A Statistical Insight into Graduate Income: An IRLS-Optimized Generalized Linear Modelling Approach

Abstract

This study examines the statistical determinants of graduate income using Maximum Likelihood Estimation implemented through the Iteratively Reweighted Least Squares algorithm. A generalized linear model with a logit link was estimated to assess how technical competence, English proficiency, and tuition-financing type influence the probability of earning above the regional minimum wage. The IRLS procedure enabled iterative refinement of coefficient estimates and standard errors, with convergence achieved as successive updates stabilized, indicating a well-behaved likelihood surface. Results show that academic competence and English proficiency significantly increase the log-odds of higher income, highlighting the importance of human-capital attributes in early-career outcomes. Study duration also exhibits a positive and statistically significant association with the likelihood of earning above the minimum wage. In contrast, tuition-financing type shows a positive but statistically non-significant effect, suggesting that its influence on graduate income requires further investigation. Although the model identifies several meaningful determinants, its moderate discriminative capacity indicates that pre-graduation characteristics alone cannot fully explain income variation. By explicitly incorporating MLE with IRLS, this study provides a transparent statistical framework for analyzing graduate employability outcomes and contributes empirical evidence on factors associated with early-career income attainment. The findings encourage future research integrating labor-market, employer, and longitudinal career information to improve predictive performance.