This dataset combines Gapminders long historic trends with recent releases from modern sources. The specific sources may vary from time to time as better estimates are published.
- Gini coefficient
Gapminder has compiled the data you see in this dataset from several sources, such as official international statistics, various historical sources and our own estimates.
There are many different ways to estimate and compare income. Different methods are used in different countries and years. Unfortunately no data source exists that would enable comparisons across all countries, not even for one single year. Gapminder has managed to adjust the picture for some differences in the data, but there are still large issues in comparing individual countries. The precise values for countries should be taken with a large grain of salt.
Gapminder strongly agrees with Branko Milanovic about the urgent need for a comparable global income survey, especially for the purpose of monitoring the UN poverty-goal. We are constantly improving our datasets and methods. Please expect revision of these numbers in the future.
The details on how the compilation was done and the sources for each observation can be found in the blog post Data Sources used in Don’t Panic — End Poverty.
Gini index measures the extent to which the distribution of income (or, in some cases, consumption expenditure) among individuals or households within an economy deviates from a perfectly equal distribution. A Lorenz curve plots the cumulative percentages of total income received against the cumulative number of recipients, starting with the poorest individual or household. The Gini index measures the area between the Lorenz curve and a hypothetical line of absolute equality, expressed as a percentage of the maximum area under the line. Thus a Gini index of 0 represents perfect equality, while an index of 100 implies perfect inequality.
Years
current version: 20150922 - together with the documentary Don't Panic — End Poverty
Authors: Ola Rosling