Theft and Deterrence
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Theft and Deterrence William T. Harbaugh & Naci Mocan & Michael S. Visser
Published online: 8 September 2013 # Springer Science+Business Media New York 2013
Abstract We report results from economic experiments of decisions that are best described as petty larceny, with high school and college students who can anonymously steal real money from each other. Our design allows exogenous variation in the rewards of crime, and the penalty and probability of detection. We find that the probability of stealing is increasing in the amount of money that can be stolen, and that it is decreasing in the probability of getting caught and in the penalty for getting caught. Furthermore, the impact of the certainty of getting caught is larger when the penalty is bigger, and the impact of the penalty is bigger when the probability of getting caught is larger. Keywords Experiments . Crime . Larceny . Theft . Deterrence . Penalty . Rationality
Introduction Becker (1968) created the foundation for the economic analysis of criminal behavior. Since then, economists have extended his basic theoretical framework in several directions, but the basic argument remains – participation in crime is the result of an optimizing individual’s response to incentives such as the expected payoffs from
This research was supported by a grant from the NSF. Steve Levitt provided useful comments on an earlier draft, and Duha Altindag provided excellent research assistance. W. T. Harbaugh University of Oregon, Eugene, OR 97403-1285, USA e-mail: [email protected] N. Mocan (*) Department of Economics, Louisiana State University and NBER, 3039 Business Education Complex, Baton Rouge, LA 70803, USA e-mail: [email protected] M. S. Visser Sonoma State University, 1801 East Cotati Ave., Rohnert Park, CA 94928, USA e-mail: [email protected]
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J Labor Res (2013) 34:389–407
criminal activity and the costs, notably the probability of apprehension and the severity of punishment.1 Since the early empirical research that reported evidence that deterrence reduces crime (Ehrlich 1973, 1975; Witte 1980; Layson 1985), the main challenge in empirical analysis has been to tackle the simultaneity between criminal activity and deterrence. Specifically, an increase in deterrence is expected to reduce criminal activity, but a change in crime is also expected to prompt an increase in the certainty and severity of punishment, through mechanisms such as an increase in the arrest rate and/or the size of the police force. This makes it difficult to identify the causal impact of deterrence on crime.2 Recent research has employed three types of strategies to overcome the simultaneity problem. The first solution is to find an instrument that is correlated with deterrence measures, but uncorrelated with crime. An example is Levitt (2002) who used the number of per capita municipal firefighters as an instrument for police effort. The second strategy is to use high-frequency time-series data. For example, in monthly data, an increase in the police force in a given month will affect crim
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