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Characterizing the Instrumental Variable Identifying Assumption as Sample Selection Conditions

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  • Belzil, Christian

    ()
    (Ecole Polytechnique, Paris)

  • Hansen, Jörgen

    ()
    (Concordia University)

Abstract

We build on Rosenzweig and Wolpin (2000) and Keane (2010) and show that in order to fulfill the Instrumental variable (IV) identifying moment condition, a policy must be designed so that compliers and non-compliers either have the same average error term, or have an error term ratio equal to their relative share of the population. The former condition (labeled Choice Orthogonality) is essentially a no-selection condition. The latter one, referred to as Weighted Opposite Choices, may be viewed as a distributional (functional form) assumption necessary to match the degree of selectivity between compliers and noncompliers to their relative population proportions. Those conditions form a core of implicit IV assumptions that are present in any empirical applications. They allow the econometrician to gain substantial insight about the validity of a specific instrument, and they illustrate the link between identification and the statistical strength of an instrument. Finally, our characterization may also help designing a policy generating a valid instrument.

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Bibliographic Info

Paper provided by Institute for the Study of Labor (IZA) in its series IZA Discussion Papers with number 6339.

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Length: 23 pages
Date of creation: Feb 2012
Date of revision:
Handle: RePEc:iza:izadps:dp6339

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Keywords: instrumental variable methods; implicit assumptions; treatment effects;

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  1. Edward Vytlacil, 2002. "Independence, Monotonicity, and Latent Index Models: An Equivalence Result," Econometrica, Econometric Society, vol. 70(1), pages 331-341, January.
  2. Christian Belzil & J. Hansen, 2010. "The distinction between dictatorial and incentive policy interventions and its implication for IV estimation," Working Papers hal-00463877, HAL.
  3. Christian Belzil, 2006. "The Return to Schooling in Structural Dynamic Models: A Survey," Post-Print halshs-00142538, HAL.
  4. James Heckman, 1997. "Instrumental Variables: A Study of Implicit Behavioral Assumptions Used in Making Program Evaluations," Journal of Human Resources, University of Wisconsin Press, vol. 32(3), pages 441-462.
  5. Abbring, Jaap H. & Heckman, James J., 2007. "Econometric Evaluation of Social Programs, Part III: Distributional Treatment Effects, Dynamic Treatment Effects, Dynamic Discrete Choice, and General Equilibrium Policy Evaluation," Handbook of Econometrics, in: J.J. Heckman & E.E. Leamer (ed.), Handbook of Econometrics, edition 1, volume 6, chapter 72 Elsevier.
  6. Keane, Michael P., 2010. "Structural vs. atheoretic approaches to econometrics," Journal of Econometrics, Elsevier, vol. 156(1), pages 3-20, May.
  7. Joshua D. Angrist & Guido W. Imbens, 1995. "Identification and Estimation of Local Average Treatment Effects," NBER Technical Working Papers 0118, National Bureau of Economic Research, Inc.
  8. Heckman, James, 2013. "Sample selection bias as a specification error," Applied Econometrics, Publishing House "SINERGIA PRESS", vol. 31(3), pages 129-137.
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