Wittgenstein Projection: Percentage of the population age 20-64 by highest level of educational attainment. No Education. Male by country
Share of the population of the stated age group that has never attended school. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility...
What the numbers show
Wittgenstein Projection: Percentage of the population age 20-64 by highest level of educational attainment. No Education. Male is currently reported for 133 countries. The highest value is 0.1% in Chad; the lowest is 0.0% in Zimbabwe.
The median across all reporting countries is 0.0%, and the mean is 0.0%.
Wittgenstein Projection: Percentage of the population age 20-64 by: full country ranking
| # | Country | Latest | Year | 10-year change | Trend |
|---|---|---|---|---|---|
| 1 | Chad | 0.1% | 2100 | down 38.5% | falling |
| 2 | Mali | 0.1% | 2100 | down 41.7% | volatile |
| 2 | Niger | 0.1% | 2100 | down 36.4% | volatile |
| 4 | Burkina Faso | 0.1% | 2100 | down 44.4% | volatile |
| 4 | Liberia | 0.1% | 2100 | down 37.5% | volatile |
| 6 | Mozambique | 0.0% | 2100 | down 33.3% | volatile |
| 7 | Benin | 0.0% | 2100 | down 40.0% | volatile |
| 7 | Sierra Leone | 0.0% | 2100 | down 40.0% | volatile |
| 9 | Guinea | 0.0% | 2100 | down 60.0% | volatile |
| 9 | Madagascar | 0.0% | 2100 | down 33.3% | volatile |
| 9 | Senegal | 0.0% | 2100 | down 33.3% | volatile |
| 12 | Burundi | 0.0% | 2100 | down 50.0% | volatile |
| 12 | Bangladesh | 0.0% | 2100 | unchanged | volatile |
| 12 | Ethiopia | 0.0% | 2100 | down 50.0% | volatile |
| 12 | Ghana | 0.0% | 2100 | unchanged | volatile |
| 12 | Gambia | 0.0% | 2100 | unchanged | volatile |
| 12 | Guinea-Bissau | 0.0% | 2100 | down 66.7% | volatile |
| 12 | Lao People's Democratic Republic | 0.0% | 2100 | unchanged | volatile |
| 12 | Pakistan | 0.0% | 2100 | down 66.7% | volatile |
| 12 | Rwanda | 0.0% | 2100 | unchanged | volatile |
| 12 | Sudan | 0.0% | 2100 | down 50.0% | volatile |
| 12 | Somalia | 0.0% | 2100 | down 50.0% | volatile |
| 23 | Aruba | 0.0% | 2100 | — | volatile |
| 23 | Albania | 0.0% | 2100 | — | volatile |
| 23 | United Arab Emirates | 0.0% | 2100 | — | volatile |
| 23 | Argentina | 0.0% | 2100 | — | volatile |
| 23 | Azerbaijan | 0.0% | 2100 | — | volatile |
| 23 | Belgium | 0.0% | 2100 | — | volatile |
| 23 | Bulgaria | 0.0% | 2100 | — | volatile |
| 23 | Bahrain | 0.0% | 2100 | — | volatile |
| 23 | Bahamas | 0.0% | 2100 | — | volatile |
| 23 | Bosnia and Herzegovina | 0.0% | 2100 | — | volatile |
| 23 | Belize | 0.0% | 2100 | — | volatile |
| 23 | Bolivia, Plurinational State of | 0.0% | 2100 | — | volatile |
| 23 | Brazil | 0.0% | 2100 | — | volatile |
| 23 | Bhutan | 0.0% | 2100 | down 100.0% | volatile |
| 23 | Central African Republic | 0.0% | 2100 | — | volatile |
| 23 | Canada | 0.0% | 2100 | — | volatile |
| 23 | Chile | 0.0% | 2100 | — | volatile |
| 23 | China | 0.0% | 2100 | — | volatile |
| 23 | Côte d'Ivoire | 0.0% | 2100 | down 100.0% | volatile |
| 23 | Cameroon | 0.0% | 2100 | — | volatile |
| 23 | Democratic Republic of Congo | 0.0% | 2100 | — | volatile |
| 23 | Congo | 0.0% | 2100 | — | volatile |
| 23 | Colombia | 0.0% | 2100 | — | volatile |
| 23 | Comoros | 0.0% | 2100 | — | volatile |
| 23 | Cape Verde | 0.0% | 2100 | — | volatile |
| 23 | Costa Rica | 0.0% | 2100 | — | volatile |
| 23 | Cuba | 0.0% | 2100 | — | volatile |
| 23 | Germany | 0.0% | 2100 | — | volatile |
| 23 | Dominican Republic | 0.0% | 2100 | — | volatile |
| 23 | Algeria | 0.0% | 2100 | — | volatile |
| 23 | Ecuador | 0.0% | 2100 | — | volatile |
| 23 | Egypt | 0.0% | 2100 | — | volatile |
| 23 | Spain | 0.0% | 2100 | — | volatile |
| 23 | France | 0.0% | 2100 | — | volatile |
| 23 | Gabon | 0.0% | 2100 | — | volatile |
| 23 | United Kingdom of Great Britain and Northern Ireland | 0.0% | 2100 | — | volatile |
| 23 | Equatorial Guinea | 0.0% | 2100 | — | volatile |
| 23 | Greece | 0.0% | 2100 | — | volatile |
