IDEAS home Printed from https://ideas.repec.org/p/arx/papers/2209.11840.html
   My bibliography  Save this paper

Revisiting the Analysis of Matched-Pair and Stratified Experiments in the Presence of Attrition

Author

Listed:
  • Yuehao Bai
  • Meng Hsuan Hsieh
  • Jizhou Liu
  • Max Tabord-Meehan

Abstract

In this paper we revisit some common recommendations regarding the analysis of matched-pair and stratified experimental designs in the presence of attrition. Our main objective is to clarify a number of well-known claims about the practice of dropping pairs with an attrited unit when analyzing matched-pair designs. Contradictory advice appears in the literature about whether or not dropping pairs is beneficial or harmful, and stratifying into larger groups has been recommended as a resolution to the issue. To address these claims, we derive the estimands obtained from the difference-in-means estimator in a matched-pair design both when the observations from pairs with an attrited unit are retained and when they are dropped. We find limited evidence to support the claims that dropping pairs helps recover the average treatment effect, but we find that it may potentially help in recovering a convex weighted average of conditional average treatment effects. We report similar findings for stratified designs when studying the estimands obtained from a regression of outcomes on treatment with and without strata fixed effects.

Suggested Citation

  • Yuehao Bai & Meng Hsuan Hsieh & Jizhou Liu & Max Tabord-Meehan, 2022. "Revisiting the Analysis of Matched-Pair and Stratified Experiments in the Presence of Attrition," Papers 2209.11840, arXiv.org, revised Oct 2023.
    • Handle: RePEc:arx:papers:2209.11840
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/2209.11840
    File Function: Latest version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Miriam Bruhn & David McKenzie, 2009. "In Pursuit of Balance: Randomization in Practice in Development Field Experiments," American Economic Journal: Applied Economics, American Economic Association, vol. 1(4), pages 200-232, October.
    2. Suresh de Mel & David McKenzie & Christopher Woodruff, 2019. "Labor Drops: Experimental Evidence on the Return to Additional Labor in Microenterprises," American Economic Journal: Applied Economics, American Economic Association, vol. 11(1), pages 202-235, January.
    3. Federico A. Bugni & Ivan A. Canay & Azeem M. Shaikh, 2018. "Inference Under Covariate-Adaptive Randomization," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 113(524), pages 1784-1796, October.
    4. Esther Duflo & Pascaline Dupas & Michael Kremer, 2015. "Education, HIV, and Early Fertility: Experimental Evidence from Kenya," American Economic Review, American Economic Association, vol. 105(9), pages 2757-2797, September.
    5. Clément de Chaisemartin & Jaime Ramirez-Cuellar, 2024. "At What Level Should One Cluster Standard Errors in Paired and Small-Strata Experiments?," American Economic Journal: Applied Economics, American Economic Association, vol. 16(1), pages 193-212, January.
    6. Jeffrey M. Wooldridge, 2002. "Inverse probability weighted M-estimators for sample selection, attrition, and stratification," Portuguese Economic Journal, Springer;Instituto Superior de Economia e Gestao, vol. 1(2), pages 117-139, August.
    7. Yuehao Bai, 2022. "Optimality of Matched-Pair Designs in Randomized Controlled Trials," Papers 2206.07845, arXiv.org.
    8. Groh, Matthew & McKenzie, David, 2016. "Macroinsurance for microenterprises: A randomized experiment in post-revolution Egypt," Journal of Development Economics, Elsevier, vol. 118(C), pages 13-25.
    9. Joshua Angrist & Victor Lavy, 2009. "The Effects of High Stakes High School Achievement Awards: Evidence from a Randomized Trial," American Economic Review, American Economic Association, vol. 99(4), pages 1384-1414, September.
    10. Rachel Glennerster & Kudzai Takavarasha, 2013. "Running Randomized Evaluations: A Practical Guide," Economics Books, Princeton University Press, edition 1, number 10085.
    11. Keisuke Hirano & Guido W. Imbens & Geert Ridder & Donald B. Rubin, 2001. "Combining Panel Data Sets with Attrition and Refreshment Samples," Econometrica, Econometric Society, vol. 69(6), pages 1645-1659, November.
    12. Gary King & Emmanuela Gakidou & Nirmala Ravishankar & Ryan T. Moore & Jason Lakin & Manett Vargas & Martha María Téllez-Rojo & Juan Eugenio Hernández Ávila & Mauricio Hernández Ávila & Héctor Hernánde, 2007. "A “politically robust” experimental design for public policy evaluation, with application to the Mexican Universal Health Insurance program," Journal of Policy Analysis and Management, John Wiley & Sons, Ltd., vol. 26(3), pages 479-506.
    13. Heckman, James, 2013. "Sample selection bias as a specification error," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 31(3), pages 129-137.
    14. 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.
    15. Yuehao Bai, 2022. "Optimality of Matched-Pair Designs in Randomized Controlled Trials," American Economic Review, American Economic Association, vol. 112(12), pages 3911-3940, December.
    16. repec:adr:anecst:y:1999:i:55-56:p:05 is not listed on IDEAS
    17. Callen, Michael & Gulzar, Saad & Hasanain, Ali & Khan, Muhammad Yasir & Rezaee, Arman, 2020. "Data and policy decisions: Experimental evidence from Pakistan," Journal of Development Economics, Elsevier, vol. 146(C).
