2023, Vol. 4, Issue 1, Part A
Multiple logistic regression model on microfinance loan default (Case study agave rural limited)
Author(s): Pascal Gidigah and Sampson Twumasi-Ankrah
Abstract: The impoverished in Ghana, who often lack the collateral required to secure traditional bank loans, have turned increasingly to microfinance as an alternative. This has contributed to a rising tide of default that has threatened to drown Ghana's rural and community banks. The purpose of this research is to determine whether or not demographic characteristics of borrowers (such as age, gender, marital status, number of children (dependency ratio), and loan size) play a role in the probability that a borrower will default on their loan. The data for this study came from the microfinance department at Agave Rural Bank Limited, a secondary source. For example, the coefficient of the p-values of the predictors shows that the risk of default increases by 0.42 times for every unit increase in Gender and by 1.00 times for every unit increase in Loan size. Due to differences in gender and in the ability to accurately appraise financial risk, the size of the loan and the number of dependents (dependence ratio) are statistically significant predictors of loan default. The study's author suggests including additional variables or switching to alternative variables to further examine repayment status variance. To go deeper into the central issue, this research provides essential groundwork.
Pages: 54-61 | Views: 369 | Downloads: 157
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How to cite this article:
Pascal Gidigah and Sampson Twumasi-Ankrah. Multiple logistic regression model on microfinance loan default (Case study agave rural limited). Journal of Mathematical Problems, Equations and Statistics. 2023; 4(1): 54-61.
