Combining Information from Heckman and Matching Estimators: Testing and Controlling for Hidden Bias
Economics Bulletin, 33 (3), pp. 2422-2436, 2013
15 Pages Posted: 20 Sep 2013
Date Written: September 19, 2013
We demonstrate how the Heckman treatment methodology can be applied to the Rosenbaum sensitivity model and the Rubin matched difference estimator. We develop a statistical test of the conditional independence assumption (CIA), based on Heckit for matched pairs. If the CIA is rejected, the method facilitates the estimation of matched treatment effects adjusted for hidden bias. We illustrate this methodology empirically for the full-time/part-time pay gap for British women. The proposed method has clear utility in establishing whether propensity score matched treatment estimates are prone to unobserved selection bias and for controlling for such bias.
Keywords: propensity score matching, unobserved bias, incorporating Heckman estimates, Rubin’s difference model, Rosenbaum bounds, part-time women’s pay penalty
JEL Classification: C14, C31, J16, J31
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