social capital and crime: a cross-national multilevel study

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International Journal of Law, Crime and Justice

41 (2013) 58e80www.elsevier.com/locate/ijlcj

Social capital and crime: A cross-national multilevelstudy*

Sunghoon Roh a, Ju-Lak Lee b,*

aKorean National Police University, Republic of KoreabKyonggi University, Department of Security Management, Suwon-si, Kyeonggi-do 443-760, Republic of Korea

Abstract

Numerous studies have repeatedly supported the negative influence of social capital upon crime rates.Although the relationship between social capital and crime is theoretically persuasive and empiricallyrobust, only a handful of studies have looked into its cross-national variation. Furthermore, no research insocial capital has yet applied a multilevel approach to take into account both macro- and micro-leveldeterminants of crime. In an attempt to fill in this research gap, we conducted multilevel analyses ofcountry-level and individual-level factors of criminal victimization. Following the lead of previous studies,it was hypothesized that social capitaldestimated as generalized trust, social norms, and civic engage-mentdreduces criminal victimization, net of individual-level determinants, and other well-establishedcountry-level factors. The results revealed that while a higher level of social capital was found toreduce the likelihood of robbery victimization, no significant impact was observed on burglary victimi-zation. With regard to the three dimensions of social capital, generalized trust and social norms exertedsignificant effects on robbery victimization in the expected direction.� 2012 Elsevier Ltd. All rights reserved.

Keywords: Social capital; Criminal victimization; Cross-national; Multilevel analysis

1. Introduction

Since social capital was introduced as a correlate of crime, the inverse association betweencrime and social capital has been repeatedly demonstrated in numerous studies (Buonanno

*This work was supported by Kyonggi University Research Grant 2010.

* Corresponding author.

E-mail addresses: [email protected] (S. Roh), [email protected] (J.-L. Lee).

1756-0616/$ - see front matter � 2012 Elsevier Ltd. All rights reserved.

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59S. Roh, J.-L. Lee / International Journal of Law, Crime and Justice 41 (2013) 58e80

et al., 2009; Galela et al., 2002; Hawdon and Ryan, 2009; Lederman et al., 2002; Martin, 2002;Messner et al., 2004; Rosenfeld et al., 2001). According to Robert Putnam (1993: 35), socialcapital refers to the “features of social organizations, such as networks, norms, and trust, thatfacilitate action and cooperation for mutual benefit.” Given the affinity and relevance inmeaning between social capital and other sociological concepts such as social integration,social disorganization, and collective efficacy, more affluent literatures support the negativeimpact of social capital on crime.

Although the relationship between social capital and crime is theoretically persuasive andempirically robust, few studies of social capital have attempted to explain cross-nationalvariation in crime. Most comparative studies that explain the uneven distribution of crimeacross the world has relied on so-called “grand theories,” including modernization theory(e.g. urbanization), world system theory (e.g. economic inequality), and ecological theory(e.g. ethnic heterogeneity) (Neuman and Berger, 1988). Furthermore, no research on socialcapital has yet applied a multilevel approach to take into account both macro- and micro-leveldeterminants. Studies of crime at the macro-structural level look into criminogenic charac-teristics derived from social and cultural environments. On the other hand, studies centering onthe micro-level correlates of crime pay attention to a variety of individual factors that makea crime target more attractive and accessible to potential criminals. Abundant previous studieshave shown that crime can be better understood when taking into consideration dynamic andcomplex interactions among factors at different levels (Capowich, 2003; Miethe andMcDowall, 1993; Pizarro et al., 2007; Roh et al., 2010).

In an attempt to fill in this research gap, we conducted multilevel analyses of country-leveland individual-level factors of criminal victimization. Following the lead of previous studies, itwas hypothesized that social capitaldestimated as generalized trust, social norms, and civicengagementdreduces criminal victimization, net of individual-level determinants, and otherwell-established country-level factors. Unlike previous studies, which mostly paid attention toviolent crimes such as homicide, we observed differences between robbery, a type of violentcrime, and burglary, a type of property crime, in their association with social capital.

2. Literature review

2.1. Meaning of social capital

Recent decades have seen the concept of social capital become a popular topic in manydifferent academic disciplines. Although some credit the introduction of social capital toscholars in eras as early as the 1910s (see Woolcock and Narayan, 2000), the burgeoninginterest in the concept in recent decades can be largely attributed to the work of JamesColeman (1990) and Robert Putnam (1993). The early work of Pierre Bourdieu (1986) is alsoseen as having been seminal in inspiring later researchers. Possibly due to its nascency in theacademic realm, however, a universally accepted definition of social capital does not exist.Instead, social capital has been used to refer to a diverse range of phenomena or has been usedinterchangeably with other sociological concepts, such as social solidarity or collective effi-cacy. Several compelling criticisms have been recently leveled against such practices(Edwards and Foley, 1998; Woolcock, 1998). Nonetheless, it is safe to argue that the conceptof social capital has already gained a strong foothold in the sociological paradigm and is hereto stay, as indicated by the ever-increasing volume of empirical research and theory devel-opment on the concept.

60 S. Roh, J.-L. Lee / International Journal of Law, Crime and Justice 41 (2013) 58e80

The definitions of social capital laid out by early scholars such as Bourdieu (1986), Coleman(1990), and Putnam (1993) share a common thread in that they see social capital as beinginherent in the structure of human relationships. Bourdieu used the concept of social capital inthe context of education. He distinguishes three forms of capitaldeconomic, cultural, andsocialdthat can be converted to each other under certain conditions (1986). According to him,social capital is “the aggregate of the actual or potential resources which are linked topossession of a durable network of more or less institutionalized relationships of mutualacquaintance or recognition” (1986, p. 249). Coleman sets out his definition of social capital bynoting not only the social aspect of social capital but also the capital aspect of it. According tohim, all the components of social capital consist of, first, some aspect of a social structure andsecond, the facilitation of certain actions of individuals within the structure (1994, p. 302).When viewed from the capital aspect, social capital facilitates actions that are productive oradvantageous to the actors. Were it not for social capital, such actions would not occur. Finally,Putnam’s (1993) work on good governance in Italy is probably the most widely cited in termsof the definition of social capital. Redefining social capital as an attribute of communities ornations, Putnam (1993, p. 35) writes that social capital is the “features of social organizations,such as networks, norms, and trust, that facilitate action and cooperation for mutual benefit.”

