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This book addresses systematically and quantitatively the role of educational attainment in global population trends and models. By adding education to the traditional demographic characteristics of age and sex, this distinguishing feature substantially alters the way we look at changes in populations and how we project them into the future. In most societies, particularly during the process of demographic transition, women with more education have fewer children, both because they want fewer and because they find better access to birth control. And better educated men and women in virtually all societies have lower mortality rates and their children have a better chance of survival. Migration flows also differ by level of education, and better educated migrants integrate more easily into receiving societies. These pervasive demographic differentials by level of education matter greatly for population dynamics. When we explicitly address this important source of population heterogeneity the projected future population trends are different from those based on the conventional stratifications that include only age and sex. In addition, the future educational attainment levels of the adult population are of great interest in their own right as a key determinant of outcomes ranging across economic growth, quality of governance, and adaptive capacity to environmental change. Traditionally in demography, the sex of a person is considered the most fundamental characteristic because it is essential for studying the process of reproduction. Mortality and migration also show significant variation by gender. Age is another key characteristic because it is the main driver of biological maturation at an early age and is directly related to school attendance, labour force entry, and retirement, all landmarks that are important for social institutions. Because there are distinct age-related patterns of fertility, mortality, and migration intensities, gender and age are considered the most fundamental demographic dimensions. In addition, demographers frequently take into account other biological, social, and economic characteristics, including place of residence (especially urban or rural), citizenship, marital status, race, migration status, employment status, health/disability status, and educational attainment. These additional characteristics are not systematically considered in every study, but tend to appear in corresponding topical studies.

The preceding chapters have all contributed to building the knowledge base for the actual Wittgenstein Centre for Demography and Global Human Capital (WIC) projections that will be presented and discussed in the second part of this book. This chapter stands as a bridge between the two parts. Its focus is the translation and operationalization of the empirical evidence and the substantive arguments presented so far into specific population projections by age, sex, and level of educational attainment for all countries in the world. This is a complex exercise in which data and methodology play the crucial roles. The cohort–component multidimensional projections presented in this volume require a large amount of information, ranging from base-year data on population disaggregated by levels of educational attainment by age and sex, to data on fertility, mortality, and migration by age, sex, and education for the base year, and, finally, to the assumed numerical values of these determinants according to the different scenarios. This new set of expert argument-based projections by age, sex, and educational attainment presents an important new step at the forefront of international population projections. As discussed in Chapter 1, this is a logical next step in the tradition of international population projections by the World Population Program of the International Institute for Applied Systems Analysis (IIASA). This effort also goes beyond what the United Nations (UN) and other agencies have been doing in two important ways: it provides the most comprehensive and systematic summary of expert knowledge on future fertility, mortality, and migration to date—including the input of hundreds of demographers from around the world—and it translates this into the most comprehensive set of human capital projections for 195 countries. The WIC projections cover all countries in the world with more than 100,000 inhabitants. In this effort, the study builds on and significantly expands earlier IIASA reconstructions and projections of the population by age, sex, and educational attainment for 120 countries published in 2007 and 2010 (KC et al., 2010; Lutz et al., 2007).

Chapter 1 introduces the book as the most significant effort so far to systematically and quantitatively address the role of educational attainment in global population trends and models. It shows that by adding education to the conventional demographic characteristics of age and sex, this distinguishing feature substantially alters the perception of population changes and their projections into the future. As background, the chapter also presents a brief history of world population projections and describes the Institute for Applied Systems Analysis tradition since 1988 of producing international projections. This tradition includes the innovative production of probabilistic population projections and the development of structured and well-documented processes for converting expert knowledge into numerical assumptions about future trends. The chapter concludes with an overview of the book’s chapters.

This chapter describes the construction and simulation of the IGEM base case, illustrating how the various components of the model combine to achieve the economy's growth path. Key projections for variables external (exogenous) to IGEM are developed for: (i) population, time endowment, labor quality and household demographics from Census Bureau projections; (ii) government deficits, expenditures and tax rates from CBO 10-year and 75-year projections; (iii) current account deficits from IMF short-term forecasts; (iv) world energy prices from EIA projections. Projections of productivity-related variables and trends are derived from IGEM's underlying econometrics. Also described is the procedure by which IGEM's productivity terms are used to target energy consumption patterns and real GDP for those applications requiring calibrated base cases and cross-model comparisons. Base case (endogenous) results are reported at both the macroeconomic and industry levels of detail and for both uncalibrated and calibrated simulations.

