Female Enrollment, Child Mortality and Corruption Are Good Predictors of a Country’s UN Education Index.

Abstract

The United Nations (UN) Education Index (EI), a widely accepted measure of educational attainment within a country and a component of the Human Development Index, is measured by combining the average adult years of schooling with expected years of schooling for children. It is concerning that sub-Saharan African (SSA) countries tend to have a relatively low average EI of 0.45 compared to a global average of 0.65 for all countries. This suggests that SSA countries are underperforming in this regard. Insufficient attention has been devoted to identifying key factors that could explain the variability in educational attainment and most especially the underperformance in SSA countries. This study investigates the predictive power of female enrollment, child mortality and corruption, among other variables to explain the EI. The data comprised of 165 countries and 47 explanatory variables and one target variable (EI), obtained from the World Bank and Transparency International. Machine learning and variable selection techniques were utilized to identify the relevant indicators. Predictive performance (in-sample and out-of-sample using cross validation) facilitated a deeper understanding of the indicators that best explain the variation in the EI. The variable selection process was accomplished using least absolute shrinkage and selection operator (LASSO). Mortality rate of children under the age of five and the corruption perception index are two non-education related indicators that contributed most to the prediction of the dependent variable (EI). The education related predictor that was identified was the net female enrollment in secondary schools. Afterwards, a linear regression was performed on the selected variables. To ensure that the model is capable of generalizing, a cross validation technique was applied using the leave one out (LOO) approach. The outof-sample performance of R2 = 0.89 implies that the model explains much of the variability in the EI. The identified relationships offer a basis for policy recommendations.