As indicated from the varying definitions, social capital is far from a single, unidimensionalconcept; rather, it is a complex and multifaceted construct. Although a concrete consensus onthe nature and number of the multiple facets of social capital has not been attained yet, theabove definition stressing the multidimensional aspect, forwarded by Putnam, has been mostwidely cited by researchers. Indeed, operationalizing social capital in terms of networks, norms,and trust in social organizations has an intuitive appeal because it is easy for people to relate tothese features in their own lives and societies (Bjørnskov, 2006).

In empirical research, drawing on Putnam’s work (1993, 2000), two aspects of social cap-italdinterpersonal trust and civic engagementdhave been most frequently examined as coreelements of social capital. With regard to trust and civic engagement, Putnam (2000) maintainsthat individuals who voluntarily participate in networks and organizations of different typeslearn to trust each other through repeated interactions, which also underlies the creation ofsocial norms and general trust. Regarding trust, he argues that the mobilization of social capitalin a community requires the community members to trust and cooperate with each other, evenwhen they do not know each other or no direct contact has been made between them (2001).Thusly formed generalized trust in turn promotes so-called spontaneous sociability, renderingindividuals more willing to engage in further cooperation (Fukuyama, 1995; Putnam, 2001).Civic engagement yields social capital by creating and maintaining organizations that are usefulfor pursuing collective goals that go beyond their original goals (Coleman, 1990). Trust andcivic engagement mutually reinforce each other. As individuals participate and engage in theorganizations in their community, they are more likely to trust each other. The more they trusteach other, the more they participate in civic matters.

2.2. Social capital and crime

Extant research shows that social capital is connected to an array of social phenomena,including education and academic development (Coleman, 1990; Meier, 1999), dropping out ofschool (Teachman et al., 1996), health (Kawachi et al., 1997), and general community devel-opment (Temkin and Rohe, 1998). Given the propitious characteristic of social capital, it is nottoo much of a leap of logic to assume a negative relationship between crime and social capital.


Putnam himself states that “In high-social-capital areas public spaces are cleaner, people arefriendlier, and the streets are safer” (2000, p. 307, emphasis added). He further notes that theincreasing crime in the 1960s in the United States was associated with the reduction of socialcapital. In fact, an increasing volume of empirical studies support a negative associationbetween social capital and crime, albeit with some exceptions (Chamlin and Cochran, 1997;Heaton, 2006). The negative linkage is relatively consistent and robust in that it holds acrossvarious levels of analysis, including census blocks, cities, counties, states, and nation states(Martin, 2002; Lederman et al., 2002; Messner et al., 2004; Rosenfeld et al., 2001). Theassociation even holds across different types of crimes, such as homicide (Lederman et al.,2002; Messner et al., 2004), violence (Galela et al., 2002), and burglary (Martin, 2002).

What theoretical framework best describes the negative association between social capitaland crime? Scholars typically draw on the Chicago School’s social disorganization theory. Themain idea of social disorganization theory is that the structural disadvantages of a communitytranslate into the weakening of informal social control, which in turn leads to high crime rates.According to Shaw (1931), disorganized communities share the common structural features ofethnic heterogeneity, poverty, and family disruption, and these features are likely to be asso-ciated with a dearth of effective relational networks and civic participation, and lowered trustamong neighbors. In contrast, strongly organized communities are characterized by individuals’active participation in civic matters and strong organizations at various levels. Informal socialcontrol abounds, which yields low crime rates (Shaw and McKay, 1942).

Social capital also has a conceptual affinity with collective efficacy, which is a recentrefinement of social disorganization theory. In particular, Sampson and colleagues (Sampson,2001; Sampson et al., 1997) integrate interpersonal trust, one of the core elements of socialcapital, into the concept of collective efficacy. Sampson defines collective efficacy as “thelinkage of mutual trust and shared willingness and intention to intervene for the common good”(Sampson, 2001, p. 95). Collective efficacy theorists posit that a community with mutual trustand shared expectations for intervening on behalf of the collective good is characterized byheightened capability to undertake actions for intended effects, including lowering crime rates(Sampson, 2001; Sampson et al., 1997). Supporting their proposition, an impressive volume ofempirical research has accumulated attesting to the crime-reducing effect of collective efficacy(Sampson, 2001; Sampson et al., 1997; Browning, 2004).

Social disorganization theory and collective efficacy find the connection between socialcapital and crime in informal social control. Bursik & Grasmick’s (1993) systemic model ofcrime finds the connection in both informal social and formal public control. Communities withhigh levels of civic engagement are likely to better secure public services and goods, includinglaw enforcement services. The ability to secure adequate public services conducive to publicsafety will lead to low crime and violence in the community. Civic engagement and theresultant public safety will in turn yield a high degree of trust among community members(Bursik and Grasmick, 1993; Rosenfeld et al., 2001). In conclusion, both social disorganizationtheory and the systemic model of crime indicate that civic engagement and trust, the two coreelements of social capital, are likely to reduce crime, the former through informal social controland the latter through both informal and formal social control.

2.3. Multi-level factors and criminal victimization across countries

There has been an increasing recognition among researchers that taking into account bothmicro- and macro-level correlates does a better job of explaining the variance in criminal


victimization (Capowich, 2003; Pizarro et al., 2007). Adoption of this integrative approachstands to reason because the degree of criminal victimization is not solely affected by indi-viduals’ demographic characteristics or lifestyles, but by the community’s structural andcultural features.

