The purpose of this paper is to explore the linkage between agricultural sector and macroeconomic factors with farm financial health. It considers whether agricultural lenders can more accurately anticipate changes in the credit quality of their portfolios by considering broad economic indicators outside the agriculture sector.
This paper examines firm, sector, and macroeconomic drivers of probability of default (PD) migrations from a sample of 153 grain farms of actual lender data from Farm Credit Mid-America’s portfolio. A series of ordered logit models are developed.
Farm-level and sector-level variables have the most significant impact on PD migrations. Equity to asset ratios, working capital to gross farm income ratios, and gross corn income per acre are found to be the most significant drivers of PD migrations. Macroeconomic variables are shown to unreliably forecast PD migrations, suggesting that agricultural lenders should emphasize firm and sector variables over macroeconomic factors in credit risk models.
This paper builds the literature on agricultural credit risk by testing a broader set of sector and macroeconomic variables than previous articles. Also, prior articles measured the direction but not magnitude of PD migrations; the ordered model in the analysis measures both.
The authors are indebted to Farm Credit Mid-America for commissioning this research and providing the data for this analysis. In particular, the authors acknowledges Gordon Hanson, Greg Beachy, Matthew Gerstle, and Byron Bennett for patiently explaining the finer points of farm Credit Mid-America’s credit philosophy and astutely reviewing paper drafts. The authors also acknowledge the helpful comments of AFR editor Calum Turvey and the anonymous AFR reviewer.
Johnson, A.M., Boehlje, M.D. and Gunderson, M.A. (2017), "Agricultural credit risk and the macroeconomy: Determinants of Farm Credit Mid-America PD migrations", Agricultural Finance Review, Vol. 77 No. 1, pp. 164-180. https://doi.org/10.1108/AFR-06-2016-0057
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