Credit worthiness and repayment performance among small – holder farmers in Sri lanka: Application of probit model

Abstract

The objective of the study is to examine the factors which determine the credit worthiness and loan repayment performance among the small-holder farmers in Vavuniya district in Sri Lanka. A sample of 113 small –holder agricultural loan borrowers from five villages who get the loans from SANASA TCCS served as the respondents in the study. A set of structured questionnaire was used to collect the primary data from the respondents who lives in the five villages located in Marukkarambali GS division in Vavuniya district, Sri Lanka during the period of 2018/2019. The dependent variable is the credit worthiness measured as binary variables where it takes as one for defaulters and zero for non - defaulters and the selected demographic characters, farming characters and farmers’ attributes were taken as explanatory variables in the study. To identify the above characters on the credit worthiness of the farmers’ descriptive statistics, and binary probit model were employed. The results of the descriptive statistics revealed that, 43.4% of the respondents belonged to the defaulters while 56.6% of them belonged to the non – defaulters in the study. Estimated results of the probit model suggest that among the demographic characteristics, age of the farmers, levels of education, number of family members positively influenced the loan repayment performance of smallholder farmers, while among farming characters, income, farm size, land ownership, farming experience, off-farm activities, purpose of loan and possibility of crop failure were positively impact on credit worthiness and repayment performance at different significant levels. On the other hand, knowledge about the loan and responsible guarantors were the major factors of farmers’ attributes influencing the repayment performance in the study. The overall findings of the study may help to the farmers as well as to the micro finance institutions to predict the repayment behaviour of the new loan applicants and to make the decision to grant loans in future.