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Marketing Agencies and Collusive Bidding in Online Ad Auctions

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  • Francesco Decarolis
  • Maris Goldmanis
  • Antonio Penta

Abstract

The transition of the advertising market from traditional media to the internet has induced a proliferation of marketing agencies specialized in bidding in the auctions that are used to sell ad space on the web. We analyze how collusive bidding can emerge from bid delegation to a common marketing agency and how this can undermine the revenues and allocative effciency of both the Generalized Second Price auction (GSP, used by Google and Microsoft-Bing and Yahoo!) and the of VCG mechanism (used by Facebook). We nd that, despite its well-known susceptibility to collusion, the VCG mechanism outperforms the GSP auction both in terms of revenues and effciency.

Suggested Citation

  • Francesco Decarolis & Maris Goldmanis & Antonio Penta, 2019. "Marketing Agencies and Collusive Bidding in Online Ad Auctions," Working Papers 1088, Barcelona School of Economics.
    • Handle: RePEc:bge:wpaper:1088
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    Cited by:

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    2. John Asker & Mariagiovanna Baccara & SangMok Lee, 2021. "Patent auctions and bidding coalitions: structuring the sale of club goods," RAND Journal of Economics, RAND Corporation, vol. 52(3), pages 662-690, September.
    3. Xiuzhi Zhang & Ying Zhang & Zhijie Lin, 2023. "Online Advertising and Real Estate sales: evidence from the Housing Market," Electronic Commerce Research, Springer, vol. 23(1), pages 605-622, March.
    4. Thomas W. L. Norman, 2021. "Evolutionary stability in the generalized second-price auction," Economic Theory, Springer;Society for the Advancement of Economic Theory (SAET), vol. 71(1), pages 235-250, February.
    5. Sadek, Biland, 2024. "Components and Strategic Routes of Corporate Transformations," MPRA Paper 120332, University Library of Munich, Germany, revised 26 Feb 2024.
    6. Sylvain Chassang & Juan Ortner, 2019. "Collusion in Auctions with Constrained Bids: Theory and Evidence from Public Procurement," Journal of Political Economy, University of Chicago Press, vol. 127(5), pages 2269-2300.
    7. Hannes Wallimann & David Imhof & Martin Huber, 2023. "A Machine Learning Approach for Flagging Incomplete Bid-Rigging Cartels," Computational Economics, Springer;Society for Computational Economics, vol. 62(4), pages 1669-1720, December.
    8. Dirk Bergemann & Alessandro Bonatti & Nicholas Wu, 2023. "How Do Digital Advertising Auctions Impact Product Prices?," Papers 2304.08432, arXiv.org, revised Apr 2024.
    9. Margarida V. B. Santos & Isabel Mota & Pedro Campos, 2023. "Analysis of online position auctions for search engine marketing," Journal of Marketing Analytics, Palgrave Macmillan, vol. 11(3), pages 409-425, September.
    10. Maximilian Schäfer & Geza Sapi, 2020. "Learning from Data and Network Effects: The Example of Internet Search," Discussion Papers of DIW Berlin 1894, DIW Berlin, German Institute for Economic Research.
    11. Peng Hao & Jun-Peng Guo & Eoghan O’Neill & Yong-Heng Shi, 2023. "When Will First-Price Work Well? The Impact of Anti-Corruption Rules on Photovoltaic Power Generation Procurement Auctions," Sustainability, MDPI, vol. 15(4), pages 1-24, February.
    12. Francesco Decarolis & Gabriele Rovigatti, 2017. "Online Auctions and Digital Marketing Agencies," Working Papers 17-08, NET Institute.
    13. Xinyu Cao & T. Tony Ke, 2019. "Cooperative Search Advertising," Marketing Science, INFORMS, vol. 38(1), pages 44-67, January.
    14. Calvano, Emilio & Polo, Michele, 2021. "Market power, competition and innovation in digital markets: A survey," Information Economics and Policy, Elsevier, vol. 54(C).
    15. Amin Sayedi & Kinshuk Jerath & Marjan Baghaie, 2018. "Exclusive Placement in Online Advertising," Marketing Science, INFORMS, vol. 37(6), pages 970-986, November.
    16. Zhao, Cui & Xiao, Yongbo & Yang, Jun & Mu, Jianliang, 2024. "Fighting against de-pooling effect of airport advertising spaces: A supply chain perspective," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 186(C).

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    More about this item

    Keywords

    Collusion; digital marketing agencies; facebook; Google; GSP; internet auctions; online advertising; VCG;
    All these keywords.

    JEL classification:

    • C72 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - Noncooperative Games
    • D44 - Microeconomics - - Market Structure, Pricing, and Design - - - Auctions
    • L81 - Industrial Organization - - Industry Studies: Services - - - Retail and Wholesale Trade; e-Commerce

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