The following regression will assist to look at past levels of educational attainment and since Table 1 already indicated the existence of regional differences in educational attainment, regional dummies will be used for this regression as well:
E_(j,t)=b_(0,t)+b_1 E_(j,t-1)+b_2 σ_(j,t-1)^E+b_3 〖logy〗_(j,t-1)+b_4 [〖logy〗_(j,t-1) ]^2+b_D D_j+v_(j,t) (2)
“The regression includes one-period-lagged values of educational inequality and income …show more content…
The results indicate that African countries have by far the lowest level of education. Regardless of monitoring for its low income, Africa still suffers from the lowest level of education with about 2.3 year of schooling. Latin America has the second highest level of education with around 0.79 years of schooling. Finally, Asia flourishes with the highest level with approximately 0.24 years of schooling. But the regional dummies for Asia and Latin America are not statistically significant. In regards to income, the coefficient turn out to be statistically significant. They indicate a positive correlation between educational achievement and income, yet, the rate declines. De Gregorio and Lee also suggest, that once the log of income per capita reaches 10.4, educational attainment drops. Additionally, they propound, that at the mean value of the log income per capita, which is 8.1, its net marginal value on educational achievement would be 0.84 “(3.83-2*0.184*8.1)”. For instance, if a country “that is twice as wealthy as another one will have about 0.6 years (0.84*ln(2)) more schooling per person” compared to the less wealthy