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http://hsx.sagepub.com/content/15/2/103The online version of this article can be found at:

DOI: 10.1177/1088767911406397

2011 15: 103 originally published online 27 April 2011Homicide Studies Amy E. Nivette

Cross-National Predictors of Crime: A Meta-Analysis

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1University of Cambridge, Cambridge, UK

Corresponding Author:Amy E. Nivette, Institute of Criminology, University of Cambridge,Sidgwick Avenue, Cambridge CB3 9DA, UKEmail: [email protected]

Cross-National Predictorsof Crime: A Meta-Analysis

Amy E. Nivette 1

AbstractCross-national research has increased in the past few decades, resulting in a large bodyof empirical research. In particular, cross-national studies are often limited in datasources, which restrict variable selection to debatable proxy indicators. This studytherefore uses meta-analytic techniques to examine major cross-national predictorsof homicide to determine strengths and weaknesses in theory and design. Thefindings indicate several critical limitations to cross-national research, including biasedsample composition, a lack of theoretical clarity in predictor operationalizations, andan overwhelming reliance on cross-sectional design. The predictors that showedthe strongest mean effects were Latin American regional dummy variables, incomeinequality indicators and the Decommodification Index. Conversely, static populationindicators, democracy indices, and measures of economic development had the weakesteffects on homicide.

Keywordsmeta-analysis, homicide, cross-national, predictors

Introduction

In recent decades, a large body of research has emerged to explain variations in levelsof homicide across nations (Marshall, Marshall, & Ren, 2009; Stamatel, 2009a; VanDijk, 2008). These explanations have been primarily drawn from the Durkheimian-Modernization theoretical tradition, which suggests rapid societal and structuralchange to be the primary source of deviance within a society (Kick & LaFree, 1985;LaFree, 1999). Cross-national literature consequently reflects this long-standing influencethrough studies that focus on examining correlations between homicide and population

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104 Homicide Studies 15(2)

composition, economic conditions, and levels of deprivation. Despite the pervasiveinfluence of the Durkheimian-Modernization perspective in cross-national research,evidence concerning this and other theories remains unclear.

Certain soft concepts such as trust and cultural values remain scarcely tested atthe international level. Soft, or sociocultural, predictors are measures of attitudesand perceptions, norms and values, or religious beliefs that are often considered medi-ators of structural variables in neighborhood-level studies (Sampson, 2006a). Althoughthe development of worldwide sociocultural data sets (e.g., the World Values Survey)have led to more direct testing of these concepts, their impact remains unknown. Asmuch of the focus in cross-national research concentrates on structural covariates,assessing the predictive potential of these sociocultural concepts can encourage newavenues of cross-national research.

As yet, no study has attempted to statistically analyze the status of cross-nationalempirical research, which is somewhat surprising due to the proclaimed importance ofthe comparative perspective for theoretical development (Bennett, 2004; Braithwaite,1989; Evans, LaGrange, & Willis, 1996). There are a few narrative research syntheses(LaFree, 1999; Pridemore, 2002; Pridemore & Trent, 2010; Stamatel, 2006), but onlytwo studies have attempted to statistically assess the aggregate effect of macro-levelindicators and theories (see Hsieh & Pugh, 1993; Pratt & Cullen, 2005). Even so, theserely heavily on U.S.-based studies, with only a handful of effect sizes drawn fromstudies using a non-U.S. sample, and even fewer from the cross-national level. For

instance, Pratt and Cullen (2005) included 12 studies from the cross-national level outof 214, whereas Hsieh and Pugh (1993) included only 10 out of 34. Furthermore, themost recent meta-analysis (Pratt & Cullen, 2005) only includes studies up to the year1999. However, many new studies have emerged since, reflecting the recent momen-tum in cross-national interest that incorporates improvements and expansions in datasources, analytic techniques, and theoretical perspectives (Marshall & Block, 2004;Van Dijk, 2008).

Although most studies at this level focus on structural influences drawn from theDurkheimian-Modernization perspective, many different theories have been applied

to cross-national data (e.g., strain, social disorganization, democratization, and routineactivity theories). The majority of studies focus on homicide since it is considered themost reliable crime indicator in cross-national research (Neapolitan, 1997), although ahandful of studies examine other types of crime. Despite the known reliability ofhomicide data, slight differences in data-collection techniques and definitions betweeninternational data sources may affect the results. Thus, it is necessary to organize theseresearch findings so that clear information regarding the size and direction of commoneffects can guide a more efficient research design. A meta-analysis can assess the rela-tive empirical impact and robustness of variables associated with these theories. The

current study therefore has three main objectives: (a) assess the mean effect sizes forcross-national predictors of homicide, (b) determine the impact of methodologicalvariations on study outcomes, and (c) evaluate the relative strength of sociocultural ver-sus structural predictors.

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The outcome variable was restricted to homicide due to the lack of validity in allother categories of international crime data (Neapolitan, 1997). Cross-national researchis highly conditional on the category of the crime statistics used due to cross-cultural

and operational differences in definitions and reporting (Marshall & Block, 2004; VanDijk, 2008). Homicide data are generally considered to be the most reliable by comparative criminologists as a victims body is most likely to come to the attention ofofficials, regardless of reporting trends (Neapolitan, 1997). However, while focusingon homicide may aid cross-cultural comparability, this restricts the interpretation andgeneralization of results.

Relevant quantitative studies that fell within the time frame 1960-2010 were searchedfor using the keywords cross-national or cross-cultural and crime , violence , murder , orhomicide . The search proceeded in three parts. First, major criminological journals were

canvassed issue by issue, especially those with a comparative and international focus.1

Second, scholarly electronic databases were searched using the keywords stated above(Criminal Justice Abstracts, National Criminal Justice Reference Service, SociologicalAbstracts, Worldwide Political Science Abstracts, Web of Knowledge, and GoogleScholar). In addition, international organizations publication databases were searchedto incorporate research outside journal publications (World Health Organization[WHO] Library, United Nations Office on Drugs and Crime [UNODC], and EuropeanInstitute for Crime Prevention and Control [HEUNI]). Third, previous syntheses werescanned for cross-national studies that may have been missed. A total of 54 studies were

included (see appendix for a complete list), resulting in 316 effect sizes.

