Dimension 1.5: Data use by international organizations in Myanmar
Myanmar: Dimension 1.5: Data use by international organizations was 0.9 in 2023. ◆ Volatile
Dimension 1.5: Data use by international organizations in Myanmar, 2004–2023
Source: Statistical Performance Indicators, The World Bank (https://datacatalog.worldbank.org/dataset/statistical-performance-indicators).
Analysis
In 2023, dimension 1.5: data use by international organizations in Myanmar stood at 0.9.
Compared with earlier readings it is up 350.0% over ten years.
Over the whole period, dimension 1.5: data use by international organizations in Myanmar peaked at 1 in 2019 and was at its lowest, 0.2, in 2004.
Myanmar ranks 66th of 214 countries on this measure, in the middle of the range.
The series is highly variable year to year, so single readings are best treated with caution.
Averages by decade
| Decade | Average | Lowest | Highest | Years |
|---|---|---|---|---|
| 2000s | 0.2 | 0.2 | 0.2 | 6 |
| 2010s | 0.4998 | 0.2 | 1 | 10 |
| 2020s | 0.925 | 0.9 | 1 | 4 |
Countries ranked near Myanmar
- 66 Argentina 0.9 compare
- 66 Australia 0.9 compare
- 66 Benin 0.9 compare
- 66 Bhutan 0.9 compare
- 66 Egypt 0.9 compare
- 66 El Salvador 0.9 compare
- 66 Gambia 0.9 compare
- 66 Guinea-Bissau 0.9 compare
- 66 Honduras 0.9 compare
- 66 Ireland 0.9 compare
- 66 Kazakhstan 0.9 compare
- 66 Kenya 0.9 compare
- 66 Laos 0.9 compare
- 66 Liberia 0.9 compare
- 66 Lithuania 0.9 compare
- 66 Malawi 0.9 compare
- 66 Mauritania 0.9 compare
- 66 Mauritius 0.9 compare
- 66 Montenegro 0.9 compare
- 66 Pakistan 0.9 compare
- 66 Paraguay 0.9 compare
- 66 Rwanda 0.9 compare
- 66 Seychelles 0.9 compare
- 66 Slovakia 0.9 compare
- 66 Tanzania 0.9 compare
- 66 Togo 0.9 compare
- 66 Tunisia 0.9 compare
- 66 Turkey 0.9 compare
- 66 United Arab Emirates 0.9 compare
- 66 Palestine 0.9 compare
- 66 Zambia 0.9 compare
More reference data data for Myanmar
- Emission Totals - Indirect emissions (N2O) - Agricultural Soils 11.17 (2050)
- Emission Totals - Indirect emissions (N2O) - Manure left on Pasture 4.58 (2050)
- Emission Totals - Indirect emissions (N2O) - Manure applied to Soils 3.28 (2050)
- Emission Totals - Indirect emissions (N2O) - IPCC Agriculture 11.17 (2050)
- Emission Totals - Indirect emissions (N2O) - Crop Residues 2.53 (2050)
- Temperature anomalies by month 0.4249 °C (2026)
- Summer temperature anomalies 0.0987 °C (2026)
- Spring temperature anomalies -0.0963 °C (2026)
- Emission Totals - Direct emissions (N2O) - Crop Residues 11.26 (2050)
- Emission Totals - Emissions (CO2eq) (AR5) - Crop Residues 3,656 (2050)
Frequently asked questions
- What is dimension 1.5: data use by international organizations in Myanmar?
- Dimension 1.5: data use by international organizations in Myanmar was 0.9 in 2023, according to Statistical Performance Indicators, The World Bank (https://datacatalog.worldbank.org/dataset/statistical-performance-indicators).
- What is the highest dimension 1.5: data use by international organizations recorded in Myanmar?
- The highest recorded value was 1 in 2019.
- What is the lowest dimension 1.5: data use by international organizations recorded in Myanmar?
- The lowest recorded value was 0.2 in 2004.
- How does Myanmar rank for dimension 1.5: data use by international organizations?
- Myanmar ranks 66th out of 214 countries with data for 2023.
- Is dimension 1.5: data use by international organizations rising or falling in Myanmar?
- Over the last ten years it is up 350.0%. The long-run trend across the full record is volatile.
- Where does this Myanmar data come from?
- The figures come from Statistical Performance Indicators, The World Bank (https://datacatalog.worldbank.org/dataset/statistical-performance-indicators), published as part of Dimension 1.5: Data use by international organizations. Statizoid updates them automatically from the source API.
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CSV · JSON — 20 observations, free to reuse under CC BY 4.0 (World Bank Open Data).
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