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The purpose of this paper is to investigate changes proposed in 2012 to commodity programs for the new Farm Bill. Both the Senate and House Agriculture Committee versions of the new Farm Bill eliminate current commodity programs including direct payments, create new revenue‐based commodity program options designed to cover “shallow” revenue losses, and also introduce supplemental crop insurance coverage for shallow revenue losses.

This paper documents the payment functions for the new revenue programs proposed in both the Senate and House Ag Committee Farm Bills, and also estimates expected payments for each using a model based on historical county yield data, farmer‐level risk rates from RMA, and commodity price levels from the March 2012 CBO baseline projections.

The authors find significant variation in expected per acre payment across programs, crops, and regions. In general, the Senate's bill would be expected to be preferred over the House's bill for corn and soybean producers, particularly those in the Midwest. Also, the RLC program in the House's Bill typically would be projected to pay much less than the Senate's SCO or ARC programs for most producers in the Midwest.

This study develops an extensive nationwide model of county and farm yield and price risks for the five major US crops and employs the model to evaluate expected payment rates and the distribution of payments under the House and Senate Farm Bill proposals. These analyses are important for program evaluation and should be of great interest to producers and policymakers.

The purpose of this paper is to use simulation analysis to assess farmer choice between crop insurance and supplemental revenue options as proposed during development of the Agricultural Act of 2014.

The certainty equivalent of wealth is used to rank farm choices and assess the effects of supplemental revenue options on the crop insurance plan and coverage level chosen by the producer under a range of farm attributes. The risk-reducing effectiveness of the select programs is also examined through their impact on the farm revenue distribution. The dependence structure of yield and prices is modeled by applying copula techniques on historical data.

Farm program supplemental revenue programs generally have no effect on crop insurance choices. Crop insurance supplemental revenue programs typically reduce crop insurance coverage at high coverage levels. An individual plan of crop insurance combined with a supplemental revenue insurance plan may substitute for incumbent area crop insurance plans.

The analysis provides insights into farmers’ possible choices by focussing on alternative crops and farm attributes and extensive scenarios, using current data, crop insurance plans and programs contained in the 2014 Farm Bill and related bills. The results should be of value to policy officials and producers in regards to the design and use of risk management tools.

The outstanding reform of the Common Agriculture Policy allows for changes regarding its most criticized component, the direct payment scheme. The purpose of this paper is to investigate farmers’ acceptance of subsidized whole farm income insurance (WFI) and single-crop, multi-peril revenue insurance (RI) that are associated with a reduction of direct payments.

By applying a generalized multinomial logit model on data of a discrete choice experiment, German farmers’ preferences, expressed as their willingness to pay (WTP), for WFI and RI are revealed.

The results show a positive WTP for WFI and RI. The average farmer has a higher WTP for WFI than for RI. By increasing the coverage level, the negative influence of a reduction of direct payments on WTP for insurance can be compensated. Individual risk attitude and assessed importance of direct payments for the farm business show a statistically significant influence on the WTP.

The results suggest that, even if direct payments were abolished in order to subsidize WFI or RI, German farmers’ WTP for both insurance products would remain positive. However, to finally assess whether subsidizing insurance is the right means of providing public support, it is necessary to assess whether farmers’ WTP meets the costs for such an insurance scheme.

To the authors’ knowledge, this is the first study investigating German farmers’ WTP for WFI and RI using an experimental approach by explicitly considering the partial to complete replacement of direct payments by subsidized insurance.

The purpose of this paper is to provide a general discussion of how techniques from financial engineering can be used to investigate the economic costs of farm programs and to aid in the design of new financial products to implement margin protection for dairy farmers. Specifically the paper investigates the Milk Income Loss Contract (MILC) and the Dairy Margin Protection (DMP) program. In addition the paper introduces the concept of the Milk to Corn Price ratio to protect margins.

The paper introduces and reviews the tools of financial engineering. These include the stochastic calculus and Itô's Lemma. The empirical tool is Monte Carlo simulations. The approach is part pedagogy and part practice.

In this paper the authors illustrate how financial engineering can be used to price complex price stabilization formula in the USA and to illustrate its use in the design of new products.

In this paper the authors illustrate how financial engineering can be used to price complex price stabilization formula in the USA and to illustrate its use in the design of new products.

Farm programs designed to protect dairy farmers margins are designed in a seemingly ad hoc fashion. Assessments of programs such as MILC or DMP are conducted on an ex-post basis using historical data. The financial engineering approach presented in this paper provides the means to add significant depth to the assessment of such programs which can be used in conjunction with Monte Carlo simulation to identify alternative model structures before they are written into law.

This paper builds upon an existing literature. Its originality is in the application of financial engineering techniques to farm dairy policy.

Rent seeking is endemic to the process through which any policy or regulatory initiative is developed in the USA. The purpose of this paper is to show how farm and other interest groups have formed coalitions to benefit themselves at the expense of the federal government by examining the legislative history of the federal crop insurance program.

The federal crop insurance legislation and the way in which the USDA Risk Management Agency manages federal crop insurance program are replete with complex and subtle policy initiatives. Using a new theoretical framework, the study examines how, since 1980, three major legislative initiatives – the 1980 Federal Crop Insurance Act, the 1994 Crop Insurance Reform Act and the 2000 Agricultural Risk Protection Act – were designed to jointly benefit farm interest groups and the agricultural insurance industry, largely through increases in government subsidies.

