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Integration and search engine bias

Author

Listed:
  • Alexandre de Cornière

    (University of Oxford)

  • Greg Taylor

    (University of Oxford)

Abstract

We study the effects of integration between a search engine and a publisher. In a model in which the search engine (i) allocates users across publishers and (ii) competes with publishers to attract advertisers, we find that the search engine is biased against publishers that display many ads – even without integration. Integration can (but need not) lead to own-content bias. It can also benefit consumers by reducing the nuisance costs due to excessive advertising. Advertisers are more likely to suffer from integration than consumers. On net, the welfare effects of integration are ambiguous.

Suggested Citation

  • Alexandre de Cornière & Greg Taylor, 2014. "Integration and search engine bias," Post-Print halshs-01510254, HAL.
    • Handle: RePEc:hal:journl:halshs-01510254
    as

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    References listed on IDEAS

    as
    1. Susan Athey & Glenn Ellison, 2011. "Position Auctions with Consumer Search," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 126(3), pages 1213-1270.
    2. Attila Ambrus & Emilio Calvano & Markus Reisinger, 2016. "Either or Both Competition: A "Two-Sided" Theory of Advertising with Overlapping Viewerships," American Economic Journal: Microeconomics, American Economic Association, vol. 8(3), pages 189-222, August.
    3. Benjamin Edelman & Michael Ostrovsky & Michael Schwarz, 2007. "Internet Advertising and the Generalized Second-Price Auction: Selling Billions of Dollars Worth of Keywords," American Economic Review, American Economic Association, vol. 97(1), pages 242-259, March.
    4. White, Alexander, 2013. "Search engines: Left side quality versus right side profits," International Journal of Industrial Organization, Elsevier, vol. 31(6), pages 690-701.
    5. Yongmin Chen & Chuan He, 2011. "Paid Placement: Advertising and Search on the Internet," Economic Journal, Royal Economic Society, vol. 121(556), pages 309-328, November.
    6. Mark Armstrong & Jidong Zhou, 2011. "Paying for Prominence," Economic Journal, Royal Economic Society, vol. 121(556), pages 368-395, November.
    7. Matthew Ellman & Fabrizio Germano, "undated". "What Do the Papers Sell?," Working Papers 149, Barcelona School of Economics.
    8. Varian, Hal R., 2007. "Position auctions," International Journal of Industrial Organization, Elsevier, vol. 25(6), pages 1163-1178, December.
    9. Kfir Eliaz & Ran Spiegler, 2011. "A Simple Model of Search Engine Pricing," Economic Journal, Royal Economic Society, vol. 121(556), pages 329-339, November.
    10. Jay Pil Choi & Byung‐Cheol Kim, 2010. "Net neutrality and investment incentives," RAND Journal of Economics, RAND Corporation, vol. 41(3), pages 446-471, September.
    11. Tarantino, Emanuele, 2013. "A simple model of vertical search engines foreclosure," Telecommunications Policy, Elsevier, vol. 37(1), pages 1-12.
    12. Simon P. Anderson & Stephen Coate, 2005. "Market Provision of Broadcasting: A Welfare Analysis," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 72(4), pages 947-972.
    13. Greg Taylor, 2013. "Search Quality and Revenue Cannibalization by Competing Search Engines," Journal of Economics & Management Strategy, Wiley Blackwell, vol. 22(3), pages 445-467, September.
    14. Matthew Ellman & Fabrizio Germano, 2009. "What do the Papers Sell? A Model of Advertising and Media Bias," Economic Journal, Royal Economic Society, vol. 119(537), pages 680-704, April.
    15. Roman Inderst & Marco Ottaviani, 2012. "Competition through Commissions and Kickbacks," American Economic Review, American Economic Association, vol. 102(2), pages 780-809, April.
    16. Andrei Hagiu & Bruno Jullien, 2011. "Why do intermediaries divert search?," RAND Journal of Economics, RAND Corporation, vol. 42(2), pages 337-362, June.
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    More about this item

    Keywords

    search engine;

    JEL classification:

    • L1 - Industrial Organization - - Market Structure, Firm Strategy, and Market Performance
    • L4 - Industrial Organization - - Antitrust Issues and Policies
    • L86 - Industrial Organization - - Industry Studies: Services - - - Information and Internet Services; Computer Software

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