The 1970s saw the development of methods of multistate population analysis and their application to the description of the dynamics of multiregional population systems. For a multiregional system it is possible to write down for each of these concepts a set of components of change equations in flow terms, and a set of components of change equations in rate terms suitable for projection or stationary and stable population analysis. Rates are normally measured using transition data in a rather different fashion from movement data. Rates that assess the likelihood of given outcomes over a time interval (i.e. transitions) are computed by dividing the transition flow term by the initial population in the state. Researchers may be interested in projecting population stocks or life years lived classified by current region, or by current region and region of previous residence (such as region of birth).

The preparation of forecasts for small and local area populations involves many challenges. Standard cohort-component models are problematic because of small numbers, which make estimation of rates unreliable. Because of this, the Synthetic Migration Population Projection (SYMPOPP) model was designed to forecast local populations without need for detailed area-specific information. This model had been used successfully for small area forecasts in Australia. The objective of the paper is to assess its performance when applied to local areas in England. The model uses a bi-regional structure based on a movement population account. Sub-models of total population change are employed to control future change. Fertility, mortality and migration rates are borrowed from national statistics, constrained to small area indicators. The model uses an Excel workbook with VBA routines and is relatively easy and quick to use. Model inputs were calibrated for 2006–2011 and used to forecast for 2011–2021. Results were tested against the census-based 2021 mid-year populations. A new error statistic, Age Structure Error, was used to evaluate Basic and Refined model versions against official projections. The two versions of SYMPOPP posted lower errors. The simple models had fewer areas with errors of 10% or more (12.3–12.6%) compared with the official projections (14.5% of areas). Investigation revealed that these errors occurred in local authorities with high military, student, prison, or ethnic minority populations, influenced by factors not captured in a projection model for the general population.

Over the past intercensal period, national population growth in the United Kingdom (UK) almost ceased. There is a long history of interest in migration and its quantitative modeling. The attraction of forging the connection is to link, for a multi-regional system, the population and economic systems by using various economic indicators in the destination attractiveness terms of the migration model. In studying spatial population change a choice has to be made from among the possible sets of areal units within a given universe. The difference in growth rates between metropolitan and non-metropolitan zones has been 2-4 times that between northern and southern zones. Over the two decades from 1961 the dominance of the metropolitan/non-metropolitan pattern increases as the overall variance decreases. The hierarchy generally places non-metropolitan zones above metropolitan, except for Greater London which is placed higher than several of the northern non-metropolitan zones.

Background There are surprisingly few resources available which offer an introductory guide to preparing a national population projection using a cohort-component model. Many demography textbooks cover projections quite briefly, and many academic papers on projections focus on advanced technical issues. Aims The aim of this paper is to provide a short and accessible guide to producing a national-scale population projection using the cohort-component model. Data and methods The paper describes the cohort-component model from a population accounting perspective, presents all the necessary projection calculations, and covers the key steps which form part of the projections preparation process – from gathering input data to validating outputs. An accompanying Excel workbook implements the model and contains example projections for Australia. Conclusions Calculating a national population projection using a cohort-component model involves fairly simple algebra, but the broader projections preparation process is more complex, and requires careful consideration and judgement.

Small area population forecasts are widely used by government and business for a variety of planning, research and policy purposes, and often influence major investment decisions. Yet the toolbox of small area population forecasting methods and techniques is modest relative to that for national and large subnational regional forecasting. In this paper we assess the current state of small area population forecasting, and suggest areas for further research. The paper provides a review of the literature on small area population forecasting methods published over the period 2001-2020. The key themes covered by the review are: extrapolative and comparative methods, simplified cohort-component methods, model averaging and combining, incorporating socio-economic variables and spatial relationships, ‘downscaling’ and disaggregation approaches, linking population with housing, estimating and projecting small area component input data, microsimulation, machine learning, and forecast uncertainty. Several avenues for further research are then suggested, including more work on model averaging and combining, developing new forecasting methods for situations which current models cannot handle, quantifying uncertainty, exploring methodologies such as machine learning and spatial statistics, creating user-friendly tools for practitioners, and understanding more about how forecasts are used.

