999 resultados para Notice (Law)
Resumo:
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.
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In this paper, we focus on the problem created by asymmetric informationabout the enforcer's (agent's) costs associated to enforcement expenditure. This adverse selection problem affects optimal law enforcement because a low cost enforcer may conceal its information by imitating a high cost enforcer, and must then be given a compensation to be induced to reveal its true costs. The government faces a trade-off between minimizing the enforcer's compensation and maximizing the net surplus of harmful acts. As a consequence, the probability of apprehension and punishment is usually reduced leading to more offenses being committed. We show that asymmetry of information does not affect law enforcement as long as raising public funds is costless. The consideration of costly raising of public funds permits to establish the positive correlation between asymmetry of information between government and enforcers andthe crime rate.
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Audit report on the Webster County Metropolitan Law Enforcement Telecommunications Board for the year ended June 30, 2008
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