IDEAS home Printed from https://ideas.repec.org/a/dem/demres/v50y2024i25.html
   My bibliography  Save this article

The importance of correcting for health-related survey non-response when estimating health expectancies: Evidence from The HUNT Study

Author

Listed:
  • Fred Schroyen

    (Norges Handelshøyskole)

Abstract

Background: Most studies on health expectancies rely on self-reported health from surveys to measure the prevalence of disabilities or ill health in a population. At best, such studies only correct for sample selection based on a limited number of characteristics observed on the invitees. Objective: Using longitudinal data from the Trøndelag Health Study (HUNT), I investigate the extent to which adjustments for a health-related sample selection affect the age profiles for the prevalence of functional impairment (FI) and the associated disability-free life expectancy (DFLE). Methods: I estimate a probit model with sample selection under the identifying restriction that the strength of the health-related selection is of similar order to the strength of the selection on observable characteristics. I then compute the selection-adjusted FI prevalence rates and trace out the implications for DFLE using the Sullivan method. Results: The analysis confirms that poor health measured at younger ages correlates with nonresponse behaviour in later waves of the survey, and that even for a conservative lower bound for the assumed degree of health-related selection, the estimated age profiles for DFLE lie systematically below the corresponding profiles when controlling only for selection on observable characteristics. Conclusions: Health related non-response downwardly biases the raw sample prevalence rates for FI obtained from survey data and contributes to overestimating the expansion in DFLE. Contribution: I present a statistical framework for taking health-related survey non-responses into account when estimating the prevalence rate of FI. The framework can be used to gauge the sensitivity of estimated (changes in) DFLE to health-related sample selection.

Suggested Citation

  • Fred Schroyen, 2024. "The importance of correcting for health-related survey non-response when estimating health expectancies: Evidence from The HUNT Study," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 50(25), pages 667-732.
    • Handle: RePEc:dem:demres:v:50:y:2024:i:25
    DOI: 10.4054/DemRes.2024.50.25
    as

