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Dynamic law enforcement with learning

Author

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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.

Suggested Citation

  • Jellal, Mohamed & Garoupa, Nuno, 2004. "Dynamic law enforcement with learning," MPRA Paper 38480, University Library of Munich, Germany.
  • Handle: RePEc:pra:mprapa:38480
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    File URL: https://mpra.ub.uni-muenchen.de/38480/1/MPRA_paper_38480.pdf
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    References listed on IDEAS

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    1. 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.
    2. Sah, Raaj K, 1991. "Social Osmosis and Patterns of Crime," Journal of Political Economy, University of Chicago Press, vol. 99(6), pages 1272-1295, December.
    3. 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.
    4. Mohamed Jellal & Nuno Garoupa, 1999. "Dynamic optimal law enforcement with learning," Economics Working Papers 402, Department of Economics and Business, Universitat Pompeu Fabra.
    5. Marcel Boyer & Tracy R. Lewis & Wei Lin Liu, 2000. "Setting standards for credible compliance and law enforcement," Canadian Journal of Economics, Canadian Economics Association, vol. 33(2), pages 319-340, May.
    6. Davis, Michael L, 1988. "Time and Punishment: An Intertemporal Model of Crime," Journal of Political Economy, University of Chicago Press, vol. 96(2), pages 383-390, April.
    7. Ziggy MacDonald, 2002. "Official Crime Statistics: Their Use and Interpretation," Economic Journal, Royal Economic Society, vol. 112(477), pages 85-106, February.
    8. Rubinstein, Ariel, 1980. "On an anomaly of the deterrent effect of punishment," Economics Letters, Elsevier, vol. 6(1), pages 89-94.
    9. 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.
    10. 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.
    11. 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 232-255, October.
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    Citations

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    Cited by:

    1. Lisa R. Anderson & Gregory DeAngelo & Winand Emons & Beth Freeborn & Hannes Lang, 2017. "Penalty Structures And Deterrence In A Two-Stage Model: Experimental Evidence," Economic Inquiry, Western Economic Association International, vol. 55(4), pages 1833-1867, October.
    2. Thomas J. Miceli, 2012. "Escalating Interest in Escalating Penalties," Working papers 2012-08, University of Connecticut, Department of Economics.
    3. 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.
    4. Stan Miles & Derek Pyne, 2015. "Deterring repeat offenders with escalating penalty schedules: a Bayesian approach," Economics of Governance, Springer, vol. 16(3), pages 229-250, August.
    5. João Ricardo Faria & Gonçalo Monteiro, "undated". "The Tenure Game: Building Up Academic Habits," Discussion Papers 05/32, Department of Economics, University of York.

    More about this item

    Keywords

    fine; probability of detection and punishment; learning;

    JEL classification:

    • K42 - Law and Economics - - Legal Procedure, the Legal System, and Illegal Behavior - - - Illegal Behavior and the Enforcement of Law
    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness

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