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Horizontal Mergers of Online Firms: Structural Estimation and Competitive Effects

  • Yonghong An
  • Michael R. Baye
  • Yingyao Hu
  • John Morgan
  • Matt Shum

This paper (1) presents a general model of online price competition, (2) shows how to structurally estimate the underlying parameters of the model when the number of competing firms is unknown or in dispute, (3) estimates these parameters based on UK data for personal digital assistants, and (4) uses these estimates to simulate the competitive effects of horizontal mergers. Our results suggest that competitive effects in this online market are more closely aligned with the simple homogeneous product Bertrand model than might be expected given the observed price dispersion and number of firms. Our estimates indicate that so long as two firms remain in the market post merger, the average transaction price is roughly unaffected by horizontal mergers. However, there are potential distributional effects; our estimates indicate that a three-to-two merger raises the average transaction price paid by price sensitive "shoppers" by 2.88 percent, while lowering the average transaction price paid by consumers "loyal" to a particular firm by 1.37 percent.

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Paper provided by The Johns Hopkins University,Department of Economics in its series Economics Working Paper Archive with number 564.

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Date of creation: Jul 2010
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Handle: RePEc:jhu:papers:564
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  1. Michael R. Baye & John Morgan & Patrick Scholten, 2004. "Price Dispersion In The Small And In The Large: Evidence From An Internet Price Comparison Site," Journal of Industrial Economics, Wiley Blackwell, vol. 52(4), pages 463-496, December.
  2. Michael R. Baye & John Morgan & Patrick Scholten, 2006. "Information, Search, and Price Dispersion," Working Papers 2006-11, Indiana University, Kelley School of Business, Department of Business Economics and Public Policy.
  3. Michael R. Baye & J. Rupert J. Gatti & Paul Kattuman & John Morgan, 2009. "Clicks, Discontinuities, and Firm Demand Online," Journal of Economics & Management Strategy, Wiley Blackwell, vol. 18(4), pages 935-975, December.
  4. Ganesh Iyer & David Soberman & J. Miguel Villas-Boas, 2005. "The Targeting of Advertising," Marketing Science, INFORMS, vol. 24(3), pages 461-476, May.
  5. Rosenthal, Robert W, 1980. "A Model in Which an Increase in the Number of Sellers Leads to a Higher Price," Econometrica, Econometric Society, vol. 48(6), pages 1575-79, September.
  6. Mark Armstrong, 2008. "Interactions between Competition and Consumer Policy," CPI Journal, Competition Policy International, vol. 4.
  7. Yingyao Hu & Matthew Shum, 2007. "Estimating First-Price Auctions with an Unknown Number of Bidders: A Misclassification Approach," Economics Working Paper Archive 541, The Johns Hopkins University,Department of Economics.
  8. Hu, Yingyao, 2008. "Identification and estimation of nonlinear models with misclassification error using instrumental variables: A general solution," Journal of Econometrics, Elsevier, vol. 144(1), pages 27-61, May.
  9. Baye, Michael & Morgan, John, 2004. "Brand and Price Advertising in Online Markets," Competition Policy Center, Working Paper Series qt9760k3hz, Competition Policy Center, Institute for Business and Economic Research, UC Berkeley.
  10. Michael R. Baye & John Morgan, 2001. "Information Gatekeepers on the Internet and the Competitiveness of Homogeneous Product Markets," American Economic Review, American Economic Association, vol. 91(3), pages 454-474, June.
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