ABSTRACT
This paper develops a model of data-driven price discrimination to study the welfare-optimal allocation of data rights between consumers and platforms. Consumers understand that their platform usage generates data that may reveal their willingness to pay and may therefore behave strategically by distorting usage. The platform invests in data-processing technology to improve inference and enable personalized pricing. A key feature of the model is that better data processing improves inference only conditional on observed behavior; it does not by itself detect whether the underlying behavior has been strategically manipulated. As a result, stronger data-processing capability can increase high-value consumers’ incentives to mimic lower-value consumers. Under binary types, a fully separating equilibrium exists, but the platform optimally caps its investment to sustain informative behavior. Under a uniform distribution of types, no pure-strategy equilibrium can feature local full separation; instead, equilibrium takes the form of multiple pooling intervals. Welfare analysis delivers a simple criterion for data-rights allocation: data rights should be assigned to the platform when data-driven pricing creates additional trade, but to consumers when it merely redistributes surplus. The paper also shows how consent walls can unravel consumer data rights in practice and how regulated pricing can mitigate overinvestment and reveal the shadow value of data rights.
Wang, Jianyun, Data Rights and Price Discrimination (July 6, 2026).
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