Malawi vs Sao Tome and Principe: Statistical performance indicators (SPI): Pillar 4 data sources score

Malawi
49.38 scale 0-100
in 2024
Sao Tome and Principe
48.59 scale 0-100
in 2024
Malawi rank
114th
Sao Tome and Principe rank
117th

Statistical performance indicators (SPI): Pillar 4 data sources score over time

  • Malawi
  • Sao Tome and Principe
0204060201520192024

How they compare

Malawi currently reports 49.38 scale 0-100 against 48.59 scale 0-100 in Sao Tome and Principe, a difference of 0.79 scale 0-100.

The two have swapped places 2 times across 10 shared years of data; in 2015 it was Malawi ahead.

Malawi ranks 114th and Sao Tome and Principe ranks 117th of 182 countries.

Across the 2 decades both report, Malawi averaged higher in 1 and Sao Tome and Principe in 1.

Head to head by decade

Decade Malawi Sao Tome and Principe Difference Ahead
2010s 44.94 scale 0-100 40.05 scale 0-100 4.89 scale 0-100 Malawi
2020s 48.82 scale 0-100 50.08 scale 0-100 1.26 scale 0-100 Sao Tome and Principe

Averages of every year both report within each decade.

Frequently asked questions

Which has higher statistical performance indicators (spi): pillar 4 data sources score, Malawi or Sao Tome and Principe?
Malawi, at 49.38 scale 0-100 against 48.59 scale 0-100 in Sao Tome and Principe as of 2024.
What is the difference in statistical performance indicators (spi): pillar 4 data sources score between Malawi and Sao Tome and Principe?
0.79 scale 0-100, with Malawi ahead.
How many years of comparable data are there for Malawi and Sao Tome and Principe?
10 years are reported by both, from 2015 to 2024.
How do Malawi and Sao Tome and Principe rank globally for statistical performance indicators (spi): pillar 4 data sources score?
Malawi ranks 114th and Sao Tome and Principe ranks 117th 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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Malawi vs Sao Tome and Principe: Statistical performance indicators (SPI): Pillar 4 data sources score. Statizoid, drawing on Statistical Performance Indicators, World Bank (WB). Retrieved 24 August 2026, from https://public-sector.statizoid.com/compare/statistical-performance-indicators-spi-pillar-4-data-sources-score-scale-0-100/malawi/sao-tome-and-principe/

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Paste this into a page to link back to these figures. The data itself is free to reuse under CC BY 4.0 (World Bank Open Data); please keep the attribution.

<a href="https://public-sector.statizoid.com/compare/statistical-performance-indicators-spi-pillar-4-data-sources-score-scale-0-100/malawi/sao-tome-and-principe/">Malawi vs Sao Tome and Principe: Statistical performance indicators (SPI): Pillar 4 data sources score</a> — Statizoid

About this data

Indicator
Statistical performance indicators (SPI): Pillar 4 data sources score (scale 0-100)
Unit
scale 0-100
Source
Statistical Performance Indicators, World Bank (WB)
Licence
CC BY 4.0 (World Bank Open Data)
Coverage
186 places, 1,766 data points, 2015–2024
Last refreshed

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.