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Online Auction Demand

Listed author(s):
  • Song Yao

    ()

    (Fuqua School of Business, Duke University, Durham, North Carolina 27708)

  • Carl F. Mela

    ()

    (Fuqua School of Business, Duke University, Durham, North Carolina 27708)

Registered author(s):

With $40 billion in annual gross merchandise volume, electronic auctions comprise a substantial and growing sector of the retail economy. Using unique data on Celtic coins, we estimate a structural model of buyer and seller behavior via Markov chain Monte Carlo (MCMC) with data augmentation. Results indicate that buyer valuations are affected by item, seller, and auction characteristics; buyer costs are affected by bidding behavior; and seller costs are affected by item characteristics and the number of listings. The model enables us to compute fee elasticities even though there is no variation in fees in our data. We find that commission elasticities exceed per item fee elasticities because they target high-value sellers and enhance their likelihood of listing. By targeting commission reductions to high-value sellers, auction house revenues can be increased by 3.9%. Computing customer value, we find that attrition of the largest seller would decrease fees paid to the auction house by $97. Given the seller paid $127 in fees, competitive effects offset only 24% of those fees. In contrast, competition offsets 81% of the buyer attrition effect. In both events, the auction house would overvalue its customers by neglecting competitive effects.

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File URL: http://dx.doi.org/10.1287/mksc.1070.0351
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Article provided by INFORMS in its journal Marketing Science.

Volume (Year): 27 (2008)
Issue (Month): 5 (09-10)
Pages: 861-885

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Handle: RePEc:inm:ormksc:v:27:y:2008:i:5:p:861-885
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  1. Emmanuel Guerre & Isabelle Perrigne & Quang Vuong, 2000. "Optimal Nonparametric Estimation of First-Price Auctions," Econometrica, Econometric Society, vol. 68(3), pages 525-574, May.
  2. Paarsch, Harry J., 1992. "Deciding between the common and private value paradigms in empirical models of auctions," Journal of Econometrics, Elsevier, vol. 51(1-2), pages 191-215.
  3. Sandro Brusco & Giuseppe Lopomo, 2009. "Simultaneous ascending auctions with complementarities and known budget constraints," Economic Theory, Springer;Society for the Advancement of Economic Theory (SAET), vol. 38(1), pages 105-124, January.
  4. Jean‐Charles Rochet & Jean Tirole, 2006. "Two‐sided markets: a progress report," RAND Journal of Economics, RAND Corporation, vol. 37(3), pages 645-667, 09.
  5. Eric A. Greenleaf & Atanu R. Sinha, 1996. "Combining Buy-In Penalties with Commissions at Auction Houses," Management Science, INFORMS, vol. 42(4), pages 529-540, April.
  6. Mireia Jofre-Bonet & Martin Pesendorfer, 2003. "Estimation of a Dynamic Auction Game," Econometrica, Econometric Society, vol. 71(5), pages 1443-1489, 09.
  7. David H. Reiley, 2006. "Field experiments on the effects of reserve prices in auctions: more Magic on the Internet," RAND Journal of Economics, RAND Corporation, vol. 37(1), pages 195-211, 03.
  8. Wagner Kamakura & Carl Mela & Asim Ansari & Anand Bodapati & Pete Fader & Raghuram Iyengar & Prasad Naik & Scott Neslin & Baohong Sun & Peter Verhoef & Michel Wedel & Ron Wilcox, 2005. "Choice Models and Customer Relationship Management," Marketing Letters, Springer, vol. 16(3), pages 279-291, December.
  9. Susan Athey & Philip A. Haile, 2002. "Identification of Standard Auction Models," Econometrica, Econometric Society, vol. 70(6), pages 2107-2140, November.
  10. Philip A. Haile & Han Hong & Matthew Shum, 2003. "Nonparametric Tests for Common Values at First-Price Sealed-Bid Auctions," NBER Working Papers 10105, National Bureau of Economic Research, Inc.
  11. Bajari, Patrick & Hortacsu, Ali, 2003. " The Winner's Curse, Reserve Prices, and Endogenous Entry: Empirical Insights from eBay Auctions," RAND Journal of Economics, The RAND Corporation, vol. 34(2), pages 329-355, Summer.
  12. Jonathan Levin & Susan Athey & Enrique Seira, 2004. "Comparing Open and Sealed Bid Auctions: Theory and Evidence from Timber Auctions," Working Papers 2004.142, Fondazione Eni Enrico Mattei.
  13. Laffont, Jean-Jacques & Vuong, Quang, 1996. "Structural Analysis of Auction Data," American Economic Review, American Economic Association, vol. 86(2), pages 414-420, May.
  14. Roson Roberto, 2005. "Two-Sided Markets: A Tentative Survey," Review of Network Economics, De Gruyter, vol. 4(2), pages 1-19, June.
  15. Peter E. Rossi & Robert E. McCulloch & Greg M. Allenby, 1996. "The Value of Purchase History Data in Target Marketing," Marketing Science, INFORMS, vol. 15(4), pages 321-340.
  16. Milgrom, Paul R & Weber, Robert J, 1982. "A Theory of Auctions and Competitive Bidding," Econometrica, Econometric Society, vol. 50(5), pages 1089-1122, September.
  17. William Vickrey, 1961. "Counterspeculation, Auctions, And Competitive Sealed Tenders," Journal of Finance, American Finance Association, vol. 16(1), pages 8-37, 03.
  18. Han Hong & Matthew Shum, 2002. "Increasing Competition and the Winner's Curse: Evidence from Procurement," Review of Economic Studies, Oxford University Press, vol. 69(4), pages 871-898.
  19. Michael Lewis & Vishal Singh & Scott Fay, 2006. "An Empirical Study of the Impact of Nonlinear Shipping and Handling Fees on Purchase Incidence and Expenditure Decisions," Marketing Science, INFORMS, vol. 25(1), pages 51-64, 01-02.
  20. Luis Cabral & Ali Hortacsu, 2006. "The Dynamics of Seller Reputation: Evidence from eBay," Working Papers 06-32, New York University, Leonard N. Stern School of Business, Department of Economics.
  21. Matt Shum & Phil Haile & Han Hong, 2003. "Nonparametric Tests for Common Values in First-Price Auctions," Economics Working Paper Archive 501, The Johns Hopkins University,Department of Economics.
  22. Eric T. Bradlow & Young-Hoon Park, 2007. "Bayesian Estimation of Bid Sequences in Internet Auctions Using a Generalized Record-Breaking Model," Marketing Science, INFORMS, vol. 26(2), pages 218-229, 03-04.
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