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Robustness in health research: Do differences in health measures, techniques, and time frame matter?

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  • Frijters, Paul
  • Ulker, Aydogan

Abstract

Survey-based health research is in a boom phase following an increased amount of health spending in OECD countries and the interest in ageing. A general characteristic of survey-based health research is its diversity. Different studies are based on different health questions in different datasets; they use different statistical techniques; they differ in whether they approach health from an ordinal or cardinal perspective; and they differ in whether they measure short-term or long-term effects. The question in this paper is simple: do these differences matter for the findings? We investigate the effects of life-style choices (drinking, smoking, exercise) and income on six measures of health in the US Health and Retirement Study (HRS) between 1992 and 2002: (1) self-assessed general health status, (2) problems with undertaking daily tasks and chores, (3) mental health indicators, (4) BMI, (5) the presence of serious long-term health conditions, and (6) mortality. We compare ordinal models with cardinal models; we compare models with fixed effects to models without fixed-effects; and we compare short-term effects to long-term effects. We find considerable variation in the impact of different determinants on our chosen health outcome measures; we find that it matters whether ordinality or cardinality is assumed; we find substantial differences between estimates that account for fixed effects versus those that do not; and we find that short-run and long-run effects differ greatly. All this implies that health is an even more complicated notion than hitherto thought, defying generalizations from one measure to the others or one methodology to another.

Suggested Citation

  • Frijters, Paul & Ulker, Aydogan, 2008. "Robustness in health research: Do differences in health measures, techniques, and time frame matter?," Journal of Health Economics, Elsevier, vol. 27(6), pages 1626-1644, December.
  • Handle: RePEc:eee:jhecon:v:27:y:2008:i:6:p:1626-1644
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    Cited by:

    1. Gunasekara, Fiona Imlach & Carter, Kristie & Blakely, Tony, 2011. "Change in income and change in self-rated health: Systematic review of studies using repeated measures to control for confounding bias," Social Science & Medicine, Elsevier, vol. 72(2), pages 193-201, January.
    2. 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.
    3. Reeves, Aaron & McKee, Martin & Mackenbach, Johan & Whitehead, Margaret & Stuckler, David, 2017. "Introduction of a national minimum wage reduceddepressive symptoms in low-wage workers:a quasi-natural experiment in the UK," LSE Research Online Documents on Economics 66485, London School of Economics and Political Science, LSE Library.
    4. Cuesta, Maite Blázquez & Budría, Santiago, 2015. "Income deprivation and mental well-being: The role of non-cognitive skills," Economics & Human Biology, Elsevier, vol. 17(C), pages 16-28.
    5. Wouterse, Bram & Huisman, Martijn & Meijboom, Bert R. & Deeg, Dorly J.H. & Polder, Johan J., 2013. "Modeling the relationship between health and health care expenditures using a latent Markov model," Journal of Health Economics, Elsevier, vol. 32(2), pages 423-439.
    6. Au, Nicole & Johnston, David W., 2014. "Self-assessed health: What does it mean and what does it hide?," Social Science & Medicine, Elsevier, vol. 121(C), pages 21-28.
    7. Maite Blázquez Cuesta & Santiago Budría, 2013. "Does income deprivation affect people’s mental well-being?," Working Papers 1312, Banco de España;Working Papers Homepage.

    More about this item

    Keywords

    Morbidity Mortality Lifestyle Income;

    JEL classification:

    • Z1 - Other Special Topics - - Cultural Economics
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions; Probabilities
    • I31 - Health, Education, and Welfare - - Welfare, Well-Being, and Poverty - - - General Welfare, Well-Being

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