Comparative Evaluation of Estimated Private Rates of Return to General and Vocational Upper Secondary Education in Greece: Mincer and Machine Learning Approaches

This study recognizes education as an investment and estimates the private rates of return to upper secondary education in Greece, overall, by type (general or vocational) and by gender. Earnings data were collected through primary research using stratified sampling from the private sector of the economy. The analysis is based on the Mincer method and is complemented by machine learning methods, including Support Vector Regression, Random Forests, and Extreme Gradient Boosting. The empirical analysis shows that investing in upper secondary education (general and vocational) is profitable. The private rates of return in upper general secondary education are higher than those in vocational education, and female graduates exhibit higher returns than male graduates. Machine learning models achieve modest improvements in predictive performance, as reflected in higher adj. R2 values and lower prediction errors. However, the estimated rates of return remain broadly consistent with those obtained from the Mincer method. This convergence suggests that the Mincer specification captures the core structural relationship between education and earnings, while machine learning models primarily enhance predictive accuracy without substantially altering the estimated economic returns. This finding highlights the robustness of the traditional econometric framework and clarifies the complementary role of machine learning techniques in empirical labor economics.