We hereby present a dataset produced at the Wittgenstein Centre (WIC) containing comprehensive time series on educational attainment and mean years of schooling (MYS). The dataset is split by 5-year age groups and sex for 171 countries and covers the period between 1970 and 2010. It also contains projections of educational attainment to 2060 based on several scenarios of demographic and educational development. The dataset is constructed around collected and harmonized empirical census and survey data sets for the projection base year. The article present the principles and methodology associated with the reconstruction and the projection, and how it differs from several previous exercises. It also proposes a closer look at the diffusion of education in world regions and how the existing gaps in terms of generations, gender, and geography -hence called the 3Gs -have been evolving in the last forty years. (max. 150 words)

SignificanceIndia will soon be the world’s most populous country, but in terms of human capital and, consequently, Gross Domestic Product per capita, it has been trailing behind China. While some economists believe that India’s younger population will be an advantage over China’s aging one, here we show that much will depend on future investments in education and health and thus human capital. In terms of methodology, this paper addresses the question of what sources of observable population heterogeneity should be explicitly incorporated in population projections. It suggests that the dominant model of considering only the age and sex structures at the national level should be complemented by multidimensional models depending on the importance of heterogeneity and substantive user interest in the additional dimensions.

The industrial revolution marked a turning point in mankind as it not only initiated an economic turn from predominantly agricultural to industrialized societies but also shaped the need for an education revolution. This was the period when most industrialized societies implemented compulsory schooling systems and created the opportunity for universal access to basic education and later medium and higher education levels. However, this did not occur at the same speed everywhere, generating divergence between countries, and subsocieties within countries, whether it was at the level of residence, gender, generation, or class. Based on a dataset developed at the Wittgenstein Centre for Demography and Global Human Capital reconstructing levels of education in 5-year steps by age (5-year age groups) and sex for a large number of countries in the world, we look at the education transition from 1900 to 2015 to uncover different patterns and pathways of educational improvements that might explain the differences in the level of human capital today.

In Austria, the first confirmed COVID-19 death occurred in early March 2020. Since then, the question as to whether and, if so, to what extent the COVID-19 pandemic has increased overall mortality has been raised in the public and academic discourse. In an effort to answer this question, Statistics Vienna (City of Vienna, Department for Economic Affairs, Labour and Statistics) has evaluated the weekly mortality trends in Vienna, and compared them to the trends in other Austrian provinces. For our analysis, we draw on data from Statistics Austria and the Austrian Agency for Health and Food Safety (AGES), which are published along with data on the actual and the expected weekly numbers of deaths via the Vienna Mortality Monitoring website. Based on the definition of excess mortality as the actual number of reported deaths from all causes minus the expected number of deaths, we calculate the weekly prediction intervals of the expected number of deaths for two age groups (0 to 64 years and 65 years and older). The temporal scope of the analysis covers not only the current COVID-19 pandemic, but also previous flu seasons and summer heat waves. The results show the actual weekly numbers of deaths and the corresponding prediction intervals for Vienna and the other Austrian provinces since 2007. Our analysis underlines the importance of comparing time series of COVID-19-related excess deaths at the sub-national level in order to highlight within-country heterogeneities.

This paper documents the rationale, the data and the methodology for reconstructing the population of 185 countries by levels of educational attainment for the period 1950-2015, by age and sex. The reconstruction uses four main input types for each country: (1) The most recent and reliable education structure by age and sex, (2) any reliable historical education data by age and sex to use as marker points in the reconstruction to increase output accuracy, (3) a set of age-and sex-specific mortality differentials and education transition by education and (4) population estimates by age and sex. The methodology relies on the fact that education is acquired at young ages and does not change much over the life course. In the first part we present the reconstruction principle. In the second one, we document the methodology and the data. The third section compares the reconstructed estimates to other existing estimates including the past reconstruction effort of the Wittgenstein Centre for Demography and Human Capital. The data are available at: www.wittgensteincentre.org/dataexplorer (version 2.0). Supplementary to this Working Paper a detailed data documentation Excel file can be downloaded via: https://www.oeaw.ac.at/vid/publications/serial-publications/vid-working-papers/.