IDEAS home Printed from https://ideas.repec.org/p/nbr/nberwo/30291.html

Simple Tests for Selection: Learning More from Instrumental Variables

Author

Listed:
  • Dan A. Black
  • Joonhwi Joo
  • Robert LaLonde
  • Jeffrey A. Smith
  • Evan J. Taylor

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 A. Black & Joonhwi Joo & Robert LaLonde & Jeffrey A. Smith & Evan J. Taylor, 2022. "Simple Tests for Selection: Learning More from Instrumental Variables," NBER Working Papers 30291, National Bureau of Economic Research, Inc.
    • Handle: RePEc:nbr:nberwo:30291
    Note: LS
    as

    Download full text from publisher

    File URL: http://www.nber.org/papers/w30291.pdf
    Download Restriction: no
    ---><---

    Other versions of this item:

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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. 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).
    3. Valentina Corradi & Daniel Gutknecht, 2019. "Testing for Quantile Sample Selection," Papers 1907.07412, arXiv.org, revised Jan 2021.
    4. Bjerk, David J., 2025. "Understanding IV Versus OLS Estimates of Treatment Effects and the Coefficient Difference Check," IZA Discussion Papers 18274, IZA Network @ LISER.
    5. Fenoll, Ainoa Aparicio & Campaniello, Nadia & Monzón, Ignacio, 2025. "Parental love is not blind: Identifying selection into early school start," European Economic Review, Elsevier, vol. 180(C).
    6. 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.
    7. Nocito, Samuel, 2021. "The effect of a university degree in english on international labor mobility," Labour Economics, Elsevier, vol. 68(C).
    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. Jeffrey Smith, 2022. "Treatment Effect Heterogeneity," Evaluation Review, , vol. 46(5), pages 652-677, October.
    10. 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.

    More about this item

    JEL classification:

    • C26 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Instrumental Variables (IV) Estimation
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C93 - Mathematical and Quantitative Methods - - Design of Experiments - - - Field Experiments

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:nbr:nberwo:30291. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: the person in charge (email available below). General contact details of provider: https://edirc.repec.org/data/nberrus.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.