The random variables measured for these countries are their Inequality (Gini Coefficient) (which is some numerical measurement of how much economic inequality there is in the country; see http://en.wikipedia.org/wiki/Gini_coefficient); variable, and their Intergenerational Earnings Elasticity (which is a numerical measurement of how much ecnomic outcomes tend to change from generation to generation in a country; see http://en.wikipedia.org/wiki/Social_mobility), as response variable.
A scatterplot is given of this data.
The scatterplot shows a fairly strong, positive, linear relationship between the explanatory and response variables. In short: the more inequality in a country, the more likely that inequality is to persist from generation to generation; the more equality in a country, the more likely it is for one generation to be able to change its economic outcomes from those of the previous generation.
The only slightly negative comment I have is about the line: a regression line was drawn on the scatterplot (which is fine), but it would have been nice for the accompanying article to give some numerical value along with this line. In particular, the coefficients of the equation of the regression line would have been nice to know — the slope would tell us how much the Gini coefficient predicts income elasticity. It would also be nice to know the correlation coefficient $r$ of this 2-d dataset, to know exactly how strong is that linear relationship which is visible.