Agricultural credit risk and the macroeconomy

Purpose 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 indic...

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Bibliographic Details
Published in:Agricultural finance review 2017-05, Vol.77 (1), p.164-180
Main Authors: Johnson, Andrew M, Boehlje, Michael D, Gunderson, Michael A
Format: Article
Language:English
Subjects:
Agricultural credit
Agricultural economics
Agricultural industry
Agricultural lending
Agronomy
Credit risk
Default
Economic factors
Farm income
Farms
Logit models
Macroeconomics
Risk
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Summary:Purpose 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. Design/methodology/approach 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. Findings 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. Originality/value 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.
ISSN:0002-1466
2041-6326
DOI:10.1108/AFR-06-2016-0057