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Let the Punishment Fit the Criminal
joint with Jose-Antonio Espin-Sanchez (Northwestern University)
(Current version: August 2013 - Incomplete and Preliminary)

Abstract: We investigate the role of punishment progressivity and individual characteristics in the determination of crime. To analyze welfare implications we model individuals' response to judges' optimal punishment in a dynamic setting. We introduce two distinctive features motivated by our empirical setting. First, judges rarely imposes maximum punishment for first time offenders. Instead, we observe low fines (or just a warning) even when crime detection technology is efficient and punishment is not costly. We account for this by allowing an unobservable (to the judge) individual state to be correlated with a public signal (the environment). This generates an optimal punishment that is conditional on individual observables. Second, judges punishments follow a progressive system: conditioning on type, recidivists are punished harsher than first-time offenders for the same crime. We account for these dynamics by introducing a persistent unobservable (to the judge) component. Depending on whether the individual committed a crime in the previous period, the judge updates her beliefs about the individual; this gives rise to progressivity in the optimal punishment system. For the empirical analysis we examine a novel trial data set from a self-governed community of farmers in Southern Spain. We find that judges vary the degree of imposed punishments based on individual characteristics—such as when the victim or the accused have a Don honorific title indicating he is a wealthy person. Recidivists are punished harsher than first time offenders.

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