw productivity outputs such as misdemeanor or felony arrests
administrators must recognize that factors beyond the control of patrol officers might affect
monthly or annual productivity Our research suggests that a patrol arearsquos crime-to-cop ratio can
substantially affect a patrol officerrsquos productivity regardless of the actual crime rate If the
characteristics of a patrol area affect raw productivity outputs police departments should employ
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more advanced productivity analytics (ie VORC) that account for activities which reduce
productive time and evaluate an officerrsquos productivity in terms of available minutes for
proactivity
Nonetheless evaluating both patrol officers and police departments using traffic citations
warrants misdemeanors and felonies is useful because this type of data is readily understood by
citizens and civic officials A neighborhood watch group for instance might care more about
the number of arrests their officers make and less about those officersrsquo productivity scores Our
data suggests that if citizens want patrol officers to write more traffic citations and make more
arrests they should lobby for more police officers on the street It is true that reducing an arearsquos
crime rate impacts the crime-to-cop ratio but our results indicate that putting police officers on
the street may be the most direct way to affect the crime-to-cop ratio
Our study is not without limitations We only examined a police department which uses
the generalist style of policing and it is unknown if our results are applicable to police
departments which use a specialist style of policing Theoretically a departmentrsquos policing
styles can affect factors that increase or decrease a patrol officerrsquos productive time It is possible
that the number of property crimes and violent crimes per officer do not significantly affect a
specialist patrol officerrsquos productivity outputs versus a generalist officerrsquos outputs Future
studies should investigate whether our results can be generalized to all police departments
We have only identified two macro-level variables which affect patrol officer
productivity and we recognize there may be other variables In addition we recommend that
future studies incorporate a wider array of variables and test competing models to determine
whether micro or macro-level variables exhibit stronger effects on productivity In the end
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managing patrol officers and improving productivity requires a greater understanding of both
patrol officers and the areas they work
Acknowledgements and Credits We would like to thank Denver Police Department Crime
Analyst Melissa Ohlhorst and Pennsylvania State University Professor Barry Ruback as well as
the three anonymous reviewers for their insightful comments We also salute Lincoln Police
Sergeant Jake Dilsaver for his logistical support of this project
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References
Amaranto E Steinberg J Castellano C and Mitchell R (2003) ldquoPolice stress interventionsrdquo
Brief Treatment and Crisis Intervention Vol 3 No 1 pp 47-53
Becker G (1985) ldquoHuman capital effort and the sexual division of laborrdquo The Journal of
Labor EconomicsVol 3 No 1 pp 33-58
Bonkiewicz L (2015) ldquoBobbies and baseball players Evaluating patrol officer productivity
using sabermetricsrdquo Police Quarterly Vol 18 No 1 pp 55-78
Braga A and Bond J (2008) ldquoPolicing crime and disorder hot spots A randomized controlled
trialrdquo Criminology Vol 46 No 3 pp 577-607
Brown J and Campbell E (1990) ldquoSources of occupational stress in the policerdquo Work
and Stress Vol 4 No 4 pp 305-318
Chappell A MacDonald J and Manz P (2006) ldquoThe organizational determinants
of police arrest decisionsrdquo Crime and Delinquency Vol 52 No 2 pp 287-306
Cloninger D and Sartorius L (1979) ldquoCrime rates clearance rates and enforcement effort
The case of Houston Texasrdquo American Journal of Economics and Sociology Vol 38
No 4 pp 389-402
Corman H and Mocan N (2005) ldquoCarrots sticks and broken windowsrdquo Journal of Law and
Economics Vol 48 No 1 pp 235-266
Crank J (1990) ldquoThe influence of environmental and organizational factors on police style in
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pp 166-189
Goodman A (1990) ldquoA model for police officer burnoutrdquo Journal of Business and
Psychology Vol 5 No 1 pp 85-99
Gould E Weinberg B and Mustard D (2002) Crime rates and local labor marketopportunities in the United States 1979ndash1997rdquo Review of Economics and Statistics Vol
84 No 1 pp 45-61
Hipp J and Roussell A (2013) ldquoMicro-and macro-environment population and the
consequences for crime ratesrdquo Social Forces Vol 92 No 2 pp 563-595
Hatry H (1976) ldquoApproaches to productivity measurement and program evaluationrdquo Public
Productivity Review Vol 1 No 3 pp21-28
Hawkins H (2001) ldquoPolice officer burnout a partial replication of Maslachs Burnout
Inventoryrdquo Police Quarterly Vol 4 No 3 pp 343-360
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Herbert S (1998)ldquoPolice subculture reconsideredrdquo Criminology Vol 36 No 2 pp 343-370
Huizinga D and Henry K (2008) ldquoThe effect of arrest and justice system sanctions on
subsequent behavior Findings from longitudinal and other studiesrdquo In The long view of
crime A synthesis of longitudinal research Springer New York pp 220-254
Johnson R (2011) ldquoOfficer attitudes and management influences on police work productivityrdquo
American Journal of Criminal Justice Vol 36 pp 293-306
Kane R (2006) ldquoOn the limits of social control Structural deterrence and the policing of
lsquosuppressiblersquo crimesrdquo Justice Quarterly Vol 23 No 2 pp 186-213
Kubrin C Messner S Deane G McGeever K and Stucky T (2010) ldquoProactive
policing and robbery rates across US citiesrdquo Criminology Vol 48 No 1 pp 57-97
Lersch K (2002) ldquoAre citizen complaints just another measure of officer productivity An
analysis of citizen complaints and officer activity measuresrdquo Police Practice and
Research Vol 3 pp 135-147
Light M and Harris C (2012) Race space and violence exploring spatial dependence in
structural covariates of white and black violent crime in US countiesrdquo Journal of
Quantitative Criminology Vol 28 No 4 pp 559-586
Maxfield M (1982) ldquoService time dispatch time and demand for police services Helping
more by serving lessrdquo Public Administration Review pp 252-263
Maxfield M Lewis D and Szoc R (1980) ldquoProducing official crimes Verified crime reports
as measures of police outputrdquo Social Science Quarterly pp 221-236
Nakosteen R and Zimmer M (2001) ldquoSpouse selection and earnings Evidence of marital
sortingrdquo Economic InquiryVol 39 No 2 pp 201-2013
Shane J (2010) ldquoOrganizational stressors and police performancerdquo Journal of Criminal
Justice Vol 38 No 4 pp 807-818
Shane J (2011) ldquoDaily work experiences and police performancerdquo Police Practice and
Research Vol 13 No 3 pp 1-19
Sherman L Gartin P and Buerger M (1989) ldquoHot spots of predatory crime Routine
activities and the criminology of placerdquoCriminology Vol 27 No 1 pp27-56
Truman J and Rand M (2010) ldquoBureau of Justice Statistics Bulletin National Crime
Victimization Survey Criminal Victimization 2009rdquo US Department of Justice Office of
Justice programs
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Van Meter D (2001) Evaluating dysfunctional police performance A zero-based approach
Charles C Thomas Publisher Springfield IL
Vila B and Kenney D (2002) ldquoTired cops The prevalence and potential consequences of
police fatiguerdquo National Institute of Justice
Vila B Morrison G and Kenney D (2002) ldquoImproving shift schedule and work-hour
policies and practices to increase police officer performance health and safetyrdquo Police
Quarterly Vol 5 No 1 pp 4-24
Vollaard B and Koning P (2009) ldquoThe effect of police on crime disorder and victim
PrecautionEvidence from a Dutch victimization surveyrdquo International Review of Law
and Economics Vol 29 No 4 pp 336-348
Weisburd D Maher L Sherman L Buerger M Cohn E and Petrisino A (1993)
ldquoContrasting crime general and crime specific theory The case of hot spots of crimerdquo
New Directions in Criminological Theory Advances in Criminological Theory Vol 4
pp 45-70
Author Biography
Luke Bonkiewicz is a patrol officer with the Lincoln Police Department
7252019 PIJPSM-05-2015-0064
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983086 983097) lowast )
7252019 PIJPSM-05-2015-0064
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983120983151983152983157983148983137983156983145983151983150 983140983141 983150983155983145983156983161 20229830856956 1687 4436
983112983151983157983155983141983144983151983148983140 983149983141983140983145983137983150 983145983150983139983151983149983141 ($) 2628198308576020 16535 49756
983159983144983145983156983141 7398308593 007 083
983126983145983151983148983141983150983156 983139983154983145983149983141 983154983137983156983141 (983152983141983154 100000) 8009830854800 1203 2521
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983120983154983151983152983141983154983156983161 983139983154983145983149983141983155 983152983141983154 983151983142983142983145983139983141983154 5329830851097 1663 8167
983126983145983151983148983141983150983156 983139983154983145983149983141 983152983141983154 983151983142983142983145983139983141983154 194983085372 559 2929
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983111983141983150983140983141983154 (983142983141983149983137983148983141=0 983149983137983148983141=1) 09830851 24 94
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983151983142 C983144983145983148983140983154983141983150 09830855 170 90
983129983141983137983154983155 983141983160983152983141983154983145983141983150983139983141 198308537 779 1063
C983137983148983148983155 983142983151983154 983155983141983154983158983145983139983141 4819830851011 16121 61365
983124983154983137983142983142983145983139 983155983156983151983152983155 16983085901 14376 20624
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983117983145983155983140983141983149983141983137983150983151983154 983137983154983154983141983155983156983155 19983085403 5965 10663
983110983141983148983151983150983161 983137983154983154983141983155983156983155 098308571 1066 852
983126983119983122C 9830851959830851043 151 24
1983155983156 2983150983140 3983154983140
104 96 102983123983144983145983142983156
7252019 PIJPSM-05-2015-0064
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983124983137983138983148983141 3 983112983145983141983154983137983154983139983144983145983139983137983148 983116983145983150983141983137983154 983117983151983140983141983148983155 E983155983156983145983149983137983156983145983150983143 983119983142 983142983145983139983141983154983155 A983150983150983157983137983148 983124983154983137983142983142983145983139 C983145983156983137983156983145983151983150983155 983127983137983154983154983137983150983156983155 983117983145983155983140983141983149983141983137983150983151983154983155 983137983150983140 983110983141983148983151983150 983145983141983155
983123983141983139983151983150983140983085983116983141983158983141983148 983126983137983154983145983137983138983148983141983155 (983120983137983156983154983151983148 A983154983141983137) 983118=40 983138 983123E 983138 983123E 983138 983123E 983138 983123E
983120983151983152983157983148983137983156983145983151983150 983140983141983150983155983145983156983161 001 003 001 001 98308501 01 00 00
983112983151983157983155983141983144983151983148983140 983149983141983140983145983137983150 983145983150983139983151983149983141 ($) 002 001 983085000 000 00 00 98308500 00
983159983144983145983156983141 13363 32472 9830858921 5664 9830853274 19211 9830854447 3255
983126983145983151983148983141983150983156 983139983154983145983149983141 983154983137983156983141 (983152983141983154 100000) 006 004 001 001 98308501 02 01 02
983120983154983151983152983141983154983156983161 983139983154983145983149983141 983154983137983156983141 (983152983141983154 100000) 983085003 005 983085002 001 02 03 98308501 00
983120983154983151983152983141983154983156983161 983139983154983145983149983141983155 983152983141983154 983151983142983142983145983139983141983154 983085225 99 98308515 03 98308552 24 98308503 03
983126983145983151983148983141983150983156 983139983154983145983149983141 983152983141983154 983151983142983142983145983139983141983154 983085168 68 29 14 76 31 22 12
983110983145983154983155983156983085983116983141983158983141983148 983126983137983154983145983137983138983148983141983155 (983119983142983142983145983139983141983154) 983118=302
983117983137983148983141 4673 1622 366 147 1613 1051 91 158
983117983137983154983154983145983141983140C983151983144983137983138983145983156983145983150983143 9830857516 3254 201 278 9830851158 1169 98308595 167
983151983142 C983144983145983148983140983154983141983150 183 702 75 77 983085262 217 983085396 207
983129983141983137983154983155 983141983160983152983141983154983145983141983150983139983141 9830851065 492 98308512 13 983085163 33 98308506 05C983137983148983148983155 983142983151983154 983155983141983154983158983145983139983141 24 13 02 02 14 01 98308508 05
983124983154983137983142983142983145983139 983155983156983151983152983155 983085983085 983085983085 03 00 09 02 00 00
983110983145983141983148983140 983145983150983142983151983154983149983137983156983145983151983150 983154983141983152983151983154983156983155 209 33 20 04 79 19 10 03
983123983141983139983151983150983140 983155983144983145983142983156 (983154983141983142983141983154983141983150983139983141 983143983154983151983157983152 983142983145983154983155983156) 2603 1778 187 195 809 730 147 160
983124983144983145 983154983140 983155983144983145 983142983156 (983154983141 983142983141 983154983141 983150983139983141 983143983154983151983157983152 983142983145 983154983155983156) 9872 2155 79 213 2532 952 393 132
983113983150983156983141983154983139983141983152983156 32591 5361 758 339 4147 1912 310 484
983123983145983143983149983137983085983155983153983157983137983154983141983140 1541467 19287 206702 5598
C983144983145983085983155983153983157983137983154983141983140 8294 6133 12348 10663
983152 lt 05
983152lt01
983152lt001
983118983151983156983141 A983148983148 983158983137983154983145983137983138983148983141983155 983137983154983141 983157983150983139983141983150983156983141983154983141983140
983127A983122983122A983118983124 A983122983122E983123983124983123 983117983113983123DE983117EA983118983119983122 A983122983122E983123983124983123 983110E983116983119983118983129 A983122983122E983123983124983123983124983122A983110983110983113C C983113983124A983124983113983119983118983123
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983124983137983138983148983141 4 983112983145983141983154983137983154983139983144983145983139983137983148 983116983145983150983141983137983154 983117983151983140983141983148983155 E983155983156983145983149983137983156983145983150983143 983119983142983142983145983139983141983154983155 A983150983150983157983137983148 983126983137983148983157983141 983119983158983141983154 983122983141983152983148983137983139983141983149983141983150983156 C983151983152 (983126983119983122C)
983123983141983139983151983150983140983085983116983141983158983141983148 983126983137983154983145983137983138983148983141983155 (983120983137983156983154983151983148 A983154983141983137) 983118=40 983138 983123E
983120983151983152983157983148983137983156983145983151983150 983140983141983150983155983145983156983161 98308500 00
983112983151983157983155983141983144983151983148983140 983149983141983140983145983137983150 983145983150983139983151983149983141 ($) 00 01
983159983144983145983156983141 318 345
983126983145983151983148983141983150983156 983139983154983145983149983141 983154983137983156983141 (983152983141983154 100000) 98308500 00
983120983154983151983152983141983154983156983161 983139983154983145983149983141 983154983137983156983141 (983152983141983154 100000) 00 00
983120983154983151983152983141983154983156983161 983139983154983145983149983141983155 983152983141983154 983151983142983142983145983139983141983154 98308502 04
983126983145983151983148983141983150983156 983139983154983145983149983141 983152983141983154 983151983142983142983145983139983141983154 02 05
983110983145983154983155983156983085983116983141983158983141983148 983126983137983154983145983137983138983148983141983155 (983119983142983142983145983139983141983154) 983118=302
983117983137983148983141 27 15
983117983137983154983154983145983141983140C983151983144983137983138983145983156983145983150983143 98308532 31
983151983142 C983144983145983148983140983154983141983150 05 03
983129983141983137983154983155 983141983160983152983141983154983145983141983150983139983141 00 01
C983137983148 983148983155 983142983151983154 983155983141983154983158983145983139983141 02 01
983124983154983137983142983142983145983139 983155983156983151983152983155 01 00
983110983145 983141983148983140 983145983150983142983151983154983149983137983156983145 983151983150 983154983141 983152983151983154983156983155 02 00
983123983141983139983151983150983140 983155983144983145983142983156 ( 983154983141983142983141983154983141983150983139983141 983143983154983151983157983152 983142 983145983154983155983156) 23 12
983124983144983145983154983140 983155983144983145983142983156 (983154983141983142983141983154983141983150983139983141 983143983154983151983157983152 983142 983145983154983155983156) 55 14
983113983150983156983141983154983139983141983152983156 165 36
983123983145983143983149983137983085983155983153983157983137983154983141983140 66
C983144983145983085983155983153983157983137983154983141983140 9846
983152 lt 05
983152lt01
983152lt001
983118983151983156983141 A983148983148 983158983137983154983145983137983138983148983141983155 983137983154983141 983157983150983139983141983150983156983141983154983141983140
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We believe our explanation has a stronger theoretical foundation The ratio of property