Effect Size EstimateTo precisely gauge how much a certain predictor influences the outcome, the effectsize, or the measure of the strength . . . and direction of a relationship between vari-ables (Littell, Corcoran, & Pillai, 2008, p. 80) must be considered. These effect sizes,when combined, can reveal the overall magnitude of like predictors. The measure ofrelationship strength used here is the standardized correlation coefficient r . The r index

is used because it is most commonly employed in analyses concerning two continuousvariables and easily interpreted (Pratt & Cullen, 2005; Wilson, 2001). In correla-tional research, results are presented as either zero-order correlations or standardizedregression coefficients (). 2 The latter is favored in the current analyses because-coefficients can be considered a more accurate representation of the relationship,whereas bivariate correlations may be inflated due to failure to account for exogenousinfluences. Thus, with the caveat that effect size must be considered in context withother predictors, mean effect sizes can be safely combined (Pratt & Cullen, 2005).

Fixed Versus Random EffectsThere are two models available in meta-analysis: the fixed effects and random effectsmodel. The former rests on the assumption that there is one true effect size within the



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population, and all differences between effects can be accounted for by sampling erroralone (Cooper, 2010; Littell et al., 2008). In random effects models, variations withinand between studies are thought to differ according to model specification, concurrent

variables, or exogenous factors (Littell et al., 2008). Due to the nature of the effectsizes preferred in this meta-analysis (-coefficients), the random effects model isideal. That is to say, as is an effect size controlling for all other included factors, therandom effects model can appropriately account for any variations occurring due todifferences in the regression model specification.

IndependenceIn many cases, research findings present more than one effect size. Multiple models

are often constructed to test for various moderating factors on one or more dependentvariables. Including more than one effect size drawn from the same sample threatensassumptions of independence in meta-analytic procedure (Littell et al., 2008; Wilson,2001). In cross-national research in particular, this assumption is in danger due to theoverreliance on convenience sampling (see Stamatel, 2006), which leads to similarsample composition across studies. These overlapping samples create the potential fora lack of statistical independence in both within- and between-study effect size esti-mates. However, as almost all studies include Western societies while very few man-age to include Islamic or African nations, most studies will have a sample bias in the

same direction (i.e., toward developed rather than less developed societies). This biascannot be easily overcome, yet it remains important to conduct a meta-analysis to geta comprehensive picture of cross-national knowledge. Therefore, one must proceedwith the added caveat that the results may be limited to developed countries.

There are several options to account for potential bias stemming from dependence:to average the effect sizes for each predictor within each study to produce an overallstudy estimate (Wilson, 2001), to choose only one effect size per study or, when mul-tiple predictors are assessed against one dependent variable (e.g., income inequalityand urbanization on homicide), to assess each predictor separately (Littell et al., 2008).

The first and third options are preferable because, although averaging effect sizes andanalyzing separately somewhat reduce the power of the statistical procedures, theoption to select only one effect size may introduce unnecessary researcher bias whendeciding which effect size within a study to choose. Thus in the present analysis, anaverage effect size was calculated per study for each predictor, then pooled and ana-lyzed against methodological variations separately.

Heterogeneity of Effect Sizes

Pooling effect sizes must be done with careful consideration to both underlying con-ceptual and theoretical differences between studies as well as statistical heterogeneity(Cooper, 2010; Littell et al., 2008). To ensure against this issue, only measures usedto operationalize the same concept are grouped together (e.g., gross domestic product



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[GDP], gross national product [GNP], and average income were grouped underEconomic Development), and a coefficient is calculated to test for extreme differ-ences between grouped indicators using the meta-analytic analogue to the ANOVA

(Q) Borenstein, Hedges, Higgins, & Rothstein, 2009; Wilson, 2002). Any predictors thatdemonstrate significant statistical heterogeneity are analyzed separately (e.g., ethnicheterogeneity and ethnic homogeneity).

Predictor DomainsDue to considerable limitations to the availability and reliability of data, cross-nationalresearch suffers from a rather modest development of theory (LaFree, 1999).However, 11 perspectives were identified for exploration in this meta-analysis, from

which 30 separate predictors emerged. As a general rule, any variable with two ormore contributing independent effect sizes are included in the analysis (Littell et al.,2008). This unfortunately led to the exclusion of several interesting predictors due tounique operationalizations or a simple lack of contributing studies. What follows is a

brief overview of each theory, its primary predictor domains, and operationalizations. 3

Absolute DeprivationA link between area poverty and homicide is a consistent finding in most crimino-

logical research (Pratt & Cullen, 2005; Pridemore, 2002, 2008) stemming from theidea that resource deprivation causes frustration, which ultimately may lead to aggres-sion (Hsieh & Pugh, 1993). The pursuit of the povertycrime link is common in U.S.literature but relatively lacking in cross-national research. Indeed, Pridemore (2008)argues that the substantial evidence behind this association in the American contextdemands the inclusion of poverty in comparative explanations and models. Povertyindicators are not unknown to cross-national research but are often expressed by argu-ably theoretically ineffectual proxies such as GDP (Pridemore, 2008). Pridemore insteadadvocates the more appropriate and conceptually encompassing proxy of Infant Mortality,

which is used here.4

Relative DeprivationThe theory of relative deprivation stems from a Mertonian anomie perspective, in that

blocked opportunities to achieving prescribed cultural goals cause individual frustra-tion and aggression, which may lead to homicide (Chamlin & Cochran, 2006; LaFree,1999). This societal imbalance is most often conceptualized by disparities in the dis-tribution of wealth. Expressed in ratios or indices, the higher proportion of wealth

held by the top deciles compared with the bottom theoretically indicates a greater potential for feelings of relative deprivation. Due to significant statistical heterogene-ity, three predictors emerged for analysis: Economic Discrimination of Minorities,Income Inequality Measured by Ratios, and Income Inequality Measured by Indices(including the Gini Index).



