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Social Interactions in Multilayered Observational Networks | Juan Estrada

Social Interactions in Multilayered Observational Networks


This paper proposes a new method to identify and estimate the parameters of an extension of a linear model of peer effects where individuals form different types of social and professional connections that can affect their outcomes. A stylized model provides a theoretical framework for the peer effects linear specification and the main identifying assumptions, which are used to provide identification results in a setting that allows all layers in the multilayer network to not be strictly exogenous. I show that identifying heterogeneous network effects is possible under the assumption that the dependence between individuals in the population is characterized by a stochastic process where dependence vanishes in the network space. I offer a novel multilayer measure of distance that provides a source of exogenous variation used to form identifying moment conditions. I propose a Generalized Method of Moments estimator that is consistent and asymptotically normal at the standard rate, for which I characterize the asymptotic variance-covariance matrix that considers the intrinsic network dependence among individuals. A Monte Carlo experiment confirms the desirable finite properties of the proposed estimator, and an empirical application finds positive and significant peer effects in citations from a multilayer network of professional connections.

Under Revision
Presentations: Eighth Annual Conference on Network Science and Economics 2023., European Winter Meetings of The Econometric Society 2021 and 2022., 16th Economics Graduate Students’ Conference Washington University in St.Louis., IAAE 2022 Annual Conference., Midwest Econometrics Group (MEG) 2022 Conference