Paper #402

Título:

Dynamic optimal law enforcement with learning

Autores:

Mohamed Jellal y Nuno Garoupa

Data:

Junio 1999

Resumen:

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.

Palabras clave:

Fine, probability of detection and punishment, learning

Códigos JEL:

K4

Área de investigación:

Economía de la Empresa y Organización Industrial

Publicado en:

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

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