Abstract
The UN released official probabilistic population projections (PPP) for all countries for the first time in July 2014. These were obtained by projecting the period total fertility rate (TFR) and life expectancy at birth (e 0) using Bayesian hierarchical models, yielding a large set of future trajectories of TFR and e 0 for all countries and future time periods to 2100, sampled from their joint predictive distribution. Each trajectory was then converted to age-specific mortality and fertility rates, and population was projected using the cohort-component method. This yielded a large set of trajectories of future age- and sex-specific population counts and vital rates for all countries. In this chapter we describe the methodology used for deriving the age-specific mortality and fertility rates in the 2014 PPP, we identify limitations of these methods, and we propose several methodological improvements to overcome them. The methods presented in this chapter are implemented in the publicly available bayesPop R package.
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Notes
- 1.
This general approach applies to countries experiencing normal mortality trends. For countries having ever experienced 2 % or more adult HIV prevalence during the period 1980–2010, all projected trajectories of life expectancy by sex for each of these countries were adjusted in such a way as to ensure that the median trajectory for each country was consistent with the 2012 Revision of the World Population Prospects deterministic projection that incorporates the impact of HIV/AIDS on mortality, as well as assumptions about future potential improvements both in the reduction of the epidemic and survival due to treatment.
- 2.
The Coale and Demeny West region formulae are used as follows. When \(_{\,\,\,0}\!\!m_{1}\geqslant 0.107\), then1 A 0 = 0. 33 for males and 0. 35 for females;4 A 1 = 1. 352 for males and 1. 361 for females. When1 m 0 < 0. 107, \(_{1}A_{0} = 0.045 + (2.684 \cdot _{\,\,\,1}\!\!m_{0}\)) for males and \(_{1}A_{0} = 0.053 + (2.800 \cdot _{\,\,\,1}\!\!m_{0})\) for females; \(_{4}A_{1} = 1.651 - (2.816 \cdot _{\,\,\,1}\!\!m_{0})\) for males and \(_{4}A_{1} = 1.522 - (1.518 \cdot _{\,\,\,1}\!\!m_{0})\) for females.
- 3.
In the bayesPop package this country-specific set of options is controlled through two dummy variables in the vwBaseYear2012 dataset: (1) whether the most recent estimate of age mortality pattern should be used (LatestAgeMortalityPattern) and (2) whether it should be smoothed (SmoothLatestAgeMortalityPattern). See help(vwBaseYear2012) in R.
- 4.
In the bayesPop package this country-specific set of options is controlled through two variables in the vwBaseYear2012 dataset: (1) the type of age mortality pattern used for the estimation period (AgeMortalityType with the option “Model life tables”) and (2) the specific mortality pattern used (AgeMortalityPattern with options like “CD West”).
- 5.
In the bayesPop package this specific-set of countries are identified through a dummy variable (WPPAIDS) in the vwBaseYear2012 dataset.
- 6.
In the bayesPop package the global model pattern is created as an average of most recent PASFRs for a set of countries (selected through a dummy variable in the vwBaseYear2012 dataset). For the purpose of the current analysis, the low fertility countries selected have already reached their Phase III and represent later childbearing patterns with mean age at childbearing close to or above 30 years in 2010–2015: Austria, the Czech Republic, Denmark, France, Germany, Japan, the Netherlands, Norway and the Republic of Korea. The specification of the countries used for the global model pattern can be changed in input file.
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Acknowledgements
This research was supported by NIH grants R01 HD054511 and R01 HD070936. The views expressed in this article are those of the authors and do not necessarily reflect those of NIH or the United Nations. The authors are grateful to the editor for very helpful comments.
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Ševčíková, H., Li, N., Kantorová, V., Gerland, P., Raftery, A.E. (2016). Age-Specific Mortality and Fertility Rates for Probabilistic Population Projections. In: Schoen, R. (eds) Dynamic Demographic Analysis. The Springer Series on Demographic Methods and Population Analysis, vol 39. Springer, Cham. https://doi.org/10.1007/978-3-319-26603-9_15
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