The Relationship Between Justice System Size and Punishment Across
NationsThe Relationship Between Justice System Size
and Punishment Across Nations
Alyssa K. Mendlein1
Received: 13 October 2020 / Accepted: 24 March 2021 / Published
online: 16 April 2021 © The Author(s), under exclusive licence to
Springer Nature Switzerland AG 2021
Abstract Nations utilize imprisonment to different extents, but
scholars have yet to fully explain why. One hypothesis, proposed
here and previously unexamined, is that the size of the front-end
justice system workforce (police, prosecution, judiciary) is
related to incarceration rates. Previous literature has examined
why these workforces are of a certain size, but largely ignores the
implications of their size in regards to incarceration. Supported
by a conflict perspective and a systems approach, this research
examines the relationship between justice system workforce size and
incarceration rates cross-nationally, control- ling for other
relevant factors supported by the literature. The study relies on a
compilation of data on 47 countries from several international
databases, with United Nations Surveys on Crime Trends and
Operations of Criminal Justice Systems (UN-CTS) as the primary
sources for main variables of interest. Findings suggest that there
is a positive relationship between prosecution workforce size and
incarceration rates in the sample of countries examined, and a
weaker, likely indirect, negative relationship between judiciary
size and incarceration rate. No relationship between police
personnel size and incarceration rate is found. The paper also
discusses study limitations and implications for future comparative
research on incarceration.
Keywords Comparative criminology · Incarceration rates ·
Justice workforce size · General systems theory ·
UN-CTS
Introduction
Countries use incarceration to differing degrees to meet pun-
ishment and deterrence goals, and scholars have attempted to
explain the differences among countries in a variety of ways, such
as through the political and social arrangements of nations.
Another way, which has been relatively ignored in the past, is
through the size of the criminal justice work- forces in charge of
handling criminal cases up to the point of incarceration—the
police, prosecutors, and judges. Prior research has examined some
factors that impact these work- force sizes, such as minority
threat on police force size, but the implications of size,
including on incarceration, have been less of a focus. Some
scholars have suggested one or more of these agencies’ size may
have impacted incarcera- tion in certain jurisdictions (e.g.
prosecutors in the U.S.: Pfaff, 2017), but research has mostly
overlooked effects of their size on incarceration.
Two lenses potentially link justice system workforce size and
incarceration rates: a conflict perspective and a systems approach.
A conflict perspective would suggest a positive link between the
size of police forces (and poten- tially prosecutorial staff and
the judiciary) and incarceration rates; when economic inequality or
population heterogene- ity is seemingly threatening the social
order, crime control bureaucracies could increase (Liska
et al., 1981), which includes police, prosecutors, and judges.
Implicit in the con- flict perspective is the systems approach to
criminal justice, which suggests that criminal justice as a system
includes subsystems (agencies) linked by a common goal and the
processing of cases (Bernard et al., 2005). Regardless of why
agencies might be bigger or smaller, through this focus on case
processing a systems perspective likely suggests a positive
relationship between workforce size in police agen- cies,
prosecutors’ offices, and judiciaries, and incarceration rates as
one of the final case outcomes; the more cases that can be
processed by each of these agencies, the more cases that may
process through correctional institutions.
It is against this backdrop that this paper explores the
relationship between front-end criminal justice system per- sonnel
size and incarceration rates cross-nationally. This
* Alyssa K. Mendlein
[email protected]
1 Department of Criminal Justice, Temple University,
Philadelphia, PA, USA
1 3
effort builds on the available comparative literature focused on
explaining variation in the use of imprisonment by adopt- ing a
systems perspective; the results should have significant
implications for the distribution of justice around the world.
Nations should understand how deprivation of their citi- zens’
liberty is occurring and ensure that this is in line with conscious
goals of punishment and incarceration. Without understanding the
functions of agencies and their workforces within justice systems,
national governments may be inad- vertently impacting their
incarcerated populations through unrelated hiring practices within
subsystems.
Literature Review
Justice System Personnel Size
Much research has attempted to describe and explain vari- ations in
the size of criminal justice system workforces, within countries
and cross-nationally. For example, studies have used due process vs
crime control hypotheses to pre- dict justice system workforce
sizes and process rates cross- nationally, with partial support
(Sung, 2006). In terms of police size, or “strength”, hypotheses
have been tested such as a conflict perspective (within the US:
Liska et al., 1981; and outside its borders: Kent &
Jacobs, 2004), political indi- cators (Ruddell & Thomas, 2009),
and police decentraliza- tion (Lowatcharin & Stallmann, 2019),
with some support for these explanations in specific conditions.
Less research has examined the size of prosecutorial and judiciary
staff, with studies of the legal profession in Japan (Chan, 2012)
and cross-nationally (Galanter, 2011) descriptively show- ing
growth during the periods of study, but not testing any
explanations for these changes in size.
Despite the above descriptions of and explanations for the size of
the factions of criminal justice systems, few studies have examined
what these variations in size mean for major justice system
outcomes, such as incarceration—or, if it is these back-end
outcomes that are driving changes in the front-end workforce size.
Many policing studies have exam- ined how police force size might
contribute to crime rates, and vice versa (as summarized in Levitt
& Miles, 2006), but few have analyzed the impact of police size
on other criminal justice system outcomes that depend on output
from police activities. Kleck and Barnes (2014) discuss the
possibility of a relationship between police force size and crime
through increased incarceration (although this discussion was
limited to the US): “larger numbers of police officers make larger
numbers of arrests, which result in more criminals being
incarcerated” and therefore incapacitated (p. 730). However, they
reasoned that prison capacity may limit any effect of police force
size on incarceration rates (Kleck & Barnes, 2014). In Ruddell
and Thomas’ (2009) study, incarceration
rate was one criminal justice variable used to predict police force
size, and the study reported no significant relationship between
them. It is worth noting, though, that the authors posited that
incarceration rate predicted police force size, rather than police
force size predicting incarceration rate (the suggested direction
in the current research), although this may not be of great
importance when using cross-sectional data.
In terms of prosecutorial staff, Pfaff (2017) has suggested that
workforce size could have contributed to mass incar- ceration in
the USA. In his book Locked In, part of Pfaff’s argument centers
around the increasing numbers of prosecu- tors in the USA during
the period of growing incarceration rates. Although this
relationship was not hypothesized to be simple or direct, there is
some thought that a shift from part- time to full-time prosecutors
could have impacted the growth in imprisonment in rural counties in
the US, where rates of incarceration increased the most (Pfaff,
2017). In addition, prosecutors in an expanding number of
jurisdictions have access to some form of plea bargaining, which is
the ability to offer more lenient treatment than would be given if
con- victed at trial in exchange for a guilty plea (Feeley, 1982).
Plea bargaining may have different characteristics in each country
that the US model has been exported to (Cavise, 2007; Solomon,
2012), but is thought to increase efficiency through shorter case
processing times by avoiding a trial (Pfaff, 2017). Although other
countries have not faced the imprisonment crisis to the extent that
the USA has (Mauer, 2003), the potential connection between
prosecutor work- force size and imprisonment could still be valid
and is worth investigating empirically.
