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Has Uber Made It Easier to Get a Ride in the Rain?

Author

Listed:
  • Brodeur, Abel

    (University of Ottawa)

  • Nield, Kerry

    (Carleton University)

Abstract
In New York City (NYC), it has been a common complaint that it is difficult to find a taxi in the rain. Using all Uber rides in NYC from April to September 2014 and January to June 2015, we show that the number of Uber rides is significantly correlated with whether it rained. The number of Uber rides per hour is about 25 percent higher when it is raining, suggesting that surge pricing encourages an increase in supply. During the same time period, the number of taxi rides per hour increases by only 4 percent in rainy hours. We then show that the number of taxi rides per hour decreased by approximately 8 percent after Uber entered the New York market in May 2011, confirming that Uber is depressing taxi demand. Last, we test whether the total (Uber plus taxi) number of rides in rainy hours increased since May 2011. Our estimates suggest that the total number of rides increased by approximately 9 percent since Uber entered the market and that it is relatively easier to get a ride in rainy than in non-rainy hours in post-Uber years.

Suggested Citation

  • Brodeur, Abel & Nield, Kerry, 2016. "Has Uber Made It Easier to Get a Ride in the Rain?," IZA Discussion Papers 9986, Institute of Labor Economics (IZA).
    • Handle: RePEc:iza:izadps:dp9986
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    1. M. Keith Chen & Judith A. Chevalier & Peter E. Rossi & Emily Oehlsen, 2019. "The Value of Flexible Work: Evidence from Uber Drivers," Journal of Political Economy, University of Chicago Press, vol. 127(6), pages 2735-2794.
    2. Chang, Tom & Gross, Tal, 2014. "How many pears would a pear packer pack if a pear packer could pack pears at quasi-exogenously varying piece rates?," Journal of Economic Behavior & Organization, Elsevier, vol. 99(C), pages 1-17.
    3. Colin Camerer & Linda Babcock & George Loewenstein & Richard Thaler, 1997. "Labor Supply of New York City Cabdrivers: One Day at a Time," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 112(2), pages 407-441.
    4. Vincent P. Crawford & Juanjuan Meng, 2011. "New York City Cab Drivers' Labor Supply Revisited: Reference-Dependent Preferences with Rational-Expectations Targets for Hours and Income," American Economic Review, American Economic Association, vol. 101(5), pages 1912-1932, August.
    5. Orley Ashenfelter & Kirk Doran & Bruce Schaller, 2010. "A Shred of Credible Evidence on the Long‐run Elasticity of Labour Supply," Economica, London School of Economics and Political Science, vol. 77(308), pages 637-650, October.
    6. Botond Kőszegi & Matthew Rabin, 2006. "A Model of Reference-Dependent Preferences," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 121(4), pages 1133-1165.
    7. Amos Tversky & Daniel Kahneman, 1991. "Loss Aversion in Riskless Choice: A Reference-Dependent Model," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 106(4), pages 1039-1061.
    8. Blundell, Richard & Macurdy, Thomas, 1999. "Labor supply: A review of alternative approaches," Handbook of Labor Economics, in: O. Ashenfelter & D. Card (ed.), Handbook of Labor Economics, edition 1, volume 3, chapter 27, pages 1559-1695, Elsevier.
    9. Doran, Kirk, 2014. "Are long-term wage elasticities of labor supply more negative than short-term ones?," Economics Letters, Elsevier, vol. 122(2), pages 208-210.
    10. Marie Connolly, 2008. "Here Comes the Rain Again: Weather and the Intertemporal Substitution of Leisure," Journal of Labor Economics, University of Chicago Press, vol. 26(1), pages 73-100.
    11. Judd Cramer & Alan B. Krueger, 2016. "Disruptive Change in the Taxi Business: The Case of Uber," American Economic Review, American Economic Association, vol. 106(5), pages 177-182, May.
    12. Henry S. Farber, 2015. "Why you Can’t Find a Taxi in the Rain and Other Labor Supply Lessons from Cab Drivers," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 130(4), pages 1975-2026.
    13. repec:pri:cepsud:199ashenfelter is not listed on IDEAS
    14. Yuan K. Chou, 2002. "Testing Alternative Models Of Labour Supply: Evidence From Taxi Drivers In Singapore," The Singapore Economic Review (SER), World Scientific Publishing Co. Pte. Ltd., vol. 47(01), pages 17-47.
    15. Daniel Kahneman & Amos Tversky, 2013. "Prospect Theory: An Analysis of Decision Under Risk," World Scientific Book Chapters, in: Leonard C MacLean & William T Ziemba (ed.), HANDBOOK OF THE FUNDAMENTALS OF FINANCIAL DECISION MAKING Part I, chapter 6, pages 99-127, World Scientific Publishing Co. Pte. Ltd..
    16. Ernst Fehr & Lorenz Goette, 2007. "Do Workers Work More if Wages Are High? Evidence from a Randomized Field Experiment," American Economic Review, American Economic Association, vol. 97(1), pages 298-317, March.
    17. Kareem Haggag & Brian McManus & Giovanni Paci, 2017. "Learning by Driving: Productivity Improvements by New York City Taxi Drivers," American Economic Journal: Applied Economics, American Economic Association, vol. 9(1), pages 70-95, January.
    18. Gerald S. Oettinger, 1999. "An Empirical Analysis of the Daily Labor Supply of Stadium Vendors," Journal of Political Economy, University of Chicago Press, vol. 107(2), pages 360-392, April.
    19. Jonathan V. Hall & Alan B. Krueger, 2015. "An Analysis of the Labor Market for Uber's Driver-Partners in the United States," Working Papers 587, Princeton University, Department of Economics, Industrial Relations Section..
    20. Henry S. Farber, 2008. "Reference-Dependent Preferences and Labor Supply: The Case of New York City Taxi Drivers," American Economic Review, American Economic Association, vol. 98(3), pages 1069-1082, June.
    21. Henry S. Farber, 2005. "Is Tomorrow Another Day? The Labor Supply of New York City Cabdrivers," Journal of Political Economy, University of Chicago Press, vol. 113(1), pages 46-82, February.
    22. Kareem Haggag & Giovanni Paci, 2014. "Default Tips," American Economic Journal: Applied Economics, American Economic Association, vol. 6(3), pages 1-19, July.
    23. Peter Cohen & Robert Hahn & Jonathan Hall & Steven Levitt & Robert Metcalfe, 2016. "Using Big Data to Estimate Consumer Surplus: The Case of Uber," NBER Working Papers 22627, National Bureau of Economic Research, Inc.
    24. 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.
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    Cited by:

