A Statistical Insight into Graduate Income: An IRLS-Optimized Generalized Linear Modelling Approach
Keywords:
MLE, IRLS algorithm, graduate income prediction, GLM, odds ratio analysis
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 technical competence and English proficiency significantly increase the log-odds of higher income, highlighting the importance of human-capital attributes in early-career outcomes. Tuition-financing type appears as the strongest predictor, with scholarship-funded graduates demonstrating substantially higher income likelihood than their self-funded peers, suggesting structural advantages associated with financial support. Although the model demonstrates meaningful associations, its moderate discriminative capacity underscores that pre-graduation characteristics alone cannot fully explain income variation. By explicitly incorporating MLE with IRLS, this study provides a robust statistical framework for analyzing employability patterns and contributes methodological clarity to graduate income research. The findings encourage future work integrating richer labor-market indicators and continuous income measures to enhance predictive accuracy.
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References
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[27] R. E. B. Gufron, M. Simanjuntak, and T. Novianti, “Program Beasiswa dan Peningkatan Kinerja Dampak Kepemimpinan, Kontribusi Sosial dan Ekonomi Alumni LPDP,” Jurnal Aplikasi Bisnis dan Manajemen, Sep. 2022, doi: 10.17358/jabm.8.3.926.
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[35] N. S. Al-Nassar, A. A. Alhajjaj, and A. Bleady, “Potential predictors of length of study for finance and accounting degrees: evidence from a public business school in Saudi Arabia,” Heliyon, vol. 8, no. 6, p. e09636, Jun. 2022, doi: 10.1016/j.heliyon.2022.e09636.
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[37] Md. H. Rahman, Md. M. Islam, M. Parvin, N. Sultana, and Md. A. Rahman, “Institutional Assistance and Adaptation of Fresh Graduates Facing Employment Crisis and Poverty,” Journal of Business Studies, vol. 03, no. 01, pp. 119–143, 2022, doi: 10.58753/jbspust.3.1.2022.8.
[38] A. M. Al Shukaili, K. Al Kindi, N. M. Kassim, Z. Ahmed, and K. Al Hosni, “Can government financial support enhance job creations: insights from Oman,” Journal of Science and Technology Policy Management, vol. 14, no. 5, pp. 807–829, Aug. 2023, doi: 10.1108/JSTPM-07-2021-0100.
[39] G. Groves and I. Palmer, “Tracking Graduates into the Workforce: Connecting Education and Labor Market,” 2015. [Online]. Available: www.nga.org.
[40] F. R. Jones, M. Mardis, C. McClure, and E. Randeree, “Alumni tracking: Promising practices for collecting, analyzing, and reporting employment data,” Journal of Higher Education Management, vol. 32, no. 1, 2017.
[2] J. Robson, “Graduate employability and employment,” in Assessing the Contributions of Higher Education, Edward Elgar Publishing, 2023, pp. 177–196. doi: 10.4337/9781035307173.00019.
[3] R. Blokker, J. Akkermans, M. Tims, P. Jansen, and S. Khapova, “Building a sustainable start: The role of career competencies, career success, and career shocks in young professionals’ employability,” J Vocat Behav, vol. 112, pp. 172–184, Jun. 2019, doi: 10.1016/j.jvb.2019.02.013.
[4] N. Nacong, Hajar, and Andri, “Pengaruh Nilai Perilaku Dan Nilai Akademik Terhadap Keberhasilan Alumni Saat di Dunia Kerja Menggunakan Regresi Linear Berganda,” Innovative: Journal Of Social Science Research, vol. 4, no. 1, 2024, Accessed: Dec. 01, 2025. [Online]. Available: https://j-innovative.org/index.php/Innovative/article/view/9022
[5] M. Irfany, “ANALISIS FAKTOR-FAKTOR YANG MEMPENGARUHI MASA,” Universitas Islam Indonesia, Jogjakarta, 2007.
[6] S.-I. Lee, H. Lee, P. Abbeel, and A. Y. Ng, “Efficient L 1 Regularized Logistic Regression,” in In Proceedings of the 21st national conference on Artificial intelligence-Volume 1, 2006. [Online]. Available: www.aaai.org
[7] D. Straszak and N. K. Vishnoi, “IRLS and Slime Mold: Equivalence and Convergence,” Jan. 2016, [Online]. Available: http://arxiv.org/abs/1601.02712
[8] L. Fahrmeir and G. Tutz, Multivariate Statistical Modelling Based on Generalized Linear Models, 2nd ed. New York, NY: Springer New York, 2001. doi: 10.1007/978-1-4757-3454-6.