Scholars have suggested that investigating both units simultaneously offers a more preciseportrait of criminal victimization by taking into account the nested nature of data (Roh et al.,2010; Stein, 2010). The individual-level correlates of criminal victimization mostly stem fromthe opportunity perspective of crime, including rational choice theory (Cornish and Clarke,1986), routine activity theory (Cohen and Felson, 1979), and lifestyle theory (Hindelanget al., 1978). Assuming offenders are led by rational decision-making, this line of theoriesrenders crime opportunities to be foremost among the determinants of crime. According toroutine activity theory, crime opportunities are present when a motivated offender encountersa vulnerable target that is not watched by a capable guardian. Lifestyle theory posits that theextent to which someone is exposed to crime opportunities is determined by his or her dailylifestyle. On the other hand, the macro-level approach to criminal victimization stresses thestructural characteristics of crime-prone communities. Following the intellectual tradition of theChicago School, people living in socially-disorganized communities, characterized by a highlevel of poverty, residential mobility, and ethnic heterogeneity, are regarded as more frequentvictims of crime (Shaw and McKay, 1942).

Albeit numerous studies have repeatedly demonstrated the inverse relationship betweensocial capital and crime, no study has yet to investigate it in a multi-level context. Additionally,previous endeavors to estimate the effect of social capital on crime and victimization have beenlargely confined to the U.S. context (Gottfredson and DiPietro, 2011; Hawdon and Ryan, 2009).Few studies have explored whether social capital decreases victimization on a cross-nationallevel. Thus, the generalizability of the effect of social capital beyond the U.S. contextremains, for the most part, an open empirical question requiring more cross-national studies.

In order to fill this research gap, we attempt to estimate the effects of social capital oncriminal victimization at the cross-national level. Our study extends previous studies byincorporating both country-level and individual-level factors in a multilevel model. Themultilevel modeling approach to cross-national victimization studies enables comparisonsacross individuals within specific countries, while simultaneously allowing for elements of thecountry have an effect on the results. Our study adds to the social capital literature as the firstmultilevel study that examines the effect of social capital on victimization at the cross-nationallevel.

3. Methods

3.1. Measurement of criminal victimization

We used the International Crime Victims Survey (ICVS) to measure criminal victimizationand individual-level correlates among 57 countries. Four waves of surveys, from the secondwave (1992) through the fifth wave (2004, 2005), were included in the current study. The ICVSconsists of a national survey that takes nationwide samples and a city survey that drawsa sample from each country’s largest city. The data from those two surveys were combined andonly the responses from cities with more than 100,000 people were selected.

Several countries were excluded from the analysis for different reasons. For example, afterthe survey was conducted, Yugoslavia was divided into different political entities after the civil


war. Some countries were ruled out because no data were available to measure country-levelpredictors (i.e., social capital). We also excluded several countries with a small sample size(less than 100) after eliminating missing cases. Finally, conducting Mahalanobis Distances, twooutlier countries (Argentina in robbery analysis and Zambia in burglary analysis) wereexcluded from a total of 57 countries.1 As a result of the filtering process, 56,071 respondentsfrom 56 countries for robbery analysis and 60,008 respondents from 56 countries for burglaryanalysis were included in this study.

This study included two types of criminal victimization: robbery victimization and burglaryvictimization. These two crimes were selected to identify any differences or similaritiesbetween street crimes (i.e., robbery) and residential crimes (i.e., burglary) in terms of correlatesof victimization. This classification was based on the assumption that the context of criminalvictimization may be different depending on where the crime occurs. For example, thevulnerability of a robbery victim can be more affected by personal characteristics (e.g. age, sex)than that of a burglary victim because the former is more directly exposed to potential crim-inals. To measure criminal victimization, respondents were asked if they had ever beenvictimized by 1) robbery or 2) burglary during the last five years. Their answers weredichotomized into “yes” (coded 1) and “no” (coded 0).

3.2. Measurement of country-level variables

Social capital, the main country-level predictor, was measured using the World ValuesSurvey (WVS), a cross-national research project that has gathered data on social and politicalindicators from more than 80 countries. Using four waves of surveys from 1990 through 2005,the survey year of WVS and that of ICVS were matched for each country. In keeping withprevious studies of social capital, we took into consideration three major dimensions of socialcapital in our measurement: generalized trust, social norms, and civic engagement. Themeasurement of generalized trust was based on the question, “In general, do you think that mostpeople can be trusted or can’t you be too careful?” The answers were dichotomized into “mostpeople can be trusted” and “you can’t be too careful.” The percentage of respondents whoanswered that most people can be trusted was used as a measure of generalized trust. Socialnorms were measured by the extent of respondents’ agreement that the following four actionscould be justifiable: “claiming government benefits to which you are not entitled,” “avoidingfares on public transport,” “cheating on taxes if you have a chance,” and “accepting a bribe inthe course of duty.” For the purpose of analysis, the original answers were reverse coded into“never justifiable” (10) and “always justifiable” (1). The scores from the four questions weresummed up and divided by four, generating an average score of social norms that ranged from 1to 10. The reliability test revealed that the summated scale was acceptable (Cronbach’sa ¼ .747). Civic engagement, the third facet of social capital, was measured by asking ifrespondents had ever signed a petition. The percentage of respondents who answered yes to thequestion indicates the level of political proactivity in each country. Other country-level

1The 57 countries in this study consists of 17 countries from the fifth wave (Argentina, Australia, Canada, Finland,

France, Germany, Italy, Mexico, Netherlands, Norway, South Africa, Spain, Sweden, Turkey, Great Britain, United

States, Zambia), 31 countries from the fourth wave (Albania, Austria, Belgium, Bulgaria, Croatia, Czech Republic,

Denmark, Egypt, Greece, Hungary, Iceland, Indonesia, Ireland, Japan, Kyrgyzstan, South Korea, Latvia, Lithuania,

Malta, Nigeria, Philippines, Poland, Portugal, Romania, Russian Federation, Slovakia, Slovenia, Uganda, Ukraine,

Zimbabwe), 8 countries from the third wave (Azerbaijan, Brazil, Colombia, Georgia, India, Macedonia, New Zealand,

Switzerland), and 1 country from the second wave (China).


variables included in the models were Urbanization, economic inequality, and ethnic hetero-geneity. These control variables were chosen from three main theoretical perspectives on crimethat explain cross-national differences in crime rates.

