No abstract

This study investigates important factors that should be used by lenders in riskrating their farm customers. These factors predict actual farm performance and debt repayment ability. Linear and logistic regression models are used to identify the debt-to-asset ratio as a major predictor of repayment ability. In addition, the rate of asset turnover and family living expenses are strong predictors of farm performance. The results are tested over several time periods to verify the robustness of the predictors.

Purpose -The purpose of this paper is to determine if there are positive microeconomic effects from a state-funded loan participation program on farm productivity and investment behavior. Design/methodology/approach -The authors take the approach that access to credit solves a liquidity problem. If a credit constraint exists it results in a suboptimal allocation of resources and a reduction in farm output and profitability. A two-stage regression model approach is used to analyze farmer survey and loan application data. In the first stage, a probit regression model is used to identify the farmers who are likely to be credit rationed. In the second stage, switching regression models are used to observe the effect of credit rationing on farm productivity and on farm investment behavior. Findings -It is found that there are liquidity effects of credit constraints for a significant share of the beginning and low-resource farmers who participated in the state-funded farm loan program. After controlling for various farm and farmer characteristics, the estimated productivity and investment demand equations imply that a 1 percent increase in credit received by credit constrained farmers under the state program increased their gross income by about 0.49 percent, and their investments in depreciable assets by about 0.33 percent. Originality/value -This paper is the first to apply the switching regression model to a state-funded farm loan program for the purpose of evaluating the financial impacts on farmer participants.

In this paper, the authors evaluate the impact of access to ICT-based market information on prices received by farmers and the intensity of adoption of improved hybrid and composite maize varieties. Propensity score matching is applied to cross-sectional survey data from farmers whose major cash crop is maize. Results indicate that adoption of improved maize has a positive and significant effect on maize yields, gross maize revenue per acre, and gross margins. The authors find that access to ICT-based market information has a positive and significant impact on the level of output prices received and the intensity of adoption of improved maize. Access to ICT-based market information implies better prices and this positively affects the intensity of adoption of improved seed. The implication is that improving food security and farm incomes should consider both the promotion of yield-augmenting agricultural technologies and improved access to ICT-based market information.

Lenders, regulatory agencies, and investors have increased their demand for credit risk exposure information to appropriately price risk and evaluate risk migration patterns that affect institution safety and soundness. This review provides a synthesis of the advances in credit risk assessment made through journal articles and other professional reports. Contributions in three primary areas are considered: (a) how the credit risk assessment problem has been defined and redefined over time in response to the changing information needs of lenders and regulators, (b) how methodological innovations have improved credit assessment procedures, and (c) how the efficiency of financial markets has changed due to the evolution of credit risk assessment. The paper concludes with a discussion of how transactional and relationship lending approaches are expected to evolve in the future and whether measures can be developed to more accurately assess factors such as management capacity and commitment to repay.