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

See all articles by Gerry Makepeace

Gerry Makepeace

Cardiff University; IZA Institute of Labor Economics

Michael J. Peel

Cardiff University - Cardiff Business School

Date Written: September 19, 2013

Abstract

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

Suggested Citation

Makepeace, Gerald H and Peel, Michael J., Combining Information from Heckman and Matching Estimators: Testing and Controlling for Hidden Bias (September 19, 2013). Economics Bulletin, 33 (3), pp. 2422-2436, 2013, Available at SSRN: https://ssrn.com/abstract=2328422

Gerald H Makepeace

Cardiff University ( email )

Economics Section
Cardiff Business School
Cardiff, Wales CF10 3EU
United Kingdom

IZA Institute of Labor Economics

Schaumburg-Lippe-Str. 7 / 9
Bonn, D-53072
Germany

Michael J. Peel (Contact Author)

Cardiff University - Cardiff Business School ( email )

Cardiff
United Kingdom

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