An analysis of factors affecting access to credit in Lesotho's smallholder agricultural sector

Financing small-holder cattle fattening in Indonesia: integrating demand, supply and institutions

“…One explanation is that farmers with more land assets are better able to access credit because land is an important form of collateral for bank loans. However, this result is opposite to the study by Motsoari et al (2015), where there was a negative relationship between land ownership and the ability of farmers to obtain loans. In that case, the borrowers came under a Government credit scheme that did not require land as collateral.…”

“…This contrasts with the case of farmers in Lesotho, Africa where an increase in total non-farm income reduced the probability of obtaining a loan (Motsoari et al 2015), because they were likely to use that non-farm income to purchase inputs for their agricultural activity. Farmers who are also fattening cattle in both sites in East Java usually use their external earnings to support their priority business which is their farming enterprise.…”

Factors Affecting Successful Agricultural Loan Applications: The Case of a South African Credit Provider

“…This is indicated by the negative influence of account standing and credit history of the applicant (p < 0.01), research should, therefore include the credit providers assessment on the clients account standings as opposed to just an indication whether savings exists. Motsoari et al [14] also mention that a positive relationship is expected between repayment record (credit record) and access to credit, meaning that a good record would increase the changes of access to credit in the future. Apart from an overall credit history, financial institutions also look at the years of business of their clients.…”

“…To eliminate multi-collinearity (structure between the independent variables) a principal component regression (PCR) approach was used to determine the uncorrelated components to be used in the logistic regression. The PCR has been used in previous research by Motsoari et al [14], while logistic regression has been applied in credit research by several authors including Motsoari et al [14], Kohansal and Mansoori [26], and Eze et al [27]. The PCR initially applies a principal component analysis (PCA) to reduce the number of relatable or correlated variables into uncorrelated components, while maintaining variation.…”

“…Internationally, researchers have successfully applied neural networks [8,12], decision trees [8], and logistic regression [8,12,13] to determine the factors affecting access to agricultural finance. Of the different methods, a regression framework proved to be the preferred approach [14]. The challenge of the regression framework, however, is that there are various personal and business characteristics of applicants, which may influence their ability to repay the loan.…”