Female enrollment, child mortality and corruption are good predictors of a country’s UN Education Index

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 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 the least absolute shrinkage and selection operator (LASSO). The 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 was identified as the net female enrollment in secondary schools. Afterward, a linear regression was performed on the selected variables. To ensure that the model can generalize, a cross-validation technique was applied using the leave one out (LOO) approach. The out-of-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.