The rise of large digital platforms — from Facebook, Google, and Amazon in the US to JD, Tencent, and Alibaba in China — has led to the unprecedented collection and commercial use of individual data.
The paper “The Economics of Social Data” argues that a central, underappreciated feature of those data is their social aspect: data captured from an individual user describe not only that individual, but other users with similar characteristics or behaviours. The policy implications of this insight include the need for privacy regulations focused less on personalised prices, and more on group-based price discrimination. The authors 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.
The authors 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.