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Sponsored-Search Auctions: Empirical and Experimental Works

Author

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  • Davydov, D.

    (New Economic School, Moscow, Russia
    Center for the Study of Diversity and Social Interactions, Moscow, Russia)

  • Izmalkov, S.

    (New Economic School, Moscow, Russia
    Center for the Study of Diversity and Social Interactions, Moscow, Russia)

  • Smirnov, A.

    (New Economic School, Moscow, Russia
    Center for the Study of Diversity and Social Interactions, Moscow, Russia)

Abstract

One of the most popular and rapidly growing directions of research in economics in the last decade is the analysis of online advertisement auctions. These auctions are of particular interest due to vast expansion of online advertisement market and new exiting possibilities for theoretical analysis of this market, especially from the mechanism design perspective. In this paper we offer a short survey of the most influential empirical works on the subject. The applied researcher or analyst in this market faces one of the two main challenges: which auction model and under what assumptions to choose for the analysis, and how to use existing models to explain or predict behavior of users and advertisers? Accordingly, in our survey we focus on the main papers that test assumptions of existing theoretical models, based on the real bidding data and experimentally. We also describe and discuss the most influential structural model, which allows to recover the main preference parameters, and which can be used to predict how the users and advertisers will change their behavior if the auction rules change. We also discuss possible directions for future research.

Suggested Citation

  • Davydov, D. & Izmalkov, S. & Smirnov, A., 2015. "Sponsored-Search Auctions: Empirical and Experimental Works," Journal of the New Economic Association, New Economic Association, vol. 28(4), pages 56-73.
    • Handle: RePEc:nea:journl:y:2015:i:28:p:56-73
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    References listed on IDEAS

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    1. 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.
    2. 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.
    3. Tilman B?rgers & Ingemar Cox & Martin Pesendorfer & Vaclav Petricek, 2013. "Equilibrium Bids in Sponsored Search Auctions: Theory and Evidence," American Economic Journal: Microeconomics, American Economic Association, vol. 5(4), pages 163-187, November.
    4. Pakes, Ariel & Pollard, David, 1989. "Simulation and the Asymptotics of Optimization Estimators," Econometrica, Econometric Society, vol. 57(5), pages 1027-1057, September.
    5. Varian, Hal R., 2007. "Position auctions," International Journal of Industrial Organization, Elsevier, vol. 25(6), pages 1163-1178, December.
    6. Przemyslaw Jeziorski & Ilya Segal, 2015. "What Makes Them Click: Empirical Analysis of Consumer Demand for Search Advertising," American Economic Journal: Microeconomics, American Economic Association, vol. 7(3), pages 24-53, August.
    7. 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.
    8. Mas-Colell, Andreu & Whinston, Michael D. & Green, Jerry R., 1995. "Microeconomic Theory," OUP Catalogue, Oxford University Press, number 9780195102680.
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    Cited by:

    1. Jian Pei, 2020. "A Survey on Data Pricing: from Economics to Data Science," Papers 2009.04462, arXiv.org, revised Nov 2020.

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    More about this item

    Keywords

    sponsored-search auctions; generalized second-price auction; online advertising; structural models;
    All these keywords.

    JEL classification:

    • C57 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Econometrics of Games and Auctions
    • D44 - Microeconomics - - Market Structure, Pricing, and Design - - - Auctions
    • D47 - Microeconomics - - Market Structure, Pricing, and Design - - - Market Design
    • L81 - Industrial Organization - - Industry Studies: Services - - - Retail and Wholesale Trade; e-Commerce
    • M37 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Marketing and Advertising - - - Advertising

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