Wittgenstein Projection: Mean Years of Schooling. Age 25+. Gender Gap in Rwanda
Rwanda: Wittgenstein Projection: Mean Years of Schooling. Age 25+. Gender Gap was 0.13 in 2100. ◆ Volatile
Wittgenstein Projection: Mean Years of Schooling. Age 25+. Gender Gap in Rwanda, 2010–2100
Source: Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/.
Analysis
The most recent figure for wittgenstein projection: mean years of schooling. age 25+. gender gap in Rwanda is 0.13, measured in 2100.
The figure is up 8.3% over ten years.
Over the whole period, wittgenstein projection: mean years of schooling. age 25+. gender gap in Rwanda peaked at 0.7 in 2010 and was at its lowest, 0.08, in 2065.
That places Rwanda 35th out of 166 countries with data for 2100, putting it in the top quarter.
The series is highly variable year to year, so single readings are best treated with caution.
Averages by decade
| Decade | Average | Lowest | Highest | Years |
|---|---|---|---|---|
| 2010s | 0.625 | 0.55 | 0.7 | 2 |
| 2020s | 0.39 | 0.34 | 0.44 | 2 |
| 2030s | 0.225 | 0.19 | 0.26 | 2 |
| 2040s | 0.125 | 0.11 | 0.14 | 2 |
| 2050s | 0.095 | 0.09 | 0.1 | 2 |
| 2060s | 0.085 | 0.08 | 0.09 | 2 |
| 2070s | 0.085 | 0.08 | 0.09 | 2 |
| 2080s | 0.105 | 0.1 | 0.11 | 2 |
| 2090s | 0.12 | 0.12 | 0.12 | 2 |
| 2100s | 0.13 | 0.13 | 0.13 | 1 |
Countries ranked near Rwanda
More reference data data for Rwanda
- Emission Totals - Indirect emissions (N2O) - Manure applied to Soils 0.2282 (2050)
- Emission Totals - Indirect emissions (N2O) - Manure left on Pasture 0.9517 (2050)
- Emission Totals - Indirect emissions (N2O) - IPCC Agriculture 1.38 (2050)
- Emission Totals - Indirect emissions (N2O) - Crop Residues 0.1223 (2050)
- Emission Totals - Indirect emissions (N2O) - Agricultural Soils 1.38 (2050)
- Emission Totals - Emissions (N2O) - Manure applied to Soils 0.7651 (2050)
- Emission Totals - Emissions (N2O) - Manure Management 0.9517 (2050)
- Emission Totals - Emissions (N2O) - Manure left on Pasture 4.27 (2050)
- Emission Totals - Emissions (N2O) - IPCC Agriculture 6.98 (2050)
- Emission Totals - Emissions (N2O) - Crop Residues 0.666 (2050)
Frequently asked questions
- What is wittgenstein projection: mean years of schooling. age 25+. gender gap in Rwanda?
- Wittgenstein projection: mean years of schooling. age 25+. gender gap in Rwanda was 0.13 in 2100, according to Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/.
- What is the highest wittgenstein projection: mean years of schooling. age 25+. gender gap recorded in Rwanda?
- The highest recorded value was 0.7 in 2010.
- What is the lowest wittgenstein projection: mean years of schooling. age 25+. gender gap recorded in Rwanda?
- The lowest recorded value was 0.08 in 2065.
- How does Rwanda rank for wittgenstein projection: mean years of schooling. age 25+. gender gap?
- Rwanda ranks 35th out of 166 countries with data for 2100.
- Is wittgenstein projection: mean years of schooling. age 25+. gender gap rising or falling in Rwanda?
- Over the last ten years it is up 8.3%. The long-run trend across the full record is volatile.
- Where does this Rwanda data come from?
- The figures come from Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/, published as part of Wittgenstein Projection: Mean Years of Schooling. Age 25+. Gender Gap. Statizoid updates them automatically from the source API.
Download this data
CSV · JSON — 19 observations, free to reuse under CC BY 4.0 (World Bank Open Data).
About this data
The difference between male and female mean number of years spent in school by age group. It is calculated by subtracting the female value from the male value. Data can be negative if the mean years of schooling for females is higher than for males. 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/