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Running head: VIOLENT CRIME IN AMERICA 1
Violent Crime in America: An Empirical Analysis of Predictors and Policy Areas
Michael Montanye
University of Wisconsin-Whitewater
VIOLENT CRIME IN AMERICA 2
Abstract
The reduction of violent crime rates has been, and continues to be a significant source of debate
in both the academic and political realms. The theories, related to the factors affecting violent
crime rates, can impact legislation in various policy areas such as: education, economics, and law
enforcement. To study these relationships, a cross-sectional analysis of the violent crime rates in
the United States was conducted. It included each of the 50 states as well as the District of
Columbia. The regression analysis showed links between the gun control variable and violent
crime rate when the District of Columbia was included, and education and violent crime rate
when the District of Columbia was omitted. Race and gender were both significant factors in
determining the violent crime rates, but neither was related to the policy areas that were being
studied. The results prompt potential attention to the gun control and education policy areas.
However, overall there was a lack of conclusive evidence, which promotes the need for further
research on the subject. It also shows the need for improvement of the predictive model used in
the analysis.
VIOLENT CRIME IN AMERICA 3
Violent Crime in America: An Empirical Analysis of Predictors and Policy Areas
According to the FBI (2012), there was a violent crime committed every 26 seconds in
the United States last year. It was also reported that 1,214,462 violent crimes were committed
across the United States in 2012 alone; up from the 2011 total of 1,206,005 (FBI, 2012). This
was the first increase in total violent crimes since 2006 when overall violent crime rose by
44,378 from the 2005 numbers (FBI, 2012). With statistics like that, it is not hard to understand
why the reduction of violent crime is on the public policy agenda.
In addition, specific policy areas related to reduction of violent crime have recently
reached the national spotlight. The always-contentious gun control argument has once again
been renewed by a string of high-profile shootings. Most notable among the shootings were the
Aurora theater shooting, and the Sandy Hook Elementary School shooting. Closer to home,
there was also a shooting at a Sikh temple in Oak Creek, Wisconsin (Follman, Aronsen, Pan, and
Caldwell, 2012). These events lead to a public display of action by President Barack Obama
who signed executive orders regarding the strengthening of gun control. While mass shootings
are just a small proportion of the total violent crimes committed in America, the media attention
intensifies the concern over violent crimes and highlights the importance of reducing them.
While the increase in total violent crimes in 2012 is concerning, it is also important to
note that the crime rate per 100,000 inhabitants stayed relatively the same from 2011 to 2012
(FBI, 2012). This suggests that an increase in population could explain the jump in volume of
violent crimes. Further data shows that 2012 could just be a small irregularity in an overall
positive trend for violent crime reduction. Since 1993, the overall number of violent crimes fell
from 1,926,017 to the current 1,214,462 and the rate per 100,000 inhabitants fell from 747.1 to
VIOLENT CRIME IN AMERICA 4
the current 386.9 (FBI, 2012). This trend shows improvement in violent crime reduction, but the
overall number of violent crimes still being committed is what keeps it a relevant policy issue.
In response to this, I will be looking at violent crime rates at the state level and their
relationship with a number of different variables. Each variable, or combination of variables,
will represent a potential policy area to be addressed in an effort to decrease violent crime rates.
The variables are split into four groups, three of which correspond with a policy area. The policy
areas include law enforcement, economics, and education. In addition, there are several
variables that are included as control variables and as such do not correlate with any specific
policy area.
This idea springs from two separate sources. The first source inspired my predictive
model. It was an unpublished econometrics paper, written by a peer, which looked at economic
factors in relation to the volume of murders at the state level (Mjelde, 2013). The inspiration for
adding the other policy areas came from Neumayer’s article in the Journal of Peace Research
(2003). The article found that good policies regarding governance and the economy were able to
lower homicide rates. While neither of these dealt directly with violent crime, they both dealt
with the adjacent topic of murders. I decided instead to work with violent crime as a whole.
I hypothesize that there are certain variables, corresponding with policy areas that will
have a more significant impact on violent crime rates than other variables. By creating a model,
where the violent crime rate is the dependent variable, I will attempt to figure out which
variables affect the violent crime rates the most. Based on the results of the model, I will explain
which variables and their corresponding policy areas should take precedence in the effort to
decrease violent crimes. Previous policy initiatives from those areas will be discussed as
possible solutions.
