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

Germany: Statistical performance indicators (SPI): Pillar 4 data sources score was 86.1 scale 0-100 in 2024. ▲ Rising

Latest (2024)
86.1 scale 0-100
Change on year
down 0.7%
World rank
14th
of 182 countries
All-time high
86.67 scale 0-100
in 2022
All-time low
71.62 scale 0-100
in 2017
Years of data
9
2016–2024

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

0204060802016202020242016: 72.8 scale 0-1002017: 71.6 scale 0-1002018: 74.9 scale 0-1002019: 76.6 scale 0-1002020: 77.1 scale 0-1002021: 85.4 scale 0-1002022: 86.7 scale 0-1002023: 86.7 scale 0-1002024: 86.1 scale 0-100

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

Analysis

The most recent figure for statistical performance indicators (spi): pillar 4 data sources score in Germany is 86.1 scale 0-100, measured in 2024.

That represents a change of down 0.7% on the previous year and up 18.3% over ten years.

Over the whole period, statistical performance indicators (spi): pillar 4 data sources score in Germany peaked at 86.67 scale 0-100 in 2022 and was at its lowest, 71.62 scale 0-100, in 2017.

Germany ranks 14th of 182 countries on this measure, in the top 10%.

Averages by decade

DecadeAverage LowestHighest Years
2010s 73.97 scale 0-100 71.62 scale 0-100 76.57 scale 0-100 4
2020s 84.38 scale 0-100 77.07 scale 0-100 86.67 scale 0-100 5

Countries ranked near Germany

  1. 11 Finland 87.1 scale 0-100 compare
  2. 12 United States 86.92 scale 0-100 compare
  3. 13 Malta 86.53 scale 0-100 compare
  4. 15 Portugal 85.6 scale 0-100 compare
  5. 16 Australia 84.45 scale 0-100 compare
  6. 17 Saudi Arabia 84.38 scale 0-100 compare

See the full ranking of 186 places →

More public sector data for Germany

All data for Germany →

Frequently asked questions

What is statistical performance indicators (spi): pillar 4 data sources score in Germany?
Statistical performance indicators (spi): pillar 4 data sources score in Germany was 86.1 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 Germany?
The highest recorded value was 86.67 scale 0-100 in 2022.
What is the lowest statistical performance indicators (spi): pillar 4 data sources score recorded in Germany?
The lowest recorded value was 71.62 scale 0-100 in 2017.
How does Germany rank for statistical performance indicators (spi): pillar 4 data sources score?
Germany ranks 14th out of 182 countries with data for 2024.
Is statistical performance indicators (spi): pillar 4 data sources score rising or falling in Germany?
Over the last ten years it is up 18.3%. The long-run trend across the full record is rising.
Where does this Germany 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.