Factors Contributing to Repayment Behavior of Micro Loans in Agricultural Bank of Meshkinshahr

Abstract

The purpose of this study was to identify factors influencing on repayment behavior of loans by farmers who received loans from rural branches of Agricultural Bank in Meshkinshahr. Data for this study was obtained from a sample of one-hundred-and -fifty farmers who defaulted in paying their loans to Agricultural Bank. Logistic regression through strata software was used to analyze data. According to the results, the variables of supervision, occupation, other credit received, and education were found to be significant predictors of repayment in Agricultural Bank of Meshginshahr. Findings of this study will give valuable insights to policy makers and planners in the bank in terms of awarding micro loans which can be used in credit policies of the bank.

Challenges Faced by Petty Traders in Accessing Loans from Financial Institutions: Evidence from the Upper West Region of Ghana

“…The result is a statement that even securing a loan through collateral is not enough as the lending institution must take steps to ensure timely repayment schedule of the loan than to be happy with the collateral. The result amplified the assertion in [22] that effective monitoring and supervision is a significant predictor of loan repayment behaviour of borrowers. Another 16% stressed that they had other sources of income aside the business they are currently into which suppose that a large majority of the respondents had no other source of income as an alternative to turn to in case this particular business fails to enable them repay their loans which means they could easily default.…”

“…One problem for the entrepreneur is determining how to successfully secure a loan from a bank [21]. A study into factors contributing to repayment behaviour of micro loans in Agricultural Bank of Meshkinshahr revealed that supervision, occupation and education were found to be significant predictors of repayment behaviours of borrowers [22].…”

Identifying Relevant Predictor Variables for a Credit Scoring Model using Compromised-Analytic Hierarchy Process (Compromised-AHP)

“…Loan applicant's demographic indicators involve the characteristics of the applicant which among others cover age [38][39]47,48,[50][51][52][53][54], marital status [2,47,50-52]), gender [2,47,50,54], race [2], number of dependents [40][41][42]47,50,52,53], and social status [43,[52][53][54]…”

“…Employment indicators cover the industry and nature of work that the applicant involved in together with the employment and unemployment benefit [44]. Some of the important indicator variables discussed in the literature are type of employer [41,44,54,57], job position [45,54,57], length of year in current employment [43,46], and length of year in previous employment [2].…”

“…Potential client's behaviour is usually related to behaviour that is relevant to money management [31]. Thus, some of the potential indicator variables are the checking accounts [43,54,56],the number of payments per year and payment intervals [48,53,54,55,57]), and the loan/credit history [35,41,49,55].…”