Algeria vs Saint Kitts and Nevis: Statistical performance indicators (SPI): Pillar 4 data sources score
Statistical performance indicators (SPI): Pillar 4 data sources score over time
- Algeria
- Saint Kitts and Nevis
How they compare
Algeria currently reports 44.3 scale 0-100 against 43.56 scale 0-100 in Saint Kitts and Nevis, a difference of 0.74 scale 0-100.
Across all 5 years both countries report, Algeria has been ahead every year.
Algeria ranks 132nd and Saint Kitts and Nevis ranks 135th of 182 countries.
Algeria has averaged higher in every one of the 1 decades both report.
Frequently asked questions
- Which has higher statistical performance indicators (spi): pillar 4 data sources score, Algeria or Saint Kitts and Nevis?
- Algeria, at 44.3 scale 0-100 against 43.56 scale 0-100 in Saint Kitts and Nevis as of 2024.
- What is the difference in statistical performance indicators (spi): pillar 4 data sources score between Algeria and Saint Kitts and Nevis?
- 0.74 scale 0-100, with Algeria ahead.
- How many years of comparable data are there for Algeria and Saint Kitts and Nevis?
- 5 years are reported by both, from 2020 to 2024.
- How do Algeria and Saint Kitts and Nevis rank globally for statistical performance indicators (spi): pillar 4 data sources score?
- Algeria ranks 132nd and Saint Kitts and Nevis ranks 135th of 182 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.
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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.