UNIVERSITÀ DEGLI STUDI DI PADOVA. Dipartimento di Scienze Economiche Marco Fanno - PDF


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UNIVERSITÀ DEGLI STUDI DI PADOVA Dipartimento di Scienze Economiche Marco Fanno DOES SOCIAL CAPITAL REDUCE CRIME? PAOLO BUONANNO Università di Bergamo DANIEL MONTOLIO University of Barcelona PAOLO VANIN Università di Padova e Pompeu Fabra University of Barcelona October 2006 MARCO FANNO WORKING PAPER N.29 DOES SOCIAL CAPITAL REDUCE CRIME? Paolo Buonanno University of Bergamo Daniel Montolio University of Barcelona Paolo Vanin University of Padua and Pompeu Fabra University of Barcelona October, 19, 2006 Abstract We investigate the effects of civic norms and associational networks on crime rates. Through their impact on trust and economic development, civic norms may raise the expected returns to crime, but they may also increase its opportunity cost and the feelings of guilt and shame attached to it. Associational networks may increase returns to non-criminal activities and raise detection probabilities, but they may also provide communication channels for criminals. The empirical assessment of these effects poses serious problems of endogeneity, omitted variables and measurement error. Italy s great variance in social and economic characteristics, its homogeneity in policies and institutions, and the availability of historical data on social capital in its regions allow us to minimise the first two problems. To tackle the third one, we exploit high and stable report rates for some forms of property crime. Once we address these problems, we find that both civic norms and associational networks have a negative and significant impact on property crimes across Italian provinces. JEL-Classification: A14, K42, Z13 Key-words: Civic norms, Associational networks, Property crime, Italy We thank Robert Putnam for providing his data on social capital in Italy, and Federico Cingano and participants to a conference in Bologna for useful comments. Usual disclaimers apply. Corresponding author: Department of Economics, University of Padua, Via del Santo, Padova (PD) - Italy. Tel.: Fax : 1. Introduction Crime and the fear it generates are among the most important determinants of individual welfare and of the expected returns to many economic activities. It is both intuitive and confirmed by recent theoretical literature that individual choices of crime participation may be significantly affected by the presence of civic norms and associational networks. Yet it is not a priori clear whether their effect should be expected to be positive or negative. Indeed, while civic norms may attach guilt and shame to criminal behaviour, they may also stimulate trust in others, lower resources and effort devoted to selfdefence and thus multiply opportunities for crime. Similarly, associational networks may increase returns to non-criminal activities and raise detection probabilities, but they may also work as communication channels for criminals. Further, whatever the empirical correlation, one may wonder whether it reflects a causal link, in what direction and with what implications for anti-crime policy. Yet such questions have received little attention, particularly by economists. 1 This is especially surprising in light of the recent economic literature on the micro-effects on crime of some forms of social interaction, like peer effects, neighbourhood effects, family background effects, and so on, and, more importantly, in light of the increasing relevance in the economic debate of the concept of social capital, often conceived in terms of social norms and networks that favour coordination and cooperation. Starting from the seminal work by Putnam (1993) on the role of social capital for government well functioning, several contributions in this literature have focused on Italian data. 2 This is due to the fact that Italy displays large and persistent provincial disparities in social and economic characteristics in spite of having common policies, institutions, laws, justice system and school system, of having police forces organised at national level, and of being ethnically and religiously quite homogeneous. Thus, changes in these factors are not responsible for socio-economic differences across Italian provinces, and this in turn substantially reduces the omitted variable problems affecting many cross-country studies. 3 In this paper we exploit provincial level variations in civic norms and associational networks in Italy to investigate their effects on crime rates. We focus on property crimes, because they are more likely to depend on economic motivations than violent crimes. Since social characteristics in Italy are often peculiar traits of single cities, provincial data are probably best suited to capture social capital in this country. We are not aware of any previous studies on the impact of social capital on crime in Italian provinces. 4 Several studies, from Knack and Keefer (1997) to Bjørnskov (2006), find empirically that social capital is best described as a collection of three main dimensions, namely generalised trust, civic norms and associational networks, and that these dimensions have different impacts on economic outcomes. Since our aim is to study the latter two dimensions, we separately consider provincial level measures of cultural and 1 In Section 2.3 we discuss more in depth these mechanisms, together with some recent empirical works by sociologists and criminologists, which start to address these questions. 2 Among others, two recent examples are Guiso et al. (2004) and Peri s (2004) investigations of the role of social capital for financial and economic development. 3 For instance, in Lederman s (2002) cross-country investigation of the relationship between social capital and crime it is difficult to disentangle what is due to differences in social capital from what is due to different institutional settings. 4 Gatti, Tremblay and Larocque (2003) investigate the relationship between civicness and juvenile crime in Italian regions, but their use of regional data imposes serious limitations. Moreover, they do not adequately tackle endogeneity. recreational associations, voluntary associations, voter turnout at referenda and blood donations. 