IDEAS home Printed from https://ideas.repec.org/a/sae/somere/v54y2025i2p500-533.html

Addressing Non-ignorable Panel Attrition Using External Population Data: Analysis of Demographic Events From Survey Data

Author

Listed:
  • John Ermisch

Abstract

Empirical analysis of variation in demographic events within the population is facilitated by using longitudinal survey data because of the richness of covariate measures in such data, but there is wave-on-wave dropout. When attrition is related to the event, it precludes consistent estimation of the impacts of covariates on the event and on event probabilities in the absence of additional assumptions. The paper introduces an adjustment procedure based on Bayes Theorem that directly addresses the problem of nonignorable dropout. It uses population information external to the survey sample to convert estimates of event probabilities and marginal effects of covariates on them that are conditional on retention in the longitudinal data to unconditional estimates of these quantities. In many plausible and verifiable circumstances, it produces estimates of the marginal effect of covariates closer to the true unconditional quantities than the conditional estimates obtained from estimation using the survey data alone.

Suggested Citation

  • John Ermisch, 2025. "Addressing Non-ignorable Panel Attrition Using External Population Data: Analysis of Demographic Events From Survey Data," Sociological Methods & Research, , vol. 54(2), pages 500-533, May.
    • Handle: RePEc:sae:somere:v:54:y:2025:i:2:p:500-533
    DOI: 10.1177/00491241231186659
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.1177/00491241231186659
    Download Restriction: no
    File URL: https://libkey.io/10.1177/00491241231186659?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Mark Handcock & Sami Huovilainen & Michael Rendall, 2000. "Combining registration-system and survey data to estimate birth probabilities," Demography, Springer;Population Association of America (PAA), vol. 37(2), pages 187-192, May.
    2. Michael Rendall & Mark Handcock & Stefan Jonsson, 2009. "Bayesian estimation of hispanic fertility hazards from survey and population data," Demography, Springer;Population Association of America (PAA), vol. 46(1), pages 65-83, February.
    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. Michael S. Rendall & Bonnie Ghosh-Dastidar & Margaret M. Weden & Elizabeth H. Baker & Zafar Nazarov, 2013. "Multiple Imputation for Combined-survey Estimation With Incomplete Regressors in One but Not Both Surveys," Sociological Methods & Research, , vol. 42(4), pages 483-530, November.
    2. Lv, Yang & Qin, Guoyou & Zhu, Zhongyi, 2024. "Population-level information for improving quantile regression efficiency," Statistics & Probability Letters, Elsevier, vol. 215(C).
    3. Michaela R. Kreyenfeld & Rembrandt D. Scholz & Frederik Peters & Ines Wlosnewski, 2010. "The German Birth Order Register - order-specific data generated from perinatal statistics and statistics on out-of-hospital births 2001-2008," MPIDR Working Papers WP-2010-010, Max Planck Institute for Demographic Research, Rostock, Germany.
    4. Michael S. Rendall & Mark S. Handcock & Stefan H. Jonsson, 2007. "Bayesian Estimation of Hispanic Fertility Hazards from Survey and Population Data," Working Papers WR-496, RAND Corporation.
    5. F Bravo, 2008. "Effcient M-estimators with auxiliary information," Discussion Papers 08/26, Department of Economics, University of York.
    6. Michael S. Rendall & Ryan Admiraal & Alessandra De Rose & Paola Di Giulio & Mark S. Handcock & Filomena Racioppi, 2006. "Population constraints on pooled surveys in demographic hazard modeling," MPIDR Working Papers WP-2006-039, Max Planck Institute for Demographic Research, Rostock, Germany.
    7. Kryštof Zeman, 2018. "Cohort fertility and educational expansion in the Czech Republic during the 20th century," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 38(56), pages 1699-1732.
    8. Petropoulos, Fotios & Apiletti, Daniele & Assimakopoulos, Vassilios & Babai, Mohamed Zied & Barrow, Devon K. & Ben Taieb, Souhaib & Bergmeir, Christoph & Bessa, Ricardo J. & Bijak, Jakub & Boylan, Joh, 2022. "Forecasting: theory and practice," International Journal of Forecasting, Elsevier, vol. 38(3), pages 705-871.
      • Fotios Petropoulos & Daniele Apiletti & Vassilios Assimakopoulos & Mohamed Zied Babai & Devon K. Barrow & Souhaib Ben Taieb & Christoph Bergmeir & Ricardo J. Bessa & Jakub Bijak & John E. Boylan & Jet, 2020. "Forecasting: theory and practice," Papers 2012.03854, arXiv.org, revised Jan 2022.
    9. Michael Rendall & Ryan Admiraal & Alessandra DeRose & Paola DiGiulio & Mark Handcock & Filomena Racioppi, 2008. "Population constraints on pooled surveys in demographic hazard modeling," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 17(4), pages 519-539, October.
    10. Michael S. Rendall & Mark S. Handcock & Stefan H. Jonsson, 2007. "Bayesian Estimation of Hispanic Fertility Hazards from Survey and Population Data," Working Papers 496, RAND Corporation.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;
    ;

    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:sae:somere:v:54:y:2025:i:2:p:500-533. 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: SAGE Publications (email available below). General contact details of provider: .

    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.