crimes per officer affects productivity outputs differently than the ratio of violent crimes per
officer for two reasons First violent crimes are different in nature than property crimes
especially with regard to clearance rates Second property crimes often require more
investigative and follow-up time than violent crimes thereby reducing an officerrsquos amount of
productive time and proactive activity
Table 3 also provides a key piece of evidence to support this assertion Table 3 shows
that the number of reported violent crimes per officer increases an officerrsquos number of warrant
and misdemeanor arrests Yet the relationship is the opposite for traffic enforcement the violent
crime-to-cop ratio actually decreases traffic citations This change is due to the fact that while
violent crime investigations often yield warrant and misdemeanor arrests they do not produce
traffic citations Furthermore responding to a greater number of violent crimes decreases the
amount of available time to conduct traffic enforcement in turn reducing the number of traffic
citations In other words the violent crime-to-cop ratio affects traffic stops the same way that
the property crime-to-cop ratio affects all productivity outputs by reducing an officerrsquos
productive time and opportunities for proactivity In fact we found a moderate correlation
between a patrol arearsquos crime-to-cop ratio and the amount of productive time enjoyed by officers
who worked that area The correlation between an arearsquos violent crime-to-cop ratio and
productive time was -21 while the correlation between an arearsquos property crime-to-cop ratio and
productive time was -33 We believe this evidence indicates that officers who work in areas
with a high crime-to-cop ratio have less time for self-initiated activities which drives down
productivity
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We suggest that the number of police officers and crime rates of an area need to be
analyzed in conjunction with each other Prior theories of police productivity have viewed and
examined these variables separately when in reality we should view these variables in tandem
Combining these variables into crime-to-cop ratios provides a more nuancedmdashand accuratemdash
understanding of patrol activities and their relationship to productivity
Although we found strong evidence that an officerrsquos patrol area affects his or her
productivity (in terms of raw outputs) we also found that a few officer-level variables affected
productivity as well including traffic stops intelligence reports and working third shift Our
results appear to substantiate what many police practitioners already suspect officers who are
proactive in contacting citizens (traffic stops) and building investigations (intelligence reports)
generate more warrant and misdemeanor arrests In addition while some newer police officers
are forced to work third shift because they do not have sufficient seniority to bid for better days
off and shift times many officers work nights because they enjoy the types of calls (eg in-
progress felonies) meaning they may be more motivated to stop vehicles make field contacts
and in turn engage in activities that generate arrests (Brehm amp Gates 1993)
These results have important implications for police researchers and administrators
First our results suggest a need for a deeper understanding of how the staffing levels crime
rates and the other characteristics of a patrol area intersect Second if departments evaluate
their patrol officers using raw productivity outputs such as misdemeanor or felony arrests
administrators must recognize that factors beyond the control of patrol officers might affect
monthly or annual productivity Our research suggests that a patrol arearsquos crime-to-cop ratio can
substantially affect a patrol officerrsquos productivity regardless of the actual crime rate If the
characteristics of a patrol area affect raw productivity outputs police departments should employ
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more advanced productivity analytics (ie VORC) that account for activities which reduce
productive time and evaluate an officerrsquos productivity in terms of available minutes for
proactivity
Nonetheless evaluating both patrol officers and police departments using traffic citations
warrants misdemeanors and felonies is useful because this type of data is readily understood by
citizens and civic officials A neighborhood watch group for instance might care more about
the number of arrests their officers make and less about those officersrsquo productivity scores Our
data suggests that if citizens want patrol officers to write more traffic citations and make more
arrests they should lobby for more police officers on the street It is true that reducing an arearsquos
crime rate impacts the crime-to-cop ratio but our results indicate that putting police officers on
the street may be the most direct way to affect the crime-to-cop ratio
Our study is not without limitations We only examined a police department which uses
the generalist style of policing and it is unknown if our results are applicable to police
departments which use a specialist style of policing Theoretically a departmentrsquos policing
styles can affect factors that increase or decrease a patrol officerrsquos productive time It is possible
that the number of property crimes and violent crimes per officer do not significantly affect a
specialist patrol officerrsquos productivity outputs versus a generalist officerrsquos outputs Future
studies should investigate whether our results can be generalized to all police departments
We have only identified two macro-level variables which affect patrol officer
productivity and we recognize there may be other variables In addition we recommend that
future studies incorporate a wider array of variables and test competing models to determine
whether micro or macro-level variables exhibit stronger effects on productivity In the end
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managing patrol officers and improving productivity requires a greater understanding of both
patrol officers and the areas they work
Acknowledgements and Credits We would like to thank Denver Police Department Crime
Analyst Melissa Ohlhorst and Pennsylvania State University Professor Barry Ruback as well as
the three anonymous reviewers for their insightful comments We also salute Lincoln Police
Sergeant Jake Dilsaver for his logistical support of this project
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References
Amaranto E Steinberg J Castellano C and Mitchell R (2003) ldquoPolice stress interventionsrdquo
Brief Treatment and Crisis Intervention Vol 3 No 1 pp 47-53
Becker G (1985) ldquoHuman capital effort and the sexual division of laborrdquo The Journal of
Labor EconomicsVol 3 No 1 pp 33-58
Bonkiewicz L (2015) ldquoBobbies and baseball players Evaluating patrol officer productivity
using sabermetricsrdquo Police Quarterly Vol 18 No 1 pp 55-78
Braga A and Bond J (2008) ldquoPolicing crime and disorder hot spots A randomized controlled
trialrdquo Criminology Vol 46 No 3 pp 577-607
Brown J and Campbell E (1990) ldquoSources of occupational stress in the policerdquo Work
and Stress Vol 4 No 4 pp 305-318
Chappell A MacDonald J and Manz P (2006) ldquoThe organizational determinants
of police arrest decisionsrdquo Crime and Delinquency Vol 52 No 2 pp 287-306
Cloninger D and Sartorius L (1979) ldquoCrime rates clearance rates and enforcement effort
The case of Houston Texasrdquo American Journal of Economics and Sociology Vol 38
No 4 pp 389-402
Corman H and Mocan N (2005) ldquoCarrots sticks and broken windowsrdquo Journal of Law and
Economics Vol 48 No 1 pp 235-266
Crank J (1990) ldquoThe influence of environmental and organizational factors on police style in
urban and rural environmentsrdquo Journal of Research in Crime and Delinquency Vol 27
pp 166-189
Goodman A (1990) ldquoA model for police officer burnoutrdquo Journal of Business and
Psychology Vol 5 No 1 pp 85-99
Gould E Weinberg B and Mustard D (2002) Crime rates and local labor marketopportunities in the United States 1979ndash1997rdquo Review of Economics and Statistics Vol
84 No 1 pp 45-61
Hipp J and Roussell A (2013) ldquoMicro-and macro-environment population and the
consequences for crime ratesrdquo Social Forces Vol 92 No 2 pp 563-595
Hatry H (1976) ldquoApproaches to productivity measurement and program evaluationrdquo Public
Productivity Review Vol 1 No 3 pp21-28
Hawkins H (2001) ldquoPolice officer burnout a partial replication of Maslachs Burnout
Inventoryrdquo Police Quarterly Vol 4 No 3 pp 343-360
7252019 PIJPSM-05-2015-0064
httpslidepdfcomreaderfullpijpsm-05-2015-0064 2530
Herbert S (1998)ldquoPolice subculture reconsideredrdquo Criminology Vol 36 No 2 pp 343-370
Huizinga D and Henry K (2008) ldquoThe effect of arrest and justice system sanctions on
subsequent behavior Findings from longitudinal and other studiesrdquo In The long view of
crime A synthesis of longitudinal research Springer New York pp 220-254
Johnson R (2011) ldquoOfficer attitudes and management influences on police work productivityrdquo
American Journal of Criminal Justice Vol 36 pp 293-306
Kane R (2006) ldquoOn the limits of social control Structural deterrence and the policing of
lsquosuppressiblersquo crimesrdquo Justice Quarterly Vol 23 No 2 pp 186-213
Kubrin C Messner S Deane G McGeever K and Stucky T (2010) ldquoProactive
policing and robbery rates across US citiesrdquo Criminology Vol 48 No 1 pp 57-97
Lersch K (2002) ldquoAre citizen complaints just another measure of officer productivity An
analysis of citizen complaints and officer activity measuresrdquo Police Practice and
Research Vol 3 pp 135-147
Light M and Harris C (2012) Race space and violence exploring spatial dependence in
structural covariates of white and black violent crime in US countiesrdquo Journal of
Quantitative Criminology Vol 28 No 4 pp 559-586
Maxfield M (1982) ldquoService time dispatch time and demand for police services Helping
more by serving lessrdquo Public Administration Review pp 252-263
Maxfield M Lewis D and Szoc R (1980) ldquoProducing official crimes Verified crime reports
as measures of police outputrdquo Social Science Quarterly pp 221-236
Nakosteen R and Zimmer M (2001) ldquoSpouse selection and earnings Evidence of marital
sortingrdquo Economic InquiryVol 39 No 2 pp 201-2013
Shane J (2010) ldquoOrganizational stressors and police performancerdquo Journal of Criminal
Justice Vol 38 No 4 pp 807-818
Shane J (2011) ldquoDaily work experiences and police performancerdquo Police Practice and
Research Vol 13 No 3 pp 1-19
Sherman L Gartin P and Buerger M (1989) ldquoHot spots of predatory crime Routine
activities and the criminology of placerdquoCriminology Vol 27 No 1 pp27-56
Truman J and Rand M (2010) ldquoBureau of Justice Statistics Bulletin National Crime
Victimization Survey Criminal Victimization 2009rdquo US Department of Justice Office of
Justice programs
7252019 PIJPSM-05-2015-0064
httpslidepdfcomreaderfullpijpsm-05-2015-0064 2630
Van Meter D (2001) Evaluating dysfunctional police performance A zero-based approach
Charles C Thomas Publisher Springfield IL
Vila B and Kenney D (2002) ldquoTired cops The prevalence and potential consequences of
police fatiguerdquo National Institute of Justice
Vila B Morrison G and Kenney D (2002) ldquoImproving shift schedule and work-hour
policies and practices to increase police officer performance health and safetyrdquo Police
Quarterly Vol 5 No 1 pp 4-24
Vollaard B and Koning P (2009) ldquoThe effect of police on crime disorder and victim
PrecautionEvidence from a Dutch victimization surveyrdquo International Review of Law
and Economics Vol 29 No 4 pp 336-348
Weisburd D Maher L Sherman L Buerger M Cohn E and Petrisino A (1993)
ldquoContrasting crime general and crime specific theory The case of hot spots of crimerdquo
New Directions in Criminological Theory Advances in Criminological Theory Vol 4
pp 45-70
Author Biography
Luke Bonkiewicz is a patrol officer with the Lincoln Police Department
7252019 PIJPSM-05-2015-0064
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983086 983097) lowast )
7252019 PIJPSM-05-2015-0064
httpslidepdfcomreaderfullpijpsm-05-2015-0064 2830
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983119983142983142983145983139983141983154 983120983154983151983155983141983139983157983156983145983151983150
983122983137983156983141C983110983123
983117983145983150983157983156983141983155983110983125983120
983117983145983150983157983156983141983155
983119983142983142983145983139983141983154
A983155983155983145983155983156983117983145983150983157983156983141983155
983113983150983139983145983140983141983150983156983122983141983152983151983154983156983155
AC983113983122983141983152983151983154983156983155 983123983141983148983141983139983156983145983158983141983155 983127983137983154983150983145983150983143983155 983119983142983142983145983139983145983137983148983155 983127983137983154983154983137983150983156983155 D983125983113983155 983117983145983155983140983141983149983141983137983150983151983154983155 983110983141983148983151983150983145983141983155
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2 95 30072 5424 4116 204 696 228 312 432 84 48 120 8
3 72 32496 6312 6600 144 564 444 720 540 84 36 24 12
4 90 33000 5232 4980 216 192 480 768 240 144 12 72 12
5 91 41724 4824 6048 312 636 132 504 324 108 7 24 24
6 83 23916 7056 6192 144 756 528 996 336 132 36 72 12
7 95 24468 6840 5892 156 624 324 456 480 72 36 120 24
8 90 39972 5196 4920 240 504 384 492 264 36 2 72 24
9 79 25500 4068 5100 336 396 192 684 204 24 36 120 10
10 87 21984 5412 4524 348 552 480 792 312 108 48 84 36
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983120983151983152983157983148983137983156983145983151983150 983140983141 983150983155983145983156983161 20229830856956 1687 4436
983112983151983157983155983141983144983151983148983140 983149983141983140983145983137983150 983145983150983139983151983149983141 ($) 2628198308576020 16535 49756
983159983144983145983156983141 7398308593 007 083
983126983145983151983148983141983150983156 983139983154983145983149983141 983154983137983156983141 (983152983141983154 100000) 8009830854800 1203 2521
983120983154983151983152983141983154983156983161 983139983154983145983149983141 983154983137983156983141 (983152983141983154 100000) 31009830859800 2027 6524
983120983154983151983152983141983154983156983161 983139983154983145983149983141983155 983152983141983154 983151983142983142983145983139983141983154 5329830851097 1663 8167
983126983145983151983148983141983150983156 983139983154983145983149983141 983152983141983154 983151983142983142983145983139983141983154 194983085372 559 2929
983110983145983154983155983156983085983116983141 983158983141983148 983126 983137983154983145983137983138983148983141 983155 (983119983142983142983145983139983141983154) 983118=302 983122983137983150983143983141 983123D 983117983141 983137983150
983111983141983150983140983141983154 (983142983141983149983137983148983141=0 983149983137983148983141=1) 09830851 24 94
983117983137983154983154983145983141983140C983151983144983137983138983145983156983145983150983143 09830851 18 79
983151983142 C983144983145983148983140983154983141983150 09830855 170 90
983129983141983137983154983155 983141983160983152983141983154983145983141983150983139983141 198308537 779 1063
C983137983148983148983155 983142983151983154 983155983141983154983158983145983139983141 4819830851011 16121 61365
983124983154983137983142983142983145983139 983155983156983151983152983155 16983085901 14376 20624
983110983145983141983148983140 983145983150983142983151983154983149983137983156983145983151983150 983154983141983152983151983154983156983155 0983085227 2153 2530
983124983154983137983142983142983145983139 983139983145983156983137983156983145983151983150983155 249830851246 14632 18642
983117983145983155983140983141983149983141983137983150983151983154 983137983154983154983141983155983156983155 19983085403 5965 10663
983110983141983148983151983150983161 983137983154983154983141983155983156983155 098308571 1066 852
983126983119983122C 9830851959830851043 151 24
1983155983156 2983150983140 3983154983140
104 96 102983123983144983145983142983156
7252019 PIJPSM-05-2015-0064
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983124983137983138983148983141 3 983112983145983141983154983137983154983139983144983145983139983137983148 983116983145983150983141983137983154 983117983151983140983141983148983155 E983155983156983145983149983137983156983145983150983143 983119983142 983142983145983139983141983154983155 A983150983150983157983137983148 983124983154983137983142983142983145983139 C983145983156983137983156983145983151983150983155 983127983137983154983154983137983150983156983155 983117983145983155983140983141983149983141983137983150983151983154983155 983137983150983140 983110983141983148983151983150 983145983141983155
983123983141983139983151983150983140983085983116983141983158983141983148 983126983137983154983145983137983138983148983141983155 (983120983137983156983154983151983148 A983154983141983137) 983118=40 983138 983123E 983138 983123E 983138 983123E 983138 983123E
983120983151983152983157983148983137983156983145983151983150 983140983141983150983155983145983156983161 001 003 001 001 98308501 01 00 00
983112983151983157983155983141983144983151983148983140 983149983141983140983145983137983150 983145983150983139983151983149983141 ($) 002 001 983085000 000 00 00 98308500 00
983159983144983145983156983141 13363 32472 9830858921 5664 9830853274 19211 9830854447 3255
983126983145983151983148983141983150983156 983139983154983145983149983141 983154983137983156983141 (983152983141983154 100000) 006 004 001 001 98308501 02 01 02
983120983154983151983152983141983154983156983161 983139983154983145983149983141 983154983137983156983141 (983152983141983154 100000) 983085003 005 983085002 001 02 03 98308501 00
983120983154983151983152983141983154983156983161 983139983154983145983149983141983155 983152983141983154 983151983142983142983145983139983141983154 983085225 99 98308515 03 98308552 24 98308503 03