The aim of this commentary is to illuminate, for a wider audience, the essential features of the analysis described in the paper on “China’s low fertility may not hinder future prosperity” (1), to place the paper in the context of the program of work on the role of education in demography at the Wittgenstein Centre, Vienna, and to evaluate its claims. Tables 1–4 provide an overview of the options for forecasting a country’s demographic future used in the Wittgenstein program. The paper’s authors chose from the options set out. Tables 1–4 provide an interpretation of their work, based on reading the paper and discussion with the authors. Table 1 characterizes the model, its inputs, and its outputs. Table 2 provides details of alternative demographic models that might be used. In Marois et al. (1), a microsimulation model based on sample data is used, with the sample numbers factored up to a larger population. The demographic assumptions of the model embed the key relationships between fertility, mortality, and migration by educational attainment. The model for China focuses on the national population, with no role for internal migration. International migration into and out of China is very small in relation to its 1.4 billion 2020 population, so it does not play much part in determining future populations, in contrast to Western countries, where immigration is a vital contribution, and to developing countries, where emigration is important. Table 3 presents information on indicators of population aging. The companion paper (1) shows that very different results are obtained when education and productivity are included in the dependency ratios. Table 4 reports the implications of the work for national policy in China. There are also pointers to improvements that might be introduced, such as integrating recent estimates of educational quality (2), the skills and knowledge that pupils and students gain through education.

Methods for forecasting households in London and the Thames Valley were developed for input to forecasts of domestic water consumption. Households were forecast by ethnicity, size and property type. South Asian-headed households consumed more water (per capita) than average. Forecast populations for 60 Local Authorities were extracted from a UK-wide forecast and aggregated to six Water Resource Zones (WRZs). Household populations by age, sex and ethnicity were multiplied by trended headship rates to forecast households. Households were classified by size and property type using census microdata. Water demand was generated using modelled consumption rates, based on policy interventions. Between 2011 and 2101, the region will experience 85% growth in populations and 113% in households. The household growth will vary across WRZs between 54% and 126%. Water demand in London and the Thames Valley is forecast to grow by 90%, 69% and 46% under status quo, moderate and extreme conservation scenarios. The future growth in water demand under all scenarios poses a huge challenge for the region, already under water stress.

This article reviews Alan Wilson's research on population and migration in the 1970s and the 2010s, which supplements his principal contribution - mathematical modelling of urban and regional systems. In the 1970s, drawing on input-output models of economies and working with Philip Rees, Wilson established the accounting basis for Andrei Rogers' multi-regional projection model, adding international migration. Innovative methods were developed to complete demographic accounts, where there were data gaps. In the 2010s, working with Adam Dennett, Wilson systematized methods for estimating migration flows between regions in Europe, employing his family of spatial interaction models. The key aim of both research strands was to ensure that no information was ignored to ensure consistency in population and migration models. The influence of Wilson's contributions to research on population and migration is traced through a survey of subsequent research.

Europe has experienced a growing divergence of trends in population growth and age structure across cities and regions. A key driver of this divergence is internal migration, which also drives disparities in labour markets and economic development. This special issue focuses on the role of internal migration as a driver of regional population change in Europe, and relates current research to the early works on the “laws of migration” by Ernst Georg Ravenstein. The topic of internal migration and regional population change is important and timely, given the ongoing social scientifi c and political debate within Europe about the causes and consequences of regional disparities and the design of appropriate policies to reduce inequalities. The European Union, for example, has the goal of reduction of inequalities across member states and across regions within them. The instruments for achieving this goal are the Cohesion Policy (European Commission 2020a), the Regional Development Fund (European Commission 2020b) and the Social Fund (European Commission 2020c). Previous research on the role of internal migration on past and future population dynamics within the context of regional disparities has largely focussed on population projections at regional and national scales. This includes a project on Demographic and Migratory Flows affecting European Regions and Cities (DEMIFER) (ESPON 2013). One task in DEMIFER was to forecast regional populations and therefore the component fl ows (births, deaths and internal, inter-member state and extra-European migration), with specifi c scenarios showing outcomes under different policies (Rees et al. 2012). Because of time constraints, the scenarios for migration assumed convergence, divergence or stasis in the attractiveness of regions to the three spatial categories of migrants, uninformed by an analysis of trends over space and time. More recent projections of European country populations have adopted either high, medium and low scenarios (Cafaro/Derer 2019) or combinations of high, central and low with differing levels of human capital of migrants (Lutz et al. 2019). However, EUROSTAT have not published regional population projections since 2008. There is therefore a gap in our knowledge of migration trends across Europe which could inform population projections under plausible policy Comparative Population Studies Vol. 44 (2019): 533-544 (Date of release: 08.09.2020)

The United Kingdom faces demographic uncertainty, as negotiations for leaving the European Union (Brexit) proceed. Brexit has implications for international migration into and out of the UK, dependent on future immigration policy and on how attractive the UK will be as a labour market. At the same time, the UK population is experiencing ethnic diversification, consequent on past immigration. To explore the UK's future ethnic diversity, we run four projection scenarios. Three international migration scenarios, varying by the extent of the break with the EU, are implemented together with a reference projection assuming zero international migration. Ethnic groups are differently affected by these migration scenarios, depending on the contribution of international migration to population growth and the extent of demographic momentum. The White British and Irish lose population under all Brexit scenarios and the Black Caribbean population declines in all but one scenario. The White Other, Indian, Chinese, Other Asian and Other groups will show much lower growth under Soft and Hard Brexit scenarios. The growth of the Mixed, Pakistani, Bangladeshi, Black African and Black Other groups will only be affected marginally. Under every scenario, however, the UK's population is projected to continue to grow, age and diversify.

