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The purpose of this paper is to consider how the federal crop insurance (FCI) program influences farm debt use, one of the key financial decisions made by farm operators.

Using data from the nationally representative Agricultural Resource Management Survey, the paper implements a propensity score matching model of the impact of FCI participation on various measures of farm business debt use. To account for the simultaneity of financial decisions, the paper further tests this relationship using a seemingly unrelated regression model.

FCI participation is associated with an increase in use of short-term farm debt, but not long-term debt, consistent with risk balancing behavior and current trends in the farm sector.

In addition to risk balancing, the results are also consistent with credit constraints or lender preferences. The paper cannot fully establish causality between crop insurance participation and short-term debt levels. Future research should address these limitations.

Agricultural lending standards are generally conservative and the farm sector as a whole currently has historically low leverage, which implies that an increase in debt use may not be a threat to the financial health of the farm sector.

The results indicate that the reduction in total risk facing the farm sector is significantly less than the decline in risk provided by FCI, which is an important consideration for policymakers.

This is the first paper to use an econometric model to analyze the relationship between FCI and farm debt use decisions. This paper can inform future research on the FCI program and farm financial decisions.

The growth of lending from nontraditional lenders may pose challenges for official US Department of Agriculture (USDA) farm sector debt estimates, but it is difficult to find data to assess official estimates. The purpose of this study is to examine whether debt provided by nontraditional lenders is accurately accounted for in official estimates.

We compare traditional and nontraditional lending data from farm equipment lien collateral values and the USDA Agricultural Resource Management Survey (ARMS). After analyzing trends in equipment lending implied by farm equipment lien data and ARMS, we estimate whether changes in farm equipment lien values predict changes in equipment debt reported in ARMS and whether lender type influences that relationship.

We find that credit provided by nontraditional lenders is likely underreported in ARMS. Our econometric model shows that equipment debt volumes for nontraditional lenders are consistently lower than traditional loan volumes in ARMS across a variety of model specifications. We also find that an increase in lien values for nontraditional lenders is less likely to predict an increase in ARMS equipment debt volumes than an increase for traditional lenders.

Official farm sector debt estimates may not fully account for nontraditional lenders.

This study demonstrates how the growth of nontraditional lending poses challenges for estimating US farm sector debt. We evaluate farm sector debt estimates and advance knowledge of the role of nontraditional lenders in farm equipment credit provision. The farm equipment lien dataset provides a rich source of novel data for research on local and national equipment debt and investment.

Dairy farms, along with livestock and specialty crop farms, face a tight labor supply and increasing labor costs. To overcome the challenging labor market, farm managers can increase labor-use efficiency through both human resource and capital investments. However, little is known about the relationship between such investments and farm profitability. The purpose of this paper is to examine the relationship between dairy farm financial performance and labor-use efficiency, as measured by labor productivity (milk sold per worker equivalent); labor costs (hired labor cost per unit of milk sold and hired labor cost per worker); and investment in labor-saving equipment.

Cluster analysis is applied to partition dairy farms into three performance categories (high/middle/low), based on farms’ rate of return on equity, asset turnover ratios and net dairy income per hundredweight of milk. Next, the annual financial rank is fitted into both random- and farm-level fixed-effects ordered logit and linear models to estimate the relationship between dairy farms’ financial performance and labor-use efficiency. This study also investigates the implications of using a single financial indicator as a measure of financial performance, which is the dominant approach in literature.

The study finds that greater labor productivity and cost efficiency (as measured by hired labor cost per unit of milk sold) are associated with better farm financial performance. No statistically significant relationship is found between farm financial performance and both hired labor cost per worker and advance milking systems (a proxy of capital investment in labor-saving technology). Future studies would benefit from better measurements of labor-saving technology. This study also demonstrates inconsistency in regression results when individual financial variables are used as a measure of financial performance. The greater labor-use efficiency on high-performing farms may be a combination of hiring more-skilled workers and managerial strategies of reducing unnecessary labor activities. The results emphasize the importance of managerial strategies that improve overall labor-use efficiency, instead of simply minimizing total labor expenses or labor cost per worker.