    Download full text from publisher

    File URL: https://www.demographic-research.org/volumes/vol50/25/50-25.pdf
    Download Restriction: no
    File URL: https://libkey.io/10.4054/DemRes.2024.50.25?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. James J. Heckman, 1976. "The Common Structure of Statistical Models of Truncation, Sample Selection and Limited Dependent Variables and a Simple Estimator for Such Models," NBER Chapters, in: Annals of Economic and Social Measurement, Volume 5, number 4, pages 475-492, National Bureau of Economic Research, Inc.
    2. Joseph G. Altonji & Todd E. Elder & Christopher R. Taber, 2005. "Selection on Observed and Unobserved Variables: Assessing the Effectiveness of Catholic Schools," Journal of Political Economy, University of Chicago Press, vol. 113(1), pages 151-184, February.
    3. 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.
    4. Giuseppe De Luca & Valeria Perotti, 2011. "Estimation of ordered response models with sample selection," Stata Journal, StataCorp LP, vol. 11(2), pages 213-239, June.
    5. Paul Contoyannis & Andrew M. Jones & Nigel Rice, 2004. "The dynamics of health in the British Household Panel Survey," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 19(4), pages 473-503.
    6. F. Kreuter & K. Olson & J. Wagner & T. Yan & T. M. Ezzati‐Rice & C. Casas‐Cordero & M. Lemay & A. Peytchev & R. M. Groves & T. E. Raghunathan, 2010. "Using proxy measures and other correlates of survey outcomes to adjust for non‐response: examples from multiple surveys," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 173(2), pages 389-407, April.
    7. repec:cai:popine:popu_p1999_11n1_0034 is not listed on IDEAS
    8. Patrick Puhani, 2000. "The Heckman Correction for Sample Selection and Its Critique," Journal of Economic Surveys, Wiley Blackwell, vol. 14(1), pages 53-68, February.
    9. Van de Ven, Wynand P. M. M. & Van Praag, Bernard M. S., 1981. "The demand for deductibles in private health insurance : A probit model with sample selection," Journal of Econometrics, Elsevier, vol. 17(2), pages 229-252, November.
    10. 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.
    11. Imai, Kosuke & Soneji, Samir, 2007. "On the Estimation of Disability-Free Life Expectancy: Sullivan's Method and Its Extension," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 1199-1211, December.
    12. Liming Cai & James Lubitz, 2007. "Was there compression of disability for older Americans from 1992 to 2003?," Demography, Springer;Population Association of America (PAA), vol. 44(3), pages 479-495, August.
    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. Christine Fauvelle-Aymar & Abel François, 2018. "Place of registration and place of residence: the non-linear detrimental impact of transportation cost on electoral participation," Public Choice, Springer, vol. 176(3), pages 405-440, September.
    2. Cumming, Douglas & Fleming, Grant & Schwienbacher, Armin, 2006. "Legality and venture capital exits," Journal of Corporate Finance, Elsevier, vol. 12(2), pages 214-245, January.
    3. Maarten Goos & Anna Salomons, 2017. "Measuring teaching quality in higher education: assessing selection bias in course evaluations," Research in Higher Education, Springer;Association for Institutional Research, vol. 58(4), pages 341-364, June.
    4. Watanabe, Hajime & Maruyama, Takuya, 2024. "A Bayesian sample selection model with a binary outcome for handling residential self-selection in individual car ownership," Journal of choice modelling, Elsevier, vol. 51(C).
    5. Croux, Christophe & Jagtiani, Julapa & Korivi, Tarunsai & Vulanovic, Milos, 2020. "Important factors determining Fintech loan default: Evidence from a lendingclub consumer platform," Journal of Economic Behavior & Organization, Elsevier, vol. 173(C), pages 270-296.
    6. Cristian Castillo & Julimar Da Silva & Sandro Monsueto, 2020. "Objectives of Sustainable Development and Youth Employment in Colombia," Sustainability, MDPI, vol. 12(3), pages 1-18, January.
    7. Arndt Reichert & Harald Tauchmann, 2014. "When outcome heterogeneously matters for selection: a generalized selection correction estimator," Applied Economics, Taylor & Francis Journals, vol. 46(7), pages 762-768, March.
    8. Andy Cosh & Douglas Cumming & Alan Hughes, 2009. "Outside Enterpreneurial Capital," Economic Journal, Royal Economic Society, vol. 119(540), pages 1494-1533, October.
    9. Annick Pamen Nyola & Alain Sauviat & Amine Tarazi, 2022. "How does regulation affect the organizational form of foreign banks' presence in developing versus developed countries?," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 27(2), pages 2367-2419, April.
    10. Fabrice Le Guel & Thierry Pénard & Raphaël Suire, 2005. "Adoption et usage marchand de l'Internet : une étude économétrique sur données bretonnes," Economie & Prévision, La Documentation Française, vol. 167(1), pages 67-84.
    11. Kuester, Sabine & Homburg, Christian & Hildesheim, Andreas, 2017. "The catbird seat of the sales force: How sales force integration leads to new product success," International Journal of Research in Marketing, Elsevier, vol. 34(2), pages 462-479.
    12. Glenn W. Harrison & Morten I. Lau & Hong Il Yoo, 2020. "Risk Attitudes, Sample Selection, and Attrition in a Longitudinal Field Experiment," The Review of Economics and Statistics, MIT Press, vol. 102(3), pages 552-568, July.
    13. Ugur, Mehment & Vivarelli, Marco, 2020. "The role of innovation in industrial dynamics and productivity growth: a survey of the literature," GLO Discussion Paper Series 648, Global Labor Organization (GLO).
    14. Goic, Marcel & Rojas, Andrea & Saavedra, Ignacio, 2021. "The Effectiveness of Triggered Email Marketing in Addressing Browse Abandonments," Journal of Interactive Marketing, Elsevier, vol. 55(C), pages 118-145.
    15. Boons, Mark & Stam, Daan, 2019. "Crowdsourcing for innovation: How related and unrelated perspectives interact to increase creative performance," Research Policy, Elsevier, vol. 48(7), pages 1758-1770.
    16. Pourya Darnihamedani & Joern Hendrich Block & Jolanda Hessels & Aram Simonyan, 2018. "Taxes, start-up costs, and innovative entrepreneurship," Small Business Economics, Springer, vol. 51(2), pages 355-369, August.
    17. Li, Phillip, 2011. "Estimation of sample selection models with two selection mechanisms," Computational Statistics & Data Analysis, Elsevier, vol. 55(2), pages 1099-1108, February.
    18. Marjan Petreski & Nikica Blazevski & Blagica Petreski, 2014. "Gender Wage Gap when Women are Highly Inactive: Evidence from Repeated Imputations with Macedonian Data," Journal of Labor Research, Springer, vol. 35(4), pages 393-411, December.
    19. Dan A. Black & Lars Skipper & Jeffrey A. Smith & Jeffrey Andrew Smith, 2023. "Firm Training," CESifo Working Paper Series 10268, CESifo.
    20. Nooraisah Katmon & Omar Al Farooque, 2017. "Exploring the Impact of Internal Corporate Governance on the Relation Between Disclosure Quality and Earnings Management in the UK Listed Companies," Journal of Business Ethics, Springer, vol. 142(2), pages 345-367, May.

    More about this item

    Keywords

    healthy life expectancy; functional impairment risk; functionally impaired life expectancy; disability-free life expectancy; attrition; sample selection; inverse-probability weighting; the HUNT Study;
    All these keywords.

    JEL classification:

    • J1 - Labor and Demographic Economics - - Demographic Economics
    • Z0 - Other Special Topics - - General

    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:dem:demres:v:50:y:2024:i:25. 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: Editorial Office (email available below). General contact details of provider: https://www.demogr.mpg.de/ .

    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.