South Sudan vs Suriname: Dimension 1.5: Data use by international organizations
Dimension 1.5: Data use by international organizations over time
- South Sudan
- Suriname
How they compare
Suriname currently reports 0.466 against 0.4 in South Sudan, a difference of 0.066.
That makes Suriname's figure about 1.2 times South Sudan's.
The two have swapped places 2 times across 20 shared years of data; in 2004 it was Suriname ahead.
South Sudan ranks 190th and Suriname ranks 187th of 217 countries.
Suriname has averaged higher in every one of the 3 decades both report.
Head to head by decade
| Decade | South Sudan | Suriname | Difference | Ahead |
|---|---|---|---|---|
| 2000s | 0.2 | 0.2 | 0 | β |
| 2010s | 0.22 | 0.32 | 0.1 | Suriname |
| 2020s | 0.4 | 0.4915 | 0.0915 | Suriname |
Averages of every year both report within each decade.
Frequently asked questions
- Which has higher dimension 1.5: data use by international organizations, South Sudan or Suriname?
- Suriname, at 0.466 against 0.4 in South Sudan as of 2023.
- What is the difference in dimension 1.5: data use by international organizations between South Sudan and Suriname?
- 0.066, with Suriname ahead.
- How many years of comparable data are there for South Sudan and Suriname?
- 20 years are reported by both, from 2004 to 2023.
- How do South Sudan and Suriname rank globally for dimension 1.5: data use by international organizations?
- South Sudan ranks 190th and Suriname ranks 187th of 217 countries.
- Where does this data come from?
- Statistical Performance Indicators, The World Bank (https://datacatalog.worldbank.org/dataset/statistical-performance-indicators), published as Dimension 1.5: Data use by international organizations. Statizoid refreshes it automatically from the source and publishes the full history for both places.
Individual pages
About this data
Five measures usefulness or reliability of country produced measures for international organizations. First, on comparability of poverty estimates for the World Bank reporting on international poverty (Source: Povcalnet). Second on usable surveys for statistics on child mortality for the UN Inter-agency Group for Child Mortality Estimation (Source: https://childmortality.org/). Third on accuracy of debt reporting as classified by the World Bank (Source: World Bank WDI metadata). Fourth, on availability of safely managed drinking water data for use by JMP. Fifth, on labor force participation data for use by ILO. We recognize that these data sources provide only partial coverage but consider that they do at least provide some indication of the performance of the national statistical system. With more complete data sources it would be possible to assess this further