Article Contentsp. 329p. 330p. 331p. 332p. 333p. 334p. 335p. 336p. 337p. 338p. 339p. 340p. 341p. 342p. 343p. 344p. 345p. 346p. 347p. 348p. 349p. 350p. 351p. 352p. 353p. 354p. 355Issue Table of ContentsThe RAND Journal of Economics, Vol. 34, No. 2 (Summer, 2003), pp. 205-411Front Matter [pp. 305 - 305]An Experimental Comparison of Reliance Levels under Alternative Breach Remedies [pp. 205 - 222]Supplier Surfing: Competition and Consumer Behavior in Subscription Markets [pp. 223 - 246]Leasing, Lemons, and Buybacks [pp. 247 - 265]Price and Quality Competition under Adverse Selection: Market Organization and Efficiency [pp. 266 - 286]The Effects of Mergers in Open-Auction Markets [pp. 287 - 304]Symposium on the Economics of the Internet and Software IndustriesIntroduction [pp. 307 - 308]Chicken & Egg: Competition among Intermediation Service Providers [pp. 309 - 328]The Winner's Curse, Reserve Prices, and Endogenous Entry: Empirical Insights from eBay Auctions [pp. 329 - 355]Ownership and Control Rights in Internet Portal Alliances, 1995-1999 [pp. 356 - 369]Internet Interconnection and the Off-Net-Cost Pricing Principle [pp. 370 - 390]Antitrust Limits to Patent Settlements [pp. 391 - 411]Back MatterThe RAND CorporationThe Winner's Curse, Reserve Prices, and Endogenous Entry: Empirical Insights from eBayAuctionsAuthor(s): Patrick Bajari and Ali HortacsuSource: The RAND Journal of Economics, Vol. 34, No. 2 (Summer, 2003), pp. 329-355Published by: Blackwell Publishing on behalf of The RAND CorporationStable URL: http://www.jstor.org/stable/1593721Accessed: 11/01/2010 12:31Your use of the JSTOR archive indicates your acceptance of JSTOR's Terms and Conditions of Use, available athttp://www.jstor.org/page/info/about/policies/terms.jsp. 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For more information about JSTOR, please contact [email protected] RAND Corporation and Blackwell Publishing are collaborating with JSTOR to digitize, preserve andextend access to The RAND Journal of Economics.http://www.jstor.orgRAND Journal of Economics Vol. 34, No. 2, Summer 2003 pp. 329-355 The winner's curse, reserve prices, and endogenous entry: empirical insights from eBay auctions Patrick Bajari* and Ali HortaSsu** Internet auctions have recently gained widespread popularity and are one of the most successful forms of electronic commerce. We examine a unique dataset of eBay coin auctions to explore the determinants of bidder and seller behavior We first document a number of empirical regularities. We then specify and estimate a structural econometric model of bidding on eBay. Using our parameter estimates from this model, we measure the extent of the winner's curse and simulate seller revenue under different reserve prices. 1. Introduction * Auctions have found their way into millions of homes with the recent proliferation of auction sites on the Interet. The web behemoth eBay is by far the most popular online auction site. In 2001, 423 million items in 18,000 unique categories were listed for sale on eBay. Since eBay archives detailed records of completed auctions, it is a source for immense amounts of high-quality data and serves as a natural testing ground for existing theories of bidding and market design. In this article we explore the determinants of bidder and seller behavior in eBay auctions.1 For our study, we collected a unique dataset of bids at eBay coin auctions from September 28 to October 2, 1998. Our research strategy has three distinct stages. We first describe the market and document several empirical regularities in bidding and selling behavior that occur in these auctions. Second, motivated by these regularities, we specify and estimate a structural econometric model of bidding on eBay. Finally, we simulate our model at the estimated parameter values to quantify the extent of the winner's curse and to characterize profit-maximizing seller behavior. The bidding format used on eBay is called "proxy bidding." Here's how it works. When a * Stanford University and NBER;[email protected]. ** University of Chicago; [email protected]. We would like to thank Robert Porter and three anonymous referees for their comments, which helped us to improve this work considerably. We also would like to thank Susan Athey, Philip Haile, John Krainer, Pueo Keffer, Jon Levin, David Lucking-Reiley, Steve Tadelis, and participants at a number of seminars for their helpful comments. The first author would like to acknowledge the generous research support of SIEPR, the National Science Foundation, grant nos. SES-0112106 and SES-0122747, and the Hoover Institution's National Fellows program. 1 For a broad survey article covering 142 online auction sites, see Lucking-Reiley (2000). Copyright ? 2003, RAND. 329330 / THE RAND JOURNAL OF ECONOMICS bidder submits a proxy bid, she is asked by the eBay computer to enter the maximum amount she is willing to pay for the item. Suppose that bidder A is the first bidder to submit a proxy bid on an item with a minimum bid of $10 (as set by the seller) and a bid increment of $.50. Let the amount of bidder A's proxy bid be $25. eBay automatically sets the high bid to $10, just enough to make bidder A the high bidder. Next suppose that bidder B enters the auction with a proxy bid of $13. eBay then raises bidder A's bid to $13.50. If another bidder submits a proxy bid above $25.50 ($25 plus one bid increment), bidder A is no longer the high bidder, and the eBay computer will notify her of this via e-mail. If bidder A wishes, she can submit a new proxy bid. This process continues until the auction ends. The high bidder ends up paying the