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Characterizing the Instrumental Variable Identifying Assumption as Sample Selection Conditions

Author

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  • Christian Belzil

    (X-DEP-ECO - Département d'Économie de l'École Polytechnique - X - École polytechnique - IP Paris - Institut Polytechnique de Paris, ENSAE Paris - École Nationale de la Statistique et de l'Administration Économique)

  • Jörgen Hansen

    (Department of Economics, Concordia University - Concordia University [Montreal], CIRANO - Centre interuniversitaire de recherche en analyse des organisations - UQAM - Université du Québec à Montréal = University of Québec in Montréal)

Abstract

We build on Rosenzweig and Wolpin (2000) and Keane (2010) and show that in order to ful ll 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 speci c instrument, and they illustrate the link between identi cation and the statistical strength of an instrument. Finally, our characterization may also help designing a policy generating a valid instrument.

Suggested Citation

  • Christian Belzil & Jörgen Hansen, 2012. "Characterizing the Instrumental Variable Identifying Assumption as Sample Selection Conditions," Working Papers hal-00753539, HAL.
    • Handle: RePEc:hal:wpaper:hal-00753539
    Note: View the original document on HAL open archive server: https://hal.science/hal-00753539
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    References listed on IDEAS

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    1. Keane, Michael P., 2010. "Structural vs. atheoretic approaches to econometrics," Journal of Econometrics, Elsevier, vol. 156(1), pages 3-20, May.
    2. Belzil, Christian, 2007. "The return to schooling in structural dynamic models: a survey," European Economic Review, Elsevier, vol. 51(5), pages 1059-1105, July.
    3. Belzil, Christian & Hansen, Jörgen, 2010. "The Distinction between Dictatorial and Incentive Policy Interventions and its Implication for IV Estimation," IZA Discussion Papers 4835, Institute of Labor Economics (IZA).
    4. Douglas Staiger & James H. Stock, 1997. "Instrumental Variables Regression with Weak Instruments," Econometrica, Econometric Society, vol. 65(3), pages 557-586, May.
    5. Imbens, Guido W & Angrist, Joshua D, 1994. "Identification and Estimation of Local Average Treatment Effects," Econometrica, Econometric Society, vol. 62(2), pages 467-475, March.
    6. 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.
    7. Heckman, James, 2013. "Sample selection bias as a specification error," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 31(3), pages 129-137.
    8. 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.
    9. Edward Vytlacil, 2002. "Independence, Monotonicity, and Latent Index Models: An Equivalence Result," Econometrica, Econometric Society, vol. 70(1), pages 331-341, January.
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    More about this item

    Keywords

    Treatment E ects; Instrumental Variable methods; Implicit Assumptions; Treatment E ects.;
    All these keywords.

    JEL classification:

    • B4 - Schools of Economic Thought and Methodology - - Economic Methodology
    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General
    • C3 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables

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