Chapter 9 describes the baseline data and summarizes the methodology that underlies the projections presented in this book for 171 countries of the world by age, sex, and educational attainment. These multidimensional cohort–component projections require a large amount of empirical information, ranging from base year data on populations disaggregated by levels of educational attainment by age and sex to data on educational differentials of fertility and mortality. The chapter also summarizes the procedures by which the assumed trajectories for future fertility, mortality, and migration were derived by combining structured expert judgements with statistical models. It also describes in detail the procedures by which assumptions on aggregate fertility, mortality, and migration trends were translated into education-specific trajectories in order to then calculate the implications of alternative education scenarios.

This is the first of three chapters that present the population projections by age, sex, and level of educational attainment for all countries in the world with a time horizon of 2060, and extensions to 2100. Before discussing the Wittgenstein Centre for Demography and Global Human Capital (WIC) projections, however, it is worth stepping back to consider how social structures change over time. While understanding the evolution of social structures is important under the conventional demographic approach that breaks down populations by age and sex, a more in-depth understanding of the changes in human capital requires that the interplay between different levels of schooling over time (the flow variable), and the changing educational attainment composition of the adult population (the stock variable) be taken into account. Societies can be stratified along several dimensions. In conventional social science the divisions studied refer to social class, race, or ethnicity. Demographers routinely break down populations by age and sex. Another important demographic dimension is that of birth cohorts or generations, that is, persons born and socialized during the same historical period. Particularly during periods of rapid social change, young cohorts tend to differ from older ones in important respects, and the demographic process of generational replacement is a powerful driver of socio-economic change. This process is analytically described by the theory of ‘Demographic Metabolism’, recently introduced as a generalized predictive demographic theory of socio-economic change by the first author (Lutz, 2013), building on earlier work by Mannheim (1952) and Ryder (1965). Ryder, who introduced the notion of Demographic Metabolism in a qualitative way, saw it as the main force of social change. While this theory applies to many stable human characteristics that are acquired at young age and remain invariant over a lifetime, it is particularly appropriate for studying and modelling the dynamics of the change in the distributions of highest educational attainment by age and sex over time. This perspective on human capital formation is the main focus of this book. This first of the three results chapters will highlight the results with respect to future population numbers by level of education in different parts of the world.

This chapter argues that contemporary global migration presents a new challenge. It challenges a fundamental premise in liberal thought under which majority groups can perpetuate their culture by controlling entry into the state. Grouping together nine macro and micro—level changes, the chapter traces a transformation in the relationship between majority groups and minority groups. The nine changes occur in migration patterns (scale, character, intensity), in Western society (population decline, changing lifestyles, new human rights regime), and in the world (geopolitics, technology, globalization). Each of these changes, individually, may not be entirely new, yet the combination of them all together at one time has no historical precedent. This reality brings about political and legal responses and raises normative questions. The aim of this chapter is to document the changing reality and show that it is not adequately addressed by political theory and international human rights law.

This chapter presents some findings from the research project ‘Modelling Needs and Resources of Older People to 2030’ (MAP2030). The project developed a set of projection models to estimate future family circumstances, incomes, pensions, savings, disability and care needs of older people in England. These projections included public and private expenditure on pensions, disability benefits and care services under different scenarios for reform of pensions and long-term care funding under a range of alternative population futures. The chapter focuses on the projected future costs and impacts for the different income quintiles of the older population of proposed reforms to the system of funding adult social care, in particular the impact of a cap on individual liability to meet care costs.

To date, one's thinking around ‘accommodation and care’ in later life focuses on very old people who are seen as unable to fit into mainstream housing and whose needs have been labelled as special, segregated, or separate, rather than accepting accommodation and care as part of society's needs in general. However, at present, it is undeniable that people are becoming a part of an ageing population whose structure is changing and where there will be a great diversity of household types. This chapter explores the changing population, the type of society that is developing, how this is affecting where people live, and the experiences that many older people now face. It includes a brief discussion on population projections, geographical variations, and the changing nature of households and the social, psychological, and physiological issues relating to it.

This chapter describes the IGEM in detail – the demand and supply sides of the economy, the inter-industry (input-output) structure and the determination of intertemporal equilibrium. The production function allows for substitution among inputs, autonomous technical change, and induced technical change. The household model allows an aggregate demand function to be expressed as a sum of individual household demand functions; this aggregate function is non-homothetic and depends on the demographic projections of the population. Household optimization also generates goods demand and labor supply. Intertemporal optimization generates savings and hence investment for capital accumulation and the cost-of-capital equation. The model is made consistent with the National Accounts and the Input-Output accounts. We describe the solution algorithm.