[9] R. Finnie and M. Frenette, “Earning differences by major field of study: evidence from three cohorts of recent Canadian graduates,” Econ Educ Rev, vol. 22, no. 2, pp. 179–192, Apr. 2003, doi: 10.1016/S0272-7757(02)00003-1.
[10] M. Francesconi and M. Parey, “Early gender gaps among university graduates,” Eur Econ Rev, vol. 109, pp. 63–82, Oct. 2018, doi: 10.1016/j.euroecorev.2018.02.004.
[11] Z. S. Syafhil and L. Kartika, “ANALISIS KESUKSESAN KARIR ALUMNI PROGRAM MAHASISWA BERPRESTASI DI PERGURUAN TINGGI,” Perspektif Ilmu Pendidikan, vol. 35, no. 1, pp. 9–24, Apr. 2021, doi: 10.21009/PIP.351.2.
[12] T. T. A. Ngo, “The Importance of Soft Skills for Academic Performance and Career Development—From the Perspective of University Students,” International Journal of Engineering Pedagogy (iJEP), vol. 14, no. 3, pp. 53–68, Apr. 2024, doi: 10.3991/ijep.v14i3.45425.
[13] M. V. Gruzdev, I. V. Kuznetsova, I. Y. Tarkhanova, and E. I. Kazakova, “University graduates’ soft skills: The employers’ opinion,” European Journal of Contemporary Education, vol. 7, no. 4, pp. 690–698, 2018, doi: 10.13187/ejced.2018.4.690.
[14] J. Gleeson, R. Black, A. Keddie, and C. Charles, “Graduate capitals and employability: Insights from an Australian university co-curricular scholarship program,” Pedagogy, Culture & Society, vol. 32, no. 2, pp. 341–359, Mar. 2024, doi: 10.1080/14681366.2022.2038251.
[15] C. Succi and M. Canovi, “Soft skills to enhance graduate employability: comparing students and employers’ perceptions,” Studies in Higher Education, vol. 45, no. 9, pp. 1834–1847, Sep. 2020, doi: 10.1080/03075079.2019.1585420.
[16] D. W. Hosmer, S. Lemeshow, and R. X. Sturdivant, Applied Logistic Regression. Wiley, 2013. doi: 10.1002/9781118548387.
[17] S. Pérez-Fernández, P. Martínez-Camblor, P. Filzmoser, and N. Corral, “Visualizing the decision rules behind the ROC curves: understanding the classification process,” AStA Advances in Statistical Analysis, vol. 105, no. 1, pp. 135–161, Mar. 2021, doi: 10.1007/s10182-020-00385-2.
[18] M. Buda, A. Maki, and M. A. Mazurowski, “A systematic study of the class imbalance problem in convolutional neural networks,” Neural Networks, vol. 106, pp. 249–259, Oct. 2018, doi: 10.1016/j.neunet.2018.07.011.
[19] P. Peduzzi, J. Concato, E. Kemper, T. R. Holford, and A. R. Feinstein, “A simulation study of the number of events per variable in logistic regression analysis,” J Clin Epidemiol, vol. 49, no. 12, pp. 1373–1379, Dec. 1996, doi: 10.1016/S0895-4356(96)00236-3.
[20] P. McCullagh and J. A. Nelder, Generalized Linear Models. Routledge, 2019. doi: 10.1201/9780203753736.
[21] A. F. Zuur, E. N. Ieno, N. Walker, A. A. Saveliev, and G. M. Smith, Mixed effects models and extensions in ecology with R. New York, NY: Springer New York, 2009. doi: 10.1007/978-0-387-87458-6.
[22] D. A. Gomez-Cravioto, R. E. Diaz-Ramos, N. Hernandez-Gress, J. L. Preciado, and H. G. Ceballos, “Supervised machine learning predictive analytics for alumni income,” J Big Data, vol. 9, no. 1, Dec. 2022, doi: 10.1186/s40537-022-00559-6.
[23] B. Henshaw, B. K. Mishra, W. Sayers, and Z. Pervez, “Unveiling the Impact of Socioeconomic and Demographic Factors on Graduate Salaries: A Machine Learning Explanatory Analytical Approach Using Higher Education Statistical Agency Data,” Analytics, vol. 4, no. 1, Mar. 2025, doi: 10.3390/analytics4010010.
[24] O. Galvez-Soriano, “Foreign language skills and labor market outcomes: The case of English in Mexico,” Econ Educ Rev, vol. 108, p. 102702, Oct. 2025, doi: 10.1016/j.econedurev.2025.102702.
