Treatment Effects With Heterogeneous Externalities
提出一种新方法，用于估计政策分析中线性均值形式社会互动下的异质性外部性，并通过PROGRESA项目数据发现超过50%的入学效应来自外部性，且该效应在贫困与非贫困家庭内外部存在差异。
This article proposes a new method for estimating heterogeneous externalities in policy analysis when social interactions take the linear-in-means form. We establish that the parameters of interest can be identified and consistently estimated using specific functions of the share of the eligible population. We also study the finite sample performance of the proposed estimators using Monte Carlo simulations. The method is illustrated using data on the PROGRESA program. We find that more than 50% of the effects of the program on schooling attendance are due to externalities, which are heterogeneous within and between poor and nonpoor households.