Each of the three legislative initiatives examined here included provisions that, when considered individually, benefitted farmers and adversely affected the insurance industry, and vice versa. However, the joint effects of the multiple adjustments included in each of those legislative initiatives generated net benefits for both sets of interest groups. The evidence, therefore, indicates that coalitions formed between the farm and insurance lobbies to obtain policy changes that, when aggregated, benefited both groups, as well as banks with agricultural lending portfolios. However, those benefits came at an increasingly substantial cost to taxpayers through federal government subsidies.

This is the first analysis of the US federal crop insurance program to examine the issue of coalition formation.

Since 1980, the principal form of crop loss assistance in the United States has been provided through the Federal Crop Insurance Program. The Federal Crop Insurance Act of 1980 was intended to replace disaster programs with a subsidized insurance program that farmers could depend on in the event of crop losses. Crop insurance was seen as preferable to disaster assistance because it was less costly and hence could be provided to more producers, was less likely to encourage moral hazard, and less likely to encourage producers to plant crops on marginal lands. Despite substantial growth in the program, the crop insurance program has failed to replace other disaster programs as the sole form of assistance. Over the past 20 years, producers received an estimated $15 billion in supplemental disaster payments in addition to $22 billion in crop insurance indemnities.

This paper examines the effect of overconfident yield forecasting (optimism bias) on crop insurance coverage level choices across both yield and revenue insurance.

This study simulates a representative producer’s preferred coverage level for both yield and revenue insurance under three potential models of decision-making and four potential manifestations of overconfident yield forecasting. The study then uses this framework to examine how coverage level choices change as overconfidence increases (decreases).

As overconfidence increases, producers prefer lower levels of crop insurance coverage than they would otherwise prefer, with extreme overconfidence inducing farmers to buy no insurance at all. While overconfidence affects cross-coverage demand for revenue and yield insurance similarly, this effect is more pronounced for yield insurance. Cross-coverage level demand for revenue insurance is relatively stable across changes in the correlation between prices and yields.

This research has important implications for crop insurance subsidy design and crop insurance demand modeling.

There is a growing body of literature suggesting that producers are overconfident with regard to their future yield risk and that this bias reduces their willingness to pay for risk management tools such as crop insurance. This is the first study to look at how such overconfidence affects cross-coverage level demand for crop insurance.

This paper examines the relationship between farm-level variables related to cash flow and premium rates on federal crop insurance coverage selection.

Using farm-level data from the Agricultural Resource Management Survey (ARMS), the authors estimate a linear fixed effects model to evaluate the relationship between farm-level and regional variables and federal crop insurance coverage selections.

The authors find evidence indicating that expected cash flow plays an important role in coverage level decisions. For example, variables directly related to cash flow, such as higher costs, are associated with significant differences in coverage level selection, though the direction of the association is dependent on the type of costs, whether fixed or variable, while higher revenue higher acreage farms insure at higher coverage levels. In addition, higher premium costs are associated with lower coverage level selections, despite subsidy incentives.

This is the first paper that identifies a potential solution to the puzzling finding that farmers do not consistently maximize coverage level. This research points to the influence of credit constraints as playing a role in limiting coverage level selections.

The purpose of this paper is to examine the sources and magnitude of variation in accrual adjusted gross farm revenue and farm revenue net of feed purchases on Michigan dairy farms representative of Upper Midwest dairy farms. The paper aims to assess whether adjusted gross revenue‐type insurance instruments meet insurability conditions when applied to dairy farms.

Accrual adjusted dairy farm revenue and revenue net of feed purchased from Michigan dairy farm panel data from 1995 through 2006 were detrended and summarized. Variance decomposition was used to identify sources of variation in adjusted gross revenue and adjusted gross revenue less feed purchases. In‐sample insurance premiums were estimated and Monte Carlo simulations were used to adjust these premiums for out‐of‐sample considerations.

Milk price variation was the largest source of variation while milk production per cow varied little. Farms with smaller herds and those with larger percentages of farm revenue from crop sales had higher relative revenue variability and would trigger a higher frequency of indemnities under a whole farm revenue insurance contract.

Because the data analyzed conclude in 2006, the volatility of the past couple of years is not reflected. Therefore, researchers are encouraged to test the proposed insurance feasibility further with more recent data.

The paper addresses considerations for the development and commercialization of a feasible dairy revenue insurance instrument.

This paper fulfils a need to understand magnitude and source of revenue variation on dairy farms and how insurance might mitigate negative consequences of this variation.

The USDA’s Risk Management Agency (RMA) made several changes to the crop insurance products available to organic growers for the 2014 crop year. Most notably, a 5 percent premium surcharge was removed and organic-specific transitional yields (t-yields) were issued for the first time. The purpose of this paper is to use farm-level organic crop yield data to analyze the impact of these reforms on producer insurance outcomes and compare the insurance options for new organic growers.

This study uses a unique panel data set of organic corn and soybean yields to analyze the impact of organic crop insurance reforms. Actual Production History values and premium rates are calculated for each farm and crop yield sequence. Producer loss ratios and subsidized premium wedges are compared for yield, revenue and area-risk products before and after the instituted reforms.

Results indicate that RMA succeeded in improving the actuarial soundness of the organic insurance program, though further refinement of organic t-yields may be necessary to accurately reflect the yield potential of organic producers and avoid reductions in program participation.

This paper provides insight into the effectiveness of reforms intended to improve the actuarial soundness of organic crop insurance and demonstrates the effect that the reforms are likely to have on new and existing organic farms. Because this analysis uses data collected independently of RMA and includes farms that may or may not have purchased crop insurance, it avoids the self-selection problems that might affect analyses using crop insurance program data.