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Modernization/Development

The Durkheimian-Modernization perspective is the earliest and consequentially most

common theoretical avenue explored in cross-national literature (LaFree, 1999). Althoughtechnically encompassing a wider range of theories including anomie and social disor-ganization, the Durkheimian-Modernization perspective aims to explain the increasein crime through changing social patterns, which have as a result disrupted traditionalmethods of social control (Hartnagel & Mizanuddin, 1986; LaFree, 1999; Shichor,1990). The shift from agricultural to industrial labor, advances in technology and com-munication, increases in access to education, and breakdowns in traditional commu-nity structure are considered evidence of modernization (Austin & Kim, 1999; Howard& Smith, 2003). Development is thought to adversely affect crime rates in a similar

fashion to modernization (Bennett, 1991), yet researchers have noted that the associa-tion between measures of modernization/development and homicide appears to benegative rather than positive (DiCristina, 2004; Kick & LaFree, 1985). Because of this,scholars have turned to more opportunity-based interpretations to explain this relation-ship: modernization works to increase the level of available goods and motivatedoffenders for theft, while simultaneously breaking down interpersonal ties that conse-quently diminish interpersonal violence (Kick & LaFree, 1985).

In practice, modernization and development predictors are dispersed across manydifferent theories, factored into indices, and variously operationalized. Thus the group-

ing done here focuses on what underlying concept the predictor is trying to capture.The Modernization predictor included three operationalizations: newspapers per1,000, energy consumption per capita, and an index created by Howard and Smith(2003). Predictors encompassing access to education were Adult Literacy and FemaleHigher Education Enrolment. Development was subdivided into two separate predic-tors: economic and human. Economic Development combined measures of GDP percapita, GNP per capita, and average income per capita. Human Development measuresranged from factored indices created by the author to the use of the Human DevelopmentIndex drawn from the United Nations.

Social DisorganizationSocial disorganization theory follows much of the same line of thought as Durkheimian-Modernization perspectives and therefore will only be discussed briefly. Social changesthat alter the family and community structure are thought to affect the efficiency ofinformal controls that previously prevented deviant behavior (Shaw & McKay, 1942).Population heterogeneity and household disruption are seen as the primary sources ofdiscord in maintaining networks of control, leading to higher homicide rates (Gartner,

1990). Two conceptually distinct predictors emerged for social disorganization: house-hold disruption as measured by the Divorce Rate and ethnic composition measured interms of Ethnic Heterogeneity and Ethnic Homogeneity. Jensens (2001) multiculturaland Antonaccio and Tittles (2007) population heterogeneity variables were combinedinto the ethnic heterogeneity predictor.



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Institutional Anomie Theory

Drawing on Esping-Andersons concept of decommodification and Mertons strain

theory, Messner and Rosenfeld (1997) devised institutional anomie theory to explainthe interrelated effects of social and economic structures on crime. Simplisticallyspeaking, it follows the Marxist line of thought that the dominance of economic institu-tions creates a harmful environment for the populace, leading to crime. In combatingthis harm, the welfare state provides services, rights, and safety from the anomic pres-sures of a market-dominant, goal-oriented society (Altheimer, 2008; Bjerregaard &Cochran, 2008; Messner & Rosenfeld, 1997). Thus the higher the levels of decom-modificationor protection from the severity of the market, whether through policy orfamilythe lower the potential for crime. The primary predictor used to measure insti-

tutional anomie theory is the Decommodification Index, although several studies alsoemploy measures of welfare expenditures. The Decommodification Index is created bycombining measures of social welfare expenditures as a percentage of GDP, averageannual benefits per capita, and the percentage of benefits given to employment injuries(Altheimer, 2008, p. 107). Straightforward measures of welfare expenditures, however,will be grouped under social support theory. In addition, Bjerregaard and Cochran(2008) opted to measure the strength of economic institutions with the EconomicFreedom Index and therefore could not be appropriately included in the analysis.

Social SupportInterrelated with institutional anomie theory, the social support (or altruism, seeChamlin & Cochran, 1997) perspective aims to explain homicide based on levels oflocal and government welfare provisions. This can be in the form of social security,health care, a community program, or family bonds, as they all commonly promote anenvironment of mutual trust and reciprocity that effectively prevents crime (Colvin,Cullen, & Ven, 2002). At the national level, social support is expressed as governmentwelfare expenditures, although emerging research investigating levels of generalized

trust and volunteerism (e.g., Halpern, 2001) may also be construed as indicators ofinformal support. Thus the predictors comprising Social Support are percentage GNPspent on welfare, health care, or education. Effect sizes within studies that includedmore than one of these variables were averaged.

Routine Activity Situational perspectives encompass routine activity theory, claiming that changes

brought by modernization have altered population patterns, effectively increasing the

opportunity for crime (Cohen & Felson, 1979; LaFree, 1999). The theoretical con-cepts of routine activity theory are interpersonal contacts, opportunity/access togoods, pool of motivated offenders, and guardianship. Essentially, changing structuralfactors (e.g., population growth, urbanization) bring together both potential offenders



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(e.g., youth population) and potential targets (e.g., industrial goods) in unsupervisedsituations (due to female labor force participation), which can lead to acts of crime.Like modernization and development theories, the predictors used for these concepts

tend to overlap with other perspectives. Consequently, three general predictors weredrawn from the pool of studies: Persons per Household for interpersonal contacts,Female Labor Participation for guardianship, and Unemployment for motivatedoffenders.

DeterrenceBased on the assumption that offenders make rational decisions to commit a crime byweighing potential consequences and benefits, deterrence theory posits that the more

the criminal justice system activity, the lower the crime (Pratt, Cullen, Blevins, Daigle,& Madensen, 2006). In other words, the more visible the potential costs to commit-ting crime (i.e., the death penalty, high clearance rates, longer sentencing), the lesslikely an individual would be to act. Although extensively studied on the macro levelwithin the United States, deterrence theory is somewhat rare in cross-nationalresearch, producing only six effect sizes from studies, of which the majority are includedas secondary or control variables. The Deterrence predictor was formed combiningthree measures of criminal justice system activity: the number of police personnel,clearance rate, and a death penalty dummy variable.