983126983145983151983148983141983150983156 983139983154983145983149983141 983152983141983154 983151983142983142983145983139983141983154 983085168 68 29 14 76 31 22 12
983110983145983154983155983156983085983116983141983158983141983148 983126983137983154983145983137983138983148983141983155 (983119983142983142983145983139983141983154) 983118=302
983117983137983148983141 4673 1622 366 147 1613 1051 91 158
983117983137983154983154983145983141983140C983151983144983137983138983145983156983145983150983143 9830857516 3254 201 278 9830851158 1169 98308595 167
983151983142 C983144983145983148983140983154983141983150 183 702 75 77 983085262 217 983085396 207
983129983141983137983154983155 983141983160983152983141983154983145983141983150983139983141 9830851065 492 98308512 13 983085163 33 98308506 05C983137983148983148983155 983142983151983154 983155983141983154983158983145983139983141 24 13 02 02 14 01 98308508 05
983124983154983137983142983142983145983139 983155983156983151983152983155 983085983085 983085983085 03 00 09 02 00 00
983110983145983141983148983140 983145983150983142983151983154983149983137983156983145983151983150 983154983141983152983151983154983156983155 209 33 20 04 79 19 10 03
983123983141983139983151983150983140 983155983144983145983142983156 (983154983141983142983141983154983141983150983139983141 983143983154983151983157983152 983142983145983154983155983156) 2603 1778 187 195 809 730 147 160
983124983144983145 983154983140 983155983144983145 983142983156 (983154983141 983142983141 983154983141 983150983139983141 983143983154983151983157983152 983142983145 983154983155983156) 9872 2155 79 213 2532 952 393 132
983113983150983156983141983154983139983141983152983156 32591 5361 758 339 4147 1912 310 484
983123983145983143983149983137983085983155983153983157983137983154983141983140 1541467 19287 206702 5598
C983144983145983085983155983153983157983137983154983141983140 8294 6133 12348 10663
983152 lt 05
983152lt01
983152lt001
983118983151983156983141 A983148983148 983158983137983154983145983137983138983148983141983155 983137983154983141 983157983150983139983141983150983156983141983154983141983140
983127A983122983122A983118983124 A983122983122E983123983124983123 983117983113983123DE983117EA983118983119983122 A983122983122E983123983124983123 983110E983116983119983118983129 A983122983122E983123983124983123983124983122A983110983110983113C C983113983124A983124983113983119983118983123
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983124983137983138983148983141 4 983112983145983141983154983137983154983139983144983145983139983137983148 983116983145983150983141983137983154 983117983151983140983141983148983155 E983155983156983145983149983137983156983145983150983143 983119983142983142983145983139983141983154983155 A983150983150983157983137983148 983126983137983148983157983141 983119983158983141983154 983122983141983152983148983137983139983141983149983141983150983156 C983151983152 (983126983119983122C)
983123983141983139983151983150983140983085983116983141983158983141983148 983126983137983154983145983137983138983148983141983155 (983120983137983156983154983151983148 A983154983141983137) 983118=40 983138 983123E
983120983151983152983157983148983137983156983145983151983150 983140983141983150983155983145983156983161 98308500 00
983112983151983157983155983141983144983151983148983140 983149983141983140983145983137983150 983145983150983139983151983149983141 ($) 00 01
983159983144983145983156983141 318 345
983126983145983151983148983141983150983156 983139983154983145983149983141 983154983137983156983141 (983152983141983154 100000) 98308500 00
983120983154983151983152983141983154983156983161 983139983154983145983149983141 983154983137983156983141 (983152983141983154 100000) 00 00
983120983154983151983152983141983154983156983161 983139983154983145983149983141983155 983152983141983154 983151983142983142983145983139983141983154 98308502 04
983126983145983151983148983141983150983156 983139983154983145983149983141 983152983141983154 983151983142983142983145983139983141983154 02 05
983110983145983154983155983156983085983116983141983158983141983148 983126983137983154983145983137983138983148983141983155 (983119983142983142983145983139983141983154) 983118=302
983117983137983148983141 27 15
983117983137983154983154983145983141983140C983151983144983137983138983145983156983145983150983143 98308532 31
983151983142 C983144983145983148983140983154983141983150 05 03
983129983141983137983154983155 983141983160983152983141983154983145983141983150983139983141 00 01
C983137983148 983148983155 983142983151983154 983155983141983154983158983145983139983141 02 01
983124983154983137983142983142983145983139 983155983156983151983152983155 01 00
983110983145 983141983148983140 983145983150983142983151983154983149983137983156983145 983151983150 983154983141 983152983151983154983156983155 02 00
983123983141983139983151983150983140 983155983144983145983142983156 ( 983154983141983142983141983154983141983150983139983141 983143983154983151983157983152 983142 983145983154983155983156) 23 12
983124983144983145983154983140 983155983144983145983142983156 (983154983141983142983141983154983141983150983139983141 983143983154983151983157983152 983142 983145983154983155983156) 55 14
983113983150983156983141983154983139983141983152983156 165 36
983123983145983143983149983137983085983155983153983157983137983154983141983140 66
C983144983145983085983155983153983157983137983154983141983140 9846
983152 lt 05
983152lt01
983152lt001
983118983151983156983141 A983148983148 983158983137983154983145983137983138983148983141983155 983137983154983141 983157983150983139983141983150983156983141983154983141983140
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We suggest that the number of police officers and crime rates of an area need to be
analyzed in conjunction with each other Prior theories of police productivity have viewed and
examined these variables separately when in reality we should view these variables in tandem
Combining these variables into crime-to-cop ratios provides a more nuancedmdashand accuratemdash
understanding of patrol activities and their relationship to productivity
Although we found strong evidence that an officerrsquos patrol area affects his or her
productivity (in terms of raw outputs) we also found that a few officer-level variables affected
productivity as well including traffic stops intelligence reports and working third shift Our
results appear to substantiate what many police practitioners already suspect officers who are
proactive in contacting citizens (traffic stops) and building investigations (intelligence reports)
generate more warrant and misdemeanor arrests In addition while some newer police officers
are forced to work third shift because they do not have sufficient seniority to bid for better days
off and shift times many officers work nights because they enjoy the types of calls (eg in-
progress felonies) meaning they may be more motivated to stop vehicles make field contacts
and in turn engage in activities that generate arrests (Brehm amp Gates 1993)
These results have important implications for police researchers and administrators
First our results suggest a need for a deeper understanding of how the staffing levels crime
rates and the other characteristics of a patrol area intersect Second if departments evaluate
their patrol officers using raw productivity outputs such as misdemeanor or felony arrests
administrators must recognize that factors beyond the control of patrol officers might affect
monthly or annual productivity Our research suggests that a patrol arearsquos crime-to-cop ratio can
substantially affect a patrol officerrsquos productivity regardless of the actual crime rate If the
characteristics of a patrol area affect raw productivity outputs police departments should employ
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more advanced productivity analytics (ie VORC) that account for activities which reduce
productive time and evaluate an officerrsquos productivity in terms of available minutes for
proactivity
Nonetheless evaluating both patrol officers and police departments using traffic citations
warrants misdemeanors and felonies is useful because this type of data is readily understood by
citizens and civic officials A neighborhood watch group for instance might care more about
the number of arrests their officers make and less about those officersrsquo productivity scores Our
data suggests that if citizens want patrol officers to write more traffic citations and make more
arrests they should lobby for more police officers on the street It is true that reducing an arearsquos
crime rate impacts the crime-to-cop ratio but our results indicate that putting police officers on
the street may be the most direct way to affect the crime-to-cop ratio
Our study is not without limitations We only examined a police department which uses
the generalist style of policing and it is unknown if our results are applicable to police
departments which use a specialist style of policing Theoretically a departmentrsquos policing
styles can affect factors that increase or decrease a patrol officerrsquos productive time It is possible
that the number of property crimes and violent crimes per officer do not significantly affect a
specialist patrol officerrsquos productivity outputs versus a generalist officerrsquos outputs Future
studies should investigate whether our results can be generalized to all police departments
We have only identified two macro-level variables which affect patrol officer
productivity and we recognize there may be other variables In addition we recommend that
future studies incorporate a wider array of variables and test competing models to determine
whether micro or macro-level variables exhibit stronger effects on productivity In the end
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managing patrol officers and improving productivity requires a greater understanding of both
patrol officers and the areas they work
Acknowledgements and Credits We would like to thank Denver Police Department Crime
Analyst Melissa Ohlhorst and Pennsylvania State University Professor Barry Ruback as well as
the three anonymous reviewers for their insightful comments We also salute Lincoln Police
Sergeant Jake Dilsaver for his logistical support of this project
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References
Amaranto E Steinberg J Castellano C and Mitchell R (2003) ldquoPolice stress interventionsrdquo
Brief Treatment and Crisis Intervention Vol 3 No 1 pp 47-53
Becker G (1985) ldquoHuman capital effort and the sexual division of laborrdquo The Journal of
Labor EconomicsVol 3 No 1 pp 33-58
Bonkiewicz L (2015) ldquoBobbies and baseball players Evaluating patrol officer productivity
using sabermetricsrdquo Police Quarterly Vol 18 No 1 pp 55-78
Braga A and Bond J (2008) ldquoPolicing crime and disorder hot spots A randomized controlled
trialrdquo Criminology Vol 46 No 3 pp 577-607
Brown J and Campbell E (1990) ldquoSources of occupational stress in the policerdquo Work
and Stress Vol 4 No 4 pp 305-318
Chappell A MacDonald J and Manz P (2006) ldquoThe organizational determinants
of police arrest decisionsrdquo Crime and Delinquency Vol 52 No 2 pp 287-306
Cloninger D and Sartorius L (1979) ldquoCrime rates clearance rates and enforcement effort
The case of Houston Texasrdquo American Journal of Economics and Sociology Vol 38
No 4 pp 389-402
Corman H and Mocan N (2005) ldquoCarrots sticks and broken windowsrdquo Journal of Law and
Economics Vol 48 No 1 pp 235-266
Crank J (1990) ldquoThe influence of environmental and organizational factors on police style in
urban and rural environmentsrdquo Journal of Research in Crime and Delinquency Vol 27
pp 166-189
Goodman A (1990) ldquoA model for police officer burnoutrdquo Journal of Business and
Psychology Vol 5 No 1 pp 85-99
Gould E Weinberg B and Mustard D (2002) Crime rates and local labor marketopportunities in the United States 1979ndash1997rdquo Review of Economics and Statistics Vol
84 No 1 pp 45-61
Hipp J and Roussell A (2013) ldquoMicro-and macro-environment population and the
consequences for crime ratesrdquo Social Forces Vol 92 No 2 pp 563-595
Hatry H (1976) ldquoApproaches to productivity measurement and program evaluationrdquo Public
Productivity Review Vol 1 No 3 pp21-28
Hawkins H (2001) ldquoPolice officer burnout a partial replication of Maslachs Burnout
Inventoryrdquo Police Quarterly Vol 4 No 3 pp 343-360
7252019 PIJPSM-05-2015-0064
httpslidepdfcomreaderfullpijpsm-05-2015-0064 2530
Herbert S (1998)ldquoPolice subculture reconsideredrdquo Criminology Vol 36 No 2 pp 343-370
Huizinga D and Henry K (2008) ldquoThe effect of arrest and justice system sanctions on
subsequent behavior Findings from longitudinal and other studiesrdquo In The long view of
crime A synthesis of longitudinal research Springer New York pp 220-254
Johnson R (2011) ldquoOfficer attitudes and management influences on police work productivityrdquo
American Journal of Criminal Justice Vol 36 pp 293-306
Kane R (2006) ldquoOn the limits of social control Structural deterrence and the policing of
lsquosuppressiblersquo crimesrdquo Justice Quarterly Vol 23 No 2 pp 186-213
Kubrin C Messner S Deane G McGeever K and Stucky T (2010) ldquoProactive
policing and robbery rates across US citiesrdquo Criminology Vol 48 No 1 pp 57-97
Lersch K (2002) ldquoAre citizen complaints just another measure of officer productivity An
analysis of citizen complaints and officer activity measuresrdquo Police Practice and
Research Vol 3 pp 135-147
Light M and Harris C (2012) Race space and violence exploring spatial dependence in
structural covariates of white and black violent crime in US countiesrdquo Journal of
Quantitative Criminology Vol 28 No 4 pp 559-586
Maxfield M (1982) ldquoService time dispatch time and demand for police services Helping
more by serving lessrdquo Public Administration Review pp 252-263
Maxfield M Lewis D and Szoc R (1980) ldquoProducing official crimes Verified crime reports
as measures of police outputrdquo Social Science Quarterly pp 221-236
Nakosteen R and Zimmer M (2001) ldquoSpouse selection and earnings Evidence of marital
sortingrdquo Economic InquiryVol 39 No 2 pp 201-2013
Shane J (2010) ldquoOrganizational stressors and police performancerdquo Journal of Criminal
Justice Vol 38 No 4 pp 807-818
Shane J (2011) ldquoDaily work experiences and police performancerdquo Police Practice and
Research Vol 13 No 3 pp 1-19
Sherman L Gartin P and Buerger M (1989) ldquoHot spots of predatory crime Routine
activities and the criminology of placerdquoCriminology Vol 27 No 1 pp27-56
Truman J and Rand M (2010) ldquoBureau of Justice Statistics Bulletin National Crime
Victimization Survey Criminal Victimization 2009rdquo US Department of Justice Office of
Justice programs
7252019 PIJPSM-05-2015-0064
httpslidepdfcomreaderfullpijpsm-05-2015-0064 2630
Van Meter D (2001) Evaluating dysfunctional police performance A zero-based approach
Charles C Thomas Publisher Springfield IL
Vila B and Kenney D (2002) ldquoTired cops The prevalence and potential consequences of
police fatiguerdquo National Institute of Justice
Vila B Morrison G and Kenney D (2002) ldquoImproving shift schedule and work-hour
policies and practices to increase police officer performance health and safetyrdquo Police
Quarterly Vol 5 No 1 pp 4-24
Vollaard B and Koning P (2009) ldquoThe effect of police on crime disorder and victim
PrecautionEvidence from a Dutch victimization surveyrdquo International Review of Law
and Economics Vol 29 No 4 pp 336-348
Weisburd D Maher L Sherman L Buerger M Cohn E and Petrisino A (1993)
ldquoContrasting crime general and crime specific theory The case of hot spots of crimerdquo
New Directions in Criminological Theory Advances in Criminological Theory Vol 4
pp 45-70
Author Biography
Luke Bonkiewicz is a patrol officer with the Lincoln Police Department
7252019 PIJPSM-05-2015-0064
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983101 983089983088983088lowast + + ) + ) + ) + ) lowast )) minus + + + 983090983088 + ) +983089983088)) minus 983089983088983088 lowast
983086 983097) lowast )
7252019 PIJPSM-05-2015-0064
httpslidepdfcomreaderfullpijpsm-05-2015-0064 2830
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983119983142983142983145983139983141983154 983120983154983151983155983141983139983157983156983145983151983150
983122983137983156983141C983110983123
983117983145983150983157983156983141983155983110983125983120
983117983145983150983157983156983141983155
983119983142983142983145983139983141983154
A983155983155983145983155983156983117983145983150983157983156983141983155
983113983150983139983145983140983141983150983156983122983141983152983151983154983156983155
AC983113983122983141983152983151983154983156983155 983123983141983148983141983139983156983145983158983141983155 983127983137983154983150983145983150983143983155 983119983142983142983145983139983145983137983148983155 983127983137983154983154983137983150983156983155 D983125983113983155 983117983145983155983140983141983149983141983137983150983151983154983155 983110983141983148983151983150983145983141983155
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2 95 30072 5424 4116 204 696 228 312 432 84 48 120 8
3 72 32496 6312 6600 144 564 444 720 540 84 36 24 12
4 90 33000 5232 4980 216 192 480 768 240 144 12 72 12
5 91 41724 4824 6048 312 636 132 504 324 108 7 24 24
6 83 23916 7056 6192 144 756 528 996 336 132 36 72 12
7 95 24468 6840 5892 156 624 324 456 480 72 36 120 24
8 90 39972 5196 4920 240 504 384 492 264 36 2 72 24
9 79 25500 4068 5100 336 396 192 684 204 24 36 120 10
10 87 21984 5412 4524 348 552 480 792 312 108 48 84 36
983124983137983138983148983141 2 983123983157983149983149983137983154983161 983123983156983137983156983145983155983156983145983139983155 983142983151983154 983126983137983154983145983137983138983148983141983155
983123983141983139983151983150983140983085983116983141983158983141983148 983126983137983154983145983137983138983148983141983155 (983120983137983156983154983151983148 A983154983141983137) 983118=40 983122983137983150983143983141 983123D 983117983141983137983150
