We are back again, having fun correlating variables. In this case, we will take a look at two very well known indicators. The GINI Coefficient, and the Human Development Index.
According to World Bank, the GINI Coefficient “measures the extent to which the distribution of income or consumption expenditure among individuals or households within an economy deviates from a perfectly equal distribution.” The data was compiled by Wikipedia here, and it is one of the sources used for this article. As it can be in the previous link, these values don’t belong all to the same exact year, but they actually range from 2008 to 2011. Still, it is assumed that the GINI Coefficient will not suffer sudden changes inside a country.
This coefficient ranged from the value of Denmark (0.24, the country with the most distributed income) to Seychelles (0.658, the country with the highest variation in income). This coefficient has been normalized and inverted, so Denmark receives a score of 100%, while Seychelles obtains a 0%.
The map of the inequality in the world looks as the following:
The other variable we will correlate is the Human Development Index, elaborated by the UNDP (United Nations Development Program). This index “is a comparative measure of life expectancy, literacy, education, standards of living, and quality of life for countries worldwide. It is a standard means of measuring well-being, especially child welfare. It is used to distinguish whether the country is a developed, a developing or an underdeveloped country, and also to measure the impact of economic policies on quality of life.” The report is published by the UNDP, but the data for this article was obtained from the wikipedia publication, here. But if you want to take a look at the complete report you can always check it here.
The map of Human Development looks like this:
In order to compare Humand Development with GINI Coefficient, the first index was noramalized as well, where the lowest country (DRC and Niger share the same score) obtain a 0%, while Norway obtains a 100%.
Once both indexes have been normalized, the comparison comes from a simple substraction. values close to 0% will imply a high level of correlation (distribution of wealth very uneven AND low human development or the opposite, even distribution of wealth AND high human development). Values close to 100% or -100% will imply an OR relationship between both variables rather than an AND relationship.
Let’s take a look at the resulting map, shall we?
Well, green colours are associated to a high degree of correlation, while brown and yellow mean a low degree of correlation (for different reasons). Countries like Zambia or Haiti are vey unfair in the distribution of wealth, and at the same time they have a very low degree level of Human Development. They share colour with countries like Finland or Sweden, where an even distribution of wealth comes together with a high degree of Human Development.
Brown countries, such as Chile, USA or Sudafrica, show a very high degree of difference between the poor and the rich, but they still do have a high level of Human Development. Other countries, such as Afghanistan or Mali, don’t show that strong differences in the distribution of wealth, but their Humand Development Index is still too low.
Well, conclusions up to the reader as you know. The data used for this can be found here: