Peer effects heterogeneity and social networks in education

This study focuses on the role of heterogeneity in network peer effects by accounting for network-specific factors and different driving mechanisms of peer behavior. We propose a novel Multivariate Instrumental Variable (MVIV) estimator which is consistent for a large number of networks keeping the individual network size bounded. We apply this approach to estimate peer effects on school achievement exploiting the network structure of friendships within classrooms. The empirical evidence presented is based on a unique network dataset from German upper secondary schools. We show that accounting for heterogeneity is not only crucial from a statistical perspective, but also yields new structural insights into how class size and gender composition affect school achievement through peer behavior.