Gabon vs Mauritania: Statistical performance indicators (SPI): Pillar 4 data sources score
Statistical performance indicators (SPI): Pillar 4 data sources score over time
- Gabon
- Mauritania
How they compare
Gabon currently reports 29.78 scale 0-100 against 25.54 scale 0-100 in Mauritania, a difference of 4.24 scale 0-100.
That makes Gabon's figure about 1.2 times Mauritania's.
The two have swapped places 1 time across 10 shared years of data; in 2015 it was Mauritania ahead.
Gabon ranks 161st and Mauritania ranks 164th of 181 countries.
Across the 2 decades both report, Gabon averaged higher in 1 and Mauritania in 1.
Head to head by decade
| Decade | Gabon | Mauritania | Difference | Ahead |
|---|---|---|---|---|
| 2010s | 14.45 scale 0-100 | 24.58 scale 0-100 | 10.13 scale 0-100 | Mauritania |
| 2020s | 30.66 scale 0-100 | 26.58 scale 0-100 | 4.08 scale 0-100 | Gabon |
Averages of every year both report within each decade.
Frequently asked questions
- Which has higher statistical performance indicators (spi): pillar 4 data sources score, Gabon or Mauritania?
- Gabon, at 29.78 scale 0-100 against 25.54 scale 0-100 in Mauritania as of 2024.
- What is the difference in statistical performance indicators (spi): pillar 4 data sources score between Gabon and Mauritania?
- 4.24 scale 0-100, with Gabon ahead.
- How many years of comparable data are there for Gabon and Mauritania?
- 10 years are reported by both, from 2015 to 2024.
- How do Gabon and Mauritania rank globally for statistical performance indicators (spi): pillar 4 data sources score?
- Gabon ranks 161st and Mauritania ranks 164th of 181 countries.
- Where does this data come from?
- Statistical Performance Indicators, World Bank (WB), published as Statistical performance indicators (SPI): Pillar 4 data sources score (scale 0-100). Statizoid refreshes it automatically from the source and publishes the full history for both places.
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About this data
The data sources overall score is a composite measure of whether countries have data available from the following sources: Censuses and surveys, administrative data, geospatial data, and private sector/citizen generated data. The data sources (input) pillar is segmented by four types of sources generated by (i) the statistical office (censuses and surveys), and sources accessed from elsewhere such as (ii) administrative data, (iii) geospatial data, and (iv) private sector data and citizen generated data. The appropriate balance between these source types will vary depending on a country's institutional setting and the maturity of its statistical system. High scores should reflect the extent to which the sources being utilized enable the necessary statistical indicators to be generated. For example, a low score on environment statistics (in the data production pillar) may reflect a lack of use of (and low score for) geospatial data (in the data sources pillar). This type of linkage is inherent in the data cycle approach and can help highlight areas for investment required if country needs are to be met.