Urbanization was derived from the Durkheimian-modernization perspective, whichcontends that crime rates increase in the wake of modernization as normative breakdown occursduring the transition from a traditional society to a modern one. The degree of urbanization hasoften been regarded as a proxy measurement of modernization in many studies. In this study,urbanization was operationalized as the percentage of urban population. The data was drawnfrom the World Development Indicators (WDI), a database compiled by the World Bank basedon officially-recognized international resources.

Economic inequality constitutes a key element in the Marxian-world system perspective (orconflict perspective) of cross-national crime variation. According to the Marxian-world systemperspective, crime rates are associated with the expansion of capitalist market economies,which grow economic inequalities between the rich and the poor and therefore cause tensionand conflict in society. In order to measure economic inequality, this study relied upon the Giniindex compiled by the World Bank. The Gini index ranges between 0 (perfect equality) and 100(perfect inequality).

Lastly, the ecological perspective views crime as a social phenomenon resulting fromstructural changes in social environment and the consequential impact upon the informal socialcontrol mechanism. Ethnic heterogeneity has been treated as one of the factors that reduces theinfluence of community control upon individuals’ behavior, and therefore results in a crimi-nogenic environment. The measurement of ethnic heterogeneity in this study relied upon themeasures of fractionalization computed by Alesin et al. (2003). A fractionalization scoreindicates the probability that two randomly selected individuals from a population belong todifferent ethnic groups. The values range between 0 (perfect homogeneity) and 1 (perfectheterogeneity).2

Multicollinearity among country-level factors is often a problem in a cross-national studybecause of relatively small sample size. Two analyses were therefore conducted to test mul-ticollinearity. Correlation analysis found that the highest correlation coefficient was only .610between economic inequality and ethnic heterogeneity in a correlation coefficient matrix withall of the country-level independent variables (see Appendix A). We also assessed multi-collinearity with measures of variation inflation factor (VIF) and tolerance and found that VIFsdid not exceed 2.0 and the level of tolerance was over .5. These results indicate that multi-collinearity is not a problem in this study.

3.3. Measurement of individual-level variables

Using the ICVS, we measured several individual-level variables that might have an influenceon the likelihood of criminal victimization.

Preventive measures is conceptually defined as a variety of proactive efforts that an indi-vidual adopts to protect him/herself from being victimized. Respondents were asked if they hadone or more of the following: burglar alarm, special door locks, special grills, a watch dog, anda high fence. The answers were dummy-coded by assigning “1” to those who adopted one ormore of those preventive measures and “0” to those who adopted none of them.

2The formula to compute the measures of fractionalization is written as FRACTj ¼ 1�Pni¼1 s

2ij , where Sij is the share

of group iði ¼ 1.NÞ in country j (Alesin et al. (2003): 159).


Risky lifestyle is an indicator of someone’s lifestyle that may heighten the risk of criminalvictimization. Following the lifestyle perspective, we assumed that individuals who tended togo out in the evening more often than others were more likely to be victimized on the street byexposing themselves to potential offenders. Also, as routine activities theory puts it, a tendencyto vacate a house more frequently may provide crime opportunities for potential burglars.Respondents were asked how often they went out in the evening for recreational purposes (forexample, to go to a pub, cinema, or restaurant, or to see friends). The answers were coded from5 to 1 depending on frequency (5 ¼ almost every day, 4 ¼ once a week, 3 ¼ once a month,2 ¼ less than once a month, 1 ¼ never).

Perceived unsafety indicates how unsafe people feel in their communities. An unsafecommunal environment is associated with higher crime rates and greater risk of criminalvictimization. Respondents were asked if they felt unsafe after dark. The answers were codedfrom 4 to 1 (4 ¼ very unsafe, 3 ¼ a bit unsafe, 2 ¼ fairly safe, 1 ¼ very safe).

Four sociodemographic variables were also included. Age consisted of 12 age categoriesranging from 1 (16e19 years old) to 12 (70 years old or higher) with a four- or five-yearinterval. Sex was dummy-coded as “1” for males and “0” for females. Respondents’income levels were measured based on the perception of their household income whencompared with others. High income was dummy-coded as “1” for those who perceived theirhousehold incomes were within the upper 25% and “0” for all others. Low income wasdichotomized into “1” for those who answered that their income levels were in the lower 25%and “0” for all others. Finally, marital status was dummy-coded as “1” for single and “0” forall others.

4. Results

4.1. Descriptive statistics

Descriptive statistics of the variables are shown in Table 1. About 6% of the respondents saidthat they were robbery victims and about 11% said that they were victimized by burglary. Onaverage, about 28.8% of respondents thought that most people could be trusted. Norwayshowed the highest level of generalized trust (74.17%), while Brazil had the lowest measure oftrust (2.8%). The observed values of social norms ranged between 7.16 (Belarus) and 9.71(Malta). The average civic engagement measure was 36.91 and ranged from 90.58 (NewZealand) to 4.54 (Zimbabwe).

Bivariate correlations between each indicator of social capital and criminal victimization aredisplayed by scatter plots. Figs. 1e3 show inverse relationships between social capital androbbery victimization with a greater likelihood of robbery victimization among countries of lowsocial capital. The R2 value for the linear line for generalized trust and robbery victimization isthe highest, indicating the strongest relationship. The scatter plots for burglary victimization aredisplayed in Figs. 4e6. The linear line for generalized trust and burglary victimization isnegative, with a very low R2 value. In Figs. 5 and 6, most of the data points are randomlyscattered and do not cluster around the regression line, indicating no relationship.

4.2. Multilevel analysis

Hierarchical Generalized Linear Modeling (HGLM) was used to examine the impact ofsocial capital on the risk of criminal victimization, taking into account both country-level


factors and individual-level factors. HGLM with a log link function is appropriate because thedependent variables are dichotomized (Raudenbush and Bryk, 2002).

