Consumers' valuation for privacy plays a crucial role in determining how much data a firm can collect to drive its managerial decisions. Meanwhile, previous literature that measures consumers’ dollar value for privacy often features small incentives to encourage data sharing. In this paper, we estimate consumers’ valuation for privacy on social network information by examining their data sharing decisions under a wide range of incentives, for customers of a microloan provider in Hong Kong. During the application process, the loan provider uses interest rate discounts to incentivize applicants to provide additional personal data regarding the social network. This percentage discount translates to dollar gains from 4,643 to 9,285 Hong Kong dollars (amount to 30 to 50 percent of their monthly salaries) for applicants who request different loan terms and expect different repayment behaviors. Using a dynamic structural model, we calculate the dollar price for personal data as a function of applicant risk type and the characteristics of the loan that they apply for. Despite the substantial benefits of sharing data, only 19 percent of applicants choose to share any optional personal data requested. In the counterfactual, we show how the company can collect that private information in a more economic way.

AI, Big Data, and Behavioral Science Workshop Series

• PK Kannan (U of Maryland): October 4, 2019
  
• Suzanne Shu (UCLA): November 18, 2019
  
• Mandy Hu (CUHK): January 10, 2020