Regarding the judicial workforce, there is an assumption that
increasing the number of judges should help with case- loads and
backlog (Beenstock & Haitovsky, 2004), meaning that more cases
would get processed, and in a timelier man- ner. Studies have found
that judicial expansion in the USA has often coincided with
caseload pressure (e.g. Carlton, 1997), enforcing the belief in the
connection between the size of the judiciary and case
processing/disposition. Been- stock and Haitovsky (2004) tested
this expectation in Israel, using annual observations from three
court systems. They reported that only in smaller magistrate courts
was there any evidence that case dispositions depend on the number
of judges, and this impact was still small; for the most part, they
found that changes in the number of judges were offset by changes
in productivity, as measured by completed cases per judge
(Beenstock & Haitovsky, 2004). While this finding suggests that
increasing judiciary size is unlikely to affect incarceration
rates, given limited external validity, it is use- ful to test this
notion in other areas.
Finally, there are only two studies, from D’Amico and Williamson
(2015, 2019), that include multiple front-end criminal justice
personnel rates in analyses of incarceration
109International Criminology (2021) 1:107–122
1 3
rates cross-nationally. Variables measuring police and judi- cial
personnel rates were used as controls in regression mod- els of
incarceration use in over 100 countries. These studies did not
examine prosecution personnel. In analyses primar- ily geared
towards understanding the relationship between incarcerate rates
and legal traditions—along with other political, economic, and
social variables—judicial person- nel size was found to be
significantly negatively related to incarceration rates in certain
models across both studies and police personnel size showed mixed
(both positive and nega- tive) significant findings in certain
models, but primarily was found to be non-significant (D’Amico
& Williamson, 2015, 2019). While the directionality for judges
is the opposite to what the systems theory approach would suggest,
these findings are inconclusive enough to be of further interest in
comparative incarceration research.
Theory Connecting Justice Workforce Size
with Incarceration
Conflict theory is one perspective that might help link front- end
criminal justice workforce size, such as the police, with
incarceration rates. The idea that crime control agencies are used
to provide increased formal social control amid racial or economic
threat has been the theoretical backdrop for many examinations of
police personnel size (Liska et al., 1981; Ruddell &
Thomas, 2009) and use of imprisonment (Jacobs & Kleban, 2003;
Ruddell, 2005; Ruddell & Urbina, 2004). It follows that social
conflict could impact both, potentially one (incarceration) through
the other (police), and that this could also reach prosecution and
judiciary personnel.
That social conflict may affect use of imprisonment through justice
agencies also suggests another perspective that might be helpful to
understand how justice personnel size could affect incarceration
rate: a systems approach. This approach, first conceived by
biologist Bertalanffy in the 1940s, suggests that a whole system is
more than the sum of its parts, and therefore its parts are best
examined in the context of the whole (Bernard et al., 2005;
Kraska, 2004). Criminal justice operations have been seen as part
of a system since at least the 1960s (although this view is not
without its detractors) (Bernard et al., 2005). A key applica-
tion was performed by Van Gigch (1978). He analyzed the criminal
justice system as a “total system”, with “subsys- tems” (agencies)
working to accomplish a common goal, situated in the context of a
“whole system”, including social, legal, technological, and
political systems (p. 24). Bernard et al. (2005) argued that
these subsystems are linked through the input, processing, and
output of “cases”. There are also countervailing pressures that are
exerted upon justice agency actors as they process cases through
their subsystems. These include a “backward pressure” to reduce the
flow of cases
from one agency to the next, due to declining capacity in each
subsequent agency, and a “forward pressure” to send cases to the
next agency, to minimize blame if cases are dropped and become
“output” back to society too soon (Ber- nard et al.,
2005).
This paper argues that it is this approach that may be par-
ticularly suitable to explain a potential relationship between
justice agency workforce size and use of imprisonment. Within this
context, arguably, the size of the subsystem workforce may affect
the size of the incarcerated population, incarceration being one of
the final options for processing before a “case” is determined to
be output of the criminal justice system and returned to the rest
of the “whole system” (Van Gigch, 1978). Workforce size may impact
the number of cases that can be processed through each agency, thus
affecting the “backward pressure” to drop cases before they have
been fully processed. Therefore, from a systems per- spective, one
might expect workforce size of these front-end subsystems (police,
prosecution, and judiciary) to positively relate to rates of
incarceration. It should be noted that con- flict theories, the
most prevalent in examinations of incar- ceration rates, are
implicitly rooted in a systems perspective, too. For imprisonment,
at the back-end of the system, to be used as a control for
perceived threats to powerful interests, the rest of the justice
system would need to support the flow of these cases, as threats
cannot (usually) be immediately removed through
incarceration.
In sum, while several studies to date have examined how and why
workforce size within the front-end of the crimi- nal justice
system varies, we do not yet fully understand the implications of
this phenomenon for the criminal jus- tice process more broadly.
There is potential—based on ideas advanced by other scholars, as
well as suggested by a systems framework of the criminal justice
process and outcomes—for workforce size of police, prosecutors, and
judges to impact other aspects of the criminal justice system, such
as incarceration rates. Before describing this study’s methods to
begin testing this idea, the next section will detail the previous
literature examining incarceration rates cross-nationally.
Traditional Explanations for Incarceration Rates
A variety of explanations have been offered and tested in the
literature for differences in incarceration rates cross-
nationally. To start, it is well established that incarceration
rates vary significantly among countries (Walmsley, 2003);
countries are not equal in their use of incarceration as pun-
ishment. Scholars have identified and analyzed a range of reasons
for this discrepancy. This section reviews some of the most
influential explanations.
Crime rates and prison capacity are two of the most rational
explanations for variation in incarceration rates
110 International Criminology (2021) 1:107–122
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cross-nationally. First, incarceration rates could differ because
countries have more or fewer crime events within their borders.
However, overall crime rates have been found to account for only a
small part of cross-national differ- ences in imprisonment (Young
& Brown, 1993). Homicide rates, in contrast, are strong
predictors of incarceration rates cross-nationally (Jacobs &
Kleban, 2003; Neapolitan, 2001); however, this impact could result
from the fear of crime that more homicides induce, which may lead
to greater public support for imprisonment, rather than through the
incarcera- tion of homicide perpetrators. This fear of crime and
public support for imprisonment could, in turn, influence penal
policy (Lappi-Seppälä, 2011). Second, based on Parkin- son’s
Law—growth is elastic in its demands on space—the number of
incarcerated individuals could grow to fit prison capacity
(D’Alessio & Stolzenberg, 1997). There have been arguments in
support of this explanation over police size in determining the
size of the incarcerated population in the USA (Kleck & Barnes,
2014) and arguments in opposition cross-nationally (Young &
Brown, 1993), but there has been little empirical testing of this
relationship.
Other explanations of cross-national differences in incar- ceration
center around the development and values of these societies. The
civilization thesis, first proposed by Norbert Elias, suggests that
as nations modernize and the condi- tions of life become less harsh
and more orderly, social con- trol shifts inward and there is less
need to rely on external sanctions to control people (Neapolitan,
2001). Evidence is mostly unsupportive of this hypothesis, as
multiple stud- ies have not found civilization, often
operationalized with the Human Development Index, to be a
significant predictor of differences in incarceration rates across
countries (e.g. Jacobs & Kleban, 2003; Neapolitan, 2001).
