Paper #402

Títol:

Dynamic optimal law enforcement with learning

Autors:

Mohamed Jellal i Nuno Garoupa

Data:

Juny 1999

Resum:

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.

Paraules clau:

Fine, probability of detection and punishment, learning

Codis JEL:

K4

Àrea de Recerca:

Economia de l'Empresa i Organització Industrial

Publicat a:

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

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