Working Paper: CEPR ID: DP17740
Authors: Niccol Lomys; Emanuele Tarantino
Abstract: We theoretically study how social information affects agents’ search behavior and the resulting observable outcomes that identify search models. We generalize canonical empirical search models by allowing a share of agents in the population to observe some peers’ choices. Social information changes optimal search. First, we show that neglecting social information leads to non-identification and inconsistent estimation of search cost distributions under various standard datasets. Whether search costs are under or overestimated depends on the dataset. Second, we propose several remedies—such as data requirements, offline estimation techniques, exogenous variations, and partial identification approaches—that restore identification and consistent estimation.
Keywords: search; learning; social information; identification; networks
JEL Codes: C1; C5; C8; D1; D6; D8
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| social information (Z13) | identification of search cost distributions (D39) |
| neglecting social information (D91) | non-identification of search cost distributions (D39) |
| social information (Z13) | utility distributions (L97) |
| utility distributions (L97) | search decisions (D87) |
| social information (Z13) | search behavior (D83) |
| social information (Z13) | utility (L90) |
| isolated agents (Y50) | search behavior (D83) |