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

Key figures

Over five years it is up 39.65% from 57.4 in 2019 (6.91% a year on average, compounded). Since 2015 it is up 39% from 57.67 in 2015 (3.73% a year on average, compounded).

What the numbers say

In 2024, Malaysia's statistical performance indicators (SPI): Pillar 4 data sources score (scale 0-100) rose 7.8 on 2023 (from 72.36 to 80.16).

Across the 8 yearly changes before that (2016 to 2023), the average change was +1.84 and a typical year strayed about 3.14 from that (the standard deviation). This year's change was 1.9 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 8.33 in 2021 and the biggest yearly fall was 1.72 in 2016. 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 the straight-line trend is upward, about 2.64 a year. A straight line explains 84% of the movement over 10 observed years.

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

How it compares

In 2024, Malaysia had the 11th 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 Malaysia in the 2024 ranking

• Bulgaria — 9th of 123: 80.75
• North Macedonia — 10th of 123: 80.4
• Kazakhstan — 11th of 123: 80.16
• Belarus — 13th of 123: 77.1
• Albania — 14th of 123: 74.6
• Panama — 15th of 123: 74.48

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

• Cambodia — 46.58 (2024)
• China — 40.7 (2024)
• Fiji — 55.95 (2024)
• Indonesia — 58.73 (2024)
• Kiribati — 31.92 (2024)
• Lao PDR — 44.43 (2024)
• Marshall Islands — 36.1 (2024)
• Micronesia, Fed. Sts. — 22.1 (2024)

More public sector data for Malaysia

• Military expenditure (current USD) — 4.93 billion (2025)
• Military expenditure (current LCU) — 21.13 billion (2025)
• Military expenditure (% of GDP) — 1.04% (2025)
• Arms imports (SIPRI trend indicator values) — 7 million (2024)
• Military expenditure (% of general government expenditure) — 4.43% (2025)
• Intentional homicides (per 100,000 people) — 0.73 (2023)
• Armed forces personnel, total — 136,000 (2020)
• Armed forces personnel (% of total labor force) — 0.81% (2020)
• Central government debt, total (current LCU) — 1.25 trillion (2024)
• Central government debt, total (% of GDP)

Reading data like this

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Questions people ask

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

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

How has it changed since 2015?

It is up 39% from 57.67 in 2015 (3.73% a year on average, compounded). The highest value was 80.16 in 2024 and the lowest 55.95 in 2016.

How does Malaysia rank?

11th 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 Malaysia?

In 2024, Malaysia's statistical performance indicators (SPI): Pillar 4 data sources score (scale 0-100) rose 7.8 on 2023 (from 72.36 to 80.16). Across the 8 yearly changes before that (2016 to 2023), the average change was +1.84 and a typical year strayed about 3.14 from that (the standard deviation). This year's change was 1.9 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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