Côte d’Ivoire vs Luxembourg: Dimension 1.5: Data use by international organizations
Dimension 1.5: Data use by international organizations over time
- Côte d’Ivoire
- Luxembourg
How they compare
Côte d’Ivoire currently reports 1 against 1 in Luxembourg, a difference of 0.
The two have swapped places 9 times across 20 shared years of data; in 2004 it was Côte d’Ivoire ahead.
Côte d’Ivoire ranks 1st and Luxembourg ranks 1st of 217 countries.
Across the 3 decades both report, Côte d’Ivoire averaged higher in 1 and Luxembourg in 1.
Head to head by decade
| Decade | Côte d’Ivoire | Luxembourg | Difference | Ahead |
|---|---|---|---|---|
| 2000s | 0.3 | 0.2333 | 0.0667 | Côte d’Ivoire |
| 2010s | 0.6 | 0.6 | 0 | — |
| 2020s | 0.975 | 1 | 0.025 | Luxembourg |
Averages of every year both report within each decade.
Frequently asked questions
- Which has higher dimension 1.5: data use by international organizations, Côte d’Ivoire or Luxembourg?
- Côte d’Ivoire, at 1 against 1 in Luxembourg as of 2023.
- What is the difference in dimension 1.5: data use by international organizations between Côte d’Ivoire and Luxembourg?
- 0, with Côte d’Ivoire ahead.
- How many years of comparable data are there for Côte d’Ivoire and Luxembourg?
- 20 years are reported by both, from 2004 to 2023.
- How do Côte d’Ivoire and Luxembourg rank globally for dimension 1.5: data use by international organizations?
- Côte d’Ivoire ranks 1st and Luxembourg ranks 1st 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