Research‎ > ‎

Let the Punishment Fit the Criminal

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.