Nico Keilman

How accurate are the United Nations world population projections

This paper investigates the accuracy of the UN world population forecasts of the age structure and crude birth and death rates in seven major regions of the world. Focus is on comparative accuracy across regions and over time utilizing data from 12 UN forecasts for Africa Asia Europe the former Soviet Union Latin America northern America and Oceania prepared between 1950 and 1985. Overall it is noted that the strength of these projections is that they contain detailed results for each country of the world computed on the basis of one consistent methodology and that they are updated frequently in the light of the most available data and methods. Compared with ex-post observed real trends the 12 sets of projections show a clear tendency over time toward greater accuracy. Part of the accuracy improvement is attributable to better data for base populations. In addition there is considerable regional heterogeneity in the accuracy of the UN projections. It is recommended however that the UN should include more than one variant for mortality in the projections due to difficulties with extrapolation and the considerable impact of exempting a variant on the results.

Biodiversity: The threat of small households.

Many studies have suggested that the increasing global human population is having a negative effect on biodiversity. According to new work, another threat comes from the rising number of households.

Assumptions for long-term stochastic population forecasts in 18 European countries

The aim of the ‘Uncertain Population of Europe’(UPE) project was to compute long-term stochastic (probabilistic) population forecasts for 18 European countries. We developed a general methodology for constructing predictive distributions for fertility, mortality and migration. The assumptions underlying stochastic population forecasts can be assessed by analysing errors in past forecasts or model-based estimates of forecast errors, or by expert judgement. All three approaches have been used in the project. This article summarizes and discusses the results of the three approaches. It demonstrates how the—sometimes conflicting—results can be synthesized into a consistent set of assumptions about the expected levels and the uncertainty of total fertility rate, life expectancy at birth of men and women, and net migration for 18 European countries.RésuméLe but du projet ‘Uncertain population of Europe’(UPE) était de calculer des projections de population stochastiques (probabilistes)àlong terme des populations de 18 pays européens. Nous avons développé une méthodologie générale pour construire des distributions prédictives de fécondité, mortalité et migration. Les hypothèses sous-jacentes aux projections stochastiques de populations peuvent être élaborées en analysant les erreurs de projections passées, en effectuant une modélisation pour estimer les erreurs de projection, ou par un jugement d’expert. Les trois approches ont été appliquées dans le projet, et leurs résultats sont résumés et discutés dans cet article. Nous démontrons que les résultats, parfois contradictoires, peuvent être synthétisés pour former un ensemble cohérent d’hypothèses concernant les niveaux attendus et l’incertitude autour de l’indice synthétique de fécondité, de l’espérance de vieàla naissance des hommes et des femmes, et de la migration nette dans 18 pays européens.

Childbearing impeded education more than education impeded childbearing among Norwegian women

In most societies, women at age 39 with higher levels of education have fewer children. To understand this association, we investigated the effects of childbearing on educational attainment and the effects of education on fertility in the 1964 birth cohort of Norwegian women. Using detailed annual data from ages 17 to 39, we estimated the probabilities of an additional birth, a change in educational level, and enrollment in the coming year, conditional on fertility history, educational level, and enrollment history at the beginning of each year. A simple model reproduced a declining gradient of children ever born with increasing educational level at age 39. When a counterfactual simulation assumed no effects of childbearing on educational progression or enrollment (without changing the estimated effects of education on childbearing), the simulated number of children ever born decreased very little with increasing completed educational level, contrary to data. However, when another counterfactual simulation assumed no effects of current educational level and enrollment on childbearing (without changing the estimated effects of childbearing on education), the simulated number of children ever born decreased with increasing completed educational level nearly as much as the decrease in the data. In summary, in these Norwegian data, childbearing impeded education much more than education impeded childbearing. These results suggest that women with advanced degrees have lower completed fertility on the average principally because women who have one or more children early are more likely to leave or not enter long educational tracks and never attain a high educational level.

Demography: Uncertain population forecasts

Traditional population forecasts made by statistical agencies do not quantify uncertainty. But demographers and statisticians have developed methods to calculate probabilistic forecasts.

