Liberia: Statistical performance indicators (SPI): Pillar 4 data sources score (scale 0-100)

Key figures

Over five years it is up 36.17% from 32.35 in 2019 (6.37% a year on average, compounded). Since 2015 it is up 62.95% from 27.03 in 2015 (5.57% a year on average, compounded).

What the numbers say

In 2024, Liberia's statistical performance indicators (SPI): Pillar 4 data sources score (scale 0-100) rose 10.53 on 2023 (from 33.52 to 44.05).

Across the 8 yearly changes before that (2016 to 2023), the average change was +0.81 and a typical year strayed about 4.9 from that (the standard deviation). This year's change was 2.0 standard deviations above that average, so it was larger than a typical year, though not out of the range seen before.

On record, the biggest yearly rise was 10.53 in 2024 and the biggest yearly fall was 5.82 in 2019. The numbers show how large and when, not why; for the cause, look at what happened in that country in those years.

From 2015 to 2024 there is no clear straight-line trend: a straight line explains 20% of the movement over 10 observed years, so the value is moving around more than it is heading anywhere.

Worked out from the yearly values above. How the checks work: standard deviation, z-scores and straight-line trends.

How it compares

In 2024, Liberia had the 83rd highest value of the 123 economies reporting that year. Higher is not always better: for some indicators the lowest value is the one to aim for. See the full ranking.

Closest to Liberia in the 2024 ranking

• Uganda — 80th of 123: 45.11
• Lao PDR — 81st of 123: 44.43
• Algeria — 82nd of 123: 44.3
• Togo — 84th of 123: 43.58
• Vanuatu — 85th of 123: 43.48
• Côte d'Ivoire — 86th of 123: 43.38

What this measures

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.

Limitations: In the data sources pillar, more information is needed in the areas of administrative data, geospatial data, and private and citizen generated data. On administrative data, the picture is incomplete with no measures of whether countries have administrative data systems in place to measure health, education, labor, and social protection program statistics. For the geospatial indicator, there is a proxy measure of whether the country is able to produce indicators at the sub-national level, but as yet, no understanding of how countries are using geospatial information in other ways, for instance using satellite data.

Regional and income-group figures are computed by the World Bank as a weighted average.

All 10 values

Statistical performance indicators (SPI): Pillar 4 data sources score (scale 0-100) in nearby economies

• Angola — 48.58 (2024)
• Benin — 49.31 (2024)
• Botswana — 67.25 (2024)
• Burkina Faso — 49.23 (2024)
• Burundi — 18.72 (2024)
• Cabo Verde — 67.9 (2024)
• Cameroon — 39.83 (2024)
• Chad — 24.74 (2024)

More public sector data for Liberia

• Military expenditure (current USD) — 44.21 million (2025)
• Military expenditure (current LCU) — 44.21 million (2025)
• Military expenditure (% of GDP) — 0.85% (2025)
• Armed forces personnel, total — 2,000 (2020)
• Armed forces personnel (% of total labor force) — 0.09% (2020)
• Military expenditure (% of general government expenditure) — 3.89% (2025)
• Statistical performance indicators (SPI): Pillar 1 data use score (scale 0-100) — 60 (2024)
• Statistical performance indicators (SPI): Pillar 3 data products score (scale 0-100) — 71.63 (2024)
• CPIA business regulatory environment rating (1=low to 6=high)

Reading data like this

• Where to find free public data, and how to check it before you trust it — 2 min read
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• Real change or random noise? A plain guide to reading your numbers — 2 min read

Questions people ask

What is Liberia's statistical performance indicators (SPI): Pillar 4 data sources score (scale 0-100)?

44.05 in 2024, according to the World Bank (World Development Indicators).

How has it changed since 2015?

It is up 62.95% from 27.03 in 2015 (5.57% a year on average, compounded). The highest value was 44.05 in 2024 and the lowest 27.03 in 2015.

How does Liberia rank?

83rd highest of 123 economies with data for 2024.

Is the latest change in statistical performance indicators (SPI): Pillar 4 data sources score (scale 0-100) unusual for Liberia?

In 2024, Liberia's statistical performance indicators (SPI): Pillar 4 data sources score (scale 0-100) rose 10.53 on 2023 (from 33.52 to 44.05). Across the 8 yearly changes before that (2016 to 2023), the average change was +0.81 and a typical year strayed about 4.9 from that (the standard deviation). This year's change was 2.0 standard deviations above that average, so it was larger than a typical year, though not out of the range seen before.

Source

World Bank, World Development Indicators, indicator IQ.SPI.PIL4, licensed CC BY 4.0. Original source: Statistical Performance Indicators, World Bank (WB), uri: https://datacatalog.worldbank.org/dataset/statistical-performance-indicators Figures are fetched from the World Bank API and refreshed monthly; see the series on data.worldbank.org. The full definition and method are in the World Bank's metadata glossary. Comparisons use the same World Bank release.

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