تأثیر سرمایه اجتماعی بر جرم و جنایت : مدارک و شواهد از هلند

This research shows that social capital is important in explaining why crime is so heterogeneous across space. Social capital is considered as a latent construct composed of a variety of indicators, such as blood donations, voter turnout, voluntary contributions to community well-being, and trust. To isolate exogenous variation in social capital, three historical variables are used as instruments: the fraction of foreigners, the number of schools and the fraction of Protestants in 1859. The historical information provides heterogeneity across municipalities in these three variables. In an application to Dutch municipalities the 2SLS estimates suggest that the exogenous component of social capital is significantly and negatively correlated with current crime rates, after controlling for a range of contemporaneous socio-economic indicators. Next, the robustness analysis shows why some social capital indicators are more useful than others in applied economic research.

One of the most puzzling elements of crime is its heterogeneity across space. Even after controlling for a range of variables, there remains a high variance of crime across space.2 How can we explain these differences in crime rates across space? The overall annual crime rate in our data varies between 1.6 and 14.6 incidents per capita, with observable factors, such as population density and size, the youth unemployment rate, the mean level of education and income inequality explaining only a small fraction of this difference. Next to that, consider the following example: The cities of Utrecht and Leiden are comparable on various socio-economic indicators, but Utrecht faces a crime rate of 14.3 per capita, relative to a rate of only 6.3 in Leiden. In this research, we argue that differences in social capital account for a significant part of the observed differences in crime rates across cities. We test our ideas using a dataset with elaborate information of about 140 Dutch municipalities. To do so, we view history as a main determinant of present outcomes and show that we can isolate exogenous variation in social capital by using historical institutions as instruments, following a recent body of empirical studies (e.g., Guiso et al., 2008a, Akçomak and ter Weel, 2009 and Tabellini, 2010). Our estimates suggest that differences in crime rates can for some part be traced back to historical differences in social capital between Dutch municipalities. To what extent do these historical indicators shape current social capital? We employ a variety of social capital measures. Previous research treats social capital as a positive sum in the sense that social capital is an asset to the individual and the community.3Fukuyama (1996) suggests that it might be easier to measure the absence of social capital through traditional measures of social dysfunction such as, family breakdown, migration and erosion in intermediate social structures. This approach hinges on the assumption that just as involvement in civic life is associated with higher levels of social capital, social deviance reflects lower levels of social capital. We use voluntary contributions to charity, electoral turnout, blood donations and trust to measure the presence of social capital. Divorce rates and population heterogeneity are used as indicators for the absence of social capital. These indicators are highly correlated to each other and a common denominator, combining several multifaceted dimensions, may serve as a useful proxy for social capital (see e.g., Table 1 and Fig. 1). We treat social capital as a latent construct and build a number of social capital indices using principal component analysis (PCA).What is the causal effect of social capital on crime? Sampson (1988) argues that communities are empowered through their trust in each other, which enables them to take action against crime and to cooperate with formal control, such as the police.4Ferrer (2010) shows that crime rates fall if communication between the police and the general public increases because community involvement stimulates the productivity of law enforcement. Involvement in community activities leads to strong social bonds by which conflicts are resolved in a more peaceful way compared to communities with weak social bonds (e.g., Hirschi, 1969). Hence, the cost of conflict resolution decreases and more conflicts will be solved. Consequently, social capital increases the probability of being caught and the costs of crime, which reduces the crime rate. This effect of social capital on crime is different from the effects of more traditional measures to explain crime, such as unemployment and inequality. These measures focus on the difference between earnings from legal and illegal activities to explain crime rates. We use three historical “institutions” to instrument social capital. First, we measure the opportunities for formal education by measuring the number of schools in 1859. Goldin and Katz (1999) show that historical differences in human capital investments help to explain differences in current levels of social capital.5 Second, we measure population heterogeneity by the percentage of foreign inhabitants in 1859. Population heterogeneity is a factor that may trigger disattachment because higher levels of heterogeneity would break closure, reduce acquaintance among residents and may result in lower trust among members of the community (Rose and Clear, 1998 and Rosenfeld et al., 2001).6 Third, we use the number of mainline Protestants in 1859 as an indicator for social capital. Mainline Protestants participate more in community-wide activities which build bonds across communities (Beyerlein and Hipp, 2005). Recent studies show the validity of such an approach by consistently highlighting the role of history in explaining current social capital and culture (e.g., Guiso et al., 2008a, Akçomak and ter Weel, 2009 and Tabellini, 2010).7 Our estimates show that social capital is negatively associated with crime rates across Dutch municipalities. On average a one standard deviation increase in social capital would reduce crime rates by 0.32 of a standard deviation. This implies that the inclusion of social capital explains about 10% of the total variation in crime rates. Given that standard determinants explain only about half of the variation in crime rates, our estimates are of a substantial magnitude. The findings reveal that non-survey indicators such as voluntary contributions and voter turnout are more robust when compared to survey indicators such as generalized trust. The empirical results are robust to the inclusion of other variables, to the exclusion of influential observations, to alternative specifications, to the use of different subsamples and regional definitions, and across different types of crime. This paper contributes to the literature in several aspects. First, we treat social capital as a latent construct. There are only a number of recent studies that follow a similar approach using survey data at the individual level to measure the presence of social capital (e.g., Svendsen and Bjørnskov, 2007, Owen and Videras, 2009 and Sabatini, 2009). We measure and compare both the presence (e.g., blood donations and voluntary givings) and absence of social capital (e.g., family breakdown and population heterogeneity) using survey and non-survey data, which differentiates our study from the existing literature. This allows us to assess the quality of the different indicators in more detail. Simple correlations between survey and non-survey indicators of social capital display quite high coefficients (see Table 1). For instance, the average of the correlation coefficients between survey based trust and non-survey based social capital indicators is roughly 0.40. Second, we try to provide an explanation for how social capital forms. This aspect is largely ignored in the literature and only took attention recently (e.g., Tabellini, 2008a). We argue that the history of a municipality a century ago shapes current social capital. Third, though crime is a global phenomenon most of the literature is based on the evidence from the United States, the United Kingdom and Canada.8 The Netherlands is an interesting case with homogeneous economic conditions, a high concentration of foreigners and a liberal attitude toward soft drugs. Finally, our units (municipalities) are much smaller in scale and much more homogeneous when compared to other studies using regional information for country analysis. Thus, the results are less likely to be affected by differences in government policies, laws and regulations. Given the high level of homogeneity, the probability of finding a significant correlation between social capital and crime is low, making us confident of the robustness of our estimates. This paper proceeds as follows. We explain how social capital can be integrated into the standard economic model of crime in Section 2. Section 3 presents information on the data and defines our measures of social capital. The empirical strategy and the way we treat social capital in estimating its causal effect on crime is presented in Section 4. Section 5 presents the estimates and a number of robustness checks. Section 6 concludes.

