The major objective of this study is to assess factors affecting loan repayment efficiency of borrowers and assess impact of efficient utilization of loan for the borrowers in Hawassa city in Ethiopia. Data used for this study was collected through a structured questionnaire. Classical and Bayesian logistic regression technique were used for data analysis. Factor analysis was used to reduce data and to incorporate the major determinants that the efficient utilization of loan have to the borrowers, whereas logistic regression is used to obtained factors affecting loan repayment performance of borrowers and it was extended to the Bayesian frame work using prior information that the parameter follows. Results of the classical binary logistic regression indicate that better repayment efficiency is associated with borrowers: sex, educational status, number of dependent family member, monthly income, loan size, additional source of income, motivation of repayment, time given for repayment, interest rate and screening mechanism when individuals apply for the loan. Also by using Bayesian logistic regression age, loan type, using loan for intended purpose and experience are significant in addition to significant predictors in classical logistic regression. From factor analysis, 27 factor used for impact assessment in which all the factor loaded highly in 7 significant factors like:-Benefit and obstacle related factor, capital effect, saving habit, expenditure, government spending, satisfaction level on the service and consumption change that has been seen after using loan. Thus, in order to improve repayment performance of borrowers, increasing loan size, training and giving some incentive in business areas, increasing awareness in different ways and studying factors which has significant impact on borrowers creditworthiness and giving solution to reduce that problems must be improved.
CITATION STYLE
Shuke Kitawa, Y. (2015). Statistical Analysis on the Loan Repayment Efficiency and Its Impact on the Borrowers: a Case Study of Hawassa City, Ethiopia. American Journal of Theoretical and Applied Statistics, 4(6), 562. https://doi.org/10.11648/j.ajtas.20150406.28
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