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The Income–Health Relationship ‘Beyond the Mean’: New Evidence from Biomarkers

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  • Vincenzo Carrieri
  • Andrew M. Jones

Abstract

The relationship between income and health is one of the most explored topics in health economics but less is known about this relationship at different points of the health distribution. Analysis based solely on the mean may miss important information in other parts of the distribution. This is especially relevant when clinical concern is focused on the tail of the distribution and when evaluating the income gradient at different points of the distribution and decomposing income‐related inequalities in health is of interest. We use the unconditional quantile regression approach to analyse the income gradient across the entire distribution of objectively measured blood‐based biomarkers. We apply an Oaxaca–Blinder decomposition at various quantiles of the biomarker distributions to analyse gender differentials in biomarkers and to measure the contribution of income (and other covariates) to these differentials. Using data from the Health Survey for England, we find a non‐linear relationship between income and health and a strong gradient with respect to income at the highest quantiles of the biomarker distributions. We find that there is heterogeneity in the association of health to income across genders, which accounts for a substantial percentage of the gender differentials in observed health. Copyright © 2016 John Wiley & Sons, Ltd.

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  • Vincenzo Carrieri & Andrew M. Jones, 2017. "The Income–Health Relationship ‘Beyond the Mean’: New Evidence from Biomarkers," Health Economics, John Wiley & Sons, Ltd., vol. 26(7), pages 937-956, July.
    • Handle: RePEc:wly:hlthec:v:26:y:2017:i:7:p:937-956
    DOI: 10.1002/hec.3372
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    Cited by:

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    2. Chris Muris & Pedro Raposo & Sotiris Vandoros, 2020. "A dynamic ordered logit model with fixed effects," Papers 2008.05517, arXiv.org.
    3. Peng Nie & Qing Li & Alan A. Cohen & Alfonso Sousa-Poza, 2021. "In search of China’s income-health gradient: a biomarker-based analysis," Applied Economics, Taylor & Francis Journals, vol. 53(48), pages 5599-5618, October.
    4. Gabriella Berloffa & Francesca Paolini, 2019. "Decomposing Immigrant Differences in Physical and Mental Health: A 'Beyond the Mean' Analysis," DEM Working Papers 2019/4, Department of Economics and Management.
    5. Davillas, Apostolos & Pudney, Stephen, 2020. "Biomarkers, disability and health care demand," Economics & Human Biology, Elsevier, vol. 39(C).
    6. Davillas, Apostolos & Pudney, Stephen, 2019. "Baseline health and public healthcare costs five years on: a predictive analysis using biomarker data in a prospective household panel," ISER Working Paper Series 2019-01, Institute for Social and Economic Research.
    7. Atkins, Rose & Turner, Alex James & Chandola, Tarani & Sutton, Matt, 2020. "Going beyond the mean in examining relationships of adolescent non-cognitive skills with health-related quality of life and biomarkers in later-life," Economics & Human Biology, Elsevier, vol. 39(C).
    8. Alexander Silbersdorff & Julia Lynch & Stephan Klasen & Thomas Kneib, 2018. "Reconsidering the income‐health relationship using distributional regression," Health Economics, John Wiley & Sons, Ltd., vol. 27(7), pages 1074-1088, July.
    9. Apostolos Davillas & Andrew M. Jones, 2018. "Parametric models for biomarkers based on flexible size distributions," Health Economics, John Wiley & Sons, Ltd., vol. 27(10), pages 1617-1624, October.
    10. Davillas, Apostolos & Pudney, Stephen, 2020. "Biomarkers as precursors of disability," Economics & Human Biology, Elsevier, vol. 36(C).
    11. Swaminathan, Harini & Sharma, Anurag & Shah, Narendra G., 2019. "Does the relationship between income and child health differ across income groups? Evidence from India," Economic Modelling, Elsevier, vol. 79(C), pages 57-73.
    12. Andrew M. Jones, 2019. "Equity, opportunity and health," Empirica, Springer;Austrian Institute for Economic Research;Austrian Economic Association, vol. 46(3), pages 413-421, August.
    13. 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.
    14. Zhong, Meirui & Qiang, Dan & Wang, Jinxian & Sun, Weizeng, 2024. "Improving health and reducing health inequality: An innovation of digitalization?," Social Science & Medicine, Elsevier, vol. 348(C).

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