Identification in Search Models with Social Information

30 Pages Posted: 7 Dec 2022 Last revised: 26 Feb 2024

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: November 24, 2023

Abstract

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.

Keywords: Search & Learning; Social Information; Identification; Networks; Robustness; Partial Identification.

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

Suggested Citation

Lomys, Niccolò and Tarantino, Emanuele, Identification in Search Models with Social Information (November 24, 2023). 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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