IDEAS home Printed from https://ideas.repec.org/p/aim/wpaimx/1905.html
   My bibliography  Save this paper

“If You Were Me”: Proxy Respondents’ Biases in Population Health Surveys

Author

Abstract

Proxy respondents are widely used in population health surveys to maximize response rates. When surveys target frail elderly, the measurement error is expected to be smaller than selection or participation biases. However, in the literature on elderly needs for care, proxy use is most often considered with a dummy variable in which endogeneity with subjects’ health status is rarely scrutinised in a robust way. Pitfalls of this choice extend beyond methodological issues. Indeed, the mismeasurement of needs for care with daily activities might lead to irrelevant social policies or to private initiatives that try to address those needs. This paper proposes a comprehensive and tractable strategy supported by various robustness checks to cope with the suspected endogeneity of proxy use to the unobserved health status of subjects in reports of needs for care with activities of daily living. Proxy respondents’ subjectivity is found to inflate the needs of the elderly who are replaced or assisted in answering the questionnaire and to deflate the probability of unmet or undermet needs.

Suggested Citation

  • Bérengère Davin & Xavier Joutard & Alain Paraponaris, 2019. "“If You Were Me”: Proxy Respondents’ Biases in Population Health Surveys," AMSE Working Papers 1905, Aix-Marseille School of Economics, France.
  • Handle: RePEc:aim:wpaimx:1905
    as

