Iran vs Papua New Guinea: Dimension 1.5: Data use by international organizations
Dimension 1.5: Data use by international organizations over time
- Iran
- Papua New Guinea
How they compare
Iran currently reports 0.666 against 0.6 in Papua New Guinea, a difference of 0.066.
That makes Iran's figure about 1.1 times Papua New Guinea's.
The two have swapped places 1 time across 20 shared years of data; in 2004 it was Papua New Guinea ahead.
Iran ranks 145th and Papua New Guinea ranks 148th of 217 countries.
Iran has averaged higher in every one of the 3 decades both report.
Head to head by decade
| Decade | Iran | Papua New Guinea | Difference | Ahead |
|---|---|---|---|---|
| 2000s | 0.35 | 0.2 | 0.15 | Iran |
| 2010s | 0.72 | 0.4 | 0.32 | Iran |
| 2020s | 0.7665 | 0.6 | 0.1665 | Iran |
Averages of every year both report within each decade.
Frequently asked questions
- Which has higher dimension 1.5: data use by international organizations, Iran or Papua New Guinea?
- Iran, at 0.666 against 0.6 in Papua New Guinea as of 2023.
- What is the difference in dimension 1.5: data use by international organizations between Iran and Papua New Guinea?
- 0.066, with Iran ahead.
- How many years of comparable data are there for Iran and Papua New Guinea?
- 20 years are reported by both, from 2004 to 2023.
- How do Iran and Papua New Guinea rank globally for dimension 1.5: data use by international organizations?
- Iran ranks 145th and Papua New Guinea ranks 148th 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