Identification in Search Models with Social Information

Niccolò Lomys and Emanuele Tarantino

No 17740, CEPR Discussion Papers from Centre for Economic Policy Research

Abstract: We study the identification of empirical search models when agents access social information, i.e., peers’ choices and experiences. We show that social information changes optimal search and the observable model implications. Consequently, neglecting social information, even in its simplest form, leads to misspecification of the search model. Depending on the dataset, search frictions are underestimated or overestimated. Next, we propose partial identification approaches to recover robust bounds on search cost distributions while imposing minimal assumptions on social information. Stronger assumptions or data requirements help refine these bounds. We evaluate the implications of our findings for quantifying welfare, demand, and counterfactuals.

JEL-codes: C1 C5 C8 D1 D6 D8 (search for similar items in EconPapers)
Date: 2022-12
References: Add references at CitEc
Citations:

Downloads: (external link)
https://cepr.org/publications/DP17740 (application/pdf)

Related works:
Working Paper: Identification in Search Models with Social Information (2023)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:cpr:ceprdp:17740

Ordering information: This working paper can be ordered from
https://cepr.org/publications/DP17740

Access Statistics for this paper

More papers in CEPR Discussion Papers from Centre for Economic Policy Research 33 Great Sutton Street, London EC1V 0DX, UK.
Bibliographic data for series maintained by CEPR ().