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Farmer Credit Delinquency in Southeastern US: Factors and Behavior Prediction

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  • Quaye, Frederick
  • Hartarska, Valentina
  • Nadolnyak, Denis

Abstract

This study examines the factors and behaviors that affect Southeast US farmers’ ability to meet their loan payment obligations within the stipulated loan term. The study also estimates a credit risk model using farm-level financial information to determine the credit worthiness of various different farmers in different states and their possible repayment capabilities. The study uses a 10-year (2003-2012) pooled cross-sectional data from the USDA ARMS survey data (Phase III). A probit approach is used to regress delinquency against various borrower-specific, loan-specific, lender-specific, macroeconomic and climatic variables for the first part, whilst a logistic approach is used to regress farmers’ coverage ratio (repayment capacity) on financial variables (liquidity, solvency, profitability, and financial efficiency) in addition with tenure, to determine how creditworthy the various kinds of farmers are, and in what particular states. The results show that farmers with larger farms, farmers with insurance, farmers with higher net income, farmers with smaller debt to asset ratio, farmers with single loans and those that take majority of their loans from sources apart from commercial banks are those that are less likely to be delinquent. Temperature and precipitation increases also lowers farmer delinquency, unless in excessive quantities where certain thresholds are exceeded. The results for credit model also show which particular farmers and in what states are more likely to be creditworthy based on their financial variable information.

Suggested Citation

  • Quaye, Frederick & Hartarska, Valentina & Nadolnyak, Denis, 2015. "Farmer Credit Delinquency in Southeastern US: Factors and Behavior Prediction," 2015 Annual Meeting, January 31-February 3, 2015, Atlanta, Georgia 196914, Southern Agricultural Economics Association.
    • Handle: RePEc:ags:saea15:196914
    DOI: 10.22004/ag.econ.196914
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    References listed on IDEAS

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    1. Benjamin Collier & Ani L. Katchova & Jerry R. Skees, 2011. "Loan portfolio performance and El Niño, an intervention analysis," Agricultural Finance Review, Emerald Group Publishing Limited, vol. 71(1), pages 98-119, May.
    2. Erica Field & Rohini Pande, 2008. "Repayment Frequency and Default in Microfinance: Evidence From India," Journal of the European Economic Association, MIT Press, vol. 6(2-3), pages 501-509, 04-05.
    3. Escalante, Cesar L. & Brooks, Rodney L. & Epperson, James E. & Stegelin, Forrest E., 2006. "Credit Risk Assessment and Racial Minority Lending at the Farm Service Agency," Journal of Agricultural and Applied Economics, Southern Agricultural Economics Association, vol. 38(1), pages 1-15, April.
    4. Crook, Jonathan & Banasik, John, 2012. "Forecasting and explaining aggregate consumer credit delinquency behaviour," International Journal of Forecasting, Elsevier, vol. 28(1), pages 145-160.
    5. Cai, Ruohong & Bergstrom, John C. & Mullen, Jeffrey D. & Wetzstein, Michael E., 2011. "Assessing the Effects of Climate Change on Farm Production and Profitability: Dynamic Simulation Approach," 2011 Annual Meeting, July 24-26, 2011, Pittsburgh, Pennsylvania 103420, Agricultural and Applied Economics Association.
    6. Allen M. Featherstone & Laura M. Roessler & Peter J. Barry, 2006. "Determining the Probability of Default and Risk-Rating Class for Loans in the Seventh Farm Credit District Portfolio," Review of Agricultural Economics, Agricultural and Applied Economics Association, vol. 28(1), pages 4-23.
    7. Escalante, Cesar L. & Barry, Peter J. & Park, Timothy A. & Demir, Ebru, 2004. "Farm-Level And Macroeconomic Determinants Of Farm Credit Migration Rates," 2004 Annual meeting, August 1-4, Denver, CO 20227, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
    8. Durguner, Seda & Katchova, Ani L., 2007. "Credit Scoring Models in Illinois by Farm Type: Hog, Dairy, Beef and Grain," 2007 Annual Meeting, July 29-August 1, 2007, Portland, Oregon 9356, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
    9. Ani L. Katchova & Peter J. Barry, 2005. "Credit Risk Models and Agricultural Lending," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 87(1), pages 194-205.
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