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Decreasing fare evasion without fines? A microeconomic analysis

Author

Listed:
  • Guarda, Pablo
  • Galilea, Patricia
  • Handy, Susan
  • Muñoz, Juan Carlos
  • Ortúzar, Juan de Dios

Abstract

Fare evasion is a problem in many public transport systems around the world. Policies to reduce this problem are generally aimed at improving control systems and increasing fines for offenders. In this paper, we attempt to identify the joint impact of different variables explaining fare evasion using an econometric study. The variables found to be statistically significant are the level of inspection, the proximity to a Metro or intermodal station, the bus occupancy level, the period of the day, the geographic location and number of passengers boarding and alighting at each bus stop, among others. We propose a novel approach to perform cost-benefit evaluation in order to help authorities increase the cost-effectiveness of ticket inspection strategies on a given time horizon. We obtain new evidence that indicates that inspection strategies can be cost-effective even when evaders are not given a fine.

Suggested Citation

  • Guarda, Pablo & Galilea, Patricia & Handy, Susan & Muñoz, Juan Carlos & Ortúzar, Juan de Dios, 2016. "Decreasing fare evasion without fines? A microeconomic analysis," Research in Transportation Economics, Elsevier, vol. 59(C), pages 151-158.
    • Handle: RePEc:eee:retrec:v:59:y:2016:i:c:p:151-158
    DOI: 10.1016/j.retrec.2016.06.001
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    References listed on IDEAS

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    1. Bucciol, Alessandro & Landini, Fabio & Piovesan, Marco, 2013. "Unethical behavior in the field: Demographic characteristics and beliefs of the cheater," Journal of Economic Behavior & Organization, Elsevier, vol. 93(C), pages 248-257.
    2. Barabino, Benedetto & Salis, Sara & Useli, Bruno, 2014. "Fare evasion in proof-of-payment transit systems: Deriving the optimum inspection level," Transportation Research Part B: Methodological, Elsevier, vol. 70(C), pages 1-17.
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    Cited by:

    1. Felipe González & Carolina Busco & Katheryn Codocedo, 2019. "Fare Evasion in Public Transport: Grouping Transantiago Users’ Behavior," Sustainability, MDPI, vol. 11(23), pages 1-17, November.
    2. Valenzuela-Levi, Nicolás, 2023. "Income inequality and rule-systems within public transport: A study of Medellín (Colombia) and Santiago (Chile)," Journal of Transport Geography, Elsevier, vol. 112(C).
    3. Ramos, Raúl & Silva, Hugo E., 2023. "Fare evasion in public transport: How does it affect the optimal design and pricing?," Transportation Research Part B: Methodological, Elsevier, vol. 176(C).
    4. Allen, Jaime & Muñoz, Juan Carlos & Ortúzar, Juan de Dios, 2019. "On evasion behaviour in public transport: Dissatisfaction or contagion?," Transportation Research Part A: Policy and Practice, Elsevier, vol. 130(C), pages 626-651.
    5. Ayal, Shahar & Celse, Jérémy & Hochman, Guy, 2021. "Crafting messages to fight dishonesty: A field investigation of the effects of social norms and watching eye cues on fare evasion," Organizational Behavior and Human Decision Processes, Elsevier, vol. 166(C), pages 9-19.
    6. Guzman, Luis A. & Arellana, Julian & Camargo, José Pablo, 2021. "A hybrid discrete choice model to understand the effect of public policy on fare evasion discouragement in Bogotá's Bus Rapid Transit," Transportation Research Part A: Policy and Practice, Elsevier, vol. 151(C), pages 140-153.
    7. Batarce, Marco & Ávila, Franco, 2020. "Misguided quality incentives: The case of the Santiago bus system," Transportation Research Part A: Policy and Practice, Elsevier, vol. 139(C), pages 376-399.
    8. Porath, Keiko & Galilea, Patricia, 2020. "Temporal analysis of fare evasion in Transantiago: A socio-political view," Research in Transportation Economics, Elsevier, vol. 83(C).

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    More about this item

    Keywords

    Fare evasion; Count regression models; Transantiago; Cost-benefit analysis;
    All these keywords.

    JEL classification:

    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General
    • C5 - Mathematical and Quantitative Methods - - Econometric Modeling
    • R4 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Transportation Economics

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