Data-Driven Price Discrimination, Regulation, and Platform Compliance: Evidence From a Field Experiment and a Natural Experiment
通过在中国电商平台进行实地实验和利用政府2020年禁止数据驱动价格歧视的法规作为自然实验，发现平台对数据多的用户歧视更严重，但法规实施后歧视程度显著下降。
Ensuring fair platform practices has become a critical challenge in Operations Management. Increasing anecdotal evidence suggests that digital platforms may leverage consumer data to engage in data-driven price discrimination (DDPD)—charging discriminatory prices to customers based on inferred willingness to pay. Yet, despite widespread concern, no platform has publicly acknowledged the operation of DDPD, and academic research has thus far lacked the means and empirical evidence to quantify its magnitude in practice. Against this backdrop, the first objective of this study therefore is to provide direct evidence on the presence of DDPD. To this end, we conducted a field experiment with a leading e-retailing platform in China. The results reveal that customers for whom the platform possesses more data are subject to a higher level of price discrimination. Recently, various regulations have been introduced to protect consumers from unfair use of their data, but there is a lack of evidence on platform compliance to these regulations. The second research objective is then to investigate whether and to what degree platforms complied with these regulations. We leverage a unique natural experiment in which the Chinese government implemented a new regulation banning DDPD in 2020. The findings reveal that while DDPD practice did not disappear entirely, the level decreased significantly post-regulation. As the first empirical study to present direct evidence of DDPD presence and platform compliance, the findings have significant implications for policymakers, platforms, and customers.