Crime and Local Inequality in South Africa

Literature › Works

Gabriel Demombynes; Berk Ozler (2002). Crime and Local Inequality in South Africa. World Bank Policy Research Working Paper 2925. The World Bank, Development Research Group, Poverty Team.

Type	working paper
Scope	Post-1994 South African economy
Method	econometric
Period studied	1996–1996
Regions	South Africa (all nine provinces), police station jurisdictions (1,066)
Themes	inequality poverty unemployment, transition post apartheid, demography urbanisation, methods data sources
Citation key	demombynes2002a

Argument

The paper tests economic (Becker-type) and sociological theories of crime using cross-sectional data for South African police station jurisdictions in 1996. Property crime, especially burglary, tracks local mean expenditure, which proxies the returns to crime. Burglary is also higher in the jurisdiction that is richest within its 'criminal catchment area', consistent with criminals travelling to wealthier neighbouring areas. Inequality retains a positive association with burglary and violent crime after controls, which the authors read as support for sociological theories. Decomposition shows that almost all of this association comes 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. 1996 South African Police Service crime counts for 1,066 jurisdictions. Mean per capita expenditure and inequality (mean log deviation) 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, used to adjust for underreporting of burglary.

Findings

• Simple elasticities of crime with respect to local inequality are positive for all four crimes, ranging from rape to burglary. (p. 21)

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

• Property crimes, unlike violent crimes, respond strongly to mean expenditure, which the authors take as a proxy for returns to 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.

• Vehicle theft's apparent link to inequality is entirely a mean-expenditure effect. (p. 21)

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

• Being the wealthiest jurisdiction among neighbours raises burglary, supporting the criminal travel hypothesis. (p. 22)

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

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

  Table 6 shows that, on average, 36% of inequality in a police station jurisdiction can be attributed to differences in mean levels of expenditure between racial groups.

• The inequality-crime association comes from within-race rather than between-race inequality. (p. 23)

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

• Reporting rates differ widely by crime, so police data may bias estimates. Correcting burglary counts for underreporting attenuates but does not overturn the results. (p. 24)

  According to the VCS data, underreporting is a particularly serious problem for robbery and is least serious for theft of vehicles.

• The authors conclude the evidence favours sociological theories, particularly for violent crime. (p. 30)

  The results support the sociological theories of crime, especially with respect to violent crimes.

• Policy implication: extra prevention spending aimed at property crime 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.