The future of world religions: population growth projections, 2010-2015

Environmental effects of reactive nitrogen (Nr) are a result of intense anthropogenic activities.1 Thy are evident in different parts of the world. Proper treatment is, inter alia, a scale issue. With individual countries in mostof Europe too small for comprehensive coverage of all effects, regulation of pollution in Europe co-emerged with political inegration in general. In this article, we describe the most important frameworks of international regulation in Europe. We als discuss the interaction of such institutional setting with the scientific initiatives at the samelevel, and finaly assess how scientific results have been fed with some success into strategies to mitigate pollution levels

Consequences of International Migration on the Size and Composition of Religious Groups in Austria

Scientific knowledge on a population’s religious composition is essential to understand the challenges faced by societies today. It arises in opposition to speculations about the actual size of religious groups that have been increasingly present in the public discourse in Europe for many years. This is particularly the case in Austria where the flows of refugees and migrants coming from the Middle East and Afghanistan have intensified since 2011 and culminated in 2015. These sparked a debate on the actual size of the Muslim population in Austria. This study fills the gap by presenting estimates of the religious composition for 2016 and projections until 2046 based on several scenarios related to the three major forces affecting the religious composition: migration (including asylum seekers), differential fertility and secularisation. The projections demonstrate that religious diversity is bound to increase, mostly through immigration and fertility. We further focus on the role and implications of international migration on the age and sex composition within the six religious groups: Roman Catholics, Protestants, Orthodox, Muslims, other religions and unaffiliated. We find that the volume and composition of international migrants can maintain youthful age compositions in minority religions—Muslims and Orthodox. Sustained immigration leads to slower ageing but does not stop or reverse the process. The disparity between older majority and younger minority religious groups will further increase the cultural generation gap

Global Estimates of Mean Years of Schooling: A New Methodology

The indicator of mean years of schooling (MYS) has the advantage of expressing the distribution of educational attainment in a single number. It is often used for cross-country comparisons and in economic and environmental models as the unique indicator of educational attainment and human capital stock. The computation of MYS from a given educational attainment distribution is complex for two main reasons. First, the standard duration of different levels of schooling varies from country to country, and within countries each school level can have different lengths depending on the type of studies, for example, studies of general secondary as opposed to vocational secondary. Secondly, the calculation is biased by the presence of pupils/students who do not complete the full course at any level, which can amount to a substantial share in some countries. To overcome these difficulties, the methodology used and detailed in this paper computes MYS as the weighted mean of six educational levels based on ISCED 1997 classification - no formal education, incomplete primary, completed primary, completed lower secondary, upper secondary and post-secondary education . and the procedure takes into account country-specific educational systems as well as changes in these systems over time. To adjust for the proportion with incomplete educational levels, we developed regional sets of regression models to improve estimates of MYS for the incomplete primary category and a set of correction factors to adjust higher levels. The models are built using detailed data on duration of schooling by grades completed within primary level for 54 countries. We apply the method to estimate MYS for 171 countries in the Wittgenstein Centre (WIC) dataset on educational attainment, which served as the base for the population projections by levels of education until 2100. Detailed data are available online at www.wittgensteincentre.org/dataexplorer. In the paper we compare our method and results for 2010 to the widely used Barro & Lee data and to that of UNESCO, the main provider of global data on education statistics, and explain the differences

Religious Denominations in Vienna & Austria: Baseline Study for 2016-Scenarios until 2046

The publication presents the results of the research project “Religious Denominations in Vienna & Austria: Baseline study for 2016 – Scenarios until 2046” conducted by the Vienna Institute of Demography of the Austrian Academy of Sciences. The aim of this study project was twofold: Firstly, to estimate the religious composition of the population of Austria and Vienna in 2016 taking into account the most recent migration movements; and secondly, to project the population of Austria and Vienna from 2016 to 2046 based on several scenarios related mostly to the three major forces affecting religious composition, migration (including asylum-seekers), differential fertility, and religious conversion. While the projections demonstrate some of the possible futures that Austria and its capital city could experience in the coming decades, and those are all quite dissimilar, they also show that religious diversity is bound to increase, and there are no reasons to think that any of the trends that have been in place already for several decades in the country will stop and that the country would move back to the situation of the early 1970s

Populations for 171 Countries by Age, Sex, and Level of Education around 2010: Harmonized Estimates of the Baseline Data for the Wittgenstein Centre Projections

Data series on levels of educational attainment of the adult population consistent across time and space cannot be found as such, readily available, not in an aggregated form and not by age and sex. This is a pity because levels of educational attainment of the working age population are the main component of human capital that is used in many models, mostly related to economics, IT and health. Researchers at IIASA have developed a methodology to reconstruct and project levels of educational attainment (see Lutz et al. 2007) based on the information contained in the best source for the most recent year. An improved and increased version will become available in 2013. We are showing in this paper that it does not really make sense to keep the data as close as possible to those directly available datasets since a large majority of those suffer from severe flaws, hampering any trend and regression analysis on levels of educational attainment. We show how picking the right dataset for the starting year can be a real hassle and point towards the necessity to invest in harmonizing and mapping levels of education to facilitate academic research for the benefit of societies

European Demographic Datasheet 2016

The European Demographic Datasheet 2016 shows key demographic data, population trends and projections until 2050. It covers fertility, mortality, migration and population structure, including population ageing, and their changes. The datasheet combines data for all countries of Europe and for broader European regions, as well as maps, population pyramids, tables, graphs and featured thematic boxes. It pays special attention to the importance of migration for the current and future population changes across the continent and to the alternative indicators of population ageing. In comparison to the Data Sheet Poster, the online version provides expanded data coverage, additional maps and population pyramids, ranking charts and details about data sources and definitions. It also allows users to download all data and images

Validation of the Wittgenstein Centre Back-projections for Populations by Age, Sex, and Six Levels of Education from 2010 to 1970

There have been few attempts at creating data series on levels of educational attainment of the adult population consistent across time and space by age and sex. They would be needed to estimate the role played by education and human capital in economic, technological, environmental models as correctly as possible. In 2007, Lutz et al developed a methodology to reconstruct (and project) levels of educational attainment based on the information contained in the base-year source of choice for the most recent period (Lutz et al. 2007a). The methodology was applied again in the framework of a new round of population projections published in 2014 online (www.wittgensteincentre.org/dataexplorer) and in the Oxford University book "World Population and Human Capital in the Twenty-First Century" edited by Lutz, Butz and KC. There, the coverage increased to 171 countries and the number of education categories to six. The back-projection methodology was applied to the updated base-year sample in 2010 to arrive at the reconstruction of levels of educational attainment by age and sex for the period 1970-2005. The purpose of this paper is to compare the reconstructed datasets to other existing sources of historical data on education, including the former reconstruction from 2007, collection and other reconstruction exercises. The validation of the Wittgenstein Centre back-projection model outcomes with available empirical data source enables the evaluation of our back-projection method for the establishment of harmonized and consistent time series on the educational composition of 171 countries in the world. In comparison, the most other available datasets suffer from severe flaws, hampering any valid trend and regression analysis on levels of educational attainment. The back-projection methodology is explained in Section 2 and Section 3 describes the collection of empirical data for the validation of the WIC 2015 dataset and associated challenges. The validation methodology and results are developed in Section 4. Detailed documentation about the country-specific validation is available from the Appendices