Identification and Estimation of Endogenous Peer Effects Using Partial Network Data from Multiple Reference Groups

Reza, S; Manchanda, P.; Chong, JK. (2020). Identification and estimation of endogenous peer effects using partial network data from multiple reference groups. Management Science, Forthcoming. https://doi.org/10.1287/mnsc.2020.3769

68 Pages Posted: 10 Jan 2018 Last revised: 14 Jan 2021

See all articles by Sadat Reza

Sadat Reza

Nanyang Business School, Nanyang Technological University

Puneet Manchanda

University of Michigan, Stephen M. Ross School of Business

Juin-Kuan Chong

National University of Singapore (NUS) - NUS Business School

Date Written: July 25, 2019

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 the social influence is truly due to agents affecting each other through their choices simultaneously (endogenous effect) or due to 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 viz. that consumers are usually part of multiple, as opposed to a single, networks. We discuss the sufficient conditions for identification when the standard linear-in-means models 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, Reference Groups, Linear-in-Means Models

JEL Classification: C31, C5

Suggested Citation

Reza, Sadat and Manchanda, Puneet and Chong, Juin-Kuan, Identification and Estimation of Endogenous Peer Effects Using Partial Network Data from Multiple Reference Groups (July 25, 2019). Reza, S; Manchanda, P.; Chong, JK. (2020). Identification and estimation of endogenous peer effects using partial network data from multiple reference groups. Management Science, Forthcoming. https://doi.org/10.1287/mnsc.2020.3769, Available at SSRN: https://ssrn.com/abstract=3097792 or http://dx.doi.org/10.2139/ssrn.3097792

Sadat Reza

Nanyang Business School, Nanyang Technological University ( email )

Singapore, 639798
Singapore
93849872 (Phone)

Puneet Manchanda (Contact Author)

University of Michigan, Stephen M. Ross School of Business ( email )

701 Tappan Street
Ann Arbor, MI MI 48109
United States
734-936-2445 (Phone)
734-936-8716 (Fax)

Juin-Kuan Chong

National University of Singapore (NUS) - NUS Business School ( email )

1 Business Link
Singapore, 117592
Singapore

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