IMPACT OF CREDIT RATIONING ON RICE PRODUCTIVITY IN BANGLADESH

Abstract

Bangladesh, a predominantly agricultural country, has a significant agricultural sector that contributes 11.22% of the country's GDP. This study aims to investigate socio-economic characteristics of Boro rice growers, factors influencing credit rationing, and the effect of credit rationing on Boro rice productivity in Kushtia district, one of the highest rices producing upazilas in the country. The study used a simple random sample strategy and a field survey to gather data on rice production and agricultural credit use. About 210 households were selected as sample households, with 120 farmers being credit constrained and 90 farmers unconstrained. The data was analyzed using descriptive statistics and a logit model with marginal impact. The Cobb-Douglas production function was used to assess the contribution of agricultural credit rationing to Boro rice production. Among the sample population, 22.40% of farmers were aged 0-35 years, 29.50% were 36-45 years, and 48.10% were above 46 years. Notably, 74.30% of the farmers' spouses were 0-35 years old, 17.10% were 36-45, and 8.60% were above 46. Educationally, 22.40% of the farmers were illiterate, 27.60% had primary education, 21% had secondary education, and 20% had higher secondary education. In contrast, 24.80% of the spouses were illiterate, 27.60% had primary education, 3.80% had secondary education, and 16% had higher secondary education. Land possession classified 7.10% of farmers as small, 59.50% as medium, and 33.30% as large. Financially, 21% of farmers had annual savings between BDT 100,000 and 50,000, while 75.70% saved more than BDT 50,000 annually, and 3.30% saved less than BDT 10,000. Agriculture was the primary income source for 90.50% of farmers, with only 9.50% relying solely on agriculture. Key determinants of credit rationing were identified, including age (coefficient 0.197, p=0.013), farm size (coefficient 0.703, p=0.095), and distance to the bank (coefficient 0.280, p=0.000). The model explained 52.5% of the variability in credit rationing outcomes. Credit rationing significantly impacted yield, with fertilizer cost (coefficient 0.845, p<0.001) being a crucial factor, while credit rationing itself negatively affected yield (coefficient -0.087, p=0.001). The study highlights critical constraints such as high collateral requirements (99.50% concern), distant financial institutions (100% concern), and fear of loan repayments (98.60% concern). Additionally, 89.50% of farmers acknowledged liquidity issues, and 100% linked credit constraints to technology adoption. These findings suggest a need for improved credit policies and support systems to enhance Boro rice production and alleviate financial constraints for farmers in Kushtia district.