A Structural Model of Crime: Evidence from Baltimore's Light Rail System (Work in Progress)

Abstract

We present a static model aimed to capture the dynamic aspect of passenger behavior in the Baltimore Light Rail system. We argue that given a set of policy variables established by the public judiciary system, and the enforcement strategy adopted by the local law enforcement officials, individuals optimize their behavior to maximize their expected payoff. In this respect, the regular light rail passenger, through holding a set of beliefs regarding the behavior of the judicial and law enforcement authorities, optimally chooses the probability of purchasing a ticket each time he or she boards the light rail. This framework could be interpreted as a game between passengers and “the law”, where in a mixed strategy Bayesian Nash equilibrium, passengers choose the optimal probability of purchasing tickets in response to the beliefs they hold concerning the probability of apprehension, the level of sanction, as well as the probability of enforcement. The policy maker, on the other hand, has already set in motion, a level of sanction, an optimal level of police presence (through which, the true distribution for the probability of apprehension is realized) , as well as a level of enforcement, through which it can play tight or loose with the public.