VIOLENT CRIME IN AMERICA 5
Literature Review
Violent crime, the factors that affect it, and solutions to reduce it have been widely
studied. There is a general agreement in the literature that violent crime is a problem, and that
certain factors affect violent crime rates. Where the disagreement originates from are the
theories on how the factors affect violent crime rates, and which factors are the most significant.
The theories point to everything from economic factors, to education levels, to demographics of
the population.
One particular article in the Journal of Peace Research by Eric Neumayer (2003) played
a big role in the development of my model. The study looked at 117 countries over the period of
1980-97 in regards to homicide rates (Neumayer, 2003). An important finding from the study
was that, “good policies in terms of political governance and the economy can lower homicide
rates” (Neumayer, 2003, pg. 620). In a separate article by Haynie and Armstrong (2006), they
describe how things like poverty and unemployment, which are factors of socioeconomic
disadvantage, have been studied in relationship to violent crime. They state, “The results of this
research suggest that measures of concentrated disadvantage are relevant predictors of both
Black and White homicide rates” (pg. 6). Kubrin, Messner, Deane, McGeever, and Stucky
(2010) also discussed the relationship of socioeconomic status with crime rates (pg. 58). Both
articles support what Neumayer found in his study, and made it clear that economic policies
should be one of my policy areas of study.
Haynie and Armstrong (2006), also discussed the role that race and gender play in
homicides. They noted that, “compared to men, women commit a very small number of
homicides” (pg. 3) and, “mounting evidence indicates wide racial disparities in homicide rates,
with homicides committed disproportionately by African Americans” (pg. 4). Kubrin et al
VIOLENT CRIME IN AMERICA 6
(2010) did not mention gender, but included “the proportion of the population that is non-
Hispanic Black” (pg. 69) as a variable in their model. Lance Lochner (2007) also used race in
his model where one of the variables was, “the percent of males that are black” (pg. 8). The use
of race as a variable in these sources convinced me to use it as a control variable in my model.
While he mentions race, Lochner (2007) focuses more on education in his article, which
elaborates on his theory that by increasing education you can decrease violent crime with and
economic bonus. Lochner, “estimate[s] that a one percent increase in high school graduation
rates would save the U.S. economy nearly $2 billion from reduced costs associated with criminal
activity” (2007, pg. 12). This combined economic benefit and violent crime reduction that he
proposes is caused by education convinced me to add an education variable to my model.
Of the variables discussed in the literature regarding the reduction of violent crime, the
most contentious is gun control laws. Lott and Mustard (1997) found in their study on concealed
handguns that giving people the right to carry concealed handguns, assuming they do not have
criminal records or mental illness, actually helps to deter violent crime (pg. 64). Black and
Nagin (1998) directly respond to Lott and Mustard (1997) and conclude that there is no evidence
to show a significant impact of right to carry laws deterring violent crime (pg. 218). They go on
to state that Lott and Mustard’s study is, “highly sensitive to small changes in the sample and the
model” (pg. 218-19). This debate shows that there is more research to be done in that area, and
provides a good reason to include some sort of gun control variable in my model.
Methods
I did a cross-sectional analysis of the violent crime rates in the United States. In order to
do this, I accumulated data on all 50 states, plus the District of Columbia. I used states as my
unit of analysis due to the fact that many of the policy areas, including education, law
VIOLENT CRIME IN AMERICA 7
enforcement, and economic factors, are implemented at the state level. The data for states are
also readily available, and include the particular statistics that I am looking for. The data for
other variables, which I will discuss later in the paper, would have been more difficult to track
down and were therefore omitted from the model due to time constraints.
