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The purpose of this paper is to examine the financial performance and stress of beginning farmers in the USA with emphasis on the agricultural downturn experienced since 2013.

Using the US Department of Agriculture’s Agricultural Resource Management Survey (ARMS) data, probit models are estimated to study the personal and farm characteristics that affect whether or not the financial ratios fall into critical zones as defined by the Farm Financial Standards Council. The financial ratios involve liquidity, solvency, profitability, efficiency, and repayment capacity.

Beginning farmers are at a greater risk of financial stress on average, with higher likelihood of financial stress in liquidity and efficiency. Further, the recent agricultural downturn has negatively affected liquidity, solvency, and profitability for farmers while repayment capacity does not appear to be affected. During the downturn, beginning farmers are better positioned than the general farming population with respect to liquidity and repayment capacity.

This paper applies current lending practices to a nationally representative sample of farms over a time of changing economic conditions for the agricultural sector.

The purpose of this paper is to evaluate farm financial stress within the USA over the past 20 years and the agricultural and economic factors which have impacted farm businesses. The effect of the 2005 Bankruptcy Abuse Prevention and Consumer Protection Act (BAPCPA) on farm financial stress is further evaluated. In particular, Chapter 12 bankruptcies – which can only be filed by farmers – were only a temporary measure until BAPCPA made Chapter 12 a permanent fixture in bankruptcy law.

Chapter 12 bankruptcy filings from 1997 until 2016 are used as a proxy for farm financial stress. Panel fixed effects models are used to determine relevant factors affecting financial stress for farmers from agricultural and macroeconomic perspectives. Further, models incorporating pre- and post-BAPCPA regimes are utilized.

The results show that macroeconomic factors (interest and unemployment rates) are strong predictors of farm bankruptcies for farms while agricultural land values are the only consistent strong predictor among the agricultural factors. When evaluating the post-BAPCPA regime, only agricultural land values continue to be a significant predictor of farm bankruptcies. The findings also indicate a dynamic relationship with agricultural land values, where current year values are negatively related but previous year land values are positively related to bankruptcies.

The authors provide an analysis of the post-BAPCPA regime on farm bankruptcies that has not been evaluated within the literature yet. Further, the findings illuminate discussion on a potentially dynamic relationship with financial stress and agricultural land values.

The purpose of this paper is to determine the effects of the 2018–2020 China–US trade war on US farm bankruptcies as filed under Chapter 12. The key task is to identify the economic factors affecting farm bankruptcies generally, and to then control for the trade war impacts including the Market Facilitation Program (MFP), floods, agricultural conditions and the health of agricultural finance leading into the trade war.

Results were obtained using ordinary least square regression and panel fixed effect model using bankruptcy rates and number as the dependent variable. Independent variables included market effects, credit conditions, yield variation, trade impacts, 2019 flooding, macroeconomic conditions and regional fixed effects. The authors use cubic splines to interpolate annual and quarterly data to a monthly base.

Based on a fixed effect model, the authors find that all other things being equal the China–USA trade war would have had a significant impact on Chapter 12 farm bankruptcies, increasing the bankruptcy rate by 25.7%. The flooding in 2009 had minor effects of increasing the rate by only 0.05%. The overall impact will, however be substantially lower than the 25.7% because of the MFP. The MFP variables (binary) had mixed effects and its true impact is unknowable at this time; however, the authors also find that a 1% increase in the producer price index decreases bankruptcy rates by 2.62% and farm bankruptcy numbers by 3.70%. Likewise a 1% increase in GDP reduces bankruptcies by 3.25%. These suggest that the MFP program will have likely reduced farm bankruptcies considerably than what would have occurred in their absence. The authors also find that states heavily dependent on trade faced lower market uncertainty. Broader economic factors (net charge-offs of farm loans held by insured commercial banks, US real GDP, the average effective interest rate on nonreal estate farm loans) affect farm bankruptcy.

The authors use monthly bankruptcy statistics, however not all data were available in monthly measures requiring interpolation using cubic spline functions to approximate monthly changes in some variables. Although the MFP had mixed effects in the model, the mid- to longer-term effects may be more impactful. These longer-term effects (and even shorter-term effects through 2020) are complicated by the coronavirus disease 2019 (COVID-19) pandemic, which will require a different identification strategy than that employed in this paper.

The analysis and results of this paper are, to the authors' knowledge, the first to investigate the impact of the China–US trade war on Chapter 12 farm bankruptcy filings. The use of cubic splines in the interpolation of agricultural data is also a technical innovation.