Let the Punishment Fit the Criminal
Javier Donna and
Jose Espin Sanchez
MPRA Paper from University Library of Munich, Germany
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.
Keywords: Auctions; Contracts; Crime; Fines; Punishment (search for similar items in EconPapers)
JEL-codes: C13 D44 K14 K42 L14 (search for similar items in EconPapers)
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