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Dynamic Law Enforcement with Learning

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  • Nuno Garoupa

Abstract

This article modifies a standard model of law enforcement to allow for learning by doing. We incorporate the process of enforcement learning by assuming that the agency's current marginal cost is a decreasing function of its past experience of detecting and convicting. The agency accumulates data and information (on criminals, on opportunities of crime), enhancing the ability of future apprehension at a lower marginal cost. We focus on the impact of enforcement learning on optimal compliance rules. In particular, we show that the optimal fine could be less than maximal and the optimal probability of detection could be higher than otherwise. It is also suggested that the optimal imprisonment sentence could be higher than otherwise. Copyright 2004, Oxford University Press.

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Bibliographic Info

Article provided by Oxford University Press in its journal The Journal of Law, Economics, and Organization.

Volume (Year): 20 (2004)
Issue (Month): 1 (April)
Pages: 192-206

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Handle: RePEc:oup:jleorg:v:20:y:2004:i:1:p:192-206

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References

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  1. Marcel Boyer & Tracy Lewis & Wei Lin Liu, 1996. "Setting Standards for Credible Compliance and Law Enforcement," CIRANO Working Papers 96s-27, CIRANO.
  2. Emons, Winand, 2003. "A note on the optimal punishment for repeat offenders," International Review of Law and Economics, Elsevier, vol. 23(3), pages 253-259, September.
  3. Sah, R.K., 1990. "Social Osmosis And Patterns Of Crime: A Dynamic Economic Analysis," Papers 609, Yale - Economic Growth Center.
  4. O'Flaherty, Brendan, 1998. "Why Repeated Criminal Opportunities Matter: A Dynamic Stochastic Analysis of Criminal Decision Making," Journal of Law, Economics and Organization, Oxford University Press, vol. 14(2), pages 232-55, October.
  5. Mohamed Jellal & Nuno Garoupa, 1999. "Dynamic optimal law enforcement with learning," Economics Working Papers 402, Department of Economics and Business, Universitat Pompeu Fabra.
  6. Gary S. Becker, 1968. "Crime and Punishment: An Economic Approach," Journal of Political Economy, University of Chicago Press, vol. 76, pages 169.
  7. Ziggy MacDonald, 2002. "Official Crime Statistics: Their Use and Interpretation," Economic Journal, Royal Economic Society, vol. 112(477), pages F85-F106, February.
  8. Nuno Garoupa & Daniel Klerman, 2002. "Optimal Law Enforcement with a Rent-Seeking Government," American Law and Economics Review, Oxford University Press, vol. 4(1), pages 116-140, January.
  9. Davis, Michael L, 1988. "Time and Punishment: An Intertemporal Model of Crime," Journal of Political Economy, University of Chicago Press, vol. 96(2), pages 383-90, April.
  10. Rubinstein, Ariel, 1980. "On an anomaly of the deterrent effect of punishment," Economics Letters, Elsevier, vol. 6(1), pages 89-94.
  11. Leung, Siu Fai, 1991. "How to make the fine fit the corporate crime? : An analysis of static and dynamic optimal punishment theories," Journal of Public Economics, Elsevier, vol. 45(2), pages 243-256, July.
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Cited by:
  1. Mungan, Murat C., 2010. "Repeat offenders: If they learn, we punish them more severely," International Review of Law and Economics, Elsevier, vol. 30(2), pages 173-177, June.
  2. João Ricardo Faria & Gonçalo Monteiro, . "The Tenure Game: Building Up Academic Habits," Discussion Papers 05/32, Department of Economics, University of York.
  3. Thomas J. Miceli, 2012. "Escalating Interest in Escalating Penalties," Working papers 2012-08, University of Connecticut, Department of Economics.

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