IDEAS home Printed from https://ideas.repec.org/p/arx/papers/2610.01654.html

Identifying Panel Conditioning with Refreshment Samples: Sharp Bounds and Design Assumptions

Author

Listed:
  • Shoki Okubo

Abstract

Refreshment samples are the standard remedy for panel attrition, and the identification results behind them maintain that participation does not change measurement. We characterize what a refreshment sample identifies about panel conditioning, modelled as a deterministic monotone map at reinterview, when attrition is unrestricted. A candidate map is consistent with the data if and only if the retention-scaled distribution of the stayers' implied latent outcomes is setwise dominated by the refreshment distribution; every such map is rationalized by an explicit attrition process. Without attrition the map is identified on the latent-outcome support; with attrition, a density-ratio condition governs the identified set, and tail behaviour alone does not determine it. For an unrestricted map the survivors' mean effect has the familiar trimming bounds; for an item with all categories reported, the model reduces to a test of no conditioning. Within a cohort, other waves, dropout patterns and entry-wave items leave the set unchanged unless restrictions link selection across waves. Under explicit selection restrictions, survival matching, symmetric matching and entry-wave correction identify survivor effects. We derive their biases and give rank conditions under which refreshment schedules identify curvature in the conditioning path. A Japanese panel illustrates the results.

Suggested Citation

  • Shoki Okubo, 2026. "Identifying Panel Conditioning with Refreshment Samples: Sharp Bounds and Design Assumptions," Papers 2610.01654, arXiv.org.
  • Handle: RePEc:arx:papers:2610.01654
    as

    Download full text from publisher

    File URL: https://arxiv.org/pdf/2610.01654
    File Function: Latest version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Van Landeghem, Bert, 2014. "A test based on panel refreshments for panel conditioning in stated utility measures," Economics Letters, Elsevier, vol. 124(2), pages 236-238.
    2. Alan B. Krueger & Alexandre Mas & Xiaotong Niu, 2017. "The Evolution of Rotation Group Bias: Will the Real Unemployment Rate Please Stand Up?," The Review of Economics and Statistics, MIT Press, vol. 99(2), pages 258-264, May.
    3. Marcel Das & Vera Toepoel & Arthur van Soest, 2011. "Nonparametric Tests of Panel Conditioning and Attrition Bias in Panel Surveys," Sociological Methods & Research, , vol. 40(1), pages 32-56, February.
    4. Chadi, Adrian, 2021. "Identification of attrition bias using different types of panel refreshments," Economics Letters, Elsevier, vol. 201(C).
    5. 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.
    6. Shuaizhang Feng & Yingyao Hu & Jiandong Sun, 2022. "Rotation group bias and the persistence of misclassification errors in the Current Population Surveys," Econometric Reviews, Taylor & Francis Journals, vol. 41(9), pages 1077-1094, September.
    7. 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.
    8. Dalia Ghanem & Sarojini Hirshleifer & Karen Ortiz-Becerra, 2026. "Testing Attrition Bias in Field Experiments," Journal of Human Resources, University of Wisconsin Press, vol. 61(2), pages 690-731.
    9. Andrew Halpern-Manners & John Warren, 2012. "Panel Conditioning in Longitudinal Studies: Evidence From Labor Force Items in the Current Population Survey," Demography, Springer;Population Association of America (PAA), vol. 49(4), pages 1499-1519, November.
    10. Horowitz, Joel L & Manski, Charles F, 1995. "Identification and Robustness with Contaminated and Corrupted Data," Econometrica, Econometric Society, vol. 63(2), pages 281-302, March.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Shoki Okubo, 2026. "Panel Conditioning in Fixed-Effects Models: Identification and Bias Propagation," Papers 2609.28871, arXiv.org.

    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. Chadi, Adrian, 2021. "Identification of attrition bias using different types of panel refreshments," Economics Letters, Elsevier, vol. 201(C).
    2. 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.
    3. Shoki Okubo, 2026. "Panel Conditioning in Fixed-Effects Models: Identification and Bias Propagation," Papers 2609.28871, arXiv.org.
    4. Bhalotra, Sonia & Clarke, Damian & Mühlrad, Hanna & Palme, Mårten, 2021. "Health and Labor Market Impacts of Twin Birth : Evidence from a Swedish IVF Policy Mandate," The Warwick Economics Research Paper Series (TWERPS) 1391, University of Warwick, Department of Economics.
    5. Karsten Marshall Elseth Rieck & Kjetil Telle, 2012. "Sick leave before, during and after pregnancy," Discussion Papers 690, Statistics Norway, Research Department.
    6. Esmerelda A. Ramalho & Richard Smith, 2003. "Discrete choice non-response," CeMMAP working papers 07/03, Institute for Fiscal Studies.
    7. 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.
    8. Molinari, Francesca, 2020. "Microeconometrics with partial identification," Handbook of Econometrics, in: Steven N. Durlauf & Lars Peter Hansen & James J. Heckman & Rosa L. Matzkin (ed.), Handbook of Econometrics, edition 1, volume 7, chapter 0, pages 355-486, Elsevier.
    9. Markus Frölich & Martin Huber, 2014. "Treatment Evaluation With Multiple Outcome Periods Under Endogeneity and Attrition," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 109(508), pages 1697-1711, December.
    10. C de Chaisemartin & X D’HaultfŒuille, 2018. "Fuzzy Differences-in-Differences," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 85(2), pages 999-1028.
    11. Hie Joo Ahn & James Hamilton, 2022. "Measuring Labor-Force Participation and the Incidence and Duration of Unemployment," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 44, pages 1-32, April.
    12. Gevorg Khandamiryan & Vira Semenova, 2026. "Adaptive Estimation of Aggregated Values of Conditional Linear Programs," Papers 2606.08359, arXiv.org.
    13. Stephan, Gesine & Hetschko, Clemens & Schmidtke, Julia & Eid, Michael & Lawes, Mario, 2024. "Feeling Observed? A Field Experiment on the Effects of Intense Survey Participation on Job Seekers' Labour Market Outcomes," IZA Discussion Papers 17347, IZA Network @ LISER.
    14. Martin Huber & Giovanni Mellace, 2014. "Testing exclusion restrictions and additive separability in sample selection models," Empirical Economics, Springer, vol. 47(1), pages 75-92, August.
    15. Yingying Dong & Phillip Heiler, 2026. "Sharp Bounds and Inference in Sample Selection Models with Treatment Endogeneity," Papers 2606.09223, arXiv.org.
    16. Hsu, Yu-Chin & Shiu, Ji-Liang & Wan, Yuanyuan, 2024. "Testing identification conditions of LATE in fuzzy regression discontinuity designs," Journal of Econometrics, Elsevier, vol. 241(1).
    17. Bartalotti, Otávio & Kédagni, Désiré & Possebom, Vitor, 2023. "Identifying marginal treatment effects in the presence of sample selection," Journal of Econometrics, Elsevier, vol. 234(2), pages 565-584.
    18. Adrian Chadi, 2019. "Dissatisfied with life or with being interviewed? Happiness and the motivation to participate in a survey," Social Choice and Welfare, Springer;The Society for Social Choice and Welfare, vol. 53(3), pages 519-553, October.
    19. Fakih, Ali & Makdissi, Paul & Marrouch, Walid & Tabri, Rami V. & Yazbeck, Myra, 2022. "A stochastic dominance test under survey nonresponse with an application to comparing trust levels in Lebanese public institutions," Journal of Econometrics, Elsevier, vol. 228(2), pages 342-358.
    20. 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.

    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:2610.01654. 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: https://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.