5 To account for criminal networks, we also include a measure of criminal association. In our estimates we control for other major socioeconomic determinants of crime rate, such as income, unemployment rate, education, urbanisation rate, share of youth and clear-up rate. Furthermore, we control for time-invariant local determinants (geographical dummies) and for the length of judicial proceedings, which exhibits great variability across provinces. Empirical work on crime and on social capital is typically affected by several methodological problems, the main of which are, besides omitted variables, measurement errors and endogeneity. Measured crime rates crucially depend on report rates, which vary significantly across crimes and space. If report rates are positively related to social capital, our estimates would be upwards biased. Endogeneity problems arise from the fact that certain forms of crime might affect social capital because they constrain social interaction, as shown, among others, by Liska and Warner (1991). 6 Such a negative effect, due to reverse causation, would downwards bias our estimates. As far as endogeneity is concerned, we consider blood donations and referenda turnout as safely exogenous variables with respect to crime rates. Indeed, blood donations seem to be as exogenous a variable as possible, and the referenda we consider have never concerned issues either related to crime or of direct interest to criminal groups. To control for the possible endogeneity of association density, we exploit the fact that associational networks in Italy are to a significant extent a historical heritage. We then use Putnam s (1993) historical data on associations in Italy as an instrument for current associations, arguing that it is unlikely that such instrument is correlated to current crime rates through other channels. Measurement errors represent a relevant problem in the empirical analysis on crime determinants because of the underreporting that affects crime variables, which is likely to be determined not only by random errors but also by specific and persistent characteristics of each province, among which its level of social capital. To address this methodological issue we need a measure of crime that not only presents a low rate of underreporting, but, more importantly, a homogeneous report rate across space. In particular, thefts and robberies not only display a high degree of underreporting, but also a high heterogeneity in report rates across provinces. By contrast, car thefts do not suffer from underreporting (more than 94% of car thefts are reported) and the rate of report is almost identical across provinces. We present regressions for these three crimes, discuss the implications of high and heterogeneous underreporting and argue that regressions based on car thefts are the most reliable ones. Our evidence, based on an original dataset, which merges existing sources with data collected by the authors, indicates that both civic norms and associational networks have a negative and significant impact on property crimes across Italian provinces. We find that, contrary to expectations, the presence of associational networks and of civic and altruistic norms are positively associated with thefts and robberies, with an effect that in some specifications is significant. Yet, we argue that a possible interpretation is that this result is driven by a positive impact of social capital on rates of report. Indeed, when we study car thefts and thus eliminate any bias due to 5 While several perspectives on trust are possible, the fact that we do not include measures of trust. corresponds to the view that it is rather a (long run) equilibrium outcome than a structural variable. We refer to Glaeser et al. (2000) for the meaning of most commonly used measures of trust. 6 They analyse 26 big U.S. cities in the mid Seventies and show that some forms of crime (e.g., robberies) generate fear and constrain social interaction, which, in turn, reduces opportunities for other forms of crime. 3 heterogeneous report rates, we find a negative and significant effect of social capital on crime, which is very robust across all specifications. In our baseline specification, a standard deviation increase in association density and in blood donations are associated with a reduction in car thefts by 13 and 9 percentage points, respectively. When we use instrumental variables these effects are even stronger, confirming that they are not due to reverse causation. The remainder of the paper is organised as follows. In Section 2 we review the main literature on the effects of social environment on crime. In Section 3 we present our data and empirical strategy. Section 4 contains the results and Section 5 concludes. 2. Literature on social determinants of crime There are at least four strands of literature that are related to our investigation: first, theoretical economic models of the social determinants of crime, many of which are based on multiple equilibria; second, empirical studies on such determinants; third, the few specific analyses of the effects of social capital on crime; and finally, works that measure social capital in Italy and evaluate its effects, although possibly not on crime but on different variables. 2.1 Economic models based on multiple equilibria Following Becker (1968), most economists consider individual decisions of crime participation as rational choices, taken by comparison of expected costs and benefits. At the core of several theoretical models, which extend the basic paradigm to include the social component of such costs and benefits, lies some form of strategic complementarity: my returns to becoming criminal, relative to not doing so, are higher, the more other individuals choose criminal behaviours. This gives rise to multiple equilibria, which help explain variation of crime rates across regions with similar fundamentals. Several specific mechanisms that yield such result are explored in the literature. For instance, Sah (1991) focuses on punishment probability, which is perceived to be lower, the higher the expected number of criminals; Murphy et al. (1993) emphasise that criminal behaviour may crowd out legal productive activities, thus becoming relatively more rewarding, the more it expands (see also Burdett et al and Rasmusen 1996). Calvó-Armengol and Zenou (2004) show that denser social networks may increase aggregate crime levels, by facilitating know-how sharing among criminals; and Weibull and Villa (2005) argue that the effectiveness of social norms against crime, due to both guilt and shame, is decreasing in crime rates, thus being yet another possible source of multiple equilibria. 7 Several extensions of these frameworks have been considered, especially with the aim of studying determination and effects of anti-crime policies, as well as their interaction with various aspects of the social structure (Kugler et al., 2004; Silverman, 2004 and Calvó-Armengol et al., 2004). 2.2 Empirical evidence on social determinants of crime 7 Most economists in the tradition of methodological individualism would consider exogenous social norms as an ad hoc explanation. Weibull and Villa s (2005) model yields a unique equilibrium if social norms effectiveness (the degree to which they generate guilt and shame) is exogenous, whereas multiple equilibria are possible when it is endogenous. In such case, social norms effectiveness and crime rates are jointly determined in equilibrium. 4 Since the early Nineties, economists have collected more and more empirical evidence on social determinants of criminal behaviour. Rather than from multiple equilibria models, most of the theoretically driven empirical studies start from models with a unique equilibrium, on which comparative statics exercises may be conducted. Among other factors, attention has been devoted to criminal records in residence neighbourhood and in the family, as in Case and Katz (1991), to imitation of peers behaviour and the degree of social interaction characteristic of each crime, as in Glaeser et al. (1996) and Patacchini and Zenou (2005), and to structural properties of relational networks, or of an individual s position therein, as in Haynie (2001) and Calvó-Armengol et al. (2005). Just to mention few results, which are of direct relevance for our study, Glaeser et al. (1996) find that the degree of social interaction is particularly high for auto theft, for crimes committed by younger criminals and in cities with more female-headed households (which is interpreted to mean that the average social interactions among criminals are higher when there are not intact family units, p. 543). Different and convincing evidence of the relevance of social and non-pecuniary factors for crime decisions is also provided by Levitt and Venkatesh s (2000) analysis of a drug-selling gang. Rubio (1997) points out that in Colombia, where incentives for crime are high, social capital may have the perverse effects of reinforcing crime choices. One major problem of most empirical studies of neighbourhood effects on crime is that it is hard to control for self-selection and endogeneity, so that it is often hard to draw causal inferences. Ludwig et al. (2001) and Kling et al. (2005) are able to overcome this problem, thanks to the natural experiment constituted by the Moving to Opportunity (MTO) randomised housing mobility program of the U.S. Department of Housing and Urban Development, which since 1994 has relocated families from high to low poverty neighbourhoods. It is found that relocating sharply reduces juvenile arrests for violent crimes, but that after several years it has the effect of actually raising male arrests for property crimes, possibly because it offers, especially to males, new opportunities for property offences. While this literature is more concerned with the micro-effects of local interaction patterns on crime rates than with the impact of more aggregate aspects of the social structure, like widespread civic norms and associational networks, as we are in the present study, it confirms three aspects which are important for us: the general relevance of social determinants of crime, the endogeneity problems that arise when one tries to evaluate such determinants, and the possibility that apparently desirable aspects of the social structure turn out to be conducive to crime. 2.3 Effects of social capital on crime: positive or negative? It is intuitive to think of a positive link between social capital and crime, at least in the short run: if areas with higher social capital display higher trust, citizens in those areas may feel less threatened and put less resources and effort to defend themselves and their properties, thus remaining potentially more exposed to criminals. Crime opportunities would then be higher and this would both stimulate local criminality and attract criminals from other regions. Such attraction effect would be reinforced by the positive correlation between trust and economic development (Knack and Keefer 1997). 8 Yet 8 If higher crime rates, in turn, induce a fast reduction in trust, because individuals become more careful, this crime-attraction effect of trust would not last long enough to be detected in cross-sectional data; but if, by contrast, trust is not so much responsive to crime, then we could expect a positive correlation between crime and social capital. 5 several theories, developed by sociologists and criminologists, imply a negative effect of social capital on crime. For instance, Rosenfeld et al. (2001) argue that theories of social disorganisation, anomie and strain all predict that civic engagement and social trust (which they refer to as social capital) should reduce crime, because they increase formal and informal social control, strengthen the effectiveness of social norms and provide resources for individual goal attainment. Very few empirical studies relate social capital to crime. Among them, most attention has been devoted to violent rather than property crime. Using U.S. data for the Nineties, and controlling for a number of covariates, Rosenfeld et al. (2001) find a negative and significant impact of social capital on homicide rate, but they also find some evidence of reverse causation. 9 One difficulty in interpreting their results is that their measure of social capital merges together different variables, so that it is not entirely clear what it indicates. Again with U.S. data, but with a different dataset, Messner et al. (2004) disentangle different dimensions of social capital and find that homicide rates decrease in social trust, as they expected, but, surprisingly, increase in community and political activism, a result which they find puzzling. In two other interesting studies, both conducted with a 2SLS approach, Chamlin and Cochran (1997) find that social altruism, proxied by charitable donations, has a negative and significant impact on both property and violent crime in a sample of U.S. cities, and Heaton (2006) finds that most of the negative correlation between crime and religious participation is indeed due to an effect of the former on the latter, so that, when historical data on religious adherence are used to control for endogeneity, religion is found to have no significant effect on either property or violent crime. These results convey two messages: at the methodological level, they confirm both the opportunity to disentangle the different dimensions of social capital and the relevance of reverse causation problems; at the substantive level, they offer mixed evidence on the impact of different forms of social capital on crime and they make clear that more and careful empirical analysis is needed. 2.4 On the effects of social capital in Italy
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