    Download full text from publisher

    File URL: https://new.amse-aixmarseille.fr/sites/default/files/working_papers/wp_2019_-_nr_05.pdf
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Myoung‐jae Lee & Young‐sook Kim, 2012. "Zero‐Inflated Endogenous Count In Censored Model: Effects Of Informal Family Care On Formal Health Care," Health Economics, John Wiley & Sons, Ltd., vol. 21(9), pages 1119-1133, September.
    2. Bent Jesper Christensen & Malene Kallestrup‐Lamb, 2012. "The Impact Of Health Changes On Labor Supply: Evidence From Merged Data On Individual Objective Medical Diagnosis Codes And Early Retirement Behavior," Health Economics, John Wiley & Sons, Ltd., vol. 21(S1), pages 56-100, June.
    3. Bent Jesper Christensen & Malene Kallestrup‐Lamb, 2012. "The Impact Of Health Changes On Labor Supply: Evidence From Merged Data On Individual Objective Medical Diagnosis Codes And Early Retirement Behavior," Health Economics, John Wiley & Sons, Ltd., vol. 21(S1), pages 56-100, June.
    4. Valeria Bordone & Helga A. G. Valk, 2016. "Intergenerational support among migrant families in Europe," European Journal of Ageing, Springer, vol. 13(3), pages 259-270, September.
    5. Gurmu, Shiferaw & Elder, John, 2008. "A bivariate zero-inflated count data regression model with unrestricted correlation," Economics Letters, Elsevier, vol. 100(2), pages 245-248, August.
    6. So, Sunha & Lee, Dong-Hee & Jung, Byoung Cheol, 2011. "An alternative bivariate zero-inflated negative binomial regression model using a copula," Economics Letters, Elsevier, vol. 113(2), pages 183-185.
    7. Matteo Lippi Bruni & Cristina Ugolini, 2016. "Delegating home care for the elderly to external caregivers? An empirical study on Italian data," Review of Economics of the Household, Springer, vol. 14(1), pages 155-183, March.
    8. Hugo Benítez-Silva & Moshe Buchinsky & Hiu Man Chan & Sofia Cheidvasser & John Rust, 2004. "How large is the bias in self-reported disability?," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 19(6), pages 649-670.
    9. Fabrice Etilé & Carine Milcent, 2006. "Income‐related reporting heterogeneity in self‐assessed health: evidence from France," Health Economics, John Wiley & Sons, Ltd., vol. 15(9), pages 965-981, September.
    10. Keay, Myoung-Jin, 2016. "Partial copula methods for models with multiple discrete endogenous explanatory variables and sample selection," Economics Letters, Elsevier, vol. 144(C), pages 85-87.
    11. Denise Doiron & Denzil G. Fiebig & Meliyanni Johar & Agne Suziedelyte, 2015. "Does self-assessed health measure health?," Applied Economics, Taylor & Francis Journals, vol. 47(2), pages 180-194, January.
    12. repec:hal:psewpa:halshs-00590524 is not listed on IDEAS
    13. Kim, Kyoo il, 2006. "Sample selection models with a common dummy endogenous regressor in simultaneous equations: A simple two-step estimation," Economics Letters, Elsevier, vol. 91(2), pages 280-286, May.
    14. John Bound, 1991. "Self-Reported Versus Objective Measures of Health in Retirement Models," Journal of Human Resources, University of Wisconsin Press, vol. 26(1), pages 106-138.
    15. Fabrice Etilé & Carine Milcent, 2006. "Income-related reporting heterogeneity in self-assessed health: evidence from France," Health Economics, John Wiley & Sons, Ltd., vol. 15(9), pages 965-981.
    16. repec:hal:wpaper:halshs-00590524 is not listed on IDEAS
    17. Bound, John & Brown, Charles & Mathiowetz, Nancy, 2001. "Measurement error in survey data," Handbook of Econometrics, in: J.J. Heckman & E.E. Leamer (ed.), Handbook of Econometrics, edition 1, volume 5, chapter 59, pages 3705-3843, Elsevier.
    18. Murray D. Smith, 2003. "Modelling sample selection using Archimedean copulas," Econometrics Journal, Royal Economic Society, vol. 6(1), pages 99-123, June.
    19. Todorov, A. & Kirchner, C., 2000. "Bias in proxies' reports of disability: Data from the National Health Interview Survey on disability," American Journal of Public Health, American Public Health Association, vol. 90(8), pages 1248-1253.
    20. Han, Sukjin & Vytlacil, Edward J., 2017. "Identification in a generalization of bivariate probit models with dummy endogenous regressors," Journal of Econometrics, Elsevier, vol. 199(1), pages 63-73.
    21. Massimiliano Bratti & Alfonso Miranda, 2011. "Endogenous treatment effects for count data models with endogenous participation or sample selection," Health Economics, John Wiley & Sons, Ltd., vol. 20(9), pages 1090-1109, September.
    22. Max Groneck, 2017. "Bequests and Informal Long-Term Care: Evidence from HRS Exit Interviews," Journal of Human Resources, University of Wisconsin Press, vol. 52(2), pages 531-572.
    23. P Grootendorst & D Feeny & W Furlong, 1994. "Does It Matter Whom and How You Ask? Inter and Intra-rater Agreement in the Ontario Health Survey," Centre for Health Economics and Policy Analysis Working Paper Series 1994-12, Centre for Health Economics and Policy Analysis (CHEPA), McMaster University, Hamilton, Canada.
    24. Ann Cartwright, 1957. "The Effect of Obtaining Information from Different Informants on a Family Morbidity Inquiry," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 6(1), pages 18-25, March.
    25. Gurmu, Shiferaw & Elder, John, 2000. "Generalized bivariate count data regression models," Economics Letters, Elsevier, vol. 68(1), pages 31-36, July.