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    10. Yash Babar & Gordon Burtch, 2020. "Examining the Heterogeneous Impact of Ride-Hailing Services on Public Transit Use," Information Systems Research, INFORMS, vol. 31(3), pages 820-834, September.
    11. Yang Si & Hongzhi Guan & Yuchao Cui, 2019. "Research on the Choice Behavior of Taxis and Express Services Based on the SEM-Logit Model," Sustainability, MDPI, vol. 11(10), pages 1-13, May.
    12. Xavier Fageda, 2021. "Measuring the impact of ride‐hailing firms on urban congestion: The case of Uber in Europe," Papers in Regional Science, Wiley Blackwell, vol. 100(5), pages 1230-1253, October.
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    14. Li, Ze-Tao & Nie, Wei-Peng & Cai, Shi-Min & Zhao, Zhi-Dan & Zhou, Tao, 2023. "Exploring the topological characteristics of urban trip networks based on taxi trajectory data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 609(C).
    15. Ravula, Prashanth, 2022. "Monetary and hassle savings as strategic variables in the ride-sharing market," Research in Transportation Economics, Elsevier, vol. 94(C).
    16. Aguilera-García, Álvaro & Gomez, Juan & Velázquez, Guillermo & Vassallo, Jose Manuel, 2022. "Ridesourcing vs. traditional taxi services: Understanding users’ choices and preferences in Spain," Transportation Research Part A: Policy and Practice, Elsevier, vol. 155(C), pages 161-178.
    17. Guilherme Mendes Resende & Ricardo Carvalho de Andrade Lima, 2018. "Working Paper No. 001/2018 - Competition Effects of the Sharing Economy in Brazil: Has Uber's entry affected the cab-hailing app market from 2014 to 2016?," Documentos de Trabalho 2018011, Conselho Administrativo de Defesa Econômica (Cade), Departamento de Estudos Econômicos.
    18. Loa, Patrick & Ong, Felita & Hawkins, Jason & Nurul Habib, Khandker, 2023. "Unravelling the relationship between ride-sourcing services and conventional modes in the city of Toronto: A stated preference study," Transport Policy, Elsevier, vol. 141(C), pages 209-220.
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    20. Thomas J. Weinandy & Michael J. Ryan, 2021. "Flexible Ubers and Fixed Taxis: the Effect of Fuel Prices on Car Services," Journal of Industry, Competition and Trade, Springer, vol. 21(2), pages 139-168, June.

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    More about this item

    Keywords

    rain; Uber; taxi; dynamic pricing;
    All these keywords.

    JEL classification:

    • D01 - Microeconomics - - General - - - Microeconomic Behavior: Underlying Principles
    • D03 - Microeconomics - - General - - - Behavioral Microeconomics: Underlying Principles
    • L92 - Industrial Organization - - Industry Studies: Transportation and Utilities - - - Railroads and Other Surface Transportation
    • J22 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Time Allocation and Labor Supply

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