Identification in Search Models with Social Information

43 Pages Posted: 7 Dec 2022 Last revised: 12 Dec 2022

See all articles by Niccolò Lomys

Niccolò Lomys

CSEF - University of Naples Federico II

Emanuele Tarantino

Luiss Guido Carli University; Einaudi Institute for Economics and Finance (EIEF)

Date Written: December 11, 2022

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 of search cost distributions under various standard datasets. Whether search costs are under or overestimated depends on the dataset. Next, we propose remedies---such as data requirements, offline estimation techniques, exogenous variations, and partial identification approaches---that restore identification and consistent estimation.

Keywords: Search and Learning, Social Information, Identification, Networks, Robustness.

JEL Classification: C1, C5, C8, D1, D6, D8.

Suggested Citation

Lomys, Niccolò and Tarantino, Emanuele, Identification in Search Models with Social Information (December 11, 2022). Available at SSRN: https://ssrn.com/abstract=4288045 or http://dx.doi.org/10.2139/ssrn.4288045

Niccolò Lomys (Contact Author)

CSEF - University of Naples Federico II ( email )

via Cinthia, 4
Naples, Caserta 80126
Italy

Emanuele Tarantino

Luiss Guido Carli University ( email )

Via O. Tommasini 1
Rome, Roma 00100
Italy

Einaudi Institute for Economics and Finance (EIEF) ( email )

Via Due Macelli, 73
Rome, 00187
Italy

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