Congo vs Samoa: Statistical performance indicators (SPI): Pillar 5 data
Statistical performance indicators (SPI): Pillar 5 data over time
- Congo
- Samoa
How they compare
Congo currently reports 50 scale 0-100 against 50 scale 0-100 in Samoa, a difference of 0 scale 0-100.
Across all 9 years both countries report, Samoa has been ahead every year.
Congo ranks 138th and Samoa ranks 138th of 192 countries.
Samoa has averaged higher in every one of the 2 decades both report.
Head to head by decade
| Decade | Congo | Samoa | Difference | Ahead |
|---|---|---|---|---|
| 2010s | 22.5 scale 0-100 | 45 scale 0-100 | 22.5 scale 0-100 | Samoa |
| 2020s | 35 scale 0-100 | 53 scale 0-100 | 18 scale 0-100 | Samoa |
Averages of every year both report within each decade.
Frequently asked questions
- Which has higher statistical performance indicators (spi): pillar 5 data, Congo or Samoa?
- Congo, at 50 scale 0-100 against 50 scale 0-100 in Samoa as of 2024.
- What is the difference in statistical performance indicators (spi): pillar 5 data between Congo and Samoa?
- 0 scale 0-100, with Congo ahead.
- How many years of comparable data are there for Congo and Samoa?
- 9 years are reported by both, from 2016 to 2024.
- How do Congo and Samoa rank globally for statistical performance indicators (spi): pillar 5 data?
- Congo ranks 138th and Samoa ranks 138th of 192 countries.
- Where does this data come from?
- Statistical Performance Indicators, World Bank (WB), published as Statistical performance indicators (SPI): Pillar 5 data infrastructure score (scale 0-100). Statizoid refreshes it automatically from the source and publishes the full history for both places.
Individual pages
About this data
The data infrastructure pillar overall score measures the hard and soft infrastructure segments, itemizing essential cross cutting requirements for an effective statistical system. The segments are: (i) legislation and governance covering the existence of laws and a functioning institutional framework for the statistical system; (ii) standards and methods addressing compliance with recognized frameworks and concepts; (iii) skills including level of skills within the statistical system and among users (statistical literacy); (iv) partnerships reflecting the need for the statistical system to be inclusive and coherent; and (v) finance mobilized both domestically and from donors.