Background of the study
In Kenya, the Higher Education Loans Board (HELB) serves as the primary source of financial support for students unable to fund their education Established in 1995 by an Act of Parliament, HELB operates under the Ministry of Higher Education, Science and Technology, with the aim of providing loans, bursaries, and scholarships to students in recognized institutions The Board's origins trace back to 1952, when the colonial government initiated the Higher Education Loans Fund (HELF) to assist Kenyans studying abroad in countries like Britain, the USA, the former USSR, India, and South Africa, offering loans secured against land title deeds and insurance contracts.
HELB aimed to create a Revolving Fund to provide loans to underprivileged Kenyan students seeking higher education This initiative was designed to alleviate the financial burden on the national budget, which allocates 40 percent to education funding (Source: HELB database, Loan Repayment and Recovery).
Problem Statement
The consequences of student loan default are significant, leading to substantial losses for the treasury due to the inefficiency of the Higher Education Loans Board (HELB) in recovering funds As a result, future university students may miss out on educational opportunities because the revolving fund is not being replenished adequately The increasing number of borrowers unable to repay their education loans raises concerns about the effectiveness of student loan disbursement and recovery in Kenya.
Higher education institutions face financial losses when new students cannot secure loans to pay tuition fees, leading to a population unable to pursue education and forced into informal employment This situation generates additional economic challenges linked to loan defaults Identifying systematic patterns associated with student loan defaults can inform public policy interventions and strategies aimed at decreasing the likelihood of such defaults (Muthii, 2015).
Main objectives
• To determine factors which play a key role in increasing default rates.
• To develop a quantitative model that returns an individual’s risk of default (A risk-profiling model).
• Suggest ways in which default rate can be decreased.
Significance of the Study
Higher education plays a vital role in equipping individuals with essential knowledge and skills that foster core values and enhance opportunities for personal and national economic advancement In Kenya, only 60% of students can access government student loans, while KSh 20.04 billion remains unpaid by borrowers Alarmingly, non-performing loans constitute over 50% of the total outstanding loans, according to the Kenyan Higher Education Loans Board This poor repayment performance among beneficiaries has significantly diminished the revolving fund that finances new university entrants.
Research indicates that students enrolled in two-year institutions have higher default rates compared to those at four-year public or private colleges, even when considering a six-year timeframe Additionally, institutions that invest more in resources and instructional support tend to see lower default rates among their students Generally, students attending wealthier institutions with greater access to social and economic capital are less likely to default on their loans These findings highlight the significant impact of institutional characteristics on student loan default rates.
Adequate financing for higher education relies on meaningful collaboration among key stakeholders, including the government, parents, students, employers, and educational institutions The primary funding source for the Higher Education Loans Board (HELB) comes from repayments of loans by former students Although it is challenging to recover all disbursed loans due to graduate unemployment, the Board aims to achieve a recovery rate of over 85 percent However, current recovery rates indicate that this target is not being met, impacting all stakeholders involved.
An additional affected stakeholder is the general performance of the economy where the cost of education does not correspond to the benefits expected from funding it.
HELB should implement stricter measures for risk profiling potential beneficiaries to enhance loan recovery strategies and reduce default rates A proposed solution is for students to obtain their Kenya Revenue Authority (KRA) personal identity number (PIN) upon turning eighteen, similar to the process for national identity cards This initiative would enable the KRA to monitor tax-related financial transactions, offering valuable background information during HELB loan applications and serving as a reference for the student's credit score.
HELB should gather childhood behavioral data on loan applicants, particularly noting any records of delinquency from primary and secondary school teachers This information can help assess whether such behaviors correlate with a higher likelihood of loan default By implementing this strategy, HELB can reduce default rates and ensure that loans are granted to individuals who are more likely to repay them.
This study employs a quantitative approach to identify various factors influencing an individual's loan repayment ability, aiming to reduce default rates By decreasing these rates, more financial resources will be accessible to students in need of assistance for their education.
This chapter explores the literature on factors contributing to default on higher education student loans, primarily focusing on applicants' personal details and background information (Hillman, 2014) It examines the impact of personal characteristics, socio-economic status, educational experiences, and post-university outcomes on student loan defaults.
Personal Characteristics
Research indicates that age is positively correlated with the likelihood of defaulting on loans, with older individuals facing a higher risk than their younger counterparts Woo (2000) suggests that this increased probability may stem from a reduced reliance on parental support during financial hardships Additionally, Herr (2004) highlights that older borrowers often encounter greater financial responsibilities, such as career commitments and family obligations, which can hinder their ability to repay loans promptly.