However, one study found that there may be a moderating role for
civi- lization, with predictors and the models’ predictive power
differing between two country groups split by development level
(Ruddell, 2005). In addition, a society’s “penal values”,
potentially related to civilization theory through the need for
external social control, have been argued to impact the form and
severity of punishment (Young & Brown, 1993). How “civilized” a
society is might impact its people’s sensibilities about what the
“right” punishment is. Although difficult to test, there has been
some support for a relationship between penal values and
incarceration rates using regional prox- ies (Neapolitan, 2001) and
public penal attitudes gathered through survey research
(Lappi-Seppälä, 2011).
The translation of penal values, and public sensibilities in
general, may be related to the political arrangements of nations
and the structural arrangement of the justice systems. Jacobs and
Kleban (2003) hypothesized that more “direct” democracy would be
associated with higher incarceration rates, as the more
decentralized governments are, the more influential the public is
in punishment decisions, and public
sentiment is often harsh. The authors tested this hypothesis in 13
countries, and results supported this expectation, with countries
low in corporatism and high in federalism hav- ing higher
imprisonment rates. The political economy of a country, including
level of corporatism, has also been sug- gested to relate to
imprisonment levels through the effects of associated values and
governmental actions—such as social inclusion and welfare
provision—on penal policy, and there have been encouraging findings
in this realm (Cavadino & Dignan, 2006; Lappi-Seppälä, 2011).
Although less studied until recently, the structural arrangement of
the justice sys- tems may also play a role in incarceration rates,
through the ease of penal value translation. Amongst other things,
two major legal traditions (common law and civil law) vary in their
incorporation of the public in legal decision-making (Wills, 2017),
which could translate into a different levels of public sentiment
incorporation in determinations of guilt and use of imprisonment.
Whether because of this or other possible mechanisms associated
with underlying differences between the legal traditions, previous
research has found a relationship between legal tradition and
imprisonment rates (D’Amico & Williamson, 2015, 2019; Ruddell,
2005).
In addition to research looking into the role certain politi- cal
arrangements play in impacting incarceration rates, stud- ies have
also found effects of societal arrangements on use of imprisonment.
Research has found evidence to support the minority threat
hypothesis, which posits that dominant racial groups may be
intimidated by increases in minority populations and respond with
increased efforts to maintain their dominant position, such as
through the use of incar- ceration. Studies have been supportive of
this argument, as large minority presence and population
heterogeneity have been found to predict higher incarceration rates
(Jacobs & Kleban, 2003; Ruddell, 2005; Ruddell & Urbina,
2004). Also, there is some limited evidence that political threat
through income inequality may impact incarceration rates, with
countries characterized by more wealth, dictatorships, and greater
economic disparity relying more on the use of incarceration than
others (Killias, 1986).
All the explanations reviewed here provide important insights into
the factors that influence incarceration rates across countries.
However, there is still more to uncover, as evidenced by the amount
of variation still left unexplained. For example, Neapolitan’s
(2001) cross-national analysis of 180 countries, relying on
fourteen explanatory variables, showed that over half of the
variation in incarceration rates still needed to be
explained.
The Current Research
With the amount of variation left to explain in incarceration rates
cross-nationally still fairly large, the current research proposes
and tests the following expectation: the use of
111International Criminology (2021) 1:107–122
1 3
imprisonment across nations varies by the size of the front- end
criminal justice workforce. The study uses data from 47 countries
in Europe, South and Central America, Asia, and Africa to examine
the relationship between police, prosecu- tion, and judge personnel
size and a major back-end justice outcome: incarceration. As the
above review suggests, there has been some discussion of why police
agencies, prose- cutorial staff, and judiciaries might be a certain
size, but the effects of this size have received much less
attention. Conflict and systems perspectives, though, might suggest
that front-end justice system workforce size would impact
incarceration rates. Therefore, this research asks the follow- ing
main question: accounting for other relevant factors, is the size
of a nation’s police forces, prosecution personnel, and judiciary
related to its incarceration rate, as compared to other
nations?1
Regardless of what might explain variation in workforce size,
justice officials could find it desirable to either expand or
contract the sizes of these workforces, if there is sufficient
evidence to indicate a relationship between them and incar-
ceration rates. There could be implications for justice system
efficiency2 if workforce size is inadvertently contributing to an
excessive prison population, such as is currently found in the US.
This research explores this possible relationship in a
cross-national sample of a large and diverse group of
countries.
Methods
Primary Data Source
This research uses the United Nations Surveys of Crime Trends and
Operations of Criminal Justice Systems (UN- CTS) as the primary
source of data on criminal justice sys- tems and personnel. At the
onset, it is worth noting that the UN-CTS is the only available and
centralized source of comparable data on a global scale when it
comes to the criminal justice system operations—main processes and
various components.
For this study, data on the primary variables of inter- est were
compiled from 47 nations, including countries in
Africa, Europe, and South and Central America3 (refer to
Table 2 means for the proportion of countries from each region
in the sample, with Southern Europe as the missing reference
group). The UN-CTS relies on a survey method- ology; a national
coordinating officer from each country is responsible for providing
the data, after identifying the correct national authorities and
official sources of relevant statistics (Bennett, 2009). Since the
first round of data col- lection completed in 1978 (Newman &
DiCristina, 2009), the UN-CTS has collected data on a biennial or
triennial basis throughout the 1980s and 1990s and then annually
from 1997 onwards (Bennett, 2009), although the UNODC data portal
only provides data from 2003 to 2017 (United Nations Office at
Vienna, 2010; UNODC n.d.).
These 2003–2017 annual data from the UNODC data por- tal have been
utilized for this research. Despite the semi-reg- ular accumulation
of data from 1970, not all UN countries have consistently
contributed data, and among those that do, they do not all provide
data on all variables. The countries examined in this study were
thus based on UN-CTS data availability and completeness; each had
provided at least one observation for the primary data of interest
(police, prosecu- tor, and judiciary size, incarcerated population)
within the 2003–2017 time frame.4
As alluded to earlier, comparative data in general, and the UN-CTS
data in particular, have many advantages and limi- tations. Despite
developing relatively independently in many countries, criminal
justice system structures look remarkably similar across nations,
and different aspects of the abstract system, such as police,
courts, and prisons, play relatively similar roles across countries
(Maguire et al., 1998). This similarity in function across
systems makes comparison pos- sible and, many (like this author)
would argue, instructive. Comparative data make this study of
national justice systems possible.
The UN-CTS, specifically, is the only centralized source of
information on criminal justice systems worldwide, in addition to
providing comprehensive crime data (Bennett, 2009). Although the
issue of differential definitions across
1 Another goal of this research was examination of mediating rela-
tionships suggested by the two theoretical lenses. However, no
medi- ating effects were found, and thus due to space limitations,
the medi- ation analyses will not be further discussed, but are
available upon request. 2 It should be noted that justice workforce
size is not being equated to case processing efficiency here, and
size may have little relation to efficient use of resources. Other
factors, such as organizational structure and processing procedure,
could have more influence on the speediness of case processing,
whereas workforce size purely sug- gests the general capacity for
case processing in the system.