    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. Thomas Barnay, 2016. "Health, work and working conditions: a review of the European economic literature," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 17(6), pages 693-709, July.
    2. Johnston, David W. & Propper, Carol & Shields, Michael A., 2009. "Comparing subjective and objective measures of health: Evidence from hypertension for the income/health gradient," Journal of Health Economics, Elsevier, vol. 28(3), pages 540-552, May.
    3. Kreider, Brent & Pepper, John V., 2007. "Disability and Employment: Reevaluating the Evidence in Light of Reporting Errors," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 432-441, June.
    4. Boman, Anders, 2015. "Spending time together? Effects on the retirement decision from partner’s labour market status," Working Papers in Economics 618, University of Gothenburg, Department of Economics.
    5. Doreen Wing Han Au & Thomas F. Crossley & Martin Schellhorn, 2005. "The effect of health changes and long‐term health on the work activity of older Canadians," Health Economics, John Wiley & Sons, Ltd., vol. 14(10), pages 999-1018, October.
    6. Thomas Barnay & Karine Briard, 2011. "Health and Early Retirement: Evidence from French Data for individuals," Economics Bulletin, AccessEcon, vol. 31(1), pages 324-341.
    7. Davillas, Apostolos & de Oliveira, Victor Hugo & Jones, Andrew M., 2023. "Is inconsistent reporting of self-assessed health persistent and systematic? Evidence from the UKHLS," Economics & Human Biology, Elsevier, vol. 49(C).
    8. Teresa Bago d'Uva & Eddy Van Doorslaer & Maarten Lindeboom & Owen O'Donnell, 2008. "Does reporting heterogeneity bias the measurement of health disparities?," Health Economics, John Wiley & Sons, Ltd., vol. 17(3), pages 351-375, March.
    9. Thomas Barnay & Julie Favrot & Catherine Pollak, 2015. "L'effet des arrêts maladie sur les trajectoires professionnelles," Économie et Statistique, Programme National Persée, vol. 475(1), pages 135-156.
    10. Valerii Baidin & Christopher J. Gerry & Maria Kaneva, 2021. "How Self-Rated is Self-Rated Health? Exploring the Role of Individual and Institutional Factors in Reporting Heterogeneity in Russia," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 155(2), pages 675-696, June.
    11. Au, N. & Johnston, D. W., 2013. "An econometric analysis of self-assessed health: what does it mean and what is it hiding?," Health, Econometrics and Data Group (HEDG) Working Papers 13/31, HEDG, c/o Department of Economics, University of York.
    12. Greene, William & Harris, Mark N. & Knott, Rachel & Rice, Nigel, 2023. "Reporting heterogeneity in modeling self-assessed survey outcomes," Economic Modelling, Elsevier, vol. 124(C).
    13. Black, Nicole & Johnston, David W. & Shields, Michael A. & Suziedelyte, Agne, 2017. "Who provides inconsistent reports of their health status? The importance of age, cognitive ability and socioeconomic status," Social Science & Medicine, Elsevier, vol. 191(C), pages 9-18.
    14. Johansson, Per & Skedinger, Per, 2005. "Are objective, official measures of disability reliable?," Working Paper Series 2005:14, IFAU - Institute for Evaluation of Labour Market and Education Policy.
    15. Aparajita Dasgupta, 2018. "Systematic measurement error in self-reported health: is anchoring vignettes the way out?," IZA Journal of Migration and Development, Springer;Forschungsinstitut zur Zukunft der Arbeit GmbH (IZA), vol. 8(1), pages 1-30, December.
    16. Pfarr, Christian & Schmid, Andreas & Schneider, Udo, 2011. "Reporting Heterogeneity in Self-Assessed Health among Elderly Europeans: The Impact of Mental and Physical Health Status," MPRA Paper 29900, University Library of Munich, Germany.
    17. Subhasree Basu Roy, 2018. "Effect of Health on Retirement of Older Americans: a Competing Risks Study," Journal of Labor Research, Springer, vol. 39(1), pages 56-98, March.
    18. Udo Schneider & Christian Pfarr & Brit Schneider & Volker Ulrich, 2012. "I feel good! Gender differences and reporting heterogeneity in self-assessed health," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 13(3), pages 251-265, June.
    19. Ilmakunnas, Pekka & Ilmakunnas, Seija, 2018. "Health and retirement age: Comparison of expectations and actual retirement," MPRA Paper 102618, University Library of Munich, Germany.
    20. Rhys Davies & Melanie Jones & Huw Lloyd-Williams, 2016. "Age and Work-Related Health: Insights from the UK Labour Force Survey," British Journal of Industrial Relations, London School of Economics, vol. 54(1), pages 136-159, March.

    More about this item

    Keywords

    proxy respondent; measurement bias; endogeneity; selection; Copula; needs for care; ADLs; IADLs;
    All these keywords.

    JEL classification:

    • C31 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Social Interaction Models
    • C35 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions
    • C36 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Instrumental Variables (IV) Estimation
    • I12 - Health, Education, and Welfare - - Health - - - Health Behavior
    • J14 - Labor and Demographic Economics - - Demographic Economics - - - Economics of the Elderly; Economics of the Handicapped; Non-Labor Market Discrimination

    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:aim:wpaimx:1905. 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: Gregory Cornu (email available below). General contact details of provider: https://edirc.repec.org/data/amseafr.html .

    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.