[25] R. Godoy et al., “Language skills and earnings: Evidence from a pre-industrial economy in the Bolivian Amazon,” Econ Educ Rev, vol. 26, no. 3, pp. 349–360, Jun. 2007, doi: 10.1016/j.econedurev.2005.12.001.
[26] C. Dustmann and F. Fabbri, “LANGUAGE PROFICIENCY AND LABOUR MARKET PERFORMANCE OF IMMIGRANTS IN THE UK*.”
[27] R. E. B. Gufron, M. Simanjuntak, and T. Novianti, “Program Beasiswa dan Peningkatan Kinerja Dampak Kepemimpinan, Kontribusi Sosial dan Ekonomi Alumni LPDP,” Jurnal Aplikasi Bisnis dan Manajemen, Sep. 2022, doi: 10.17358/jabm.8.3.926.
[28] J. Mountjoy, “Community Colleges and Upward Mobility,” American Economic Review, vol. 112, no. 8, pp. 2580–2630, Aug. 2022, doi: 10.1257/aer.20181756.
[29] G. L. Bernal, L. K. Abadia, L. E. Alvarez-Arando, K. De Witte, L. K. Abadía, and L. E. Álvarez-Arando, “Financial Aid Uncertainty and Low-Income Students’ Higher Education Preferences Financial Aid Uncertainty and Low-Income Students’ Higher Education Preferences 1,” 2023.
[30] A. Delis and C. Jones, “The impact of work placements on graduate earnings,” Studies in Higher Education, vol. 48, no. 11, pp. 1708–1723, Nov. 2023, doi: 10.1080/03075079.2023.2211999.
[31] P. Wang, W. Liao, Z. Zhao, and F. Miu, “Prediction of Factors Influencing the Starting Salary of College Graduates Based on Machine Learning,” Wirel Commun Mob Comput, vol. 2022, no. 1, Jan. 2022, doi: 10.1155/2022/7845545.
[32] F. Li, N. A. Majid, and S. Ding, “Unlocking the potential of LSTM for accurate salary prediction with MLE, Jeffreys prior, and advanced risk functions,” PeerJ Comput Sci, vol. 10, p. e1875, Feb. 2024, doi: 10.7717/peerj-cs.1875.
[33] M. Zhou and R. Ramezani, “A Deep Dive into the Factors Influencing Financial Success: A Machine Learning Approach.”
[34] A. Payne, “Assessing Post-Secondary Graduates’ Soft Skills, Job Search and Employment Outcomes,” University of Guelph, 2018.
[35] N. S. Al-Nassar, A. A. Alhajjaj, and A. Bleady, “Potential predictors of length of study for finance and accounting degrees: evidence from a public business school in Saudi Arabia,” Heliyon, vol. 8, no. 6, p. e09636, Jun. 2022, doi: 10.1016/j.heliyon.2022.e09636.
[36] J. E. Hunter and F. L. Schmidt, “Dichotomization of continuous variables: The implications for meta-analysis.,” Journal of Applied Psychology, vol. 75, no. 3, pp. 334–349, Jun. 1990, doi: 10.1037/0021-9010.75.3.334.
[37] Md. H. Rahman, Md. M. Islam, M. Parvin, N. Sultana, and Md. A. Rahman, “Institutional Assistance and Adaptation of Fresh Graduates Facing Employment Crisis and Poverty,” Journal of Business Studies, vol. 03, no. 01, pp. 119–143, 2022, doi: 10.58753/jbspust.3.1.2022.8.
[38] A. M. Al Shukaili, K. Al Kindi, N. M. Kassim, Z. Ahmed, and K. Al Hosni, “Can government financial support enhance job creations: insights from Oman,” Journal of Science and Technology Policy Management, vol. 14, no. 5, pp. 807–829, Aug. 2023, doi: 10.1108/JSTPM-07-2021-0100.
[39] G. Groves and I. Palmer, “Tracking Graduates into the Workforce: Connecting Education and Labor Market,” 2015. [Online]. Available: www.nga.org.
[40] F. R. Jones, M. Mardis, C. McClure, and E. Randeree, “Alumni tracking: Promising practices for collecting, analyzing, and reporting employment data,” Journal of Higher Education Management, vol. 32, no. 1, 2017.
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2026-09-11
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Copyright (c) 2026 Yusma Yanti, Hutomo Atman Maulana, Salmun K Nasib, Anang Kurnia
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