Educational Attainment of Population 15 Years and Over, by Age and Sex (All Races):2004

2004 ◽  
Keyword(s):  
Educational Attainment ◽  
Age And Sex

The Rise of Global Human Capital and the End of World Population Growth

2017 ◽  
Author(s):  
Wolfgang Lutz ◽  
KC Samir
Keyword(s):  
Human Capital ◽  
Social Change ◽  
Educational Attainment ◽  
Economic Change ◽  
Adult Population ◽  
Social Structures ◽  
Historical Period ◽  
The World ◽  
Over Time ◽  
Age And Sex

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.

Selectivity profiles of recently arrived refugees and labour migrants in Germany

Soziale Welt ◽  
2020 ◽  
Vol 71 (1-2) ◽  
pp. 54-89
Author(s):  
Christoph Spörlein ◽  
Cornelia Kristen ◽  
Regine Schmidt ◽  
Jörg Welker
Keyword(s):  
Educational Attainment ◽  
Country Of Origin ◽  
Destination Country ◽  
Data Sources ◽  
Syrian Refugees ◽  
German Microcensus ◽  
Migrant Selectivity ◽  
Stay Behind ◽  
Age And Sex

Migrant selectivity refers to the idea that immigrants differ in certain characteristics from individuals who stay behind in their country of origin. In this article, we describe the selectivity profiles of recent migrants to Germany with respect to educational attainment, age and sex. We illustrate how refugees differ from labour migrants, and we compare the profiles of Syrian refugees who successfully completed the long journey to Europe to Syrian refugees who settled in neighbouring Lebanon or Jordan. We rely on destination-country data from the IAB-BAMF-GSOEP Survey of Refugees, the Arab Barometer, and the German Microcensus, as well as on a broad range of origin-country data sources. Regarding sex selectivity, males dominate among refugees in Germany, while among economic migrants, sex distributions are more balanced. Relative to their societies of origin, labour migrants are younger than refugees. At the same time, both types of migrants are drawn from the younger segments of their origin populations. In terms of educational attainment, many refugees compare rather poorly with average Germans’ attainment, but well when compared to their origin populations. The educational profiles for labour migrants are mixed. Finally, Syrians who settle in Germany are younger, more likely to be male and relatively better educated than Syrians migrating to Jordan or Lebanon.

scholarly journals Predicting cognitive and mental health traits and their polygenic architecture using large-scale brain connectomics

2019 ◽  
Author(s):  
Luigi A. Maglanoc ◽  
Tobias Kaufmann ◽  
Dennis van der Meer ◽  
Andre F. Marquand ◽  
Thomas Wolfers ◽  
...  
Keyword(s):  
Mental Health ◽  
Mental Disorders ◽  
Educational Attainment ◽  
Cognitive Abilities ◽  
Prediction Accuracy ◽  
Fluid Intelligence ◽  
Chance Level ◽  
Polygenic Scores ◽  
Brain Connectomics ◽  
Age And Sex

AbstractCognitive abilities and mental disorders are complex traits sharing a largely unknown neuronal basis and aetiology. Their genetic architectures are highly polygenic and overlapping, which is supported by heterogeneous phenotypic expression and substantial clinical overlap. Brain network analysis provides a non-invasive means of dissecting biological heterogeneity yet its sensitivity, specificity and validity in clinical applications remains a major challenge. We used machine learning on static and dynamic temporal synchronization between all brain network nodes in 10,343 healthy individuals from the UK Biobank to predict (i) cognitive and mental health traits and (ii) their genetic underpinnings. We predicted age and sex to serve as our reference point. The traits of interest included individual level educational attainment and fluid intelligence (cognitive) and dimensional measures of depression, anxiety, and neuroticism (mental health). We predicted polygenic scores for educational attainment, fluid intelligence, depression, anxiety, and different neuroticism traits, in addition to schizophrenia. Beyond high accuracy for age and sex, permutation tests revealed above chance-level prediction accuracy for educational attainment and fluid intelligence. Educational attainment and fluid intelligence were mainly negatively associated with static brain connectivity in frontal and default mode networks, whereas age showed positive correlations with a more widespread pattern. In comparison, prediction accuracy for polygenic scores was at chance level across traits, which may serve as a benchmark for future studies aiming to link genetic factors and fMRI-based brain connectomics.SignificanceAlthough cognitive abilities and susceptibility to mental disorders reflect individual differences in brain function, neuroimaging is yet to provide a coherent account of the neuronal underpinnings. Here, we aimed to map the brain functional connectome of (i) cognitive and mental health traits and (ii) their polygenic architecture in a large population-based sample. We discovered high prediction accuracy for age and sex, and above-chance accuracy for educational attainment and intelligence (cognitive). In contrast, accuracies for dimensional measures of depression, anxiety and neuroticism (mental health), and polygenic scores across traits, were at chance level. These findings support the link between cognitive abilities and brain connectomics and provide a reference for studies mapping the brain connectomics of mental disorders and their genetic architectures.

