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The Income-Health Relationship “Beyond the Mean†: New Evidence from Biomarkers

Author

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  • Carrieri, V.
  • Jones, A.M.
Abstract
This paper offers new evidence on the income-health relationship by analyzing the income gradient across the full distribution of four blood-based biomarkers: cholesterol, fibrinogen, glycated haemoglobin and ferritin. We use an unconditional quantile approach based on recentered influence function (RIF) regressions and apply an Oaxaca-Blinder decomposition at various quantiles of biomarker distributions to explain gender differentials in biomarkers. Using ten waves of the Health Survey for England (from 2003 to 2012) we find a non-linear relationship between income and biomarkers and a higher income gradient at the highest quantiles of the biomarker distributions. Moreover, we find that there is an important heterogeneity in the association of health to income across genders which varies significantly along the biomarker distribution and accounts for a substantial percentage of the gender differentials in observed health.

Suggested Citation

  • Carrieri, V. & Jones, A.M., 2015. "The Income-Health Relationship “Beyond the Mean†: New Evidence from Biomarkers," Health, Econometrics and Data Group (HEDG) Working Papers 15/22, HEDG, c/o Department of Economics, University of York.
  • Handle: RePEc:yor:hectdg:15/22
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    References listed on IDEAS

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    Cited by:

    1. Sinha, K.; & Davillas, A.; & Jones, A.M.; & Sharma, A.;, 2018. "Distributional analysis of the role of breadth and persistence of multiple deprivation in the health gradient measured by biomarkers," Health, Econometrics and Data Group (HEDG) Working Papers 18/31, HEDG, c/o Department of Economics, University of York.
    2. Vincenzo Carrieri & Andrew M. Jones, 2018. "Inequality of opportunity in health: A decomposition‐based approach," Health Economics, John Wiley & Sons, Ltd., vol. 27(12), pages 1981-1995, December.
    3. Amélie Adeline & Eric Delattre, 2017. "Some microeconometric evidence on the relationship between health and income," Health Economics Review, Springer, vol. 7(1), pages 1-18, December.
    4. Lucia Corno & Áureo de Paula, 2019. "Risky Sexual Behaviours: Biological Markers and Self‐reported Data," Economica, London School of Economics and Political Science, vol. 86(342), pages 229-261, April.
    5. Davillas, A.; Jones, A.M.; Benzeval, M.;, 2017. "The income-health gradient: Evidence from self-reported health and biomarkers using longitudinal data on income," Health, Econometrics and Data Group (HEDG) Working Papers 17/04, HEDG, c/o Department of Economics, University of York.
    6. Alexander Silbersdorff & Julia Lynch & Stephan Klasen & Thomas Kneib, 2017. "Reconsidering the Income-Illness Relationship using Distributional Regression: An Application to Germany," Courant Research Centre: Poverty, Equity and Growth - Discussion Papers 231, Courant Research Centre PEG.
    7. Gloria Moroni, 2018. "Explaining Divorce Gaps in Cognitive and Noncognitive Skills of Children," Discussion Papers 18/16, Department of Economics, University of York.

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    More about this item

    Keywords

    biomarkers; unconditional quantile regression; decomposition analysis; health inequalities;
    All these keywords.

    JEL classification:

    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General
    • C5 - Mathematical and Quantitative Methods - - Econometric Modeling
    • I14 - Health, Education, and Welfare - - Health - - - Health and Inequality

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