3 Countries included are Albania, Austria, Bosnia &
Herzegovina, Bulgaria, Cabo Verde, Chile, Colombia, Costa Rica,
Croatia, Czech Republic, Denmark, Estonia, Finland, Germany,
Greece, Guyana, Honduras, Hungary, Iceland, Italy, Japan, Kenya,
Latvia, Lithuania, Luxembourg, Mexico, Mongolia, Montenegro,
Morocco, Panama, Paraguay, Peru, Philippines, Poland, Portugal,
Republic of Korea, Romania, Russian Federation, Serbia, Singapore,
Slovakia, Slovenia, Spain, Sweden, Switzerland, and Northern
Ireland and Scotland in the UK. 4 The initial number of countries
that met this requirement was 55. However, seven countries had to
be excluded due to missing data for other variables of interest. In
addition, the USA was excluded because of questionable reliability
of its three observations for prosecutor size (extremely low
numbers).
112 International Criminology (2021) 1:107–122
1 3
countries plagues comparative research (Neapolitan, 2001), the
UN-CTS survey does its reasonable best to provide respondents with
definitions corresponding to the items of interest (Newman &
DiCristina, 2009). However, these data, understandably, have
limitations—which affected the choices made in this study. Agencies
reporting their statistics to the UN do so voluntarily; the dataset
is therefore limited by non-response bias (Bennett, 2009). Within
the data that are collected, there is also the possibility of
instrumentation, recorder, and respondent error, as the survey
methodology may oversimplify the data, and no quality control
measures are conducted (Bennett, 2009). Despite these limitations,
the data do allow investigation, though restricted, into a crucial
comparative issue.
Study Measures
The dependent variable in these analyses, compiled from the UN-CTS,
is the incarcerated population, which is defined as “persons held
in prisons, penal institutions or correctional institutions”
(UNODC, 2018, “5—Prisons”). To allow com- parison, variables for
incarceration rate per 100,000 popula- tion were created from 2003
to 2017, using estimates from the UN 2019 Revision of World
Population Prospects for each year (https:// popul ation. un. org/
wpp/).5
The independent variables of primary interest are coun- try-level
estimates of police, prosecutorial, and judicial workforce sizes,
also gathered from the UN-CTS data. These refer to personnel “whose
principal functions are the preven- tion, detection and
investigation of crime and the apprehen- sion of alleged offenders”
(police), “whose duty is to initiate and maintain criminal
proceedings on behalf of the state in relation to a criminal
offence” (prosecutors), or who are “authorized to hear specifically
criminal cases, including in appeal courts, and to make
dispositions in a court of law” (judges) (UNODC, 2018,
“Definitions”).
The study employed a host of other independent vari- ables. Some
were included in the study because of their expected connection to
the primary predictor variables (i.e., personnel variables). Prior
literature informed the inclusion of other variables, as they were
frequently included and/ or found to be important in other
research. Table 1 gives details about the other independent
variables, including their source, description, and rationale for
inclusion.
Data Analysis
The study generated several Ordinary Least Squares (OLS) regression
models to answer the research questions about the relationship
between justice personnel size and use of incarcera- tion across
countries. Variables were entered in blocks in order of theoretical
interest and importance in previous literature, and therefore the
analysis is a form of hierarchical linear regression. To address
the assumption of linearity, the dependent variable (imprisonment
rate) was log-transformed. Several independent variables (justice
system personnel rates, process rates, homi- cide rate, and prison
occupancy) were also log-transformed as they were highly skewed;
thus, the transformation prevented violation of the
homoskedasticity assumption and also gave more easily interpretable
results.
Table 2 presents descriptive statistics for variables in the
models. Median and interquartile range (IQR) are provided for
variables that are highly skewed, and therefore natural-log trans-
formed in analyses. Mean or proportion and standard deviation (SD)
are provided for variables that are less skewed. The means of dummy
variables for legal tradition, life imprisonment, and region
indicate the proportion of that category in the sample; for
instance, the mean of .06 for Africa indicates that 6% of the
sample are African countries. The median incarceration rate for the
sample is 143.85 per 100,000 population, averaged from 2003 to
2017, before log transformation. The lowest average incarceration
rate in the sample is 43.10 (Iceland) and the high- est is 525.65
(Russian Federation). The following descriptive statistics about
front-end justice system personnel are also prior to log
transformation, and rates are per 100,000 population. The median
police personnel rate is 323.23, with a minimum of 96.34 (Kenya)
and a maximum of 697.69 (Montenegro). The median prosecutor rate is
10.08; Honduras has the lowest prosecutor personnel rate (0.66) and
Panama has the highest (81.19). For judicial personnel, the median
rate is 16.35, with a minimum value of 0.99 (Kenya) and a maximum
of 47.02 (Slovenia). The other descriptive statistics indicate that
a major- ity of countries in the sample have a relatively low
homicide rate (2 per 100,000 population or below), have prison
popula- tions at or below 100% occupancy level, are more ethnically
homogenous than fractionalization (mean = 0.32), skew more towards
income equality than inequality (mean = 36.54), have more
centralized governments (mean = 5.64), have a civil law tradition
(79%), are medium to very highly developed (.52–.92 range), and
have some form of life imprisonment (with parole: 60%; without
parole: 19%).
In a preliminary stage, the analysis examined bivariate
relationships (shown in Appendix), to understand the baseline
relationships and screen for multicollinearity issues. At the mul-
tivariate stage, the study estimated OLS models, incorporating
variables as conceptual blocks that added on to the previous model.
The first model included only justice system personnel variables,
to analyze their relationships with incarceration rate
5 Unless noted, the estimates from 2003 to 2017 were averaged to
create one measure for each variable in the study. These average
rates were used to explore the main question of the study, which is
focused on overall differences in workforce size rather than
changes over time. Although examining changes over time would be
potentially instruc- tive, it is not possible due to data
limitations within the main predic- tors of interest.
1 3
Ta bl
e 1
D et
ai ls
o n
ad di
tio na
1 3
alone, without any controls. The next models (2 and 3) added in
variables thought to relate to the personnel variables through the
systems (formal contact rate) and conflict (ethnic fraction-
alization, income inequality) perspectives, respectively. Model 4
controlled for homicide rate and prison occupancy. The next model
then also controlled for government (decentralization) and legal
system (civil law) structure. Model 6 incorporated controls for
variation in civilization (human development) and penal values
(life imprisonment). Finally, Model 7 also accounted for
differences in world region. Variation Inflation Factors (VIFs) for
each variable were examined (not shown) to make sure that they did
not exceed an acceptable level. VIFs above 10 were considered to
indicate issues with multicollin- earity (Hair et al. 1995)
and one variable in Model 7 did exceed this value (HDI) and was
therefore excluded.
