Search results

Logistic regression techniques for panel data are used to identify factors affecting farm credit transition probabilities. Results indicate that most farm‐specific factors do not have adequate explanatory influence on the probability of farm credit risk transition. Class upgrade probabilities are more significantly affected by changes in certain macroeconomic factors, such as economic growth signals (from changes in stock price indexes and farm real estate values) and larger money supply that relax the credit constraint. Increases in interest rates, on the other hand, negatively affect such probabilities.

This study adds a new dimension in the study of racial and gender bias in farm lending. Most previous studies analyzed the separate effects of race and gender attributes on loan approval decisions. The analysis focuses on the stipulation of loan terms (loan amount, interest rate and maturity) among approved farm loan applications. The time period analyzed spans from 2004 until 2014 during which the government has undertaken reforms to improve delivery of loan services to its clientele of minority farmers. Thus, this study's findings could help validate the effectivity of such institutional reforms affecting Farm Service Agency (FSA) lending operations.

This study utilizes a national direct loan origination data from the FSA of the U.S. Department of Agriculture (USDA) collected from 2004 to 2014. The analysis begins by identifying significant differences in cross-tabulations of loan terms among different racial and gender classes. Seemingly unrelated regression (SUR) regression techniques are then applied for a system of equations involving the three loan packaging components. The combined effects of the prescribed loan packaging terms are subsequently analyzed under a simulation-optimization framework.

Regression results validate that indeed, relative to White American borrowers, certain minority borrowers are accommodated with lower loan amounts at higher interest rates and with shorter maturities. However, these decisions seem to be prompted by credit risk management considerations. The most compelling findings include the insignificance of all double minority labeling variables, except for the interest rate equation that even produced favorable results for Hispanic American females. Simulation-optimization results further reinforce that even when one or two unfavorable loan terms are included in the packaging, double minority borrowers end up with better profitability and liquidity positions.

This study provides a different perspective in dealing with the controversial minority bias in lending by presenting evidence gathered from a government farm lending institution. The USDA-FSA has been sued in numerous occasions by minority borrowers. Since then, however, it has deliberately implemented institutional reforms to rectify previous errors. This study provides empirical evidence strengthening FSA's claim of its intention to improve its delivery of loan services, especially for its socially disadvantaged borrowers with double minority classification.

This study pioneers the analysis of the double minority labeling effect on farm lending decisions. Its contributions to literature are further enhanced by its goal to validate the effectiveness of FSA institutional reforms undertaken since the early 2000s in order to improve credit access of and delivery of credit services to minority farm borrowers, especially those that belong to more than one minority classification.

This study utilizes an expected utility framework to conceptualize the risk‐adjusted valuation of cash versus share leases for farmers and landowners. Farm‐level data then are used to empirically estimate the rental spread between these leases in Illinois, and to econometrically evaluate how these spreads are related to risks and other farm characteristics. The results indicate that non‐risk factors likely are the primary determinants of the magnitude and sign of the rental spread. In particular, high cash rent may be a bidding strategy to control additional leased acreage and thus expand farm size.

This study identifies key strategies employed by Illinois grain farms to prevent the erosion of their equity positions due to significant downturns in commodity prices during the implementation of the 1996 farm bill. The econometric results emphasize the collective importance of revenue enhancement, cost reduction, and capital management strategies. Nonfarm‐related strategies aimed at minimizing equity withdrawals through regulated family living expenditures, as well as supplementing low farm incomes with receipts from nonfarm employment and investments, significantly affect cost value equity growth rates. Moreover, significant financial and asset management strategies include those that minimize the costs of borrowing and maintain high asset productivity levels through elimination of excess farm capacity.

This is a comparative study of the nature of operating decisions made by agricultural and non-agricultural banks, affecting their actual growth plans in the years around and during the Great Recession of 2008. The main empirical question is whether banks under greater economic stress shortly before, during, and immediately after the recession made deliberate adjustments in their growth decisions vis-à-vis predetermined sustainable levels.

Higgins' sustainable growth challenge is employed to evaluate banks' growth decisions involving four growth levers (profitability, earnings retention, asset management, and financial leverage). Actual growth trends are related to business growth rates deemed sustainable given available financial capability as prescribed by Higgins' model.

Both banking groups made cautious growth decisions during the sample period. Actual growth rates were below sustainable levels. Agricultural banks registered steadily increasing sustainable growth rates from the pre-recession years until the recovery period, while non-agricultural banks were more constrained to grow given their declining sustainable growth levels. Notably, agricultural banks showed relatively more aggressiveness in raising slightly actual revenue growth to levels much closer to sustainable levels. This could have resulted from their less volatile profit margin trends and usual pressure to maintain acceptable liquidity conditions in order to gain access to external funds.

This study presents an additional application of Higgins' model to agricultural finance. The comparative analysis of banking groups becomes even more relevant these days as recent economic discussions focus on indicators of an imminent recessionary period.

The migration approach to credit risk measurement is based on historic rates of movements of individual loans among the classes of a lender’s risk‐rating or credit‐scoring system. This article applies the migration concept to farm‐level data from Illinois to estimate migration rates for a farmer’s credit score and other performance measures under different time‐averaging approaches. Empirical results suggest greater stability in rating migrations for longer time‐averaging periods (although less stable than bond migrations), and for the credit score criterion versus ROE and repayment capacity.

This study uses farm‐level data from the Illinois Farm Business Farm Management Association to determine whether the variability of net farm income is significantly influenced by farm size, financial structure, and other structural characteristics of farm businesses. The econometric results indicate that under a cross‐sectional model the relative variability of real net farm income is not significantly influenced by farm size, measured either by acreage or value of farm production. However, under a time‐series/cross‐section model, periodic variations in farm size, along with differences in the relative crop price received, crop yield, degree of enterprise diversification, and geographic location, can significantly influence changes in farm income variability.

This paper reviews various optimization approaches used to address a variety of issues related to risk in agricultural finance and farm management. The central focus is in the Markowitz mean‐variance model, which represents the classical approach to balancing risk and returns in an optimization framework. We also review other models that have been used historically to solve linearizations of the mean‐variance problem including MOTAD and target MOTAD. Specialized optimization models such as Target semivariance and direct expected utility maximization are also discussed.

The increasing demand for highly differentiated products in today’s enterprise economies has emphasized the small firms’ comparative advantage over larger firms. Business mortality rates, however, remained very high among more vulnerable start‐up businesses still in their earliest stage of business development. The challenges experienced by agribusiness entrepreneurs and their counterparts from other industries in their start‐up years are analyzed using case‐study research techniques. Results indicate that highly differentiated start‐up conditions between industries and among firms usually resulted in varied survival strategies. Notable differences include pricing policies dependent on market structures, more consultative management styles, inadequate start‐up resources, and preferences for brand new equipment.

This study introduces two Markov chain time approaches, time‐homogeneous and nonhomogeneous models, for analyzing farm credit risk migration as alternatives to the traditional discrete‐time (cohort) method. The Markov chain models are found to produce more accurate, reliable transition probability rates using the 3 x 1 migration measurement method used by farm lenders. Compared to corporate bond ratings migration results, this study obtained larger mean differences in singular value decomposition between the cohort matrix and each of the Markov chain matrices. This finding suggests that the omission of transient, indirect migration activities under the cohort method is more costly when applied to farm credit analysis. This discrepancy could lead to understated transition probability estimates which, in turn, could produce misleading indicators of farm loan portfolio quality.