Working Paper: CEPR ID: DP6990
Authors: Paul J. Devereux; Gautam Tripathi
Abstract: We develop a simple semiparametric framework for combining censored and uncensored samples so that the resulting estimators are consistent, asymptotically normal, and use all information optimally. No nonparametric smoothing is required to implement our estimators.To illustrate our results in an empirical setting, we show how to estimate the effect of changes in compulsory schooling laws on age at first marriage, a variable that is censored for younger individuals. We find positive effects of the laws on age at first marriage but the effects are much smaller than would be inferred if one ignored the censoring problem. Results from a small simulation experiment suggest that the estimator proposed in this paper can work very well in finite samples.
Keywords: Age at first marriage; Censored data; Compulsory schooling
JEL Codes: C34; J12
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| Age at first marriage (J12) | Censoring problem (C24) |
| Compulsory schooling laws (I21) | Censoring problem (C24) |
| Compulsory schooling laws (I21) | Age at first marriage (J12) |