Working Paper: CEPR ID: DP2604
Authors: Thomas Brodaty; Bruno Crépon; Denis Fougre
Abstract: In this paper we apply the statistical framework recently proposed by Imbens (1999) and Lechner (1999) to identify the causal effects of multiple treatments under the conditional independence assumption. We show that under this assumption, matching with respect to the ratio of the scores allows to estimate nonparametrically the average conditional treatment effect for any pair of treatments. Consequently it is possible to estimate this effect by implementing nonparametric matching estimators, which were recently studied by Heckman, Ichimura, Smith and Todd (1998) and Heckman, Ichimura and Todd (1998). The application concerns the youth employment programs that were set up in France during the eighties to improve the labour market prospects of the most disadvantaged and unskilled young workers. The empirical analysis makes use of nonexperimental longitudinal micro data collected by INSEE (Institut National de la Statistique et des Etudes Economiques, Paris) from 1986 to 1988.
Keywords: Competing Risks; Duration Models; Econometric Evaluation Methods; Matching Estimators; Multiple Treatments; Youth Employment Policies
JEL Codes: J24; J68
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| Application of matching estimators (C51) | Estimation of average conditional treatment effect (C22) |
| Higher ratio of propensity scores (C52) | Relative efficiency of programs (C88) |
| On-the-job training programs in the private sector (M53) | Better employment outcomes (J68) |
| Higher conditional probabilities of participation (J49) | More significant positive effects (F69) |