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 ORCID: https://orcid.org/0000-0003-1677-4409 and Pervez, Zeeshan (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). p. 10. doi:10.3390/analytics4010010

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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.

Item Type:	Article
Article Type:	Article
Uncontrolled Keywords:	Graduate salaries; Higher education; Machine learning; Socioeconomic and demographic factors; Statistical analysis; SHAP; Analysis of variance (ANOVA)
Subjects:	H Social Sciences > HA Statistics L Education > LB Theory and practice of education > LB2300 Higher Education
Divisions:	Schools and Research Institutes > School of Business, Computing and Social Sciences
Depositing User:	Kamila Niekoraniec
Date Deposited:	21 Mar 2025 10:27
Last Modified:	10 Apr 2025 09:15
URI:	https://eprints.glos.ac.uk/id/eprint/14895

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