983120983151983152983157983148983137983156983145983151983150 983140983141 983150983155983145983156983161 20229830856956 1687 4436
983112983151983157983155983141983144983151983148983140 983149983141983140983145983137983150 983145983150983139983151983149983141 ($) 2628198308576020 16535 49756
983159983144983145983156983141 7398308593 007 083
983126983145983151983148983141983150983156 983139983154983145983149983141 983154983137983156983141 (983152983141983154 100000) 8009830854800 1203 2521
983120983154983151983152983141983154983156983161 983139983154983145983149983141 983154983137983156983141 (983152983141983154 100000) 31009830859800 2027 6524
983120983154983151983152983141983154983156983161 983139983154983145983149983141983155 983152983141983154 983151983142983142983145983139983141983154 5329830851097 1663 8167
983126983145983151983148983141983150983156 983139983154983145983149983141 983152983141983154 983151983142983142983145983139983141983154 194983085372 559 2929
983110983145983154983155983156983085983116983141 983158983141983148 983126 983137983154983145983137983138983148983141 983155 (983119983142983142983145983139983141983154) 983118=302 983122983137983150983143983141 983123D 983117983141 983137983150
983111983141983150983140983141983154 (983142983141983149983137983148983141=0 983149983137983148983141=1) 09830851 24 94
983117983137983154983154983145983141983140C983151983144983137983138983145983156983145983150983143 09830851 18 79
983151983142 C983144983145983148983140983154983141983150 09830855 170 90
983129983141983137983154983155 983141983160983152983141983154983145983141983150983139983141 198308537 779 1063
C983137983148983148983155 983142983151983154 983155983141983154983158983145983139983141 4819830851011 16121 61365
983124983154983137983142983142983145983139 983155983156983151983152983155 16983085901 14376 20624
983110983145983141983148983140 983145983150983142983151983154983149983137983156983145983151983150 983154983141983152983151983154983156983155 0983085227 2153 2530
983124983154983137983142983142983145983139 983139983145983156983137983156983145983151983150983155 249830851246 14632 18642
983117983145983155983140983141983149983141983137983150983151983154 983137983154983154983141983155983156983155 19983085403 5965 10663
983110983141983148983151983150983161 983137983154983154983141983155983156983155 098308571 1066 852
983126983119983122C 9830851959830851043 151 24
1983155983156 2983150983140 3983154983140
104 96 102983123983144983145983142983156
7252019 PIJPSM-05-2015-0064
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983124983137983138983148983141 3 983112983145983141983154983137983154983139983144983145983139983137983148 983116983145983150983141983137983154 983117983151983140983141983148983155 E983155983156983145983149983137983156983145983150983143 983119983142 983142983145983139983141983154983155 A983150983150983157983137983148 983124983154983137983142983142983145983139 C983145983156983137983156983145983151983150983155 983127983137983154983154983137983150983156983155 983117983145983155983140983141983149983141983137983150983151983154983155 983137983150983140 983110983141983148983151983150 983145983141983155
983123983141983139983151983150983140983085983116983141983158983141983148 983126983137983154983145983137983138983148983141983155 (983120983137983156983154983151983148 A983154983141983137) 983118=40 983138 983123E 983138 983123E 983138 983123E 983138 983123E
983120983151983152983157983148983137983156983145983151983150 983140983141983150983155983145983156983161 001 003 001 001 98308501 01 00 00
983112983151983157983155983141983144983151983148983140 983149983141983140983145983137983150 983145983150983139983151983149983141 ($) 002 001 983085000 000 00 00 98308500 00
983159983144983145983156983141 13363 32472 9830858921 5664 9830853274 19211 9830854447 3255
983126983145983151983148983141983150983156 983139983154983145983149983141 983154983137983156983141 (983152983141983154 100000) 006 004 001 001 98308501 02 01 02
983120983154983151983152983141983154983156983161 983139983154983145983149983141 983154983137983156983141 (983152983141983154 100000) 983085003 005 983085002 001 02 03 98308501 00
983120983154983151983152983141983154983156983161 983139983154983145983149983141983155 983152983141983154 983151983142983142983145983139983141983154 983085225 99 98308515 03 98308552 24 98308503 03
983126983145983151983148983141983150983156 983139983154983145983149983141 983152983141983154 983151983142983142983145983139983141983154 983085168 68 29 14 76 31 22 12
983110983145983154983155983156983085983116983141983158983141983148 983126983137983154983145983137983138983148983141983155 (983119983142983142983145983139983141983154) 983118=302
983117983137983148983141 4673 1622 366 147 1613 1051 91 158
983117983137983154983154983145983141983140C983151983144983137983138983145983156983145983150983143 9830857516 3254 201 278 9830851158 1169 98308595 167
983151983142 C983144983145983148983140983154983141983150 183 702 75 77 983085262 217 983085396 207
983129983141983137983154983155 983141983160983152983141983154983145983141983150983139983141 9830851065 492 98308512 13 983085163 33 98308506 05C983137983148983148983155 983142983151983154 983155983141983154983158983145983139983141 24 13 02 02 14 01 98308508 05
983124983154983137983142983142983145983139 983155983156983151983152983155 983085983085 983085983085 03 00 09 02 00 00
983110983145983141983148983140 983145983150983142983151983154983149983137983156983145983151983150 983154983141983152983151983154983156983155 209 33 20 04 79 19 10 03
983123983141983139983151983150983140 983155983144983145983142983156 (983154983141983142983141983154983141983150983139983141 983143983154983151983157983152 983142983145983154983155983156) 2603 1778 187 195 809 730 147 160
983124983144983145 983154983140 983155983144983145 983142983156 (983154983141 983142983141 983154983141 983150983139983141 983143983154983151983157983152 983142983145 983154983155983156) 9872 2155 79 213 2532 952 393 132
983113983150983156983141983154983139983141983152983156 32591 5361 758 339 4147 1912 310 484
983123983145983143983149983137983085983155983153983157983137983154983141983140 1541467 19287 206702 5598
C983144983145983085983155983153983157983137983154983141983140 8294 6133 12348 10663
983152 lt 05
983152lt01
983152lt001
983118983151983156983141 A983148983148 983158983137983154983145983137983138983148983141983155 983137983154983141 983157983150983139983141983150983156983141983154983141983140
983127A983122983122A983118983124 A983122983122E983123983124983123 983117983113983123DE983117EA983118983119983122 A983122983122E983123983124983123 983110E983116983119983118983129 A983122983122E983123983124983123983124983122A983110983110983113C C983113983124A983124983113983119983118983123
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983124983137983138983148983141 4 983112983145983141983154983137983154983139983144983145983139983137983148 983116983145983150983141983137983154 983117983151983140983141983148983155 E983155983156983145983149983137983156983145983150983143 983119983142983142983145983139983141983154983155 A983150983150983157983137983148 983126983137983148983157983141 983119983158983141983154 983122983141983152983148983137983139983141983149983141983150983156 C983151983152 (983126983119983122C)
983123983141983139983151983150983140983085983116983141983158983141983148 983126983137983154983145983137983138983148983141983155 (983120983137983156983154983151983148 A983154983141983137) 983118=40 983138 983123E
983120983151983152983157983148983137983156983145983151983150 983140983141983150983155983145983156983161 98308500 00
983112983151983157983155983141983144983151983148983140 983149983141983140983145983137983150 983145983150983139983151983149983141 ($) 00 01
983159983144983145983156983141 318 345
983126983145983151983148983141983150983156 983139983154983145983149983141 983154983137983156983141 (983152983141983154 100000) 98308500 00
983120983154983151983152983141983154983156983161 983139983154983145983149983141 983154983137983156983141 (983152983141983154 100000) 00 00
983120983154983151983152983141983154983156983161 983139983154983145983149983141983155 983152983141983154 983151983142983142983145983139983141983154 98308502 04
983126983145983151983148983141983150983156 983139983154983145983149983141 983152983141983154 983151983142983142983145983139983141983154 02 05
983110983145983154983155983156983085983116983141983158983141983148 983126983137983154983145983137983138983148983141983155 (983119983142983142983145983139983141983154) 983118=302
983117983137983148983141 27 15
983117983137983154983154983145983141983140C983151983144983137983138983145983156983145983150983143 98308532 31
983151983142 C983144983145983148983140983154983141983150 05 03
983129983141983137983154983155 983141983160983152983141983154983145983141983150983139983141 00 01
C983137983148 983148983155 983142983151983154 983155983141983154983158983145983139983141 02 01
983124983154983137983142983142983145983139 983155983156983151983152983155 01 00
983110983145 983141983148983140 983145983150983142983151983154983149983137983156983145 983151983150 983154983141 983152983151983154983156983155 02 00
983123983141983139983151983150983140 983155983144983145983142983156 ( 983154983141983142983141983154983141983150983139983141 983143983154983151983157983152 983142 983145983154983155983156) 23 12
983124983144983145983154983140 983155983144983145983142983156 (983154983141983142983141983154983141983150983139983141 983143983154983151983157983152 983142 983145983154983155983156) 55 14
983113983150983156983141983154983139983141983152983156 165 36
983123983145983143983149983137983085983155983153983157983137983154983141983140 66
C983144983145983085983155983153983157983137983154983141983140 9846
983152 lt 05
983152lt01
983152lt001
983118983151983156983141 A983148983148 983158983137983154983145983137983138983148983141983155 983137983154983141 983157983150983139983141983150983156983141983154983141983140
7252019 PIJPSM-05-2015-0064
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more advanced productivity analytics (ie VORC) that account for activities which reduce
productive time and evaluate an officerrsquos productivity in terms of available minutes for
proactivity
Nonetheless evaluating both patrol officers and police departments using traffic citations
warrants misdemeanors and felonies is useful because this type of data is readily understood by
citizens and civic officials A neighborhood watch group for instance might care more about
the number of arrests their officers make and less about those officersrsquo productivity scores Our
data suggests that if citizens want patrol officers to write more traffic citations and make more
arrests they should lobby for more police officers on the street It is true that reducing an arearsquos
crime rate impacts the crime-to-cop ratio but our results indicate that putting police officers on
the street may be the most direct way to affect the crime-to-cop ratio
Our study is not without limitations We only examined a police department which uses
the generalist style of policing and it is unknown if our results are applicable to police
departments which use a specialist style of policing Theoretically a departmentrsquos policing
styles can affect factors that increase or decrease a patrol officerrsquos productive time It is possible
that the number of property crimes and violent crimes per officer do not significantly affect a
specialist patrol officerrsquos productivity outputs versus a generalist officerrsquos outputs Future
studies should investigate whether our results can be generalized to all police departments
We have only identified two macro-level variables which affect patrol officer
productivity and we recognize there may be other variables In addition we recommend that
future studies incorporate a wider array of variables and test competing models to determine
whether micro or macro-level variables exhibit stronger effects on productivity In the end
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managing patrol officers and improving productivity requires a greater understanding of both
patrol officers and the areas they work
Acknowledgements and Credits We would like to thank Denver Police Department Crime
Analyst Melissa Ohlhorst and Pennsylvania State University Professor Barry Ruback as well as
the three anonymous reviewers for their insightful comments We also salute Lincoln Police
Sergeant Jake Dilsaver for his logistical support of this project
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References
Amaranto E Steinberg J Castellano C and Mitchell R (2003) ldquoPolice stress interventionsrdquo
Brief Treatment and Crisis Intervention Vol 3 No 1 pp 47-53
Becker G (1985) ldquoHuman capital effort and the sexual division of laborrdquo The Journal of
Labor EconomicsVol 3 No 1 pp 33-58
Bonkiewicz L (2015) ldquoBobbies and baseball players Evaluating patrol officer productivity
using sabermetricsrdquo Police Quarterly Vol 18 No 1 pp 55-78
Braga A and Bond J (2008) ldquoPolicing crime and disorder hot spots A randomized controlled
trialrdquo Criminology Vol 46 No 3 pp 577-607
Brown J and Campbell E (1990) ldquoSources of occupational stress in the policerdquo Work
and Stress Vol 4 No 4 pp 305-318
Chappell A MacDonald J and Manz P (2006) ldquoThe organizational determinants
of police arrest decisionsrdquo Crime and Delinquency Vol 52 No 2 pp 287-306
Cloninger D and Sartorius L (1979) ldquoCrime rates clearance rates and enforcement effort
The case of Houston Texasrdquo American Journal of Economics and Sociology Vol 38
No 4 pp 389-402
Corman H and Mocan N (2005) ldquoCarrots sticks and broken windowsrdquo Journal of Law and
Economics Vol 48 No 1 pp 235-266
Crank J (1990) ldquoThe influence of environmental and organizational factors on police style in
urban and rural environmentsrdquo Journal of Research in Crime and Delinquency Vol 27
pp 166-189
Goodman A (1990) ldquoA model for police officer burnoutrdquo Journal of Business and
Psychology Vol 5 No 1 pp 85-99
Gould E Weinberg B and Mustard D (2002) Crime rates and local labor marketopportunities in the United States 1979ndash1997rdquo Review of Economics and Statistics Vol
84 No 1 pp 45-61
Hipp J and Roussell A (2013) ldquoMicro-and macro-environment population and the
consequences for crime ratesrdquo Social Forces Vol 92 No 2 pp 563-595
Hatry H (1976) ldquoApproaches to productivity measurement and program evaluationrdquo Public
Productivity Review Vol 1 No 3 pp21-28
Hawkins H (2001) ldquoPolice officer burnout a partial replication of Maslachs Burnout
Inventoryrdquo Police Quarterly Vol 4 No 3 pp 343-360
7252019 PIJPSM-05-2015-0064
httpslidepdfcomreaderfullpijpsm-05-2015-0064 2530
Herbert S (1998)ldquoPolice subculture reconsideredrdquo Criminology Vol 36 No 2 pp 343-370
Huizinga D and Henry K (2008) ldquoThe effect of arrest and justice system sanctions on
subsequent behavior Findings from longitudinal and other studiesrdquo In The long view of
crime A synthesis of longitudinal research Springer New York pp 220-254
Johnson R (2011) ldquoOfficer attitudes and management influences on police work productivityrdquo
American Journal of Criminal Justice Vol 36 pp 293-306
Kane R (2006) ldquoOn the limits of social control Structural deterrence and the policing of
lsquosuppressiblersquo crimesrdquo Justice Quarterly Vol 23 No 2 pp 186-213
Kubrin C Messner S Deane G McGeever K and Stucky T (2010) ldquoProactive
policing and robbery rates across US citiesrdquo Criminology Vol 48 No 1 pp 57-97
Lersch K (2002) ldquoAre citizen complaints just another measure of officer productivity An
analysis of citizen complaints and officer activity measuresrdquo Police Practice and
Research Vol 3 pp 135-147
Light M and Harris C (2012) Race space and violence exploring spatial dependence in
structural covariates of white and black violent crime in US countiesrdquo Journal of
Quantitative Criminology Vol 28 No 4 pp 559-586
Maxfield M (1982) ldquoService time dispatch time and demand for police services Helping
more by serving lessrdquo Public Administration Review pp 252-263
Maxfield M Lewis D and Szoc R (1980) ldquoProducing official crimes Verified crime reports
as measures of police outputrdquo Social Science Quarterly pp 221-236
Nakosteen R and Zimmer M (2001) ldquoSpouse selection and earnings Evidence of marital
sortingrdquo Economic InquiryVol 39 No 2 pp 201-2013
Shane J (2010) ldquoOrganizational stressors and police performancerdquo Journal of Criminal
Justice Vol 38 No 4 pp 807-818
Shane J (2011) ldquoDaily work experiences and police performancerdquo Police Practice and
Research Vol 13 No 3 pp 1-19
Sherman L Gartin P and Buerger M (1989) ldquoHot spots of predatory crime Routine
activities and the criminology of placerdquoCriminology Vol 27 No 1 pp27-56
Truman J and Rand M (2010) ldquoBureau of Justice Statistics Bulletin National Crime
Victimization Survey Criminal Victimization 2009rdquo US Department of Justice Office of
Justice programs
7252019 PIJPSM-05-2015-0064
httpslidepdfcomreaderfullpijpsm-05-2015-0064 2630
Van Meter D (2001) Evaluating dysfunctional police performance A zero-based approach
Charles C Thomas Publisher Springfield IL
Vila B and Kenney D (2002) ldquoTired cops The prevalence and potential consequences of
police fatiguerdquo National Institute of Justice
Vila B Morrison G and Kenney D (2002) ldquoImproving shift schedule and work-hour
policies and practices to increase police officer performance health and safetyrdquo Police
Quarterly Vol 5 No 1 pp 4-24
Vollaard B and Koning P (2009) ldquoThe effect of police on crime disorder and victim
PrecautionEvidence from a Dutch victimization surveyrdquo International Review of Law
and Economics Vol 29 No 4 pp 336-348
Weisburd D Maher L Sherman L Buerger M Cohn E and Petrisino A (1993)
ldquoContrasting crime general and crime specific theory The case of hot spots of crimerdquo
New Directions in Criminological Theory Advances in Criminological Theory Vol 4
pp 45-70
Author Biography
Luke Bonkiewicz is a patrol officer with the Lincoln Police Department
