Access the dataset Global and regional Gini coefficients.
Read the Blueprint An anatomy of inclusive growth in Europe.
Zsolt Darvas compares four methodologies to estimate the global distribution of income and find that many methods work well, but the method based on two-parameter distributions is more accurate than other methods.
This method is simpler, easier to implement and relies on a more internationally-comparable dataset of national income distributions than other approaches used in the literature to calculate the global distribution of income. Zsolt suggests a simulation-based technique to estimate the standard error of the global Gini coefficient.
Global income inequality among the citizens of 128 countries gradually declined in 1989-2013, largely due to convergence of income per capita, which was offset by a small degree the increase in within-country inequalities. The standard error of the global Gini coefficient is very small.
After 1994, market income inequality in the EU28 was at a level similar to market inequality in other parts of the world, but net inequality (after taxes and transfers) is at a much lower level and it declined between 1994 and 2008, since when it remained relatively stable.
Regional income inequality is much higher in Asia, Africa, the Commonwealth of Independent states and Latin America than in the EU28.In Asia, regional inequality has increased recent years, while it declined in the other three non-European regions.