Credit risks are unanticipated variations in costs and availability of credit that arise from forces in financial markets or from lenders' responses to risks in agricultural markets and farmers' creditworthiness. An extension of mean‐variance portfolio theory shows how credit risks combine with other financial and business risks to determine total risk. Empirical evidence from lender surveys about risks shows that farmers' credit is positively correlated with changes in farm income, although the correlation is stronger for capital credit than for operating credit, and that variability in fund availability from rural banks has contributed to high credit risks.
It is argued that the equilibrium conditions traditionally used by economists must be modified to provide criteria for optima useful to the firm. Important modifications are associated with liquidity attributes of the firm organization. Credit, defined as borrowing capacity, constitutes an important source of liquidity. Accordingly, borrowing generates a cost from loss of liquidity as well as from interest charges on loans. Modifications are suggested in the relevant optimizing criteria relating to the firm to account for liquidity losses associated with borrowing. Finally, the modifications are reflected in models and observational techniques suggested to make the conceptual notions operationally useful.
This paper uses quadratic programming to calculate the variance‐efficient mean income path and associated lower income bounds and suggests a way to select an optimum farm plan under risk based on the farmer's own self‐assessed income‐risk preference function. An empirical example from a Midwest corn‐soybean farm is presented.
Liquidity in the form of reserved credit is valuable to a business manager because it is available to counter uncertain expectations. This paper investigates the definition and method of estimating values or reservation prices associated with firm liquidity provided by unused credit. A multiperiod linear programming model is taken to reflect the behavior of decision makers in empirically observed situations. Credit reservation prices of decision makers were inferred by comparing growth information for real borrowers with growth information generated by the comparable model at alternative reservation prices. This method succeeded in associating a high credit reservation price with the conservative borrower and low credit reservation prices with a more liberal borrower.
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