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Red Queen Pricing Effects in E-Retail Markets

Author

Listed:
  • Michael R. Baye

    (Department of Business Economics and Public Policy, Indiana University Kelley School of Business)

  • John Morgan

    (University of California at Berkeley)

Abstract

A standard “solution” offered to the deleterious effects of all-out price competition is for firms to engage in differentiation strategies. This solution, however, depends critically on the inability of rivals to imitate a successful differentiation strategy. With imitation, we show how “Red Queen” pricing effects can arise: All firms have an incentive to vertically differentiate and increase markups, yet imitation by rivals drives prices down toward pre-differentiation levels. Thus, the price premia arising from differentiation strategies in eretailing critically depend on the number of other firms that imitate the strategies. Based on data from Shopper.com, we find that an online firm that unilaterally differentiates itself from its rivals by participating in CNet’s Certified Merchant program enjoys a 5 to 17 percent price premium. However, when other firms also follow this strategy, the price premium vanishes.

Suggested Citation

  • Michael R. Baye & John Morgan, 2005. "Red Queen Pricing Effects in E-Retail Markets," Working Papers 2005-07, Indiana University, Kelley School of Business, Department of Business Economics and Public Policy.
    • Handle: RePEc:iuk:wpaper:2005-07
    as

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    File URL: http://kelley.iu.edu/riharbau/RePEc/iuk/wpaper/bepp2005-07-baye-morgan.pdf
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    References listed on IDEAS

    as
    1. Karen Clay & Ramayya Krishnan & Eric Wolff & Danny Fernandes, 2002. "Retail Strategies on the Web: Price and Non–price Competition in the Online Book Industry," Journal of Industrial Economics, Wiley Blackwell, vol. 50(3), pages 351-367, September.
    2. Kathy Baylis & Jeffrey Perloff, 2002. "Price Dispersion on the Internet: Good Firms and Bad Firms," Review of Industrial Organization, Springer;The Industrial Organization Society, vol. 21(3), pages 305-324, November.
    3. Gary E. Bolton & Elena Katok & Axel Ockenfels, 2004. "How Effective Are Electronic Reputation Mechanisms? An Experimental Investigation," Management Science, INFORMS, vol. 50(11), pages 1587-1602, November.
    4. John G. Lynch , Jr. & Dan Ariely, 2000. "Wine Online: Search Costs Affect Competition on Price, Quality, and Distribution," Marketing Science, INFORMS, vol. 19(1), pages 83-103, April.
    5. repec:bla:jindec:v:49:y:2001:i:4:p:541-58 is not listed on IDEAS
    6. Eric K. Clemons & Il-Horn Hann & Lorin M. Hitt, 2002. "Price Dispersion and Differentiation in Online Travel: An Empirical Investigation," Management Science, INFORMS, vol. 48(4), pages 534-549, April.
    7. Glenn Ellison & Sara Fisher Ellison, 2009. "Search, Obfuscation, and Price Elasticities on the Internet," Econometrica, Econometric Society, vol. 77(2), pages 427-452, March.
    8. repec:bla:jindec:v:50:y:2002:i:3:p:351-67 is not listed on IDEAS
    9. Michael D. Smith & Erik Brynjolfsson, 2001. "Consumer Decision-making at an Internet Shopbot: Brand Still Matters," NBER Chapters, in: E-commerce, pages 541-558, National Bureau of Economic Research, Inc.
    10. Rajiv Lal & Miklos Sarvary, 1999. "When and How Is the Internet Likely to Decrease Price Competition?," Marketing Science, INFORMS, vol. 18(4), pages 485-503.
    11. 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.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    Pricing; Product Differentiation; Red Queen Effect; Internet; Reputation;
    All these keywords.

    JEL classification:

    • D4 - Microeconomics - - Market Structure, Pricing, and Design
    • D8 - Microeconomics - - Information, Knowledge, and Uncertainty
    • M3 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Marketing and Advertising
    • L13 - Industrial Organization - - Market Structure, Firm Strategy, and Market Performance - - - Oligopoly and Other Imperfect Markets

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