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Mortality forecasting using a modified CMI Mortality Projections Model for China II: cities, towns and counties

Published online by Cambridge University Press:  11 October 2016

Fei Huang
Fei Huang*
Affiliation:
Research School of Finance, Actuarial Studies and Applied Statistics, College of Business and Economics, Australian National University, Canberra, ACT 2601, Australia
*
*Correspondence to: Fei Huang, Research School of Finance, Actuarial Studies and Statistics, College of Business and Economics, Australian National University, Canberra, ACT 2601, Australia. Tel: +(61) 2 612 57390. Fax: +(61) 2 612 50087. E-mail: fei.huang@anu.edu.au
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Abstract

In this paper, we conduct the study of long-term age-sex-specific mortality forecasting for subpopulations in different areas of China: cities, towns and counties. We use a modified CMI (Continuous Mortality Investigation) Mortality Projections Model, which has been discussed in Huang & Browne (Paper I), for modelling purposes. From the historical experience, we find that people in cities have lower mortality rates and higher mortality improvement rates than people in towns and counties for most ages. If this trend continues, the mortality of different areas will diverge further in the future. From the projection results, we find that there will be significant mortality and life expectancy differences between cities, towns and counties for both males and females. Sensitivity analysis for long-term rates of mortality improvement and the speed of convergence from “initial” to “long-term” rates of mortality improvement are conducted. Uncertainties are attached to the central estimates to overcome the limitation of the original CMI approach from which only deterministic results can be obtained.

Keywords

Mortality forecastingCMI Mortality Projections ModelChinaSubpopulations
Type
Papers
Information
Annals of Actuarial Science , Volume 11 , Issue 1 , March 2017 , pp. 46 - 66
Copyright
© Institute and Faculty of Actuaries 2016 

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References

Bound, J., Geronimus, A. & Rodriguez, J. (2014). The implications of differential trends in mortality for social security policy. 16th Annual Joint Meeting of the Retirement Research Consortium, August 7–8, 2014, Washington, DC.Google Scholar
Browne, B., Duchassaing, J. & Suter, F. (2009). Longevity: a “simple” stochastic modelling of mortality. British Actuarial Journal, 15 (Supplement S1), 249–265.CrossRefGoogle Scholar
Eggleston, K. (2012). Health care for 1.3 billion: an overview of China’s health system, Technical Report, Stanford University, Stanford, California, USA.Google Scholar
Frazier, M.W. (2010). Socialist Insecurity: Pensions and Politics of Uneven Development in China. Cornell University Press, Ithaca, New York.Google Scholar
Herd, R., Hu, Y.-W. & Koen, V. (2010). Improving China’s health care system, Technical Report, Economic Department, OECD, Paris, France.Google Scholar
Hu, F., Xu, Z. & Chen, Y. (2011). Circular migration, or permanent stay? Evidence from China’s rural-urban migration. China Economic Review, 22(1), 64–74.CrossRefGoogle Scholar
Huang, F. & Browne, B. (2014). Mortality forecasting using the CMI Mortality Projections Model: a case study of China, Technical Report, Australian National University, Canberra, Australia.Google Scholar
Koller, M. (2011). Life Insurance Risk Management Essentials. Springer, Berlin, Heidelberg.Google Scholar
Lee, R.D. & Carter, L.R. (1992). Modeling and forecasting U. S. mortality. Journal of the American Statistical Association, 87(419), 659–671.Google Scholar
Li, N., Lee, R. & Tuljapurkar, S. (2004). Using the Lee-Carter method to forecast mortality for populations with limited data. International Statistical Review, 72(1), 19–36.Google Scholar
Lu, F. & Yin, S. (2005). An application of Lee-Carter method to forecast Chinese mortality (in Chinese). Journal of Insurance Professional College, (6), 9–11.Google Scholar
Lu, J., Wong, W. & Bajekal, M. (2014). Mortality improvement by social-economic circumstances in England (1982–2006). British Actuarial Journal, 19(1), 1–35.Google Scholar
Luo, C. & Shu, Y. (2013). On Chinese urbanization comparing with city and town level: an observation based on population census. Population and Economics, 4(199), 3–11.Google Scholar
Olshansky, S.J., Antonucci, T., Berkman, L. & Binstock, R.H. (2012). Differences in life expectancy due to race and educational differences are widening. Health Affairs, 31(8), 1803–1813.Google Scholar
Wilkinson, R. & Marmot, M. (2003). Social Determinants of Health: The Solid Facts. World Health Organization, Geneva, Switzerland.Google Scholar
Zhao, B.B. (2012). A modified Lee-Carter model for analysing short-base-period data. Population Studies, 66(1), 39–52.Google Scholar
Zhao, B.B., Liang, X., Zhao, W. & Hou, D. (2013). Modeling of group-specific mortality in China using a modified Lee-Carter model. Scandinavian Actuarial Journal, 2013(5), 383–402.Google Scholar