Abstract

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.

摘要

本文提出了一种在社会互动采取均值线性形式时估算政策分析中异质外部性的新方法。我们建立了感兴趣的参数可以使用合格人口份额的特定功能进行识别和一致估计的功能。我们还使用蒙特卡洛模拟研究了提出的估计量的有限样本性能。使用PROGRESA程序上的数据说明了该方法。我们发现，该计划对入学率的影响超过50％是由于外部因素造成的，这些因素在贫困家庭和非贫困家庭之间以及贫困家庭和非贫困家庭之间是异质的。