IDEAS home Printed from https://ideas.repec.org/p/nbr/nberwo/22627.html
   My bibliography  Save this paper

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
    Note: IO LS PE
    as

    Download full text from publisher

    File URL: http://www.nber.org/papers/w22627.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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, Oxford University Press, vol. 112(2), pages 407-441.
    2. Gregory S. Crawford & Robin S. Lee & Michael D. Whinston & Ali Yurukoglu, 2018. "The Welfare Effects of Vertical Integration in Multichannel Television Markets," Econometrica, Econometric Society, vol. 86(3), pages 891-954, May.
    3. 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.
    4. Steven T. Berry & Philip A. Haile, 2014. "Identification in Differentiated Products Markets Using Market Level Data," Econometrica, Econometric Society, vol. 82, pages 1749-1797, September.
    5. 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, Oxford University Press, vol. 130(4), pages 1975-2026.
    6. Julie Holland Mortimer, 2005. "Price Discrimination, Copyright Law, and Technological Innovation: Evidence from the Introduction of DVDs," NBER Working Papers 11676, National Bureau of Economic Research, Inc.
    7. Brynjolfsson, Erik & Smith, Michael D. & Yu, (Jeffrey) Hu, 2003. "Consumer Surplus in the Digital Economy: Estimating the Value of Increased Product Variety at Online Booksellers," Working papers 4305-03, Massachusetts Institute of Technology (MIT), Sloan School of Management.
    8. Thomas W. Quan & Kevin R. Williams, 2016. "Product Variety, Across-Market Demand Heterogeneity, and the Value of Online Retail," Cowles Foundation Discussion Papers 2054, Cowles Foundation for Research in Economics, Yale University.
    9. Aviv Nevo, 2000. "Mergers with Differentiated Products: The Case of the Ready-to-Eat Cereal Industry," RAND Journal of Economics, The RAND Corporation, vol. 31(3), pages 395-421, Autumn.
    10. Gregory S. Crawford & Ali Yurukoglu, 2012. "The Welfare Effects of Bundling in Multichannel Television Markets," American Economic Review, American Economic Association, vol. 102(2), pages 643-685, April.
    11. Erik Brynjolfsson & Yu (Jeffrey) Hu & Michael D. Smith, 2003. "Consumer Surplus in the Digital Economy: Estimating the Value of Increased Product Variety at Online Booksellers," Management Science, INFORMS, vol. 49(11), pages 1580-1596, November.
    12. Amil Petrin, 2002. "Quantifying the Benefits of New Products: The Case of the Minivan," Journal of Political Economy, University of Chicago Press, vol. 110(4), pages 705-729, August.
    13. 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.
    14. Julie Holland Mortimer, 2007. "Price Discrimination, Copyright Law, and Technological Innovation: Evidence from the Introduction of DVDs," The Quarterly Journal of Economics, Oxford University Press, vol. 122(3), pages 1307-1350.
    15. 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.
    16. Baker, Jonathan B. & Bresnahan, Timothy F., 1988. "Estimating the residual demand curve facing a single firm," International Journal of Industrial Organization, Elsevier, vol. 6(3), pages 283-300.
    17. Miroslav Svoboda, 2008. "History and troubles of consumer surplus," Prague Economic Papers, Prague University of Economics and Business, vol. 2008(3), pages 230-242.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Donna, Javier D. & Pereira, Pedro & Pires, Tiago & Trindade, Andre, 2018. "Measuring the Welfare of Intermediaries in Vertical Markets," MPRA Paper 90465, University Library of Munich, Germany.
    2. Javier Donna & Andre Trindade & Pedro Pereira & Tiago Pires, 2018. "Measuring the Welfare of Intermediation in Vertical Markets," 2018 Meeting Papers 984, Society for Economic Dynamics.
    3. 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.
    4. Yingjie Zhang & Beibei Li & Ramayya Krishnan, 2020. "Learning Individual Behavior Using Sensor Data: The Case of Global Positioning System Traces and Taxi Drivers," Information Systems Research, INFORMS, vol. 31(4), pages 1301-1321, December.
    5. Leong, Kaiwen & Li, Huailu & Xu, Haibo, 2019. "Effect of Enforcement Shock on Pushers' Activities: Evidence from an Asian Drug-Selling Gang," IZA Discussion Papers 12083, Institute of Labor Economics (IZA).
    6. 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.
    7. Wang, Ao, 2021. "A BLP Demand Model of Product-Level Market Shares with Complementarity," The Warwick Economics Research Paper Series (TWERPS) 1351, University of Warwick, Department of Economics.
    8. Heiko Karle & Dirk Engelmann & Martin Peitz, 2022. "Student performance and loss aversion," Scandinavian Journal of Economics, Wiley Blackwell, vol. 124(2), pages 420-456, April.
    9. Philip G. Gayle & Ying Lin, 2022. "Market effects of new product introduction: Evidence from the brew‐at‐home coffee market," Journal of Economics & Management Strategy, Wiley Blackwell, vol. 31(3), pages 525-557, August.
    10. 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.
    11. 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.
    12. Eric J. Allen & Patricia M. Dechow & Devin G. Pope & George Wu, 2017. "Reference-Dependent Preferences: Evidence from Marathon Runners," Management Science, INFORMS, vol. 63(6), pages 1657-1672, June.
    13. 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.
    14. Hammarlund, Cecilia, 2018. "A trip to reach the target? – The labor supply of Swedish Baltic cod fishermen," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 75(C), pages 1-11.
    15. 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.
    16. Dupas, Pascaline & Robinson, Jonathan & Saavedra, Santiago, 2020. "The daily grind: Cash needs and labor supply," Journal of Economic Behavior & Organization, Elsevier, vol. 177(C), pages 399-414.
    17. Soetevent, Adriaan R., 2022. "Short run reference points and long run performance. (No) Evidence from running data," Journal of Economic Psychology, Elsevier, vol. 89(C).
    18. MacDonald, Daniel & Mellizo, Philip, 2017. "Reference dependent preferences and labor supply in historical perspective," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 69(C), pages 117-124.
    19. Barbos, Andrei & Kaisen, Joshua, 2022. "An Example of Negative Wage Elasticity for YouTube Content Creators," Journal of Economic Behavior & Organization, Elsevier, vol. 203(C), pages 382-400.
    20. 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.

    More about this item

    JEL classification:

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

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:nbr:nberwo:22627. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: the person in charge (email available below). General contact details of provider: https://edirc.repec.org/data/nberrus.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.