7252019 PIJPSM-05-2015-0064
httpslidepdfcomreaderfullpijpsm-05-2015-0064 2730
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983101 983089983088983088lowast + + ) + ) + ) + ) lowast )) minus + + + 983090983088 + ) +983089983088)) minus 983089983088983088 lowast
983086 983097) lowast )
7252019 PIJPSM-05-2015-0064
httpslidepdfcomreaderfullpijpsm-05-2015-0064 2830
983124983137983138983148983141 1 E983160983137983149983152983148983141 983151983142 983156983144983141 983116983145983150983139983151983148983150 983120983151983148983145983139983141 D983141983152983137983154983156983149983141983150983156983155 A983150983150983157983137983148 983120983154983151983140983157983139983156983145983158983145983156983161 983122983141983152983151983154983156
983119983142983142983145983139983141983154 983120983154983151983155983141983139983157983156983145983151983150
983122983137983156983141C983110983123
983117983145983150983157983156983141983155983110983125983120
983117983145983150983157983156983141983155
983119983142983142983145983139983141983154
A983155983155983145983155983156983117983145983150983157983156983141983155
983113983150983139983145983140983141983150983156983122983141983152983151983154983156983155
AC983113983122983141983152983151983154983156983155 983123983141983148983141983139983156983145983158983141983155 983127983137983154983150983145983150983143983155 983119983142983142983145983139983145983137983148983155 983127983137983154983154983137983150983156983155 D983125983113983155 983117983145983155983140983141983149983141983137983150983151983154983155 983110983141983148983151983150983145983141983155
1 88 22812 5208 6132 216 348 396 396 264 60 24 72 7
2 95 30072 5424 4116 204 696 228 312 432 84 48 120 8
3 72 32496 6312 6600 144 564 444 720 540 84 36 24 12
4 90 33000 5232 4980 216 192 480 768 240 144 12 72 12
5 91 41724 4824 6048 312 636 132 504 324 108 7 24 24
6 83 23916 7056 6192 144 756 528 996 336 132 36 72 12
7 95 24468 6840 5892 156 624 324 456 480 72 36 120 24
8 90 39972 5196 4920 240 504 384 492 264 36 2 72 24
9 79 25500 4068 5100 336 396 192 684 204 24 36 120 10
10 87 21984 5412 4524 348 552 480 792 312 108 48 84 36
983124983137983138983148983141 2 983123983157983149983149983137983154983161 983123983156983137983156983145983155983156983145983139983155 983142983151983154 983126983137983154983145983137983138983148983141983155
983123983141983139983151983150983140983085983116983141983158983141983148 983126983137983154983145983137983138983148983141983155 (983120983137983156983154983151983148 A983154983141983137) 983118=40 983122983137983150983143983141 983123D 983117983141983137983150
983120983151983152983157983148983137983156983145983151983150 983140983141 983150983155983145983156983161 20229830856956 1687 4436
983112983151983157983155983141983144983151983148983140 983149983141983140983145983137983150 983145983150983139983151983149983141 ($) 2628198308576020 16535 49756
983159983144983145983156983141 7398308593 007 083
983126983145983151983148983141983150983156 983139983154983145983149983141 983154983137983156983141 (983152983141983154 100000) 8009830854800 1203 2521
983120983154983151983152983141983154983156983161 983139983154983145983149983141 983154983137983156983141 (983152983141983154 100000) 31009830859800 2027 6524
983120983154983151983152983141983154983156983161 983139983154983145983149983141983155 983152983141983154 983151983142983142983145983139983141983154 5329830851097 1663 8167
983126983145983151983148983141983150983156 983139983154983145983149983141 983152983141983154 983151983142983142983145983139983141983154 194983085372 559 2929
983110983145983154983155983156983085983116983141 983158983141983148 983126 983137983154983145983137983138983148983141 983155 (983119983142983142983145983139983141983154) 983118=302 983122983137983150983143983141 983123D 983117983141 983137983150
983111983141983150983140983141983154 (983142983141983149983137983148983141=0 983149983137983148983141=1) 09830851 24 94
983117983137983154983154983145983141983140C983151983144983137983138983145983156983145983150983143 09830851 18 79
983151983142 C983144983145983148983140983154983141983150 09830855 170 90
983129983141983137983154983155 983141983160983152983141983154983145983141983150983139983141 198308537 779 1063
C983137983148983148983155 983142983151983154 983155983141983154983158983145983139983141 4819830851011 16121 61365
983124983154983137983142983142983145983139 983155983156983151983152983155 16983085901 14376 20624
983110983145983141983148983140 983145983150983142983151983154983149983137983156983145983151983150 983154983141983152983151983154983156983155 0983085227 2153 2530
983124983154983137983142983142983145983139 983139983145983156983137983156983145983151983150983155 249830851246 14632 18642
983117983145983155983140983141983149983141983137983150983151983154 983137983154983154983141983155983156983155 19983085403 5965 10663
983110983141983148983151983150983161 983137983154983154983141983155983156983155 098308571 1066 852
983126983119983122C 9830851959830851043 151 24
1983155983156 2983150983140 3983154983140
104 96 102983123983144983145983142983156
7252019 PIJPSM-05-2015-0064
httpslidepdfcomreaderfullpijpsm-05-2015-0064 2930
983124983137983138983148983141 3 983112983145983141983154983137983154983139983144983145983139983137983148 983116983145983150983141983137983154 983117983151983140983141983148983155 E983155983156983145983149983137983156983145983150983143 983119983142 983142983145983139983141983154983155 A983150983150983157983137983148 983124983154983137983142983142983145983139 C983145983156983137983156983145983151983150983155 983127983137983154983154983137983150983156983155 983117983145983155983140983141983149983141983137983150983151983154983155 983137983150983140 983110983141983148983151983150 983145983141983155
983123983141983139983151983150983140983085983116983141983158983141983148 983126983137983154983145983137983138983148983141983155 (983120983137983156983154983151983148 A983154983141983137) 983118=40 983138 983123E 983138 983123E 983138 983123E 983138 983123E
983120983151983152983157983148983137983156983145983151983150 983140983141983150983155983145983156983161 001 003 001 001 98308501 01 00 00
983112983151983157983155983141983144983151983148983140 983149983141983140983145983137983150 983145983150983139983151983149983141 ($) 002 001 983085000 000 00 00 98308500 00
983159983144983145983156983141 13363 32472 9830858921 5664 9830853274 19211 9830854447 3255
983126983145983151983148983141983150983156 983139983154983145983149983141 983154983137983156983141 (983152983141983154 100000) 006 004 001 001 98308501 02 01 02
983120983154983151983152983141983154983156983161 983139983154983145983149983141 983154983137983156983141 (983152983141983154 100000) 983085003 005 983085002 001 02 03 98308501 00
983120983154983151983152983141983154983156983161 983139983154983145983149983141983155 983152983141983154 983151983142983142983145983139983141983154 983085225 99 98308515 03 98308552 24 98308503 03
983126983145983151983148983141983150983156 983139983154983145983149983141 983152983141983154 983151983142983142983145983139983141983154 983085168 68 29 14 76 31 22 12
983110983145983154983155983156983085983116983141983158983141983148 983126983137983154983145983137983138983148983141983155 (983119983142983142983145983139983141983154) 983118=302
983117983137983148983141 4673 1622 366 147 1613 1051 91 158
983117983137983154983154983145983141983140C983151983144983137983138983145983156983145983150983143 9830857516 3254 201 278 9830851158 1169 98308595 167
983151983142 C983144983145983148983140983154983141983150 183 702 75 77 983085262 217 983085396 207
983129983141983137983154983155 983141983160983152983141983154983145983141983150983139983141 9830851065 492 98308512 13 983085163 33 98308506 05C983137983148983148983155 983142983151983154 983155983141983154983158983145983139983141 24 13 02 02 14 01 98308508 05
983124983154983137983142983142983145983139 983155983156983151983152983155 983085983085 983085983085 03 00 09 02 00 00
983110983145983141983148983140 983145983150983142983151983154983149983137983156983145983151983150 983154983141983152983151983154983156983155 209 33 20 04 79 19 10 03
983123983141983139983151983150983140 983155983144983145983142983156 (983154983141983142983141983154983141983150983139983141 983143983154983151983157983152 983142983145983154983155983156) 2603 1778 187 195 809 730 147 160
983124983144983145 983154983140 983155983144983145 983142983156 (983154983141 983142983141 983154983141 983150983139983141 983143983154983151983157983152 983142983145 983154983155983156) 9872 2155 79 213 2532 952 393 132
983113983150983156983141983154983139983141983152983156 32591 5361 758 339 4147 1912 310 484
983123983145983143983149983137983085983155983153983157983137983154983141983140 1541467 19287 206702 5598
C983144983145983085983155983153983157983137983154983141983140 8294 6133 12348 10663
983152 lt 05
983152lt01
983152lt001
983118983151983156983141 A983148983148 983158983137983154983145983137983138983148983141983155 983137983154983141 983157983150983139983141983150983156983141983154983141983140
983127A983122983122A983118983124 A983122983122E983123983124983123 983117983113983123DE983117EA983118983119983122 A983122983122E983123983124983123 983110E983116983119983118983129 A983122983122E983123983124983123983124983122A983110983110983113C C983113983124A983124983113983119983118983123
7252019 PIJPSM-05-2015-0064
httpslidepdfcomreaderfullpijpsm-05-2015-0064 3030
983124983137983138983148983141 4 983112983145983141983154983137983154983139983144983145983139983137983148 983116983145983150983141983137983154 983117983151983140983141983148983155 E983155983156983145983149983137983156983145983150983143 983119983142983142983145983139983141983154983155 A983150983150983157983137983148 983126983137983148983157983141 983119983158983141983154 983122983141983152983148983137983139983141983149983141983150983156 C983151983152 (983126983119983122C)
983123983141983139983151983150983140983085983116983141983158983141983148 983126983137983154983145983137983138983148983141983155 (983120983137983156983154983151983148 A983154983141983137) 983118=40 983138 983123E
983120983151983152983157983148983137983156983145983151983150 983140983141983150983155983145983156983161 98308500 00
983112983151983157983155983141983144983151983148983140 983149983141983140983145983137983150 983145983150983139983151983149983141 ($) 00 01
983159983144983145983156983141 318 345
983126983145983151983148983141983150983156 983139983154983145983149983141 983154983137983156983141 (983152983141983154 100000) 98308500 00
983120983154983151983152983141983154983156983161 983139983154983145983149983141 983154983137983156983141 (983152983141983154 100000) 00 00
983120983154983151983152983141983154983156983161 983139983154983145983149983141983155 983152983141983154 983151983142983142983145983139983141983154 98308502 04
983126983145983151983148983141983150983156 983139983154983145983149983141 983152983141983154 983151983142983142983145983139983141983154 02 05
983110983145983154983155983156983085983116983141983158983141983148 983126983137983154983145983137983138983148983141983155 (983119983142983142983145983139983141983154) 983118=302
983117983137983148983141 27 15
983117983137983154983154983145983141983140C983151983144983137983138983145983156983145983150983143 98308532 31
983151983142 C983144983145983148983140983154983141983150 05 03
983129983141983137983154983155 983141983160983152983141983154983145983141983150983139983141 00 01
C983137983148 983148983155 983142983151983154 983155983141983154983158983145983139983141 02 01
983124983154983137983142983142983145983139 983155983156983151983152983155 01 00
983110983145 983141983148983140 983145983150983142983151983154983149983137983156983145 983151983150 983154983141 983152983151983154983156983155 02 00
983123983141983139983151983150983140 983155983144983145983142983156 ( 983154983141983142983141983154983141983150983139983141 983143983154983151983157983152 983142 983145983154983155983156) 23 12
983124983144983145983154983140 983155983144983145983142983156 (983154983141983142983141983154983141983150983139983141 983143983154983151983157983152 983142 983145983154983155983156) 55 14
983113983150983156983141983154983139983141983152983156 165 36
983123983145983143983149983137983085983155983153983157983137983154983141983140 66
C983144983145983085983155983153983157983137983154983141983140 9846
983152 lt 05
983152lt01
983152lt001
983118983151983156983141 A983148983148 983158983137983154983145983137983138983148983141983155 983137983154983141 983157983150983139983141983150983156983141983154983141983140
7252019 PIJPSM-05-2015-0064
httpslidepdfcomreaderfullpijpsm-05-2015-0064 2330
managing patrol officers and improving productivity requires a greater understanding of both
patrol officers and the areas they work
Acknowledgements and Credits We would like to thank Denver Police Department Crime
Analyst Melissa Ohlhorst and Pennsylvania State University Professor Barry Ruback as well as
the three anonymous reviewers for their insightful comments We also salute Lincoln Police
Sergeant Jake Dilsaver for his logistical support of this project
7252019 PIJPSM-05-2015-0064
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References
Amaranto E Steinberg J Castellano C and Mitchell R (2003) ldquoPolice stress interventionsrdquo
Brief Treatment and Crisis Intervention Vol 3 No 1 pp 47-53
Becker G (1985) ldquoHuman capital effort and the sexual division of laborrdquo The Journal of
Labor EconomicsVol 3 No 1 pp 33-58
Bonkiewicz L (2015) ldquoBobbies and baseball players Evaluating patrol officer productivity
using sabermetricsrdquo Police Quarterly Vol 18 No 1 pp 55-78
Braga A and Bond J (2008) ldquoPolicing crime and disorder hot spots A randomized controlled
trialrdquo Criminology Vol 46 No 3 pp 577-607
Brown J and Campbell E (1990) ldquoSources of occupational stress in the policerdquo Work
and Stress Vol 4 No 4 pp 305-318
Chappell A MacDonald J and Manz P (2006) ldquoThe organizational determinants
of police arrest decisionsrdquo Crime and Delinquency Vol 52 No 2 pp 287-306
Cloninger D and Sartorius L (1979) ldquoCrime rates clearance rates and enforcement effort
The case of Houston Texasrdquo American Journal of Economics and Sociology Vol 38
No 4 pp 389-402
Corman H and Mocan N (2005) ldquoCarrots sticks and broken windowsrdquo Journal of Law and
Economics Vol 48 No 1 pp 235-266
Crank J (1990) ldquoThe influence of environmental and organizational factors on police style in
urban and rural environmentsrdquo Journal of Research in Crime and Delinquency Vol 27
pp 166-189
Goodman A (1990) ldquoA model for police officer burnoutrdquo Journal of Business and
Psychology Vol 5 No 1 pp 85-99
Gould E Weinberg B and Mustard D (2002) Crime rates and local labor marketopportunities in the United States 1979ndash1997rdquo Review of Economics and Statistics Vol
84 No 1 pp 45-61
Hipp J and Roussell A (2013) ldquoMicro-and macro-environment population and the
consequences for crime ratesrdquo Social Forces Vol 92 No 2 pp 563-595
Hatry H (1976) ldquoApproaches to productivity measurement and program evaluationrdquo Public
Productivity Review Vol 1 No 3 pp21-28
Hawkins H (2001) ldquoPolice officer burnout a partial replication of Maslachs Burnout
Inventoryrdquo Police Quarterly Vol 4 No 3 pp 343-360
7252019 PIJPSM-05-2015-0064
httpslidepdfcomreaderfullpijpsm-05-2015-0064 2530
Herbert S (1998)ldquoPolice subculture reconsideredrdquo Criminology Vol 36 No 2 pp 343-370
Huizinga D and Henry K (2008) ldquoThe effect of arrest and justice system sanctions on
subsequent behavior Findings from longitudinal and other studiesrdquo In The long view of
crime A synthesis of longitudinal research Springer New York pp 220-254
Johnson R (2011) ldquoOfficer attitudes and management influences on police work productivityrdquo
American Journal of Criminal Justice Vol 36 pp 293-306
Kane R (2006) ldquoOn the limits of social control Structural deterrence and the policing of
lsquosuppressiblersquo crimesrdquo Justice Quarterly Vol 23 No 2 pp 186-213
Kubrin C Messner S Deane G McGeever K and Stucky T (2010) ldquoProactive
policing and robbery rates across US citiesrdquo Criminology Vol 48 No 1 pp 57-97
Lersch K (2002) ldquoAre citizen complaints just another measure of officer productivity An
analysis of citizen complaints and officer activity measuresrdquo Police Practice and
Research Vol 3 pp 135-147
Light M and Harris C (2012) Race space and violence exploring spatial dependence in
structural covariates of white and black violent crime in US countiesrdquo Journal of
Quantitative Criminology Vol 28 No 4 pp 559-586
Maxfield M (1982) ldquoService time dispatch time and demand for police services Helping
more by serving lessrdquo Public Administration Review pp 252-263
Maxfield M Lewis D and Szoc R (1980) ldquoProducing official crimes Verified crime reports
as measures of police outputrdquo Social Science Quarterly pp 221-236
Nakosteen R and Zimmer M (2001) ldquoSpouse selection and earnings Evidence of marital
sortingrdquo Economic InquiryVol 39 No 2 pp 201-2013
Shane J (2010) ldquoOrganizational stressors and police performancerdquo Journal of Criminal
Justice Vol 38 No 4 pp 807-818
Shane J (2011) ldquoDaily work experiences and police performancerdquo Police Practice and
Research Vol 13 No 3 pp 1-19
Sherman L Gartin P and Buerger M (1989) ldquoHot spots of predatory crime Routine
activities and the criminology of placerdquoCriminology Vol 27 No 1 pp27-56
Truman J and Rand M (2010) ldquoBureau of Justice Statistics Bulletin National Crime
Victimization Survey Criminal Victimization 2009rdquo US Department of Justice Office of
Justice programs
7252019 PIJPSM-05-2015-0064
httpslidepdfcomreaderfullpijpsm-05-2015-0064 2630
Van Meter D (2001) Evaluating dysfunctional police performance A zero-based approach
Charles C Thomas Publisher Springfield IL
Vila B and Kenney D (2002) ldquoTired cops The prevalence and potential consequences of
police fatiguerdquo National Institute of Justice
Vila B Morrison G and Kenney D (2002) ldquoImproving shift schedule and work-hour
