Equatorial Guinea vs Gabon: Statistical performance indicators (SPI): Pillar 4 data sources score
Statistical performance indicators (SPI): Pillar 4 data sources score over time
- Equatorial Guinea
- Gabon
How they compare
Gabon currently reports 29.78 scale 0-100 against 25.69 scale 0-100 in Equatorial Guinea, a difference of 4.09 scale 0-100.
That makes Gabon's figure about 1.2 times Equatorial Guinea's.
Across all 5 years both countries report, Gabon has been ahead every year.
Equatorial Guinea ranks 163rd and Gabon ranks 161st of 181 countries.
Gabon has averaged higher in every one of the 1 decades both report.
Frequently asked questions
- Which has higher statistical performance indicators (spi): pillar 4 data sources score, Equatorial Guinea or Gabon?
- Gabon, at 29.78 scale 0-100 against 25.69 scale 0-100 in Equatorial Guinea as of 2024.
- What is the difference in statistical performance indicators (spi): pillar 4 data sources score between Equatorial Guinea and Gabon?
- 4.09 scale 0-100, with Gabon ahead.
- How many years of comparable data are there for Equatorial Guinea and Gabon?
- 5 years are reported by both, from 2020 to 2024.
- How do Equatorial Guinea and Gabon rank globally for statistical performance indicators (spi): pillar 4 data sources score?
- Equatorial Guinea ranks 163rd and Gabon ranks 161st 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.