Austria vs Lithuania: Statistical performance indicators (SPI): Pillar 4 data sources score
Statistical performance indicators (SPI): Pillar 4 data sources score over time
- Austria
- Lithuania
How they compare
Lithuania currently reports 80.5 scale 0-100 against 80.25 scale 0-100 in Austria, a difference of 0.25 scale 0-100.
The two have swapped places 2 times across 9 shared years of data; in 2016 it was Lithuania ahead.
Austria ranks 35th and Lithuania ranks 33rd of 181 countries.
Lithuania has averaged higher in every one of the 2 decades both report.
Head to head by decade
| Decade | Austria | Lithuania | Difference | Ahead |
|---|---|---|---|---|
| 2010s | 71.55 scale 0-100 | 73.58 scale 0-100 | 2.03 scale 0-100 | Lithuania |
| 2020s | 78.42 scale 0-100 | 79.55 scale 0-100 | 1.13 scale 0-100 | Lithuania |
Averages of every year both report within each decade.
Frequently asked questions
- Which has higher statistical performance indicators (spi): pillar 4 data sources score, Austria or Lithuania?
- Lithuania, at 80.5 scale 0-100 against 80.25 scale 0-100 in Austria as of 2024.
- What is the difference in statistical performance indicators (spi): pillar 4 data sources score between Austria and Lithuania?
- 0.25 scale 0-100, with Lithuania ahead.
- How many years of comparable data are there for Austria and Lithuania?
- 9 years are reported by both, from 2016 to 2024.
- How do Austria and Lithuania rank globally for statistical performance indicators (spi): pillar 4 data sources score?
- Austria ranks 35th and Lithuania ranks 33rd of 181 countries.
- Where does this data come from?
- Statistical Performance Indicators, World Bank (WB), published as Statistical performance indicators (SPI): Pillar 4 data sources 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 sources overall score is a composite measure of whether countries have data available from the following sources: Censuses and surveys, administrative data, geospatial data, and private sector/citizen generated data. The data sources (input) pillar is segmented by four types of sources generated by (i) the statistical office (censuses and surveys), and sources accessed from elsewhere such as (ii) administrative data, (iii) geospatial data, and (iv) private sector data and citizen generated data. The appropriate balance between these source types will vary depending on a country's institutional setting and the maturity of its statistical system. High scores should reflect the extent to which the sources being utilized enable the necessary statistical indicators to be generated. For example, a low score on environment statistics (in the data production pillar) may reflect a lack of use of (and low score for) geospatial data (in the data sources pillar). This type of linkage is inherent in the data cycle approach and can help highlight areas for investment required if country needs are to be met.