    18. Callen, Michael, 2020. "Data and policy decisions: Experimental evidence from Pakistan," CEPR Discussion Papers 15169, C.E.P.R. Discussion Papers.
    19. Yuehao Bai & Jizhou Liu & Max Tabord-Meehan, 2022. "Inference for Matched Tuples and Fully Blocked Factorial Designs," Papers 2206.04157, arXiv.org, revised Nov 2023.
    20. 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.
    21. Roland G. Fryer Jr., 2018. "The "Pupil" Factory: Specialization and the Production of Human Capital in Schools," American Economic Review, American Economic Association, vol. 108(3), pages 616-656, March.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Yuehao Bai, 2022. "Optimality of Matched-Pair Designs in Randomized Controlled Trials," Papers 2206.07845, arXiv.org.
    2. Yuehao Bai & Jizhou Liu & Azeem M. Shaikh & Max Tabord-Meehan, 2023. "On the Efficiency of Finely Stratified Experiments," Papers 2307.15181, arXiv.org, revised Feb 2024.
    3. Liang Jiang & Xiaobin Liu & Peter C. B. Phillips & Yichong Zhang, 2020. "Bootstrap Inference for Quantile Treatment Effects in Randomized Experiments with Matched Pairs," Papers 2005.11967, arXiv.org, revised May 2021.
    4. Yuehao Bai & Jizhou Liu & Max Tabord-Meehan, 2022. "Inference for Matched Tuples and Fully Blocked Factorial Designs," Papers 2206.04157, arXiv.org, revised Nov 2023.
    5. McGovern, Mark E. & Canning, David & Bärnighausen, Till, 2018. "Accounting for non-response bias using participation incentives and survey design: An application using gift vouchers," Economics Letters, Elsevier, vol. 171(C), pages 239-244.
    6. Yuehao Bai & Liang Jiang & Joseph P. Romano & Azeem M. Shaikh & Yichong Zhang, 2023. "Covariate Adjustment in Experiments with Matched Pairs," Papers 2302.04380, arXiv.org, revised Oct 2023.
    7. Max Tabord-Meehan, 2018. "Stratification Trees for Adaptive Randomization in Randomized Controlled Trials," Papers 1806.05127, arXiv.org, revised Jul 2022.
    8. Mark McGovern & David Canning & Till Bärnighausen, 2018. "Accounting for Non-Response Bias using Participation Incentives and Survey Design," CHaRMS Working Papers 18-02, Centre for HeAlth Research at the Management School (CHaRMS).
    9. Jizhou Liu, 2023. "Inference for Two-stage Experiments under Covariate-Adaptive Randomization," Papers 2301.09016, arXiv.org, revised Oct 2023.
    10. Clément de Chaisemartin & Jaime Ramirez-Cuellar, 2024. "At What Level Should One Cluster Standard Errors in Paired and Small-Strata Experiments?," American Economic Journal: Applied Economics, American Economic Association, vol. 16(1), pages 193-212, January.
    11. Martin Huber, 2012. "Identification of Average Treatment Effects in Social Experiments Under Alternative Forms of Attrition," Journal of Educational and Behavioral Statistics, , vol. 37(3), pages 443-474, June.
    12. Martin Huber, 2010. "Identification of average treatment effects in social experiments under different forms of attrition," University of St. Gallen Department of Economics working paper series 2010 2010-22, Department of Economics, University of St. Gallen.
    13. Andrew Dustan & Stanislao Maldonado & Juan Manuel Hernandez-Agramonte, 2018. "Motivating bureaucrats with non-monetary incentives when state capacity is weak: Evidence from large-scale field experiments in Peru," Working Papers 136, Peruvian Economic Association.
    14. Augurzky, Boris & Bauer, Thomas K. & Reichert, Arndt R. & Schmidt, Christoph M. & Tauchmann, Harald, 2014. "Small Cash Rewards for Big Losers – Experimental Insights Into the Fight Against the Obesity Epidemic," Ruhr Economic Papers 530, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen.
    15. Jiang, Liang & Phillips, Peter C.B. & Tao, Yubo & Zhang, Yichong, 2023. "Regression-adjusted estimation of quantile treatment effects under covariate-adaptive randomizations," Journal of Econometrics, Elsevier, vol. 234(2), pages 758-776.
    16. Federico A. Bugni & Ivan A. Canay & Azeem M. Shaikh, 2018. "Inference Under Covariate-Adaptive Randomization," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 113(524), pages 1784-1796, October.
    17. Federico A. Bugni & Ivan A. Canay & Azeem M. Shaikh, 2019. "Inference under covariate‐adaptive randomization with multiple treatments," Quantitative Economics, Econometric Society, vol. 10(4), pages 1747-1785, November.
    18. 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.
    19. Paloyo, Alfredo R. & Rogan, Sally & Siminski, Peter, 2016. "The effect of supplemental instruction on academic performance: An encouragement design experiment," Economics of Education Review, Elsevier, vol. 55(C), pages 57-69.
    20. Joachim Inkmann, 2010. "Estimating Firm Size Elasticities of Product and Process R&D," Economica, London School of Economics and Political Science, vol. 77(306), pages 384-402, April.

    More about this item

    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:arx:papers:2209.11840. 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.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with 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: arXiv administrators (email available below). General contact details of provider: http://arxiv.org/ .

    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.