Results
Table 3 summarizes the results from the OLS regression mod-
els. As indicated earlier, Model 1 includes only the three justice
system personnel variables of interest (police, prosecution, and
judiciary workforces) predicting incarceration rate. According to
the ANOVA chart, Model 1 is statistically significant, and the
adjusted R2 suggests that just these workforce size vari- ables
account for 11.4% of the variation in incarceration rates across
the 47 countries considered. Both the rates of prosecu- tion and
judicial personnel are significant, while rates of police are not.
An increase of 1% in prosecution or judicial personnel per 100,000
population would lead to a .2% change in incarcer- ated persons per
100,000 population, with that change being an increase for larger
prosecution personnel (positive relationship) and a decrease for
larger judicial personnel (negative relation- ship). In Model 2,
when rate of formal contact is added, pros- ecution personnel rate
retains its significance, while judicial personnel rate drops below
statistical significance. However, both variables lose significance
in Model 3 when the threat variables are added. In Model 4, when
the other justice and crime variables are incorporated, only
homicide rates are a significant predictor of incarceration rate.
In Model 5, which accounts for process, threat variables, crime and
system con- trols, and structural variables, none of the variables
reach statis- tical significance. After adding civilization
variables in Model 6 prosecutorial force size regains its
significance. However, in the final model, also accounting for
regional effects, prosecutor personnel size drops back below
statistical significance. The judiciary personnel rate never
regains significance after Model 1, and police personnel rate stays
non-significant throughout.
The final model has an adjusted R2 of .582, indicating that,
accounting for the number of variables in the model, these pre-
dictors account for 58.2% of the variation in incarceration rates.
In looking across Models 1–7, the greatest increase in adjusted
Ta
bl e
1 (c
on tin
ue d)
Va ria
bl es
So ur
ce D
es cr
ip tio
n R
at io
na le
1 3
R2 occurs when the threat variables are incorporated, increasing
from .095 to .349. Each subsequent block adds about .05–.08 to the
explained variation in incarceration rates.
In the final model, the two threat variables are statistically
significant, and their relationships with incarceration rate are
both positive, meaning that as threat increases, either through
multiculturalism or income inequality, so do incarceration rates.
Income inequality (economic threat) was significant when first
incorporated in Model 3, lost significance once homicide rate was
incorporated in Model 4, and then regained significance in Model 6,
once both structural and civilization/penal values variables were
also taken into account. By contrast, ethnic frac- tionalization
(minority threat) was non-significant until the final model. In
examining the standardized regression coefficients (β), it appears
that income inequality is the strongest factor in the final model.
A one-standard-deviation increase in income inequality is
associated with an increase of almost two-thirds of the
log-transformed incarcerate rate’s standard deviation. This value
is almost double the values of any of the other variables in the
final model.
In addition, there are a couple factors that were significant in
earlier models, but found to be non-significant by the final
model. Homicide rates were significant in Models 4 and 5,
indicating that countries with higher homicide rates also have
higher incarceration rates; however, once civilization variables
were incorporated, homicide rates lost their significance and did
not regain it once regional variables were also controlled for in
Model 7. An additional variable, life imprisonment with the option
of parole, was significant in Model 6, but lost statistical
significance in the final model. Specifically, those countries that
allow for life sentences with the possibility of parole are more
likely to have higher incarceration rates than those that have no
life imprisonment option, controlling for all other non-regional
factors. However, this factor did not retain significance when
controlling for region as well. It should be noted, lastly, that
the final model did not include the development indicator, HDI, due
to model issues with multicollinearity.
Discussion
These results suggest that there is reason to believe that crimi-
nal justice system personnel size affects incarceration rates
around the world. Although there was no link found between
Table 2 Descriptive statistics
Median and IQR are provided for heavily skewed variables,
Mean/Proportion and SD for the less skewed variables; IQR
interquartile range, SD standard deviation
Variables n Median IQR Mean/proportion SD Minimum Maximum
Incarceration rate 47 143.85 114.78 43.10 525.65 Police rate 47
323.23 189.20 96.34 697.69 Prosecutor rate 47 10.08 10.71 0.66
81.19 Judge rate 47 16.35 15.38 0.99 47.02 Formal contact rate 47
953.86 889.69 30.71 6225.04 Prosecution rate 42 990.17 1302.66
33.13 5291.28 Criminal court rate 41 692.04 694.84 158.92 3853.75
Conviction rate 44 484.69 770.50 6.01 3833.09 Homicide rate 47 1.69
6.63 0.37 59.42 Prison occupancy 47 0.97 0.33 0.57 4.64 Ethnic
fractionalization 47 0.32 0.21 0.00 0.86 Income inequality 47 36.54
8.01 25.40 54.20 Government closeness index 47 5.64 7.41 0.00 31.96
Civil law legal tradition 47 0.79 0.41 0.00 1.00 Human development
47 0.79 0.10 0.52 0.92 Life with possibility of parole 47 0.60 0.50
0.00 1.00 Life without possibility of parole 47 0.19 0.40 0.00 1.00
Africa 47 0.06 0.25 0.00 1.00 South America 47 0.11 0.31 0.00 1.00
Central America 47 0.09 0.28 0.00 1.00 Asia 47 0.11 0.31 0.00 1.00
Eastern Europe 47 0.15 0.36 0.00 1.00 Northern Europe 47 0.19 0.40
0.00 1.00 Western Europe 47 0.09 0.28 0.00 1.00
116 International Criminology (2021) 1:107–122
1 3
Ta bl
e 3
O LS
re gr
es si
on re
su lts
Pr ed
ic to
rs M
od el
1 M
od el
2 M
od el
3 M
od el
4 M
od el
5 M
od el
6 M
od el
1 3
police personnel size and incarceration rates, there was some
evidence that judiciary size may be related to incarceration and
even stronger evidence for prosecution personnel size.
First, judicial personnel rate is negatively related to incar-
ceration rate, in analyses controlling for other personnel. How-
ever, once other variables were introduced into these models, rate
of judicial personnel did not retain its significance. These
findings are consistent with those of D’Amico and Williamson (2015,
2019), who found judicial personnel size to be nega- tively related
to incarceration rates across nations, but only significant in
certain regression models, suggesting that this relationship is not
strong or direct.
The findings offer stronger evidence for the influence of
prosecution personnel size, although not the strongest or most
direct predictor in the models. The rate of prosecutor personnel is
positively associated with incarceration rates, controlling for
other personnel and formal contact rate. However, once threat
variables are controlled for, prosecutor personnel size loses sig-
nificance. As more variables are incorporated into the model,
prosecutor personnel size regains its significance, except in the
final model when it fails to reach statistical significance. These
findings suggest that incarceration rates around the world may be
directly related to prosecutorial force size, among other jus- tice
agency factors; the relative change in results when various factors
are controlled for, in contrast, also suggests that the rela-
tionship with prosecutorial force size is at least partially
attrib- utable to some indirect mechanisms. This relationship
occurs in spite of the fact that countries vary on the exact nature
of the prosecutor’s role, in terms of direct or political
appointment, length of appointment, and use of plea bargaining,
among other factors; however, some of this is controlled for by
including a measurement of civil law legal tradition. Mediation
analyses explored (but not reported, see Footnote 1) in this study
were unable to uncover mechanisms of the association. Thus, this
remains a line of inquiry that needs to be further explored and the
results here, though not always reaching statistical signifi-
cance, show that this is a worthy line of inquiry. A different
theoretical lens would likely be useful in this endeavor.
