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Pigovian Transport Pricing in Practice

Author

Listed:
  • Beat Hintermann
  • Beaumont Schoemann
  • Joseph Molloy
  • Thomas Götschi
  • Alberto Castro
  • Christopher Tchervenkov
  • Uros Tomic
  • Kay W. Axhausen

Abstract

We implement Pigovian transport pricing in a field experiment in urban agglomerations of Switzerland over the course of 8 weeks. Our pricing considers the external costs from climate damages, health outcomes from pollution, accidents and physical activity, and congestion. It varies across time, space and mode of transport and is deducted from a budget provided to GPS-tracked participants. The treatment reduces the external costs of transport by 4.6% during the course of the experiment. The main underlying mechanism is a shift away from driving towards other modes, such as public transport, walking and cycling. Providing information about the external costs of transport alone is insufficient to change the transport behavior for the sample majority. We compute the welfare improvement due to mode shift to be 77 Swiss francs (or US dollars) per person and year, and that a fuel tax would achieve 70% of this gain.

Suggested Citation

  • Beat Hintermann & Beaumont Schoemann & Joseph Molloy & Thomas Götschi & Alberto Castro & Christopher Tchervenkov & Uros Tomic & Kay W. Axhausen, 2025. "Pigovian Transport Pricing in Practice," CESifo Working Paper Series 11871, CESifo.
    • Handle: RePEc:ces:ceswps:_11871
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    References listed on IDEAS

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    1. Sun, Liyang & Abraham, Sarah, 2021. "Estimating dynamic treatment effects in event studies with heterogeneous treatment effects," Journal of Econometrics, Elsevier, vol. 225(2), pages 175-199.
    2. K. W. Axhausen & M. Löchl & R. Schlich & T. Buhl & P. Widmer, 2007. "Fatigue in long-duration travel diaries," Transportation, Springer, vol. 34(2), pages 143-160, March.
    3. Hess, Stephane & Train, Kenneth E. & Polak, John W., 2006. "On the use of a Modified Latin Hypercube Sampling (MLHS) method in the estimation of a Mixed Logit Model for vehicle choice," Transportation Research Part B: Methodological, Elsevier, vol. 40(2), pages 147-163, February.
    4. Ernst R. Berndt & Bronwyn H. Hall & Robert E. Hall & Jerry A. Hausman, 1974. "Estimation and Inference in Nonlinear Structural Models," NBER Chapters, in: Annals of Economic and Social Measurement, Volume 3, number 4, pages 653-665, National Bureau of Economic Research, Inc.
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    Cited by:

    1. Roth, Jakob & Schwab, Laura & Hintermann, Beat & Götschi, Thomas & Meister, Adrian & Meyer de Freitas, Lucas & Axhausen, Kay W., 2025. "Transport Pricing to Promote E-biking and Reduce Externalities: Insights from a GPS-Tracked Experiment," Working papers 2025/05, Faculty of Business and Economics - University of Basel.

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    Keywords

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    JEL classification:

    • H23 - Public Economics - - Taxation, Subsidies, and Revenue - - - Externalities; Redistributive Effects; Environmental Taxes and Subsidies
    • H31 - Public Economics - - Fiscal Policies and Behavior of Economic Agents - - - Household
    • I18 - Health, Education, and Welfare - - Health - - - Government Policy; Regulation; Public Health
    • Q52 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Pollution Control Adoption and Costs; Distributional Effects; Employment Effects
    • Q54 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Climate; Natural Disasters and their Management; Global Warming
    • R41 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Transportation Economics - - - Transportation: Demand, Supply, and Congestion; Travel Time; Safety and Accidents; Transportation Noise
    • R48 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Transportation Economics - - - Government Pricing and Policy

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