IDEAS home Printed from https://ideas.repec.org/p/arx/papers/2503.04489.html
   My bibliography  Save this paper

Welfare Effects of Self-Preferencing by a Platform: Empirical Evidence from Airbnb

Author

Listed:
  • Kaede Hanazawa

Abstract

This paper studies the welfare effects of self-preferencing by Airbnb, a practice where Airbnb utilizes its pricing algorithm to prioritize maximizing platform-wide commission revenue rather than optimizing individual host revenues. To examine this welfare implication, I construct a Bertrand competition model with differentiated products between Airbnb hosts and hotels. Using unique data from Tokyo's 23 wards, I estimate the model and conduct counterfactual simulations to evaluate the welfare effects of self-preferencing. Counterfactual simulations reveal that self-preferencing reduces social welfare by 5.08% on average, equivalent to an annual loss of about 14.90% of Tokyo's vacation rental market size in 2023 while increasing Airbnb's commission revenue by an average of 37.73%. These findings highlight the significant trade-offs between platform-driven revenue optimization and market efficiency, emphasizing the urgent need for competition policy reforms and greater transparency and accountability in platform practices.

Suggested Citation

  • Kaede Hanazawa, 2025. "Welfare Effects of Self-Preferencing by a Platform: Empirical Evidence from Airbnb," Papers 2503.04489, arXiv.org.
    • Handle: RePEc:arx:papers:2503.04489
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/2503.04489
    File Function: Latest version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Belleflamme, Paul & Peitz, Martin, 2021. "The Economics of Platforms : Concepts and Strategy," LIDAM Reprints CORE 3185, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    2. Horn, Keren & Merante, Mark, 2017. "Is home sharing driving up rents? Evidence from Airbnb in Boston," Journal of Housing Economics, Elsevier, vol. 38(C), pages 14-24.
    3. Zach Y. Brown & Alexander MacKay, 2023. "Competition in Pricing Algorithms," American Economic Journal: Microeconomics, American Economic Association, vol. 15(2), pages 109-156, May.
    4. Ali Hortaçsu & Olivia R Natan & Hayden Parsley & Timothy Schwieg & Kevin R Williams, 2024. "Organizational Structure and Pricing: Evidence from a Large U.S. Airline★," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 139(2), pages 1149-1199.
    5. Small, Kenneth A & Rosen, Harvey S, 1981. "Applied Welfare Economics with Discrete Choice Models," Econometrica, Econometric Society, vol. 49(1), pages 105-130, January.
    6. Sherry He & Brett Hollenbeck & Davide Proserpio, 2022. "The Market for Fake Reviews," Marketing Science, INFORMS, vol. 41(5), pages 896-921, September.
    7. Kittaka, Yuta & Sato, Susumu & Zennyo, Yusuke, 2023. "Self-preferencing by platforms: A literature review," Japan and the World Economy, Elsevier, vol. 66(C).
    8. Igal Hendel & Aviv Nevo, 2013. "Intertemporal Price Discrimination in Storable Goods Markets," American Economic Review, American Economic Association, vol. 103(7), pages 2722-2751, December.
    9. Mark J. Zbaracki & Mark Ritson & Daniel Levy & Shantanu Dutta & Mark Bergen, 2004. "Managerial and Customer Costs of Price Adjustment: Direct Evidence from Industrial Markets," The Review of Economics and Statistics, MIT Press, vol. 86(2), pages 514-533, May.
    10. Federico Etro, 2023. "Hybrid Marketplaces with Free Entry of Sellers," Review of Industrial Organization, Springer;The Industrial Organization Society, vol. 62(2), pages 119-148, March.