I collected data from several sources including the Federal Bureau of Investigation (FBI)
Uniform Crime Reports, the Rhode Island Department of Labor and Training, and the United
States Census Bureau. I chose these sources because they are reliable and have consistent data
reporting. This is important because I, or another person, could easily repeat my model with data
from other years. The data that I collected from those sources includes: violent crime rates,
background check numbers, population and income statistics, unemployment numbers, education
levels, and demographic information. In addition, I added a gun control variable based on
individual state laws. These variables were aggregated into a dataset that I used for the analysis
of my model. I plan to estimate the following equation:
CrimeRate = β + β1BackChecks + β2Strictness + β3Income + β4Pop + β5LawEnforce +
β6HisPop + β7BlackPop + β8UnempRate + β9Education + β10Gender + £
Where CrimeRate is the rate per 100,000 population in a state in a given year, BackChecks is
how many National Instant Criminal Background Check System (NICS) firearm background
checks are conducted in a state that year, Strictness is the relative strictness of the gun laws in a
state compared to the rest of the states, Income is median household income for each state that
year, Pop is the total estimated population for each state that year, LawEnforce is the number of
full-time law enforcement officers that are employed in a state that year, HisPop is the
percentage of Hispanic people living in a state that year, BlackPop is the percentage of African
Americans living in a state that year, UnempRate is the unemployment rate in a state that year,
VIOLENT CRIME IN AMERICA 8
Education is the percentage of people (who are older than 25) in a state that have a high school
diploma or higher education, and Gender is the percentage of males in a state that year. β₀ is the
constant; β1, β2, β3, β4, β5, β6, β7, β8, β9, and β10 are the estimable coefficients; and £ is the
stochastic error term.
Using Ordinary Least Squares Regression, I tested this model in order to discover what
effects the variables have on the violent crime rates. I actually ran the model six separate times
using different combinations of the data. The first time I ran the model I used both the data from
2010 and 2011. However, I felt compelled to run the model on each year separately as well. I
was concerned that using two data points from each state in the first model would cause there to
be a lack of independence of the observations. The data points were independent to a point,
since they came from different years, but to be safe I ran the models separately. I also ran each
model again omitting the District of Columbia because I felt it may have been skewing the data.
The crime rate numbers were collected from Table 5 of the 2010 Uniform Crime Report
(FBI, 2010) and 2011 Uniform Crime Report (FBI, 2011). The NICS background check
numbers were collected from the NICS Firearm Background Checks by State report (FBI, 2013).
The gun control strictness variable was taken from an unpublished paper. The author
looked at state laws regarding, “conceal carry, open carry, permit for right-to-carry, permit to
purchase, assault weapons bans, requirements to register guns, license to own a gun, and a ban
on NFA [National Firearms Act] weapons” (Montanye, 2013, pg. 5). He described his
methodology of creating the strictness variable in depth. The aforementioned variables were
coded and added together for each state to create a new scale variable. The variable ranges from
0 being the least strict, to 1 being the strictest. The new strictness variable, which I used in my
model, measures the overall strictness of gun laws of that state (Montanye, 2013, pg. 6).
VIOLENT CRIME IN AMERICA 9
The median household income, gender, population, Black population, Hispanic
population, number of law enforcement officers, and education data was collected from the 2010
American Community Survey (United States Census Bureau, 2010) and the 2011 American
Community Survey (United States Census Bureau, 2011). The unemployment data came from a
report comparing the average annual unemployment rates of states since 2006 (Rhode Island
Department of Labor and Training, 2013).
I grouped variables together based on which policy area each variable correlated with. I
arranged the remaining variables into a group of control variables. The group of control
variables included population, Hispanic population, Black population, and gender. These
variables were meant to help prevent the other variables from seeming more significant than they
really were. For example, let us say that the total population strongly correlates with education
levels. If the population variable is left out, the education level variable could be shown to
correlate stronger with violent crime, when in reality the population variable could be the real
reason for the fluctuations in crime rates.
To begin, I arranged NICS background checks, and the gun strictness variable together
with the number of law enforcement officers to form the law enforcement policy group. The
median household income, and unemployment rate data were arranged together to form the
economic policy group. The study by Mjelde (2013) had two separate variables for education;
high school graduates and bachelor’s degree holders. For my model I combined the two into one
variable that I used for education policy. I created these groups so that I could add up the effects
of each individual variable to create a total effect for the policy area as a whole. This would give
me an idea of which policy areas should be given priority when trying to reduce the violent
crime rates in states across America.
