Projection of Subnational Social Heterogeneity in India

This study is motivated by two research questions: (1) How does the accounting for socioeconomic heterogeneity, measured by educational attainment, improve population projections for India?, and (2) How will changing patterns in urbanization affect the population projection, depending on the spatial scale (national vs. subnational) considered in the projections? Projections at national and subnational level can provide essential information for planning and implementing government policies, including the allocation of budget and resources. In a country like India national projections would be too short sighted considering its sheer population size of 1.2 billion inhabitants in 2011. We aim to show not only the spatial and social heterogeneity of urban and rural India, but also how we implemented this in our subnational projection model. This allows us to show the potential population development of India up to 2050 and how and why the consideration of different spatial levels affect the projection outcome

Population projection by age, sex, and educational attainment in rural and urban regions of 35 provinces of India, 2011-2101: Technical report on projecting the regionally explicit socioeconomic heterogeneity in India

This working paper documents and explains our methodological approaches and technical details about how we conducted subnational population projections for India. This research is motivated by two research questions: (1) How does the accounting of socioeconomic heterogeneity, measured by educational attainment, improve population projections for India?, and (2) How will changing patterns in urbanization affect the population projections, depending on the spatial scale (national vs. subnational) considered in the projections? Projections at national and subnational level can provide essential information for planning and implementing government policies, including the allocation of budget and resources. In a country like India national projections ignoring spatial and socioeconomic heterogeneity would be too short-sighted considering its sheer population size of 1.2 billion in 2011. It was surprising to see that our population projections for India with baseline scenario were consistent with the UN medium variant and Wittgenstein Centre SSP2 until 2070. We found that while our fertility assumptions are lower, our mortality assumptions were also lower and compensated for the lower number of births (and no international migration) with higher number of survivors. The results show that the overall fertility for India is lower than estimated/assumed by UN and Wittgenstein Centre due to lower starting values in our projection as well as due to explicit consideration of education in the model. This results in a rapid TFR decline to about 1.85 children per woman in the next two decades and stabilization for the rest of the century. The projection resulted in slower rate of urbanization in India from 31% in 2011 to 40% in 2051, compared to the UN urbanization projection and we presented several explanations for that

Future Population and Human Capital in Heterogeneous India

Within the next decade India is expected to surpass China as the world’s most populous country due to still higher fertility and a younger population. Around 2025 each country will be home to around 1.5 billion people. India is demographically very heterogeneous with some rural illiterate populations still having more than four children on average while educated urban women have fewer than 1.5 children and with great differences between states. We show that the population outlook greatly depends on the degree to which this heterogeneity is explicitly incorporated into the population projection model used. The conventional projection model, considering only the age and sex structures of the population at the national level, results in a lower projected population than the same model applied at the level of states because over time the high-fertility states gain more weight, thus applying the higher rates to more people. The opposite outcome results from an explicit consideration of education differentials because over time the proportion of more educated women with lower fertility increases, thus leading to lower predicted growth than in the conventional model. To comprehensively address this issue, we develop a five-dimensional model of India’s population by state, rural/urban place of residence, age, sex, and level of education and show the impacts of different degrees of aggregation. We also provide human capital scenarios for all Indian states that suggest that India will rapidly catch up with other more developed countries in Asia if the recent pace of education expansion is maintained

Projecting Nepal's Demographic Future- How to deal with spatial and demographic heterogeneity

This Working Paper outlines the efforts of the cooperation between researchers at IIASA’s World Population Program and the Ministry of Health and Population, Nepal to conduct small-area population projections on Village Development Committee and Municipality levels for Nepal from 2011 to 2031. In order to fulfil this aim it was necessary to compile, harmonize and estimate small-area population data based on the latest census and survey data. Due to the lack of comprehensive fine-grained data on the demographic determinants fertility, mortality, and migration we estimate those with different methodological approaches like the Child-Women-Ratio or mortality corrections. In recent time, internal and international migration has become the most common of the three demographic components; therefore, most efforts went into estimating the rates of migration flows to and from several directions. The creation of this small-area fertility, mortality and migration data by age and sex enables us to apply the well-known cohort component method in a multi-state framework (each district as a state) and to create reasonable scenarios on the prospective population development for Nepal on regional and local level. This will help national, regional and local actors and policymakers to set appropriate measures to steer and adapt to the future characteristics of the Nepalese society on all administrative levels

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

A Harmonized Dataset on Global Educational Attainment between 1970 and 2060 – An Analytical Window into Recent Trends and Future Prospects in Human Capital Development

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 paper presents 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 generation, gender, and geography have been evolving in the last 40 years

Global Reconstruction of Educational Attainment, 1950 to 2015: Methodology and Assessment

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 educationtransition 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/

Religions in Vienna in the Past, Present and Future - Key Findings from the WIREL Project

The role of religion is currently a topic of considerable public interest in Vienna as well as across Europe. Over the course of the last half-century, Vienna has witnessed rapidly changing religious composition accompanied by consistently increasing religious diversity. The various aspects of research conducted by WIREL facilitate the global assessment of both quantitative and qualitative aspects of religious diversity in Vienna. A short report – Religions in Vienna in the Past, Present and Future –summarises the research findings with the aim of making the trends, drivers, and socio-demographic consequences of the changing religious landscape of Vienna more accessible and understandable

Projecting Nepal's Demographic Future- How to deal with spatial and demographic heterogeneity-Dataset

This dataset outlines the efforts of the cooperation between researchers at IIASA’s World Population Program and the Ministry of Health and Population, Nepal to conduct small-area population projections on Village Development Committee and Municipality levels for Nepal from 2011 to 2031. In order to fulfil this aim it was necessary to compile, harmonize and estimate small-area population data based on the latest census and survey data. Due to the lack of comprehensive fine-grained data on the demographic determinants fertility, mortality, and migration we estimate those with different methodological approaches like the Child-Women-Ratio or mortality corrections. In recent time, internal and international migration has become the most common of the three demographic components; therefore, most efforts went into estimating the rates of migration flows to and from several directions. The creation of this small-area fertility, mortality and migration data by age and sex enables us to apply the well-known cohort component method in a multi-state framework (each district as a state) and to create reasonable scenarios on the prospective population development for Nepal on regional and local level. This will help national, regional and local actors and policymakers to set appropriate measures to steer and adapt to the future characteristics of the Nepalese society on all administrative levels