Availability of agricultural credit: determinants, marginal effect, and predicted probability

Abstract

Purpose. This paper aims to identify the major determinants of agricultural credit and their marginal effects, along with describing the pattern of the predicted probability of getting credit from the agricultural credit cooperatives. Methodology / approach. We used a multi-stage stratified random sampling method to collect data from the paddy farmers of Kerala, India. Descriptive statistics are used to describe the profile of the farmers. Ordered logistic and probit regression models are used to model the credit categories. The authors analyzed the determinants of credit and their marginal effect, while the pattern of the predicted probability is described using tables and graphs. Results. Results show that age, household size, farming experience, and farm size significantly influence the probability of a farmer falling into a particular credit category. However, the estimated coefficients of other factors, like gender and occupation, are not statistically significant. The results from the study clearly show that relatively large paddy farms are not getting enough credit from the cooperatives, contrary to the common perception. An evaluation of the predicted probabilities shows that the very high and shallow categories are much more dispersed than the middle categories. Originality / scientific novelty. This is the first study that describes the predicted probability of credit availability pattern from the agricultural credit cooperatives to the paddy farmers. Moreover, this study describes the determinants and their marginal effects by credit category. Practical value / implications. The results indicate the probability of a farmer falling into a specific credit category based on his/her characteristics or background. The results can help them frame a strategy while approaching a credit cooperative for a loan. The inverse relationship between age and the likelihood of getting higher credit amounts requires government policy intervention. It will be hard for farmers to continue farming while aging if they do not get sufficient credit. The government must develop policies to counteract the influence of age on credit availability, like special schemes for older age groups.

Keywords

• agricultural credit availability
• co-operative banks
• predicted probability
• ordered logit
• ordered probit
• heckman’s model.