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.

This is a preview of subscription content, log in to check access.

Fig. 1
Fig. 2
Fig. 3
Fig. 4


  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2010 decennial Census, as the “long form” Census questionnaire, which elicited such information from respondents, has been replaced by the annual ACS.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.


  1. Autor DH, Katz LF, Kearney MS (2008) Trends in US wage inequality: revising the revisionists. Rev Econ Stat 90(2):300–323

    Article  Google Scholar 

  2. Ayres I, Levitt SD (1998) Measuring positive externalities from unobservable victim precaution: an empirical analysis of Lojack. Q J Econ 113(1):43–77

    Article  Google Scholar 

  3. Bayer P, Hjalmarsson R, Pozen D (2009) Building criminal capital behind bars: peer effects in juvenile corrections. Q J Econ 124(1):105–147

    Article  Google Scholar 

  4. Becker G (1968) Crime and punishment: an economic approach. J Polit Econ 76(2):169–217

    Article  Google Scholar 

  5. Bernasco W, Block R (2009) Where offenders choose to attack: a discrete choice model of robberies in Chicago. Criminology 47(1):93–130

    Article  Google Scholar 

  6. Bjerk D (2010) Thieves, thugs, and neighborhood poverty. J Urban Econ 68 (3):231–246

    Article  Google Scholar 

  7. Bourguignon F, Nuñez J, Sanchez F (2003) A structural model of crime and inequality in Colombia. J Eur Econ Assoc 1(2-3):440–449

    Article  Google Scholar 

  8. Brush J (2008) Does income inequality lead to more crime? A comparison of cross-sectional and time-series analyses of United States counties. Econ Lett 96 (2):264–268

    Article  Google Scholar 

  9. Burdett K, Lagos R, Wright R (2003) Crime, inequality, and unemployment. Am Econ Rev 93(5):1764–1777

    Article  Google Scholar 

  10. Burdett K, Lagos R, Wright R (2004) An on-the-job search model of crime, inequality, and unemployment. Int Econ Rev 45(3):681–706

    Article  Google Scholar 

  11. Chiu W, Madden P (1998) Burglary and income inequality. J Public Econ 69(1):123–141

    Article  Google Scholar 

  12. Choe J (2008) Income inequality and crime in the United States. Econ Lett 101(1):31–33

    Article  Google Scholar 

  13. Cook PJ (1986). In: Tonry M, Morris N (eds) The demand and supply of criminal opportunities

  14. Cook PJ, MacDonald J (2011) Public safety through private action: an economic assessment of BIDS. Econ J 121(552):445–462

    Article  Google Scholar 

  15. Crow EL, Shimizu K (1988) Lognormal distributions: theory and applications. Marcel Dekker Inc., New York

    Google Scholar 

  16. Cullen JB, Levitt SD (1999) Crime, urban flight, and the consequences for cities. Rev Econ Stat 81(2):159–169

    Article  Google Scholar 

  17. Cutler DM, Glaeser EL (1997) Are ghettos good or bad? Q J Econ 112 (3):827–872

    Article  Google Scholar 

  18. Cutler DM, Glaeser EL, Vigdor JL (1999) The rise and decline of the American ghetto. J Polit Econ 107(3):455–506

    Article  Google Scholar 

  19. Di Tella R, Schargrodsky E (2004) Do police reduce crime? Estimates using the allocation of police forces after a terrorist attack. Am Econ Rev:115–133

  20. Doyle J, Ahmed E, Horn R (1999) The effects of labor markets and income inequality on crime: evidence from panel data. Southern Econ J 65(4):717–738

    Article  Google Scholar 

  21. Ehrlich I (1973) Participation in illegitimate activities: a theoretical and empirical investigation. J Polit Econ:521–565

  22. Ellen I, O’Regan K (2010) Crime and urban flight revisited: the effect of the 1990s drop in crime on cities. J Urban Econ 68(3):247–259

    Article  Google Scholar 

  23. Evans WN, Owens EG (2007) COPS and crime. J Public Econ 91(1-2):181–201

    Article  Google Scholar 

  24. Fajnzylber P, Lederman D, Loayza N (2002) Inequality and violent crime. J Law Econ 45(1):1–39

    Article  Google Scholar 

  25. Freedman M, Owens EG (2014) Your friends and neighbors: localized economic development and criminal activity. Rev Econ Stat. forthcoming

  26. Gaviria A, Raphael S (2001) School-based peer effects and juvenile behavior. Rev Econ Stat 83(2):257–268

    Article  Google Scholar 

  27. Glaeser EL, Sacerdote B, Scheinkman JA (1996) Crime and social interactions. Q J Econ 111(2):507–548

    Article  Google Scholar 

  28. Gould ED, Weinberg BA, Mustard DB (2002) Crime rates and local labor market opportunities in the United States: 1979–1997. Rev Econ Stat 84(1):45–61