4.2.1. Unconditional modelPrior to putting predictors into the model, we estimated the amount of variation in criminal

victimization among countries. The unconditional model without predictors is written as log[rij/(1 � rij)] ¼ b0j at the individual level, where the country-level model is b0j ¼ g00 þ m0j.Here rij is the probability that respondent i from country j becomes the victim of a crime, b0j isthe average log-odds of criminal victimization in country j, g00 is the average log-odds ofcriminal victimization across countries, and m0j is the error term. The unconditional modelsshowed significant variation in the average risk of victimization among countries. The averagelog-odds of robbery victimization across countries (g00) is �2.934 (SE ¼ .105) and thevariance of the country averages around the grand average is .593 (SD ¼ .770, Chi-square ¼ 2556.824, p < .00). For burglary victimization, the average log-odds is �2.207(SE ¼ .071) and its variability at the country level is .269 (SD ¼ .518, Chi-square ¼ 1582.722,p < .00). Next, the intraclass correlation coefficient (ICC) was used to quantify the degree ofresemblance among individuals that belong to the same country (Snijders and Bosker, 2012:17). ICC for binary data is defined as rI ¼ t20 þ ft20 þ ðp2=3Þg, where t20 is variance betweengroups (Snijders and Bosker, 2012: pp. 304e305). ICC is .152 for robbery victimization and.075 for burglary victimization. These figures indicate that 15.2% of the total variance ofrobbery victimization is accounted for by the country level. In terms of burglary victimization,however, the proportion of the variance that is explained by the country level is only 7.5%.These results show that a multilevel model fits robbery victimization better than burglaryvictimization.

Table 1

Descriptive statistics.

Variable Robbery Burglary

Mean SD Min Max Mean SD Min Max

Dependent variables

Robbery victimization (1 ¼ yes) .06 .24 .00 1.00

Burglary victimization (1 ¼ yes) .11 .31 .00 1.00

Independent variables

Country-level

Generalized trust 28.82 16.63 2.80 74.17 28.91 16.55 2.80 74.17

Social norms 8.63 .61 7.16 9.71 8.65 .59 7.16 9.71

Civic engagement 36.40 24.71 4.54 90.58 36.75 24.50 4.54 90.58

Urbanization 64.16 18.58 11.70 97.00 65.13 18.56 11.70 97.00

Income inequality 35.64 9.58 20.70 65.00 35.60 9.51 20.70 65.00

Ethnic heterogeneity .33 .24 .00 .93 .32 .23 .00 .93

Individual-level

Preventive measures .62 .48 .00 1.00 .63 .48 .00 1.00

Risky lifestyle 2.96 1.31 1.00 5.00 2.94 1.30 1.00 5.00

Perceived unsafety 2.31 .94 1.00 4.00 2.35 .96 1.00 4.00

Age 6.19 3.25 1.00 12.00 6.22 3.27 1.00 12.00

Sex (male ¼ 1) .45 .50 .00 1.00 .45 .50 .00 1.00

High income (upper 25% ¼ 1) .27 .45 .00 1.00 .29 .46 .00 1.00

Low income (lower 25% ¼ 1) .22 .41 .00 1.00 .22 .42 .00 1.00

Marital status (single ¼ 1) .27 .44 .00 1.00 .27 .44 .00 1.00

Fig. 1. Generalized trust and robbery victimization.


4.2.2. Random coefficients modelAt the second stage, the outcome variables are examined as a function of the individual-level

variables. The random coefficients model is written as log [rij/(1 � rij)] ¼ b0j þ b1j(X1ij � 1) þ ..þ bkj (Xkij � Xk) at the individual level, where the country level model isb0j¼ g00þ m0j, b1j¼ g10þ m1j, bkj¼ gk0þ mkj. The random coefficients model that allows all thecoefficients to vary for different countries consists of fixed effects (grand means) and randomeffects (country deviance). A significant fixed effect means that the individual-level variable isa significant predictor of criminal victimization among all the countries. The random effects areused to determine whether there is significant variability in criminal victimization or in therelationship between individual-level variables and criminal victimization across countries.

Table 2 shows the fixed estimated results of random coefficients models. Three predictorsare significantly associated with the risk of both robbery and burglary victimization: riskylifestyle, perceived unsafety, and age. Younger respondents who go out in the evening morefrequently or feel unsafe after dark are more victimized than others. Unmarried males are morefrequent targets of robbers. People who perceive their household incomes are within the upper25% are associated with an increase in the odds of burglary.

4.2.3. Intercept-as-outcome modelsThird, only country-level predictors enter into the models to test whether each indicator of

social capital is significantly associated with criminal victimization, controlling for the othercountry-level predictors. The intercept-as-outcome model is written as log [rij/(1� rij)]¼ b0j at

Fig. 2. Social norms and robbery victimization.


the individual level, where the country-level model is b0j ¼ g00 þ g01W1j þ. þ g0qWqj þ m0j,whereWqj represent country-level variables. The country-level variation in the average log-oddsof criminal victimization is conditioned by different sets of country-level predictors.

Table 3 shows the results of intercept-as-outcome models, respectively, for both robbery andburglary victimization. In terms of robbery, generalized trust and social norms are significantlyrelated to the risk of victimization when the other country-level predictors are not controlled.Greater generalized trust and social norms are associated with a lower risk of robberyvictimization. However, civic engagement does not have a significant effect on robberyvictimization. Economic inequality and ethnic heterogeneity also show a significant andpositive association with robbery victimization. These findings reveal that generalized trust andsocial norms among the three indicators of social capital reduce the likelihood of robberyvictimization when other country-level predictors are taken into account. Unlike robberyvictimization, none of the social capital indicators are significantly associated with burglaryvictimization. Among the country-level variables, only ethnic heterogeneity shows a significantassociation, as greater heterogeneity increases the risk of burglary victimization.

4.2.4. HGLM modelsAt first, HGLM models add individual-level variables to the intercept-as-outcome models to

predict criminal victimization with both country- and individual-level variables. The models arewritten as log [rij/(1 � rij)] ¼ b0j þ b1j (X1ij � 1) þ .. þ bkj (Xkij � Xk) at the individuallevel, where the country level model is b0j ¼ g00 þ g01W1j þ .. þ g0qWqj þ m0j. The log-odds of criminal victimization at the individual level is predicted from individual-level

Fig. 3. Civic engagement and robbery victimization.

Fig. 4. Generalized trust and burglary victimization.

