Crime and local inequality in South Africa

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Abstract

We examine the effects of local inequality on property and violent crime in South Africa. The findings are consistent with economic theories relating local inequality to property crime and also with sociological theories that imply that inequality leads to crime in general. Burglary rates are 25–43% higher in police precincts that are the wealthiest among their neighbors, suggesting that criminals travel to neighborhoods where the expected returns from burglary are highest. Finally, while we find little evidence that inequality between racial groups fosters interpersonal conflict at the local level, racial heterogeneity itself is highly correlated with crime.

Introduction

Crime is among the most difficult of the many challenges facing South Africa in the post-apartheid era. The country's crime rates are among the highest in the world and no South African is insulated from its effects. Beyond the pain and loss suffered by crime victims, crime also has less direct costs. The threat of crime diverts resources to protection efforts, exacts health costs through increased stress, and generally creates an environment unconducive to productive activity. Additionally, the widespread emigration of South African professionals in recent years is attributable in part to their desire to escape a high-crime environment.1 All of these effects are likely to discourage investment and stifle long-term growth in South Africa. Consequently, it is important to understand the factors that contribute to crime.

Both economic and sociological theory has linked the distribution of welfare to criminal activity. Economists have suggested that inequality may capture the differential returns to criminal activity and thereby have an association with crime rates. If criminals travel, not only the welfare distribution in the local area, but that of neighboring areas as well, may be linked to local crime levels. Sociologists have hypothesized that inequality and social welfare in general may have effects on crime through other channels. Inequality may be associated with lack of social capital, lack of upward mobility, or social disorganization, all of which may cause higher levels of crime. Furthermore, economic inequalities between groups may engender conflict in a society by consolidating and reinforcing ethnic and class differences (Blau and Blau, 1982).

In this paper, using data on crime and estimates of welfare measures by police station jurisdiction2 in South Africa, we consider three questions. First, we examine the extent to which economic versus sociological theories explain the variation in crime rates by comparing the implications of various theories for violent crime and property crime separately. Next, we consider how the relative economic position of a community among neighboring areas may be associated with crime. Finally, we examine whether crime is particularly prevalent in areas with high inequality between racial groups.

The next section discusses the reasons why there might be an association between economic welfare and crime at the community level. Section 3 summarizes the empirical literature on inequality and crime and explains the contribution of this paper. Section 4 briefly outlines the empirical approach and describes our data sources. Section 5 presents the regression results for various types of crime and Section 6 concludes.

Section snippets

Crime and economic welfare

There are a number of reasons why the local distribution of economic welfare might be associated with the prevalence of crime. Various arguments have been made by economists, sociologists, and public health specialists.

First, community welfare measures may be associated with crime levels via a relationship with the returns from crime and noncrime activities. In his seminal work, Becker (1968) proposes an occupational choice model in which the incentives for individuals to commit crime are

Evidence from the literature

The empirical evidence on the crime–inequality relationship, mainly based on comparisons across states and cities in the United States, or across countries, generally shows a positive correlation between inequality and crime. A meta-analysis of 34 aggregate data studies (Hsieh and Pugh, 1993) shows that 97% of bivariate correlation coefficients for violent crime with either poverty or inequality were positive, with 80% of the coefficients above 0.25. Ehrlich (1973) finds a positive relationship

Empirical strategy

We first establish the bivariate relationships between two welfare indicators (inequality and mean expenditure) and various types of crime in South Africa across police precincts. Next, we consider whether the observed relationships are more consistent with the economic or sociological theories of crime, by controlling for costs and benefits of crime and by comparing the results for violent and property crimes. We also examine the effect on crime of the relative position of a community among

Main results

Fig. 1, Fig. 2, Fig. 3, Fig. 4 show log–log scatter plots of per capita crime levels versus inequality. There is a positive correlation for all four crimes shown, although this correlation is the least pronounced for vehicle thefts. The bivariate correlations between mean per capita expenditure and crime rates are shown in Fig. 5, Fig. 6, Fig. 7, Fig. 8. Fig. 5, Fig. 6 show that, absent any additional controls, property crimes are strongly and positively associated with average estimated

Conclusions

Both theoretical and empirical papers in the crime literature have called for an analysis of crime at a smaller level of geographical disaggregation than countries, states or large metropolitan areas. When the unit of analysis is large, not only is there loss of information regarding relative welfare levels across neighborhoods, but also the fact that individuals may travel to conduct criminal activities is ignored. In this paper, utilizing data on crime and welfare in all police precincts in

Acknowledgements

The authors would like to thank Statistics South Africa with help in the generation of our data set. We are grateful to Harold Alderman, Jere Behrman, Eliana La Ferrara, Peter Lanjouw, Misha Lokshin, Martin Ravallion, and two anonymous referees for comments on previous drafts of this paper. These are the views of the authors, and need not reflect those of the World Bank or any affiliated organization.

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