Farmers Characteristics and Its Influencing on Loans Resettlement Decision in Sri Lanka

Abstract

This study investigates the impact of socio-economic characteristics of the farmers and its impact on their loans resettlement behavior in the People's Bank, Puttalam branch in Sri Lanka. Secondary data were collected from the bank officials and the data were analyzed with 100 applicants who are cultivating paddy as a major crop and other field crops during the Maha and Yala season 2011/2012. This study was analyzed using descriptive statistics, Tobit model and in addition to that elasticity of the loan repayment ratio also measured. The estimated Tobit regression model indicated that family members and secondary education were significantly positive influence on the farmers' repayment behavior while loan disbursement has negative influence on their behavior in Sri Lanka. Other independent variables namely gender, age, civil status, major crops, income and higher level of education of the beneficiaries were not statistically significant influence on the farmers' behavior in the above bank branch in Sri Lanka. The elasticity of the loan repayment performance for the variables also were calculated at the mean values and according to that number of family members, size of loans and secondary education were statistically significant. The overall results revealed that, the bank managers should considered the above characters of the borrowers to increase the probability of repayment ratio and thus it will help them to improve the efficiency of lending decision of the bank loans in future.

The effects of customer segmentation, borrower behaviors and analytical methods on the performance of credit scoring models in the agribusiness sector

“…The types of variables used in the literature on credit scoring for farmers mainly describe financial ratios such as liquidity, profitability and leverage (Durguner and Katchova 2007;Jouault and Featherstone 2011;Ziari et al 1995); farmer characteristics (educational level, age, goods, etc;Limsombunchai et al (2005)); farm characteristics, such as types of crops and farm size (Limsombunchai et al 2005;Miller and LaDue 1988;Novak and LaDue 1999;Onyenucheya and Ukoha 2007); and credit features, including credit history (Aruppillai and Phillip 2014;Eyo and Ofem 2014;Hou et al 2005;Jouault and Featherstone 2011). Other studies have used weather data (Pelka et al 2015;Römer et al 2017) and variables related to the sustainability of crops (Henning and Jordaan 2016).…”

“…Limsombunchai et al (2005) defined the lending decision as a function of borrower characteristics, relationship indicators and dummy variables about the agricultural sector and loan information. Aruppillai and Phillip (2014) showed that considering socioeconomic characteristics, such as number of family members, amount of loan disbursement and secondary education, improves the efficiency of the lending decision.…”