In 1876, 1885 and 1889, Ernst Ravenstein, an Anglo-German geographer, published papers on internal and international migration in Britain, Europe and North America. He generalized his findings as “laws of migration”, which have informed subsequent migration research. This paper aims to compare Ravenstein’s approach to investigating migration with how researchers have studied the phenomenon more recently. Ravenstein used lifetime migrant tables for counties from the 1871 and 1881 censuses of the British Isles. Data on lifetime migrants are still routinely collected but, because of the indeterminate time interval, they are rarely used to study internal migration. Today, internal migration measures from alternative sources are used to measure internal migration: fixed interval migrant data from censuses and surveys, continuous records of migrations from registers, and “big data” from telecommunications and internet companies. Ravenstein described and mapped county-level lifetime migration patterns, using the concepts of “absorption” and “dispersion”, using migration rates and net balances. Recently, researchers have used lifetime migrant stocks from consecutive censuses to estimate country to country flows for the world. In the last decade, an Australian-led team has built an international database of internal migration flow data and summary measures. Methods were developed to investigate the modifiable areal unit problem (MAUP), in order to design summary internal migration measures comparable across countries. Indicators of internal migration were produced for countries covering 80 percent of the world’s population. Ravenstein observed that most migrants moved only short distances, anticipating the development of “gravity” models of migration. Recent studies calibrated the relationship between migration and distance, using gravity models. For mid-19th century Britain, Ravenstein found the dominant direction of internal migration to be towards the “centres of commerce and industry”. Urbanization is still the dominant flow direction in most countries, though, late in the process, suburbanization, counter-urbanization and re-urbanization can occur. Ravenstein focussed on place-specific migration, whereas today researchers describe migration flows using area typologies, seeking spatial generality. Ravenstein said little about migrant attributes except that women migrated more than men. In recent decades, the behaviour of migrants by age, sex, education, ethnicity, social class and partnership status have been studied intensively, using microdata from censuses and surveys. Knowledge about processes influencing internal and international migration has rarely been built into demographic projections. Scenarios that link migration with sub-national or national inequalities and with climate or environmental change are influencing the design of policies to reduce inequalities or slow global warming. * This article belongs to a special issue on “Internal Migration as a Driver of Regional Population Change in Europe: Updating Ravenstein”.

Sub-national population projections help allocate national funding to local areas for planning local services. For example, water utilities prepare plans to meet future water demand over long-term horizons. Future demand depends on projected populations and households and forecasts of per household and per capita domestic water consumption in supply zones. This paper reports on population projections prepared for a water utility, Thames Water, which supplies water to over nine million people in London and the Thames Valley. Thames Water required an evaluation of the accuracy of the delivered projections against alternatives and estimates of uncertainty. The paper reviews how such evaluations have been made by researchers. The factors leading to variation in sub-national projections are identified. The methods, assumptions and results for English sub-national areas, used in five sets of projections, are compared. There is a consensus across projections about the future fertility and mortality but varying views about the future impact of internal and international migration flows. However, the greatest differences were between projections using ethnic populations and those using homogeneous populations. Areas with high populations of ethnic minorities were projected to grow faster when an ethnic-specific model was used. This result is important for assessing projections for countries housing diverse populations with different demographic profiles. Historic empirical prediction intervals are used to assess the uncertainty of the London and the Thames Valley projections. By 2101 the preferred projection suggests that the population of the Thames Water region will have grown by 85% within an 80% empirical prediction interval between 45 and 125%.

Affordable housing has emerged as a key issue in urban development in a wide range of countries. Themes in research on affordable housing development across the world are reviewed. Affordable Housing Communities for low income households have been built on a large scale in developing countries such as China during the last two decades, mainly in urban fringe areas. Evidence on the impact of the location on access of residents to services is rare. Studying Nanjing, this paper compares spatial access to services between Affordable Housing Communities and Other Housing Communities by measuring distances and imputing walking time between residential land parcels and facilities. Affordable Housing Communities have significantly poorer access than Other Housing Communities, because of poor neighbourhood provision of low order services and poor access to high order services. A household survey of Affordable Housing Communities and Other Housing Communities records the daily lives, degrees of satisfaction and community attachments of residents. Residents in affordable housing have low degrees of satisfaction, weak community attachments and desire to move. The findings emphasize that service provision should be planned to keep pace with Affordable housing construction, so that these communities become better places to live.