policies and practices to increase police officer performance health and safetyrdquo Police
Quarterly Vol 5 No 1 pp 4-24
Vollaard B and Koning P (2009) ldquoThe effect of police on crime disorder and victim
PrecautionEvidence from a Dutch victimization surveyrdquo International Review of Law
and Economics Vol 29 No 4 pp 336-348
Weisburd D Maher L Sherman L Buerger M Cohn E and Petrisino A (1993)
ldquoContrasting crime general and crime specific theory The case of hot spots of crimerdquo
New Directions in Criminological Theory Advances in Criminological Theory Vol 4
pp 45-70
Author Biography
Luke Bonkiewicz is a patrol officer with the Lincoln Police Department
7252019 PIJPSM-05-2015-0064
httpslidepdfcomreaderfullpijpsm-05-2015-0064 2730
983110983145983143983157983154983141 983089983086 983126983119983122983107 983110983151983154983149983157983148983137
983101 983089983088983088lowast + + ) + ) + ) + ) lowast )) minus + + + 983090983088 + ) +983089983088)) minus 983089983088983088 lowast
983086 983097) lowast )
7252019 PIJPSM-05-2015-0064
httpslidepdfcomreaderfullpijpsm-05-2015-0064 2830
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983119983142983142983145983139983141983154 983120983154983151983155983141983139983157983156983145983151983150
983122983137983156983141C983110983123
983117983145983150983157983156983141983155983110983125983120
983117983145983150983157983156983141983155
983119983142983142983145983139983141983154
A983155983155983145983155983156983117983145983150983157983156983141983155
983113983150983139983145983140983141983150983156983122983141983152983151983154983156983155
AC983113983122983141983152983151983154983156983155 983123983141983148983141983139983156983145983158983141983155 983127983137983154983150983145983150983143983155 983119983142983142983145983139983145983137983148983155 983127983137983154983154983137983150983156983155 D983125983113983155 983117983145983155983140983141983149983141983137983150983151983154983155 983110983141983148983151983150983145983141983155
1 88 22812 5208 6132 216 348 396 396 264 60 24 72 7
2 95 30072 5424 4116 204 696 228 312 432 84 48 120 8
3 72 32496 6312 6600 144 564 444 720 540 84 36 24 12
4 90 33000 5232 4980 216 192 480 768 240 144 12 72 12
5 91 41724 4824 6048 312 636 132 504 324 108 7 24 24
6 83 23916 7056 6192 144 756 528 996 336 132 36 72 12
7 95 24468 6840 5892 156 624 324 456 480 72 36 120 24
8 90 39972 5196 4920 240 504 384 492 264 36 2 72 24
9 79 25500 4068 5100 336 396 192 684 204 24 36 120 10
10 87 21984 5412 4524 348 552 480 792 312 108 48 84 36
983124983137983138983148983141 2 983123983157983149983149983137983154983161 983123983156983137983156983145983155983156983145983139983155 983142983151983154 983126983137983154983145983137983138983148983141983155
983123983141983139983151983150983140983085983116983141983158983141983148 983126983137983154983145983137983138983148983141983155 (983120983137983156983154983151983148 A983154983141983137) 983118=40 983122983137983150983143983141 983123D 983117983141983137983150
983120983151983152983157983148983137983156983145983151983150 983140983141 983150983155983145983156983161 20229830856956 1687 4436
983112983151983157983155983141983144983151983148983140 983149983141983140983145983137983150 983145983150983139983151983149983141 ($) 2628198308576020 16535 49756
983159983144983145983156983141 7398308593 007 083
983126983145983151983148983141983150983156 983139983154983145983149983141 983154983137983156983141 (983152983141983154 100000) 8009830854800 1203 2521
983120983154983151983152983141983154983156983161 983139983154983145983149983141 983154983137983156983141 (983152983141983154 100000) 31009830859800 2027 6524
983120983154983151983152983141983154983156983161 983139983154983145983149983141983155 983152983141983154 983151983142983142983145983139983141983154 5329830851097 1663 8167
983126983145983151983148983141983150983156 983139983154983145983149983141 983152983141983154 983151983142983142983145983139983141983154 194983085372 559 2929
983110983145983154983155983156983085983116983141 983158983141983148 983126 983137983154983145983137983138983148983141 983155 (983119983142983142983145983139983141983154) 983118=302 983122983137983150983143983141 983123D 983117983141 983137983150
983111983141983150983140983141983154 (983142983141983149983137983148983141=0 983149983137983148983141=1) 09830851 24 94
983117983137983154983154983145983141983140C983151983144983137983138983145983156983145983150983143 09830851 18 79
983151983142 C983144983145983148983140983154983141983150 09830855 170 90
983129983141983137983154983155 983141983160983152983141983154983145983141983150983139983141 198308537 779 1063
C983137983148983148983155 983142983151983154 983155983141983154983158983145983139983141 4819830851011 16121 61365
983124983154983137983142983142983145983139 983155983156983151983152983155 16983085901 14376 20624
983110983145983141983148983140 983145983150983142983151983154983149983137983156983145983151983150 983154983141983152983151983154983156983155 0983085227 2153 2530
983124983154983137983142983142983145983139 983139983145983156983137983156983145983151983150983155 249830851246 14632 18642
983117983145983155983140983141983149983141983137983150983151983154 983137983154983154983141983155983156983155 19983085403 5965 10663
983110983141983148983151983150983161 983137983154983154983141983155983156983155 098308571 1066 852
983126983119983122C 9830851959830851043 151 24
1983155983156 2983150983140 3983154983140
104 96 102983123983144983145983142983156
7252019 PIJPSM-05-2015-0064
httpslidepdfcomreaderfullpijpsm-05-2015-0064 2930
983124983137983138983148983141 3 983112983145983141983154983137983154983139983144983145983139983137983148 983116983145983150983141983137983154 983117983151983140983141983148983155 E983155983156983145983149983137983156983145983150983143 983119983142 983142983145983139983141983154983155 A983150983150983157983137983148 983124983154983137983142983142983145983139 C983145983156983137983156983145983151983150983155 983127983137983154983154983137983150983156983155 983117983145983155983140983141983149983141983137983150983151983154983155 983137983150983140 983110983141983148983151983150 983145983141983155
983123983141983139983151983150983140983085983116983141983158983141983148 983126983137983154983145983137983138983148983141983155 (983120983137983156983154983151983148 A983154983141983137) 983118=40 983138 983123E 983138 983123E 983138 983123E 983138 983123E
983120983151983152983157983148983137983156983145983151983150 983140983141983150983155983145983156983161 001 003 001 001 98308501 01 00 00
983112983151983157983155983141983144983151983148983140 983149983141983140983145983137983150 983145983150983139983151983149983141 ($) 002 001 983085000 000 00 00 98308500 00
983159983144983145983156983141 13363 32472 9830858921 5664 9830853274 19211 9830854447 3255
983126983145983151983148983141983150983156 983139983154983145983149983141 983154983137983156983141 (983152983141983154 100000) 006 004 001 001 98308501 02 01 02
983120983154983151983152983141983154983156983161 983139983154983145983149983141 983154983137983156983141 (983152983141983154 100000) 983085003 005 983085002 001 02 03 98308501 00
983120983154983151983152983141983154983156983161 983139983154983145983149983141983155 983152983141983154 983151983142983142983145983139983141983154 983085225 99 98308515 03 98308552 24 98308503 03
983126983145983151983148983141983150983156 983139983154983145983149983141 983152983141983154 983151983142983142983145983139983141983154 983085168 68 29 14 76 31 22 12
983110983145983154983155983156983085983116983141983158983141983148 983126983137983154983145983137983138983148983141983155 (983119983142983142983145983139983141983154) 983118=302
983117983137983148983141 4673 1622 366 147 1613 1051 91 158
983117983137983154983154983145983141983140C983151983144983137983138983145983156983145983150983143 9830857516 3254 201 278 9830851158 1169 98308595 167
983151983142 C983144983145983148983140983154983141983150 183 702 75 77 983085262 217 983085396 207
983129983141983137983154983155 983141983160983152983141983154983145983141983150983139983141 9830851065 492 98308512 13 983085163 33 98308506 05C983137983148983148983155 983142983151983154 983155983141983154983158983145983139983141 24 13 02 02 14 01 98308508 05
983124983154983137983142983142983145983139 983155983156983151983152983155 983085983085 983085983085 03 00 09 02 00 00
983110983145983141983148983140 983145983150983142983151983154983149983137983156983145983151983150 983154983141983152983151983154983156983155 209 33 20 04 79 19 10 03
983123983141983139983151983150983140 983155983144983145983142983156 (983154983141983142983141983154983141983150983139983141 983143983154983151983157983152 983142983145983154983155983156) 2603 1778 187 195 809 730 147 160
983124983144983145 983154983140 983155983144983145 983142983156 (983154983141 983142983141 983154983141 983150983139983141 983143983154983151983157983152 983142983145 983154983155983156) 9872 2155 79 213 2532 952 393 132
983113983150983156983141983154983139983141983152983156 32591 5361 758 339 4147 1912 310 484
983123983145983143983149983137983085983155983153983157983137983154983141983140 1541467 19287 206702 5598
C983144983145983085983155983153983157983137983154983141983140 8294 6133 12348 10663
983152 lt 05
983152lt01
983152lt001
983118983151983156983141 A983148983148 983158983137983154983145983137983138983148983141983155 983137983154983141 983157983150983139983141983150983156983141983154983141983140
983127A983122983122A983118983124 A983122983122E983123983124983123 983117983113983123DE983117EA983118983119983122 A983122983122E983123983124983123 983110E983116983119983118983129 A983122983122E983123983124983123983124983122A983110983110983113C C983113983124A983124983113983119983118983123
7252019 PIJPSM-05-2015-0064
httpslidepdfcomreaderfullpijpsm-05-2015-0064 3030
983124983137983138983148983141 4 983112983145983141983154983137983154983139983144983145983139983137983148 983116983145983150983141983137983154 983117983151983140983141983148983155 E983155983156983145983149983137983156983145983150983143 983119983142983142983145983139983141983154983155 A983150983150983157983137983148 983126983137983148983157983141 983119983158983141983154 983122983141983152983148983137983139983141983149983141983150983156 C983151983152 (983126983119983122C)
983123983141983139983151983150983140983085983116983141983158983141983148 983126983137983154983145983137983138983148983141983155 (983120983137983156983154983151983148 A983154983141983137) 983118=40 983138 983123E
983120983151983152983157983148983137983156983145983151983150 983140983141983150983155983145983156983161 98308500 00
983112983151983157983155983141983144983151983148983140 983149983141983140983145983137983150 983145983150983139983151983149983141 ($) 00 01
983159983144983145983156983141 318 345
983126983145983151983148983141983150983156 983139983154983145983149983141 983154983137983156983141 (983152983141983154 100000) 98308500 00
983120983154983151983152983141983154983156983161 983139983154983145983149983141 983154983137983156983141 (983152983141983154 100000) 00 00
983120983154983151983152983141983154983156983161 983139983154983145983149983141983155 983152983141983154 983151983142983142983145983139983141983154 98308502 04
983126983145983151983148983141983150983156 983139983154983145983149983141 983152983141983154 983151983142983142983145983139983141983154 02 05
983110983145983154983155983156983085983116983141983158983141983148 983126983137983154983145983137983138983148983141983155 (983119983142983142983145983139983141983154) 983118=302
983117983137983148983141 27 15
983117983137983154983154983145983141983140C983151983144983137983138983145983156983145983150983143 98308532 31
983151983142 C983144983145983148983140983154983141983150 05 03
983129983141983137983154983155 983141983160983152983141983154983145983141983150983139983141 00 01
C983137983148 983148983155 983142983151983154 983155983141983154983158983145983139983141 02 01
983124983154983137983142983142983145983139 983155983156983151983152983155 01 00
983110983145 983141983148983140 983145983150983142983151983154983149983137983156983145 983151983150 983154983141 983152983151983154983156983155 02 00
983123983141983139983151983150983140 983155983144983145983142983156 ( 983154983141983142983141983154983141983150983139983141 983143983154983151983157983152 983142 983145983154983155983156) 23 12
983124983144983145983154983140 983155983144983145983142983156 (983154983141983142983141983154983141983150983139983141 983143983154983151983157983152 983142 983145983154983155983156) 55 14
983113983150983156983141983154983139983141983152983156 165 36
983123983145983143983149983137983085983155983153983157983137983154983141983140 66
C983144983145983085983155983153983157983137983154983141983140 9846
983152 lt 05
983152lt01
983152lt001
983118983151983156983141 A983148983148 983158983137983154983145983137983138983148983141983155 983137983154983141 983157983150983139983141983150983156983141983154983141983140
7252019 PIJPSM-05-2015-0064
httpslidepdfcomreaderfullpijpsm-05-2015-0064 2430
References
Amaranto E Steinberg J Castellano C and Mitchell R (2003) ldquoPolice stress interventionsrdquo
Brief Treatment and Crisis Intervention Vol 3 No 1 pp 47-53
Becker G (1985) ldquoHuman capital effort and the sexual division of laborrdquo The Journal of
Labor EconomicsVol 3 No 1 pp 33-58
Bonkiewicz L (2015) ldquoBobbies and baseball players Evaluating patrol officer productivity
using sabermetricsrdquo Police Quarterly Vol 18 No 1 pp 55-78
Braga A and Bond J (2008) ldquoPolicing crime and disorder hot spots A randomized controlled
trialrdquo Criminology Vol 46 No 3 pp 577-607
Brown J and Campbell E (1990) ldquoSources of occupational stress in the policerdquo Work
and Stress Vol 4 No 4 pp 305-318
Chappell A MacDonald J and Manz P (2006) ldquoThe organizational determinants
of police arrest decisionsrdquo Crime and Delinquency Vol 52 No 2 pp 287-306
Cloninger D and Sartorius L (1979) ldquoCrime rates clearance rates and enforcement effort
The case of Houston Texasrdquo American Journal of Economics and Sociology Vol 38
No 4 pp 389-402
Corman H and Mocan N (2005) ldquoCarrots sticks and broken windowsrdquo Journal of Law and
Economics Vol 48 No 1 pp 235-266
Crank J (1990) ldquoThe influence of environmental and organizational factors on police style in
urban and rural environmentsrdquo Journal of Research in Crime and Delinquency Vol 27
pp 166-189
Goodman A (1990) ldquoA model for police officer burnoutrdquo Journal of Business and
Psychology Vol 5 No 1 pp 85-99
Gould E Weinberg B and Mustard D (2002) Crime rates and local labor marketopportunities in the United States 1979ndash1997rdquo Review of Economics and Statistics Vol
84 No 1 pp 45-61
Hipp J and Roussell A (2013) ldquoMicro-and macro-environment population and the
consequences for crime ratesrdquo Social Forces Vol 92 No 2 pp 563-595
Hatry H (1976) ldquoApproaches to productivity measurement and program evaluationrdquo Public
Productivity Review Vol 1 No 3 pp21-28
Hawkins H (2001) ldquoPolice officer burnout a partial replication of Maslachs Burnout
Inventoryrdquo Police Quarterly Vol 4 No 3 pp 343-360
7252019 PIJPSM-05-2015-0064
httpslidepdfcomreaderfullpijpsm-05-2015-0064 2530
Herbert S (1998)ldquoPolice subculture reconsideredrdquo Criminology Vol 36 No 2 pp 343-370
Huizinga D and Henry K (2008) ldquoThe effect of arrest and justice system sanctions on
subsequent behavior Findings from longitudinal and other studiesrdquo In The long view of
crime A synthesis of longitudinal research Springer New York pp 220-254
Johnson R (2011) ldquoOfficer attitudes and management influences on police work productivityrdquo
American Journal of Criminal Justice Vol 36 pp 293-306
Kane R (2006) ldquoOn the limits of social control Structural deterrence and the policing of
lsquosuppressiblersquo crimesrdquo Justice Quarterly Vol 23 No 2 pp 186-213
Kubrin C Messner S Deane G McGeever K and Stucky T (2010) ldquoProactive
policing and robbery rates across US citiesrdquo Criminology Vol 48 No 1 pp 57-97
Lersch K (2002) ldquoAre citizen complaints just another measure of officer productivity An
analysis of citizen complaints and officer activity measuresrdquo Police Practice and
Research Vol 3 pp 135-147
Light M and Harris C (2012) Race space and violence exploring spatial dependence in
structural covariates of white and black violent crime in US countiesrdquo Journal of
Quantitative Criminology Vol 28 No 4 pp 559-586
Maxfield M (1982) ldquoService time dispatch time and demand for police services Helping
more by serving lessrdquo Public Administration Review pp 252-263
Maxfield M Lewis D and Szoc R (1980) ldquoProducing official crimes Verified crime reports
as measures of police outputrdquo Social Science Quarterly pp 221-236
Nakosteen R and Zimmer M (2001) ldquoSpouse selection and earnings Evidence of marital
sortingrdquo Economic InquiryVol 39 No 2 pp 201-2013
Shane J (2010) ldquoOrganizational stressors and police performancerdquo Journal of Criminal
Justice Vol 38 No 4 pp 807-818
Shane J (2011) ldquoDaily work experiences and police performancerdquo Police Practice and
Research Vol 13 No 3 pp 1-19
Sherman L Gartin P and Buerger M (1989) ldquoHot spots of predatory crime Routine
activities and the criminology of placerdquoCriminology Vol 27 No 1 pp27-56
Truman J and Rand M (2010) ldquoBureau of Justice Statistics Bulletin National Crime
Victimization Survey Criminal Victimization 2009rdquo US Department of Justice Office of
Justice programs
7252019 PIJPSM-05-2015-0064
httpslidepdfcomreaderfullpijpsm-05-2015-0064 2630
Van Meter D (2001) Evaluating dysfunctional police performance A zero-based approach
Charles C Thomas Publisher Springfield IL
Vila B and Kenney D (2002) ldquoTired cops The prevalence and potential consequences of
police fatiguerdquo National Institute of Justice
Vila B Morrison G and Kenney D (2002) ldquoImproving shift schedule and work-hour
policies and practices to increase police officer performance health and safetyrdquo Police
Quarterly Vol 5 No 1 pp 4-24
Vollaard B and Koning P (2009) ldquoThe effect of police on crime disorder and victim
