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Simple Tests for Selection: Learning More from Instrumental Variables

Author

Listed:
  • Dan Black

    (The University of Chicago / NORC / IZA)

  • Joonhwi Joo

    (University of Texas at Dallas)

  • Robert LaLonde

    (University of Chicago)

  • Jeffrey Smith

    (University of Wisconsin-Madison)

  • Evan Taylor

    (Center for the Economics of Human Development)

Abstract
We provide simple tests for selection on unobserved variables in the Vytlacil-Imbens-Angrist framework for Local Average Treatment Effects (LATEs). Our setup allows researchers not only to test for selection on either or both of the treated and untreated outcomes, but also to assess the magnitude of the selection effect. We show that it applies to the standard binary instrument case, as well as to experiments with imperfect compliance and fuzzy regression discontinuity designs, and we link it to broader discussions regarding instrumental variables. We illustrate the substantive value-added by our framework with three empirical applications drawn from the literature.

Suggested Citation

  • Dan Black & Joonhwi Joo & Robert LaLonde & Jeffrey Smith & Evan Taylor, 2020. "Simple Tests for Selection: Learning More from Instrumental Variables," Working Papers 2020-048, Human Capital and Economic Opportunity Working Group.
    • Handle: RePEc:hka:wpaper:2020-048
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    Cited by:

    1. Tarek Azzam & Michael Bates & David Fairris, 2019. "Do Learning Communities Increase First Year College Retention? Testing Sample Selection and External Validity of Randomized Control Trials," Working Papers 202002, University of California at Riverside, Department of Economics.
    2. Nocito, Samuel, 2021. "The effect of a university degree in english on international labor mobility," Labour Economics, Elsevier, vol. 68(C).
    3. Jeffrey Smith, 2022. "Treatment Effect Heterogeneity," Evaluation Review, , vol. 46(5), pages 652-677, October.
    4. Azzam, Tarek & Bates, Michael D. & Fairris, David, 2022. "Do learning communities increase first year college retention? Evidence from a randomized control trial," Economics of Education Review, Elsevier, vol. 89(C).
    5. Valentina Corradi & Daniel Gutknecht, 2019. "Testing for Quantile Sample Selection," Papers 1907.07412, arXiv.org, revised Jan 2021.
    6. Ainoa Aparicio Fenoll & Nadia Campaniello & Ignacio Monzón, 2023. "Parental Love Is Not Blind: Identifying Selection into Early School Start," Working Papers 286, Red Nacional de Investigadores en Economía (RedNIE).
    7. Seth Gershenson & Cassandra M. D. Hart & Joshua Hyman & Constance A. Lindsay & Nicholas W. Papageorge, 2022. "The Long-Run Impacts of Same-Race Teachers," American Economic Journal: Economic Policy, American Economic Association, vol. 14(4), pages 300-342, November.
    8. Daniel Litwok, 2023. "Estimating the Impact of Emergency Assistance on Educational Progress for Low-Income Adults: Experimental and Nonexperimental Evidence," Evaluation Review, , vol. 47(2), pages 231-263, April.
    9. Kim, Jun Hyung & Schulz, Wolfgang & Zimmermann, Tanja & Hahlweg, Kurt, 2018. "Parent–child interactions and child outcomes: Evidence from randomized intervention," Labour Economics, Elsevier, vol. 54(C), pages 152-171.

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    More about this item

    Keywords

    instrumental variables; local average treatment effect; selection; test;
    All these keywords.

    JEL classification:

    • C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - General
    • D04 - Microeconomics - - General - - - Microeconomic Policy: Formulation; Implementation; Evaluation

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