Can We Rely on Projections of the Immigrant Population? The Case of Norway.

Demographic forecasters must be realistic about how well they can predict future populations, and it is important that they include estimates of uncertainty in their forecasts. Here we focus on the future development of the immigrant population of Norway and their Norwegian-born children ("second generation"), grouped by three categories of country background: 1. West European countries plus the United States, Canada, Australia, and New Zealand; 2. Central and East European countries that are members of the European Union; 3. other countries. We show how to use a probabilistic forecast to assess the reliability of projections of the immigrant population and their children. We employ the method of random shares using data for immigrants and their children for 2000-2021. We model their age- and sex-specific shares relative to the whole population. Relational models are used for the age patterns in these shares, and time series models to extrapolate the parameters of the age patterns. We compute a probabilistic forecast for six population sub-groups with immigration background, and one for non-immigrants. The probabilistic forecast is calibrated against Statistics Norway's official population projection. We find that a few population trends are quite certain: strong increases to 2060 in the size of the immigrant population (more specifically those who belong to country group 3) and of Norwegian-born children of immigrants. However, prediction intervals around the forecasts of immigrants and their children by one-year age groups are so wide that these forecasts are not reliable.

Do Japanese and Italian Women Live Longer than Women in Scandinavia?

Life expectancies at birth are routinely computed from period life tables. When mortality is falling, such period life expectancies will typically underestimate real life expectancies, that is, life expectancies for birth cohorts. Hence, it becomes problematic to compare period life expectancies between countries when they have different historical mortality developments. For instance, life expectancies for countries in which the longevity improved early (like Norway and Sweden) are difficult to compare with those in countries where it improved later (like Italy and Japan). To get a fair comparison between the countries, one should consider cohort data. Since cohort life expectancies can only be computed for cohorts that were born more than a hundred years ago, in this paper we suggest that for younger cohorts one may consider the expected number of years lost up to a given age. Contrary to the results based on period data, our cohort results then indicate that Italian women may expect to lose more years than women in Norway and Sweden, while there are no indications that Japanese women will lose fewer years than women in Scandinavia. The large differences seen for period data may just be an artefact due to the distortion that period life tables imply in times of changing mortality.

Letter to the Editor: Probabilistic population forecasts for informed decision making.

Demographic forecasts are inherently uncertain. Nevertheless, an appropriate description of this uncertainty is a key underpinning of informed decision making. In recent decades various methods have been developed to describe the uncertainty of future populations and their structures, but the uptake of such tools amongst the practitioners of official population statistics has been lagging behind. In this letter we revisit the arguments for the practical uses of uncertainty assessments in official population forecasts, and address their implications for decision making. We discuss essential challenges, both for the forecasters and forecast users, and make recommendations for the official statistics community.

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.

Assumptions for long-term stochastic population forecasts in 18 European countries: Hypothèses de projections stochastiquesàlong terme des populations de 18 pays européens.

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.