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Testing for Attrition Bias in Field Experiments

Author

Listed:
  • Dalia Ghanem

    (UC Davis)

  • Sarojini Hirshleifer

    (Department of Economics, University of California Riverside)

  • Karen Ortiz-Becerra

    (UC Davis)

Abstract

We approach attrition in field experiments with baseline outcome data as an identification problem in a panel model. A systematic review of the literature indicates that there is no consensus on how to test for attrition bias. We establish identifying assumptions for treatment effects for both the respondent subpopulation and the study population. We then derive their sharp testable implications on the baseline outcome distribution and propose randomization procedures to test them. We demonstrate that the most commonly used test does not control size in general when internal validity holds. Simulations and applications illustrate the empirical relevance of our analysis.

Suggested Citation

  • Dalia Ghanem & Sarojini Hirshleifer & Karen Ortiz-Becerra, 2019. "Testing for Attrition Bias in Field Experiments," Working Papers 202010, University of California at Riverside, Department of Economics, revised Mar 2020.
    • Handle: RePEc:ucr:wpaper:202010
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    References listed on IDEAS

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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. Ben Weidmann & Luke Miratrix, 2021. "Missing, presumed different: Quantifying the risk of attrition bias in education evaluations," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 184(2), pages 732-760, April.
    3. Fulya Ersoy, 2021. "Returns to effort: experimental evidence from an online language platform," Experimental Economics, Springer;Economic Science Association, vol. 24(3), pages 1047-1073, September.
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    6. Simon Calmar Andersen & Louise Beuchert & Phillip Heiler & Helena Skyt Nielsen, 2023. "A Guide to Impact Evaluation under Sample Selection and Missing Data: Teacher's Aides and Adolescent Mental Health," Papers 2308.04963, arXiv.org.
    7. Annie Alcid & Erwin Bulte & Robert Lensink & Aussi Sayinzoga & Mark Treurniet, 2023. "Short- and Medium-term Impacts of Employability Training: Evidence from a Randomised Field Experiment in Rwanda," Journal of African Economies, Centre for the Study of African Economies, vol. 32(3), pages 296-328.
    8. Coali, Andrea & Gambardella, Alfonso & Novelli, Elena, 2024. "Scientific decision-making, project selection and longer-term outcomes," Research Policy, Elsevier, vol. 53(6).
    9. Rafkin, Charlie & Shreekumar, Advik & Vautrey, Pierre-Luc, 2021. "When guidance changes: Government stances and public beliefs," Journal of Public Economics, Elsevier, vol. 196(C).

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    More about this item

    Keywords

    attrition; non-response; treatment effects; field experiment; randomized experiment; randomized control trial; internal validity; identifying assumptions; randomization test; panel data;
    All these keywords.

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • C93 - Mathematical and Quantitative Methods - - Design of Experiments - - - Field Experiments

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