VIOLENT CRIME IN AMERICA 10
Discussion
By analyzing these variables with respect to their effects on the violent crime rates across
the United States, I hope to discover which areas have the most significant impact on violent
crime rates. By doing so, I hope to be able to add to the public policy discussion and promote
the further study of particular policy areas based on my results. These aspirations are fairly lofty,
but the goal is worthy of the pursuit. Decreasing the violent crime in America, and creating a
safer environment for the next generation is important. After all, politicians love to say that the
children are our future. By conducting this study I am trying to put those words into action.
The first model that I ran was the one using just the 2010 data. The result was Table 1,
which can be seen in Appendix A. The adjusted R-square for this model was .431. That means
the variables that I chose accounted for 43.1 percent of the variation in violent crime rates across
states. When I ran the analysis, several variables stood out. Of these variables, Black population
was variable the one that I could use to predict violent crime rates with the most confidence. As
the percentage of Black population increased within a state, crime rates also went up. Table 1
shows that for every one percent increase of Blacks in the total population, ceteris paribus, we
can expect there to be an increase in the crime rate of that state by 13.863 crimes per 100,000
inhabitants. I can say this with a 99 percent confidence, which means there is less than a 0.01
probability of a type I error. The type I error in this case would be if the increase in Black
population would not actually have any effect on the crime rate in the state where the increase
occurred.
Along with the Black population, the Hispanic population was another significant
predictor of the violent crime rate in a state. As the percentage of Hispanic population increased
within a state, crime rates also went up. Table 1 shows that for every one percent increase of
VIOLENT CRIME IN AMERICA 11
Hispanics in the total population, ceteris paribus, we can expect there to be an increase in the
crime rate of that state by 5.645 crimes per 100,000 inhabitants. I can say this with 90 percent
confidence, which means there is less than a 0.10 probability of a type I error.
Gender also seemed to be a significant predictor of the violent crime rate in a state. As
the percentage of males in the population increased within a state, crime rates also went up.
Table 1 shows that for every one percent increase of males in the total population, ceteris
paribus, we can expect there to be an increase in the crime rate of that state by 72.022 crimes per
100,000 inhabitants. I can say this with 90 percent confidence.
The last variable that was significant, from the first round of analysis, was the gun control
strictness variable. Contrary to what may have been expected, the data showed that as the gun
control laws became stricter, there was a higher rate of violent crime. Table 1 shows that for
every 1 increase in the gun control strictness, remember the strictness variable is on a scale from
0 to 1, we can expect there to be an increase in the crime rate of that state by 244.440 crimes per
100,000 inhabitants, ceteris paribus. I can say this with 90 percent confidence.
The second model that I ran was the one using just the 2011 data. The result was Table
2, which can be seen in Appendix A. The adjusted R-square for this model dropped to .415. In
addition to explaining less of the variation in violent crime rates across states, this analysis
yielded very few significant results. In fact, only one of the variables met the threshold of
significance. It is, however, important to note that both the strictness and gender variables were
close to meeting the threshold. The variable that did meet the threshold was the Black
population, which once again showed that when the Black population increased so did crime
rates in that state. Table 2 shows that for every one percent increase of Blacks in the total
VIOLENT CRIME IN AMERICA 12
population, ceteris paribus, we can expect there to be an increase in the crime rate of that state by
11.758 crimes per 100,000 inhabitants. I can say this with a 99 percent confidence.
The third model that I ran was the one using both the 2010 and 2011 data. The result was
Table 3, which can be seen in Appendix A. The adjusted R-square for this model jumped up
to .482, and the results became more significant. This time both gender and Black population
had a 99 percent confidence, while Hispanic population and the gun control strictness variable
each had a 95 percent confidence.
The directionality remained the same on each of the variables, but the numbers changed.
This time for every percent increase of Blacks in the total population there is expected to be an
increase of violent crime rates of 12.729, for every percent increase of Hispanics in the total
population there is expected to be an increase of violent crime rates of 4.410, for every percent
increase of males in the total population there is expected to be an increase of violent crime rates
of 67.681, and for every 1 increase in strictness there is expected to be an increase of violent
crime rates of 207.459 crimes per 100,000 inhabitants.
Based on the three models that I ran, a trend seemed to be emerging. There were four
variables that seemed to be the most significant in predicting violent crime. These variables
were the Black population, Hispanic population, gender, and gun control strictness variables.
