DETERMINANT OF CREDIT DEFAULT OF MICRO FINANCE INSTITUTION BORROWERS THE CASE OF HAWASSA CITY,

Abstract

This study investigated the determinants of credit default among microfinance institution (MFI) borrowers in Hawassa City, Ethiopia. MFIs are essential in extending financial services to underserved populations, yet loan default remains a significant challenge. Using data from 296 MFI borrowers, the study examines borrower and loan characteristics to identify factors influencing loan default. The independent variables analyzed include demographic factors such as age, gender, education, and household size, along with financial and business-related factors like business experience, secondary income sources, and prior credit experience. Loan-specific factors such as credit size, repayment amount, and timing of credit release are also assessed. Descriptive statistics and correlation analysis reveal significant relationships between several variables and loan default. Notably, education level, household size, business experience, and gender emerged as key predictors. Higher education and more extensive business experience are associated with lower default rates, whereas larger household sizes and gender dynamics impact repayment behavior. On the other hand, factors such as income from other sources and the timeline of credit disbursement show weaker associations with default, though they are still relevant. Logistic regression results further underscore the predictive power of business experience and education, with borrowers possessing these traits showing a significantly reduced likelihood of defaulting. The model demonstrates strong explanatory power, with Cox & Snell R Square = 0.695 and Nagelkerke R Square = 0.929. The findings suggest that targeted support, such as business training and financial education, could enhance repayment performance. MFIs are encouraged to consider borrower profiles when designing loan conditions and to support clients’ capacity to repay by aligning credit terms with their financial situations. The study recommends future research into social and technological influences on repayment, as well as longitudinal analyses to track borrower behavior over time. Overall, the study contributes valuable insights for improving MFI operations and reducing credit default in Ethiopia