Inequality and crime revisited: effects of local inequality and economic segregation on crime


Economic inequality has long been considered an important determinant of crime. Existing evidence, however, is mostly based on inadequately aggregated data sets, making its interpretation less than straightforward. Using tract- and county-level U.S. Census panel data, I decompose county-level income inequality into its within- and across-tract components and examine the extent to which county-level crime rates are influenced by local inequality and economic segregation. I find that the previously reported positive correlation between violent crime and economic inequality is largely driven by economic segregation across neighborhoods instead of within-neighborhood inequality. Moreover, there is little evidence of a significant empirical link between overall inequality and crime when county- and time-fixed effects are controlled for. On the other hand, a particular form of economic inequality, namely, poverty concentration, remains an important predictor of county-level crime rates.

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  1. 1.

    Information on socioeconomic characteristics of the population at the tract-level is not available in the 2010 decennial Census, as the “long form” Census questionnaire, which elicited such information from respondents, has been replaced by the annual ACS.

  2. 2.

    Part I index crimes are murder, rape, robbery, aggravated assault, burglary, larceny, and motor vehicle theft.

  3. 3.

    On the other hand, residential segregation may be the outcome of the spatial distribution of crime. High-income households may have chosen to live far from high-crime, disadvantaged neighborhoods to avoid the risk of victimization. Cullen and Levitt (1999) describe empirical evidence on the “urban flight” of highly-educated households following increases in inner-city crime rates.

  4. 4.

    The above argument based on the supply and demand of criminal opportunities complements the existing peer effects literature on negative spillovers of criminality (Bayer et al. 2009; Gaviria and Raphael 2001; Glaeser et al. 1996), which explains high concentration of crime in economically disadvantaged neighborhoods. This argument is also closely related to the extensive sociology literature on the poverty concentration effect on crime (Sampson et al. 1997; Wilson 1987).

  5. 5.

    There are other theoretical models which can explain observed high rates of violent crime victimization among the poor. For example, one may have to kill another to avoid being killed (O’Flaherty and Sethi 2010) or want to build a reputation of being a thug to lower his risk of victimization (Silverman 2004; Bjerk 2010). Moreover, criminals may use violence as an instrument to successfully carry out economically-motivated crimes (Grogger 2000).

  6. 6.

    Due to the data availability issue, most existing empirical evidence on the link between inequality and crime at a local level come from cross-sectional analyses (Hipp 2007; Messner and Tardiff 1986). A notable exception is Freedman and Owens (2014), who examine the effect of local inequality on the residents’ criminal risks using a plausibly exogenous variation in localized economic development.

  7. 7.

    Counties in the state of Illinois are dropped from the sample because rape statistics were not available for these counties. Including these counties in the sample in the analysis for Part I index crimes other than rape, however, results in similar findings.

  8. 8.

    Neighborhood Change Database is a product of Geolytics, inc ( See Tatian (2003) for technical details on the tract boundary normalization process used.

  9. 9.

    The sample choice also matches the empirical analysis reported in Kelly (2000).

  10. 10.

    Dissimilarity index has been widely used as a standard measure of economic segregation (e.g., Cutler and Glaeser 1997, Cutler et al. 1999). Massey and Denton (1988) present a detailed description of various segregation measures, including dissimilarity and isolation indices.

  11. 11.

    Local linear regression results in all four panels of Fig. 4 are computed using the triangle kernel with a bandwidth size of 0.02.

  12. 12.

    I also estimated Eq. 11 using the rate of arrest as the dependent variables and obtained similar results.

  13. 13.

    Following the official UCR classification, I define murder, rape, robbery, and aggravated assault as violent crimes and burglary, larceny, and motor vehicle as property crimes. Robbery is considered as violent crime in the UCR crime categories, but is similar to property crimes for its pecuniary motivation. In an alternative specification, I defined robbery as property crime and obtained similar results.

  14. 14.

    I attempted to extend the regression specification by including (log) police expenditure per population to control for the difference in police resource across counties, but this led to little change in the estimated effect of inequality on crime. The police expenditure variable is omitted from the main specification in order to avoid the well-known problem of reverse causality between police resource and crime.

  15. 15.

    Previous studies often relied on instrument variables to obtain causal estimates of the effect of an economic condition on crime. For example, Gould et al. (2002) use the variations in industry composition across states and cities as instrument variables when estimating the effect of local labor market conditions on crime, and Bjerk (2010) estimates the effect of economic segregation on crime by using the (1) shares of local government revenue coming from state and federal governments and (2) public housing assistance as instrument variables. Motivated by these studies, I ran an IV analysis using (1) the number of workers employed in manufacturing and retail industries and (2) the county-level share of individuals living in public housing. The estimation results, presented in Appendix Table 9, are generally consistent with the main results but are more imprecise.

  16. 16.

    Economic loss is defined as the value of cash and/or property taken upon victimization.

  17. 17.

    A number of empirical studies find that the deterrent effect of the perceived risks of punishment can be substantial. Sah (1991) and Lochner (2007) present models that highlight the link between perceived risk of punishment and crime participation.

  18. 18.

    Several factors may account for the observed differential in reporting rates across victims of different income groups. First, poor victims may have less incentive to report to the authority because their economic loss from victimization tend to be smaller. Second, poor victims living in disadvantaged neighborhoods may feel that the police would be ineffective or biased against them. Lastly, they may fear retribution by perpetrators, who are likely to live in proximity.

  19. 19.

    An important exception is Bjerk (2010). In addition to the usual assumption that low-income individuals are more likely to commit property crime than high-income individuals, and the expected gains from property crime is greater when fewer of one’s neighbors are low-income, he also assumes that use of violence can protect one from both physical victimization and pecuniary loss from victimization. Under these assumptions, his model predicts a positive effect of economic segregation on violent crime and little effect on property crime.

  20. 20.

    The prediction that ineffective police force further reinforces the effect of inequality on crime may be particularly relevant for Latin American countries, some of which are known for their high economic inequality and high crime rates (Soares and Naritomi 2010). Incidentally, some of the Latin American countries with high crime rates are also considered to have ineffective public police force; the 2014–2015 Global Competitiveness Report ranks Argentina 133rd, Brazil 83rd, and Venezuela 144th in terms of reliability of public police services, out of 144 countries studied.


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I am grateful to two anonymous referees, Seth Sanders, Phil Cook, Peter Arcidiacono, Joe Hotz, and Chris Timmins for valuable comments. I also thank seminar participants at Duke University and Osaka University of Economics for discussions and comments. This work was supported by the research fund of Hanyang University (HY-201400000001597).

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Correspondence to Songman Kang.

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Responsible editor: Erdal Tekin



Table 7 Description of explanatory variables
Table 8 MSA-level regression analysis
Table 9 IV analysis

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Kang, S. Inequality and crime revisited: effects of local inequality and economic segregation on crime. J Popul Econ 29, 593–626 (2016).

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  • Crime
  • Inequality
  • Poverty concentration
  • Inequality decomposition

JEL Classification

  • K42
  • I32