Default estimation in agricultural credit using a regression model: the case of a credit cooperative

Abstract

This paper aims at developing a model that may help a credit cooperative in the region of Toledo, Paraná, in the analysis and concession of agricultural credit, estimating the probability of execution of the contracts, what permits to predict a possible default, using the logistic regression model – Logit. The theoretical framework used, based on the Theory of Transaction Costs, identifies the default as the result of the incompleteness of contracts and the asymmetry of information between borrowers and the credit cooperative, in order to avoid the granting of credit to possible defaulters. To do so, we collected information on the borrowers from the records of the cooperative from 2004 to 2007, aiming at drawing a profile of the credit borrower. Later, we estimated the logistic regression model for 10 different samples to identify the one that received the greatest number of hits between payers and defaulters. We established that the estimated model was more efficient to identify the payers’ contracts than the defaulters’ contracts. Even with a rather low percentual average of accuracy the model may help the cooperative’s decision making in granting credit.