From a community governance perspective, social capital plays an important role in crime prevention by providing informal social control and support and due to network externalities. The presence of social capital provides community-oriented solutions to the crime problem and these solutions are important next to formal measures such as increasing expenditure on police or incarceration. Our findings are in line with Huck and Kosfeld (2007) who show that voluntary crime-watch programs can act as an effective tool for community crime prevention complementing other formal tools. This research contributes to the literature by trying to isolate the effect of social capital on crime rates. Our estimates for Dutch municipalities suggest that communities with higher levels of social capital have lower crime rates. We show that these estimates are robust and we have carefully examined the causality of this relationship. Generally, a one standard deviation increase in social capital reduces crime by roughly around 0.30 of a standard deviation. These estimates contribute to finding an explanation for why crime is so heterogeneous across space. We note that the empirical findings have limitations. Geography and spatial correlations may determine both social capital and crime. It might be easier to argue that crime levels in a municipality are affected by unemployment and income levels of neighboring municipalities. However, it is not straightforward to assume that this holds for the relationship between social capital and crime as well. We consider municipalities as the unit of analysis, which still have geographical boundaries. We assume that crime is mostly a local phenomenon and criminals have better knowledge about the opportunities in cities that they live in, compared to crime opportunities in neighboring cities. Moreover, ideally one should use panel data to infer causality from the data. Cross-sectional analysis has limitations in evaluating causality. Unfortunately, in our setting it is not possible to pursue panel analysis. This is because we do not have the data to do so and more importantly because social capital is a stock that does not change considerably from year to year, whereas variables such as inequality and unemployment do. Alternatively, we use an instrumental variable strategy to capture the exogenous variation in social capital. Our instruments pass over identification tests, however this is not a bullet proof that the instruments are not correlated with unobserved factors that might affect crime as well. Having used many socio-economic indicators as right-handside variables, it is hard to find good instruments that are legitimate in both statistical and economic sense. We use institutional development in the past to proxy for current levels of social capital. Hence, we treat social capital as a long-term phenomenon, which stock has been build during a long period of time. From a policy perspective, this makes our study difficult to apply because our measures of social capital cannot be changed rapidly but need long-term investment. On the positive side, we show that crime is higher in municipalities where more youth is present. Informal education in the early stages of the life cycle provided by the family and community control and support could act as an important mechanism to reduce youth crime and later on to build social networks. This is an area in which policy makers do have impact by means of preventing people to drop out of schools and by setting up schemes to stimulate youth to stay out of unemployment.