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Quasi-Experimental Evaluation of Alternative Sample Selection Corrections

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  • Robert Garlick
  • Joshua Hyman

Abstract

Researchers routinely use datasets where outcomes of interest are unobserved for some cases, potentially creating a sample selection problem. Statisticians and econometricians have proposed many selection correction methods to address this challenge. We use a natural experiment to evaluate different sample selection correction methods’ performance. From 2007, the state of Michigan required that all students take a college entrance exam, increasing the exam-taking rate from 64% to 99% and largely eliminating selection into exam-taking. We apply different selection correction methods, using different sets of covariates, to the selected exam score data from before 2007. We compare the estimated coefficients from the selection-corrected models to those from OLS regressions using the complete exam score data from after 2007 as a benchmark. We find that less restrictive semiparametric correction methods typically perform better than parametric correction methods but not better than simple OLS regressions that do not correct for selection. Performance is generally worse for models that use only a few discrete covariates than for models that use more covariates with less coarse distributions.

Suggested Citation

  • Robert Garlick & Joshua Hyman, 2022. "Quasi-Experimental Evaluation of Alternative Sample Selection Corrections," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 40(3), pages 950-964, June.
  • Handle: RePEc:taf:jnlbes:v:40:y:2022:i:3:p:950-964
    DOI: 10.1080/07350015.2021.1889566
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    Cited by:

    1. Kaitlin Anderson & Gema Zamarro & Jennifer Steele & Trey Miller, 2021. "Comparing Performance of Methods to Deal With Differential Attrition in Randomized Experimental Evaluations," Evaluation Review, , vol. 45(1-2), pages 70-104, February.
    2. Deshpande, Ashwini & Khanna, Shantanu, 2021. "Can weak ties create social capital? Evidence from Self-Help Groups in rural India," World Development, Elsevier, vol. 146(C).
    3. Zhewen Pan & Zhengxin Wang & Junsen Zhang & Yahong Zhou, 2024. "Marginal treatment effects in the absence of instrumental variables," Papers 2401.17595, arXiv.org.
    4. Joshua Hyman, 2018. "Nudges, College Enrollment, and College Persistence: Evidence From a Statewide Experiment in Michigan," Working papers 2018-10, University of Connecticut, Department of Economics.

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