The resilience of agricultural lenders and borrowers in the late 2000s financial crises
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This dissertation consists of three essays that feature different approaches in evaluating lender’s and borrower’s resilience through the late 2000s recession. The first study applied an Input Distance Stochastic Frontier function to compare estimates of the technical efficiency (TE) and allocative efficiency (AE) between agricultural banks and non-agricultural banks. This efficiency analysis was applied to a seven-year pre-recession period and is designed to identify any early warning signals that could decrease the efficiency level of banks. Results suggest that survival banks were more technically efficient than critically insolvent banks, and banks that tend to utilize cheaper inputs were more likely to withstand the economic crisis. The second study utilized a split-population survival model in analyzing the role of agricultural loan portfolios on the probability of survival and temporal endurance of commercial bank lenders in the late 2000s recession. The results establish that farm credit transactions neither increased the commercial bank lenders’ chances of failure nor expedited the deterioration of their financial conditions. Results indicate that bank failures could have resulted from changes in the quality of the banks’ portfolios of real estate, consumer, commercial and industrial loans as well as factors capturing interest rate risk, fund sourcing strategies, and certain structural attributes. The third study evaluates the credit migration probabilities among different types of farm borrowers from Farm Service Agency (FSA)’s lending program. Two time continuous Markov Chain transition matrices were applied in lieu of the traditional time discrete method, and produced more accurate transition probability estimates that capture the indirect and transient changes in credit risk ratings. Racial and gender minority farmers are found to experience a lower probability of credit rating upgrade than white male farmers. Macroeconomic factors prove to have significant impact on the farms’ transition probabilities.