Predictive Intervals for Age-Specific Fertility

A multivariate ARIMA model is combined with a Gammacurve to predict confidence intervals for age-specificbirth rates by one-year age groups. The method isapplied to observed age-specific births in Norwaybetween 1900 and 1995, and predictive intervals arecomputed for each year up to 2050. The predictedtwo-thirds confidence intervals for Total Fertility(TF) around 2010 agree well with TF errors in oldpopulation forecasts made by Statistics Norway. Themethod gives useful predictions for age-specificfertility up to the years 2020–2030. For later years,the intervals become too wide. Methods which do nottake account of estimation errors in the ARIMA modelcoefficients underestimate the uncertainty for futureTF values. The findings suggest that the marginbetween high and low fertility variants in officialpopulation forecasts for many Western countries aretoo narrow.

Data quality and accuracy of United Nations population projections, 1950-95

Between 1951 and 1998, the United Nations (UN) published 16 sets of population projections for the world, its major regions, and countries. This paper reports the accuracy of the projection results. I analyse the quality of the historical data used for the base populations of the projections, and for extrapolating fertility and mortality. I study also the impact this quality has had on the accuracy of the projection results. Results and assumptions for the sets of projections are compared with corresponding estimates from the UN 1998 Revision for total fertility and life expectancy at birth, total population, and the projected age structures. The report covers seven major regions (Africa, Asia, the former USSR, Europe, Northern America, Latin America, and Oceania) and the largest ten countries of the world as of 1998 (China, India, the USA, Indonesia, Brazil, Pakistan, Russia, Japan, Bangladesh, and Nigeria).

On the estimation of multidimensional demographic models with population registration data

In this paper the estimation of multidimensional demographic models is investigated in situations where population registration data are available. With this kind of aggregate data, estimation by traditional methods is not possible. We look at two versions of the multidimensional model: the constant intensities model and the linear integration model. Some logical inconsistencies in the derivation of the latter are discussed. In particular, we argue that the linear integration model is not compatible with a Markov process. A new algorithm for the estimation of the constant intensities model with population registration data is proposed. Some preliminary results on the mathematical and statistical properties of this method are given. The algorithm is applied to Dutch nuptiality data.

Uncertain Demographics and Fiscal Sustainability: Empirically based specification of forecast uncertainty

To make a conventional population forecast one needs to specify agespecific fertility rates for women, and mortality rates for women and men, for all future years of interest. These are used to generate births and deaths. The simplest way to handle migration is to specify net migration in absolute numbers that are added to population each year. Starting from a jump-off population, the so-called cohort-component bookkeeping (e.g. Shryock, Siegel and associates, 1976) is used recursively to keep track of the resulting changes in population, by age and sex. These methods were first used by Cannan (1895) for England and Wales, and since the 1920s and 1930s they have been widely used in Europe (DeGans, 1999). The early forecasters were aware that calculations based on the cohortcomponent method are only as reliable as the assumptions that go into making them. Alternative variants were offered from early on, but even the forecast producers themselves were uneasy about the methods that were used to prepare them (e.g. Modeen, 1934). Stochastic (or probabilistic) cohort-component forecasts are similar, but in this case future fertility and mortality rates and net migration are considered as random variables (e.g. Alho and Spencer, 2005). Their distributions can be specified in various ways. Perhaps the simplest is to give first the location of the distribution, and then to specify the spread (or scale) around it to reflect forecast uncertainty. Under a normal (Gaussian) assumption for the rate (or, for example, its log transform), the measure of location is the mean (or median) and the measure of spread is the standard deviation, for example. An advantage of the normal model is that the dependency structure of the various random variables can be given in terms of correlations in an interpretable way. From the perspective of random vital rates, cohort-component bookkeeping is a non-linear operation. Simulation is frequently used to carry out the propagation of uncertainty, from the rates to future population numbers. A joint distribution derived in this manner for the future

Translation Formulae for Non-repeatable Events

Ryders translation expressions for repeatable events are extended to the case of non-repeatable events. It is found that cohort quantum is a constant function of time when period quantum is constant, and period tempo, as measured by the moments of the age pattern of the occurrence-exposure rates, change linearly with time. The degree of distributional distortion (that is, the upward or downward shift in cohort caused by changes in the period age pattern), given a set of occurrence-exposure rates, is generally less for non-repeatable than for repeatable events in this case, in particular at high quantum levels. Furthermore, it is found that when period tempo is constant over time, and period quantum falls linearly, period quantum underestimates cohort quantum for high period quantum levels, and overestimates it for low period quantum levels.