The federal crop insurance program has been a major fixture of U.S. agricultural policy since the 1930s, and continues to grow in size and importance. Indeed, it now represents the most prominent farm policy instrument, accounting for more government spending than any other farm commodity program. The 2014 Farm Bill further expanded the crop insurance program and introduced a number of new county‐level revenue insurance plans. In 2013, over $123 billion in crop value was insured under the program. Crop revenue insurance, first introduced in the 1990s, now accounts for nearly 70% of the total liability in the program. The available plans cover losses that result from a revenue shortfall that can be triggered by multiple, dependent sources of risk—either low prices, low yields, or a combination of both. The actuarial practices currently applied when rating these plans essentially involve the application of a Gaussian copula model to the pricing of dependent risks. We evaluate the suitability of this assumption by considering a number of alternative copula models. In particular, we use combinations of pair‐wise copulas of conditional distributions to model multiple sources of risk. We find that this approach is generally preferred by model‐fitting criteria in the applications considered here. We demonstrate that alternative approaches to modeling dependencies in a portfolio of risks may have significant implications for premium rates in crop insurance.

The COVID‐19 pandemic presented a severe crisis to the agricultural sector and the economy at large. To confront it, the administration and Congress had to mobilize vast resources very quickly and introduce creative emergency measures to mitigate the unprecedented impacts on the economy. This article takes a closer look at the impacts of the COVID‐19 pandemic on the agricultural sector and the policy measures that USDA implemented to help farmers and ranchers weather the immediate crisis.

Purpose -The purpose of this paper is to investigate the effects of crop insurance premiums being determined by small samples of yields that are spatially correlated. If spatial autocorrelation and small sample size are not properly accounted for in premium ratings, the premium rates may inaccurately reflect the risk of a loss. Design/methodology/approach -The paper first examines the spatial autocorrelation among county-level yields of corn and soybeans in the Corn Belt by calculating Moran's I and the effective spatial degrees of freedom. After establishing the existence of spatial autocorrelation, copula models are used to estimate the joint distribution of corn yields and the joint distribution of soybean yields for a group of nine counties in Illinois. Bootstrap samples of the corn and soybean yields are generated to estimate copula models with the purpose of creating sampling distributions. Findings -The estimated bootstrap confidence intervals demonstrate that the copula parameter estimates and the premium rates derived from the parameter estimates can vary greatly. There is also evidence of bias in the parameter estimates. Originality/value -Although small samples will always be an issue in crop insurance ratings and assumptions must be made for the federal crop insurance program to operate at its current scale, this analysis sheds light on some of the issues caused by using small samples and will hopefully lead to the mitigation of these small sample issues. 477Crop insurance SCO in the "Stacked Income Protection Plan". The crop insurance title also mandates development of a revenue -minus -cost insurance plan. All of these new insurance plans provide revenue coverage, meaning that payments may be triggered by low prices, low yields, or a combination of both that results in a revenue shortfall.All of these plans address coverage of multiple, dependent sources of risk. Further, the largest share of liability written in the current crop insurance program is in the form of revenue coverage, which now accounts for almost 90 percent of total liability in the federal program. In terms of individual farm -level insurance coverage, the "Revenue-Protection" plan with harvest -price replacement accounts for over 80 percent of total liability. A critical parameter in the design and rating of these revenue insurance plans is the measure of dependence or correlation among the various sources of risk. In the case of revenue insurance, one is typically concerned with the inverse correlation that exists between crop yields and prices. In the case of more complex insurance instruments, such as the newly proposed revenue minus cost plan, one must be concerned with multiple dependencies. These dependencies may be complex to model and measure. In most cases, billions of dollars of liability is rated using very small numbers of observations or even by assumed values of correlation relationships that may be only weakly related to empirical measurement of actual dependencies. These dependencies are often assumed to be constant...

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