Statistical performance indicators (SPI): Pillar 4 data sources score in High income

High income: Statistical performance indicators (SPI): Pillar 4 data sources score was 75.83 scale 0-100 in 2024. ▲ Rising

Latest (2024)
75.83 scale 0-100
Change on year
up 2.1%
Rank
1st
of 5 groups
All-time high
75.83 scale 0-100
in 2024
All-time low
66.43 scale 0-100
in 2017
Years of data
9
2016–2024

Statistical performance indicators (SPI): Pillar 4 data sources score in High income, 2016–2024

0204060802016202020242016: 68 scale 0-1002017: 66.4 scale 0-1002018: 67.3 scale 0-1002019: 68.1 scale 0-1002020: 67.9 scale 0-1002021: 75.1 scale 0-1002022: 73.9 scale 0-1002023: 74.2 scale 0-1002024: 75.8 scale 0-100

Source: Statistical Performance Indicators, World Bank (WB). Measured in scale 0-100.

Analysis

In 2024, statistical performance indicators (spi): pillar 4 data sources score in High income stood at 75.83 scale 0-100. That is the highest value across all 9 years on record.

That represents a change of up 2.1% on the previous year and up 11.6% over ten years.

Over the whole period, statistical performance indicators (spi): pillar 4 data sources score in High income peaked at 75.83 scale 0-100 in 2024 and was at its lowest, 66.43 scale 0-100, in 2017.

Averages by decade

DecadeAverage LowestHighest Years
2010s 67.44 scale 0-100 66.43 scale 0-100 68.06 scale 0-100 4
2020s 73.4 scale 0-100 67.86 scale 0-100 75.83 scale 0-100 5

Countries ranked near High income

  1. 1 Singapore 95.88 scale 0-100 compare
  2. 2 Poland 94.33 scale 0-100 compare
  3. 3 Spain 92.1 scale 0-100 compare
  4. 4 Canada 91.28 scale 0-100 compare

See the full ranking of 186 places →

More public sector data for High income

All data for High income →

Frequently asked questions

What is statistical performance indicators (spi): pillar 4 data sources score in High income?
Statistical performance indicators (spi): pillar 4 data sources score in High income was 75.83 scale 0-100 in 2024, according to Statistical Performance Indicators, World Bank (WB).
What is the highest statistical performance indicators (spi): pillar 4 data sources score recorded in High income?
The highest recorded value was 75.83 scale 0-100 in 2024.
What is the lowest statistical performance indicators (spi): pillar 4 data sources score recorded in High income?
The lowest recorded value was 66.43 scale 0-100 in 2017.
How does High income rank for statistical performance indicators (spi): pillar 4 data sources score?
High income ranks 1st out of 5 groups with data for 2024.
Is statistical performance indicators (spi): pillar 4 data sources score rising or falling in High income?
Over the last ten years it is up 11.6%. The long-run trend across the full record is rising.
Where does this High income data come from?
The figures come from Statistical Performance Indicators, World Bank (WB), published as part of Statistical performance indicators (SPI): Pillar 4 data sources score (scale 0-100). Statizoid updates them automatically from the source API.

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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.