Italy vs Latvia: Statistical performance indicators (SPI): Pillar 5 data infrastructure
Statistical performance indicators (SPI): Pillar 5 data infrastructure over time
- Italy
- Latvia
How they compare
Italy currently reports 85 scale 0-100 against 85 scale 0-100 in Latvia, a difference of 0 scale 0-100.
Across all 9 years both countries report, Latvia has been ahead every year.
Italy ranks 24th and Latvia ranks 24th of 190 countries.
Head to head by decade
| Decade | Italy | Latvia | Difference | Ahead |
|---|---|---|---|---|
| 2010s | 100 scale 0-100 | 100 scale 0-100 | 0 scale 0-100 | — |
| 2020s | 97 scale 0-100 | 97 scale 0-100 | 0 scale 0-100 | — |
Averages of every year both report within each decade.
Frequently asked questions
- Which has higher statistical performance indicators (spi): pillar 5 data infrastructure, Italy or Latvia?
- Italy, at 85 scale 0-100 against 85 scale 0-100 in Latvia as of 2024.
- What is the difference in statistical performance indicators (spi): pillar 5 data infrastructure between Italy and Latvia?
- 0 scale 0-100, with Italy ahead.
- How many years of comparable data are there for Italy and Latvia?
- 9 years are reported by both, from 2016 to 2024.
- How do Italy and Latvia rank globally for statistical performance indicators (spi): pillar 5 data infrastructure?
- Italy ranks 24th and Latvia ranks 24th of 190 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.