Unfortunately, three out of the four variables were in my control variable group. Therefore, their
significance did not help point to any policy area that I had predicted and I could not make policy
suggestions based on those results. I also noted something questionable with the fourth variable.
It seemed that, because the District of Columbia had both really strict gun laws and really high
violent crime rates, D.C. had become an extreme outlier. This can be seen in Figure 1 in
Appendix A. I had a suspicion that the District of Columbia may be skewing the data.
VIOLENT CRIME IN AMERICA 13
Therefore, I decided to omit the District of Columbia from my data, and ran all three
models again. Unfortunately the results, which can be seen in Tables 4 through 6 in Appendix
A, proved my suspicion correct. The Black population and gender variables stayed significant in
all three of the new models, but the gun control strictness variable did not stay significant in any
of them. The Hispanic variable also failed to stay significant in any of the three models. This
left me with no significant policy variable, or policy area to suggest addressing, from the first
three models.
However, in the combined 2010 and 2011 model without the District of Columbia, a new
variable became significant. This new variable was education. Table 6 shows that for every one
percent increase of people with a high school diploma or higher in the total population, ceteris
paribus, we can expect there to be a decrease in the crime rate of that state by 12.748 crimes per
100,000 inhabitants. I can say this with a 90 percent confidence.
Conclusion
While most of the information that I gathered from the models that I ran was unhelpful
towards pointing to a specific policy area to address for reducing violent crime, I was able to get
some interesting results. To begin with, demographic variables seemed to play a large role in
predicting violent crime. The Black population, Hispanic population, and gender variables
specifically were the most significant. Research expanding upon the Haynie & Armstrong
(2006) article could potentially lead to explanations and solutions to this.
The second thing that I found was that, when the District of Columbia was included in
the models, stricter gun laws actually had the opposite effect of what they are intended for.
However, seeing as the significance disappeared with the omission of just one location, this is far
VIOLENT CRIME IN AMERICA 14
from conclusive evidence. It would be helpful to revisit the strictness variable and make it more
comprehensive before reintroducing it to my model for predicting violent crime.
The third thing I found was that a higher educated population could lead to lower crime
rates. Based on this information, I would suggest trying to address education reform as part of
an initiative to lower violent crime rates in states across America. Research similar to Mehta’s
(2013) article into policies like No Child Left Behind and common core standards should be
done to assess those policies’ strengths and weaknesses. This research should then be used to
create effective education reform, which could reduce violent crime rates.
The last thing that I found was actually what I did not find. I did not find a lot of
significance from my policy variables. There was also a surprising lack of explanation of the
variation of crime rates between states as shown by the adjusted R-square values. This leads me
to believe that my model might need to be changed. Upon review, there are several variables
that I would like to change or add in a future research project. There is the possibility of a
lagged effect from the NICS background checks, and therefore I would try to take that into
account if I did further research. I also mentioned that I would return to the strictness variable
and make it more comprehensive before using it in a future model.
In addition, I would also want to add a cost of living variable to complement the current
income variable that I have. The reason for that is because just having a higher median
household income does not mean anything if the cost of living where you reside is higher as
well. This could explain why the income variable did not show up as significant. I would also
like to add a Gini coefficient variable to account for income inequality. I think that is an
important economic issue that my model failed to address.
VIOLENT CRIME IN AMERICA 15
Another thing that I would do with a future model would be to substitute a population
density variable for the total population variable. That might be a better predictor of violent
crime rates. Also, instead of having Black population and Hispanic population variables, I think
one diversity variable could strengthen the statistical relationship that was already shown
between those populations and the violent crime rate.
I acknowledge that, due to time and resource constraints, I was not able to aggregate all
the data that is available on the subject. I looked at a small period of time, only two years, when
there is actually data that goes back several decades. While my sample size will be smaller
because of this, I used all 50 states plus the District of Columbia to try to make up for that. It is
also worth noting that the variables, which I have chosen, are not all inclusive. There are
enumerable theories as to what effects violent crime, and due to the same time and resource
constraints mentioned earlier, I could not include everything in my model for this research
project. There are many other variables, which I mentioned earlier, that I would like to add or
substitute into the model if I had more time or if I decided to revise the model for a future
project.
