Identification and Estimation of Endogenous Peer Effects Using Partial Network Data from Multiple Reference Groups
Sadat Reza (),
Puneet Manchanda () and
Juin-Kuan Chong
Additional contact information
Sadat Reza: Nanyang Technological University, Singapore 639798
Puneet Manchanda: University of Michigan, Ann Arbor, Michigan 48104
Management Science, 2021, vol. 67, issue 8, 5070-5105
Abstract:
There has been a considerable amount of interest in the empirical investigation of social influence in the marketing and economics literature in the last decade or so. Among the many different empirical models applied for such investigations, the most common class of model is the linear-in-means model. These models can be used to examine whether social influence is truly a result of agents affecting each other through their choices simultaneously (endogenous effect) or of having similar taste and characteristics (homophily). However, the two effects are not separately identified in general in the standard linear-in-means model unless data on all members of an individual’s network are available. With data on a sample of individuals from a network, these effects are not identified. In this research, we leverage a very natural aspect of social settings, namely that consumers are usually part of multiple—as opposed to single—networks. We discuss the sufficient conditions for identification when the standard linear-in-means model is extended to allow for multiple sources of social influence. We also show how the additional variation generated by more than one source of social influence actually allows estimation of endogenous effects with sample data. We demonstrate the feasibility of our approach via simulation and on the National Longitudinal Study on Adolescent Health data, which has been used in a number of studies examining social influence. Our approach is, therefore, likely to be useful in typical marketing settings.
Keywords: peer effects; social influence; linear-in-means models; adolescent screen time (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)
Downloads: (external link)
http://dx.doi.org/10.1287/mnsc.2020.3769 (application/pdf)
Related works:
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:inm:ormnsc:v:67:y:2021:i:8:p:5070-5105
Access Statistics for this article
More articles in Management Science from INFORMS Contact information at EDIRC.
Bibliographic data for series maintained by Chris Asher ().