Crime and arrests: deterrence or resource reallocation?

Abstract

We use monthly time-series data for 20 large US cities to test the deterrence hypothesis (arrests reduce crimes) and the resource reallocation hypothesis (arrests follow from an increase in crime). We find (1) weak support for the deterrence hypothesis, (2) much stronger support for the resource reallocation hypothesis and (3) differences in city-level estimates suggest much heterogeneity in the crime and arrest relationship across regions.

Acknowledgments

The views expressed here are those of the authors and not necessarily the views of the Federal Reserve Bank of St. Louis or the Federal Reserve System.

Notes

¹The agency-level Uniform Crime Reports (UCR) data were retrieved from the National Archive of Criminal Justice Data via the Inter-University Consortium for Political and Social Research at the University of Michigan at http://www.icpsr.umich.edu/NACJD/ucr.html (last accessed March 6, 2010). Although the UCR is the most widely used source of crime data, the fact that these data are self-reported by cities raises some possible problems. These include underreporting by police departments and differences in the collection and reporting of criminal activity across cities.

²The failure of cities to report crime data for several months or several years early or late in the sample period has shortened the sample for several cities. For some cities, the absence of offense and arrest statistics for certain crimes over an extended period mid-sample led us to omit the crime from the list of seven crime equations estimated. In addition, appropriate steps were taken to handle the occasional monthly missing observation to preserve the sample for estimation purposes (Maltz, 1999, p. 28).

³Our empirical model closely follows that of Corman and Mocan (2000, 2005).

⁴We used Newey–West SEs to correct for heteroskedasticity and serial correlation. Also, each empirical model includes an error-correction term to account for a long-run equilibrium relationship.

⁵Let Ω be a sum of coefficients. The elasticity (η) is computed as η = Ω·, where Y is the dependent variable and X is the independent variable. The variance of the elasticity is calculated as Var(η) = , where Var(Ω) is calculated using the standard formula for the variance of a sum – summing the variances of each individual coefficient and the covariance between each coefficient pair.