Voluntary Disclosure and Personalized Pricing

S. Nageeb Ali, Greg Lewis, Shoshana Vasserman

Research output: Chapter in Book/Report/Conference proceedingConference contribution

8 Scopus citations


A concern central to the economics of privacy is that firms may use consumer data to price discriminate. A common response is that consumers should have control over their data and the ability to choose how firms access it. Since firms draw inferences based on both the data seen as well as the consumer's disclosure choices, the strategic implications of this proposal are unclear. We investigate whether such measures improve consumer welfare in monopolistic and competitive environments. We find that consumer control can guarantee gains for every consumer type relative to both perfect price discrimination and no personalized pricing. This result is driven by two ideas. First, consumers can use disclosure to amplify competition between firms. Second, consumers can share information that induces a seller - -even a monopolist - -to make price concessions. Furthermore, whether consumer control improves consumer surplus depends on both the technology of disclosure and the competitiveness of the marketplace. In a competitive market, simple disclosure technologies such as "track / do-not-track'' suffice for guaranteeing gains in consumer welfare. However, in a monopolistic market, welfare gains require richer forms of disclosure technology whereby consumers can decide how much information they would like to convey.

Original languageEnglish (US)
Title of host publicationEC 2020 - Proceedings of the 21st ACM Conference on Economics and Computation
PublisherAssociation for Computing Machinery
Number of pages2
ISBN (Electronic)9781450379755
StatePublished - Jul 13 2020
Event21st ACM Conference on Economics and Computation, EC 2020 - Virtual, Online, Hungary
Duration: Jul 13 2020Jul 17 2020

Publication series

NameEC 2020 - Proceedings of the 21st ACM Conference on Economics and Computation


Conference21st ACM Conference on Economics and Computation, EC 2020
CityVirtual, Online

All Science Journal Classification (ASJC) codes

  • Computer Science (miscellaneous)
  • Economics and Econometrics
  • Statistics and Probability
  • Computational Mathematics


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