Optimal Nonlinear Pricing with Data-Sensitive Consumers

Abstract

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. (JEL D11, D42, D82, D83, L12)