The results for the judicial personnel size implicates one
theoretical lens that could be used and more fully explored in the
future. Judicial personnel size was a significant predictor of
incarceration rate in the personnel-only model; however, this
relationship is negative, the opposite direction than that pre-
dicted by the systems approach. The combined results for the
directions of both the prosecution and judicial workforce size
variables fit better with a crime control vs. due process perspec-
tive. Packer (1968) suggested these are two models of the crimi-
nal process as value systems that compete for primacy in the
operation of criminal justice. The crime control model values
repression of criminal conduct above all other justice system
functions; for this model to operate efficiently, it must produce a
high level of apprehension and conviction, which means it relies
heavily on investigative (police) and prosecutorial personnel
ln
lo g-
tra ns
fo rm
ed v
ar ia
bl e
1 3
(Packer, 1968). The due process model, in contrast, is con- cerned
with the errors that might occur in these investigations and
prosecutions and creates a sort of “obstacle course” for officials
to follow to protect the rights of the accused (Packer, 1968).
Thus, Sung’s (2006) cross-national test study concep- tualized
these models to include different sizes of workforce personnel to
achieve these aims: the crime control model relies on a large
police force, a large prosecutorial staff, a small judi- cial body,
and a large prison system; the due process model encourages the
opposite for each.
It is within this framework that the directionality of both the
prosecutorial personnel and the judiciary size results makes sense.
With judges seen as enforcers of due process, able to protect the
rights of the accused, increasing their size would decrease the
incarcerated population. In contrast, increasing the size of the
prosecutorial workforce would be a function of higher reliance on
the crime control model, and would then increase imprisonment.
However, in this study, this perspec- tive cannot account for why
judicial workforce size became insignificant in later models or why
police size was never a significant predictor of incarceration
rate.
Although police size non-significance cannot be explained by a
focus on crime control vs. due process models, and is inconsistent
with what was expected based on conflict theory or a systems
perspective, these findings are consistent with some discussion and
findings in the literature (D’Amico & William- son, 2019; Kleck
& Barnes, 2014; Ruddell & Thomas, 2009). When looking at
the association between police force size on incarceration, their
role may just be too removed from the final outcome/output, in case
processing terms, to affect ultimate incarceration. This is
supported in the current study by the fact that formal contact
rate, the processing variable for the police, also showed no
relation to incarceration. The number of opera- tional steps in
between these actors and processes might be too large to detect any
effect on the prison populace. Also, some data from the USA and
beyond have shown that police spend a majority of their time on
activities other than those focused on crime management which would
lead to arrests (Dadds & Scheide, 2000; Webster, 1970), and an
especially low percent- age of time on those crimes serious enough
to warrant incar- ceration (Asher and Horwitz, 2020). Future
research should explore these explanations for police size
non-significance in terms of incarceration, cross-nationally if
possible.
Overall, the theoretical perspectives utilized in this research, as
they relate to justice system personnel, were unsupported. There
does appear to be some relationship between prosecu- tion and
judicial personnel size and threat variables, but more research is
needed to parse out the complexities of this. The research also did
not find strong support for a systems approach, although the full
models were limited in their ability to test the processing
variables due to missing data. This being said, the study’s null
findings should not discourage future research from utilizing this
perspective; to the contrary, the results generally
show that there is more that can be done to understand the criminal
justice as a system moving forward.
The current study’s models also provide additional evidence to the
literature regarding the role of other variables proposed to
explain variation in incarceration rates cross-nationally, although
only certain additional independent variables will be addressed due
to space limitations. First, the results provide relatively strong
evidence in favor of threat hypotheses, support- ing theories
relating incarceration use to the social arrangement of nations.
Although not significant in all models, both ethnic
fractionalization and income inequality were significant in the
final model. Their positive association with incarceration sug-
gests that countries with more threat, through either higher pro-
portions of ethnic minorities or unequal income distributions, have
higher incarceration rates, controlling for other factors.
The significance of minority threat is generally in line with
previous research, although the significance of economic threat is
not. Although one study has found a measure of minority threat
significant across models incorporating a variety of other crime,
economic, and political controls (Jacobs & Kle- ban, 2003),
other research has highlighted the more conditional nature of
minority threat as a predictor of incarceration that was also found
in this research (Ruddell, 2005; Ruddell & Urbina, 2004).
However, none of these studies found a significant relationship
between economic threat and incarceration rates cross-nationally in
their main analyses, and other studies have either failed to find a
relation (Neapolitan, 2001; Clark & Her- bolsheimer, 2021) or
this effect diminished when controlling for other factors (Sutton,
2004; Davis & Gibson-Light, 2020). Therefore, this study’s
results on income inequality as a predic- tor, and the strongest
predictor in the model at that, indicate a relative departure from
prior research. This could be due to the specific sample of
countries used in this research, as Ruddell (2005) has shown that
the sample composition matters.
The current research also departs from previous research in the
significance of homicide rates. While a strong predictor when first
introduced, homicide rates lost their significance as other
variables were accounted for and did not reach signifi- cance in
the final model. These results are in contrast to previ- ous
findings, which have shown homicide rates to be important across
models for a variety of country samples and with a mul- titude of
different variables included (Jacobs & Kleban, 2003;
Neapolitan, 2001; Ruddell, 2005; Ruddell & Urbina, 2004;
Sutton, 2000, 2004). However, Ruddell (2005) did find that homicide
rate was a significant predictor for a group of 50 more developed
nations but not for a group of 50 developing nations. The current
research and Ruddell’s (2005) study indicate that the relationship
between homicide and incarceration is not as clear-cut as previous
research has indicated, calling for more nuanced investigations in
the future.
Finally, life imprisonment, included as a new proxy for penal
values, reaches statistical significance in the model controlling
for all other variables except for region, and thus deserve
some
119International Criminology (2021) 1:107–122
1 3
discussion. Countries that allow the option of imposing life
sentences had higher rates of incarceration than those without this
option. Although not reaching statistical significance in the final
model, these results offer some support for the notion that public
sentiments toward punishment (penal values) are relevant to
understanding cross-national differences in incar- ceration use,
suggested by Young and Brown (1993). These findings also suggest
that variation in life imprisonment options should be considered as
another proxy for penal values in future research, especially in
country samples that have little variation in death penalty
use.
Limitations
While this research has presented noteworthy findings, it is not
without its limitations. Limitations related to the data quality
and availability have been discussed in the methods section.
Briefly, as a survey of countries through a national coordinating
officer, there are opportunities for the introduction of error,
with no counteracting quality assurance checks in place (Bennett,
2009). In addition, voluntary response to the UN-CTS could have
created nonresponse bias, and some regions of the world, such as
Africa and Asia, were under-represented in this sample. However,
the hope is that the advantage of being able to study this topic
outweighs these limitations, in addition to prompting increased
accuracy in the data collection and curation process and providing
some direction for future research with more complete data.