Political StructurePolitical structure may influence homicide in a variety of ways. For instance, one

perspective places democracy alongside modernization forces that work to diffusesocial control and discipline among the populace (Lin, 2007). Alternatively, oppres-sive characteristics of a nondemocratic political regime may affect levels of homicide

by exposing the population to officially sanctioned violence (Stamatel, 2009b). Bothof these share the basic hypothesis, however, that democracy or democratic values are

inversely related to violence. Studies that seek to explain cross-national homicidefrom a political perspective tend to focus on levels of Democracy or Political Rights,as measured by indices. The political rights predictor consists of a combination of theCivil Liberties Index and Political Rights Index produced by Freedom House. In stud-ies where both indices were included as independent variables, these values wereaveraged to create a single effect size.

Culture

The concept of culture is captured in many ways in cross-national research. Someauthors indicate unique cultural differences through the inclusion of a regionaldummy variable. For example, Neapolitan (1994) included a dummy variable forLatin American nations in his analysis to determine if simply being a Latin American



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nation can predict homicide rates. He argues that Latin American nations maintaincertain cultural values (e.g., machismo ) that favor the use of violence. The interpreta-tion of these results consequently hinges on controlling for important structural factors,

but the dummy ultimately represents certain characteristics unmeasured by traditionalvariables. In this sense, regional dummy variables are very important in attempting toencapsulate often difficult-to-operationalize cultural predictors (Pridemore, 2002).The regional dummies included in this analysis are Latin American and Caribbean

Nations, East Asian Nations (e.g., Japan), and Western Nations (e.g., the United States,the United Kingdom). Only one study (Austin & Kim, 1999) considered an Africanregion variable and therefore could not be included.

Several interesting predictors were unfortunately excluded for their singularity:Halperns (2001) measure of moral values, Pampel and Gartners (1995) Collectivism

Scale, Lederman, Loayza, and Menendezs (2002) measurement of religiosity, Jensens(2001) religious fundamentalism variable, and McAlisters (2006) aggregated mea-surement of attitudes toward violence. 5 Although it is not possible to draw conclusionsabout the significance of these predictors on a meta-analytic level, the more generalquestion concerning structural versus sociocultural indicators can address whetherthese areas are worth further pursuit in cross-national research.

Demographic Predictors

Demographic predictors are most commonly included in cross-national research ascontrol variables, implying that each of these factors plays a significant enough rolein influencing homicide rates that they cannot be ignored in the model. Theoretically,demographic variables are generally claimed as predictors for social disorganization(e.g., urbanization, youth population, ethnic heterogeneity), routine activity (e.g., youth

population, population density), or modernization (e.g., population growth and density,urbanization). However, many studies include demographic controls merely based onthe fact that previous studies have done the same. Often ignored in results sections infavor of the target variables, the question remains as to exactly what effect these demo-

graphic predictors have on homicide.The predictors gathered considered population characteristics, youth population proportion, sex ratio, and urbanization. The population characteristics were subdi-vided into population growth, population density, and population total. Due to signifi-cant statistical heterogeneity, the proportion of youth population (i.e., age structure)was divided into two predictors: age < 15 and age 15 and above. Sex Ratio is measured

by the ratio of males to females and measures of Urbanism by the percentage of peopleliving in urban areas.

Methodological VariationsIt is important to code for variations in design, data source, and measurement toaccount for possible methodological influences in the outcome variable. These coded



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variations can then be analyzed as moderator variables when conducting theANOVA analogue and meta-regression (Cooper, 2010; Littell et al., 2008). In somecases, an inadequate number of effect sizes prevented analyses of moderator vari-

ables, and in these instances the effect sizes are merely ranked according to strength.Drawing from Stamatels (2006) criticisms of cross-national research concerning theconsiderable restrictions imposed by outdated data (time) and limited sample size(space), this study specifically aims to assess the relative impact of these varia-tions on resulting effect sizes. In addition, certain categorical variables such as datasource and a quality indicator are assessed to determine the robustness of the meaneffect sizes.

TimeA researchers choice of model and design can affect how one is to interpret theirresults. In particular, time specifications, such as cross-sectional versus longitudinaldesigns, reflect the assumptions made about the relationships between predictors andcrime. In cross-national research, an overwhelming majority of studies are specified ascross-sectional or pooled cross-sectional time series due to the assumption that timedoes not matter (Stamatel, 2006, p. 190). Dugan (2010) argues that this assumptionignores the inherent dynamic quality of crime data, effectively confound[ing] thecause with the effect (p. 741). Unfortunately, very few cross-national studies incorpo-

rated time-series analyses, making any categorical comparisons difficult. In light ofthis, time was instead conceptualized in terms of the year from which homicide datawere drawn. This time artifact may signify an important meaning between certain pre-dictors and a particular period in time. Data drawn from a certain year (or across years)are accompanied by a corresponding array of possible political, social, or culturalevents for each nation, which may or may not be reflected in the target predictor vari-ables. In other words, studies using older data may have time-specific contextual dif-ferences hitherto unknown before placed alongside more recent findings.

For the meta-regression analysis, the year from which the homicide data were

drawn was coded as a continuous variable for each study. Cross-national researchroutinely computes homicide averages over periods of time to avoid fluctuations inreported rates. In these cases, the latest year included in the average was recorded.

Sample Size and SelectionSample size is a critical issue in much cross-national research. The lack of availabledata for both crime and predictor statistics often severely limits the sample composi-tion to primarily industrialized, highly developed nations (Stamatel, 2006; Marshall

et al., 2009). This limitation may affect the outcome in both a statistical and contex-tual manner. In a statistical sense, small samples may lack the power and precision tocorrectly detect an effect (Britt & Weisburd, 2010), which can partially be solved byconducting a meta-analysis (Hunter & Schmidt, 2004). Moreover, examining the



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relationship between sample size and effect size through meta-regression can illumi-nate exactly how statistical power affects the outcome.

Alternatively, implicit in the sample size is the distribution of nations, whereby the

larger the sample, the more diverse the range of countries and contexts. This meaningof sample size attends to the idea that outcomes may be significantly biased by cross-national criminologys reliance on convenience sampling, consequently producing arather narrow picture of global crime (Stamatel, 2009a). One of the fundamental questsin comparative criminology is the search for universal relationships, trends, or mean-ings among numerous cultural contexts (Karstedt, 2001). Therefore, the samplingselection acts as a key ingredient to the testing of criminological theory: significantresults that hold over a large distribution of contexts add strength to that perspective.Conversely, if a predictors effect is significantly reduced by an increase in sample,

this might signify contextual differences held by nations that may alter the roles andmeanings of the targeted theoretical concept.

