Dynamic Law Enforcement with Learning

Posted: 19 Nov 2003

See all articles by Nuno Garoupa

Nuno Garoupa

George Mason University - Antonin Scalia Law School, Faculty

Mohamed Jellal

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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.

Keywords: fine, probability of detection and punishment, learning

JEL Classification: K4

Suggested Citation

Garoupa, Nuno and Jellal, Mohamed, Dynamic Law Enforcement with Learning. The Journal of Law, Economics, and Organization, Vol. 20, No. 1, pp. 192-206, 2004. Available at SSRN: https://ssrn.com/abstract=469941

Nuno Garoupa (Contact Author)

George Mason University - Antonin Scalia Law School, Faculty ( email )

3301 Fairfax Drive
Arlington, VA 22201
United States

No contact information is available for Mohamed Jellal

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