Fig. 5. Social norms and burglary victimization.


variables with the intercept b0j conditioned by country-level variables. Second, both theintercept and slopes of individual-level variables are conditioned by country-level variables toobserve cross-level interaction effects. The coefficients of slopes are defined by country-levelvariables and treated as random when their random effects are significant at p < .05. Forother individual-level variables, slopes are treated as fixed. The full HGLM models are locatedin Appendix B.

Table 4 shows the results of the HLM analysis for robbery victimization with bothcountry- and individual-level variables. Among three indicators of social capital, onlygeneralized trust and social norms are negatively and significantly associated with victimi-zation risk without other country-level predictors. When control variables are taken intoaccount, however, only the effect of social norms remains significant. Supporting theMarxian-world system perspective and the Durkheimian-modernization perspective, urban-ization and economic inequality is positively and significantly associated with the risk ofcrime in countries. When all three indicators of social capital and control variables enter intothe model, only economic inequality remains significant. At the individual level, risky life-style, perceived unsafety, age, sex and marital status are associated with greater odds ofrobbery victimization. Contrary to the opportunity theory of criminal victimization, however,respondents who adopt crime prevention strategies such as burglar alarms and special doorlocks are more likely to be victimized. This unusual result may be explained by the directionof causal order. The preventive efforts at the individual level may be better understood asa response to victimization experiences rather than proactive strategies to prevent victimi-zation before crime takes place (Roh et al., 2010). People who go out more frequently in the

Fig. 6. Civic engagement and burglary victimization.


evening for recreational purposes are found to have a greater risk of robbery victimization.As the lifestyle theory and routine activities theory contend, an individual’s tendency to goout in the evening provides potential offenders with more chances to detect vulnerablevictims. Living in an unsafe environment is also associated with greater risk of robberyvictimization. Respondents who perceive that they are unsafe after dark are more likely to bevictimized. In terms of sociodemographic characteristics, age, sex, and marital status aresignificantly related to robbery victimization. Young unmarried males are more frequentlyrobbed than their counterparts. Neither high income nor low income shows a significanteffect on robbery victimization.

Table 5 shows the results of the HLM analysis for burglary victimization. When individual-level variables are added, the effects of social capital and other country-level variables onvictimization risk remain unchanged. As in the intercept-as-outcome models without individualvariables, none of the social capital indicators show a significant association with burglaryvictimization. While ethnic heterogeneity is positively and significantly related to the risk ofvictimization, supporting the ecological perspective, urbanization and economic inequality arenot associated with victimization at a statistically significant level. At the individual level,preventive measures, risky lifestyle, and perceived safety are positively and significantly relatedto burglary victimization. When it comes to the positive association between preventivemeasures and victimization, the same logical explanation can be applied as in robberyvictimization; the adoption of crime prevention strategies should be understood as a conse-quence of direct criminal victimization experiences. Like robbery victimization, youngerrespondents are more frequently burglarized. In contrast, sex and marital status are not related

Table 2

Intercept-as-outcome models for robbery victimization.

Fixed effect Robbery Burglary

Coef. SE Odds ratio Coef. SE Odds ratio

Individual level

Intercept �4.189** .166 .015 �2.975** .120 .051

Preventive measures .086 .049 1.089 .201** .039 1.222

Risky lifestyle .118** .017 1.126 .053** .015 1.054

Perceived unsafety .400** .033 1.493 .226** .022 1.253

Age -.075** .011 .928 -.017* .006 .983

Sexa .452** .056 1.572 .031 .036 1.031

High income -.029 .059 .971 .143** .046 1.153

Low income .031 .062 1.032 .013 .041 1.013

Marital status .309** .054 1.362 .003 .038 1.003

Random effect Robbery Burglary

Variance df c2 Variance df c2

Intercept .871 54 169.264** .447 54 126.826**

Preventive measures .038 54 55.014 .026 54 81.170**

Risky lifestyle .002 54 48.321 .004 54 61.586

Perceived unsafety .028 54 125.273** .009 54 92.987**

Age .003 54 91.268** .001 54 72.500*

Sex .077 54 107.397** .022 54 73.108*

High income .057 54 71.745 .044 54 92.142**

Low income .054 54 50.444 .012 54 45.100

Marital status .039 54 61.175 .010 54 66.781

yp < .1, *p < .05, **p < .01 (2-tailed).


to the odds of burglary victimization at a significant level. In consistence with the opportunitytheory which contends that people of higher income, with greater potential rewards, arefrequent targets of burglary, high income is associated with an increase in the risk of burglaryvictimization.

Table 3

Intercept-as-outcome models.

Fixed effects Robbery Burglary

Model1 Model2 Model3 Model1 Model2 Model3

Coef. SE Coef. SE Coef. SE Coef. SE Coef. SE Coef. SE

Intercept �2.931** .087 �2.931** .087 �2.932** .089 �2.207** .070 �2.207** .070 �2.207** .069

Generalized trust �.011y .006 e e e e .000 .005 e e e eSocial norms e e �.299* .149 e e e e .102 .122 e e

Civic engagement e e e e �.007 .005 e e e e .004 .004

Urbanization .006 .005 .004 .005 .010 .006 .002 .004 .002 .004 �.001 .005

Economic inequality .024* .011 .030** .011 .027* .011 .004 .009 .003 .008 .005 .009

Ethnic heterogeneity .838y .463 .766 .468 .919y .467 .669y .370 .728y .371 .692y .363

Random effects Robbery Burglary

Model1 Model2 Model3 Model1 Model2 Model3

Variance SD Variance SD Variance SD Variance SD Variance SD Variance SD

Intercept .396** .629 .394** .628 .411** .641 .262** .511 .257** .507 .255** .505

yp < .1, *p < .05, **p < .01 (2-tailed).

Table 4

HGLM models for robbery victimization.

Fixed effects Model1 Model2 Model3 Model4 Model5 Model6 Model7

Coef. Coef. Coef. Coef. Coef. Coef. Coef.