| 23 | Guatemala | 0.0% | 2100 | down 100.0% | volatile |
| 23 | Guyana | 0.0% | 2100 | — | volatile |
| 23 | Hong Kong, China | 0.0% | 2100 | — | volatile |
| 23 | Honduras | 0.0% | 2100 | — | volatile |
| 23 | Haiti | 0.0% | 2100 | — | volatile |
| 23 | Indonesia | 0.0% | 2100 | — | volatile |
| 23 | India | 0.0% | 2100 | down 100.0% | volatile |
| 23 | Iran, Islamic Republic of | 0.0% | 2100 | — | volatile |
| 23 | Iraq | 0.0% | 2100 | — | volatile |
| 23 | Israel | 0.0% | 2100 | — | volatile |
| 23 | Jamaica | 0.0% | 2100 | — | volatile |
| 23 | Jordan | 0.0% | 2100 | — | volatile |
| 23 | Kenya | 0.0% | 2100 | — | volatile |
| 23 | Cambodia | 0.0% | 2100 | down 100.0% | volatile |
| 23 | Republic of Korea | 0.0% | 2100 | — | volatile |
| 23 | Kuwait | 0.0% | 2100 | — | volatile |
| 23 | Lebanon | 0.0% | 2100 | — | volatile |
| 23 | Saint Lucia | 0.0% | 2100 | — | volatile |
| 23 | Lesotho | 0.0% | 2100 | — | volatile |
| 23 | Luxembourg | 0.0% | 2100 | — | volatile |
| 23 | Macau, China | 0.0% | 2100 | — | volatile |
| 23 | Morocco | 0.0% | 2100 | — | volatile |
| 23 | Maldives | 0.0% | 2100 | — | volatile |
| 23 | Mexico | 0.0% | 2100 | — | volatile |
| 23 | North Macedonia | 0.0% | 2100 | — | volatile |
| 23 | Malta | 0.0% | 2100 | — | volatile |
| 23 | Myanmar | 0.0% | 2100 | — | volatile |
| 23 | Montenegro | 0.0% | 2100 | — | volatile |
| 23 | Mongolia | 0.0% | 2100 | — | volatile |
| 23 | Mauritius | 0.0% | 2100 | — | volatile |
| 23 | Malawi | 0.0% | 2100 | down 100.0% | volatile |
| 23 | Malaysia | 0.0% | 2100 | — | volatile |
| 23 | Namibia | 0.0% | 2100 | — | volatile |
| 23 | New Caledonia | 0.0% | 2100 | — | volatile |
| 23 | Nigeria | 0.0% | 2100 | — | volatile |
| 23 | Nicaragua | 0.0% | 2100 | — | volatile |
| 23 | Netherlands | 0.0% | 2100 | — | volatile |
| 23 | Nepal | 0.0% | 2100 | — | volatile |
| 23 | Panama | 0.0% | 2100 | — | volatile |
| 23 | Peru | 0.0% | 2100 | — | volatile |
| 23 | Philippines | 0.0% | 2100 | — | volatile |
| 23 | Puerto Rico | 0.0% | 2100 | — | volatile |
| 23 | Portugal | 0.0% | 2100 | — | volatile |
| 23 | Paraguay | 0.0% | 2100 | — | volatile |
| 23 | Palestine, State of | 0.0% | 2100 | — | volatile |
| 23 | French Polynesia | 0.0% | 2100 | — | volatile |
| 23 | Qatar | 0.0% | 2100 | — | volatile |
| 23 | Romania | 0.0% | 2100 | — | volatile |
| 23 | Saudi Arabia | 0.0% | 2100 | — | volatile |
| 23 | Singapore | 0.0% | 2100 | — | volatile |
| 23 | El Salvador | 0.0% | 2100 | — | volatile |
| 23 | Sao Tome and Principe | 0.0% | 2100 | — | volatile |
| 23 | Eswatini | 0.0% | 2100 | — | volatile |
| 23 | Syria | 0.0% | 2100 | — | volatile |
| 23 | Thailand | 0.0% | 2100 | — | volatile |
| 23 | Tajikistan | 0.0% | 2100 | — | volatile |
| 23 | Timor-Leste | 0.0% | 2100 | — | volatile |
| 23 | Tonga | 0.0% | 2100 | — | volatile |
| 23 | Trinidad and Tobago | 0.0% | 2100 | — | volatile |
| 23 | Tunisia | 0.0% | 2100 | — | volatile |
| 23 | Türkiye | 0.0% | 2100 | — | volatile |
| 23 | Tanzania, United Republic of | 0.0% | 2100 | — | volatile |
| 23 | Uganda | 0.0% | 2100 | down 100.0% | volatile |
| 23 | Uruguay | 0.0% | 2100 | — | volatile |
| 23 | United States of America | 0.0% | 2100 | — | volatile |
| 23 | Saint Vincent and the Grenadines | 0.0% | 2100 | — | volatile |
| 23 | Venezuela, Bolivarian Republic of | 0.0% | 2100 | — | volatile |
| 23 | Viet Nam | 0.0% | 2100 | — | volatile |
| 23 | Vanuatu | 0.0% | 2100 | — | volatile |
| 23 | Samoa | 0.0% | 2100 | — | volatile |
| 23 | South Africa | 0.0% | 2100 | — | volatile |
| 23 | Zambia | 0.0% | 2100 | — | volatile |
| 23 | Zimbabwe | 0.0% | 2100 | — | volatile |
Regions and income groups
Aggregates are excluded from the country ranking above so that a region can never outrank a country.
- World 0.0%
About this data
Share of the population of the stated age group that has never attended school. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/