Categorical Variables: Data Source and Quality There are several sources of international crime statistics: the Comparative CrimeData File [CCDF], the United Nations, WHO, the International Criminal and PoliceOrganization [Interpol], HEUNI, and the European Sourcebook. The strengths andlimitations of these data sources are reported extensively elsewhere (see Howard &

Smith, 2003; Marshall & Block, 2004; Marshall et al., 2009; Van Dijk, 2008) andtherefore are only briefly discussed here, but it is important to note that WHO homi-cide statistics are considered to be the most reliable (Neapolitan, 1997). Internationaldata sources have been criticized for several reasons, namely, the variation in interna-tional definitions of intentional and unintentional homicide, the ambiguity pertainingto the inclusion or exclusion or attempted homicides in overall data, and the potentialfor official misclassification of violent deaths (Marshall & Block, 2004). The use ofdifferent and potentially unreliable data sources may significantly compromise theresults, producing misleading conclusions. Therefore, each study will be coded accord-

ing to the source of data used: CCDF, United Nations, WHO, Interpol, Other (includ-ing HEUNI and European Sourcebook). In cases where data are drawn from multiplesources, these will be coded as mixed.

To account for potential differences in quality, each report was coded by whether itwas peer-reviewed. Analyzing the differences between these categories can helpassess the file-drawer problem, which is the tendency of journals to favor publishingsignificant over nonsignificant results (Rosenthal, 1979).

Statistical AnalysisFollowing standard meta-analytical procedure, effect sizes drawn from studies aretransformed into z (r ) values using Fishers r -to- z transformation and pooled into meaneffect size estimates (Littell et al., 2008; Pratt & Cullen, 2005; Wilson, 2001). This



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process normalizes the sampling distribution of r , which is skewed for all nonzerovalues. Using the random effects procedures, the estimates are then weighted by theirinverse variance (Borenstein et al., 2009). Diagnostic tests are also conducted to test

for heterogeneity by using the ANOVA analogue ( Q) to detect significant differences between predictor subcategories. 6 Any extreme effect size values are divided intoseparate predictors to minimize bias. In addition, a coefficient of heterogeneity ( Q) iscomputed to examine the distribution of effects around the mean (Borenstein et al.,2009). A heterogeneity coefficient can indicate how widely individual effect sizesvary for a given predictor. The resulting mean effect size estimates are then ranked byoverall strength. Using Wilsons (2002) macros for SPSS, the meta-analytic analogueto the ANOVA is performed on selected categorical variables to detect significantdifferences in effect sizes between the data sources used as well as whether the study

was peer reviewed. Generalized least squares meta-regression is used for analyzinginfluences of time and sample size on effect sizes. Analyses are conducted separatelyto maintain independence among effect size estimates.

ResultsThe results are presented in the following order: Information about the sample of stud-ies is described, mean effect sizes and confidence intervals organized by theory are

presented (Table 1) and then rank-ordered for clarity (Table 2), categorical influences

in design are investigated (Table 3), and finally meta-regression results are consideredin relation to time and sample size (Table 4).

Sample CharacteristicsThe number of countries included in the sampled studies ranged from 11 to 100, withan average of 44 and a mode of 44 nations. The most frequently tested indicators weremeasures of economic development ( n = 37), followed by income inequality indices(n = 31) and urbanism ( n = 27). Predictors with the fewest contributing effect sizes

are the cultural region variables ( n = 2 each), literacy rates ( n = 2), female educationindicators ( n = 3), household size ( n = 3), unemployment ( n = 4), and theDecommodification Index ( n = 4). Overall, demographic, relative deprivation, anddevelopment perspectives are most often the subject of cross-national inquiry,whereas cultural region indicators, institutional anomie theory, deterrence, and politi-cal perspectives are the most infrequent.

Strength of Effects

Table 1 presents the mean effect sizes, contributing study sample, and 95% confi-dence intervals for each predictor. Table 1 shows that there remains a need for researchin many theoretical areas, as the majority of predictors have less than 10 contributingeffect sizes. Consequently, this adds tentativeness to interpretation.



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Table 1. Mean Effect Sizes () by Predictor Domain

Predictordomain Predictor Study N Mr CIL CIU Q

Absolutedeprivation

Infant mortality 8 .196* .019 .362 27.406***

Institutionalanomietheory

Decommodification 4 .279** .415 .131 2.453

Culture West 2 .108 .302 .094 0.573East 2 .326*** .484 .149 1.053Latin America 2 .445*** .255 .602 1.654

Demographic Urbanism 27 .103 .049 .028 24.023***Population density 12 .012 .097 .074 6.104

Population growth 9 .251** .080 .407 24.321Population total 16 .024 .101 .053 22.569Sex ratio 15 .095 .243 .058 60.413***Age > 15 17 .116*** .054 .177 23.509Age < 15 8 .100 .227 .030 2.360

Deterrence 6 .072 .018 .160 5.393Relative

deprivationIncome inequality

(ratio)13 .416*** .339 .488 4.605

Income inequality(index)

31 .224*** .149 .297 80.626***

Economicdiscrimination

6 .136 .066 .328 15.282***

Development Index 14 .163** .274 .047 28.124***Economic

development37 .055 .137 .028 182.662***

Modernization 9 .173 .344 .009 33.955***Female education 3 .110 .457 .614 20.650***Literacy 2 .219 .218 .583 2.354

Politicalstructure

Democracy indices 6 .012 .366 .346 153.777***Political rights 5 .088 .230 .056 4.520

Routine

activity

Household size 3 .166 .317 .581 20.217***

Female labor 13 .223* .027 .402 85.509***Unemployment 4 .043 .042 .128 5.175

Socialdisorganization

Ethnicheterogeneity

12 .163** .046 .275 19.600

Ethnic homogeneity 5 .247* .423 .052 13.750Divorce 10 .277*** .157 .389 2.261

Social support 15 .072 .164 .020 29.785***

Note: Mr = mean effect size correlation coefficient after weighting and transformations; CI

L = 95% confidence interval

lower limit; CIU = 95% confidence interval upper limit; Q = heterogeneity coefficient indicating distribution of effects

around the mean (Borenstein, Hedges, Higgins, & Rothstein, 2009). All analyses were performed on Fishers z values andsubsequently transformed back to r for presentation.*p < .05. **p < .01. ***p < .001.



