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 alyssa.mendlein@temple.edu
1 Department of Criminal Justice, Temple University, Philadelphia, PA, USA
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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
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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
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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
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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).
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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.
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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
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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
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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
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(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
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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.
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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.
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Abstract
Introduction
Traditional Explanations for Incarceration Rates
The Current Research