Benin vs Montenegro: Statistical performance indicators (SPI): Pillar 5 data
Statistical performance indicators (SPI): Pillar 5 data over time
- Benin
- Montenegro
How they compare
Benin currently reports 70 scale 0-100 against 70 scale 0-100 in Montenegro, a difference of 0 scale 0-100.
Across all 9 years both countries report, Montenegro has been ahead every year.
Benin ranks 78th and Montenegro ranks 78th of 192 countries.
Montenegro has averaged higher in every one of the 2 decades both report.
Head to head by decade
| Decade | Benin | Montenegro | Difference | Ahead |
|---|---|---|---|---|
| 2010s | 26.25 scale 0-100 | 40 scale 0-100 | 13.75 scale 0-100 | Montenegro |
| 2020s | 54 scale 0-100 | 74 scale 0-100 | 20 scale 0-100 | Montenegro |
Averages of every year both report within each decade.
Frequently asked questions
- Which has higher statistical performance indicators (spi): pillar 5 data, Benin or Montenegro?
- Benin, at 70 scale 0-100 against 70 scale 0-100 in Montenegro as of 2024.
- What is the difference in statistical performance indicators (spi): pillar 5 data between Benin and Montenegro?
- 0 scale 0-100, with Benin ahead.
- How many years of comparable data are there for Benin and Montenegro?
- 9 years are reported by both, from 2016 to 2024.
- How do Benin and Montenegro rank globally for statistical performance indicators (spi): pillar 5 data?
- Benin ranks 78th and Montenegro 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.