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Table 2. Rank-Ordered Mean Effect Sizes

Rank Predictor M r Rank Predictor M r

1 Latin American nation .445* 16 Economic discrimination .136 2 Income inequality (ratio) .416* 17 Age structure (age > 15) .116* 3 Eastern nation .326* 18 Female education (%) .110 4 Decommodification

Index.279* 19 Western nation .109

5 Divorce rate .277* 20 Urbanism .103 6 Population growth .251* 21 Age structure (age < 15) .100 7 Ethnic homogeneity .247* 22 Sex ratio .095 8 Income inequality

(indices).224* 23 Political rights .088

9 Female laborparticipation

.223* 24 Social support .072

10 Literacy (%) .219 25 Deterrence .07211 Infant mortality .197* 26 Economic development .05512 Modernization .173 27 Unemployment .04313 Household size .166 28 Population total .02414 Development index .163* 29 Democracy index .01215 Ethnic heterogeneity .163* 30 Population density .012

Significant predictors (*p


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Table 4. Generalized Least Squares Regression Results for Time and Space on Selected MeanEffect Size Predictors

Predictor Time Sample size R2

Income inequality (indices) .190 .321* .165Income inequality (ratio) .366 .620 .507

Development index .073 .348 .095Economic development .228 .072 .054Social support .157 .162 .038Urbanism .008 .051 .003Population density .149 .169 .068Population total .298 .107 .065Sex ratio .321 .352 .166Age structure (age > 15) .495** .186 .230Female labor participation .143 .387 .183Ethnic heterogeneity .539 .877** .430Divorce .182 .793 .439

Note: Standardized regression coefficients ( ) shown represent the relationship between methodologicalmoderators and mean effect sizes (Borenstein, Hedges, Higgins, & Rothstein, 2009).*p < .05. **p < .01.

Table 3. Significant Methodological Variations Using the ANOVA Analogue for SelectedPredictors

Moderator variable

Predictor Peer-review a Data source ( df )

Income inequality (indices) 0.023 4.190 (3)Income inequality (ratio) 0.085 2.612 (4)Development index 0.004 3.795 (3)Economic development 0.399 2.631 (3)Social support 7.413** 0.997 (2)Urbanism 0.196 3.788 (4)Population density n/a b 2.249 (3)

Population total 0.170 2.217 (4)Sex ratio 0.357 8.267** (3)Age structure (age > 15) 0.037 3.728 (1)Female labor participation 1.311 12.044** (4)Ethnic heterogeneity 0.100 4.599 (2)Divorce 0.143 0.685 (3)

Note: df = degrees of freedom. Numbers in the columns are the meta-analytic analogue to the ANOVA(Q) and are interpreted as the F statistic found in traditional ANOVA analyses.a. For all peer-reviewed results, df = 1.

b. All studies including population density were peer reviewed.**p < .01.



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a wider range of societal change, displays a much stronger negative impact on homi-cide ( M

r = .163, p < .01).

Interestingly, two of the top three predictors when ranked are cultural region dum-

mies (see Table 2). Latin American and Caribbean nations show a strong positiveeffect on homicide ( M r = .445, p < .001), whereas East Asian dummies have the con-

verse effect ( M r = .326, p < .001).

Methodological VariationsThe investigation of categorical moderating variables produced few significant results(see Table 3). This may be due to insufficient or unbalanced between-group samplesizes for most predictors. Assumptions of independence prevented predictors from

being pooled for moderator analyses, which would have granted a sufficient sample. Nevertheless, predictors with more than 10 contributing studies were analyzed usingthe meta-analytic analogue to the ANOVA (Wilson, 2002).

Only three predictors showed significant fluctuations between methodologicalvariations, and even so there appears to be no discernable pattern. The effects offemale labor and sex ratio were significantly influenced by the data source used ( Q = 12.043, p < .05 and Q = 8.266, p < .05, respectively); however, the specific groupeffects vary. For female labor, only Interpol and HEUNI/European Sourcebook datashow significant positive effects ( M

Interpol = .305, p < .05, n = 4; M

Other = .546, p < .05,

n = 2). In comparison, the sex ratio predictor is only significant and negative whenusing WHO data ( M WHO

= .204, p < .05, n = 10). Interestingly, the UN and Interpoldata report a positive effect between sex ratio and homicide, although these are notsignificant.

Finally, effect sizes for social support were significantly different when divided bywhether they were peer reviewed. Here, peer-reviewed studies show more significantresults as expected due to the file-drawer problem ( M

peer = .110, p < .05, n = 15).

Importantly, the non-peer-reviewed effect sizes were positive but nearly significant( M

nonpeer = .213, p = .053, n = 2), effectively bringing the overall mean effect size to

nonsignificance.The findings reported here can only be considered tentative, and any significantdifferences must be viewed with caution. That measurement dependence is a signifi-cant factor for only two variables may be indicative of a wider problem in data reli-ability, or alternatively, as a reassuring sign that most effects are not significantlyaffected by how homicide is measured.

Time and Sample Size

Similar limitations apply to the meta-regression, although the varying results cansignify the dynamic properties of cross-national predictors. Table 4 presents the stan-dardized coefficients for data year (time) and sample size when regressed on selected

predictors (i.e., those with 10 or more contributing effect sizes). Income inequality as



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measured by indices and ethnic heterogeneity were the only predictors significantlyrelated to sample size ( = .321, p < .05 and = .877, p < .01, respectively). Incomeinequality measured by ratios and divorce rate show a similar relationship ( = .620,

p = .183 and = .793, p = .062, respectively) but do not reach significance.However, the effect of youth population (15 + ) appears to decrease through time ( = .495, p < .01).