PrecautionEvidence from a Dutch victimization surveyrdquo International Review of Law
and Economics Vol 29 No 4 pp 336-348
Weisburd D Maher L Sherman L Buerger M Cohn E and Petrisino A (1993)
ldquoContrasting crime general and crime specific theory The case of hot spots of crimerdquo
New Directions in Criminological Theory Advances in Criminological Theory Vol 4
pp 45-70
Author Biography
Luke Bonkiewicz is a patrol officer with the Lincoln Police Department
7252019 PIJPSM-05-2015-0064
httpslidepdfcomreaderfullpijpsm-05-2015-0064 2730
983110983145983143983157983154983141 983089983086 983126983119983122983107 983110983151983154983149983157983148983137
983101 983089983088983088lowast + + ) + ) + ) + ) lowast )) minus + + + 983090983088 + ) +983089983088)) minus 983089983088983088 lowast
983086 983097) lowast )
7252019 PIJPSM-05-2015-0064
httpslidepdfcomreaderfullpijpsm-05-2015-0064 2830
983124983137983138983148983141 1 E983160983137983149983152983148983141 983151983142 983156983144983141 983116983145983150983139983151983148983150 983120983151983148983145983139983141 D983141983152983137983154983156983149983141983150983156983155 A983150983150983157983137983148 983120983154983151983140983157983139983156983145983158983145983156983161 983122983141983152983151983154983156
983119983142983142983145983139983141983154 983120983154983151983155983141983139983157983156983145983151983150
983122983137983156983141C983110983123
983117983145983150983157983156983141983155983110983125983120
983117983145983150983157983156983141983155
983119983142983142983145983139983141983154
A983155983155983145983155983156983117983145983150983157983156983141983155
983113983150983139983145983140983141983150983156983122983141983152983151983154983156983155
AC983113983122983141983152983151983154983156983155 983123983141983148983141983139983156983145983158983141983155 983127983137983154983150983145983150983143983155 983119983142983142983145983139983145983137983148983155 983127983137983154983154983137983150983156983155 D983125983113983155 983117983145983155983140983141983149983141983137983150983151983154983155 983110983141983148983151983150983145983141983155
1 88 22812 5208 6132 216 348 396 396 264 60 24 72 7
2 95 30072 5424 4116 204 696 228 312 432 84 48 120 8
3 72 32496 6312 6600 144 564 444 720 540 84 36 24 12
4 90 33000 5232 4980 216 192 480 768 240 144 12 72 12
5 91 41724 4824 6048 312 636 132 504 324 108 7 24 24
6 83 23916 7056 6192 144 756 528 996 336 132 36 72 12
7 95 24468 6840 5892 156 624 324 456 480 72 36 120 24
8 90 39972 5196 4920 240 504 384 492 264 36 2 72 24
9 79 25500 4068 5100 336 396 192 684 204 24 36 120 10
10 87 21984 5412 4524 348 552 480 792 312 108 48 84 36
983124983137983138983148983141 2 983123983157983149983149983137983154983161 983123983156983137983156983145983155983156983145983139983155 983142983151983154 983126983137983154983145983137983138983148983141983155
983123983141983139983151983150983140983085983116983141983158983141983148 983126983137983154983145983137983138983148983141983155 (983120983137983156983154983151983148 A983154983141983137) 983118=40 983122983137983150983143983141 983123D 983117983141983137983150
983120983151983152983157983148983137983156983145983151983150 983140983141 983150983155983145983156983161 20229830856956 1687 4436
983112983151983157983155983141983144983151983148983140 983149983141983140983145983137983150 983145983150983139983151983149983141 ($) 2628198308576020 16535 49756
983159983144983145983156983141 7398308593 007 083
983126983145983151983148983141983150983156 983139983154983145983149983141 983154983137983156983141 (983152983141983154 100000) 8009830854800 1203 2521
983120983154983151983152983141983154983156983161 983139983154983145983149983141 983154983137983156983141 (983152983141983154 100000) 31009830859800 2027 6524
983120983154983151983152983141983154983156983161 983139983154983145983149983141983155 983152983141983154 983151983142983142983145983139983141983154 5329830851097 1663 8167
983126983145983151983148983141983150983156 983139983154983145983149983141 983152983141983154 983151983142983142983145983139983141983154 194983085372 559 2929
983110983145983154983155983156983085983116983141 983158983141983148 983126 983137983154983145983137983138983148983141 983155 (983119983142983142983145983139983141983154) 983118=302 983122983137983150983143983141 983123D 983117983141 983137983150
983111983141983150983140983141983154 (983142983141983149983137983148983141=0 983149983137983148983141=1) 09830851 24 94
983117983137983154983154983145983141983140C983151983144983137983138983145983156983145983150983143 09830851 18 79
983151983142 C983144983145983148983140983154983141983150 09830855 170 90
983129983141983137983154983155 983141983160983152983141983154983145983141983150983139983141 198308537 779 1063
C983137983148983148983155 983142983151983154 983155983141983154983158983145983139983141 4819830851011 16121 61365
983124983154983137983142983142983145983139 983155983156983151983152983155 16983085901 14376 20624
983110983145983141983148983140 983145983150983142983151983154983149983137983156983145983151983150 983154983141983152983151983154983156983155 0983085227 2153 2530
983124983154983137983142983142983145983139 983139983145983156983137983156983145983151983150983155 249830851246 14632 18642
983117983145983155983140983141983149983141983137983150983151983154 983137983154983154983141983155983156983155 19983085403 5965 10663
983110983141983148983151983150983161 983137983154983154983141983155983156983155 098308571 1066 852
983126983119983122C 9830851959830851043 151 24
1983155983156 2983150983140 3983154983140
104 96 102983123983144983145983142983156
7252019 PIJPSM-05-2015-0064
httpslidepdfcomreaderfullpijpsm-05-2015-0064 2930
983124983137983138983148983141 3 983112983145983141983154983137983154983139983144983145983139983137983148 983116983145983150983141983137983154 983117983151983140983141983148983155 E983155983156983145983149983137983156983145983150983143 983119983142 983142983145983139983141983154983155 A983150983150983157983137983148 983124983154983137983142983142983145983139 C983145983156983137983156983145983151983150983155 983127983137983154983154983137983150983156983155 983117983145983155983140983141983149983141983137983150983151983154983155 983137983150983140 983110983141983148983151983150 983145983141983155
983123983141983139983151983150983140983085983116983141983158983141983148 983126983137983154983145983137983138983148983141983155 (983120983137983156983154983151983148 A983154983141983137) 983118=40 983138 983123E 983138 983123E 983138 983123E 983138 983123E
983120983151983152983157983148983137983156983145983151983150 983140983141983150983155983145983156983161 001 003 001 001 98308501 01 00 00
983112983151983157983155983141983144983151983148983140 983149983141983140983145983137983150 983145983150983139983151983149983141 ($) 002 001 983085000 000 00 00 98308500 00
983159983144983145983156983141 13363 32472 9830858921 5664 9830853274 19211 9830854447 3255
983126983145983151983148983141983150983156 983139983154983145983149983141 983154983137983156983141 (983152983141983154 100000) 006 004 001 001 98308501 02 01 02
983120983154983151983152983141983154983156983161 983139983154983145983149983141 983154983137983156983141 (983152983141983154 100000) 983085003 005 983085002 001 02 03 98308501 00
983120983154983151983152983141983154983156983161 983139983154983145983149983141983155 983152983141983154 983151983142983142983145983139983141983154 983085225 99 98308515 03 98308552 24 98308503 03
983126983145983151983148983141983150983156 983139983154983145983149983141 983152983141983154 983151983142983142983145983139983141983154 983085168 68 29 14 76 31 22 12
983110983145983154983155983156983085983116983141983158983141983148 983126983137983154983145983137983138983148983141983155 (983119983142983142983145983139983141983154) 983118=302
983117983137983148983141 4673 1622 366 147 1613 1051 91 158
983117983137983154983154983145983141983140C983151983144983137983138983145983156983145983150983143 9830857516 3254 201 278 9830851158 1169 98308595 167
983151983142 C983144983145983148983140983154983141983150 183 702 75 77 983085262 217 983085396 207
983129983141983137983154983155 983141983160983152983141983154983145983141983150983139983141 9830851065 492 98308512 13 983085163 33 98308506 05C983137983148983148983155 983142983151983154 983155983141983154983158983145983139983141 24 13 02 02 14 01 98308508 05
983124983154983137983142983142983145983139 983155983156983151983152983155 983085983085 983085983085 03 00 09 02 00 00
983110983145983141983148983140 983145983150983142983151983154983149983137983156983145983151983150 983154983141983152983151983154983156983155 209 33 20 04 79 19 10 03
983123983141983139983151983150983140 983155983144983145983142983156 (983154983141983142983141983154983141983150983139983141 983143983154983151983157983152 983142983145983154983155983156) 2603 1778 187 195 809 730 147 160
983124983144983145 983154983140 983155983144983145 983142983156 (983154983141 983142983141 983154983141 983150983139983141 983143983154983151983157983152 983142983145 983154983155983156) 9872 2155 79 213 2532 952 393 132
983113983150983156983141983154983139983141983152983156 32591 5361 758 339 4147 1912 310 484
983123983145983143983149983137983085983155983153983157983137983154983141983140 1541467 19287 206702 5598
C983144983145983085983155983153983157983137983154983141983140 8294 6133 12348 10663
983152 lt 05
983152lt01
983152lt001
983118983151983156983141 A983148983148 983158983137983154983145983137983138983148983141983155 983137983154983141 983157983150983139983141983150983156983141983154983141983140
983127A983122983122A983118983124 A983122983122E983123983124983123 983117983113983123DE983117EA983118983119983122 A983122983122E983123983124983123 983110E983116983119983118983129 A983122983122E983123983124983123983124983122A983110983110983113C C983113983124A983124983113983119983118983123
7252019 PIJPSM-05-2015-0064
httpslidepdfcomreaderfullpijpsm-05-2015-0064 3030
983124983137983138983148983141 4 983112983145983141983154983137983154983139983144983145983139983137983148 983116983145983150983141983137983154 983117983151983140983141983148983155 E983155983156983145983149983137983156983145983150983143 983119983142983142983145983139983141983154983155 A983150983150983157983137983148 983126983137983148983157983141 983119983158983141983154 983122983141983152983148983137983139983141983149983141983150983156 C983151983152 (983126983119983122C)
983123983141983139983151983150983140983085983116983141983158983141983148 983126983137983154983145983137983138983148983141983155 (983120983137983156983154983151983148 A983154983141983137) 983118=40 983138 983123E
983120983151983152983157983148983137983156983145983151983150 983140983141983150983155983145983156983161 98308500 00
983112983151983157983155983141983144983151983148983140 983149983141983140983145983137983150 983145983150983139983151983149983141 ($) 00 01
983159983144983145983156983141 318 345
983126983145983151983148983141983150983156 983139983154983145983149983141 983154983137983156983141 (983152983141983154 100000) 98308500 00
983120983154983151983152983141983154983156983161 983139983154983145983149983141 983154983137983156983141 (983152983141983154 100000) 00 00
983120983154983151983152983141983154983156983161 983139983154983145983149983141983155 983152983141983154 983151983142983142983145983139983141983154 98308502 04
983126983145983151983148983141983150983156 983139983154983145983149983141 983152983141983154 983151983142983142983145983139983141983154 02 05
983110983145983154983155983156983085983116983141983158983141983148 983126983137983154983145983137983138983148983141983155 (983119983142983142983145983139983141983154) 983118=302
983117983137983148983141 27 15
983117983137983154983154983145983141983140C983151983144983137983138983145983156983145983150983143 98308532 31
983151983142 C983144983145983148983140983154983141983150 05 03
983129983141983137983154983155 983141983160983152983141983154983145983141983150983139983141 00 01
C983137983148 983148983155 983142983151983154 983155983141983154983158983145983139983141 02 01
983124983154983137983142983142983145983139 983155983156983151983152983155 01 00
983110983145 983141983148983140 983145983150983142983151983154983149983137983156983145 983151983150 983154983141 983152983151983154983156983155 02 00
983123983141983139983151983150983140 983155983144983145983142983156 ( 983154983141983142983141983154983141983150983139983141 983143983154983151983157983152 983142 983145983154983155983156) 23 12
983124983144983145983154983140 983155983144983145983142983156 (983154983141983142983141983154983141983150983139983141 983143983154983151983157983152 983142 983145983154983155983156) 55 14
983113983150983156983141983154983139983141983152983156 165 36
983123983145983143983149983137983085983155983153983157983137983154983141983140 66
C983144983145983085983155983153983157983137983154983141983140 9846
983152 lt 05
983152lt01
983152lt001
983118983151983156983141 A983148983148 983158983137983154983145983137983138983148983141983155 983137983154983141 983157983150983139983141983150983156983141983154983141983140
7252019 PIJPSM-05-2015-0064
httpslidepdfcomreaderfullpijpsm-05-2015-0064 2530
Herbert S (1998)ldquoPolice subculture reconsideredrdquo Criminology Vol 36 No 2 pp 343-370
Huizinga D and Henry K (2008) ldquoThe effect of arrest and justice system sanctions on
subsequent behavior Findings from longitudinal and other studiesrdquo In The long view of
crime A synthesis of longitudinal research Springer New York pp 220-254
Johnson R (2011) ldquoOfficer attitudes and management influences on police work productivityrdquo
American Journal of Criminal Justice Vol 36 pp 293-306
Kane R (2006) ldquoOn the limits of social control Structural deterrence and the policing of
lsquosuppressiblersquo crimesrdquo Justice Quarterly Vol 23 No 2 pp 186-213
Kubrin C Messner S Deane G McGeever K and Stucky T (2010) ldquoProactive
policing and robbery rates across US citiesrdquo Criminology Vol 48 No 1 pp 57-97
Lersch K (2002) ldquoAre citizen complaints just another measure of officer productivity An
analysis of citizen complaints and officer activity measuresrdquo Police Practice and
Research Vol 3 pp 135-147
Light M and Harris C (2012) Race space and violence exploring spatial dependence in
structural covariates of white and black violent crime in US countiesrdquo Journal of
Quantitative Criminology Vol 28 No 4 pp 559-586
Maxfield M (1982) ldquoService time dispatch time and demand for police services Helping
more by serving lessrdquo Public Administration Review pp 252-263
Maxfield M Lewis D and Szoc R (1980) ldquoProducing official crimes Verified crime reports
as measures of police outputrdquo Social Science Quarterly pp 221-236
Nakosteen R and Zimmer M (2001) ldquoSpouse selection and earnings Evidence of marital
sortingrdquo Economic InquiryVol 39 No 2 pp 201-2013
Shane J (2010) ldquoOrganizational stressors and police performancerdquo Journal of Criminal
Justice Vol 38 No 4 pp 807-818
Shane J (2011) ldquoDaily work experiences and police performancerdquo Police Practice and
Research Vol 13 No 3 pp 1-19
Sherman L Gartin P and Buerger M (1989) ldquoHot spots of predatory crime Routine
activities and the criminology of placerdquoCriminology Vol 27 No 1 pp27-56
Truman J and Rand M (2010) ldquoBureau of Justice Statistics Bulletin National Crime
Victimization Survey Criminal Victimization 2009rdquo US Department of Justice Office of
Justice programs
7252019 PIJPSM-05-2015-0064
httpslidepdfcomreaderfullpijpsm-05-2015-0064 2630
Van Meter D (2001) Evaluating dysfunctional police performance A zero-based approach
Charles C Thomas Publisher Springfield IL
Vila B and Kenney D (2002) ldquoTired cops The prevalence and potential consequences of
police fatiguerdquo National Institute of Justice
Vila B Morrison G and Kenney D (2002) ldquoImproving shift schedule and work-hour
policies and practices to increase police officer performance health and safetyrdquo Police
Quarterly Vol 5 No 1 pp 4-24
Vollaard B and Koning P (2009) ldquoThe effect of police on crime disorder and victim
PrecautionEvidence from a Dutch victimization surveyrdquo International Review of Law
and Economics Vol 29 No 4 pp 336-348
Weisburd D Maher L Sherman L Buerger M Cohn E and Petrisino A (1993)
ldquoContrasting crime general and crime specific theory The case of hot spots of crimerdquo
New Directions in Criminological Theory Advances in Criminological Theory Vol 4
pp 45-70
Author Biography
Luke Bonkiewicz is a patrol officer with the Lincoln Police Department
7252019 PIJPSM-05-2015-0064
httpslidepdfcomreaderfullpijpsm-05-2015-0064 2730
983110983145983143983157983154983141 983089983086 983126983119983122983107 983110983151983154983149983157983148983137
983101 983089983088983088lowast + + ) + ) + ) + ) lowast )) minus + + + 983090983088 + ) +983089983088)) minus 983089983088983088 lowast
983086 983097) lowast )
7252019 PIJPSM-05-2015-0064
httpslidepdfcomreaderfullpijpsm-05-2015-0064 2830
983124983137983138983148983141 1 E983160983137983149983152983148983141 983151983142 983156983144983141 983116983145983150983139983151983148983150 983120983151983148983145983139983141 D983141983152983137983154983156983149983141983150983156983155 A983150983150983157983137983148 983120983154983151983140983157983139983156983145983158983145983156983161 983122983141983152983151983154983156
983119983142983142983145983139983141983154 983120983154983151983155983141983139983157983156983145983151983150
983122983137983156983141C983110983123
983117983145983150983157983156983141983155983110983125983120
983117983145983150983157983156983141983155
983119983142983142983145983139983141983154
A983155983155983145983155983156983117983145983150983157983156983141983155
983113983150983139983145983140983141983150983156983122983141983152983151983154983156983155
AC983113983122983141983152983151983154983156983155 983123983141983148983141983139983156983145983158983141983155 983127983137983154983150983145983150983143983155 983119983142983142983145983139983145983137983148983155 983127983137983154983154983137983150983156983155 D983125983113983155 983117983145983155983140983141983149983141983137983150983151983154983155 983110983141983148983151983150983145983141983155
