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Crime and Local Inequality in South Africa

Literature › Works

Gabriel Demombynes; Berk Ozler (2002). Crime and Local Inequality in South Africa. Policy Research Working Paper 2925, Development Research Group, Poverty Team. The World Bank.
Type working paper
Scope Post-1994 South African economy
Method econometric
Period studied 1996–1996
Regions South Africa (all nine provinces), 1,066 police station jurisdictions
Themes inequality poverty unemployment, demography urbanisation, methods data sources, comparative theory
Citation key demombynes2002

Argument

The paper asks whether local inequality explains crime in post-apartheid South Africa, and whether economic (Becker-type) or sociological theories fit better. Using a cross-section of police station jurisdictions, it finds that property crime tracks mean expenditure (a proxy for returns to crime). Burglary is also higher in jurisdictions that are the richest in their 'criminal catchment area', which is consistent with burglars travelling to wealthier neighbours. Inequality keeps a positive association with burglary and violent crime after controls, which the authors read as support for sociological theories. Decomposition shows this association comes almost entirely from inequality within racial groups, not between them, contrary to Blau and Blau's claim that ascribed inequalities matter especially.

Evidence and method

1996 Population Census matched to police station jurisdictions; SAPS reported crime counts for 1996 (1,066 jurisdictions); mean per capita expenditure and inequality (mean log deviation) for each jurisdiction, estimated by small-area methods that combine the census with the 1995 October Household Survey and Income and Expenditure Survey; 1998 Victims of Crime Survey (3,899 observations) used to adjust for underreporting of burglary.

Findings

  • In the basic negative binomial specification, inequality is positively associated with all four crimes, with elasticities ranging from 0.53 (rape) to 1.17 (burglary). (p. 21)

    The elasticities range from 0.53 for rape to 1.17 for burglary.

  • Mean expenditure has high elasticities for property crime, consistent with it proxying returns from crime. (p. 21)

    The elasticities on mean expenditure for burglary and vehicle theft, which are 0.97 and 1.77 respectively, are high relative to those for violent crime.

  • Once mean expenditure is controlled for, the inequality-vehicle theft link vanishes while the other crimes retain smaller positive coefficients. (p. 21)

    Once mean expenditure is controlled for, the correlation between inequality and vehicle theft completely disappears

  • Unemployment in the catchment area, used as a proxy for the opportunity cost of crime, has essentially no association with crime in the main models. (p. 22)

    we find no association between unemployment and crime, other than the small negative coefficient on vehicle theft

  • Burglary is higher in the jurisdictions that are richest among their neighbours, which supports the travelling-burglar hypothesis. (p. 22)

    burglary rates are 20% higher in jurisdictions that are the wealthiest among their neighbors.

  • Racial between-group differences account for roughly a third of local inequality. (p. 23)

    on average, 36% of inequality in a police station jurisdiction can be attributed to differences in mean levels of expenditure between racial groups.

  • Almost all of the inequality-crime association is due to inequality within racial groups, which contradicts the ascribed-inequality hypothesis. (p. 23)

    Almost all of the association between inequality and levels of crime can be attributed to inequality within racial groups.

  • Victims' survey data show reporting of crime to police varies widely by crime type, so reported counts may bias estimates. (p. 24)

    underreporting is a particularly serious problem for robbery and is least serious for theft of vehicles.

  • Adjusting burglary counts for underreporting attenuates but does not overturn the main results. (p. 25)

    Compared with the earlier results the elasticities of inequality and per capita expenditure are still positive and significant but somewhat attenuated.

  • The age 21-40 share is strongly related to all crime types. (p. 26)

    Percentage of individuals aged 21-40 in the jurisdiction is strongly correlated with all types of crime, with a consistently large elasticity for all types of crime.

  • Policy implication: extra public crime-prevention resources aimed at high property-crime areas would be regressive. (p. 32)

    increasing the public supply of resources for prevention in high property crime areas would be regressive, because these crimes are most likely to occur in richer neighborhoods.