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Socio-economic and demographic variation in health and in its measures: the issue of reporting heterogeneity


  • Shmueli, Amir


True health state is an unobservable concept. Researchers and practitioners now have access to a large variety of tools to measure the health state and health related quality of life by self-reports. Socio-demographic variation in these measures is usually interpreted as variation in health. However, building on several measures simultaneously (multiple indicators), true health might be better represented, so that socio-demographic variation in any indicator can be decomposed into variation in the estimated true health, and measure-specific variation, holding true health constant. The latter variation is referred to as "reporting heterogeneity". Using structural equations models, the paper provides an empirical assessment of reporting heterogeneity in three popular measures of health and health related quality of life: the number of chronic conditions (CHRON), the SF-36 instrument and the visual analogue rating scale. Considering a large array of socio-economic and demographic characteristics from an Israeli health survey, the results indicate the existence of age-related reporting heterogeneity in the CHRON; income-related heterogeneity in the rating scale measure; and age, sex, income, ethnic origin and religiosity-related reporting heterogeneity in the SF-36 tool, in particular in its mental component scale. The main implication of reporting heterogeneity on the common uses of self-reported health measures is the need to adjust the measures not only for the determinants of health but also for the determinants of reporting heterogeneity.

Suggested Citation

  • Shmueli, Amir, 2003. "Socio-economic and demographic variation in health and in its measures: the issue of reporting heterogeneity," Social Science & Medicine, Elsevier, vol. 57(1), pages 125-134, July.
  • Handle: RePEc:eee:socmed:v:57:y:2003:i:1:p:125-134

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    1. Hsieh, Chee-Ruey & Lo, Te-Fen, 2017. "Are smokers too optimistic about their health status: Ex ante perception versus ex post observation," China Economic Review, Elsevier, vol. 43(C), pages 169-183.
    2. 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.
    3. Layes, Audrey & Asada, Yukiko & Kephart, George, 2012. "Whiners and deniers – What does self-rated health measure?," Social Science & Medicine, Elsevier, vol. 75(1), pages 1-9.
    4. 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.
    5. 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.
    6. Thomas Barnay & Eric Defebvre, 2018. "Retired, at last? The short-term impact of retirement on health status in France," TEPP Working Paper 2018-01, TEPP.
    7. Thomas Barnay & Éric Defebvre, 2019. "Gender Differences in the Influence of Mental Health on Job Retention," LABOUR, CEIS, vol. 33(4), pages 507-532, December.
    8. Sandy Tubeuf & Marc Perronnin, 2008. "New prospects in the analysis of inequalities in health: a measurement of health encompassing several dimensions of health," Health, Econometrics and Data Group (HEDG) Working Papers 08/01, HEDG, c/o Department of Economics, University of York.
    9. Paolo Li Donni & Ranjeeta Thomas, 0. "Latent class models for multiple ordered categorical health data: testing violation of the local independence assumption," Empirical Economics, Springer, vol. 0, pages 1-29.
    10. 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.
    11. Olivier Bouba-Olga & Magalie Vigé, 2014. "Le renoncement aux soins : une analyse empirique à partir de la base SHARE," Working Papers hal-01070962, HAL.
    12. Florence Jusot & Sabine Mage & Marta Menendez, 2014. "Inequality of Opportunity in Health in Indonesia," Working Papers DT/2014/06, DIAL (Développement, Institutions et Mondialisation).
    13. Lindeboom, Maarten & van Doorslaer, Eddy, 2004. "Cut-point shift and index shift in self-reported health," Journal of Health Economics, Elsevier, vol. 23(6), pages 1083-1099, November.
    14. repec:dau:papers:123456789/7005 is not listed on IDEAS
    15. Eric Defebvre, 2016. "Harder, better, faster... yet stronger? Working conditions and self-declaration of chronic diseases," TEPP Working Paper 2016-07, TEPP.
    16. repec:dau:papers:123456789/13753 is not listed on IDEAS
    17. Richard, Sébastien & Skagen, Kristian & Pedersen, Kjeld Møller & Huver, Benjamin, 2017. "Assessing the Propensity for Presenteeism with Sickness Absence Data," DaCHE discussion papers 2017:1, University of Southern Denmark, Dache - Danish Centre for Health Economics.
    18. Kaneva, Maria & Baidin, Valerii, 2018. "Heterogeneity in reporting self-assessed health of the Russians," Applied Econometrics, Publishing House "SINERGIA PRESS", vol. 51, pages 102-125.
    19. Nesson, Erik T. & Robinson, Joshua J., 2019. "On the measurement of health and its effect on the measurement of health inequality," Economics & Human Biology, Elsevier, vol. 35(C), pages 207-221.
    20. Groot, Wim & Maassen van den Brink, Henriette, 2007. "The health effects of education," Economics of Education Review, Elsevier, vol. 26(2), pages 186-200, April.
    21. 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.
    22. repec:dau:papers:123456789/423 is not listed on IDEAS
    23. repec:dau:papers:123456789/7004 is not listed on IDEAS
    24. Yang, Qingqing & Rosenman, Robert, 2015. "Adjusting Self-Assessed Health for Potential Bias Using a Random-Effects Generalized Ordered Probit model," 2015 AAEA & WAEA Joint Annual Meeting, July 26-28, San Francisco, California 205217, Agricultural and Applied Economics Association.


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