Mongolia vs Montenegro: Statistical performance indicators (SPI): Pillar 5 data
Statistical performance indicators (SPI): Pillar 5 data over time
- Mongolia
- Montenegro
How they compare
Mongolia currently reports 70 scale 0-100 against 70 scale 0-100 in Montenegro, a difference of 0 scale 0-100.
The two have swapped places 1 time across 9 shared years of data; in 2016 it was Mongolia ahead.
Mongolia ranks 78th and Montenegro ranks 78th of 192 countries.
Across the 2 decades both report, Mongolia averaged higher in 1 and Montenegro in 1.
Head to head by decade
| Decade | Mongolia | Montenegro | Difference | Ahead |
|---|---|---|---|---|
| 2010s | 55 scale 0-100 | 40 scale 0-100 | 15 scale 0-100 | Mongolia |
| 2020s | 62 scale 0-100 | 74 scale 0-100 | 12 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, Mongolia or Montenegro?
- Mongolia, 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 Mongolia and Montenegro?
- 0 scale 0-100, with Mongolia ahead.
- How many years of comparable data are there for Mongolia and Montenegro?
- 9 years are reported by both, from 2016 to 2024.
- How do Mongolia and Montenegro rank globally for statistical performance indicators (spi): pillar 5 data?
- Mongolia 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.