IDEAS home Printed from https://ideas.repec.org/p/arx/papers/2110.13814.html

Bidders' Responses to Auction Format Change in Internet Display Advertising Auctions

Author

Listed:
  • Shumpei Goke
  • Gabriel Y. Weintraub
  • Ralph Mastromonaco
  • Sam Seljan

Abstract

We study actual bidding behavior when a new auction format gets introduced into the marketplace. More specifically, we investigate this question using a novel dataset on internet display advertising auctions that exploits a staggered adoption by different publishers (sellers) of first-price auctions (FPAs), instead of the traditional second-price auctions (SPAs). We analyze the auction format change using difference-in-differences regressions and a synthetic difference-in-differences estimator, which better handles pre-trends. The results show that revenue per sold impression (price) jumps considerably for treated publishers relative to control publishers, with increases ranging from 25% to 75% of the pre-treatment price level of the treated group. Moreover, for later auction format changes, the increase in price levels under FPAs relative to those under SPAs tends to dissipate over time, reminiscent of the revenue equivalence theorem, although the extent of this reversion depends on the specification. We view these results as suggestive of initially insufficient bid shading following the format change, as opposed to an immediate transition to a new Bayesian Nash equilibrium, with prices tending to decline in several specifications in a manner consistent with gradual adjustment in bidding behavior as bidders learn to shade their bids. Our work constitutes one of the first field studies on bidders'responses to auction format changes, providing an important complement to theoretical model predictions. As such, it provides valuable information to auction designers when considering the implementation of different formats.

