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Learning from Law Enforcement

Author

Listed:
  • Libor Dusek
  • Christian Traxler

Abstract

This paper studies how punishment for past offenses 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 (tickets with higher fines) margins. 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 re-offense rates fall by 70%. Higher fines produce only a limited 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. Additional results indicate that learning from law enforcement affects drivers’ behavior more broadly, including spillovers on non-ticketed drivers.

Suggested Citation

  • Libor Dusek & Christian Traxler, 2020. "Learning from Law Enforcement," CESifo Working Paper Series 8043, CESifo.
    • Handle: RePEc:ces:ceswps:_8043
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    File URL: https://www.cesifo.org/DocDL/cesifo1_wp8043_0.pdf
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    Cited by:

    1. Browne, Oliver R. & Gazze, Ludovica & Greenstone, Michael & Rostapshova, Olga, 2022. "Man vs. Machine : Technological Promise and Political Limits of Automated Regulation Enforcement," CAGE Online Working Paper Series 646, Competitive Advantage in the Global Economy (CAGE).
    2. Agan, Amanda & Doleac, Jennifer & Harvey, Anna, 2021. "Misdemeanor Prosecution," IZA Discussion Papers 14234, Institute of Labor Economics (IZA).
    3. Rebollo-Sanz, Yolanda & Rodríguez-López, Jesús & Rodríguez-Planas, Núria, 2021. "Penalty-point system, deterrence and road safety: A quasi-experimental analysis," Journal of Economic Behavior & Organization, Elsevier, vol. 190(C), pages 408-433.
    4. Maennig, Wolfgang & Wilhelm, Stefan, 2023. "News and noise in crime politics: The role of announcements and risk attitudes," Economic Modelling, Elsevier, vol. 129(C).
    5. Libor Dušek & Christian Traxler, 2024. "Swiftness and Delay of Punishment," CESifo Working Paper Series 10906, CESifo.
    6. Christian Traxler & Libor Dušek, 2023. "Fines, Non-Payment, and Revenues: Evidence from Speeding Tickets," Berlin School of Economics Discussion Papers 0023, Berlin School of Economics.
    7. Buechel, Berno & Feess, Eberhard & Muehlheusser, Gerd, 2020. "Optimal law enforcement with sophisticated and naïve offenders," Journal of Economic Behavior & Organization, Elsevier, vol. 177(C), pages 836-857.
    8. Carl Lieberman & Elizabeth Luh & Michael Mueller-Smith, 2023. "Criminal court fees, earnings, and expenditures: A multi-state RD analysis of survey and administrative data," Working Papers 23-06, Center for Economic Studies, U.S. Census Bureau.
    9. Wolfgang Maennig & Stefan Wilhelm, 2023. "Crime Prevention Effects of Data Retention Policies," Working Papers 074, Chair for Economic Policy, University of Hamburg.
    10. Libor Dušek & Nicolas Pardo & Christian Traxler, 2022. "Salience and Timely Compliance: Evidence from Speeding Tickets," Journal of Policy Analysis and Management, John Wiley & Sons, Ltd., vol. 41(2), pages 426-449, March.

    More about this item

    Keywords

    learning deterrence; law enforcement; speeding tickets; regression discontinuity; event study;
    All these keywords.

    JEL classification:

    • K10 - Law and Economics - - Basic Areas of Law - - - General (Constitutional Law)
    • D80 - Microeconomics - - Information, Knowledge, and Uncertainty - - - General
    • K42 - Law and Economics - - Legal Procedure, the Legal System, and Illegal Behavior - - - Illegal Behavior and the Enforcement of Law

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