VIOLENT CRIME IN AMERICA 16
References
Black, D. A., & Nagin, D. S. (1998). Do right-to-carry laws deter violent crime. Journal Of
Legal Studies, 27(1), 209.
Follman, M., Aronsen, G., Pan, D., & Caldwell, M (2012, December 28). US mass shootings,
1982-2012: data from Mother Jones’ investigation. Mother Jones. Retrieved from
http://www.motherjones.com/politics/2012/12/mass-shootings-mother-jones-full-data
Haynie, D. L., & Armstrong, D. P. (2006). Race- and gender-disaggregated homicide offending
rates: differences and similarities by victim-offender relations across cities. Homicide
Studies, 10(1), 3-32.
Kubrin, C. E., Messner, S. F., Deane, G., McGeever, K., & Stucky, T. D. (2010). Proactive
policing and robbery rates across U.S. cities. Criminology, 48(1), 57-98.
Lochner, L. (2007). Education and crime. University of Western Ontario, 5(8), 1-14.
Lott, J. R., Mustard, D. B. (1997). Crime, deterrence, and right-to-carry concealed handguns.
Journal of Legal Studies, 40(1), 1-68.
Mehta, J. (2013). Why American education fails. Foreign Affairs, 92(3), 105-116.
Mjelde, K. (2013). The effect of different economic factors on the number of murders in a state
for a given year. Unpublished manuscript, University of Wisconsin-Whitewater,
Whitewater, United States.
Montanye, M. (2013). Gun control in America: a study of the effects on violent crime.
Unpublished manuscript, University of Wisconsin-Whitewater, Whitewater, United
States.
Neumayer, E. (2003). Good policy can lower violent crime: evidence from a cross-national panel
of homicide rates, 1980-97. Journal Of Peace Research, 40(6), 619-640.
VIOLENT CRIME IN AMERICA 17
U.S. Department of Commerce, United States Census Bureau. (2010). 2010 American
community survey 1-year estimates. Retrieved from http://factfinder2.census.gov/faces
/help/jsf/pages/metadata.xhtml?lang=en&type=dataset&id=dataset.en.ACS_10_1YR
U.S. Department of Commerce, United States Census Bureau. (2011). 2011 American
community survey 1-year estimates. Retrieved from http://factfinder2.census.gov/faces/
help/jsf/pages/metadata.xhtml?lang=en&type=dataset&id=dataset.en.ACS_11_1YR
U.S. Department of Justice, Federal Bureau of Investigation. (2010). Crime in the United States.
Retrieved from http://www.fbi.gov/about-us/cjis/ucr/crime-in-the-u.s/2010/crime-in-the-
u.s.-2010/tables/10tbl05.xls
U.S. Department of Justice, Federal Bureau of Investigation. (2011). Crime in the United States.
Retrieved from http://www.fbi.gov/about-us/cjis/ucr/crime-in-the-u.s/2011/crime-in-the-
u.s.-2011/tables/table-5
U.S. Department of Justice, Federal Bureau of Investigation. (2012). Crime in the United States.
Retrieved from http://www.fbi.gov/about-us/cjis/ucr/crime-in-the-u.s/2012
U.S. Department of Justice, Federal Bureau of Investigation. (2013). NICS firearm background
checks. Retrieved from http://www.fbi.gov/about-us/cjis/nics/reports/nics-firearm-
background-checks-1998_2013_state_monthly_totals-100313.pdf
Rhode Island Department of Labor and Training. (2013). Unemployment rates for states annual
average rankings. Retrieved from http://www.dlt.ri.gov/lmi/laus/us/annavg.htm
VIOLENT CRIME IN AMERICA 18
Appendix A
Table 1: 2010 with DC
VIOLENT CRIME IN AMERICA 19
Table 2: 2011 with DC
VIOLENT CRIME IN AMERICA 20
Table 3: Combined with DC
VIOLENT CRIME IN AMERICA 21
Table 4: 2010 without DC
VIOLENT CRIME IN AMERICA 22
Table 5: 2011 without DC
VIOLENT CRIME IN AMERICA 23
Table 6: Combined without DC
VIOLENT CRIME IN AMERICA 24
Figure 1: DC outlier
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