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Off‐farm investment decisions of farm households are analyzed. Farm‐level data for a sample of Kansas farms observed from 1994 through 2000 are utilized. A system of censored dependent variable models is estimated to investigate the factors that influence the composition of farm households’ portfolios. The central question underlying the analysis is whether farm income variability influences off‐farm investment decisions. Previous analyses on the determinants of non‐farm investments have failed to consider the role of income variability. Results of this study indicate that higher farm income fluctuations increase the relevance of non‐farm assets in the farm household portfolio, thus suggesting these assets are used as farm household income risk management tools.

The crop insurance purchase decision for a group of Kansas farmers is analyzed using farm‐level data from the 1990s, a period that experienced many changes in the federal crop insurance program. Results indicate a reduction in the elasticity of the demand for crop insurance with respect to premium rates by the end of the decade. The reduction in demand elasticity corresponded with a considerable increase in government subsidies by the end of the 1990s. This result may also reflect the attractiveness of new revenue insurance products which may have made producers less sensitive to premium changes.

This research examines factors influencing the adoption of crop and revenue insurance. This is accomplished by estimating a multinomial logit model of insurance choices facing U.S. farmers. Results indicate significant differences in the probabilities of adoption of each insurance plan. The levels of selected explanatory variables, such as operator’s education level, debt‐to‐asset ratio, off‐farm income, soil productivity, participation in production and marketing contracts, and type of farm ownership, appear to be the determinants of the probability of having adopted each insurance plan.

Revenue insurance is the most popular form of insurance available in the US federal crop insurance program. The majority of crop revenue policies are sold with a harvest price replacement feature that pays out on lost crop yields at the maximum of a realized or projected harvest price. The authors introduce a novel actuarial and statistical approach to rate revenue insurance policies with exotic price coverage: the payout depends on an order statistic or average of prices. The authors examine the price implications of different dependence models and demonstrate the feasibility of policies of this type.

Hierarchical Archimedean copulas and vine copulas are used to model dependence between prices and yields and serial dependence of prices. The authors construct several synthetic exotic price coverage insurance policies and evaluate the impact of copula models on policies covering different types of risk.

The authors’ findings show that the price of exotic price coverage policies is sensitive to the choice of dependence model. Serial dependence varies across the growing season. It is possible to accurately price exotic coverage policies and we suggest these add-ons as a possible avenue for developing private crop insurance markets.

The authors apply hierarchical Archimedean copulas and vine copulas that allow for flexibility in the modeling of multivariate dependence. Unlike previous research, which has primarily considered dependence across space, the form of exotic price coverage requires modeling serial dependence in relative prices. Results are important for this segment of the agricultural insurance market: one of the main areas that insurers can develop private products around the federal program.

The federal crop insurance program has become the cornerstone of US agricultural policy. Since its introduction in the mid-1990s, crop revenue insurance has grown in prominence and now represents nearly 90 percent of liability for major crops. The pricing and design of revenue insurance raises a number of important challenges. The 2014 Farm Bill brought about several important changes in the program, resulting in a moving target for analysts and researchers. The paper aims to discuss these issues.

The risks are of a multivariate nature and are likely to be highly dependent on one another. The crop insurance setting is also constantly changing, with technological changes in production practices and highly volatile commodity prices. Compounding these challenges is the fact that US policymakers continually change the program.

The program has indeed undergone many changes and a number of important research questions need to be addressed.

Original research based upon recent policy.

Much farm financial risk research has involved the application of “credit‐scoring” models. We approach the issue of measuring financial risk by using the actual interest rates charged on agricultural loans reported in the USDA’s ARMS survey as market‐based measures of the financial risk associated with individual farm operations. A simultaneous equations model relates rates to several farm, producer, and lender characteristics. Because individual loans in our sample have different dates of origination, deviations of individual rates from market rates are considered. Our results indicate that risk (as perceived by lenders) tends to be higher for farms with less wealth (net worth) and more loans. Farm operators who live on their operations are considered by lenders to be less risky. Farm diversification appears to be correlated with less financial risk. Significant differences in agricultural lending rates across different types of lenders were also revealed, with the highest rates being charged by commercial banks and savings and loans. A key element of our analysis is development of a probability‐weighted bootstrap estimator that permits consistent inferences to be drawn from the stratified ARMS data.

This article seeks to examine the reference yield calculation method used in crop insurance rating and provides recommendations that could potentially improve actuarial performance of the Federal crop insurance program.

Conceptual, numerical, and statistical analysis is utilized to evaluate the reference yield calculation method used in the US Federal crop insurance program.

The results suggest that reference yields, which at the time of this study are calculated using National Agricultural Statistics Service (NASS) data, do not accurately represent the average actual yields of the insured pool of producers in the Federal crop insurance program. In addition, it is found that not regularly updating these NASS‐based reference yields exacerbates this problem because these reference yields do not appropriately represent the current state of technological progress.

The empirical analysis leads this paper to recommend a reference yield calculation procedure that utilizes county‐average yields from the risk management agency (RMA) participation database and an approach that uses spatially aggregated average yields in cases when data for a particular county are sparse.

No previous study has investigated the reference yield calculation method in the Federal crop insurance program using both RMA and NASS data sets. Moreover, this study contributes to the small literature that examines various aspects of the actual production history (APH) rating platform and suggests refinements to improve actuarial performance.

This chapter reviews the specification and application of the Deaton and Muellbauer's (1980) almost ideal demand system (AIDS) and the Christensen et al. (1975) translog (TL) demand system. In so doing we examine various refinements to these models, including ways of incorporating demographic effects, methods by which curvature conditions can be imposed, and issues associated with incorporating structural change and seasonal effects. We also review methods for adjusting for autocorrelation in the models' residuals. A set of empirical examples for the AIDS and the log TL version of the translog based on historical meat price and consumption data for the United States are also presented.

The purpose of this paper is to be an academic inquiry into rating issues confronted by the US Federal Crop Insurance program stemming from changes in participation rates as well as the weighting of data to reflect longer‐run weather patterns.

The authors investigate two specific approaches that differ from those adopted by the Risk Management Agency, building upon standard maximum likelihood and Bayesian estimation techniques that consider parametric densities for the loss‐cost ratio.

Both approaches indicate that incorporating weights into the priors for Bayesian estimation can inform the distribution.

In most cases, the authors' results indicate that including weighting into priors for Bayesian estimation implied lower premium rates than found using standard methods.

Banking and finance during the past several decades have become “re‐internationalized,” not simply “internationalized.” This becomes clear when we compare the institutional features of banking and finance today with those in the early part of this century, the last period in which both had a substantial international dimension. It is further apparent in historical data that are analyzed in the paper: cross‐country spreads between real interest rates over the long period 1835 to 1990, and figures for gross foreign assets available for a number of major countries at key points in time from 1885 to 1994. The paper concludes by discussing the factors responsible for the changes that have occurred in banking and finance during the past several decades.