Unobserved Heterogeneous Spillover Effects in Instrumental Variable Models

TL;DR

This paper introduces a comprehensive framework for identifying and estimating causal spillover effects in instrumental variable models, extending existing methods to account for peer influences and heterogeneity.

Contribution

It develops new identification conditions and estimators for spillover effects, including generalized local average effects and marginal effects, in settings with continuous and binary instruments.

Findings

Heterogeneity in education spillovers within friend networks was observed.

New identification conditions clarify when spillover effects can be point identified.

Semiparametric and parametric estimators effectively estimate spillover effects.

Abstract

This paper develops a general framework for identifying causal effects in settings with spillovers, where both outcomes and endogenous treatment decisions are influenced by peers within a known group. It introduces the generalized local average controlled spillover and direct effects (LACSEs and LACDEs), which extend the local average treatment effect framework to settings with spillovers and establish sufficient conditions for their point identification without restricting the cardinality of the support of instrumental variables. These conditions clarify the necessity of commonly imposed restrictions to achieve point identification with binary instruments in related studies. The paper then defines the marginal controlled spillover and direct effects (MCSEs and MCDEs), which naturally extend the marginal treatment effect framework to settings with spillovers and are nonparametrically point identified from continuous variation in instruments. These marginal effects serve as building blocks for a broad class of policy-relevant treatment effects, including some causal spillover parameters in the related literature. Semiparametric and parametric estimators are developed, and an application using Add Health data reveals heterogeneity in education spillovers within best-friend networks.