DiscussionCross-national research is a comparatively recent venture in the application of crimi-nological theory. The transference of theory on this larger unit of analysis has natu-rally occurred with the advancement of international data; however, the state of

knowledge in this realm remains relatively disorganized. In particular, a focus onstructural predictors of crimeperhaps the easiest data to obtainhas obscured theinfluence of more complex sociocultural concepts such as Latin American machismo (Neapolitan, 1994), generalized trust (Halpern, 2001; Lederman et al., 2002), or reli-gious beliefs (Antonaccio & Tittle, 2007). On the large-n scale, these are difficultto reliably operationalize, and whether these concepts can or should be pursued on thislevel remains an open question. Many of the sociocultural predictors had too few con-tributing effect sizes to make accurate judgments about their worth in cross-nationalresearch. However, in contrast to the predictors that were most often included in

designs (e.g., urbanism, economic development), sociocultural predictors showed thestrongest relationship with homicide. This signifies the need to reconsider whichsocial mechanisms are important at the national level.

Individually, certain predictors such as income inequality and age structure demon-strate interesting results. Poverty, inequality, ethnic composition, youth populationabove age 15, and divorce are all well-known predictors that emerged strong and posi-tive as expected based on previous findings (Hsieh & Pugh, 1993; LaFree, 1999; Land,McCall, & Cohen, 1990). Income inequality, as measured by the Gini Index, rankedsomewhat lower than when measured by ratios, signifying a possible conceptual

departure between the two predictors. Index-measured income inequality was also theonly predictor to be significantly affected by sample size, supporting doubts about therobustness and validity of the Gini Index made by cross-national scholars (Messner,Raffalovich, & Shrock, 2002; Neapolitan, 1997; Pratt & Godsey, 2003; Pridemore &Trent, 2010). However, ratio-based income inequality, although nonsignificant, appearedto be subject to differences in methodology, whereby half of the variation betweeneffect sizes is attributable to changes in time and sample size. The observed vulnera-

bility of both indicators of income inequality to differences in contexts places thisusually robust predictor under scrutiny. As there are a decent amount of studies report-

ing these strong effect sizes for income inequality and it is unlikely that every one ofthem has made a Type II error, it is reasonable to conjecture that income inequalityoperates differently outside the developed, industrialized nations that typically com-

prise the core sample in comparative research. This does not dismiss the importanceof income inequality in explaining homicide, and indeed these findings indicate the



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need to control for such a strong predictor. Nevertheless, sample distribution, andsubsequently context, must be carefully considered in future models.

Other predictors show considerable promise based on the size of their effects, includ-

ing decommodification, regional indicators, human development indices, and adult lit-eracy. As a measure of institutional anomie theory, the Decommodification Index proves to be a significant influence on national homicide rates. However, when placedalongside the relatively weak effect of social support, the idea that protection frommarket forces via governmental aid will prevent homicide becomes dubious. The indexitself incorporates a measure of welfare expenditures, so it is unclear as to which mech-anisms are effectively operating in relation to homicide. Governmental support appearsto have very little effect on national homicide rates; however, some scholars suggestcertain types of aid may be more important in preventing frustration and aggression than

others (Altheimer, 2008). Furthermore, these predictors only capture support provided by public means, whereas nongovernmental sources, such as family, community pro-grams, and religious organizations, may provide more effective means of social support(Chamlin & Cochran, 1997; Colvin et al., 2002). Therefore, future research should con-sider developing more precise indicators of private and public social support (see alsoPratt & Cullen, 2005), and disaggregate the Decommodification Index used to test insti-tutional anomie theory to identify the specific mechanisms at work.

Regional predictors require some interpretation, as each could potentially representa number of characteristics (Pridemore, 2002). Neapolitan (1994) uses the Latin

American and Caribbean dummy variable to capture values that permeate the regionand promote violent behavior, namely machismo . By way of providing historical con-text, however, Neapolitan gives way to alternative explanations that align more closelywith a political perspective. Characterized by a long history of revolution, politicalterrorism, and authoritarian leadership, many Latin American nations have been con-tinually exposed to legitimized official violence and political oppression (Ayres,1998). Furthermore, the strong presence of the drug trade throughout many of thesecountries presents additional outlets and opportunities for violence (United NationsOffice on Drugs and Crime, 2007). However, an East Asian region dummy is expected

to have an inverse effect on homicide due to accompanying religious and culturalcharacteristics. Nations such as Japan gather significant scholarly attention in crimi-nology due to their seemingly unique maintenance of low crime rates in the face ofrapid social transformations (Leonardsen, 2004). Whether it is the communitarian val-ues conveyed through Islam and Confucianism (Antonaccio & Tittle, 2007), stronginformal social controls (Adler, 1983; Komiya, 1999), or a strong paternalistic govern-ment (Austin, 1987), the operation of social mechanisms in East Asian nations areconsidered anomalous to the West. While the significance of these cultural findings isdampened by the small amount of contributing studies, at the very least they further

attest to the consequence of spatial context in cross-national research. Future researchshould aim to qualitatively and quantitatively develop each of these cultural mecha-nisms to precisely test them on the cross-national scale.

Measures of development and modernization are some of the most frequently tested predictors in cross-national research but surprisingly demonstrate rather weak overall



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effects. Excepting education indicators, the general negative relationships support theopportunity-based theoretical explanations (Agha, 2009; Kick & LaFree, 1985;Shelley, 1981). Interestingly, only development measured by indices showed a signifi-

cant effect, and even so a relatively small one. As the majority of studies used factoredindices as an indicator of development, the exact variables and hence precise relation-ships are difficult to decipher. Development indices generally tend to represent a com-

bination of societal and economic levels of development through indicators such aseducation enrollment, human rights scales, GNP/GDP per capita, population growth,or infant mortality. Individually, these predictors portray a very different picture thanwhen they are combined: infant mortality, population growth, and education indica-tors show medium positive relationships whereas economic and political rights indica-tors show small negative relationships. Which predictor is the greater influence?

Which is more theoretically and empirically important? Some criminologists arguethat lumping together social processes and covariates merely obscures causality andhinders interpretation (Cook, Shagle, & Degirmencioglu, 1997; Land et al., 1990;Sampson, 2006b). Development is a highly complex and longitudinal concept thatdefies one-dimensional operationalizations (Inglehart & Welzel, 2005; LaFree &Tseloni, 2006). Thus the problem here is essentially a theoretical one. Alternatively, asSampson (2006b) explains, [n]o statistical method can solve what is fundamentally atheoretical issue about causal mechanisms (p. 52).

