Many scholars and policymakers view establishing functioning data markets as essential for the digital economy to bring prosperity and stability to society at large. A key challenge is to determine the value of an individual’s specific data. Is one buyer’s data more valuable than another’s for an e-commerce platform? How much should each be paid?

This project studies what determines the value of a single data record and how privacy policies can affect it. For instance, such a record can be the characteristics of a buyer that an e-commerce platform stores on its servers. When data is used by a third party (like a platform) to strategically direct interactions between multiple agents (like buyers and sellers), assessing its value is complicated and calls for a new approach. We show that this value is not just the payoff the data-user derives directly from a record (like a platform’s transaction fees). It involves other components, which can significantly bias our assessments if ignored. They capture externalities between the records of, say, different buyers not because of a statistical correlation, but because of how the platform partitions its knowledge of the buyers so as to direct sellers' responses (e.g., by pooling buyers into market segments). Such externalities can render the record of a low-spending buyer more valuable than that of a high-spending buyer. This has deep implications for our understanding of the demand side of data markets, which is essential to compare policies' welfare effects as well as to design organizations serving as ``data unions.''

This project has two parts. The first studies contexts where the data-user already owns the data and can use it without people’s consent. We show how to properly assess the value of individual records and characterize all its components. Besides its applicability to those contexts, this part offers a benchmark to understand how privacy affects the value of data. The second part focuses on this question. One key insight is that privacy may not only shift wealth from data-users to data-sources (i.e., from businesses to people), but also change the value itself of data records. For instance, it can increase the value of some people’s records at the expense of others. Thus, privacy can have redistributive effects across data-sources, which may contribute to social inequality and should be taken into account by privacy-protection policies.

Selected candidate(s) can receive a stipend directly from the faculty advisor. This is not a guarantee of payment, and the total amount is subject to available funding.

Faculty Advisor

  • Professor: Jacopo Perego
  • Center/Lab: Graduate School of Business
  • Location: Uris
  • I am an Assistant Professor of Economics at Columbia GSB and affiliated faculty member of the Department of Economics at Columbia University. My interests are in information economics and in the industrial organization of information markets

Project Timeline

  • Earliest starting date: 9/20/21
  • End date: 5/13/22
  • Number of hours per week of research expected during Fall 2021: ~5
  • Number of hours per week of research expected during Summer 2022: ~8

Candidate requirements

  • Skill sets: The ideal student is someone interested in pursuing a PhD in Economics after graduation. The ideal student has a solid quantitative background (e.g. real analysis, probability theory, linear programming, game theory, intermediate microeconomics). The ideal student is familiar with Matlab or Python or both.

  • Student eligibility: freshman, sophomore, junior, senior, master’s

  • International students on F1 or J1 visa: eligible

  • Academic Credit Possible: No