Working Paper: CEPR ID: DP13781
Authors: Eleonora Patacchini; Tiziano Arduini; Edoardo Rainone
Abstract: This paper 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 percent of the effects of the program on schooling attendance are due to externalities, which are heterogeneous within and between poor and nonpoor households.
Keywords: Program Evaluation; Two-Stage Least Squares; Indirect Treatment Effect
JEL Codes: C13; C21; D62
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| 10 percentage point increase in school enrollment of eligible students (I24) | 4 percentage point increase in school attendance among ineligible students (I24) |
| 10 percentage point increase in school attendance of ineligible students (I24) | 8 percentage point increase in school attendance among ineligible students (I24) |
| treatment effects (C22) | externalities (D62) |
| social dynamics within and between household types (J12) | indirect treatment effects (C32) |