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Dynamic law enforcement with learning

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  • Jellal, Mohamed
  • Garoupa, Nuno

Abstract

This paper 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.

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File URL: http://mpra.ub.uni-muenchen.de/38480/
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Bibliographic Info

Paper provided by University Library of Munich, Germany in its series MPRA Paper with number 38480.

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Date of creation: 2004
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Handle: RePEc:pra:mprapa:38480

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Related research

Keywords: fine; probability of detection and punishment; learning;

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References

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  1. Nuno Garoupa & Daniel Klerman, 2002. "Optimal Law Enforcement with a Rent-Seeking Government," American Law and Economics Review, Oxford University Press, Oxford University Press, vol. 4(1), pages 116-140, January.
  2. Marcel Boyer & Tracy R. Lewis & Wei Lin Liu, 2000. "Setting standards for credible compliance and law enforcement," Canadian Journal of Economics, Canadian Economics Association, Canadian Economics Association, vol. 33(2), pages 319-340, May.
  3. Davis, Michael L, 1988. "Time and Punishment: An Intertemporal Model of Crime," Journal of Political Economy, University of Chicago Press, University of Chicago Press, vol. 96(2), pages 383-90, April.
  4. Leung, S.F., 1991. "How to Make the Fine Fit the Corporate Crime? An Analysis of Static and Dynamic Optimal Punishment Theories," RCER Working Papers 261, University of Rochester - Center for Economic Research (RCER).
  5. Sah, R.K., 1990. "Social Osmosis And Patterns Of Crime: A Dynamic Economic Analysis," Papers, Yale - Economic Growth Center 609, Yale - Economic Growth Center.
  6. Ziggy MacDonald, 2002. "Official Crime Statistics: Their Use and Interpretation," Economic Journal, Royal Economic Society, Royal Economic Society, vol. 112(477), pages F85-F106, February.
  7. Gary S. Becker, 1974. "Crime and Punishment: An Economic Approach," NBER Chapters, in: Essays in the Economics of Crime and Punishment, pages 1-54 National Bureau of Economic Research, Inc.
  8. Winand Emons, 2001. "A Note on the Optimal Punishment for Repeat Offenders," Diskussionsschriften, Universitaet Bern, Departement Volkswirtschaft dp0104, Universitaet Bern, Departement Volkswirtschaft.
  9. Mohamed Jellal & Nuno Garoupa, 1999. "Dynamic optimal law enforcement with learning," Economics Working Papers, Department of Economics and Business, Universitat Pompeu Fabra 402, Department of Economics and Business, Universitat Pompeu Fabra.
  10. 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, Oxford University Press, vol. 14(2), pages 232-55, October.
  11. Rubinstein, Ariel, 1980. "On an anomaly of the deterrent effect of punishment," Economics Letters, Elsevier, Elsevier, vol. 6(1), pages 89-94.
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Citations

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Cited by:
  1. Thomas J. Miceli, 2012. "Escalating Interest in Escalating Penalties," Working papers, University of Connecticut, Department of Economics 2012-08, University of Connecticut, Department of Economics.
  2. João Ricardo Faria & Gonçalo Monteiro, . "The Tenure Game: Building Up Academic Habits," Discussion Papers, Department of Economics, University of York 05/32, Department of Economics, University of York.
  3. Mungan, Murat C., 2010. "Repeat offenders: If they learn, we punish them more severely," International Review of Law and Economics, Elsevier, Elsevier, vol. 30(2), pages 173-177, June.

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