Deterring Repeat Offenders with Escalating Penalty Schedules: A Bayesian Approach

37 Pages Posted: 21 Jul 2015 Last revised: 31 Jan 2017

See all articles by Derek Pyne

Derek Pyne

Thompson Rivers University - School of Business and Economics

Stan Miles

Thompson Rivers University

Date Written: August 1, 2015

Abstract

We model deterrence with costly punishment when criminals have different abilities. Abilities are unobserved by both criminals and the courts. Based on past successes, criminals update their priors on being high-ability criminals. Courts cannot observe a criminal’s total past offenses. They do know that criminals with more convictions were undeterred by previous penalties. Thus, they must have had more successes resulting in higher posterior probabilities of being high-ability criminals. Those with fewer convictions include more with lower posterior probabilities of being high-ability. Since they know that they are relatively more likely to be caught, they are deterred with lower penalties.

Keywords: Deterrence, crime, recidivism

JEL Classification: K4, D8

Suggested Citation

Pyne, Derek and Miles, Stan, Deterring Repeat Offenders with Escalating Penalty Schedules: A Bayesian Approach (August 1, 2015). Economics of Governance, Vol. 16, No. 3, 2015, Available at SSRN: https://ssrn.com/abstract=2633648

Derek Pyne (Contact Author)

Thompson Rivers University - School of Business and Economics ( email )

900 McGill Road
Kamloops, British Columbia V2C 0C8
Canada

Stan Miles

Thompson Rivers University ( email )

900 McGill Road
IB2008
Kamloops, BC V2C 5N3
Canada

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