AI 中文总结
本文提出基于模型的实证方法，利用Add Health数据，通过工具变量解决内生性，识别多层网络中危险行为的有影响力个体，发现朋友和同学对吸烟、吸食大麻有正向同伴效应。
AI 中文摘要
本文提出一种基于模型的实证方法，用于识别危险行为中的有影响力个体。为确定最具影响力的个体，我们利用来自多个社交关联的观测性截面数据估计同伴影响。我们的实证策略采用跨多个社交网络的远距离个体观测特征作为工具变量，以解决同质性产生的内生性问题。使用Add Health数据，我们发现朋友和同学对吸烟及吸食大麻均存在正向同伴效应。基于估计的同伴效应，我们刻画了样本中的有影响力个体。
英文摘要
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.
Comments32 pages, 3 figures, 12 tables
Journal refSocial Networks, 2026, (86), pp. 252-265
DOI：10.1016/j.socnet.2026.03.003
