Working Paper: NBER ID: w30920
Authors: Jeanine Miklsthal; Avi Goldfarb; Avery M. Haviv; Catherine Tucker
Abstract: When a user shares multi-dimensional data about themselves with a firm, the firm learns about the correlations of different dimensions of user data. We incorporate this type of learning into a model of a data market in which a firm acquires data from users with privacy concerns. User data is multi-dimensional, and each user can share no data, only non-sensitive data, or their full data with the firm. As the firm collects more data and becomes better at drawing inferences about a user’s privacy-sensitive data from their non-sensitive data, the share of new users who share no data (“digital hermits”) grows. At the same time, the share of new users who share their full data also grows. The model therefore predicts a polarization of users’ data sharing choices away from non-sensitive data sharing to no sharing and full sharing.
Keywords: No keywords provided
JEL Codes: L51; L86; M3
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| Firm's accumulation of data (D25) | Proportion of users choosing to share no data (Y10) |
| Firm's accumulation of data (D25) | Share of users who share all their data (D16) |
| Negative externality imposed by early data sharers (D62) | Compensation required for nonsensitive data sharing (D26) |
| Firm's patience (D25) | Slowing of data collection (C80) |
| Slowing of data collection (C80) | Optimization of future profits (L21) |
| Firm's accumulation of data (D25) | Polarization in user behavior towards extremes of data sharing (D16) |