The Effect of Education on Individual Earnings: A Statistical Analysis Using Quantile Regression and the Mincerian Model

Abstract

This study examines the impact of education on individual earnings in Saudi Arabia using quantile regression and the Mincerian earnings function. Unlike traditional ordinary least square (OLS) regression, quantile regression allows for a more nuanced understanding of how education influences wages across different points of the income distribution. The analysis is based on a survey of 804 respondents, collected from various regions of Saudi Arabia, and accounts for key factors such as gender, marital status, work experience, and employment sector. The findings reveal that education has a significant positive effect on earnings, but its impact varies across income levels. While higher education substantially benefits lower- and middle-income earners, its returns diminish at the highest quantiles, suggesting that Health sciences, engineering, computers, and information technology (IT) and experience, play a more prominent role in wage determination at the upper end of the income distribution. Gender disparities are evident, with men earning significantly more than women, particularly at higher wage levels, reinforcing long-term career advantages for men. Additionally, marital status is found to be a strong predictor of income, particularly for high earners, while public sector employment provides greater wage stability across all quantiles. The Mincerian model results further support these findings, showing that both education and work experience contribute significantly to earnings. However, diminishing returns to experience at later career stages highlight the need for continuous skill development. The study’s findings have critical implications for education policies, labor market strategies, and workforce planning in Saudi Arabia.