IDEAS home Printed from https://ideas.repec.org/a/gam/jrisks/v11y2023i4p77-d1126271.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-9091/11/4/77/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-9091/11/4/77/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. Unnikrishnan, Vidhya & Imai, Katsushi S., 2020. "Does the old-age pension scheme improve household welfare? Evidence from India," World Development, Elsevier, vol. 134(C).
    3. 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.
    4. 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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Kang, Ji Young & Park, Sojung & Ahn, Seoyeon, 2022. "The effect of social pension on consumption among older adults in Korea," The Journal of the Economics of Ageing, Elsevier, vol. 22(C).
    2. Yannick Markhof & Isabela Franciscon & Nicolò Bird & Pedro Arruda, 2021. "Social assistance programmes in South Asia: an evaluation of socio-economic impacts," Research Report 62, International Policy Centre for Inclusive Growth.
    3. Dong, Shizheng & Zhang, Zili & Han, Yiduo & Si, Yanwu, 2023. "Do pension subsidies reduce household education expenditure inequality? Evidence from China," International Review of Economics & Finance, Elsevier, vol. 87(C), pages 532-540.
    4. Alzua, Maria Laura & Cantet, Maria Natalia & Dammert, Ana & Olajide, Daminola, 2020. "Mental Health Effects of an Old Age Pension: Experimental Evidence for Ekiti State in Nigeria," 2020 Annual Meeting, July 26-28, Kansas City, Missouri 304176, Agricultural and Applied Economics Association.
    5. Vidhya Unnikrishnan & Kunal Sen, 2020. "Old-age pensions and female labour supply in India," WIDER Working Paper Series wp-2020-90, World Institute for Development Economic Research (UNU-WIDER).
    6. Naseem Al Rahahleh, 2022. "Financial Literacy Levels among Saudi Citizens across Budgeting, Saving, Investment, Debt, and Insurance Dimensions," JRFM, MDPI, vol. 15(12), pages 1-18, December.
    7. Sridhar Kundu & Maynor Cabrera, 2022. "Fiscal Policies and their Impact on Income Distribution in India," Commitment to Equity (CEQ) Working Paper Series 120, Tulane University, Department of Economics.
    8. Unnikrishnan, Vidhya & Pinet, Melanie & Marc, Lukasz & Boateng, Nathaniel Amoh & Boateng, Ethel Seiwaa & Pasanen, Tiina & Atta-Mensah, Maya & Bridonneau, Sophie, 2022. "Impact of an integrated youth skill training program on youth livelihoods: A case study of cocoa belt region in Ghana," World Development, Elsevier, vol. 151(C).
    9. Ira N. Gang & Rajesh Raj Natarajan & Kunal Sen, 2022. "Finance, Gender, and Entrepreneurship: India’s Informal Sector Firms," Journal of Development Studies, Taylor & Francis Journals, vol. 58(7), pages 1383-1402, July.
    10. Yang, Feng-An & Chang, Hung-Hao, 2023. "Impact of a pension program on healthcare utilization among older farmers: Empirical evidence from health claims data," World Development, Elsevier, vol. 169(C).
    11. Chatterjee, Sidharta, 2022. "Rationalizing Decision Choices: What Influences our Social Decision Making?," MPRA Paper 114985, University Library of Munich, Germany.
    12. Vidhya Unnikrishnan & Subhasish Dey, 2023. "Political meddling in social assistance programme: Panel data evidence from India," Journal of International Development, John Wiley & Sons, Ltd., vol. 35(6), pages 1346-1364, August.
    13. Beata Świecka & Paweł Terefenko & Tomasz Wiśniewski & Jingjian Xiao, 2021. "Consumer Financial Knowledge and Cashless Payment Behavior for Sustainable Development in Poland," Sustainability, MDPI, vol. 13(11), pages 1-18, June.
    14. Nawaz, Saima & Iqbal, Nasir, 2021. "How cash transfers program affects environmental poverty among ultra-poor? Insights from the BISP in Pakistan," Energy Policy, Elsevier, vol. 148(PB).
    15. Yoko Niimi & Charles Yuji Horioka, 2023. "Elderly poverty and its measurement," Chapters, in: Jacques Silber (ed.), Research Handbook on Measuring Poverty and Deprivation, chapter 29, pages 307-315, Edward Elgar Publishing.
    16. Anqi Zhang & Katsushi S. Imai, 2022. "Does a Universal Pension Reduce Elderly Poverty in China?," Economics Discussion Paper Series 2203, Economics, The University of Manchester.
    17. Nicholas Bett & Juma Kasozi & Daniel Ruturwa, 2023. "Dependency Modeling Approach of Cause-Related Mortality and Longevity Risks: HIV/AIDS," Risks, MDPI, vol. 11(2), pages 1-18, February.
    18. María Laura Alzua & Natalia Cantet & Ana C. Dammert & Damilola Olajide, 2023. "The Wellbeing Effects of an Old Age Pension: Experimental Evidence for Ekiti State in Nigeria," CEDLAS, Working Papers 0322, CEDLAS, Universidad Nacional de La Plata.
    19. Anqi Zhang & Katsushi S. Imai, 2022. "Does a Universal Pension Reduce Elderly Poverty in China?," Discussion Paper Series DP2022-30, Research Institute for Economics & Business Administration, Kobe University.
    20. Shengkun Xie & Kun Shi, 2023. "Generalised Additive Modelling of Auto Insurance Data with Territory Design: A Rate Regulation Perspective," Mathematics, MDPI, vol. 11(2), pages 1-24, January.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jrisks:v:11:y:2023:i:4:p:77-:d:1126271. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.