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Identifying Treatment Effects in the Presence of Confounded Types

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  • Kedagni, Desire

Abstract

In this paper, I consider identification of treatment effects whenthe treatment is endogenous. The use of instrumental variables is a popularsolution to deal with endogeneity, but this may give misleading answers whenthe instrument is invalid. I show that when the instrument is invalid due tocorrelation with the first stage unobserved heterogeneity, a second (alsopossibly invalid) instrument allows to partially identify not only the localaverage treatment effect but also the entire potential outcomes distributionsfor compliers. I exploit the fact that the distribution of the observedoutcome in each group defined by the treatment and the instrument is amixture of the distributions of interest. I write the identified set in theform of conditional moment inequalities, and provide an easily implementableinference procedure. Under some (testable) tail restrictions, the potentialoutcomes distributions are point-identified for compliers. Finally, Iillustrate my methodology on data from the National Longitudinal Survey ofYoung Men to estimate returns to college using college proximity as(potential) instrument. I find that a college degree increases the averagehourly wage of the compliers by 38-79%.

Suggested Citation

  • Kedagni, Desire, 2018. "Identifying Treatment Effects in the Presence of Confounded Types," ISU General Staff Papers 201809110700001056, Iowa State University, Department of Economics.
  • Handle: RePEc:isu:genstf:201809110700001056
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    Cited by:

    1. Vitor Possebom, 2019. "Sharp Bounds for the Marginal Treatment Effect with Sample Selection," Papers 1904.08522, arXiv.org.
    2. Rui Wang, 2023. "Point Identification of LATE with Two Imperfect Instruments," Papers 2303.13795, arXiv.org.

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    More about this item

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions; Probabilities
    • C26 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Instrumental Variables (IV) Estimation

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