    11. Shunyuan Zhang & Nitin Mehta & Param Vir Singh & Kannan Srinivasan, 2021. "Frontiers: Can an Artificial Intelligence Algorithm Mitigate Racial Economic Inequality? An Analysis in the Context of Airbnb," Marketing Science, INFORMS, vol. 40(5), pages 813-820, September.
    12. Belleflamme,Paul & Peitz,Martin, 2021. "The Economics of Platforms," Cambridge Books, Cambridge University Press, number 9781108710749, December.
    13. Stephanie Assad & Robert Clark & Daniel Ershov & Lei Xu, 2024. "Algorithmic Pricing and Competition: Empirical Evidence from the German Retail Gasoline Market," Journal of Political Economy, University of Chicago Press, vol. 132(3), pages 723-771.
    14. O’Connor, Jason & Wilson, Nathan E., 2021. "Reduced demand uncertainty and the sustainability of collusion: How AI could affect competition," Information Economics and Policy, Elsevier, vol. 54(C).
    15. Nathan H. Miller & Matthew C. Weinberg, 2017. "Understanding the Price Effects of the MillerCoors Joint Venture," Econometrica, Econometric Society, vol. 85(6), pages 1763-1791, November.
    16. Joseph E. Harrington, 2022. "The Effect of Outsourcing Pricing Algorithms on Market Competition," Management Science, INFORMS, vol. 68(9), pages 6889-6906, September.
    17. Justin P. Johnson & Andrew Rhodes & Matthijs Wildenbeest, 2023. "Platform Design When Sellers Use Pricing Algorithms," Econometrica, Econometric Society, vol. 91(5), pages 1841-1879, September.
    18. Timo Klein, 2021. "Autonomous algorithmic collusion: Q‐learning under sequential pricing," RAND Journal of Economics, RAND Corporation, vol. 52(3), pages 538-558, September.
    19. Emilio Calvano & Giacomo Calzolari & Vincenzo Denicolò & Sergio Pastorello, 2020. "Artificial Intelligence, Algorithmic Pricing, and Collusion," American Economic Review, American Economic Association, vol. 110(10), pages 3267-3297, October.
    20. Georgios Zervas & Davide Proserpio & John W. Byers, 2021. "A first look at online reputation on Airbnb, where every stay is above average," Marketing Letters, Springer, vol. 32(1), pages 1-16, March.
    21. Laura Grigolon & Frank Verboven, 2014. "Nested Logit or Random Coefficients Logit? A Comparison of Alternative Discrete Choice Models of Product Differentiation," The Review of Economics and Statistics, MIT Press, vol. 96(5), pages 916-935, December.
    22. Nan Chen & Hsin‐Tien Tsai, 2024. "Steering via algorithmic recommendations," RAND Journal of Economics, RAND Corporation, vol. 55(4), pages 501-518, December.
    23. Jeanine Miklós-Thal & Catherine Tucker, 2019. "Collusion by Algorithm: Does Better Demand Prediction Facilitate Coordination Between Sellers?," Management Science, INFORMS, vol. 65(4), pages 1552-1561, April.
    24. Jens-Uwe Franck & Martin Peitz, 2021. "Market Definition in the Platform Economy," CRC TR 224 Discussion Paper Series crctr224_2021_259v2, University of Bonn and University of Mannheim, Germany.
    25. Christopher Conlon & Jeff Gortmaker, 2020. "Best practices for differentiated products demand estimation with PyBLP," RAND Journal of Economics, RAND Corporation, vol. 51(4), pages 1108-1161, December.
    26. W. Ross Morrow & Steven J. Skerlos, 2011. "Fixed-Point Approaches to Computing Bertrand-Nash Equilibrium Prices Under Mixed-Logit Demand," Operations Research, INFORMS, vol. 59(2), pages 328-345, April.
    Full references (including those not matched with items on IDEAS)

