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Should instrumental variables be used as matching variables?

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  • Wooldridge, Jeffrey M.

Abstract

I show that for a linear model and estimating a coefficient on an endogenous explanatory variable, adding covariates that satisfy instrumental variables assumptions increases the amount of inconsistency. A special case is an endogenous binary treatment and estimating a constant treatment effect when matching on covariates that satisfy instrumental variables, rather than ignoribility, assumptions. I also establish a general result that implies that regression adjustment using the propensity score based on instrumental variables actually maximizes the inconsistency among regression-type estimators.

Suggested Citation

  • Wooldridge, Jeffrey M., 2016. "Should instrumental variables be used as matching variables?," Research in Economics, Elsevier, vol. 70(2), pages 232-237.
    • Handle: RePEc:eee:reecon:v:70:y:2016:i:2:p:232-237
    DOI: 10.1016/j.rie.2016.01.001
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    References listed on IDEAS

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    1. James Heckman & Salvador Navarro-Lozano, 2004. "Using Matching, Instrumental Variables, and Control Functions to Estimate Economic Choice Models," The Review of Economics and Statistics, MIT Press, vol. 86(1), pages 30-57, February.
    2. Jeffrey M Wooldridge, 2010. "Econometric Analysis of Cross Section and Panel Data," MIT Press Books, The MIT Press, edition 2, volume 1, number 0262232588, December.
    3. Wooldridge, Jeffrey M., 2005. "Violating Ignorability Of Treatment By Controlling For Too Many Factors," Econometric Theory, Cambridge University Press, vol. 21(5), pages 1026-1028, October.
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