Analysing the Determinants of Graduate Unemployment in Tunisia Using Machine Learning
This paper analyses the determinants of youth graduate unemployment in Tunisia by combining classical econometric methods (logistic regression) with three machine learning algorithms (Random Forest, XGBoost, RBF-kernel SVM) applied to an original survey of 1,200 Tunisian graduates. The econometric results reveal that female gender, belonging to the engineering field, and education-employment mismatch both vertical (overqualification) and horizontal (field misalignment) are the most significant determinants. The machine learning analysis confirms the predominance of gender and uncovers non-linear interactions: the protective effect of engineering is significantly attenuated for women, revealing that the gender gap persists even in high-demand fields. A feature importance plot derived from XGBoost quantifies each variable's contribution, making the transition from predictive modelling to policy governance more transparent. XGBoost and SVM offer the best predictive performance, outperforming logistic regression on F1-score and AUC-ROC. These findings call for targeted policies against gender discrimination, differentiated reform of the university curriculum, and improved recruitment transparency.
© The Author(s) 2026. Published by RITHA Publishing. This article is distributed under the terms of the license CC-BY 4.0., which permits any further distribution in any medium, provided the original work is properly cited maintaining attribution to the author(s) and the title of the work, journal citation and URL DOI.
Article’s history: Received 14th of May, 2026; Revised 31st of May, 2026; Accepted for publication 19th of June, 2026; Available online: 24th of June, 2026; Published as research article in Volume II, Issue 1(3), 2026.
Mestiri, S. (2026). Analysing the Determinants of Graduate Unemployment in Tunisia Using Machine Learning. Applied Journal of Economics, Law and Governance, Volume II, Issue 1(3), 125-138. https://doi.org/10.57017/ajelg.v2.i1(3).07
CRediT Authorship Contribution Statement: Mestiri S., contributed to the conceptualization and design of the study, methodology development, formal analysis, investigation, data curation, visualization, and the writing of the original draft. The author was also responsible for reviewing and editing the manuscript.
Acknowledgments: The author declares that no specific funding was received for this research.
Conflict of Interest Statement: The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Data Availability Statement: The data that support the findings of this study are available from the corresponding author upon reasonable request.
Ethical Approval Statement: This study was conducted in accordance with applicable ethical standards. It involved a voluntary anonymous survey of adult graduates. Informed consent was obtained from all participants prior to their inclusion in the study.
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