It is important to note that economic developmentincluded in almost all of the

sample studiesproved to be consistently immaterial to the explanation of homicide.This result, combined with the confusion in the theoretical literature, perhaps indicatesthat it is time to move past the use of this variable in cross-national designs. Not sur-

prisingly, it appears that the distribution of wealth in a society is more important in predicting homicide rates than total or per capita measures of wealth (see Land et al.,1990). Thus if economic development is to remain in cross-national discourse, research-ers must investigate how political and cultural factors may influence the uneven spreadof wealth in societies.

Other commonly included demographic variables show similar ineffectual pat-

terns: urbanism, population density, population total, youth population less than age15, and sex ratio. Although these are often used as control variables, the general weak-ness of these predictors carries implications for the Durkheimian-Modernization,social disorganization, and routine activity theories as well. In each of these per-spectives, there is the hypothesis that the close proximity of youth (primarily males)will increase crime. Although nonsignificant and generally negligible, the meaneffects are all negatively related to homicide. However, this must be consideredwith caution, as it is equally likely these predictors are simply irrelevant to homicideat the national level. For population density, population total and youth population

below 15, the weak findings were robust across methodological variations, indicat-ing that these can be safely excluded from future cross-national designs withoutconsequence.

Interestingly, the somewhat small effect of youth population aged above 15 is aug-mented by its susceptibility to time. The significant decrease in the effect of the size of



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youth population as data become more recent bares consequence to the proclaimedinvariance of the agecrime curve (see Hirschi & Gottfredson, 1983). The direct effectof the agecrime relationship is not disputed here, as a generous amount of studies con-

firm a positive effect. However, the strength of the association appears to have declinedwith time. Speculative reasoning may suggest that the impact of age composition mayrely on time-specific, often exogenous, factors. Alternatively, with the advance of newsources of indicator data, influences that were previously unaccounted for are now beingincluded in cross-national research, portraying a more accurate model of homicide.

Democracy indices proved rather weak influences on homicide overall, though thismay be attributable to the lack of empirical and theoretical development of the specificmechanisms involved (Stamatel, 2008). The large body of emerging research on crimein post-Communist nations suggests that processes of democratization are dependent

on political culture and economic conditions (e.g., Karstedt, 2003; Stamatel, 2008).Thus even within a region undergoing similar democratic transitions, the impact onhomicide is not always the same. Furthermore, LaFree and Tseloni (2006) stress theuse of the word processes , arguing that democracy, like modernization, development,and globalization, is a longitudinal concept that must be examined over time. Thereforemore sociohistorical comparative research that focuses on a particular region, as in the

post-Communist example above, is needed to better understand how the change todemocracy interacts with cultural institutions and values (Karstedt, 2003).

ConclusionsThis meta-analysis confirmed the importance of cross-national predictors that repre-sent levels of social integration and stability in explaining homicide. Certain predic-tors, such as population density, economic development, and deterrence, show verylittle impact on national-level homicide and must be carefully reevaluated if includedin future research. However, it also confirmed concerns about theoretical and meth-odological limitations to cross-national research (Marshall & Block, 2004; Marshallet al., 2009; Stamatel, 2006; Van Dijk, 2008), namely, the following:

Samples are small and biased. On average, only about 20% of the global population of countries are covered, of which the overwhelming majority are primarily developed and industrialized.

Indicators are theoretically vague, with considerable empirical overlap andno agreement on operationalization.

Time is nearly nonexistent in cross-national research, as designs predomi-nantly favor cross-sectional over longitudinal.

These limitations require future research to incorporate predictors into models withcareful theoretical consideration toward disentangling overlapping covariates and samplecomposition. Theories that rely on factored indices, proxy indicators, and highly correlatedvariables in particular must clarify the mechanisms involved to sort out empiricaldiscrepancies. Overall, the overwhelming use of cross-sectional design to test the



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Nivette 125

Acknowledgements

The author would like to thank the PhD writing group at the Institute of Criminology, her

supervisor Prof. Manuel Eisner, and three anonymous reviewers for their helpful comments onan earlier draft of this article.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship,and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of

this article.

Notes

1. Individual journals searched were Criminology , Homicide Studies , European Journal ofCriminology , Asian Journal of Criminology , British Journal of Criminology , Interna-tional Journal of Comparative and Applied Criminal Justice , and International Journal ofOffender Therapy and Comparative Criminology .

2. Effect sizes reported in unstandardized b-coefficient format were standardized using thefollowing equation: = b x

y

SD

SD

.Where SD x and SD y are the standard deviations of the indepen-

dent and dependent variable, respectively, and b is the reported unstandardized regressioncoefficient.

3. It is important to note that the grouping of certain predictors with theories is rather tenta-tive. For instance, infant mortality has only recently been used as an indicator for absolutedeprivation on the cross-national level (Pridemore, 2008), having previously representedconcepts of social inequality (Lee & Bankston, 1999), health care (Shichor, 1990), andsocial disorganization (Jacobs & Richardson, 2008). The following associations betweentheories and predictors follow major arguments in the literature, with conceptual overlap to

be examined in the discussion.

4. One study included a Poverty Index (Par, 2006), but due to significant heterogeneity and alack of similar operationalizations, this effect size was excluded.

5. Gartners (1990, 1991) cultural predictor Battle Deaths had to be excluded due to heruse of nearly identical samples. Unfortunately, measures of American culture wereexcluded because these studies did not employ standardized regression coefficients(Chamlin & Cochran, 2007; Jensen, 2002) or did not have a large-enough sample size(Cao, 2004). In addition, Chamlin and Cochrans (2006) study on perceptions of politi -cal legitimacy was excluded because they used negative binomial regression with met-ric coefficients.

6. This Q statistic is mathematically equivalent to the Q statistic used to examine the over-all dispersion of effect sizes around the mean effect. However, the former is modifiedto assess between-subgroup differences (Borenstein, Hedges, Higgins, & Rothstein,2009).



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Bio

Amy E. Nivette is a PhD candidate at the Institute of Criminology at the University ofCambridge, Cambridge, United Kingdom.

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