This discussion piece is an extended review of the work on projecting the world’s population and human capital by country conducted by the Wittgenstein Centre (WIC). The project was led by Wolfgang Lutz, and its outcomes were published by Oxford University Press in a book that appeared in 2014. Using statistics from the book and elsewhere, this article starts with an overview of the development of educational attainment. The role that education plays in the WIC2014 model is identified. Definitions of ‘multi-dimensional’, ‘multi-state’, and ‘micro-simulation’ are offered, and are used to characterise the model. A thumbnail sketch of the main methods used in the projections is given. The final section sets out a possible agenda for the future development of the WIC2014 model. This review is intended to help readers tackle the more than 1,000 pages of argument and analysis in the book, which represents a major contribution to demographic research in the 21st century.

This document provides additional information about the methods used to develop the international migration inputs to the projections described in the associated JEMS paper and discusses further methods that might be included in future projections of international migration by ethnicity. In order to project the population by ethnicity, it is necessary to estimate immigration and emigration for the starting baseline year (2011) for the 12 ethnic groups in our analysis. The estimates are required for the United Kingdom and for its constituent local authorities. This is a challenge because international migration is poorly measured in the United Kingdom through a survey, the International Passenger Survey (IPS), which does not include a question on migrant ethnicity. It is also necessary to decide on the model for forecasting immigration and emigration and to forecast those flows into the future to 2061.

This case study implements long-term projections of domestic water demand for a UK water company, Thames Water. Projections of per household consumption (PHC) and households were combined to yield future demand. Regression models predicted PHC using the determinants of occupancy, property type, ethnicity and rateable value, drawing on 2006-2015 domestic water-use data as a baseline. A model was developed for diffusing savings in per capita consumption (PCC), drawn from published studies of interventions. PCC declines were converted to PHC reductions using baseline ratios. Interventions were grouped into Business as Usual, Light Green (limited intervention), and Dark Green (extreme intervention) scenarios. Projected households were generated by property type, occupancy, and ethnicity for Thames Water's resource zones for 2011 to 2101 and multiplied by projected PHCs to yield water-demand projections. By 2101, the 2011 water demand of 1,225 million liters a day grew 90% under Business as Usual, 69% under Light Green, and 46% under Dark Green.

This chapter describes the creation of new estimates of ethnic populations and components of change in local authority districts (LADs) in England for years between the 2001 and 2011 Censuses. Information on ethnic populations by age and gender is provided in censuses. In between censuses, information on ethnic population change is scarce. To fill the gap we used data from the two censuses with reconciled total population and component estimates published by the Office of National Statistics. This chapter outlines the sequence of steps used to produce a ten-year time series. These reconciled population and component estimates provide a firmer foundation for ethnic-specific projections than hitherto available. The role of the census in this work is vital.

This chapter reviews the results of a series of regional projection exercises carried out for British regions in the past two decades. It describes a set of further projections carried out using some recently developed multi-regional models of population change. Comparison among the various projections will be made to expose the differences and similarities in underlying models, and in the nature of the assumptions input to those models. The first set of projections to be reviewed is that of the National Institute of Economic and Social Research carried out in 1963–4 and reported in published form in Stone. The second set of projections is that prepared by the Office of Population Censuses and Surveys and its predecessor the General Register Office. Remaining set of projections to be described are those developed by the author in a study of 'Spatial Demographic Growth in British Regions' and in a report on 'The Future Population of East Anglia and its Constituent Counties'.

Population ageing is commonly cited as one of the main drivers of increasing pressures on health care systems, as more people with chronic morbidities live to older ages. This chapter digs deeper into this presumed relationship by estimating the successive health impacts of: total population change, population ageing, changing ethnic mix of the population and trends in the age-specific incidence of disease. The decomposition of a set of health projections is developed using a micro-simulation model. These projections are based on the population of England aged 50 and over, classified by local authority of residence. The model projects forward, for the 20 years beyond 2011, the prevalence of cardiovascular disease, diabetes and respiratory illness. For diabetes the finding is that population increase alone contributes to a 24% increase in prevalence by 2031, while the changes in gender, ethnicity and age composition together contribute another 24 %. Taking account of all potential contributions, the overall diabetes prevalence count increases by 57 %. For cardio-vascular disease (CVD), population increase contributes a 23% increase; demographic composition processes a further 30 %; while decreases in CVD prevalence rates reduce prevalence by 60 %, resulting in an overall decrease of 35% in those with CVD by 2031. For respiratory illness, population increase contributes 23 %; demographic composition changes 13%; while a decrease in prevalence rates of 29% means that the burden of the disease reduces by a modest 1%. These results underline the potential for successful health intervention (as in CVD), the urgent need for prevention (as in diabetes) and the incentive to continue to care about health to very old ages (as in respiratory illness). These headline results refer to England as a whole but we also show how they vary across local authorities by area type, pointing to models of good practice in morbidity control.