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Beyond the business cycle - factors driving aggregate mortality rates

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  • Katja Hanewald

Abstract

This article provides a comprehensive econometric analysis of factors driving aggregate mortality rates over time. It differs from previous studies in this field by simultaneously considering an extensive set of macroeconomic, socio-economic and ecological factors as explanatory variables. Germany is chosen as an indicative example for other industrialized countries due to its advanced demographic transition process. Our regression analysis, which covers the time interval 1956-2004, indicates that sex- and age-specific mortality rates vary substantially in their response to external factors. Strongest associations are found with changes in real GDP, flu epidemics and the two life style variables alcohol and cigarette consumption in both univariate and multivariate setups. Further analysis shows that these effects are primarily contemporary, while other indicators such as weather conditions exert lagged effects. By combining variables in a multivariate model the share of explained data volatility can be substantially increased.

Suggested Citation

  • Katja Hanewald, 2008. "Beyond the business cycle - factors driving aggregate mortality rates," SFB 649 Discussion Papers SFB649DP2008-031, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
  • Handle: RePEc:hum:wpaper:sfb649dp2008-031
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    File URL: http://sfb649.wiwi.hu-berlin.de/papers/pdf/SFB649DP2008-031.pdf
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    References listed on IDEAS

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    4. Olivier Deschênes & Enrico Moretti, 2009. "Extreme Weather Events, Mortality, and Migration," The Review of Economics and Statistics, MIT Press, vol. 91(4), pages 659-681, November.
    5. Gerdtham, Ulf-G. & Ruhm, Christopher J., 2006. "Deaths rise in good economic times: Evidence from the OECD," Economics & Human Biology, Elsevier, pages 298-316.
    6. Christopher J. Ruhm, 2000. "Are Recessions Good for Your Health?," The Quarterly Journal of Economics, Oxford University Press, vol. 115(2), pages 617-650.
    7. Gravelle, Hugh & Wildman, John & Sutton, Matthew, 2002. "Income, income inequality and health: what can we learn from aggregate data?," Social Science & Medicine, Elsevier, vol. 54(4), pages 577-589, February.
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    Citations

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    Cited by:

    1. Katja Hanewald, 2009. "Mortality modeling: Lee-Carter and the macroeconomy," SFB 649 Discussion Papers SFB649DP2009-008, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    2. Wu, Wen-Chieh & Cheng, Hui-Pei, 2010. "Symmetric mortality and asymmetric suicide cycles," Social Science & Medicine, Elsevier, vol. 70(12), pages 1974-1981, June.
    3. Wolfgang H. Reichmuth & Samad Sarferaz, 2008. "The Influence of the Business Cycle on Mortality," SFB 649 Discussion Papers SFB649DP2008-059, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    4. Katja Hanewald & Thomas Post & Helmut Gründl, 2011. "Stochastic Mortality, Macroeconomic Risks and Life Insurer Solvency," The Geneva Papers on Risk and Insurance - Issues and Practice, Palgrave Macmillan;The Geneva Association, vol. 36(3), pages 458-475, July.
    5. Qi Ming, 2013. "The Impact of Mortality Risk on the Asset and Liability Management of Insurance Companies," Asia-Pacific Journal of Risk and Insurance, De Gruyter, vol. 7(2), pages 81-104, July.

    More about this item

    Keywords

    Aggregate mortality; business cycle; socio-economic factors; multivariate model;

    JEL classification:

    • I12 - Health, Education, and Welfare - - Health - - - Health Behavior
    • J11 - Labor and Demographic Economics - - Demographic Economics - - - Demographic Trends, Macroeconomic Effects, and Forecasts
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models

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