Heterogeneous Peer Effects with Endogenous Network Formation

2026-06-23

AI Summary

This study addresses estimation bias arising from heterogeneous peer effects and endogenous network formation in social interactions by proposing a Selection-Corrected Heterogeneous Spatial Autoregressive (SCHSAR) model. This framework achieves, for the first time, credible identification of heterogeneous spillover effects under endogenous networks by jointly modeling the processes of network formation and outcome generation. It incorporates a finite mixture structure to capture individual heterogeneity in responses to peer influence and employs a fully Bayesian data augmentation approach to overcome computational and identification challenges posed by complex endogeneity. Application to U.S. firm innovation networks reveals significant and heterogeneous positive peer effects on R&D investment. The analysis further quantifies both direct and spillover effects of policy shocks, offering empirical foundations for designing targeted innovation policies.

Abstract

This paper introduces a new econometric framework for modeling social interactions with heterogeneous peer responses, addressing endogenous link formation. Our Selection-corrected Heterogeneous Spatial Autoregressive (SCHSAR) approach jointly models link formation and outcome determination. We incorporate a finite mixture structure to capture heterogeneity in peer effects and account for unobserved individual-specific factors driving both network formation and outcome equations, addressing network endogeneity for credible estimation of heterogeneous spillover effects. We propose a fully Bayesian data augmentation approach for estimation and inference, overcoming challenges posed to standard likelihood-based methods. A simulation study validates our approach. Our empirical application to an innovation network among U.S. firms reveals significant positive, yet heterogeneous, peer effects on corporate R&D investments, after accounting for endogenous network formation. The findings highlight varying firm behaviors in response to exogenous R&D policy shocks and and quantify firm-level direct and spillover effects, offering valuable insights for evidence-based and targeted policy design.

Problem

• heterogeneous peer effects
• endogenous network formation
• social interactions
• network endogeneity
• spillover effects

Innovation

• heterogeneous peer effects
• endogenous network formation
• spatial autoregressive model
• Bayesian data augmentation
• network endogeneity