Economic Effects of the Widespread Use of Personalised Pricing
DOI:
https://doi.org/10.54097/czdhq493Keywords:
Personalised Pricing, Price Discrimination, Consumer WelfareAbstract
This study explores the economic impact of personalised pricing in digital markets, based on microeconomic models and consumer welfare analysis. It argues that, although personalised pricing achieves efficient resource allocation through matching between prices and consumers’ willingness to pay in the short term, it may distort price signals and increase transaction costs in the long term. While improving social welfare, personalised pricing can lead to the transfer of consumer welfare to suppliers, creating concerns about distributive fairness. In addition, the data advantages used for personalised pricing may strengthen platforms’ monopoly power, weakening market competition. Therefore, this study argues that regulatory measures are necessary to balance efficiency, fairness and the sustainability of market development.
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References
[1] Statista. (2025). Global number of digital buyers. Statista.
[2] Public Broadcasting Service. (2025, July 11). Delta Air Lines tests AI-driven personalized pricing. PBS News. https://www.pbs.org/newshour/economy/personalized-pricing-has-spread-across-many-industries-heres-how-consumers-can-avoid-it
[3] Industry Research. (2025). Dynamic pricing solutions market size, share, growth, and industry analysis. Industry research report.
[4] Varian, H. R. (1989). Price discrimination. In R. Schmalensee & R. D. Willig (Eds.), Handbook of industrial organization (Vol. 1, pp. 597–654). Elsevier.
[5] Pigou, A. C. (1920). The economics of welfare. Macmillan.
[6] Rhodes, A., & Zhou, J. (2024). Personalized pricing and competition. American Economic Review, 114(7), 2141–2170.
[7] Dube, J.-P., & Misra, S. (2023). Personalized pricing and consumer welfare. Journal of Political Economy, 131(1), 131–189.
[8] Woodcock, R. (2019). Personalized pricing as monopolization. Antitrust Law Journal, 86(2), 417–462.
[9] Esteves, R. B., & Resende, J. (2019). Personalized pricing and advertising: Who are the winners? International Journal of Industrial Organization, 63, 239–282.
[10] Li, X., & Li, K. J. (2023). Beating the algorithm: Consumer manipulation, personalized pricing, and big data management. Manufacturing & Service Operations Management, 25(1), 36–49.
[11] Ezrachi, A., & Stucke, M. E. (2016). The rise of behavioural discrimination. European Competition Law Review, 37(8), 485–496.
[12] Luo, X., Kou, G., Huang, Y., & Wang, Y. (2025). Personalized pricing strategies of competitive firms when consumers can manipulate data to beat AI algorithms. Information Technology and Management, 1–18.
[13] Yuan, Z. (2023). The value of personal data in Internet commerce: A high-stakes field experiment on data regulation policy. Management Science. https://doi.org/10.1287/mnsc.2023.4828
[14] Chen, Z., Choe, C., & Matsushima, N. (2020). Competitive personalized pricing. Management Science, 66(9), 4003–4023.
[15] Yun, J. M. (2025, July). Should we fear personalized pricing? CPI Antitrust Chronicle. George Mason University Law & Economics Research Paper Series, 25(10).
[16] Federal Trade Commission. (2025). FTC surveillance pricing study indicates wide range of personal data used to set individualized consumer prices. Federal Trade Commission.
[17] Kallus, N., & Zhou, Y. (2021). Fairness, welfare, and equity in personalized pricing. Operations Research, 69(4), 1184–1205.
[18] Zhao, Z. (2023). Algorithmic personalized pricing with the right to explanation. Journal of Competition Law & Economics, 19(3), 367–396.
[19] Zarsky, T. (2019). Algorithmic inequality: How big data analytics exacerbate discrimination. Harvard Journal of Law and Technology, 32(2), 457–512.
[20] Prinsloo, E., Barasz, K., & Ubel, P. A. (2022). Motivated inferences of price and quality in healthcare decisions. Journal of the Association for Consumer Research, 7(2), 186–197.
[21] Hu, Y., Li, G., Liu, M., & Qu, S. (2025). Information sharing and personalized pricing in online platforms. Production and Operations Management, 34(12), 3958–3977.
[22] Mulligan, C. E., & Godsiff, P. (2023). Datalism and data monopolies in the era of AI (arXiv preprint arXiv:2307.08049). arXiv.
[23] Nanfang Metropolitan Daily. (2025, September 18). Multiple hotels accuse Ctrip of unauthorised price adjustments amid regulatory interviews. NetEase. https://www.163.com/dy/article/K9O7KBFD0129QAF.html
[24] Ma, J. (2022). Data monopolies and competition law. In Regulating data monopolies: A law and economics perspective. Springer Nature Singapore.
[25] Pasquale, F. (2015). The black box society: The secret algorithms that control money and information. Harvard University Press.
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