Optimal Nonlinear Pricing with Data-Sensitive Consumers
研究了当部分消费者因购买行为泄露信息而产生隐私成本时，垄断企业如何设计最优定价机制，并分析了隐私偏好公开对各方福利的影响。
We study monopolistic screening when some consumers are data sensitive and incur a privacy cost if their purchase reveals information to the monopolist. The monopolist discriminates between data-sensitive and classical consumers using privacy mechanisms that consist of a direct mechanism and a privacy option. A privacy mechanism is optimal for large privacy costs and leaves classical consumers better off than data-sensitive consumers with the same valuation. When privacy preferences become public information, data-sensitive consumers and the monopolist gain, whereas classical consumers lose. Our results are relevant for policies targeting consumers’ data awareness, such as the European General Data Protection Regulation.