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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.

Suggested Citation

  • Nuno Garoupa, 2004. "Dynamic Law Enforcement with Learning," The Journal of Law, Economics, and Organization, Oxford University Press, vol. 20(1), pages 192-206, April.
    • Handle: RePEc:oup:jleorg:v:20:y:2004:i:1:p:192-206
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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.
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    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. Chu, C. Y. Cyrus & Hu, Sheng-cheng & Huang, Ting-yuan, 2000. "Punishing repeat offenders more severely," International Review of Law and Economics, Elsevier, vol. 20(1), pages 127-140, March.
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    15. 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.
    16. 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.
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    Cited by:

    1. Thomas J. Miceli, 2012. "Escalating Interest in Escalating Penalties," Working papers 2012-08, University of Connecticut, Department of Economics.
    2. 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.
    3. 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.
    4. João Ricardo Faria & Gonçalo Monteiro, "undated". "The Tenure Game: Building Up Academic Habits," Discussion Papers 05/32, Department of Economics, University of York.
    5. Miles Stan & Pyne Derek, 2017. "The Economics of Scams," Review of Law & Economics, De Gruyter, vol. 13(1), pages 1-18, March.
    6. 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.

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    More about this item

    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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