Maldives vs San Marino: Dimension 1.5: Data use by international organizations
Dimension 1.5: Data use by international organizations over time
- Maldives
- San Marino
How they compare
Maldives currently reports 0.7 against 0.7 in San Marino, a difference of 0.
The two have swapped places 1 time across 20 shared years of data; in 2004 it was Maldives ahead.
Maldives ranks 134th and San Marino ranks 134th of 214 countries.
Maldives has averaged higher in every one of the 3 decades both report.
Head to head by decade
| Decade | Maldives | San Marino | Difference | Ahead |
|---|---|---|---|---|
| 2000s | 0.2 | 0 | 0.2 | Maldives |
| 2010s | 0.47 | 0.23 | 0.24 | Maldives |
| 2020s | 0.7 | 0.65 | 0.05 | Maldives |
Averages of every year both report within each decade.
Frequently asked questions
- Which has higher dimension 1.5: data use by international organizations, Maldives or San Marino?
- Maldives, at 0.7 against 0.7 in San Marino as of 2023.
- What is the difference in dimension 1.5: data use by international organizations between Maldives and San Marino?
- 0, with Maldives ahead.
- How many years of comparable data are there for Maldives and San Marino?
- 20 years are reported by both, from 2004 to 2023.
- How do Maldives and San Marino rank globally for dimension 1.5: data use by international organizations?
- Maldives ranks 134th and San Marino ranks 134th of 214 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