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Optimizing Pension Participation in Kenya through Predictive Modeling: A Comparative Analysis of Tree-Based Machine Learning Algorithms and Logistic Regression Classifier

Author

Listed:
  • Nelson Kemboi Yego

    (African Center of Excellence in Data Science, University of Rwanda, Kigali 4285, Rwanda
    School of Economics, University of Rwanda, Kigali 4285, Rwanda
    Department of Mathematics and Computing, Moi University, Eldoret 3900-30100, Kenya)

  • Juma Kasozi

    (African Center of Excellence in Data Science, University of Rwanda, Kigali 4285, Rwanda
    Department of Mathematics, Makerere University, Kampala 7062-10218, Uganda)

  • Joseph Nkurunziza

    (African Center of Excellence in Data Science, University of Rwanda, Kigali 4285, Rwanda
    School of Economics, University of Rwanda, Kigali 4285, Rwanda)

Abstract

Pension plans play a vital role in the economy by impacting savings, consumption, and investment allocation. Despite declining mortality rates and increasing life expectancy, pension enrollment remains low, affecting the long-term financial stability and well-being of populations. To address this issue, this study was conducted to explore the potential of predictive modeling techniques in improving pension participation. The study utilized three tree-based machine learning algorithms and a logistic regression classifier to analyze data from a nationally representative 2019 Kenya FinAccess Household Survey. The results indicated that ensemble tree-based models, particularly the random forest model, were the most effective in predicting pension enrollment. The study identified the key factors that influenced enrollment, such as National Health Insurance Fund (NHIF) usage, monthly income, and bank usage. The findings suggest that collaboration among the NHIF, banks, and pension providers is necessary to increase pension uptake, along with increased financial education for citizens. The study provides valuable insight for promoting and optimizing pension participation.

Suggested Citation

  • Nelson Kemboi Yego & Juma Kasozi & Joseph Nkurunziza, 2023. "Optimizing Pension Participation in Kenya through Predictive Modeling: A Comparative Analysis of Tree-Based Machine Learning Algorithms and Logistic Regression Classifier," Risks, MDPI, vol. 11(4), pages 1-21, April.
    • Handle: RePEc:gam:jrisks:v:11:y:2023:i:4:p:77-:d:1126271
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    References listed on IDEAS

    as
    1. Nicholas Bett & Juma Kasozi & Daniel Ruturwa, 2022. "Temporal Clustering of the Causes of Death for Mortality Modelling," Risks, MDPI, vol. 10(5), pages 1-34, May.
    2. Susanna Levantesi & Giulia Zacchia, 2021. "Machine Learning and Financial Literacy: An Exploration of Factors Influencing Financial Knowledge in Italy," JRFM, MDPI, vol. 14(3), pages 1-21, March.
    3. Francis Kipkogei & Ignace H. Kabano & Belle Fille Murorunkwere & Nzabanita Joseph, 2021. "Business success prediction in Rwanda: a comparison of tree-based models and logistic regression classifiers," SN Business & Economics, Springer, vol. 1(8), pages 1-19, August.
    4. Unnikrishnan, Vidhya & Imai, Katsushi S., 2020. "Does the old-age pension scheme improve household welfare? Evidence from India," World Development, Elsevier, vol. 134(C).
    Full references (including those not matched with items on IDEAS)

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