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Using Big Data to Estimate Consumer Surplus: The Case of Uber

Author

Listed:
  • Peter Cohen
  • Robert Hahn
  • Jonathan Hall
  • Steven Levitt
  • Robert Metcalfe

Abstract

Estimating consumer surplus is challenging because it requires identification of the entire demand curve. We rely on Uber’s “surge” pricing algorithm and the richness of its individual level data to first estimate demand elasticities at several points along the demand curve. We then use these elasticity estimates to estimate consumer surplus. Using almost 50 million individual-level observations and a regression discontinuity design, we estimate that in 2015 the UberX service generated about $2.9 billion in consumer surplus in the four U.S. cities included in our analysis. For each dollar spent by consumers, about $1.60 of consumer surplus is generated. Back-of-the-envelope calculations suggest that the overall consumer surplus generated by the UberX service in the United States in 2015 was $6.8 billion.

Suggested Citation

  • 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.
  • Handle: RePEc:nbr:nberwo:22627
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Roger Fouquet, 2018. "Consumer Surplus from Energy Transitions," The Energy Journal, International Association for Energy Economics, vol. 0(Number 3).
    2. Sutirtha Bagchi, 2017. "A Tale of Two Cities: An Examination of Medallion Prices in New York and Chicago," Villanova School of Business Department of Economics and Statistics Working Paper Series 33, Villanova School of Business Department of Economics and Statistics.
    3. repec:oup:oxford:v:33:y:2017:i:4:p:541-571. is not listed on IDEAS
    4. Scott Duke Kominers & Alexander Teytelboym & Vincent P Crawford, 2017. "An invitation to market design," Oxford Review of Economic Policy, Oxford University Press, vol. 33(4), pages 541-571.
    5. Erik Brynjolfsson & Felix Eggers & Avinash Gannamaneni, 2018. "Using Massive Online Choice Experiments to Measure Changes in Well-being," NBER Working Papers 24514, National Bureau of Economic Research, Inc.
    6. Chiara Farronato & Andrey Fradkin, 2018. "The Welfare Effects of Peer Entry in the Accommodation Market: The Case of Airbnb," NBER Working Papers 24361, National Bureau of Economic Research, Inc.
    7. 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.
    8. Basil Halperin & Benjamin Ho & John List & Ian Muir, 2018. "Toward an understanding of the economics of apologies: evidence from a large-scale natural field experiment," Natural Field Experiments 00644, The Field Experiments Website.
    9. repec:kap:revind:v:53:y:2018:i:2:d:10.1007_s11151-018-9612-5 is not listed on IDEAS
    10. repec:eee:eecrev:v:110:y:2018:i:c:p:197-210 is not listed on IDEAS
    11. repec:eee:trapol:v:76:y:2019:i:c:p:149-158 is not listed on IDEAS
    12. Diogo G. R. Costa, 2016. "From Pipelines to Networks: The Intellectual Disruptions of Online Platforms," Economic Affairs, Wiley Blackwell, vol. 36(3), pages 379-389, October.
    13. Hall, Jonathan D. & Palsson, Craig & Price, Joseph, 2018. "Is Uber a substitute or complement for public transit?," Journal of Urban Economics, Elsevier, vol. 108(C), pages 36-50.
    14. Romanyuk, Gleb & Smolin, Alexey, 2018. "Cream Skimming and Information Design in Marching Markets," MPRA Paper 86713, University Library of Munich, Germany.
    15. repec:ksa:szemle:1778 is not listed on IDEAS
    16. Leon Moskatel & David Slusky, 2019. "Did UberX reduce ambulance volume?," Health Economics, John Wiley & Sons, Ltd., vol. 28(7), pages 817-829, July.

    More about this item

    JEL classification:

    • H0 - Public Economics - - General
    • J0 - Labor and Demographic Economics - - General
    • L0 - Industrial Organization - - General

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