Optimal Nonlinear Pricing with Data-Sensitive Consumers

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.