1 88 22812 5208 6132 216 348 396 396 264 60 24 72 7
2 95 30072 5424 4116 204 696 228 312 432 84 48 120 8
3 72 32496 6312 6600 144 564 444 720 540 84 36 24 12
4 90 33000 5232 4980 216 192 480 768 240 144 12 72 12
5 91 41724 4824 6048 312 636 132 504 324 108 7 24 24
6 83 23916 7056 6192 144 756 528 996 336 132 36 72 12
7 95 24468 6840 5892 156 624 324 456 480 72 36 120 24
8 90 39972 5196 4920 240 504 384 492 264 36 2 72 24
9 79 25500 4068 5100 336 396 192 684 204 24 36 120 10
10 87 21984 5412 4524 348 552 480 792 312 108 48 84 36
983124983137983138983148983141 2 983123983157983149983149983137983154983161 983123983156983137983156983145983155983156983145983139983155 983142983151983154 983126983137983154983145983137983138983148983141983155
983123983141983139983151983150983140983085983116983141983158983141983148 983126983137983154983145983137983138983148983141983155 (983120983137983156983154983151983148 A983154983141983137) 983118=40 983122983137983150983143983141 983123D 983117983141983137983150
983120983151983152983157983148983137983156983145983151983150 983140983141 983150983155983145983156983161 20229830856956 1687 4436
983112983151983157983155983141983144983151983148983140 983149983141983140983145983137983150 983145983150983139983151983149983141 ($) 2628198308576020 16535 49756
983159983144983145983156983141 7398308593 007 083
983126983145983151983148983141983150983156 983139983154983145983149983141 983154983137983156983141 (983152983141983154 100000) 8009830854800 1203 2521
983120983154983151983152983141983154983156983161 983139983154983145983149983141 983154983137983156983141 (983152983141983154 100000) 31009830859800 2027 6524
983120983154983151983152983141983154983156983161 983139983154983145983149983141983155 983152983141983154 983151983142983142983145983139983141983154 5329830851097 1663 8167
983126983145983151983148983141983150983156 983139983154983145983149983141 983152983141983154 983151983142983142983145983139983141983154 194983085372 559 2929
983110983145983154983155983156983085983116983141 983158983141983148 983126 983137983154983145983137983138983148983141 983155 (983119983142983142983145983139983141983154) 983118=302 983122983137983150983143983141 983123D 983117983141 983137983150
983111983141983150983140983141983154 (983142983141983149983137983148983141=0 983149983137983148983141=1) 09830851 24 94
983117983137983154983154983145983141983140C983151983144983137983138983145983156983145983150983143 09830851 18 79
983151983142 C983144983145983148983140983154983141983150 09830855 170 90
983129983141983137983154983155 983141983160983152983141983154983145983141983150983139983141 198308537 779 1063
C983137983148983148983155 983142983151983154 983155983141983154983158983145983139983141 4819830851011 16121 61365
983124983154983137983142983142983145983139 983155983156983151983152983155 16983085901 14376 20624
983110983145983141983148983140 983145983150983142983151983154983149983137983156983145983151983150 983154983141983152983151983154983156983155 0983085227 2153 2530
983124983154983137983142983142983145983139 983139983145983156983137983156983145983151983150983155 249830851246 14632 18642
983117983145983155983140983141983149983141983137983150983151983154 983137983154983154983141983155983156983155 19983085403 5965 10663
983110983141983148983151983150983161 983137983154983154983141983155983156983155 098308571 1066 852
983126983119983122C 9830851959830851043 151 24
1983155983156 2983150983140 3983154983140
104 96 102983123983144983145983142983156
7252019 PIJPSM-05-2015-0064
httpslidepdfcomreaderfullpijpsm-05-2015-0064 2930
983124983137983138983148983141 3 983112983145983141983154983137983154983139983144983145983139983137983148 983116983145983150983141983137983154 983117983151983140983141983148983155 E983155983156983145983149983137983156983145983150983143 983119983142 983142983145983139983141983154983155 A983150983150983157983137983148 983124983154983137983142983142983145983139 C983145983156983137983156983145983151983150983155 983127983137983154983154983137983150983156983155 983117983145983155983140983141983149983141983137983150983151983154983155 983137983150983140 983110983141983148983151983150 983145983141983155
983123983141983139983151983150983140983085983116983141983158983141983148 983126983137983154983145983137983138983148983141983155 (983120983137983156983154983151983148 A983154983141983137) 983118=40 983138 983123E 983138 983123E 983138 983123E 983138 983123E
983120983151983152983157983148983137983156983145983151983150 983140983141983150983155983145983156983161 001 003 001 001 98308501 01 00 00
983112983151983157983155983141983144983151983148983140 983149983141983140983145983137983150 983145983150983139983151983149983141 ($) 002 001 983085000 000 00 00 98308500 00
983159983144983145983156983141 13363 32472 9830858921 5664 9830853274 19211 9830854447 3255
983126983145983151983148983141983150983156 983139983154983145983149983141 983154983137983156983141 (983152983141983154 100000) 006 004 001 001 98308501 02 01 02
983120983154983151983152983141983154983156983161 983139983154983145983149983141 983154983137983156983141 (983152983141983154 100000) 983085003 005 983085002 001 02 03 98308501 00
983120983154983151983152983141983154983156983161 983139983154983145983149983141983155 983152983141983154 983151983142983142983145983139983141983154 983085225 99 98308515 03 98308552 24 98308503 03
983126983145983151983148983141983150983156 983139983154983145983149983141 983152983141983154 983151983142983142983145983139983141983154 983085168 68 29 14 76 31 22 12
983110983145983154983155983156983085983116983141983158983141983148 983126983137983154983145983137983138983148983141983155 (983119983142983142983145983139983141983154) 983118=302
983117983137983148983141 4673 1622 366 147 1613 1051 91 158
983117983137983154983154983145983141983140C983151983144983137983138983145983156983145983150983143 9830857516 3254 201 278 9830851158 1169 98308595 167
983151983142 C983144983145983148983140983154983141983150 183 702 75 77 983085262 217 983085396 207
983129983141983137983154983155 983141983160983152983141983154983145983141983150983139983141 9830851065 492 98308512 13 983085163 33 98308506 05C983137983148983148983155 983142983151983154 983155983141983154983158983145983139983141 24 13 02 02 14 01 98308508 05
983124983154983137983142983142983145983139 983155983156983151983152983155 983085983085 983085983085 03 00 09 02 00 00
983110983145983141983148983140 983145983150983142983151983154983149983137983156983145983151983150 983154983141983152983151983154983156983155 209 33 20 04 79 19 10 03
983123983141983139983151983150983140 983155983144983145983142983156 (983154983141983142983141983154983141983150983139983141 983143983154983151983157983152 983142983145983154983155983156) 2603 1778 187 195 809 730 147 160
983124983144983145 983154983140 983155983144983145 983142983156 (983154983141 983142983141 983154983141 983150983139983141 983143983154983151983157983152 983142983145 983154983155983156) 9872 2155 79 213 2532 952 393 132
983113983150983156983141983154983139983141983152983156 32591 5361 758 339 4147 1912 310 484
983123983145983143983149983137983085983155983153983157983137983154983141983140 1541467 19287 206702 5598
C983144983145983085983155983153983157983137983154983141983140 8294 6133 12348 10663
983152 lt 05
983152lt01
983152lt001
983118983151983156983141 A983148983148 983158983137983154983145983137983138983148983141983155 983137983154983141 983157983150983139983141983150983156983141983154983141983140
983127A983122983122A983118983124 A983122983122E983123983124983123 983117983113983123DE983117EA983118983119983122 A983122983122E983123983124983123 983110E983116983119983118983129 A983122983122E983123983124983123983124983122A983110983110983113C C983113983124A983124983113983119983118983123
7252019 PIJPSM-05-2015-0064
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983123983141983139983151983150983140983085983116983141983158983141983148 983126983137983154983145983137983138983148983141983155 (983120983137983156983154983151983148 A983154983141983137) 983118=40 983138 983123E
983120983151983152983157983148983137983156983145983151983150 983140983141983150983155983145983156983161 98308500 00
983112983151983157983155983141983144983151983148983140 983149983141983140983145983137983150 983145983150983139983151983149983141 ($) 00 01
983159983144983145983156983141 318 345
983126983145983151983148983141983150983156 983139983154983145983149983141 983154983137983156983141 (983152983141983154 100000) 98308500 00
983120983154983151983152983141983154983156983161 983139983154983145983149983141 983154983137983156983141 (983152983141983154 100000) 00 00
983120983154983151983152983141983154983156983161 983139983154983145983149983141983155 983152983141983154 983151983142983142983145983139983141983154 98308502 04
983126983145983151983148983141983150983156 983139983154983145983149983141 983152983141983154 983151983142983142983145983139983141983154 02 05
983110983145983154983155983156983085983116983141983158983141983148 983126983137983154983145983137983138983148983141983155 (983119983142983142983145983139983141983154) 983118=302
983117983137983148983141 27 15
983117983137983154983154983145983141983140C983151983144983137983138983145983156983145983150983143 98308532 31
983151983142 C983144983145983148983140983154983141983150 05 03
983129983141983137983154983155 983141983160983152983141983154983145983141983150983139983141 00 01
C983137983148 983148983155 983142983151983154 983155983141983154983158983145983139983141 02 01
983124983154983137983142983142983145983139 983155983156983151983152983155 01 00
983110983145 983141983148983140 983145983150983142983151983154983149983137983156983145 983151983150 983154983141 983152983151983154983156983155 02 00
983123983141983139983151983150983140 983155983144983145983142983156 ( 983154983141983142983141983154983141983150983139983141 983143983154983151983157983152 983142 983145983154983155983156) 23 12
983124983144983145983154983140 983155983144983145983142983156 (983154983141983142983141983154983141983150983139983141 983143983154983151983157983152 983142 983145983154983155983156) 55 14
983113983150983156983141983154983139983141983152983156 165 36
983123983145983143983149983137983085983155983153983157983137983154983141983140 66
C983144983145983085983155983153983157983137983154983141983140 9846
983152 lt 05
983152lt01
983152lt001
983118983151983156983141 A983148983148 983158983137983154983145983137983138983148983141983155 983137983154983141 983157983150983139983141983150983156983141983154983141983140
7252019 PIJPSM-05-2015-0064
httpslidepdfcomreaderfullpijpsm-05-2015-0064 2630
Van Meter D (2001) Evaluating dysfunctional police performance A zero-based approach
Charles C Thomas Publisher Springfield IL
Vila B and Kenney D (2002) ldquoTired cops The prevalence and potential consequences of
police fatiguerdquo National Institute of Justice
Vila B Morrison G and Kenney D (2002) ldquoImproving shift schedule and work-hour
policies and practices to increase police officer performance health and safetyrdquo Police
Quarterly Vol 5 No 1 pp 4-24
Vollaard B and Koning P (2009) ldquoThe effect of police on crime disorder and victim
PrecautionEvidence from a Dutch victimization surveyrdquo International Review of Law
and Economics Vol 29 No 4 pp 336-348
Weisburd D Maher L Sherman L Buerger M Cohn E and Petrisino A (1993)
ldquoContrasting crime general and crime specific theory The case of hot spots of crimerdquo
New Directions in Criminological Theory Advances in Criminological Theory Vol 4
pp 45-70
Author Biography
Luke Bonkiewicz is a patrol officer with the Lincoln Police Department
7252019 PIJPSM-05-2015-0064
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983086 983097) lowast )
7252019 PIJPSM-05-2015-0064
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983119983142983142983145983139983141983154 983120983154983151983155983141983139983157983156983145983151983150
983122983137983156983141C983110983123
983117983145983150983157983156983141983155983110983125983120
983117983145983150983157983156983141983155
983119983142983142983145983139983141983154
A983155983155983145983155983156983117983145983150983157983156983141983155
983113983150983139983145983140983141983150983156983122983141983152983151983154983156983155
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2 95 30072 5424 4116 204 696 228 312 432 84 48 120 8
3 72 32496 6312 6600 144 564 444 720 540 84 36 24 12
4 90 33000 5232 4980 216 192 480 768 240 144 12 72 12
5 91 41724 4824 6048 312 636 132 504 324 108 7 24 24
6 83 23916 7056 6192 144 756 528 996 336 132 36 72 12
7 95 24468 6840 5892 156 624 324 456 480 72 36 120 24
8 90 39972 5196 4920 240 504 384 492 264 36 2 72 24
9 79 25500 4068 5100 336 396 192 684 204 24 36 120 10
10 87 21984 5412 4524 348 552 480 792 312 108 48 84 36
983124983137983138983148983141 2 983123983157983149983149983137983154983161 983123983156983137983156983145983155983156983145983139983155 983142983151983154 983126983137983154983145983137983138983148983141983155
983123983141983139983151983150983140983085983116983141983158983141983148 983126983137983154983145983137983138983148983141983155 (983120983137983156983154983151983148 A983154983141983137) 983118=40 983122983137983150983143983141 983123D 983117983141983137983150
983120983151983152983157983148983137983156983145983151983150 983140983141 983150983155983145983156983161 20229830856956 1687 4436
983112983151983157983155983141983144983151983148983140 983149983141983140983145983137983150 983145983150983139983151983149983141 ($) 2628198308576020 16535 49756
983159983144983145983156983141 7398308593 007 083
983126983145983151983148983141983150983156 983139983154983145983149983141 983154983137983156983141 (983152983141983154 100000) 8009830854800 1203 2521
983120983154983151983152983141983154983156983161 983139983154983145983149983141 983154983137983156983141 (983152983141983154 100000) 31009830859800 2027 6524
983120983154983151983152983141983154983156983161 983139983154983145983149983141983155 983152983141983154 983151983142983142983145983139983141983154 5329830851097 1663 8167
983126983145983151983148983141983150983156 983139983154983145983149983141 983152983141983154 983151983142983142983145983139983141983154 194983085372 559 2929
983110983145983154983155983156983085983116983141 983158983141983148 983126 983137983154983145983137983138983148983141 983155 (983119983142983142983145983139983141983154) 983118=302 983122983137983150983143983141 983123D 983117983141 983137983150
983111983141983150983140983141983154 (983142983141983149983137983148983141=0 983149983137983148983141=1) 09830851 24 94
983117983137983154983154983145983141983140C983151983144983137983138983145983156983145983150983143 09830851 18 79
983151983142 C983144983145983148983140983154983141983150 09830855 170 90
983129983141983137983154983155 983141983160983152983141983154983145983141983150983139983141 198308537 779 1063
C983137983148983148983155 983142983151983154 983155983141983154983158983145983139983141 4819830851011 16121 61365
983124983154983137983142983142983145983139 983155983156983151983152983155 16983085901 14376 20624
983110983145983141983148983140 983145983150983142983151983154983149983137983156983145983151983150 983154983141983152983151983154983156983155 0983085227 2153 2530
983124983154983137983142983142983145983139 983139983145983156983137983156983145983151983150983155 249830851246 14632 18642
983117983145983155983140983141983149983141983137983150983151983154 983137983154983154983141983155983156983155 19983085403 5965 10663
983110983141983148983151983150983161 983137983154983154983141983155983156983155 098308571 1066 852
983126983119983122C 9830851959830851043 151 24
1983155983156 2983150983140 3983154983140
104 96 102983123983144983145983142983156
7252019 PIJPSM-05-2015-0064
httpslidepdfcomreaderfullpijpsm-05-2015-0064 2930
983124983137983138983148983141 3 983112983145983141983154983137983154983139983144983145983139983137983148 983116983145983150983141983137983154 983117983151983140983141983148983155 E983155983156983145983149983137983156983145983150983143 983119983142 983142983145983139983141983154983155 A983150983150983157983137983148 983124983154983137983142983142983145983139 C983145983156983137983156983145983151983150983155 983127983137983154983154983137983150983156983155 983117983145983155983140983141983149983141983137983150983151983154983155 983137983150983140 983110983141983148983151983150 983145983141983155
983123983141983139983151983150983140983085983116983141983158983141983148 983126983137983154983145983137983138983148983141983155 (983120983137983156983154983151983148 A983154983141983137) 983118=40 983138 983123E 983138 983123E 983138 983123E 983138 983123E
983120983151983152983157983148983137983156983145983151983150 983140983141983150983155983145983156983161 001 003 001 001 98308501 01 00 00
983112983151983157983155983141983144983151983148983140 983149983141983140983145983137983150 983145983150983139983151983149983141 ($) 002 001 983085000 000 00 00 98308500 00
983159983144983145983156983141 13363 32472 9830858921 5664 9830853274 19211 9830854447 3255
983126983145983151983148983141983150983156 983139983154983145983149983141 983154983137983156983141 (983152983141983154 100000) 006 004 001 001 98308501 02 01 02
983120983154983151983152983141983154983156983161 983139983154983145983149983141 983154983137983156983141 (983152983141983154 100000) 983085003 005 983085002 001 02 03 98308501 00
983120983154983151983152983141983154983156983161 983139983154983145983149983141983155 983152983141983154 983151983142983142983145983139983141983154 983085225 99 98308515 03 98308552 24 98308503 03
983126983145983151983148983141983150983156 983139983154983145983149983141 983152983141983154 983151983142983142983145983139983141983154 983085168 68 29 14 76 31 22 12
983110983145983154983155983156983085983116983141983158983141983148 983126983137983154983145983137983138983148983141983155 (983119983142983142983145983139983141983154) 983118=302
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