Suggested Citation

  • Shumpei Goke & Gabriel Y. Weintraub & Ralph Mastromonaco & Sam Seljan, 2021. "Bidders' Responses to Auction Format Change in Internet Display Advertising Auctions," Papers 2110.13814, arXiv.org, revised May 2026.
  • Handle: RePEc:arx:papers:2110.13814
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/2110.13814
    File Function: Latest version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Joshua D. Angrist & Jörn-Steffen Pischke, 2009. "Mostly Harmless Econometrics: An Empiricist's Companion," Economics Books, Princeton University Press, edition 1, number 8769, December.
    2. Mark Armstrong & Robert Porter (ed.), 2007. "Handbook of Industrial Organization," Handbook of Industrial Organization, Elsevier, edition 1, volume 3, number 1.
    3. Robert Zeithammer, 2019. "Soft Floors in Auctions," Management Science, INFORMS, vol. 65(9), pages 4204-4221, September.
    4. Chiara Farronato & Jessica Fong & Andrey Fradkin, 2020. "Dog Eat Dog: Measuring Network Effects Using a Digital Platform Merger," NBER Working Papers 28047, National Bureau of Economic Research, Inc.
    5. Santiago R. Balseiro & Omar Besbes & Gabriel Y. Weintraub, 2015. "Repeated Auctions with Budgets in Ad Exchanges: Approximations and Design," Management Science, INFORMS, vol. 61(4), pages 864-884, April.
    6. Clément de Chaisemartin & Xavier D'Haultfœuille, 2020. "Two-Way Fixed Effects Estimators with Heterogeneous Treatment Effects," American Economic Review, American Economic Association, vol. 110(9), pages 2964-2996, September.
    7. Jun Li & Serguei Netessine, 2020. "Higher Market Thickness Reduces Matching Rate in Online Platforms: Evidence from a Quasiexperiment," Management Science, INFORMS, vol. 66(1), pages 271-289, January.
    8. Santiago Gallino & Antonio Moreno, 2014. "Integration of Online and Offline Channels in Retail: The Impact of Sharing Reliable Inventory Availability Information," Management Science, INFORMS, vol. 60(6), pages 1434-1451, June.
    9. 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.
    10. Daniel Kahneman & Amos Tversky, 2013. "Prospect Theory: An Analysis of Decision Under Risk," World Scientific Book Chapters, in: Leonard C MacLean & William T Ziemba (ed.), HANDBOOK OF THE FUNDAMENTALS OF FINANCIAL DECISION MAKING Part I, chapter 6, pages 99-127, World Scientific Publishing Co. Pte. Ltd..
    11. Roger B. Myerson, 1981. "Optimal Auction Design," Mathematics of Operations Research, INFORMS, vol. 6(1), pages 58-73, February.
    12. Imbens,Guido W. & Rubin,Donald B., 2015. "Causal Inference for Statistics, Social, and Biomedical Sciences," Cambridge Books, Cambridge University Press, number 9780521885881, Enero-Abr.
    13. David H. Autor, 2003. "Outsourcing at Will: The Contribution of Unjust Dismissal Doctrine to the Growth of Employment Outsourcing," Journal of Labor Economics, University of Chicago Press, vol. 21(1), pages 1-42, January.
    14. L. Elisa Celis & Gregory Lewis & Markus Mobius & Hamid Nazerzadeh, 2014. "Buy-It-Now or Take-a-Chance: Price Discrimination Through Randomized Auctions," Management Science, INFORMS, vol. 60(12), pages 2927-2948, December.
    15. William Vickrey, 1961. "Counterspeculation, Auctions, And Competitive Sealed Tenders," Journal of Finance, American Finance Association, vol. 16(1), pages 8-37, March.
    16. Ulrich Doraszelski & Gregory Lewis & Ariel Pakes, 2018. "Just Starting Out: Learning and Equilibrium in a New Market," American Economic Review, American Economic Association, vol. 108(3), pages 565-615, March.
    17. Hendricks, Ken & Porter, Robert H., 2007. "An Empirical Perspective on Auctions," Handbook of Industrial Organization, in: Mark Armstrong & Robert Porter (ed.), Handbook of Industrial Organization, edition 1, volume 3, chapter 32, pages 2073-2143, Elsevier.
    18. Athey, Susan & Haile, Philip A., 2007. "Nonparametric Approaches to Auctions," Handbook of Econometrics, in: J.J. Heckman & E.E. Leamer (ed.), Handbook of Econometrics, edition 1, volume 6, chapter 60, Elsevier.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Mateusz Mysliwski & Lars Nesheim & Simeon Duckworth, 2023. "Taking the biscuit: how Safari privacy policies affect online advertising," CeMMAP working papers 04/23, Institute for Fiscal Studies.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Omid Rafieian & Hema Yoganarasimhan, 2021. "Targeting and Privacy in Mobile Advertising," Marketing Science, INFORMS, vol. 40(2), pages 193-218, March.
    2. Alexander Teytelboym & Shengwu Li & Scott Duke Kominers & Mohammad Akbarpour & Piotr Dworczak, 2021. "Discovering Auctions: Contributions of Paul Milgrom and Robert Wilson," Scandinavian Journal of Economics, Wiley Blackwell, vol. 123(3), pages 709-750, July.
    3. Anthony Kim & Vahab Mirrokni & Hamid Nazerzadeh, 2021. "Deals or No Deals: Contract Design for Online Advertising," Operations Research, INFORMS, vol. 69(5), pages 1450-1467, September.
    4. Hana Choi & Carl F. Mela & Santiago R. Balseiro & Adam Leary, 2020. "Online Display Advertising Markets: A Literature Review and Future Directions," Information Systems Research, INFORMS, vol. 31(2), pages 556-575, June.
    5. Siddharth Prasad & Maria-Florina Balcan & Tuomas Sandholm, 2025. "Revenue-Optimal Efficient Mechanism Design with General Type Spaces," Papers 2505.13687, arXiv.org.
    6. Shunda, Nicholas, 2009. "Auctions with a buy price: The case of reference-dependent preferences," Games and Economic Behavior, Elsevier, vol. 67(2), pages 645-664, November.
    7. Frank Kelly & Peter Key & Neil Walton, 2016. "Efficient Advert Assignment," Operations Research, INFORMS, vol. 64(4), pages 822-837, August.
    8. Ming Chen & Sareh Nabi & Marciano Siniscalchi, 2023. "Advancing Ad Auction Realism: Practical Insights & Modeling Implications," Papers 2307.11732, arXiv.org, revised Apr 2024.
    9. Ying-Ju Chen, 2017. "Optimal Dynamic Auctions for Display Advertising," Operations Research, INFORMS, vol. 65(4), pages 897-913, August.
    10. Luofeng Liao & Christian Kroer, 2024. "Statistical Inference and A/B Testing in Fisher Markets and Paced Auctions," Papers 2406.15522, arXiv.org, revised Mar 2025.
    11. Mahsa Derakhshan & Negin Golrezaei & Renato Paes Leme, 2022. "Linear Program-Based Approximation for Personalized Reserve Prices," Management Science, INFORMS, vol. 68(3), pages 1849-1864, March.
    12. He, Haoran & Wu, Keyu, 2016. "Choice set, relative income, and inequity aversion: An experimental investigation," Journal of Economic Psychology, Elsevier, vol. 54(C), pages 177-193.
    13. Santiago R. Balseiro & Omar Besbes & Francisco Castro, 2024. "Mechanism Design Under Approximate Incentive Compatibility," Operations Research, INFORMS, vol. 72(1), pages 355-372, January.
    14. Syngjoo Choi & Lars Nesheim & Imran Rasul, 2016. "Reserve Price Effects In Auctions: Estimates From Multiple Regression-Discontinuity Designs," Economic Inquiry, Western Economic Association International, vol. 54(1), pages 294-314, January.
    15. Sameer Mehta & Milind Dawande & Ganesh Janakiraman & Vijay Mookerjee, 2020. "Sustaining a Good Impression: Mechanisms for Selling Partitioned Impressions at Ad Exchanges," Information Systems Research, INFORMS, vol. 31(1), pages 126-147, March.
    16. Mark Kattenberg & Bas Scheer & Jurre Thiel, 2023. "Causal forests with fixed effects for treatment effect heterogeneity in difference-in-differences," CPB Discussion Paper 452, CPB Netherlands Bureau for Economic Policy Analysis.
    17. Axel Ockenfels & David Reiley & Abdolkarim Sadrieh, 2006. "Online Auctions," NBER Working Papers 12785, National Bureau of Economic Research, Inc.
    18. W. Jason Choi & Amin Sayedi, 2019. "Learning in Online Advertising," Marketing Science, INFORMS, vol. 38(4), pages 584-608, July.
    19. Eric Bax, 2020. "Heavy Tails Make Happy Buyers," Papers 2002.09014, arXiv.org.
    20. Estrella Alonso & Joaquín Sánchez-Soriano & Juan Tejada, 2020. "Mixed Mechanisms for Auctioning Ranked Items," Mathematics, MDPI, vol. 8(12), pages 1-26, December.

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:arx:papers:2110.13814. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: arXiv administrators (email available below). General contact details of provider: http://arxiv.org/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.