Estimating Social Effects with Randomized and Observational Network Data

Chan TszKin Julian, Estrada Juan, Kim Huynh, David Jacho-Chávez, Lam Chungsang Tom and Sánchez-Aragón Leonardo
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Chan TszKin Julian: Bates White Economic Consulting, Washington, USA
Estrada Juan: Analysis Group Economic Consulting, Washington, USA
Lam Chungsang Tom: Department of Finance, Florida State University, Tallahassee, USA
Sánchez-Aragón Leonardo: Facultad de Ciencias Sociales y Humanísticas, ESPOL University, Guayaquil, Ecuador

Journal of Econometric Methods, 2024, vol. 13, issue 2, 205-224

Abstract: This paper introduces an innovative approach to identifying and estimating the parameters of interest in the widely recognized linear-in-means regression model under conditions where the initial randomization of peers determines the observed network. We assert that peers who are initially randomized do not produce social effects. However, after randomization, agents can endogenously develop significant connections that potentially generate peer influences. We present a moment condition that compiles local heterogeneous identifying information for all agents within the population. Under the assumption of ψ-dependence in the endogenous network space, we propose a Generalized Method of Moments (GMM) estimator, which is proven to be consistent, asymptotically normally distributed, and straightforward to implement using commonly available statistical software due to its closed-form expression. Monte Carlo simulations demonstrate the GMM estimator’s strong small-sample performance. An empirical analysis utilizing data from Hong Kong high school students reveals substantial positive spillover effects on math test scores among study partners in our sample, provided that their seatmates were exogenously assigned by their teachers.

Keywords: social networks; instrumental variables; causal inference; ψ-dependence (search for similar items in EconPapers)
JEL-codes: C21 (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1515/jem-2023-0043

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