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Sensitivity of Bounds on ATEs under Survey Nonresponse

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  • Lafférs, Lukáš
  • Nedela, Roman

Abstract

The problem of bounding average treatment effects under survey nonresponse, when data collection entails sequential efforts made to obtain response, can be formulated as an optimization problem. It is shown that this formulation is equivalent to the original problem and further extends it into a sensitivity analysis of the identifying assumptions. Departure from the assumption of treatment exogeneity can be controlled via an interpretable parameter and thus allows to quantify the importance of the crucial identification assumption.

Suggested Citation

  • Lafférs, Lukáš & Nedela, Roman, 2025. "Sensitivity of Bounds on ATEs under Survey Nonresponse," Econometrics and Statistics, Elsevier, vol. 34(C), pages 1-13.
    • Handle: RePEc:eee:ecosta:v:34:y:2025:i:c:p:1-13
    DOI: 10.1016/j.ecosta.2022.01.005
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    References listed on IDEAS

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    1. Lafférs, Lukáš & Mellace, Giovanni, 2020. "Identification of the average treatment effect when SUTVA is violated," Discussion Papers on Economics 3/2020, University of Southern Denmark, Department of Economics.
    2. Bruce D. Meyer & Wallace K. C. Mok & James X. Sullivan, 2015. "Household Surveys in Crisis," Journal of Economic Perspectives, American Economic Association, vol. 29(4), pages 199-226, Fall.
    3. Cantoni, Eva & de Luna, Xavier, 2020. "Semiparametric inference with missing data: Robustness to outliers and model misspecification," Econometrics and Statistics, Elsevier, vol. 16(C), pages 108-120.
    4. David McKenzie, 2017. "Identifying and Spurring High-Growth Entrepreneurship: Experimental Evidence from a Business Plan Competition," American Economic Review, American Economic Association, vol. 107(8), pages 2278-2307, August.
    5. Bo E. Honoré & Elie Tamer, 2006. "Bounds on Parameters in Panel Dynamic Discrete Choice Models," Econometrica, Econometric Society, vol. 74(3), pages 611-629, May.
    6. James J. Heckman, 1977. "Sample Selection Bias As a Specification Error (with an Application to the Estimation of Labor Supply Functions)," NBER Working Papers 0172, National Bureau of Economic Research, Inc.
    7. Alexander Torgovitsky, 2019. "Nonparametric Inference on State Dependence in Unemployment," Econometrica, Econometric Society, vol. 87(5), pages 1475-1505, September.
    8. Luc Behaghel & Bruno Crépon & Marc Gurgand & Thomas Le Barbanchon, 2015. "Please Call Again: Correcting Nonresponse Bias in Treatment Effect Models," The Review of Economics and Statistics, MIT Press, vol. 97(5), pages 1070-1080, December.
    9. Charles F. Manski, 2007. "Partial Identification Of Counterfactual Choice Probabilities," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 48(4), pages 1393-1410, November.
    10. Ori Heffetz & Daniel B. Reeves, 2019. "Difficulty of Reaching Respondents and Nonresponse Bias: Evidence from Large Government Surveys," The Review of Economics and Statistics, MIT Press, vol. 101(1), pages 176-191, March.
    11. Ro'ee Levy, 2021. "Social Media, News Consumption, and Polarization: Evidence from a Field Experiment," American Economic Review, American Economic Association, vol. 111(3), pages 831-870, March.
    12. Lafférs, Lukáš & Nedela, Roman, 2017. "Sensitivity of the bounds on the ATE in the presence of sample selection," Economics Letters, Elsevier, vol. 158(C), pages 84-87.
    13. David S. Lee, 2009. "Training, Wages, and Sample Selection: Estimating Sharp Bounds on Treatment Effects," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 76(3), pages 1071-1102.
    14. repec:hal:pseose:halshs-01245539 is not listed on IDEAS
    15. Molinari, Francesca, 2008. "Partial identification of probability distributions with misclassified data," Journal of Econometrics, Elsevier, vol. 144(1), pages 81-117, May.
    16. Gerard J. Van Den Berg & Maarten Lindeboom & Peter J. Dolton, 2006. "Survey non‐response and the duration of unemployment," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 169(3), pages 585-604, July.
    17. Lovett, Nicholas & Xue, Yuhan, 2020. "Family first or the kindness of strangers? Foster care placements and adult outcomes," Labour Economics, Elsevier, vol. 65(C).
    18. Freyberger, Joachim & Horowitz, Joel L., 2015. "Identification and shape restrictions in nonparametric instrumental variables estimation," Journal of Econometrics, Elsevier, vol. 189(1), pages 41-53.
    19. Edward Vytlacil, 2002. "Independence, Monotonicity, and Latent Index Models: An Equivalence Result," Econometrica, Econometric Society, vol. 70(1), pages 331-341, January.
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    JEL classification:

    • C4 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics
    • C6 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling

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