Data availability also relates to another limitation of the
regression analyses conducted in this research. One rule of thumb
in terms of sample size is to have a minimum ratio of subjects to
predictors of 5:1 (Tabachnick & Fidell, 1989). Other rules are
even more stringent, such as one requiring at least 10 subjects per
predictor for regression equations using 6 or more predictors
(VanVoorhis & Morgan, 2007). With a sample size of 47 and over
10 predictor variables included in Models 6 and 7, these rules
suggest that these final two models might have an issue with
statistical power. This was one reason for using the hierarchical
linear regression approach; there are fewer concerns with power for
Models 1–5. With the ever-increasing number of factors found to be
related to incarceration rates cross-nationally, future research
should keep these sample size limitations in mind.
Lastly, another limitation of the study due to data availabil- ity
is that the relationships of interest could only be examined
cross-sectionally and not longitudinally. Although data were
examined from 2003 to 2017, because of missing data points, it was
determined that the most feasible way to use the data
on a large number of countries was to compute averages for this
period, rather than try to fill in the missing data and exam- ine
the relationship using nation years (although missing data
imputation was considered). In this regard, the current research
followed the path of many other comparative studies drawing on
available cross-national level data sources. However, there would
be clear value in using more time points to better under- stand the
causal relationship proposed here and study changes in the
relationships of interest over time. With the current data, this
may be an option for a smaller sample of nations in future
research, and it would be interesting to see whether changes in
justice system personnel, especially court personnel, impact
changes in incarceration, cross-nationally.
Conclusion
Despite these limitations, this research provides important
implications for future research and potentially practice. This
research showed that, while potentially more com- plex than linear
regression allows, court personnel size, especially in terms of
prosecutors, has a relationship with incarceration rates amongst a
particular sample of countries. Future research should attempt to
better parse out these relationships, and understand the conditions
under which they function, as this research did not find any
evidence of mediating relationships between threat variables,
prosecu- tion personnel, and case processing variables with
incarcera- tion. These studies may want to use a new theoretical
lens to better clarify how these relationships function.
Ultimately, if additional research confirms these findings and can
better explain the causal relationships, there are implications for
practice. These results could be important for the distribution of
justice around the world, as coun- tries may be unintentionally
increasing or decreasing prison populations due in part to
unrelated hiring practices in other criminal justice agencies, such
as prosecutors’ offices. Jus- tice officials should be cognizant of
any relevant factors that lead to more or less of their
population’s loss of liberty and take steps to limit any
inadvertent under- or over-utilization of incarceration, based on
legitimate punishment goals.
Appendix
1 3
Ta bl
e 4
C or
re la
tio n
m at
1 3
Funding The author received no financial support for the research,
authorship, and/or publication of this article.
Data Availability These data were derived from the following
resources available in the public domain: UNODC (https:// datau
nodc. un. org/); UN Population Division (https:// popul ation. un.
org/ wpp/); Alesina et al. 2002 (https:// www. nber. org/
papers/ w7207. pdf); Ivanyna & Shah, 2014; JuriGlobe (http://
www. jurig lobe. ca/ eng/?i=1); UN Development Pro- gramme (http://
hdr. undp. org/ en/ data); van Zyl Smit & Appleton, 2019; World
Bank (https:// data. world bank. org/ indic ator/ SI. POV. GINI);
and World Prison Brief (https:// www. priso nstud ies. org/).
Declarations
Conflict of interest The author declares that there is no conflict
of in- terest.
References
Alesina, A., Devleeschauwer, A., Easterly, W., Kurlat, S., &
Waczi- arg, R. (2002). Fractionalization. NBER Working Paper
Series, WP9411, 1–70.
Amnesty International. (2020). Countries. Retrieved February 13,
2020, from https:// www. amnes ty. org/ en/ count ries/.
Asher, J., & Horwitz, B. (2020). How do the police spend their
time? You might be surprised. New York Times. https:// www.
nytim es. com/ 2020/ 06/ 19/ upshot/ unrest- police- time- viole
nt- crime. html.
Beenstock, M., & Haitovsky, Y. (2004). Does the appointment of
judges increase the output of the judiciary? International Review
of Law and Economics, 24(3), 351–369.
Bennett, R. R. (2009). Comparative criminological and criminal jus-
tice research and the data that drive them. International Journal
of Comparative and Applied Criminal Justice, 33(2), 171–192.
Bernard, T. J., Paoline, E. A., III., & Pare, P. P. (2005).
General systems theory and criminal justice. Journal of Criminal
Justice, 33(3), 203–211.
Carlton, C. F. (1997). The grinding wheel of justice needs some
grease: Designing the Federal Courts of the 21st century. Kansas
Journal of Law and Public Policy, 6, 1.
Cavadino, M., & Dignan, J. (2006). Penal policy and political
economy. Criminology & Criminal Justice, 6(4), 435–456.
Cavise, L. L. (2007). The transition from the inquisitorial to the
accu- satorial system of trial procedure: Why some Latin American
lawyers hesitate. The Original Law Review, 3(1), 1–27.
Chan, K.-W. (2012). Setting the limits: Who controls the size of
the legal profession in Japan? International Journal of the Legal
Pro- fession, 19(2–3), 321–337.
Clark, R., & Herbolsheimer, C. (2021). The iron cage of
development: A crossnational analysis of incarceration, 2000 –
2015. Socio- logical Forum. Advance online publication.
D’Alessio, S. J., & Stolzenberg, L. (1997). The effect of
available capacity on jail incarceration: An empirical test of
Parkinson’s law. Journal of Criminal Justice, 25(4), 279–288.
D’Amico, D. J., & Williamson, C. (2015). Do legal origins
affect cross-country incarceration rates? Journal of Comparative
Eco- nomics, 43(3), 595–612.
D’Amico, D. J., & Williamson, C. R. (2019). An empirical
examina- tion of institutions and cross-country incarceration
rates. Public Choice, 180(3–4), 217–242.
Dadds, V., & Scheide, T. (2000). Police performance and
activity measurement. Australian Institute of Criminology.
Davis, A. P., & Gibson-Light, M. (2020). Difference and
punishment: Ethno-political exclusion, colonial institutional
legacies, and incarceration. Punishment & Society, 22(1),
3–27.
Feeley, M. M. (1982). Plea bargaining and the structure of the
crimi- nal process. The Justice System Journal, 7(3),
338–354.
Galanter, M. (2011). More lawyers than people. In S. L. Cummings
(Ed.), The paradox of professionalism. (pp. 68–89). SSRN.
Hair, J. F., Jr., Anderson, R. E., Tatham, R. L., & Black, W.
C. (1995). Multivariate data analysis. (3rd ed.). Macmillan.
Hough, J. M., & Walker, N. (1988). Public attitudes to
sentencing: Surveys from five countries. Gower Publishing
Company.
Ivanyna, M., & Shah, A. (2014). How close is your government to
its people? Worldwide indicators on localization and decen-
tralization. Economics: The Open-Access Open-Assessment E-Journal,
8, 1–61.
