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The income-health gradient: Evidence from self-reported health and biomarkers using longitudinal data on income

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  • Davillas, A.; Jones, A.M.; Benzeval, M.;
Abstract
This paper adds to the literature on the income-health gradient by exploring the association between short- and long-term income and a wide set of self-reported health measures and objective nurse-administered and blood-based biomarkers as well as employing estimation techniques that allow for analysis “beyond the mean†and accounting for unobserved heterogeneity. The income-health gradients are greater in magnitude in case of long-run rather than cross-sectional income measures. Unconditional quantile regressions reveal that the differences between the long-run and the short-run income gradients are more evident towards the tails of the distributions, where both higher risk of illnesses and steeper income gradients are observed. A two-step estimator, involving a fixed-effects income model at the first stage, shows that the individual-specific selection effects have a systematic impact in the long-run income gradients in self-reported health but not in biomarkers, highlighting the importance of reporting error in self-reported health.

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  • 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.
    • Handle: RePEc:yor:hectdg:17/04
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    2. 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.
    3. 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).
    4. Paul Allanson, 2022. "Ordinal health disparities between population subgroups: measurement and multivariate analysis with an application to the North-South divide in England," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 20(4), pages 841-860, December.
    5. 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).
    6. 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.
    7. Fernando Antonio Slaibe Postali & Maria Dolores M Diaz, Adriano Dutra Teixeira, Natalia Nunes Ferreira Batista, Rodrigo Moreno Serra, 2021. "Impact of primary care coverage on individual health: evidence from biomarkers in Brazil," Working Papers, Department of Economics 2021_01, University of São Paulo (FEA-USP).
    8. Dongling Zhang & Guoqing Zhang & Yuxin Jiao & Yanyan Wang & Pengnian Wang, 2022. "“Digital Dividend” or “Digital Divide”: What Role Does the Internet Play in the Health Inequalities among Chinese Residents?," IJERPH, MDPI, vol. 19(22), pages 1-19, November.
    9. Declan French, 2023. "From financial wealth shocks to ill‐health: Allostatic load and overload," Health Economics, John Wiley & Sons, Ltd., vol. 32(4), pages 939-952, April.
    10. Parra-Mujica, F. & Robson, M. & Cookson, R., 2021. "Socioeconomic Health Inequalities: Differences Between and Within Individuals," Health, Econometrics and Data Group (HEDG) Working Papers 21/15, HEDG, c/o Department of Economics, University of York.
    11. Kajal Lahiri & Liu Yang, 2021. "Estimating Endogenous Ordered Response Panel Data Models with an Application to Income Gradient in Child Health," Sankhya B: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 83(2), pages 207-243, November.
    12. Hou, Bingdong & Wu, Jingwen & Mi, Zhifu & Ma, Chunbo & Shi, Xunpeng & Liao, Hua, 2022. "Cooking fuel types and the health effects: A field study in China," Energy Policy, Elsevier, vol. 167(C).

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    More about this item

    Keywords

    biomarkers; health inequalities; panel data; Understanding Society;
    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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