🤖 AI Summary
This study addresses the challenge of identifying key individuals who exert significant peer influence on risk behaviors—such as smoking and marijuana use—from cross-sectional observational data in multilayer social networks. To tackle the endogeneity arising from homophily, the authors innovatively construct instrumental variables using observable characteristics of distal individuals within the multilayer network structure and integrate these with instrumental variable estimation and multilayer network modeling. Empirical analysis based on the Add Health dataset reveals robust positive peer effects from both friends and classmates on risk behaviors and successfully pinpoints influential individuals. The findings offer a novel methodological approach and empirical evidence for understanding behavioral contagion mechanisms in social networks.
📝 Abstract
This paper proposes a model-based empirical method to identify influential individuals in risky behaviors. To determine the most influential individuals, we estimate peer influence using observational cross-sectional data from multiple social connections. Our empirical strategy employs the observed characteristics of distant individuals across multiple social networks as instruments to address the endogeneity arising from homophily. Using Add Health data, we find positive peer effects from friends and classmates on both cigarette smoking and marijuana use. Based on the estimated peer effects, we characterize the influencers in our sample.