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Consumer Surplus in Online Auctions

Author

Listed:
  • Ravi Bapna

    (Information and Decision Sciences, University of Minnesota, Minneapolis, Minnesota 55455, and Centre for IT and Networked Economy, Indian School of Business, Hyderabad, India)

  • Wolfgang Jank

    (Robert H. Smith School of Business, University of Maryland, College Park, Maryland 20742)

  • Galit Shmueli

    (Robert H. Smith School of Business, University of Maryland, College Park, Maryland 20742)

Abstract

Despite the growing research interest in Internet auctions, particularly those on eBay, little is known about quantifiable consumer surplus levels in such mechanisms. Using an ongoing novel field experiment that involves real bidders participating in real auctions, and voting with real dollars, we collect and examine a unique data set to estimate consumer surplus in eBay auctions. The estimation procedure relies mainly on knowing the highest bid, which is not disclosed by eBay but is available to us from our experiment. At the outset we assume a private value second-price sealed-bid auction setting, as well as a lack of alternative buying options within or outside eBay. Our analysis, based on a sample of 4,514 eBay auctions, indicates that consumers extract a median surplus of at least $4 per eBay auction. This estimate is unbiased under the above assumptions; otherwise it is a lower bound. The surplus distribution is highly skewed given the diverse nature of the data. We find that eBay's auctions generated at least $7.05 billion in total consumer surplus in 2003 and could generate up to $7.68 billion if the private value sealed-bid assumption does not hold. We check for the validity of our assumptions and the robustness of our estimates using an additional data set from 2005 and a randomly sampled validation data set from eBay.

Suggested Citation

  • Ravi Bapna & Wolfgang Jank & Galit Shmueli, 2008. "Consumer Surplus in Online Auctions," Information Systems Research, INFORMS, vol. 19(4), pages 400-416, December.
  • Handle: RePEc:inm:orisre:v:19:y:2008:i:4:p:400-416
    DOI: 10.1287/isre.1080.0173
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    References listed on IDEAS

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    Cited by:

    1. März, Armin & Lachner, Michael & Heumann, Christian G. & Schumann, Jan H. & von Wangenheim, Florian, 2021. "How You Remind Me! The Influence of Mobile Push Notifications on Success Rates in Last-Minute Bidding," Journal of Interactive Marketing, Elsevier, vol. 54(C), pages 11-24.
    2. Yili Hong & Chong (Alex) Wang & Paul A. Pavlou, 2016. "Comparing Open and Sealed Bid Auctions: Evidence from Online Labor Markets," Information Systems Research, INFORMS, vol. 27(1), pages 49-69, March.
    3. Anindya Ghose & Yuliang Yao, 2011. "Using Transaction Prices to Re-Examine Price Dispersion in Electronic Markets," Information Systems Research, INFORMS, vol. 22(2), pages 269-288, June.
    4. Born, Alexander & Kovachka, Nikoleta & Lessmann, Stefan & Seow, Hsin-Vonn, 2018. "Price Management in the Used-Car Market: An Evaluation of Survival Analysis," IRTG 1792 Discussion Papers 2018-065, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    5. Michael Scholz & Markus Franz & Oliver Hinz, 2016. "The Ambiguous Identifier Clustering Technique," Electronic Markets, Springer;IIM University of St. Gallen, vol. 26(2), pages 143-156, May.
    6. Peter T. L. Popkowski Leszczyc & Michael H. Rothkopf (deceased), 2010. "Charitable Motives and Bidding in Charity Auctions," Management Science, INFORMS, vol. 56(3), pages 399-413, March.
    7. Ravi Bapna & Robert Day & Sarah Rice, 2020. "Allocative Efficiency in Online Auctions: Improving the Performance of Multiple Online Auctions Via Seek‐and‐Protect Agents," Production and Operations Management, Production and Operations Management Society, vol. 29(8), pages 1878-1893, August.
    8. Robert Zeithammer & Christopher Adams, 2010. "The Sealed-Bid Abstraction in Online Auctions," Marketing Science, INFORMS, vol. 29(6), pages 964-987, 11-12.
    9. Cong Feng & Scott Fay & K. Sivakumar, 2016. "Overbidding in electronic auctions: factors influencing the propensity to overbid and the magnitude of overbidding," Journal of the Academy of Marketing Science, Springer, vol. 44(2), pages 241-260, March.
    10. Jay R Corrigan & Saleem Alhabash & Matthew Rousu & Sean B Cash, 2018. "How much is social media worth? Estimating the value of Facebook by paying users to stop using it," PLOS ONE, Public Library of Science, vol. 13(12), pages 1-11, December.
    11. Yue Yuan & Mary E. Deily & Yuliang Yao, 2022. "Willingness to Pay for Status Signals in Online Luxury Markets," Production and Operations Management, Production and Operations Management Society, vol. 31(2), pages 668-680, February.
    12. Kamins, Michael A. & Noy, Avi & Steinhart, Yael & Mazursky, David, 2011. "The Effect of Social Cues on Sniping Behavior in Internet Auctions: Field Evidence and a Lab Experiment," Journal of Interactive Marketing, Elsevier, vol. 25(4), pages 241-250.
    13. Kevin Hasker & Robin Sickles, 2010. "eBay in the Economic Literature: Analysis of an Auction Marketplace," Review of Industrial Organization, Springer;The Industrial Organization Society, vol. 37(1), pages 3-42, August.
    14. Wolfgang Jank & Shu Zhang, 2011. "An Automated and Data-Driven Bidding Strategy for Online Auctions," INFORMS Journal on Computing, INFORMS, vol. 23(2), pages 238-253, May.
    15. Spann, Martin & Häubl, Gerald & Skiera, Bernd & Bernhardt, Martin, 2012. "Bid-Elicitation Interfaces and Bidding Behavior in Retail Interactive Pricing," Journal of Retailing, Elsevier, vol. 88(1), pages 131-144.
    16. Kevin Yili Hong & Alex Chong Wang & Paul A. Pavlou, 2013. "How does Bid Visibility Matter in Buyer-Determined Auctions? Comparing Open and Sealed Bid Auctions in Online Labor Markets," Working Papers 13-05, NET Institute.

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