LAUSR

Abstract We identify and estimate heterogeneous social effects within groups of individuals that make binary choices. These heterogeneous social effects, which include peer and contextual effects, are modeled through unobserved influence matrices that summarize how the members within each group affect each other’s outcomes. We recover parameters in social effects as well as the unknown influence matrices by exploiting how these matrices are linked to the reduced-form effects of multiple characteristics. Monte Carlo experiments show that a nested fixed-point maximum-likelihood estimator for the social effects has good finite-sample performance. Using a new dataset, we analyze how college roommates influence each other’s decisions to participate in volunteering activities. Our estimates reveal substantial heterogeneity in the social effects among these students.