Intercept �4.118** �4.101** �4.128** �4.107** �4.133** �4.105** �4.108**

Individual level

Preventive measures .043 .042 .043 .042 .043 .042 .042

Risky lifestyle .104** .106** .103** .105** .105** .106** .105**

Perceived unsafety .398** .392** .403** .394** .402** .393** .392**

Age �.074** �.075** �.075** �.075** �.074** �.075** �.074**

Sex .448** .443** .454** .444** .456** .446** .443**

High income .009 .012 .007 .017 .008 .013 .012

Low income .025 .022 .024 .021 .025 .022 .021

Marital status .314** .316** .315** .317** .314** .317** .317**

Country level

Generalized trust �.012* �.005 e e e e �.002

Social norms e e �.297* �.224y e e �.198

Civic engagement e e e e �.004 �.003 �.001

Urbanization e .008y e .007 e .010y .008

Economic inequality e .026* e .029** e .027** .027**

Ethnic heterogeneity e .491 e .365 e .536 .374

Random effects Model1 Model2 Model3 Model4 Model5 Model6 Model7

Variance Variance Variance Variance Variance Variance Variance

Intercept .550** .386** .619** .401** .621** .391** .397**


Age .002** .002** .002** .002** .002** .002** .002**

Sex .076** .076** .075** .076** .076** .076** .076**

yp < .1, *p < .05, **p < .01 (2-tailed).


Finally, Table 6 shows the effects of country-level variables upon the relationship betweenindividual-level variables and criminal victimization. There is no significant difference in therelationship between any individual-level variable and robbery victimization across the level ofsocial capital. In term of burglary victimization, generalized trust is negatively associated with theeffects of age and sex upon the risk of victimization. As the level of generalized trust increases, theeffect of age upon burglary victimization increases too. This indicates that age is better predictor ofburglary victimization in a country with greater generalized trust. In contrast, the positive rela-tionship between sex and burglary victimization reduces as generalized trust increases. In otherwords, the difference in burglary victimization between males and females is greater in a countrywith a lower level of generalized trust. generalized trust and civic engagement are positivelyassociatedwith the effects ofhigh incomeuponburglaryvictimization.Thus, asgeneralized trustorcivic engagement increases, the relative risk of burglary for high-income people becomes greater.

5. Discussion and conclusion

In sum, using the four waves of ICVS, which covered 57 countries over two decades, wehave investigated whether the multidimensional measures of social capital can explain thecross-national variation in criminal victimization. A multilevel analysis method has been usedto take into account not only the well-established macro-level correlates, but also individual-level variables mostly drawn from opportunity theory of criminal victimization.

Table 5

HGLM models for burglary victimization.

Fixed effects Model1 Model2 Model3 Model4 Model5 Model6 Model8

Coef. Coef. Coef. Coef. Coef. Coef. Coef.

Intercept �2.939** �2.929** �2.942** �2.934** �2.039** �2.932** �2.933**

Individual level

Preventive measures .200** .200** .200** .201** .200** .198** .199**

Risky lifestyle .046** .045** .046** .046** .045** .045** .045**


Age �.017* �.018* �.017* �.017* �.017* �.018* �.018*

Sex .032 .021 .032 .023 .033 .027 .024

High income .140** .141** .142** .141** .141** .140** .142**

Low income .013 .011 .013 .012 .013 .012 .012

Marital status �.021 �.022 �.021 �.022 �.021 �.022 �.023

Country level

Generalized trust .000 .006 e e e e .001

Social norms e e .114 .179 e e .140

Civic engagement e e e e .001 .005 .003

Urbanization e .000 e .001 e �.003 �.001

Economic inequality e .003 e �.000 e .002 .002

Ethnic heterogeneity e .827* e .858* e .756* .872*

Random effects

Intercept .431** .373** .431** .388** .435** .394** .396**

Preventive measures .029** .030** .029** .030** .029** .029** .030**


Age .001** .001** .001** .001** .001** .001** .010*

Sex .019* .019* .019* .019* .019* .019* .019*

High income .040** .040** .040** .039** .040** .040** .039**

yp < .1, *p < .05, **p < .01 (2-tailed).


The results supported the findings of previous studies only in part. While a higher level ofsocial capital was found to reduce the likelihood of robbery victimization, no significant impactwas observed upon burglary victimization by social capital. With regard to the three dimensionsof social capital, generalized trust and social norms exerted significant effects on robberyvictimization in the expected direction. Only the influence of social norms remained significantwhen controlling for both country-level and individual-level correlates. Economic inequalitywas a robust determinant of robbery victimization, whereas ethnic heterogeneity yieldeda significant effect on burglary victimization. At the individual level, risky lifestyle, andperceived unsafety were positively and significantly related to both robbery and burglaryvictimizations. While unmarried young males faced a greater risk of robbery victimization,young individuals with a high income were more frequent targets of burglary.

Why might social capital influence only robbery and not burglary? First of all, the variationof burglary victimization across countries is not as large as that of robbery victimization. Theinterclass correlation coefficients show that only 7.5% of the total variance of burglaryvictimization is explained by the country-level differences, whereas the proportion of variancethat the country level accounts for is 15.2% for robbery victimization. In other words,individual-level factors exert greater influence on the risk of burglary victimization thanrobbery victimization. Burglary may indeed be less influenced by country-level factors thanrobbery because the former is more opportunity-driven (Clarke and Cornish, 1985). Robbery is


regarded as a more serious crime than burglary because the commission of robbery requires theactual involvement of the victim and the use of violence. Thus, a perpetrator of robbery,compared with that of burglary, should bear a greater burden of harsh punishment and moralcondemnation. In contrast, a particular situation or opportunity, such as the existence of easy

Table 6

Cross-level interaction.


