Fairness Regulation of Prices in Competitive Markets - Yang, Zongsen; Fu, Xingyu; Gao, Pin; Chen, Ying-Ju (Manufacturing and Service Operations Management, 2024)
Problem definition: The loyalty penalty refers to a pricing strategy where companies charge higher prices to loyal customers for exploitation while offering lower prices to nonloyal customers for attraction. To address this unfair business practice, various regulatory agencies, such as the Competition and Markets Authority and the Financial Conduct Authority in the United Kingdom, have proposed or implemented regulations aimed at promoting fairness in pricing. In this study, we analyze the impact of such regulations on both firms and consumers. Methodology/results: We develop a stylized model to investigate duopoly competition in two symmetric markets, where consumers exhibit loyalty to different firms in each market. The regulatory intervention mandates that the price difference between the two markets, set by each firm, must not exceed a certain threshold. Our analysis reveals an intriguing interaction between market competition and price fairness regulation. When competition is intense, fairness regulation can alleviate cutthroat competition between firms, resulting in Pareto improvements compared with a scenario with no regulation. On the other hand, when competition is weak, fairness regulation can further enhance firms’ existing monopoly power, potentially leading to collusive high prices that are detrimental to consumers and society. We also consider several extensions to enrich our findings, including fairness regulation on relative price discounts, asymmetric markets, and a two-pronged policy regulating the price gap and price cap. Managerial implications: Our study reveals the economic consequences of price fairness regulations; firms can benefit by pricing fairly, but well-intended fairness requirements may have adverse effects on consumers. Funding: P. Gao is supported by the National Natural Science Foundation of China [Grants 72201234 and 72192805], the Hong Kong Research Grants Council (the Collaborative Research Fund) [Grant C6032-21G], and the Guangdong Provincial Key Laboratory of Mathematical Foundations for Artificial Intelligence [Grant 2023B1212010001]. Y.-J. Chen acknowledges financial support from the Hong Kong Research Grants Council [Grants CRF HKUST C6020-21GF, GRF 16500821, and GRF 16501722]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2022.0552 .