Jacobs, D., & Kleban, R. (2003). Political institutions,
minorities, and punishment: A pooled cross-national analysis of
imprison- ment rates. Social Forces, 82(2), 725–755.
JuriGlobe. (n.d.). Alphabetical index of the political entities and
cor- responding legal systems. Retrieved February 27, 2020, from
http:// www. jurig lobe. ca/ eng/.
Kent, S. L., & Jacobs, D. (2004). Social divisions and coercive
con- trol in advanced societies: Law enforcement strength in eleven
nations from 1975 to 1994. Social Problems, 51(3), 343.
Killias, M. (1986). Power concentration, legitimation crisis and
penal severity. In W. B. Groves & G. Newman (Eds.), Punishment
and privilege. (pp. 95–117). Harrow & Heston.
Kleck, G., & Barnes, J. C. (2014). Do more police lead to more
crime deterrence? Crime & Delinquency, 60(5), 716–738.
Kraska, P. B. (2004). Theorizing criminal justice: Eight essential
orientations. Waveland Press.
Lappi-Seppälä, T. (2011). Explaining imprisonment in Europe. Euro-
pean Journal of Criminology, 8(4), 303–328.
Levitt, S. D., & Miles, T. J. (2006). Economic contributions to
the understanding of crime. The Annual Review of Law and Social
Science, 2, 147–164.
Liska, A. E., Lawrence, J. J., & Benson, M. (1981).
Perspectives on the legal order: The capacity for social control.
American Journal of Sociology, 87(2), 413–426.
Lowatcharin, G., & Stallmann, J. I. (2019). The differential
effects of decentralization on police intensity: A cross-national
compari- son. The Social Science Journal, 56(2), 196–207.
Maguire, E. R., Howard, G. J., & Newman, G. (1998). Measuring
the performance of national criminal justice systems. Interna-
tional Journal of Comparative and Applied Criminal Justice, 22(1),
31–59.
Mauer, M. (2003). Comparative international rates of incarceration:
An examination of causes and trends presented to the US Com-
mission on Civil Rights. The Sentencing Project, 1, 1–16.
Neapolitan, J. L. (2001). An examination of cross-national
variation in punitiveness. International Journal of Offender
Therapy and Comparative Criminology, 45(6), 691–710.
Newman, G., & DiCristina, B. (2009). Data set of the 1st and
2nd United Nations World Crime Surveys. 1–11.
Packer, H. (1968). The limits of the criminal sanction. Stanford
Uni- versity Press.
Pfaff, J. (2017). Locked in: The true causes of mass incarceration-
and how to achieve real reform. Basic Books.
Ruddell, R. (2005). Social disruption, state priorities, and
minority threat: A cross-national study of imprisonment. Punishment
& Society, 7(1), 7–28.
Ruddell, R., & Thomas, M. O. (2009). Does politics matter?
Cross- national correlates of police strength. Policing: An
International Journal of Police Strategies & Management, 32(4),
654–674.
Ruddell, R., & Urbina, M. G. (2004). Minority threat and
punishment: A cross-national analysis. Justice Quarterly, 21(4),
903–931.
1 3
Solomon, P. H., Jr. (2012). Plea bargaining Russian style.
Demokrati- zatsiya, 20(3), 282–300.
Sung, H. E. (2006). Democracy and criminal justice in
cross-national perspective: From crime control to due process. The
Annals of the American Academy of Political and Social Science,
605(1), 311–337.
Sutton, J. R. (2000). Imprisonment and social classification in
five common-law democracies, 1955–1985. American Journal of Soci-
ology, 106(2), 350–386.
Sutton, J. R. (2004). The political economy of imprisonment in
afflu- ent western democracies. American Sociological Review, 69,
170–189.
Tabachnick, B. G., & Fidell, L. S. (1989). Using multivariate
statis- tics. (2nd ed.). Harper & Row.
Underhill, K. (2016). Way less than you need to know about the
civil- and common-law systems. TYL, 20(4), 8–9.
United Nations Development Programme (n.d.). Human development data
(1990–2018). Retrieved February 26, 2020, from http:// hdr. undp.
org/ en/ data.
United Nations Office at Vienna. Crime Prevention and Criminal Jus-
tice Branch. (2010). United Nations Surveys of Crime Trends and
Operations of Criminal Justice Systems Series, Waves 1–10,
1970–2006. Inter-university Consortium for Political and Social
Research [distributor]
United Nations Office on Drugs and Crime. (2018). United Nations
Survey of Crime Trends and Operations of Criminal Justice Sys- tems
(UN-CTS)—2018 [Questionnaire]. Retrieved May 11, 2020, from
https:// www. unodc. org/ unodc/ en/ data- and- analy sis/ stati
stics/ crime/ cts- data- colle ction. html.
United Nations Office on Drugs and Crime. (n.d.). Crime data.
Retrieved February 12, 2020, from https:// datau nodc. un. org/
crime.
United Nations Population Division of the Department of Economic
and Social Affairs. (2019a). 2019 Revision of World
Population
Prospects. Retrieved February 24, 2020, from https:// popul ation.
un. org/ wpp/.
United Nations Population Division of the Department of Eco- nomic
and Social Affairs. (2019b). International migrant stock
2019. Retrieved August 24, 2020, from https:// www. un.
org/ en/ devel opment/ desa/ popul ation/ migra tion/ data/ estim
ates2/ estim ates19. asp.
Van Gigch, J. P. (1978). Applied general systems theory. (2nd ed.).
Harper and Row Publishers.
van Zyl, S. D., & Appleton, C. (2019). Life imprisonment: A
global human rights analysis. Harvard University Press.
VanVoorhis, C. W., & Morgan, B. L. (2007). Understanding power
and rules of thumb for determining sample sizes. Tutorials in Quan-
titative Methods for Psychology, 3(2), 43–50.
Vîlcic, E. R., Belenko, S., Hiller, M., & Taxman, F. (2010).
Exporting court innovation from the United States to Continental
Europe: Compatibility between the drug court model and
inquisitorial jus- tice systems. International Journal of
Comparative and Applied Criminal Justice, 34(1), 139–172.
Walmsley, R. (2003). World prison population list. (pp. 1–6). Home
Office.
Webster, J. A. (1970). Police task and time study. Journal
of Crimi- nal Law, Criminology and Police
Science, 61, 94–100.
Wills, E. (2017). The roles of judges and of judge-made law in
English common law and the civil law family of legal systems.
Anglo- German Law Journal, 3, 114–128.
World Bank (n.d.). GINI index (World Bank estimate). Retrieved
Febru- ary 27, 2020, from https:// data. world bank. org/ indic
ator/ SI. POV. GINI.
World Prison Brief, Institute for Crime & Justice Policy
Research. (2020). World prison brief data. Retrieved February 27,
2020, from https:// www. priso nstud ies. org/ world- prison-
brief- data.
Young, W., & Brown, M. (1993). Cross-national comparisons of
imprisonment. Crime and Justice, 17, 1–49.
Abstract
Introduction
Traditional Explanations for Incarceration Rates
The Current Research