Statistical performance indicators (SPI): Pillar 4 data sources score in Saint Vincent and the Grenadines
Saint Vincent and the Grenadines: Statistical performance indicators (SPI): Pillar 4 data sources score was 48.31 scale 0-100 in 2024. ▬ Flat
Statistical performance indicators (SPI): Pillar 4 data sources score in Saint Vincent and the Grenadines, 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 Saint Vincent and the Grenadines stood at 48.31 scale 0-100.
That represents a change of up 0.6% on the previous year and down 1.9% over ten years.
Over the whole period, statistical performance indicators (spi): pillar 4 data sources score in Saint Vincent and the Grenadines peaked at 52.48 scale 0-100 in 2020 and was at its lowest, 48.01 scale 0-100, in 2022.
That places Saint Vincent and the Grenadines 119th out of 182 countries with data for 2024, putting it in the middle of the range.
Averages by decade
| Decade | Average | Lowest | Highest | Years |
|---|---|---|---|---|
| 2010s | 50.01 scale 0-100 | 48.35 scale 0-100 | 51.17 scale 0-100 | 5 |
| 2020s | 49.02 scale 0-100 | 48.01 scale 0-100 | 52.48 scale 0-100 | 5 |
Countries ranked near Saint Vincent and the Grenadines
More public sector data for Saint Vincent and the Grenadines
- Tax revenue 23.8% (2017)
- Taxes on income, profits and capital gains 24.7% (2017)
- Taxes on goods and services 43.5% (2017)
- Net investment in nonfinancial assets 3.6% (2017)
- Net lending (+) / net borrowing (-) -1.5% (2017)
- Interest payments 8.1% (2017)
- Grants and other revenue 11.3% (2017)
- Interest payments 8.8% (2017)
- Other taxes 2.9% (2017)
- Compensation of employees 49.8% (2017)
Frequently asked questions
- What is statistical performance indicators (spi): pillar 4 data sources score in Saint Vincent and the Grenadines?
- Statistical performance indicators (spi): pillar 4 data sources score in Saint Vincent and the Grenadines was 48.31 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 Saint Vincent and the Grenadines?
- The highest recorded value was 52.48 scale 0-100 in 2020.
- What is the lowest statistical performance indicators (spi): pillar 4 data sources score recorded in Saint Vincent and the Grenadines?
- The lowest recorded value was 48.01 scale 0-100 in 2022.
- How does Saint Vincent and the Grenadines rank for statistical performance indicators (spi): pillar 4 data sources score?
- Saint Vincent and the Grenadines ranks 119th out of 182 countries with data for 2024.
- Is statistical performance indicators (spi): pillar 4 data sources score rising or falling in Saint Vincent and the Grenadines?
- Over the last ten years it is down 1.9%. The long-run trend across the full record is flat.
- Where does this Saint Vincent and the Grenadines 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.