Unveiling the Impact of Socioeconomic and Demographic Factors on Graduate Salaries: A Machine Learning Explanatory Analytical Approach Using Higher Education Statistical Agency Data

Henshaw, Bassey; Mishra, Bhupesh Kumar; Sayers, William; Pervez, Zeeshan

Abstract

Graduate salaries are a significant concern for graduates, employers, and policymakers, as various factors influence them. This study investigates determinants of graduate salaries in the UK, utilising survey data from HESA (Higher Education Statistical Agency) and integrating advanced machine learning (ML) explanatory techniques with statistical analytical methodologies. By employing multi-stage analyses alongside machine learning models such as decision trees, random forests and the explainability with SHAP stands for (Shapley Additive exPanations), this study investigates the influence of 21 socioeconomic and demographic variables on graduate salary outcomes. Key variables, including institutional reputation, age at graduation, socioeconomic classification, job qualification requirements, and domicile, emerged as critical determinants, with institutional reputation proving the most significant. Among ML methods, the decision tree achieved a standout with the highest accuracy through rigorous optimisation techniques, including oversampling and undersampling. SHAP highlighted the top 12 influential variables, providing actionable insights into the interplay between individual and systemic factors. Furthermore, the statistical analysis using ANOVA (Analysis of Variance) validated the significance of these variables, revealing intricate interactions that shape graduate salary dynamics. Additionally, domain experts’ opinions are also analysed to authenticate the findings. This research makes a unique contribution by combining qualitative contextual analysis with quantitative methodologies, machine learning explainability and domain experts’ views on addressing gaps in the existing identification of graduate salary predicting components. Additionally, the findings inform policy and educational interventions to reduce wage inequalities and promote equitable career opportunities. Despite limitations, such as the UK-specific dataset and the focus on socioeconomic and demographic variables, this study lays a robust foundation for future research in predictive modelling and graduate outcomes.

Citation

Henshaw, B., Mishra, B. K., Sayers, W., & Pervez, Z. (2025). Unveiling the Impact of Socioeconomic and Demographic Factors on Graduate Salaries: A Machine Learning Explanatory Analytical Approach Using Higher Education Statistical Agency Data. Analytics, 4(1), Article 10. https://doi.org/10.3390/analytics4010010

• Journal Article Type: Article
• Acceptance Date: Mar 4, 2025
• Online Publication Date: Mar 11, 2025
• Publication Date: Mar 1, 2025
• Deposit Date: Mar 17, 2025
• Publicly Available Date: Mar 17, 2025
• Journal: Analytics
• Print ISSN: 2813-2203
• Electronic ISSN: 2813-2203
• Publisher: MDPI
• Peer Reviewed: Peer Reviewed
• Volume: 4
• Issue: 1
• Article Number: 10
• DOI: https://doi.org/10.3390/analytics4010010
• Keywords: Graduate salaries; Higher education; Machine learning; Socioeconomic and demographic factors; Statistical analysis; SHAP; Analysis of variance (ANOVA)
• Public URL: https://hull-repository.worktribe.com/output/5084345