Individual level Country level Coef. SE Coef. SE

Intercept �4.133** .135 �2.955** .118

Generalized trust �.007 .009 .011 .008

Social norms �.108 .207 .007 .204

Civic engagement �.006 .007 �.002 .006

Urbanization .006 .008 �.004 �.007

Economic inequality .027y .014 .012 .014

Ethnic heterogeneity .951 .610 .749 .573

Preventive measures Intercept .403** .033 .205** .043

Generalized trust e e .002 .003

Social norms e e .041 .083

Civic engagement e e �.004 .002

Urbanization e e .005 .003

Economic inequality e e �.005 .005

Ethnic heterogeneity e e .107 .236

Risky lifestyle Intercept .092** .013 .048** .012

Perceived unsafety Intercept .401** .032 .225** .023

Generalized trust .002 .003 �.003 .002

Social norms �.040 .058 .023 .043

Civic engagement .002 .002 .002 .001

Urbanization .001 .002. �.000 .001

Economic inequality .001 .004 �.001 .003

Ethnic heterogeneity �.314y .182 .035 .128

Age Intercept �.075** .010 �.017* .007

Generalized trust �.001 .001 �.001* .000

Social norms �.009 .016 .013 .012

Civic engagement �.000 .000 .000 .000

Urbanization �.001 .001 .000 .000

Economic inequality .000 .001 �.001 .001

Ethnic heterogeneity .039 .049 �.012 .035

Sex Intercept .470** .057 .027 .034

Generalized trust .007 .005 �.006* .003

Social norms .093 .103 �.001 .067

Civic engagement .001 .003 .003 .002

Urbanization .004 .004 �.002 .002

Economic inequality �.004 .007 �.000 .004

Ethnic heterogeneity .282 .312 �.379y .190

High income Intercept .012 .044 .123** .040

Generalized trust e e .006y .003

Social norms e e �.074 .084

Civic engagement e e .006* .002

Urbanization e e �.002 .003

Economic inequality e e �.003 .005

Ethnic heterogeneity e e .377y .218

(continued on next page)

Table 6 (continued )


Individual level Country level Coef. SE Coef. SE

Low income Intercept .019 .050 .007 .038

Marital status Intercept .323 .044 �.020 .035

Random effects Robbery Burglary

Variance SD Variance SD

Intercept .352** .593 414** .643

Preventive measures e e .040** .199

Perceived unsafety .024** .154 .012** .108

Age .002** .041 .001** .030

Sex .069** .263 .015 .123

High income e e .021y .144

yp < .1, *p < .05, **p < .01 (2-tailed).


targets, is more important during a burglary. Offenders of property crime, including burglaryand theft, are often tempted into transgressions simply by vulnerable targets (e.g. an unlockedvacant house, unattended small goods).

Second, the reversed causation between social capital and criminal victimization mayexplain the difference between the findings on robbery and burglary. Previous studies havepointed out that the association between social capital and crime can be reciprocal. That is, anincrease in crime rates may have a possible negative impact on social capital, as well as theother way around. Witnessing an increase in predatory crime, community members will shunrelationships with neighbors for fear of being the victim of a crime. This will lead to a dete-rioration of networks in the community and a reduction in the sense of civic obligation, mutualtrust, and belief in shared norms. However, the negative impact on social capital variesdepending on the type of crime. Generally speaking, the impact of crime on social capital isparticularly serious when it comes to violent crime (Saegert and Winkel, 2004). People tend toperceive a confrontation with a robber (sometimes armed with weapons) on the street as morefearful and intimidating than a loss of personal property while they are away from home. Thus,the inverse and significant association between robbery victimization and social capital found inthis study may indicate that an increase in robbery generates a detrimental effect on socialcapital, but that this is not the case for burglary.

This study makes a significant contribution to the literature of social capital in that it takesinto consideration both country-level and individual factors to explain the effects of socialcapital on criminal victimization. Nevertheless, several methodological limitations warrantcautious interpretation of the results. First, as discussed above, the findings may reflecta possible negative effect of robbery victimization on social capital. Without controlling for thereciprocal influence of criminal victimization on social capital, it is hard to make a causalinference between the two factors. Second, the measurement of civic engagement could beelaborated. Since Putnam (1993) measured civic engagement using the percentage of respon-dents who were active members of voluntary organizations listed in the WVS surveys, themajority of studies have followed the same manner of measurement. In this study, however, werelied solely upon a single political activity (signing a petition) to measure civic engagementbecause data on active membership in voluntary organizations were not available in manycountries that participated in the WVS surveys. Finally, among the 57 countries included in the


study, about 60% of them (34 countries) are located in Europe, compared with .9% in Africaand .7% in Asia. The lack of representation in the country sample results from the over-representative participation by European countries in the ICVS. Future research is recom-mended to include more non-Western countries, which will enhance the external validity of theresults.

Appendix A. Correlation matrix (N [ 56)

1 2 3 4 5 6 7

1. Robberyvictimization

1
�.436** �.322* �.327* �.155 .640** .502**
2. Generalized trust
1 .430** .500** .251y �.417** �.359** 3. Social norms 1 .242y .054 �.124 �.243y 4. Civic engagement 1 .627** �.390** �.362** 5. Urbanization 1 �.355** �.380** 6. Economic inequality 1 .550** 7. Ethnic heterogeneity 1
1 2 3 4 5 6 7

1. Burglaryvictimization

1
�.133 .066 �.004 �.098 .245y .308*
2. Generalized trust
1 .408** .491** .205 �.408** �.332* 3. Social norms 1 .218 .011 �.062 �.195 4. Civic engagement 1 .601** �.368** �.341** 5. Urbanization 1 �.264* �.348** 6. Economic inequality 1 .497** 7. Ethnic heterogeneity 1
yp < .1, *p < .05, **p < .01 (2-tailed).

Appendix B. Full HGLM models

Individual-level model

Criminal victimization¼ b0j þ b1jðpreventive measuresÞ þ b2jðrisky lifestyleÞþ b3jðperceived unsafetyÞ þ b4jðageÞ þ b5jðsexÞþ b6jðhigh incomeÞ þ b7jðlow incomeÞ þ b8jðmarital statusÞ

Country-level model

For robbery victimization

b0j ¼ g00 þ g01ðgeneralized trustÞ þ g02ðsocial normsÞ þ g03ðcivic engagementÞþ g04ðurbanizationÞ þ g05ðeconomic inequalityÞ þ g06ðethnic heterogeneityÞ þ m0j


b1j ¼ g10

b2j ¼ g20




b6j ¼ g60

b7j ¼ g70

b8j ¼ g80

For burglary victimization



b2j ¼ g20





b7j ¼ g70

b8j ¼ g80


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