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Combining information from Heckman and matching estimators: testing and controlling for hidden bias

  • Gerry H. Makepeace

    ()

    (Cardiff Business School, Cardiff University and IZA, Bonn)

  • Michael J. Peel

    ()

    (Cardiff Business School, Cardiff University)

We demonstrate how the Heckman 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

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Article provided by AccessEcon in its journal Economics Bulletin.

Volume (Year): 33 (2013)
Issue (Month): 3 ()
Pages: 2422-2436

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Handle: RePEc:ebl:ecbull:eb-13-00321
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  8. Black, Dan A. & Smith, J.A.Jeffrey A., 2004. "How robust is the evidence on the effects of college quality? Evidence from matching," Journal of Econometrics, Elsevier, vol. 121(1-2), pages 99-124.
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  17. Richard Blundell & Howard Reed & Thomas M. Stoker, 2003. "Interpreting Aggregate Wage Growth: The Role of Labor Market Participation," American Economic Review, American Economic Association, vol. 93(4), pages 1114-1131, September.
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