Jakub Bijak

Population and labour force projections for 27 European countries, 2002-052: impact of international migration on population ageing: Projections de population et de population active pour 27 pays européens 2002-052: impact de la migration internationale sur le vieillissement de la population

AbstractPopulation and labour force projections are made for 27 selected European countries for 2002-052, focussing on the impact of international migration on population and labour force dynamics. Starting from single scenarios for fertility, mortality and economic activity, three sets of assumptions are explored regarding migration flows, taking into account probable policy developments in Europe following the enlargement of the EU. In addition to age structures, various support ratio indicators are analysed. The results indicate that plausible immigration cannot offset the negative effects of population and labour force ageing. RésuméDes projections de population et de population active sont présentées pour 27 pays Européens pour la période 2002-052, avec un intérêt particulier pour l’impact de la migration internationale sur la dynamique des populations. A partir de scénarios uniques pour la fécondité, la mortalité et l’activité économique, trois séries d’hypothèses concernant les flux migratoires sont explorées, en intégrant des prévisions sur les développements des politiques publiquesàla suite de l’élargissement de l’Union Européenne. Les structures par âge sont analysées, de même que des indicateurs de rapports de dépendance. Les résultats indiquent que les flux d’immigration vraisemblables ne pourront pas compenser les effets négatifs du vieillissement de la population et de celui de la population active.

Forecasting international migration in Europe: a Bayesian view

This book addresses from a methodological perspective a research problem, how to forecast the international migration component in a way that could be then used for population forecasts using the probabilistic approach. All forecasts are made in the conditions of uncertainty, which is an immanent feature of every inference about the future, a key issue in forecasting becomes not to offer a point estimate of the future values of the variables under study, but rather to provide a reliable assessment of the related uncertainty span, ideally, in a coherent and quantifiable manner. It consists of three major parts: an overview of existing theories, methods and models used for forecasting migration flows, followed by a proposition of a forecasting framework based on the Bayesian approach in statistics, and then by a discussion of the predictions from the point of view of forecast users (decision-makers).

Integrated Modeling of European Migration

International migration data in Europe are collected by individual countries with separate collection systems and designs. As a result, reported data are inconsistent in availability, definition, and quality. In this article, we propose a Bayesian model to overcome the limitations of the various data sources. The focus is on estimating recent international migration flows among 31 countries in the European Union and European Free Trade Association from 2002 to 2008, using data collated by Eurostat. We also incorporate covariate information and information provided by experts on the effects of undercount, measurement, and accuracy of data collection systems. The methodology is integrated and produces a synthetic database with measures of uncertainty for international migration flows and other model parameters. Supplementary materials for this article are available online.

Bayesian population forecasting: extending the Lee-Carter Method

In this article, we develop a fully integrated and dynamic Bayesian approach to forecast populations by age and sex. The approach embeds the Lee-Carter type models for forecasting the age patterns, with associated measures of uncertainty, of fertility, mortality, immigration, and emigration within a cohort projection model. The methodology may be adapted to handle different data types and sources of information. To illustrate, we analyze time series data for the United Kingdom and forecast the components of population change to the year 2024. We also compare the results obtained from different forecast models for age-specific fertility, mortality, and migration. In doing so, we demonstrate the flexibility and advantages of adopting the Bayesian approach for population forecasting and highlight areas where this work could be extended.

International Migration Scenarios for 27 European Countries, 2002–2052

In this chapter assumptions about future developments of international migration in 27 European countries over the period 2002–2052 are summarized. The assumptions were developed to serve as an input for the forecasts and simulations of population and labour force developments in Europe. On the basis of an overview of the theories of international migration, various ‘push’ and ‘pull’ factors and recent migration trends in Europe presented before, three knowledge-based scenarios for the future are presented and subsequently quantified. The scenarios are formulated separately for the flows among the 27 countries under study and for the net migration with the other parts of the world.

Linked lives: the utility of an agent-based approach to modeling partnership and household formation in the context of social care

The UKs population is aging, which presents a challenge as older people are the primary users of health and social care services. We present an agent-based model of the basic demographic processes that impinge on the supply of, and demand for, social care: namely mortality, fertility, health-status transitions, internal migration, and the formation and dissolution of partnerships and households. Agent-based modeling is used to capture the idea of “linked lives” and thus to represent hypotheses that are impossible to express in alternative formalisms. Simulation runs suggest that the per-taxpayer cost of state-funded social care could double over the next forty years. A key benefit of the approach is that we can treat the average cost of state-funded care as an outcome variable, and examine the projected effect of different sets of assumptions about the relevant social processes.

Bayesian demography 250 years after Bayes

Bayesian statistics offers an alternative to classical (frequentist) statistics. It is distinguished by its use of probability distributions to describe uncertain quantities, which leads to elegant solutions to many difficult statistical problems. Although Bayesian demography, like Bayesian statistics more generally, is around 250 years old, only recently has it begun to flourish. The aim of this paper is to review the achievements of Bayesian demography, address some misconceptions, and make the case for wider use of Bayesian methods in population studies. We focus on three applications: demographic forecasts, limited data, and highly structured or complex models. The key advantages of Bayesian methods are the ability to integrate information from multiple sources and to describe uncertainty coherently. Bayesian methods also allow for including additional (prior) information next to the data sample. As such, Bayesian approaches are complementary to many traditional methods, which can be productively re-expressed in Bayesian terms.

Utilising Expert Opinion to Improve the Measurement of International Migration in Europe

Abstract In this article, we first discuss the need to augment reported flows of international migration in Europe with additional knowledge gained from experts on measurement, quality and coverage. Second, we present our method for eliciting this information. Third, we describe how this information is converted into prior distributions for subsequent use in a Bayesian model for estimating migration flows amongst countries in the European Union (EU) and European Free Trade Association (EFTA). The article concludes with an assessment of the importance of expert information and a discussion of lessons learned from the elicitation process.

Complex systems modeling for supply and demand in health and social care

This paper introduces a major new cross-disciplinary research project that looks at the UK health and social care system, as part of an ambitious, broader initiative to apply methods from complexity science to a range of key global challenges. This particular project aims to develop new, integrated models for the supply and demand of both health and social care, in the context of the societal change brought about by migration, mobility and the ageing population. We discuss the background to the work, and the broad way in which we intend to leverage complexity science. This is made more specific with a brief discussion on existing demographic models, and some examples of model-building in progress. We conclude with a glimpse into the subtly difficult problems of fostering such innovative interdisciplinarity.

A comparison of official population projections with Bayesian time series forecasts for England and Wales

We compare official population projections with Bayesian time series forecasts for England and Wales. The Bayesian approach allows the integration of uncertainty in the data, models and model parameters in a coherent and consistent manner. Bayesian methodology for time-series forecasting is introduced, including autoregressive (AR) and stochastic volatility (SV) models. These models are then fitted to a historical time series of data from 1841 to 2007 and used to predict future population totals to 2033. These results are compared to the most recent projections produced by the Office for National Statistics. Sensitivity analyses are then performed to test the effect of changes in the prior uncertainty for a single parameter. Finally, in-sample forecasts are compared with actual population and previous official projections. The article ends with some conclusions and recommendations for future work.