Para acceder a los documentos con el texto completo, por favor, siga el siguiente enlace:

Dynamic optimal law enforcement with learning
Jellal, Mohamed; Garoupa, Nuno
Universitat Pompeu Fabra. Departament d'Economia i Empresa
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.
Business Economics and Industrial Organization
probability of detection and punishment
L'accés als continguts d'aquest document queda condicionat a l'acceptació de les condicions d'ús establertes per la següent llicència Creative Commons
Documento de trabajo

Mostrar el registro completo del ítem