Statistical performance indicators (SPI): Pillar 4 data sources score in Montenegro
Montenegro: Statistical performance indicators (SPI): Pillar 4 data sources score was 68.71 scale 0-100 in 2024. ▲ Rising
Statistical performance indicators (SPI): Pillar 4 data sources score in Montenegro, 2015–2024
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 Montenegro stood at 68.71 scale 0-100. That is the highest value across all 10 years on record.
The figure is up 3.5% on the previous year and up 13.3% over ten years.
Over the whole period, statistical performance indicators (spi): pillar 4 data sources score in Montenegro peaked at 68.71 scale 0-100 in 2024 and was at its lowest, 59.82 scale 0-100, in 2017.
That places Montenegro 70th out of 181 countries with data for 2024, putting it in the middle of the range.
Averages by decade
| Decade | Average | Lowest | Highest | Years |
|---|---|---|---|---|
| 2010s | 61.26 scale 0-100 | 59.82 scale 0-100 | 62.07 scale 0-100 | 5 |
| 2020s | 64.17 scale 0-100 | 60.54 scale 0-100 | 68.71 scale 0-100 | 5 |
Countries ranked near Montenegro
- 67 Uruguay 69.53 scale 0-100 compare
- 68 Guatemala 69.47 scale 0-100 compare
- 69 United Arab Emirates 68.74 scale 0-100 compare
- 71 Uzbekistan 68.21 scale 0-100 compare
- 72 Moldova 67.91 scale 0-100 compare
- 73 Cape Verde 67.9 scale 0-100 compare
More public sector data for Montenegro
- Arms imports 5.00 million SIPRI trend indicator values (2024)
- Military expenditure 150.03 million current USD (2024)
- Military expenditure 1.8% (2024)
- Armed forces personnel, total 12,000 (2020)
- Armed forces personnel 4.7% (2020)
- Statistical performance indicators (SPI): Pillar 1 data use score 90 scale 0-100 (2024)
- Intentional homicides 0.7892 per 100,000 people (2023)
- Statistical performance indicators (SPI): Pillar 3 data products score 69.42 scale 0-100 (2024)
- Military expenditure 138.70 million current LCU (2024)
- Proportion of seats held by women in national parliaments 27.2% (2025)
Frequently asked questions
- What is statistical performance indicators (spi): pillar 4 data sources score in Montenegro?
- Statistical performance indicators (spi): pillar 4 data sources score in Montenegro was 68.71 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 Montenegro?
- The highest recorded value was 68.71 scale 0-100 in 2024.
- What is the lowest statistical performance indicators (spi): pillar 4 data sources score recorded in Montenegro?
- The lowest recorded value was 59.82 scale 0-100 in 2017.
- How does Montenegro rank for statistical performance indicators (spi): pillar 4 data sources score?
- Montenegro ranks 70th out of 181 countries with data for 2024.
- Is statistical performance indicators (spi): pillar 4 data sources score rising or falling in Montenegro?
- Over the last ten years it is up 13.3%. The long-run trend across the full record is rising.
- Where does this Montenegro 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
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.