Publication Date: September 2019
Revision Date: July 2020
We propose a model of data intermediation to analyze the incentives for sharing individual data in the presence of informational externalities. A data intermediary acquires signals from individual consumers regarding their preferences. The intermediary resells the information in a product market in which ﬁrms and consumers can tailor their choices to the demand data. The social dimension of the individual data - whereby an individual’s data are predictive of the behavior of others - generates a data externality that can reduce the intermediary’s cost of acquiring information. We derive the intermediary’s optimal data policy and establish that it preserves the privacy of consumer identities while providing precise information about market demand to the ﬁrms. This policy enables the intermediary to capture the total value of the information as the number of consumers becomes large.
Keywords: Social data, Personal information, Consumer privacy, Privacy paradox, Data intermediaries, Data externality, Data flow, Data policy, Data rights, Collaborative filtering
JEL Classification Codes: D44, D82, D83.CFDP 2203