Older beneficiaries of loans may face a higher risk of default due to accumulating interest and limited resources for repayment Specifically, the Higher Education Loans Board (HELB) charges an interest of KES 5,000 monthly if the loan is not consistently serviced or if repayments cease This growing financial burden can become increasingly challenging as individuals age.
Research indicates a positive correlation between gender and loan repayment, with female borrowers demonstrating a lower likelihood of default compared to their male counterparts Studies, including one by Woo in 2002, highlight that men are more prone to default on student loans An Experian analysis from May 2013 revealed that men typically carry 4.3 percent more debt than women, with their mortgages averaging 4.9 percent higher than those of female borrowers Additionally, women utilize less available credit on their credit cards, with usage rates at 30 percent for women compared to 31 percent for men.
A 2013 Experian analysis revealed that men are more prone to financial difficulties, with 5.7% falling 60 days or more behind on mortgage payments compared to 5.3% of women Rod Griffin, a director of public education, emphasizes that managing a mortgage, often the largest debt individuals face, is indicative of one's overall financial health.
Marital status significantly influences loan default risks, with singles, divorced, or widowed individuals facing over a 7 percent higher likelihood of defaulting on loans (Volkwein and Szelest, 1995) Additionally, single parents are also at an increased risk of loan default (Volkwein et al., 1998).
Social-Economic Factors
Dependents The greater the number of dependents claimed by a student, the greater the likelihood of loan default (Dynarski, 1994; Volkwein and Szelest, 1995; Woo, 2002).
Research by Volkwein and Szelest (1995) indicates that the probability of loan default rises by 4.5 percent for each dependent, such as children or younger siblings This aligns with common sense and further studies, which show that having more dependents strains financial resources, making it harder for students to repay loans (Herr and Burt, 2005) Notably, the impact of dependents on loan default rates was found to be more significant than factors like the type of institution attended, parental income, and the student’s annual earnings (Volkwein et al., 1998).
Research indicates a strong correlation between parents' education levels and student loan default rates, revealing that students with more educated parents are less likely to default compared to first-generation college students This trend applies to both mothers and fathers, highlighting the significant impact of parental education on socioeconomic status and student outcomes.
Students from low-income families typically accumulate more debt during their education compared to their wealthier counterparts, as noted in various studies (Herr and Burt, 2005; Steiner and Teszler, 2005; Volkwein and Szelest, 1995) Additionally, low-income students often experience greater stress when their loan repayments commence, with indications that this burden is becoming increasingly severe (Baum and O’Malley, 2003b).
Research indicates that higher family income correlates with a reduced likelihood of student loan default, as wealthier families can offer a financial safety net that lower-income families cannot This support is crucial for students from less affluent backgrounds, who face greater financial risks and are more likely to struggle with meeting their loan obligations during income fluctuations.
Education Experience
Research indicates that a student's choice of degree major significantly influences loan repayment outcomes Harrast (2004) highlights that certain majors, such as special education, computer engineering, sociology, art history, and risk management and insurance, are linked to higher debt levels compared to others While his study was limited to a single institution, it raised questions about the reasons behind the impact of major selection on future debt burdens, as noted by Hossler and Hillman (2010).
Research indicates that post-graduation earnings linked to a student's field of study significantly impact personal income and loan repayment capabilities (Herr and Burt, 2005; Steiner and Teszler, 2005) Lochner and Monge Naranjo (2004) discovered that the influence of major choice diminished when accounting for total debt and post-college earnings Additionally, Flint (1997) noted that a greater mismatch between a student’s undergraduate major and their employment field increases the risk of loan default.
The Kenya Universities and Colleges Central Placement Service (KUCCPS), established under the Universities Act of 2012, has taken over the responsibilities of the Joint Admissions Board (JAB) Governed by a placement board, KUCCPS aims to develop criteria that facilitate student access to their chosen courses, considering their qualifications and preferences.
Research on the connection between sponsorship applications and student loan defaults in Kenya is limited Existing studies have primarily focused on comparing the default rates of bursary and loan applicants to those who applied solely for loans Findings indicate that bursary applications are negatively correlated with loan repayment, suggesting that they may be more effective in predicting the likelihood of loan defaults.
