Dynamic optimal law enforcement with learning

Mohamed Jellal () and Nuno Garoupa

Economics Working Papers from Department of Economics and Business, Universitat Pompeu Fabra

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 (search for similar items in EconPapers)
JEL-codes: K4 (search for similar items in EconPapers)
Date: 1999-06
New Economics Papers: this item is included in nep-law
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Citations: View citations in EconPapers (12)

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Persistent link: https://EconPapers.repec.org/RePEc:upf:upfgen:402

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