Cameroon vs Mongolia: Statistical performance indicators (SPI): Pillar 5 data
Statistical performance indicators (SPI): Pillar 5 data over time
- Cameroon
- Mongolia
How they compare
Cameroon currently reports 70 scale 0-100 against 70 scale 0-100 in Mongolia, a difference of 0 scale 0-100.
Across all 9 years both countries report, Mongolia has been ahead every year.
Cameroon ranks 78th and Mongolia ranks 78th of 192 countries.
Mongolia has averaged higher in every one of the 2 decades both report.
Head to head by decade
| Decade | Cameroon | Mongolia | Difference | Ahead |
|---|---|---|---|---|
| 2010s | 35 scale 0-100 | 55 scale 0-100 | 20 scale 0-100 | Mongolia |
| 2020s | 48 scale 0-100 | 62 scale 0-100 | 14 scale 0-100 | Mongolia |
Averages of every year both report within each decade.
Frequently asked questions
- Which has higher statistical performance indicators (spi): pillar 5 data, Cameroon or Mongolia?
- Cameroon, at 70 scale 0-100 against 70 scale 0-100 in Mongolia as of 2024.
- What is the difference in statistical performance indicators (spi): pillar 5 data between Cameroon and Mongolia?
- 0 scale 0-100, with Cameroon ahead.
- How many years of comparable data are there for Cameroon and Mongolia?
- 9 years are reported by both, from 2016 to 2024.
- How do Cameroon and Mongolia rank globally for statistical performance indicators (spi): pillar 5 data?
- Cameroon ranks 78th and Mongolia ranks 78th 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.