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An Odd Couple: Monotone Instrumental Variables and Binary Treatments

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  • Jeremiah Richey
Abstract
This article investigates Monotone Instrumental Variables (MIV) and their ability to aid in identifying treatment effects when the treatment is binary in a nonparametric bounding framework. I show that an MIV can only aid in identification beyond that of a Monotone Treatment Selection assumption if for some region of the instrument the observed conditional-on-received-treatment outcomes exhibit monotonicity in the instrument in the opposite direction as that assumed by the MIV in a Simpson's Paradox-like fashion. Furthermore, an MIV can only aid in identification beyond that of a Monotone Treatment Response assumption if for some region of the instrument either the above Simpson's Paradox-like relationship exists or the instrument's indirect effect on the outcome (as through its influence on treatment selection) is the opposite of its direct effect as assumed by the MIV. The implications of the main findings for empirical work are discussed and the results are highlighted with an application investigating the effect of criminal convictions on job match quality using data from the 1997 National Longitudinal Survey of the Youth. Though the main results are shown to hold only for the binary treatment case in general, they are shown to have important implications for the multi-valued treatment case as well.

Suggested Citation

  • Jeremiah Richey, 2016. "An Odd Couple: Monotone Instrumental Variables and Binary Treatments," Econometric Reviews, Taylor & Francis Journals, vol. 35(6), pages 1099-1110, June.
  • Handle: RePEc:taf:emetrv:v:35:y:2016:i:6:p:1099-1110
    DOI: 10.1080/07474938.2014.977082
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    References listed on IDEAS

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    1. Charles F. Manski, 1997. "Monotone Treatment Response," Econometrica, Econometric Society, vol. 65(6), pages 1311-1334, November.
    2. Aviv Nevo & Adam M. Rosen, 2012. "Identification With Imperfect Instruments," The Review of Economics and Statistics, MIT Press, vol. 94(3), pages 659-671, August.
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    Cited by:

    1. Richey, Jeremiah, 2015. "Shackled labor markets: Bounding the causal effects of criminal convictions in the U.S," International Review of Law and Economics, Elsevier, vol. 41(C), pages 17-24.
    2. Siwach, Garima, 2017. "Criminal background checks and recidivism: Bounding the causal impact," International Review of Law and Economics, Elsevier, vol. 52(C), pages 74-85.
    3. Aizawa, T.;, 2019. "Reviewing the Existing Evidence of the Conditional Cash Transfer in India through the Partial Identification Approach," Health, Econometrics and Data Group (HEDG) Working Papers 19/24, HEDG, c/o Department of Economics, University of York.

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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
    • J63 - Labor and Demographic Economics - - Mobility, Unemployment, Vacancies, and Immigrant Workers - - - Turnover; Vacancies; Layoffs
    • K40 - Law and Economics - - Legal Procedure, the Legal System, and Illegal Behavior - - - General

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