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Logistic regression techniques for panel data are used to identify factors affecting farm credit transition probabilities. Results indicate that most farm‐specific factors do not have adequate explanatory influence on the probability of farm credit risk transition. Class upgrade probabilities are more significantly affected by changes in certain macroeconomic factors, such as economic growth signals (from changes in stock price indexes and farm real estate values) and larger money supply that relax the credit constraint. Increases in interest rates, on the other hand, negatively affect such probabilities.

The purpose of this paper is to examine the relative financial strength and endurance of several paired classes of farmers according to business maturity (beginning versus mature farm businesses), farm operators’ age/experience (young versus older, more experienced farm operators), and farm size (small vs large farm businesses) by utilizing random-effects ordered logistic techniques.

This study uses a credit migration approach to analyze the factors that impact the probability of farm credit migration rates. An ordered logit model is used to assess the influence that factors have on a farm upgrading, staying same, or downgrading in credit rating.

Results show that increasing farm size will lead to a higher probability of class upgrades. Being a young farm operator, meanwhile, decreases this probability. Positive changes in money supply and farm real estate values were found to increase the likelihood of credit upgrades. Results also show trend reversal of credit risk movement, where upgrades (downgrades) are more likely to be followed by downgrades (upgrades).

With farms being dependent on capital for growth, knowing what factors affect the ability of a farm to obtain credit lends insight in the agricultural credit markets. This paper is also the first to assess the impacts of these factors on small farms which constitute 92 percent of farms in the USA per the US Department of Agriculture.

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.

The purpose of this paper is to analyse how farm production and input use (land, variable inputs, labour, and capital) is related to farm access to credit in the Central and Eastern Europe (CEE) transition countries.

Drawing on a unique farm level panel data set with 37,409 observations and employing a matching estimator, this paper analyses how farm access to credit affects farm input allocation and farm efficiency in the CEE transition countries. The large size of the FADN data set has an additional advantage. It allows the authors to employ a semi‐parametric estimator based on the propensity score matching. Using more than 37,409 observations assures that the loss in efficiency of semi‐parametric estimates, as compared to parametric ones, is not a problem. This is important for at least two reasons. First, applying a semi‐parametric propensity score matching (PSM) estimator allows to control for any heterogeneity in the relationship between farm performance and their observable characteristics (in particular access to credit). Second, matching estimators are robust in situations where farms having access to credit systematically differ from those that do not.

It is found that farms are asymmetrically credit constrained between inputs. The use of variable inputs and capital investment increases up to 2.3 percent and 29 percent, respectively, per 1,000 EUR of additional credit. The authors' estimates suggest also that farm access to credit increases the total factor productivity up to 1.9 percent per 1,000 EUR of additional credit, indicating that an improved access to credit results in adjusting the relative input intensities on farms. This finding is further supported by a negative effect of better access to credit on labour, suggesting that these two are substitutes. Interestingly, farms are found not to be credit constrained with respect to land.

To the best of the authors' knowledge, the present paper is the first to investigate the importance of access to credit for farm performance in the CEE region as a whole.

The purpose of this paper is to examine the effect of falling commodity prices on farm debt usage of corn and soybean farms, and how this debt usage differs based on the financial leverage of the farm.

Using panel data on farms surveyed at least twice in the Agricultural Resource Management Survey (ARMS) from 1996 to 2015, this paper uses a difference-in-differences approach to measure the effect of low commodity price shocks on financially vulnerable farms. To account for the correlation in the error structure between the three dependent variables (real estate debt, non-real estate debt, and interest payments) we use a seemingly unrelated regression approach.

Following a commodity price shock, financially vulnerable farms (debt-to-asset ratio greater than 40 percent) were found to increase their non-real estate debt when compared with non-financially vulnerable farms. Off-farm business income was found to help farms reduce real estate debt and interest payments in the face of these shocks.

Data consist of corn and soybean farms surveyed more than once in the ARMS from 1996 to 2015 and are not representative of all US farms, but have similar characteristics to US commercial farms.

The results indicate that financially vulnerable commercial crop farms respond to lower prices by taking on non-real estate debt, increasing financial stress. Well-targeted federal programs could prevent further financial stress for this group.

This is the first paper to use unbalanced panel data from ARMS to examine how farm debt use responds to commodity prices. This paper can inform policymakers about the financial risks to farms resulting from the current low-price environment.

The paper aims to explore the relationship between accounting and racial violence through an investigation of sharecropping in the postbellum American South.

A range of primary sources including peonage case files of the US Department of Justice and the archives of the National Association for the Advancement of Colored People (NAACP) are utilised. Data are analysed by reference to Randall Collins' theory of violence. Consistent with this theory, a micro-sociological approach to examining violent encounters is employed.

It is demonstrated that the production of alternative or competing accounts, accounting manipulation and failure to account generated interactions where confrontational tension culminated in bluster, physical attacks and lynching. Such violence took place in the context of potent racial ideologies and institutions.

The paper is distinctive in its focus on the interface between accounting and “actual” (as opposed to symbolic) violence. It reveals how accounting processes and traces featured in the highly charged emotional fields from which physical violence could erupt. The study advances knowledge of the role of accounting in race relations from the late nineteenth century to the mid-twentieth century, a largely unexplored period in the accounting history literature. It also seeks to extend the research agenda on accounting and slavery (which has hitherto emphasised chattel slavery) to encompass the practice of debt peonage.