    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. Zexin Ye, 2025. "Algorithmic Collusion under Observed Demand Shocks," Papers 2502.15084, arXiv.org, revised May 2025.
    2. Martin, Simon & Rasch, Alexander, 2024. "Demand forecasting, signal precision, and collusion with hidden actions," International Journal of Industrial Organization, Elsevier, vol. 92(C).
    3. Simon Martin & Alexander Rasch, 2022. "Collusion by Algorithm: The Role of Unobserved Actions," CESifo Working Paper Series 9629, CESifo.
    4. Martin, Simon & Rasch, Alexander, 2022. "Collusion by algorithm: The role of unobserved actions," DICE Discussion Papers 382, Heinrich Heine University Düsseldorf, Düsseldorf Institute for Competition Economics (DICE).
    5. Justin P. Johnson & Andrew Rhodes & Matthijs Wildenbeest, 2023. "Platform Design When Sellers Use Pricing Algorithms," Econometrica, Econometric Society, vol. 91(5), pages 1841-1879, September.
    6. Harrington, Joseph E., 2024. "The effect of demand variability on the adoption and design of a third party’s pricing algorithm," Economics Letters, Elsevier, vol. 244(C).
    7. Fourberg, Niklas & Marques-Magalhaes, Katrin & Wiewiorra, Lukas, 2022. "They are among us: Pricing behavior of algorithms in the field," WIK Working Papers 6, WIK Wissenschaftliches Institut für Infrastruktur und Kommunikationsdienste GmbH, Bad Honnef.
    8. Abada, Ibrahim & Lambin, Xavier & Tchakarov, Nikolay, 2024. "Collusion by mistake: Does algorithmic sophistication drive supra-competitive profits?," European Journal of Operational Research, Elsevier, vol. 318(3), pages 927-953.
    9. Fourberg, Niklas & Marques Magalhaes, Katrin & Wiewiorra, Lukas, 2023. "They Are Among Us: Pricing Behavior of Algorithms in the Field," 32nd European Regional ITS Conference, Madrid 2023: Realising the digital decade in the European Union – Easier said than done? 277958, International Telecommunications Society (ITS).
    10. Marcel Wieting & Geza Sapi, 2021. "Algorithms in the Marketplace: An Empirical Analysis of Automated Pricing in E-Commerce," Working Papers 21-06, NET Institute.
    11. Werner, Tobias, 2021. "Algorithmic and human collusion," DICE Discussion Papers 372, Heinrich Heine University Düsseldorf, Düsseldorf Institute for Competition Economics (DICE).
    12. Gonzalo Ballestero, 2021. "Collusion and Artificial Intelligence: A computational experiment with sequential pricing algorithms under stochastic costs," Young Researchers Working Papers 1, Universidad de San Andres, Departamento de Economia, revised Oct 2022.
    13. Hanspach, Philip & Sapi, Geza & Wieting, Marcel, 2024. "Algorithms in the marketplace: An empirical analysis of automated pricing in e-commerce," Information Economics and Policy, Elsevier, vol. 69(C).
    14. Yassine Lefouili & Leonardo Madio, 2022. "The economics of platform liability," European Journal of Law and Economics, Springer, vol. 53(3), pages 319-351, June.
    15. John Asker & Chaim Fershtman & Ariel Pakes, 2024. "The impact of artificial intelligence design on pricing," Journal of Economics & Management Strategy, Wiley Blackwell, vol. 33(2), pages 276-304, March.
    16. Gonzalo Ballestero, 2022. "Collusion and Artificial Intelligence: A Computational Experiment with Sequential Pricing Algorithms under Stochastic Costs," Working Papers 118, Red Nacional de Investigadores en Economía (RedNIE).
    17. Francesco Angelini & Massimiliano Castellani & Lorenzo Zirulia, 2025. "Platform investment and seller competition in two-sided markets," Journal of Economics, Springer, vol. 144(1), pages 1-29, January.
    18. Werner, Tobias, 2023. "Algorithmic and Human Collusion," VfS Annual Conference 2023 (Regensburg): Growth and the "sociale Frage" 277573, Verein für Socialpolitik / German Economic Association.
    19. Alessandro De Chiara & Juan José Ganuza & Fernando Gómez & Ester Manna & Adrián Segura, 2023. "Platform liability with reputational sanctions," Economics Working Papers 1868, Department of Economics and Business, Universitat Pompeu Fabra.
    20. Brett Hollenbeck & Kosuke Uetake, 2021. "Taxation and market power in the legal marijuana industry," RAND Journal of Economics, RAND Corporation, vol. 52(3), pages 559-595, September.

    More about this item

    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:2503.04489. 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.