Peru vs Thailand: Dimension 1.5: Data use by international organizations

Peru
1
in 2023
Thailand
1
in 2023
Peru rank
1st
Thailand rank
1st

Dimension 1.5: Data use by international organizations over time

  • Peru
  • Thailand
00.20.40.60.81200420132023

How they compare

Peru currently reports 1 against 1 in Thailand, a difference of 0.

Across all 20 years both countries report, Thailand has been ahead every year.

Peru ranks 1st and Thailand ranks 1st of 214 countries.

Thailand has averaged higher in every one of the 3 decades both report.

Head to head by decade

Decade Peru Thailand Difference Ahead
2000s 0.4 0.4 0
2010s 0.68 0.78 0.1 Thailand
2020s 0.95 1 0.05 Thailand

Averages of every year both report within each decade.

Frequently asked questions

Which has higher dimension 1.5: data use by international organizations, Peru or Thailand?
Peru, at 1 against 1 in Thailand as of 2023.
What is the difference in dimension 1.5: data use by international organizations between Peru and Thailand?
0, with Peru ahead.
How many years of comparable data are there for Peru and Thailand?
20 years are reported by both, from 2004 to 2023.
How do Peru and Thailand rank globally for dimension 1.5: data use by international organizations?
Peru ranks 1st and Thailand ranks 1st 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

Indicator
Dimension 1.5: Data use by international organizations
Source
Statistical Performance Indicators, The World Bank (https://datacatalog.worldbank.org/dataset/statistical-performance-indicators)
Licence
CC BY 4.0 (World Bank Open Data)
Coverage
217 places, 4,340 data points, 2004–2023
Last refreshed

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