    Article  Google Scholar 

  29. Grogger J (2000) An economic model of recent trends in violent crime. In: Blumstein A, Wallman J (eds) The Crime Drop in America. Cambridge University Press, Cambridge, pp 266–287

    Google Scholar 

  30. Helland E, Tabarrok A (2007) Does three strikes deter? A nonparametric estimation. J Hum Resour 42(2):309–330

    Article  Google Scholar 

  31. Hipp JR (2007) Income inequality, race, and place: does the distribution of race and class within neighborhoods affect crime rates? Criminology 45(3):665–697

    Article  Google Scholar 

  32. Hipp JR (2011) Spreading the wealth: the effect of the distribution of income and race/ethnicity across households and neighborhoods on city crime trajectories. Criminology 49(3):631–665

    Article  Google Scholar 

  33. Imrohoroğlu A, Merlo A, Rupert P (2004) What accounts for the decline in crime? Int Econ Rev 45(3):707–729

    Article  Google Scholar 

  34. Kelly M (2000) Inequality and crime. Rev Econ Stat 82(4):530–539

    Article  Google Scholar 

  35. Kessler D, Levitt SD (1999) Using sentence enhancements to distinguish between deterrence and incapacitation. J Law Econ 42(S1):343–364

    Article  Google Scholar 

  36. Klick J, Tabarrok A (2005) Using terror alert levels to estimate the effect of police on crime. J Law Econ 48(1):267–279

    Article  Google Scholar 

  37. Levitt SD (1999) The changing relationship between income and crime victimization. Econ Policy Rev 5(3):87–98

    Google Scholar 

  38. Levitt SD (2004) Understanding why crime fell in the 1990s: four factors that explain the decline and six that do not. J Econ Perspect 18(1):163–190

    Article  Google Scholar 

  39. Lochner L (2007) Individual perceptions of the criminal justice system. Am Econ Rev 97(1):444–460

    Article  Google Scholar 

  40. Massey DS, Denton NA (1988) The dimensions of residential segregation. Soc Forces 67(2):281–315

    Article  Google Scholar 

  41. Messner SF, Tardiff K (1986) Economic inequality and levels of homicide: an analysis of urban neighborhoods. Criminology 24(2):297–316

    Article  Google Scholar 

  42. O’Flaherty B, Sethi R (2010) Homicide in black and white. J Urban Econ 68(3):215–230

    Article  Google Scholar 

  43. Rand M, Robinson JE (2011) Criminal victimization in the United States, 2008–statistical tables. Bureau of Justice Statistics. NCJ, 227669

  44. Rengert GF, Piquero AR, Jones PR (1999) Distance decay reexamined. Criminology 37(2):427–446

    Article  Google Scholar 

  45. Sah RK (1991) Social osmosis and patterns of crime. J Polit Econ 99(6):1272–95

    Article  Google Scholar 

  46. Sampson RJ, Raudenbush SW, Earls F (1997) Neighborhoods and violent crime: a multilevel study of collective efficacy. Science 277(5328):918–924

    Article  Google Scholar 

  47. Silverman D (2004) Street crime and street culture. Int Econ Rev 45(3):761–786

    Article  Google Scholar 

  48. Soares RR (2004) Development, crime and punishment: accounting for the international differences in crime rates. J Dev Econ 73(1):155–184

    Article  Google Scholar 

  49. Soares RR, Naritomi J (2010) Understanding high crime rates in Latin America: the role of social and policy factors. In: The Economics of Crime: Lessons for and from Latin America. University of Chicago Press, Chicago, pp 19–55

    Google Scholar 

  50. Tatian PA (2003) Census CD Neighborhood Change Database (NCDB) 1970-2000 tract data: data user’s guide, long form release. The Urban Institute, Washington DC

    Google Scholar 

  51. Thacher D (2004) The rich get richer and the poor get robbed: inequality in US criminal victimization, 1974–2000. J Quant Criminol 20(2):89–116

    Article  Google Scholar 

  52. Vollaard B, Van Ours JC (2011) Does regulation of built?in security reduce crime? Evidence from a natural experiment. Econ J 121(552):485–504

    Article  Google Scholar 

  53. Wilson WJ (1987) The truly disadvantaged: the inner city, the underclass, and public policy. University of Chicago Press, Chicago

    Google Scholar 

Download references


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).

Author information



Corresponding author

Correspondence to Songman Kang.

Additional information

Responsible editor: Erdal Tekin



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

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

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

Download citation


  • Crime
  • Inequality
  • Poverty concentration
  • Inequality decomposition

JEL Classification

  • K42
  • I32