• Login
    View Item 
    •   SU+ Home
    • Research and Publications
    • Strathmore Institute of Mathematical Sciences (SIMs)
    • SIMs Projects, Theses and Dissertations
    • MSc.MF Theses and Dissertations
    • MSc.MF Theses and Dissertations (2018)
    • View Item
    •   SU+ Home
    • Research and Publications
    • Strathmore Institute of Mathematical Sciences (SIMs)
    • SIMs Projects, Theses and Dissertations
    • MSc.MF Theses and Dissertations
    • MSc.MF Theses and Dissertations (2018)
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    A Quantitative analysis of the Kenyan students' loan default

    Thumbnail
    View/Open
    Full-text thesis (864.9Kb)
    Date
    2018
    Author
    Kamau, Pauline Nyathira
    Metadata
    Show full item record
    Abstract
    Higher education capacity, quality, and availability has driven more countries to turn to student loan schemes in order to assist students whose families are unable to meet their university costs. Ideally, all students seeking university education should be able to access these loans. It is also expected that student loan applicants pay back the entire loan in the stipulated time frame to allow other needy students joining university to utilize the repaid amounts. In this study, we seek to perform a quantitative analysis of loan applications by computing the probability of default of a given applicant using the qualitative information provided in the application forms. We apply multiple logistic regression with the binomial nominal variable defined either as defaulter or re-payer. Further, we treated different factors affecting default probability of the student as independent variables. The main objective was to find out the effect that the independent variables have on the dependent variable. We then validated the resulting model by comparing its results to observed data from the Kenyan Higher Education Loans Board. Results show the amount of loan reimbursed as the main factor affecting default. This can be an eye-opener for policy makers in their effort to mitigate non-repayment.
    URI
    http://hdl.handle.net/11071/5966
    Collections
    • MSc.MF Theses and Dissertations (2018) [9]

    DSpace software copyright © 2002-2016  DuraSpace
    Contact Us | Send Feedback
    Theme by 
    Atmire NV
     

     

    Browse

    All of SU+Communities & CollectionsBy Issue DateAuthorsTitlesSubjectsThis CollectionBy Issue DateAuthorsTitlesSubjects

    My Account

    Login

    DSpace software copyright © 2002-2016  DuraSpace
    Contact Us | Send Feedback
    Theme by 
    Atmire NV