This paper studies the value of privacy, for individuals, using data from large-scale field experiments that vary disclosure requirements for loan applicants and loan terms on an online peer-to-peer lending platform in China. The author finds that loan applicants attach positive value to personal data: Lower disclosure requirements significantly increase the rate at which applications are completed. She quantifies the monetary value of personal data—and the welfare effect of various disclosure policies—by developing a structural model that links individuals’ disclosure, borrowing, and repayment decisions. Using detailed application-level data, she estimates that social network ID and employer contact are valued at 230 RMB (i.e., $33, or 70% of the average daily salary in China); for successful borrowers, this accounts for 8% of the average net present value of a loan. Requiring answers to these application questions reduces borrower welfare by 13% and costs the platform $0.50 in expected revenue per applicant.

Speaker

Huan Tang

London School of Economics and Political Science, United Kingdom

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Date

05.11.2020 | 15:00 - 16:15 (CET)

Event Location

Online


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Contact

Head of Junior Research Group