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Testing Attrition Bias in Field Experiments

Author

Listed:
  • Dalia Ghanem

    (University of California, Davis)

  • Sarojini Hirshleifer

    (Department of Economics, University of California Riverside)

  • Karen Ortiz-Becerra

    (University of San Diego)

Abstract

We approach attrition in field experiments with baseline 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, and propose procedures to test their sharp implications. We then relate our proposed tests to current empirical practice, and demonstrate that the most commonly used test in the literature is not a test of internal validity in general. We illustrate the relevance of our analysis using several empirical applications.

Suggested Citation

  • Dalia Ghanem & Sarojini Hirshleifer & Karen Ortiz-Becerra, 2019. "Testing Attrition Bias in Field Experiments," Working Papers 202218, University of California at Riverside, Department of Economics, revised Oct 2022.
    • Handle: RePEc:ucr:wpaper:202218
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    File URL: https://economics.ucr.edu/repec/ucr/wpaper/202218.pdf
    File Function: First version, 2019
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    File URL: https://economics.ucr.edu/repec/ucr/wpaper/202218R.pdf
    File Function: Revised version, 2022
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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. 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.
    3. Guigonan S. Adjognon & Daan van Soest & Jonas Guthoff, 2021. "Reducing Hunger with Payments for Environmental Services (PES): Experimental Evidence from Burkina Faso," American Journal of Agricultural Economics, John Wiley & Sons, vol. 103(3), pages 831-857, May.
    4. 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.
    5. 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.

    More about this item

    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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