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Longitudinal methods to investigate the role of health determinants in the dynamics of income-related health inequality

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  • Allanson, Paul
  • Petrie, Dennis

Abstract

The usual starting point for understanding changes in income-related health inequality (IRHI) over time has been regression-based decomposition procedures for the health concentration index. However the reliance on repeated cross-sectional analysis for this purpose prevents both the appropriate specification of the health function as a dynamic model and the identification of important determinants of the transition processes underlying IRHI changes such as those relating to mortality. This paper overcomes these limitations by developing alternative longitudinal procedures to analyse the role of health determinants in driving changes in IRHI through both morbidity changes and mortality, with our dynamic modelling framework also serving to identify their contribution to long-run or structural IRHI. The approach is illustrated by an empirical analysis of the causes of the increase in IRHI in Great Britain between 1999 and 2004.

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  • Allanson, Paul & Petrie, Dennis, 2013. "Longitudinal methods to investigate the role of health determinants in the dynamics of income-related health inequality," Journal of Health Economics, Elsevier, vol. 32(5), pages 922-937.
    • Handle: RePEc:eee:jhecon:v:32:y:2013:i:5:p:922-937
    DOI: 10.1016/j.jhealeco.2013.07.001
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    Cited by:

    1. Roselinde Kessels & Guido Erreygers, 2014. "A unified structural equation modeling approach for the decomposition of rank-dependent indicators of socioeconomic inequality of health," EcoMod2014 7065, EcoMod.
    2. Roselinde Kessels & Guido Erreygers, 2016. "Structural equation modeling for decomposing rank-dependent indicators of socioeconomic inequality of health: an empirical study," Health Economics Review, Springer, vol. 6(1), pages 1-13, December.
    3. Jun-Yi Zheng & Li-Xia Luan & Mei Sun, 2022. "Does the National Fitness Policy Promote National Health?—An Empirical Study from China," IJERPH, MDPI, vol. 19(15), pages 1-20, July.
    4. Mahmut ZORTUK & Sinan ÇEKEN, 2015. "The Relationship between Health Care Expenditures and Income in the Selected Transition Economies: A Panel Smooth Transition Regression Analysis," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(2), pages 105-118, June.
    5. Paul Allanson & Kalina Kasprzyk & Andrew P. Barnes, 2017. "Income Mobility and Income Inequality in Scottish Agriculture," Journal of Agricultural Economics, Wiley Blackwell, vol. 68(2), pages 471-493, June.
    6. Roselinde Kessels & Guido Erreygers, 2014. "A unified structural equation modeling approach for the decomposition of rank-dependent indicators of socioeconomic inequality of health," EcoMod2014 7065, EcoMod.
    7. Apergis, Nicholas & Mustafa, Ghulam & Dastidar, Sayantan Ghosh, 2021. "An analysis of the impact of unconventional oil and gas activities on public health: New evidence across Oklahoma counties," Energy Economics, Elsevier, vol. 97(C).
    8. Yongqing Dong & Quheng Deng & Shaoping Li, 2022. "The Health Inequality of Children in China: A Regression-Based Decomposition Analysis," Child Indicators Research, Springer;The International Society of Child Indicators (ISCI), vol. 15(1), pages 137-159, February.
    9. Gustav Kjellsson & Dennis Petrie & Tom (T.G.M.) van Ourti, 2018. "Measuring income-related inequalities in risky health prospects," Tinbergen Institute Discussion Papers 18-007/V, Tinbergen Institute.
    10. Roselinde Kessels & Guido Erreygers, 2019. "A direct regression approach to decomposing socioeconomic inequality of health," Health Economics, John Wiley & Sons, Ltd., vol. 28(7), pages 884-905, July.
    11. Calara, Paul Samuel & Gerdtham, Ulf-G & Petrie, Dennis, 2016. "The Dynamics of Income-Related Health Inequalities in Australia versus Great Britain," Working Papers 2016:20, Lund University, Department of Economics.

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