This study examines the importance of labor productivity and labor cost efficiency for dairy farm management. It also develops a novel approach which brings a more comprehensive financial performance evaluation into regression models. Furthermore, this study explicitly demonstrates the potential for inconsistent results when using individual financial variable as a measure of financial performance, which is the dominant measurement of financial performance in farm management studies.

The purpose of this paper is to investigate the market microstructure related to the Farm Credit System (FCS), Commercial Banks (CB) and Farm Services Administration (FSA). The commercial banks frequently call out the FCS as having an unfair advantage in the agricultural finance market place due to tax exempt bonds, and an implied guarantee of those bonds. This paper addresses the issue by examining the interrelationships since 1939, while addressing the historically distinctive roles that the FCS, CB and FSA have played in the US agricultural credit market.

There are two components to our model. The first is the estimation of short and long run credit demand elasticities, as well as land elasticities. These are estimated from a dynamic duality model using seemingly unrelated regression. The point elasticity measures are then used as independent variables in least square regressions, combined with farm specific and related macro variables, for the Cornbelt states. The dependent variable is the year-over-year changes in paired FCS, CB and FSA loans.

The genesis of the FCS was to provide credit to farmers in good and bad years. Therefore, we expected to see a countercyclical relationship between FCS and CB. This is found for the farm crisis years in the 1980s but is not a continuous characteristic of FCS lending. In good times the FCS and CB appear to compete, albeit with differentiated market segmentation into short- and long-term credit. The FSA, which was established to provide tertiary support to both the FCS and CB, appears to be responding as designed, with greater activity in bad years. The authors find the elasticity measures to be economically significant.

The authors conclude that the market microstructure of the agricultural credit market in the US is important. Our analysis applies a broader definition of market microstructure for institutions and intermediaries and reveals that further research examining the economic frictions caused by comparative bond vs deposit funding of agricultural credit is important.

The authors believe that this is the first paper to examine agricultural finance through the market microstructure lens. In addition our long-term data measures allow us to examine the economics through various sub-periods. Finally, we believe that our introduction of credit and land demand elasticities into a comparative credit model is also a first.

Farmland plays a critical role in the financial health of the agricultural sector. As a result, a number of institutions closely monitor farm real estate markets and publicly report estimated farmland values. This study aims to compare the information content of reported farmland values from three institutions.

A state space model is formulated to link observed price estimates to the unobservable value of farmland. The model considers reported values over the period 1965‐2010 for Iowa from three surveys: Iowa State Extension Service, the Federal Reserve, and the USDA.

The values reported by Iowa State receive the greatest weight in estimating the unobservable market value, yet the appreciation rates implied by the USDA estimates most closely track those of the unobservable value.

This study is the first to estimate the unobservable value of farm real estate based on observed estimates. The empirical procedure offers a number of unique advantages. It combines information from data reported at both annual and quarterly intervals and addresses potential problems related to cointegration, nonstationarity, and nonlinearity.

The purpose of this article is to document and evaluate patterns of nontraditional credit use among Wisconsin dairy farmers. Using a survey-based case study approach, this article analyzes farmer and farm characteristics, farmers’ utilization of credit and farmers’ perceptions of nontraditional lenders. The findings are connected to ongoing structural change in the dairy sector and economic theories of trade credit.

Data were collected using an incentivized online survey of Wisconsin dairy farmers distributed through existing university and industry networks. A total of 16 farmers completed the survey. The sample is treated as a focus group case study, and participants’ responses are examined using summary statistics and correlational analyses to describe emergent patterns in the industry.

Among survey respondents who utilize agricultural credit, nearly 80% (11 of 14) borrow from at least one nontraditional lender, and nontraditional credit comprises 17% of their total borrowing, on average. Much of this borrowing occurs through the financial arm of a vendor and is used to finance equipment or machinery purchases. Despite widespread use of nontraditional credit, no surveyed farmers preferred nontraditional lenders over traditional lenders.

This is the first study to analyze the use of nontraditional credit specifically among Wisconsin dairy farmers. Dairy farming is a capital-intensive endeavor, and recent structural change in the sector has increased surviving dairy farmers' demand for credit.