Wittgenstein Centre
Human Capital Data Explorer

Explore, select and download data on past reconstructions and future projections of the global population by age, sex and education, published in Lutz, Goujon, KC, Stonawski, and Stilianakis (Eds.) (2018) .



1. Indicators



2. Geography



3. Breakdown



4. Time Horizon




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Scenario Details:


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Basic underlying assumptions used in the projection of future indicators. Available at the country level only. Full information on the scenarios can be found on the About page.





Wittgenstein Centre
Human Capital Graphic Explorer

Explore, visualise and download graphics on past reconstructions and future projections of the global population by age, sex and education, published in Lutz, Goujon, KC, Stonawski, and Stilianakis (Eds.) (2018) .




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Download profile pages of 201 countries from the appendix of Lutz, Goujon, KC, Stonawski, and Stilianakis (Eds.) (2018) (revised version).

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Note: The image to the right is a sample preview. It will not update.




Further Details

More detailed information on how to use this web application. Further reading on the data from the global population projections by age, sex and education is provided in Lutz, Goujon, KC, Stonawski, and Stilianakis (Eds.) (2018) and in Speringer et al. (2019) for the reconstruction. Further features about the methodology can be found in Lutz, Butz, and K.C. (2014).

Overview

This website presents a set of different scenarios of future population and human capital trends in 201 countries of the world to 2100. The result of the population projections by levels of educational attainment were published in 2018 in Lutz, Goujon, KC, Stonawski, and Stilianakis (Eds.) (2018). They provide an update of the projections (scope, coverage and quality) presented in Lutz, Butz and KC in 2014, based on the work of a large team of researchers at the Wittgenstein Centre for Demography and Global Human Capital and at other institutions.

The present version (2.0) benefitted from the partnership with the Joint Research Centre in the Centre of Expertise on Population and Migration (CEPAM). On top of the assumptions about future trends in fertility, mortality, and education, the projections study the effect of several migration assumptions applied to the context of the set of Shared Socioeconomic Pathways (SSP) scenarios related to the Intergovernmental Panel on Climate Change (IPCC).

The new version also includes the reconstruction of population by levels of educational attainment from 1950 to 2015 for 185 countries. More information in Speringer et al. 2019.

Citation

The suggested citation for data and plots from this website is:

Wittgenstein Centre for Demography and Global Human Capital, (2018). Wittgenstein Centre Data Explorer Version 2.0 (Beta). Available at: http://www.wittgensteincentre.org/dataexplorer

Credits

Data Explorer Team at the Wittgenstein Centre for Demography and Global Human Capital:

Concept and Coordination:

Data:

Web Interface:

Researchers who participated in the development of the projections:

M. Jalal Abbasi-Shavazi Michel Guillot Richard G. Rogers
Guy J. Abel a Graeme Hugo Anna Rotkirch
Alicia Adsera Gavin Jones Patrick Sabourin a, b
Bilal F. Barakat a Sandra Jurasszovich Nikola Sander
Stuart Basten Samir K.C. a Warren C. Sanderson a
Ramon Bauer James K.S. Zeba Sathar
Jan Van Bavel Siew-Ean Khoo Sergei Scherbov a
Alain Bélanger a, b Henri Leridon Bruno Schoumaker
Donatien Beguy Elke Loichinger a David Shapiro
Caroline Berghammer a Marc Luy a Vegard Skirbekk
Ayla Bonfiglio Wolfgang Lutz a, b Tomas Sobotka a
William P. Butz Guillaume Marois a, b Markus Speringer
Graziella Caselli Douglas Massey Nikolaos Stilianakis b
John Casterline Bruno Masquelier Marcin Stonawski a, b
Teresa Castro-Martin John F. May Erich Striessnig a
Minja Kim Choe Blessing Mberu Christian Wegner-Siegmundt a
Alessandra Conte b France Meslé Maria Rita Testa a
Sven Drefahl Melinda Mills Olivier Thévenon
Rachel E. Durham S. Philip Morgan Edward Jow-Ching Tu
Jakob Eder Elsie Pamuk Laura Wong
Regina Fuchs Michaela Potančoková a, b Marcus Wurzer
Tomas Frejka Claudia Reiter a Brenda Yepez-Martinez
Alessandra Garbero Ronald R. Rindfuss Dilek Yildiz a
Michel Garenne Fernando Riosmena Sam Hyun Yoo
Anne Goujon a Louis Rosero-Bixby Kryštof Zeman a
Erofili Grapsa b Arodys Robles Zhongwei Zhao
a Wittgenstein Centre for Demography and Global Human Capital
b Centre of Expertise on Population and Migration (CEPAM), Joint Research Center

Web interface built using:

  • shiny: Chang, W., Cheng J., Allaire, J.J., Xie, Y. & McPherson, J. (2018). shiny: Web Application Framework for R. R package version 1.1.0

Additional R packages used for data manipulations and visualisation:

  • dplyr: Wickham, H., Francois, R., Henry, L. and Müller K. (2018). dplyr: A Grammar of Data Manipulation. R package version 0.7.6.
  • googleVis: Gesmann, M., & de Castillo, D. (2011). Using the Google visualisation API with R. The R Journal, 3(2), 40-44.
  • magrittr: Bache, S.M. & Wickham, H. (2015). magrittr: A Forward-Pipe Operator for R. R package version 1.5
  • markdown: Allaire, J.J., Horner, J., Marti, V. & Porte, N. (2015). markdown: Markdown rendering for R. R package version 0.7.4
  • tidyr: Wickham, H. & Henry, L. (2018). tidyr: Easily Tidy Data with 'spread()' and 'gather()' Functions. R package version 0.8.1.
  • saves: Daróczi, G. (2013). saves: Fast load variables. R package version 0.5
  • webshot: Chang, W. (2017). webshot: Take Screenshots of Web Pages. R package version 0.5

General:

Data and Graphic Explorer:

For further details see:

For further details see: