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How can a cause-of-death reduction be compensated for by the population heterogeneity? A dynamic approach

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  • Kaakaï, Sarah
  • Labit Hardy, Héloïse
  • Arnold, Séverine
  • El Karoui, Nicole

Abstract

In the context of widening socioeconomic inequalities in mortality, it has become crucially important to understand the impact of population heterogeneity and its evolution on mortality. In particular, recent developments in multi-population mortality have raised a number of questions, among which is the issue of evaluating cause-of-death reduction targets set by national and international institutions in the presence of heterogeneity. The aim of this paper is to show how the population dynamics framework contributes to addressing these issues, relying on English population data and cause-specific number of deaths by socioeconomic circumstances, over the period 1981–2015.

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  • Kaakaï, Sarah & Labit Hardy, Héloïse & Arnold, Séverine & El Karoui, Nicole, 2019. "How can a cause-of-death reduction be compensated for by the population heterogeneity? A dynamic approach," Insurance: Mathematics and Economics, Elsevier, vol. 89(C), pages 16-37.
  • Handle: RePEc:eee:insuma:v:89:y:2019:i:c:p:16-37
    DOI: 10.1016/j.insmatheco.2019.07.005
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    1. Andrew J.G. Cairns & Malene Kallestrup-Lamb & Carsten P.T. Rosenskjold & David Blake & Kevin Dowd, 2016. "Modelling Socio-Economic Differences in the Mortality of Danish Males Using a New Affluence Index," CREATES Research Papers 2016-14, Department of Economics and Business Economics, Aarhus University.
    2. Alai, Daniel H. & Arnold (-Gaille), Séverine & Sherris, Michael, 2015. "Modelling cause-of-death mortality and the impact of cause-elimination," Annals of Actuarial Science, Cambridge University Press, vol. 9(1), pages 167-186, March.
    3. Boumezoued, Alexandre & Hardy, Héloïse Labit & El Karoui, Nicole & Arnold, Séverine, 2018. "Cause-of-death mortality: What can be learned from population dynamics?," Insurance: Mathematics and Economics, Elsevier, vol. 78(C), pages 301-315.
    4. Lu, J. L. C. & Wong, W. & Bajekal, M., 2014. "Mortality improvement by socio-economic circumstances in England (1982 to 2006)," British Actuarial Journal, Cambridge University Press, vol. 19(1), pages 1-35, March.
    5. Shang, Han Lin & Haberman, Steven, 2017. "Grouped multivariate and functional time series forecasting:An application to annuity pricing," Insurance: Mathematics and Economics, Elsevier, vol. 75(C), pages 166-179.
    6. Lu, Joseph, 2014. "Mortality Improvement by Socio-Economic Circumstances in England (1982 to 2006) ‐ Abstract of the London discussion," British Actuarial Journal, Cambridge University Press, vol. 19(1), pages 36-54, March.
    7. Meyricke, Ramona & Sherris, Michael, 2013. "The determinants of mortality heterogeneity and implications for pricing annuities," Insurance: Mathematics and Economics, Elsevier, vol. 53(2), pages 379-387.
    8. Ludkovski, Mike & Risk, Jimmy & Zail, Howard, 2018. "Gaussian Process Models For Mortality Rates And Improvement Factors," ASTIN Bulletin, Cambridge University Press, vol. 48(3), pages 1307-1347, September.
    9. Yuan Gao & Han Lin Shang, 2017. "Multivariate Functional Time Series Forecasting: Application to Age-Specific Mortality Rates," Risks, MDPI, vol. 5(2), pages 1-18, March.
    10. Jarner, Søren Fiig & Kryger, Esben Masotti, 2011. "Modelling Adult Mortality in Small Populations: The Saint Model," ASTIN Bulletin, Cambridge University Press, vol. 41(2), pages 377-418, November.
    11. Dimitrova, Dimitrina S. & Haberman, Steven & Kaishev, Vladimir K., 2013. "Dependent competing risks: Cause elimination and its impact on survival," Insurance: Mathematics and Economics, Elsevier, vol. 53(2), pages 464-477.
    12. Andrés Villegas & Steven Haberman, 2014. "On the Modeling and Forecasting of Socioeconomic Mortality Differentials: An Application to Deprivation and Mortality in England," North American Actuarial Journal, Taylor & Francis Journals, vol. 18(1), pages 168-193.
    13. Ludkovski, Mike & Risk, Jimmy & Zail, Howard, 2018. "Gaussian Process Models For Mortality Rates And Improvement Factors – Corrigendum," ASTIN Bulletin, Cambridge University Press, vol. 48(3), pages 1349-1349, September.
    14. Li, Johnny Siu-Hang & Zhou, Rui & Hardy, Mary, 2015. "A step-by-step guide to building two-population stochastic mortality models," Insurance: Mathematics and Economics, Elsevier, vol. 63(C), pages 121-134.
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    Cited by:

    1. Nicole El Karoui & Kaouther Hadji & Sarah Kaakai, 2021. "Simulating long-term impacts of mortality shocks: learning from the cholera pandemic," Papers 2111.08338, arXiv.org.

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    More about this item

    Keywords

    Population dynamics; Deprivation; Heterogeneity; Cause-of-death mortality; Cohort effect;
    All these keywords.

    JEL classification:

    • G22 - Financial Economics - - Financial Institutions and Services - - - Insurance; Insurance Companies; Actuarial Studies
    • J11 - Labor and Demographic Economics - - Demographic Economics - - - Demographic Trends, Macroeconomic Effects, and Forecasts
    • C60 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - General

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