Bayesian probabilistic population projections for all countries

Abstract

Projections of countries' future populations, broken down by age and sex, are widely used for planning and research. They are mostly done deterministically, but there is a widespread need for probabilistic projections. We propose a Bayesian method for probabilistic population projections for all countries. The total fertility rate and female and male life expectancies at birth are projected probabilistically using Bayesian hierarchical models estimated via Markov chain Monte Carlo using United Nations population data for all countries. These are then converted to age-specific rates and combined with a cohort component projection model. This yields probabilistic projections of any population quantity of interest. The method is illustrated for five countries of different demographic stages, continents and sizes. The method is validated by an out of sample experiment in which data from 1950-1990 are used for estimation, and applied to predict 1990-2010. The method appears reasonably accurate and well calibrated for this period. The results suggest that the current United Nations high and low variants greatly underestimate uncertainty about the number of oldest old from about 2050 and that they underestimate uncertainty for high fertility countries and overstate uncertainty for countries that have completed the demographic transition and whose fertility has started to recover towards replacement level, mostly in Europe. The results also indicate that the potential support ratio (persons aged 20-64 per person aged 65þ) will almost certainly decline dramatically in most countries over the coming decades.double logistic function | Lee-Carter method | life expectancy at birth | predictive distribution | United Nations World Population Prospects P rojections of countries' future populations, broken down by age and sex, are used by governments for social, economic, and infrastructure planning by international organizations for development planning and monitoring and global modeling, by the private sector for strategic and marketing decisions, and by academic and other researchers as inputs to social and health research.Most population projections are currently done deterministically, using the cohort component method (1, 2). This is an ageand sex-structured version of the basic demographic identity that the population of a country at the next time point is equal to the population at the current time point, plus the number of births, minus the number of deaths, plus the number of immigrants minus the number of emigrants. It was formulated in matrix form by Leslie (3) and is described in detail in ref. (4, chap. 6).Population projections are currently produced by many organizations, including national and local governments and private companies. The main organizations that have produced population projections for all or most of the world's countries are the United Nations (UN) (5), the World Bank (6), and the United States Census Bureau (7), all of which use the standard deterministic approach. Among these, the UN produces ...

Future Fertility in Low Fertility Countries

“…Only few expected that these increases or declines would be of a large magnitude of 0.5 or higher. 22 For more details on deriving the United Nations set of probabilistic fertility fertility projections see Alkema et al (2011), Raftery et al (2012, and the supporting documentation to the UN population projections (UN 2012). These expectations differ considerably from the higher TFR values projected by the 2010 round of the UN population projections (UN 2011).…”

Integrating uncertainty in time series population forecasts: An illustration using a simple projection model

“…Methods have also been developed to combine elements of each of these approaches, for example, the parameters from time series models have been constrained according to expert opinions (Lee and Tuljapurkar 1994) or to target levels and age distributions of fertility and mortality (Lutz, Sanderson, and Scherbov 2001). However, the use of Bayesian methods, which have the potential to bring all of these ideas together, are only recently gaining prominence in population forecasting (Bryant and Graham 2013;Raftery et al 2012). We hope this paper illustrates some of the advantages of the Bayesian approach and motivates researchers to carefully consider not only if but how they include uncertainty in their forecasts.…”

Probabilistic population projections with migration uncertainty

“…Undercoverage is less problematic in the model that conditions on knowing the true migration counts, as was used in ref. 16. The mean absolute relative error is markedly lower if we assume knowledge of net migration than if Coverage refers to the proportion of the 2000-2015 observations that fell within their prediction interval (PI), in percent.…”

“…If the current population is known, broken down by age and sex, and future age-and sex-specific rates are projected for fertility, mortality, and migration, then the cohortcomponent method produces population projections (15). However, the UN Population Division now produces probabilistic projections of population, fertility, and mortality for all countries, but these projections still condition on deterministic migration projections (16,17). The current methodology in the UN's World Population Prospects (WPP) differs from country to country but typically projects that net migration counts will remain constant until 2050 and drop deterministically to zero by 2150 (17).…”