The purpose of this paper is to provide an overview of the farm and rural credit system in China. To do this the authors use the so‐called “7 Cs” of credit (these include: Credit, Character, Capacity, Capital, Condition, Capability, and Collateral) and for each “C” provide some aspect of importance related to agricultural finance.

This paper is largely based on a survey of 897 farm households in Shaanxi and Gansu provinces, and extensive interviews of agricultural lenders conducted in the summer and fall of 2009. These data are used in simple form and in regression form to explain a variety of credit issues in China.

A number of key factors related to credit delivery and demand are found. First, using the 7 Cs as a guide proved to be very fruitful for disentangling the many institutional and cultural facets affecting rural credit in China. Under “Character” the authors discuss the cultural characteristics of the Chinese farmer in terms of informal lending and borrowing; under “Capacity” the authors discuss the challenges of delivering credit to farms with limited resources; under “Condition” the authors discuss group guarantees and credit worthy villages, credit rationing and insurance and incomplete markets; under “Capability” the authors discuss income inequality and challenges in economies of scale and size; and for “Collateral” the authors discuss the implications of lack of collateral and limitations on farm economic growth due to the collectivization of land and the potential for agricultural lending from the transferability and mortgagability of land or forestry use rights.

Although the assessment provides a great deal of breadth and depth across many credit‐related issues in China, it is not an exhaustive study. Agricultural and rural credit in China is very complex and in many instance under developed. The survey results from Shaanxi and Gansu tell a story that is consistently told throughout China, but the authors would caution against using the data to characterize farm credit across China as a whole.

Large swaths of China have either no or very rudimentary credit services. Even in areas where credit is in supply there are issues of poverty that could be aided with credit access and delivery. In order to improve livelihoods through credit institutions, it is important to understand rural credit in many dimensions. This paper takes a step in that direction.

Despite the importance of rural credit in China, it is largely understudied and not well understood. This paper makes progress in providing such an understanding. Our reasoning for using our unique approach is that by understanding the 7 Cs of credit one comes to understand the elemental characteristics of the credit decision from the lender's point of view but in a way that takes into account conditions at the farm level. The 7 Cs provide an objective approach to credit assessment that balances both the supply of and demand for credit.

The purpose of this paper is to analyze the repayment records of Farm Service Agency (FSA) borrowers in two distinct US farming regions that have been experienced serious drought conditions even as the US economy was going through a recession. The analysis will identify factors that significantly influence both the probability of FSA borrowers’ survival (capability to remain in good credit standing) and temporal endurance (or length of period of good standing with creditor).

This analysis utilizes a data set of farm borrowers of the Farm Service Agency that regular farm lenders have classified as “marginal” relative to other borrowers. The research goal is addressed by confining this study’s regional focus to the Southeast and Midwest that have both dealt with financial stress arising from abnormal natural and economic conditions prevailing during the same time period. A split population duration model is employed to separately identify determinants of the probability and duration of survival (condition of good credit standing).

This study’s results indicate that larger loan balances, declining commodity prices, and the severity of drought conditions have adversely affected both the borrowing farms’ probability of survival and temporal endurance in terms of maintaining non-delinquent borrower standing. Notably, Midwestern farms have been relatively less affected by drought conditions compared to Southeastern farms. This study’s results validate the contention that the farms’ capability to survive and the duration of their survival can be attributed to differences in regional resource endowments, farming activities, and business structures.

This study’s analytical framework departs from the basic duration model approach by considering temporal endurance, in addition to survival probability analysis. This study’s original contributions are enhanced by its specific focus on the contrasting farm business structures and operating environments in the Midwest and Southeast regions.

Organic outputs have been increasing at much lower rates than growth in consumer demand. Organic farmers’ debt aversion hinders them from obtaining business funds through borrowing. The purpose of this paper is to clarify that the farmers’ reluctance to use debt as a funding option can be more attributed to gaps in existing borrower-lender relationships, beyond sustainability principles.

Empirical evidence collected from organic farmers and farm lenders establish differing expectations and perceptions that reinforce the organic farmers’ debt aversion. The farm lender survey data set was analyzed using the Heckman approach applied to two lenders’ decisions: their interest in lending to organic farm borrowers and loan amounts approved for successful loan applicants. The econometric results were reconciled with the compiled inputs provided by organic farmers interviewed.

Results validate the farmers’ lower reliance on loans due to suspicions that lenders lack knowledge and consideration of organic farming conditions and principles. Farm lenders must depart from employing a uniform credit risk appraisal model and adopt borrower-specific versions of the model, but not necessarily delineating organic-conventional farming dichotomy that may not substantially affect credit risk measurement. Organic farms, on the other hand, need to better understand the credit risk appraisal principles and use their inherent business strengths to compete for loans with conventional farms without any special consideration.

Borrower-lender relationships can improve if information gaps between lenders and borrowers can be minimized with more extensive outreach education efforts. Better relationships would increase organic farms’ credit access to effectively address an impending supply gap in an expanding industry.

To the knowledge, a specific focus on organic farms in understanding farm borrower-lender relationships has never been explored in literature.