A Cross-National Study on Education and Employment Outcomes Using Machine Learning-Based Predictive Modeling

This paper investigates the relationship between education levels and employment outcomes across multiple countries by employing machine learning-based predictive modeling as the primary analytical framework. Drawing on cross-national data from the OECD and the World Bank, the study compares secondary and tertiary education in relation to employment rates, and examines the structural alignment between educational enrollment and labor market absorption. Beyond descriptive statistics and linear regression as baselines, the methodology emphasizes supervised machine learning algorithms—Random Forest Regressor, Support Vector Regressor, and Gradient Boosting Regressor—with hyperparameter tuning via grid search and five-fold cross-validation. Experimental results demonstrate that ensemble-based machine learning models outperform traditional statistical approaches in predictive accuracy (R<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> improved from 0.68 to 0.81) and reveal complex, nonlinear patterns in the education-employment nexus, particularly structural mismatches in developing economies. The findings underscore the potential of machine learning techniques as robust tools for evidence-based educational policy-making and workforce planning.