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’ re- sponse to judges’ optimal punishment in a dynamic setting. We introduce two distinctive features motivated by our empirical setting. First, judges rarely imposes maximum pun- ishment 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: con- ditioning 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. Judges update their beliefs about individuals depending on whether they committed a crime in the previous period; this gives rise to progressivity in the opti- mal 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 victims or accused have a Don honorific title indicating they are wealthy. Recidivists are punished harsher than first time offenders.