Introduction

2017 ◽  
Author(s):  
Wolfgang Lutz ◽  
William P. Butz
Keyword(s):  
Educational Attainment ◽  
School Attendance ◽  
Labour Force ◽  
Population Trends ◽  
Level Of Education ◽  
Age Related ◽  
The Future ◽  
Main Driver ◽  
And Migration ◽  
Age And Sex

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.

scholarly journals Projection of populations by level of educational attainment, age, and sex for 120 countries for 2005-2050

2010 ◽  
Vol 22 ◽  
pp. 383-472 ◽  
Author(s):  
Samir KC ◽  
Bilal Barakat ◽  
Anne Goujon ◽  
Vegard Skirbekk ◽  
Wolfgang Lutz
Keyword(s):  
Educational Attainment ◽  
Age And Sex

scholarly journals How Education Drives Demography and Knowledge Informs Projections

2017 ◽  
Author(s):  
Wolfgang Lutz ◽  
Vegard Skirbekk
Keyword(s):  
Human Capital ◽  
Educational Attainment ◽  
Review Article ◽  
Population Heterogeneity ◽  
Population Projections ◽  
Demographic Analysis ◽  
Underlying Assumption ◽  
Economic Implications ◽  
Explicit Consideration ◽  
Age And Sex

This chapter provides the background necessary for understanding our approach to projecting population and human capital. First, we investigate the proper place of education in demographic analysis and the evidence for an underlying causal relationship between education and demographic outcomes. Second, we emphasize the importance of explicit assumptions undergirding population projections and detail our procedures for incorporating the views of hundreds of experts into sets of assumptions that drive the Wittgenstein Centre (WIC) projections. Subsequent chapters build on this background in their detailed discussions of trends and arguments in fertility, mortality, migration, and education. A major innovative feature of this volume is the systematic addition of educational attainment as a standard demographic dimension in addition to age and sex for demographic analyses, particularly for projections. The underlying assumption is that educational attainment is not just one of many socio-economic factors that matter for population, as it is often viewed in conventional demographic analysis, but is the single most important source of empirically observable population heterogeneity next to age and sex. The suggestion of routinely adding educational attainment as a dimension of demographic analysis is not new. It was first proposed in a Population and Development Review article by Lutz et al. (1998), entitled ‘Adding Education to Age and Sex’. More recently, the idea of adding the education factor to demographic analysis was discussed by Lutz (2010) in a commentary entitled, ‘Education Will be at the Heart of 21st Century Demography’. It has also been the focus of two recent articles by Lutz and KC, one published in Philosophical Transactions entitled, ‘Dimensions of Global Population Projections’ (2010), and a review article published in Science entitled, ‘Global Human Capital: Integrating Education and Population’ (2011). In the latter paper they argue that an additional demographic dimension should be added routinely to age and sex in population analyses and projections according to three criteria: (i) its explicit consideration should be feasible in terms of available data and methodology; (ii) it should matter substantially in terms of altering population dynamics; and (iii) it should be of interest in its own right in terms of its social and economic implications.

scholarly journals Chances to be Employed for Employees at Pre-Retirement and Retirement Age

2019 ◽  
Vol 14 (2) ◽  
pp. 90-109 ◽  
Keyword(s):  
Educational Attainment ◽  
Time Series Data ◽  
Regional Analysis ◽  
Current Level ◽  
Series Data ◽  
Retirement Age ◽  
Men And Women ◽  
Market Status ◽  
Time Series Data Analysis ◽  
Age And Sex

The article deals with the labor market status of men and women at pre-retirement and retirement age by educational attainment. The analysis is based on a statistic indicator developed by the author estimating the relative chance to be employed for a certain group of workers by educational attainment (CEE), split by age and sex, relative to the chances for employment of the workforce on average. Independence from the current level of unemployment is a distinctive feature of the proposed indicator. This ensures the correctness of its use without additional adjustments in international or cross-regional analysis as well as time series data analysis. The analysis revealed disparities in CEE for men and women of preretirement and retirement ages, women having the better chance to be employed.

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