Estimating Social Effects in a Multilayered Linear-In-Means Model with Network Data

Abstract: This paper studies the identification and estimation of social parameters in a general version of the Linear-in-Means model commonly fitted in the Social Sciences with multilayered network data. A Monte Carlo exercise showcases its good small-sample properties while an empirical application to Canadian consumers’ credit usage demonstrates its applicability. Our estimates show that one’s credit-card balance increases by $0.31 for an extra $1 owed by surrounding neighbors.