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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.

This study examines credit score migration rates of farm businesses, testing whether migration probabilities differ across business cycles. Results suggest that agricultural credit ratings are more likely to improve during expansions and deteriorate during recessions. The analysis also tests whether agricultural credit ratings depend on the previous period migration trends. The findings show that credit score ratings exhibit trend reversal where upgrades (downgrades) are more likely to be followed by downgrades (upgrades).

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 use a linked-farm approach and a cohort approach to estimate farm entry and exit rates using the US Census of Agriculture. The number of new farms entering agriculture was re-estimated and adjusted upward since not all new and beginning farmers are known to US Department of Agriculture.

In addition to a linked-farm approach (linking farms over time), a cohort approach (farms that started operating in the same year) is used to determine exit rates conditional on the number of years a farm has been operating. Linear forecasting, moving-average forecasting, and using data from a later Census are used to re-estimate the number of new farms in their first year of operating.

Using the linked-farm approach, an average annual entry rate of 7.5 percent and exit rate of 8.5 percent is estimated for 2007 to 2012, which vary based on the farmer’s lifecycle. The cohort approach shows that exit rates are lower than 4 percent for the first 40 years of operating a farm business and then exit rates gradually increase. Revised estimates of approximately 70-80,000 new farms entering each year are calculated, which are considerably higher numbers than the 30-40,000 new farm entrants participating in the Census of Agriculture.

The linked-farm and cohort approaches are used to provide updated estimates for farm entry and exit using new Census data and to make comparisons with previous years. To the authors’ knowledge, this is the first study to provide revised estimates for new farm entrants into US agriculture.

Following the recent global financial crisis, US regulatory agencies issued laws to implement the Basel III accords to ensure the resiliency of the US banking sector. Theories predict that enhanced regulations may alter credit issuance of the regulated banks due to increased capital requirements, but the direction of changes might not be straightforward especially with respect to the agricultural loans. A decrease in credit availability from banks might pose a serious problem for farmers who rely on bank credit especially during economic recessions. The paper aims to discuss these issues.

In this study, the impact of Basel III regulatory framework implementation on agricultural lending in the USA is examined. Using panel data of FDIC-insured banks from 2008 to 2017, the agricultural loan volume and growth rates are examined for agricultural banks and all US banks.

The results show that agricultural loan growth rates have slowed down, but the amount of agricultural loan volume issuance still remained positive. More detailed examination finds that regulated agricultural banks have decreased both the agricultural loan volume and their loan exposure to the agricultural sector, showing a possible sign of credit crunch.

This study examines whether the implementation of the Basel III regulation has resulted in changes in agricultural loan issuance by US banks as predicted by the lending channel theory.

This study analyzes the personal and farm characteristics that influence the use of farm credit, the degree of indebtedness, and debt consolidation for U.S. farms. Whereas previous studies have examined the supply side of agricultural credit using lender‐based data, this study considers the demand side of agricultural credit using representative farm‐level data from the USDA’s 2001 Agricultural Resource Management Study (ARMS). The results show that gross farm income, risk management strategies, and operator’s age and risk aversion had significant influences on the likelihood of farm credit use by rural residence, intermediate, and commercial farms.

This paper examines the contract design problem of microfinance institutions seeking to maximize outreach to the poor while remaining financially sustainable. A dynamic model of group lending is developed that shows how optimal interest rates depend on information regarding moral hazard and adverse selection problems, correlated project risks, and strategic default. Relative to traditional static models, the results indicate a dynamic model better explains the current experience with individual and group lending in developing countries.

Economic theory implies that firms in a competitive market will adjust to long‐run equilibrium levels of profitability, resulting in mean reversion of profitability. Partial adjustment models are applied to farm‐level data from Illinois to test for mean reversion and autocorrelation in profitability. Results show that farm businesses revert to individual levels of expected profitability at an annual rate of 0.5, while the annual rate of negative autocorrelation is 0.175.

The purpose of this research is to evaluate the financial performance measures calculated and reported by the Economic Resource Service (ERS) from Agricultural Resource Management Survey (ARMS) data. The evaluation includes the calculation method and the underlying assumptions used in obtaining the reported values. Recommendations for improving the information reported are proposed to ERS.

The financial measures calculated and reported are compared with those recommended by the Farm Financial Standards Council (FFSC). The underlying assumptions are identified by analyzing the software code used in calculating the values reported. The values reported by ERS are duplicated and alternative methods for calculating the financial performance measures are considered. The values obtained from the various calculation methods are compared and contrasted.

Recommendations for ERS include: calculate and report the financial measures recommended by FFSC, note values that are imputed, periodically update and validate assumptions used in calculating imputed values, review its policy for flagging estimates as statistically unreliable, report medians and other select percentiles, and consider reporting the percent of farm businesses that have values within critical zones.

A total of four methods for calculating financial performance measures are compared and contrasted. These are the aggregate mean, sample mean, sample median, and percentage of farm businesses with values in critical zones.

The first theme addressed in this paper is agricultural asset performance. The low rate of return on agricultural assets has been of particular interest to policy makers. From a market portfolio perspective, several studies have analyzed the relationship between farm asset returns and systematic market factors, concluding that farmland adds little systematic risk to a well‐diversified portfolio. Because asset values adjust so that the return to each asset is in equilibrium with its relative risk, any persistent low return on agricultural assets may be due to differences in relative risk. The paper’s second theme is the valuation of farmland in the United States. Numerous studies have examined the factors affecting farmland values. Most have used the standard present value capitalization formula relating land values to land rents, although these models have been rejected by empirical data. Several studies have reformulated and improved the performance of the present value models. Since changes in rates of return of agricultural assets and land values can have drastic consequences for farmers’ wealth and sector solvency, future research needs in this area will continue.