Working Paper: CEPR ID: DP14225
Authors: Daron Acemoglu; Ali Makhdoumi; Asuman Ozdaglar; Azarakhsh Malekian
Abstract: When a user shares her data with an online platform, she typically reveals relevant informationabout other users. We model a data market in the presence of this type of externality in a setupwhere one or multiple platforms estimate a user’s type with data they acquire from all users and(some) users value their privacy. We demonstrate that the data externalities depress the price ofdata because once a user’s information is leaked by others, she has less reason to protect her dataand privacy. These depressed prices lead to excessive data sharing. We characterize conditionsunder which shutting down data markets improves (utilitarian) welfare. Competition betweenplatforms does not redress the problem of excessively low price for data and too much data sharing,and may further reduce welfare. We propose a scheme based on mediated data-sharing thatimproves efficiency.
Keywords: data; informational externalities; online markets; privacy
JEL Codes: D62; L86; D83
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| User data sharing (D16) | Negative impact on privacy of other users (F69) |
| User data sharing (D16) | Diminished incentives to protect own data (K24) |
| Diminished incentives to protect own data (K24) | Lower data prices (D49) |
| User data sharing (D16) | Excessive data sharing (C55) |
| Competition among platforms (L17) | Inefficiencies in data pricing (D49) |
| Mediated data-sharing scheme (D16) | Improved efficiency (H21) |