Events at CERGE-EI

Monday, 2 December, 2019 | 14:00 | Applied Micro Research Seminar

Libor Dušek, Ph.D. (Charles University) “Learning from Law Enforcement“

Libor Dušek, Ph.D.

Department of Economics, Faculty of Law, Charles University


Authors: Libor Dusek and Christian Traxlerz

Abstract:  This paper studies how punishment for past offenses shapes future compliance behaviorvia learning. The context of our study is traffic law enforcement through automated speedcameras. We use unique data on speeding tickets and full driving histories of more than onemillion cars tracked over several years in a suburb of Prague. In our setting, punishmentneither implies incapacitation nor do past tickets alter the ‘price’ for future offenses. Thisallows us to identify specific deterrence effects induced by learning from law enforcement. Wepresent results from two empirical strategies. Firstly, a regression discontinuity design exploitstwo speed level cutoffs which provide variation in punishment at the extensive (receiving aspeeding ticket) and intensive margin (tickets with low or high fines), respectively. The RDDreveals strong and precisely estimated responses to speeding tickets: the speeding rate drops bya third (10 percentage points) and chances of getting a further ticket fall by 70%. An increasein punishment at the intensive margin – a more than a doubling of fines – triggers only alimited additional effect. Secondly, an event study makes use of the high-frequency natureof our data. The average treatment effects on the treated obtained from the event studyconfirms all LATEs from the RDD. We also document that driving responses are immediateand very persistent over time. Even two years after receiving a ticket there is no evidence on‘backsliding’ towards speeding. The results reject unlearning and temporary salience effectsand support a reinforcement learning model in which agents, after experiencing punishment,update their priors on the expected costs of future offending in a discontinuous, ‘coarse’manner. Additional results indicate that learning from (local) law enforcement affects drivers’behavior more broadly, including spillovers on non-ticketed drivers.

Full Text:  “Learning from Law Enforcement