Identification in Search Models with Social Information

We theoretically study the problem of a researcher seeking to identify and estimate the search cost distribution when a share of agents in the population observes some peers’ choices. To begin with, we show that social information changes agents’ optimal search and, as a result, the distributions of observable outcomes identifying the search model. Consequently, neglecting social information leads to non-identification of the search cost distribution. Whether, as a result, search frictions are under or overestimated depends on the dataset’s content. Next, we present empirical strategies that restore identification and correct estimation. First, we show how to recover robust bounds on the search cost distribution by imposing only minimal assumptions on agents’ social information. Second, we explore how leveraging additional data or stronger assumptions can help obtain more informative estimates.