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

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  • 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.

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  • 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
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    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, July.
    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.
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