Post-University Experience
In 2018, the availability of formal employment opportunities in Kenya reached a historic low, leading many graduates to seek informal jobs This shift complicates the ability of the Higher Education Loans Board (HELB) to monitor and ensure loan repayments from these individuals.
The rise in informal employment in Kenya has significantly hindered overall economic growth, as many individuals are struggling to make ends meet and lack viable plans for future investments.
Informal employment often results in job insecurity, where a brief illness or injury can lead to dismissal and replacement This instability makes it challenging for individuals to focus on repaying student loans Additionally, many of these workers fall below the taxable income threshold, further complicating their ability to repay loans and limiting the government's tax revenue for funding HELB.
In this study, we will look at the probability of default given most of the factors studied as affecting default rates being present.
Introduction
Exploratory Analysis
An exploratory analysis is crucial for understanding the data's characteristics before modeling, providing insights into the information contained within The dataset comprises a sample of 5,100 individuals.
The table below shows HELB’s interest rates and the frequency of students in our sample who pay the rates Those who graduated between 1974/75 and 1994/95 academic years repay their loans at 2 percent, while those who took loans from 1995/96 to date repay their loans at 4 percent HELB can vary the interest rate anytime without referring to the ben- eficiary (Section 6(c) of the HELB Act) For postgraduate and continuing education stu- dents, the interest rate is 12 percent compounded annually, (http://www.helb.co.ke/loan- repayment/, 26/04/2018).
Frequency Percent Valid per- cent
The analysis revealed that an individual's relationship status significantly influences their loan repayment capabilities, as discussed in the literature review Here is a summary of the relationship status within our sample group.
Frequency Percent Valid per- cent
Receiving a bursary or scholarship significantly influences a student's willingness and capability to repay HELB loans This article provides an overview of our sample, highlighting the percentage of students who received these financial aids compared to those who did not.
Bursaries Frequency Percent Valid per- cent
Gender significantly influences loan default rates, with studies indicating that men are more likely to default on their loans compared to women The accompanying table illustrates the gender distribution within our sample, highlighting this variation.
Gender Frequency Percent Valid per- cent
Target Population
The target population refers to the specific group of individuals for whom survey data is collected to draw conclusions for the study In this case, the population encompasses HELB financial statements and data from 1995, when operations expanded nationwide, up to 2014 Due to the extensive and unrefined nature of the sample population, our research focused on data from 2009 to 2014, a period marked by significant enhancements in the Board's disbursement and recovery policies.
This study examines individuals aged 23 to 75 who have completed their higher education within the past year to 50 years, regardless of their student loan repayment status The survey encompasses a diverse group, including those who have fully paid off their loans and those who have not.
Sources of Data
Data Analysis
The data analysis utilized Visual Basic for Applications (VBA) within Microsoft Excel, a programming language that enables users to develop customized functions This approach was essential in the initial phase to refine the extensive dataset, ensuring that only relevant information was included in the sample.
The polished data sample was analyzed in R Studio to develop a multiple logistic regression model, which involved creating dummy variables for categorical data such as loan amounts and payment delays According to Hair et al (2010), categorical variables can be transformed into dummy variables for inclusion in analyses that require continuous variables This approach was utilized in the study to enhance the model's functionality by converting all relevant categorical variables into dummy variables.
Variable Selection
In regression analysis, the selection of explanatory variables is often not predetermined and is a crucial part of the process There are two primary methods for selecting these variables: the regression approach and automatic methods.
In this analysis, we employed a regression approach that evaluates all potential subsets of explanatory variables to identify the model that best fits the data We utilized the Akaike Information Criterion (AIC) as our selection criteria, which scores each model, enabling us to select the one with the optimal score A lower AIC value, in comparison to the null deviance, indicates a more effective model.
We utilized the step function for variable selection by first establishing a starting model and defining the range of models for our analysis Our approach involved employing both forward and backward selection methods to enhance the model selection process.
The step function is utilized prior to defining the logistic regression model in R When using the "both" selection method, it begins by identifying and eliminating the variable with the highest AIC of 6257.05, proceeding sequentially to the next highest AIC of 6255.05, and continues this process until reaching the lowest AIC of 6236.99.
The analysis identified loan amount and the beneficiary's father being alive as the most influential variables, as indicated by the lowest AIC These factors demonstrated a positive impact on account status, with loan amount showing significant correlation (p|z|)(Intercept) 0.089900 0.364316 0.247 0.805 loanamount 0.039585 0.003625 10.921