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Search Design and Broad Matching

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  • Kfir Eliaz
  • Ran Spiegler

Abstract

We study decentralized mechanisms for allocating firms into search pools. The pools are created in response to noisy preference signals provided by consumers, who then browse the pools via costly random sequential search. Surplus-maximizing search pools are implementable in symmetric Nash equilibrium. Full extraction of the maximal surplus is implementable if and only if the distribution of consumer types satisfies a set of simple inequalities, which involve the relative fractions of consumers who like different products and the Bhattacharyya coefficient of similarity between their conditional signal distributions. The optimal mechanism can be simulated by a keyword auction with broad matching. (JEL C78, D44, D82)

Suggested Citation

  • Kfir Eliaz & Ran Spiegler, 2016. "Search Design and Broad Matching," American Economic Review, American Economic Association, vol. 106(3), pages 563-586, March.
    • Handle: RePEc:aea:aecrev:v:106:y:2016:i:3:p:563-86
    Note: DOI: 10.1257/aer.20150076
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    References listed on IDEAS

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    1. 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.
    2. 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.
    3. Caillaud, Bernard & Jullien, Bruno, 2003. "Chicken & Egg: Competition among Intermediation Service Providers," RAND Journal of Economics, The RAND Corporation, vol. 34(2), pages 309-328, Summer.
    4. 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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    Citations

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    Cited by:

    1. Wei Zhou & Zidong Wang, 2020. "Competing for Search Traffic in Query Markets: Entry Strategy, Platform Design, and Entrepreneurship," Working Papers 20-12, NET Institute.
    2. Jinzhao Du & Ying Lei, 2022. "Information design of matching platforms when user preferences are bidimensional," Production and Operations Management, Production and Operations Management Society, vol. 31(8), pages 3320-3336, August.
    3. Yangguang Huang, 2021. "Search Algorithm and Sales on Online Platforms: Evidence from Food Delivery Platforms," HKUST CEP Working Papers Series 202101, HKUST Center for Economic Policy.
    4. Gene Moo Lee & Shu He & Joowon Lee & Andrew B. Whinston, 2020. "Matching Mobile Applications for Cross-Promotion," Information Systems Research, INFORMS, vol. 31(3), pages 865-891, September.
    5. Chen, Yongmin & Zhang, Tianle, 2018. "Intermediaries and consumer search," International Journal of Industrial Organization, Elsevier, vol. 57(C), pages 255-277.
    6. Fershtman, Chaim & Fishman, Arthur & Zhou, Jidong, 2018. "Search and categorization," International Journal of Industrial Organization, Elsevier, vol. 57(C), pages 225-254.
    7. Sangita Poddar & Tanmoyee Banerjee(Chatterjee) & Swapnendu Banerjee, 2023. "Taxation on duopoly e-commerce platforms and their search environments," SN Business & Economics, Springer, vol. 3(8), pages 1-20, August.

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    More about this item

    JEL classification:

    • C78 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - Bargaining Theory; Matching Theory
    • D44 - Microeconomics - - Market Structure, Pricing, and Design - - - Auctions
    • D82 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Asymmetric and Private Information; Mechanism Design

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