Paper #402

Title:

Dynamic optimal law enforcement with learning

Authors:

Mohamed Jellal and Nuno Garoupa

Date:

June 1999

Abstract:

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 to apprehend in the future at a lower marginal cost. We focus on the impact of enforcement learning on optimal stationary compliance rules. In particular, we show that the optimal stationary fine could be less-than-maximal and the optimal stationary probability of detection could be higher-than-otherwise.

Keywords:

Fine, probability of detection and punishment, learning

JEL codes:

K4

Area of Research:

Business Economics and Industrial Organization

Published in:

Journal of Law, Economics and Organization, 20(2004), pp. 192-206

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