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When to quit: Narrow bracketing and reference dependence in taxi drivers

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  • Martin, Vincent

Abstract

Taxi drivers provide an ideal setting for testing various models of labor supply. Despite this, the literature studying driver behavior has produced mixed evidence of which labor supply model best applies to driver labor supply. Using a novel analysis and large datasets of taxi trips from two different cities, this paper provides a unifying analysis which reconciles previous inconsistencies in evidence for reference dependence in taxi drivers. By testing for a particular non-linear relationship between shift income and drivers’ hazard of stopping, I identify behavior that is consistent with Prospect theoretic (S-shaped) reference dependence as opposed to the more extensively examined loss aversion model of reference dependence. This particular model of reference dependence in this setting allows me to estimate individual driver reference points without an explicit functional form or ex-ante assumptions about the existence of a reference point.

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  • Martin, Vincent, 2017. "When to quit: Narrow bracketing and reference dependence in taxi drivers," Journal of Economic Behavior & Organization, Elsevier, vol. 144(C), pages 166-187.
    • Handle: RePEc:eee:jeborg:v:144:y:2017:i:c:p:166-187
    DOI: 10.1016/j.jebo.2017.09.024
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    References listed on IDEAS

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    2. Brodeur, Abel & Nield, Kerry, 2018. "An empirical analysis of taxi, Lyft and Uber rides: Evidence from weather shocks in NYC," Journal of Economic Behavior & Organization, Elsevier, vol. 152(C), pages 1-16.
    3. Thanos Mergoupis & Robertas Zubrickas, 2024. "Work experience, information revelation, and study effort," Oxford Economic Papers, Oxford University Press, vol. 76(2), pages 495-513.
    4. Hai Long Duong & Junhong Chu & Dai Yao, 2023. "Taxi Drivers’ Response to Cancellations and No-Shows: New Evidence for Reference-Dependent Preferences," Management Science, INFORMS, vol. 69(1), pages 179-199, January.
    5. Jan Schlüter & Manuel Frewer & Leif Sörensen & Justin Coetzee, 2020. "A stochastic prediction of minibus taxi driver behaviour in South Africa," Palgrave Communications, Palgrave Macmillan, vol. 7(1), pages 1-12, December.
    6. Chen, Junlin & Feng, Xiaojing & Kou, Gang & Mu, Mengting, 2023. "Multiproduct newsvendor with cross-selling and narrow-bracketing behavior using data mining methods," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 169(C).
    7. Timothy J. Richards, 2020. "Income Targeting and Farm Labor Supply," American Journal of Agricultural Economics, John Wiley & Sons, vol. 102(2), pages 419-438, March.
    8. Alessandro Saia, 2022. "Trouble Underground: Demand Shocks and the Labor Supply Behavior of New York City Taxi Drivers," Italian Economic Journal: A Continuation of Rivista Italiana degli Economisti and Giornale degli Economisti, Springer;Società Italiana degli Economisti (Italian Economic Association), vol. 8(1), pages 1-27, March.
    9. Yiyuan Ma & Ke Chen & Youzhi Xiao & Rong Fan, 2022. "Does Online Ride-Hailing Service Improve the Efficiency of Taxi Market? Evidence from Shanghai," Sustainability, MDPI, vol. 14(14), pages 1-16, July.

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