Learning from Law Enforcement

Abstract

This paper studies how punishment affects future compliance behavior and isolates deterrence effects mediated by learning. Using administrative data from speed cameras that capture the full driving histories of more than a million cars over several years, we evaluate responses to punishment at the extensive (receiving a speeding ticket) and intensive margin (tickets with higher fines). Two complementary empirical strategies a regression discontinuity design and an event study coherently document strong responses to receiving a ticket: the speeding rate drops by a third and reoffense rates fall by 70% Higher fines produce a small but imprecisely estimated additional effect. All responses occur immediately and are persistent over time, with no backsliding towards speeding even two years after receiving a ticket. Our evidence rejects unlearning and temporary salience effects. Instead, it supports a learning model in which agents update their priors on the expected punishment in a coarse manner.