Working Paper: NBER ID: w9546
Authors: Pedro Carneiro; Karsten T. Hansen; James J. Heckman
Abstract: This paper uses factor models to identify and estimate distributions of counterfactuals. We extend LISREL frameworks to a dynamic treatment effect setting, extending matching to account for unobserved conditioning variables. Using these models, we can identify all pairwise and joint treatment effects. We apply these methods to a model of schooling and determine the intrinsic uncertainty facing agents at the time they make their decisions about enrollment in school. Reducing uncertainty in returns raises college enrollment. We go beyond the Veil of Ignorance' in evaluating educational policies and determine who benefits and who loses from commonly proposed educational reforms.
Keywords: No keywords provided
JEL Codes: C31
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| reducing uncertainty in returns to education (I26) | college enrollment (I23) |
| intrinsic uncertainty facing agents (D89) | educational choices (I21) |
| variability in returns to schooling (I26) | differences in individual characteristics (D29) |
| variability in returns to schooling (I26) | variability in distributions of outcomes (C46) |