Identifying Peer Effects in Networks with Unobserved Effort and Isolated Students
Aristide Houndetoungan, Cristelle Kouame, Michael Vlassopoulos
TL;DR
This paper develops a structural framework to identify peer effects on academic effort when effort is unobserved and GPA is the observed outcome. It distinguishes two school-level GPA shocks—one that affects GPA without changing effort and one that shifts preferences and effort—allowing consistent identification even in networks with isolated students. The authors derive a reduced-form GPA equation, propose an IV-GMM strategy with a double fixed-effects structure, and extend the method to endogenous networks via a nonparametric control-function approach. Empirically, using Add Health data, they find a substantially larger peer effect (0.856) under the structural model than in proxy-based specifications (0.507), and demonstrate robustness to misclassification and network endogeneity, with implications for policy analysis in education and other effort-dependent outcomes.
Abstract
Peer influence on effort devoted to some activity is often studied when effort is unobserved, and the researcher instead observes an outcome that combines effort with other shocks. For instance, in education, achievement measures such as GPA reflect both effort and idiosyncratic GPA shocks. We propose an alternative approach that circumvents this approximation. Our framework distinguishes unobserved shocks to GPA that do not affect effort from preference shocks that do affect effort levels. We show that peer effects estimates obtained using our approach can differ significantly from classical estimates (where effort is approximated) if the network includes isolated students. Applying our approach to data on high school students in the United States, we find that peer effect estimates relying on GPA as a proxy for effort are 40% lower than those obtained using our approach.