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Returns to a model farm are simulated to assess the impact of marketing and insurance risk management tools as measured by mean net returns and returns at 5% value‐at‐risk (VaR). Results indicate that revenue insurance strategies and strategies involving a combination of price and yield protection provide substantial downside revenue protection, while mean net returns only modestly differ from the benchmark harvest sale strategy when considering all years between 1986 and 2000.

Customs risk management has been widely recognized as a powerful tool to balance between trade facilitation and revenue maximization. However, most customs administrations worldwide, particularly in developing countries, are suffering from a lack of experience and knowledge to assess their risk management systems for revenue protection (RP). Customs risk management has a very limited legacy in the literature. Academic research is quite scarce and very limited, although its relevance to customs administrations. This paper aims to identify the key risk profiles and indicators that contribute to the protection of customs revenue and investigate the role of these risk profiles and indicators on customs RP using the case of Jordan Customs.

This study adopts a panel data approach by using the case of Jordan Customs. Data were collected from the risk targeting and selectivity system at Jordan Customs for the year 2019, a total of 600 observations.

The findings show that all risk targeting criteria except random selectivity (RS) and HS code have a significant positive association with RP. The findings also revealed that RS is an effective tool to prevent traders with fraud and offenses history from a prediction of targeting patterns and to assess the traders’ compliance and make sure their declarations are free from fraud or offenses. Moreover, the findings of this study indicate that customs administrations should adopt alternative programs such as authorized economic operator and post clearance audit as an effective means to measure and improve compliance.

The main contribution of this study lies in proposing a model to assist customs administrations in assessing the performance of risk management systems to protect revenue. This model provides a comprehensive conceptualization and explanations necessary for numerous aspects of risk management projects and it assists to predict the outcomes based on formulated indicators.

This study provides guidelines for risk analysts on how to identify and assess the key risk profiles and indicators that effect on maximizing the detection of revenue leakage and to obtain interpretable and predictable results. In addition, the findings of this study will assist customs administrations in supporting revenue collection, minimizing uncertainty, allocating resources more effectively to target high-risk consignments, while simplifying the procedures for the safe consignments.

This paper is of significant value because it is one of the preliminary studies that empirically identify the risk indicators/profiles that contribute to the protection of revenue and investigate the predictive power of these risk indicators/profiles as a key predictor to protect customs revenue.

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 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 examine the spatial components of producer heterogeneity in crop insurance product selection among US corn producers and identifies neighborhood spillover or agent marketing effects in these decisions.

County‐level insurance and yield data are used to demonstrate that a gradual shift from yield‐based insurance to revenue‐based insurance has spatial patterns. Conventional risk variables such as yield variability, price variability, prevalence of irrigation, other crops, and yield‐price relationships play an important role in this shift and are consistently estimated only when spatial components are included. A spatial random effects model is used to also identify the impact of spatial lag effects, which include neighborhood spillover and agent marketing effects, on the share of corn acres insured with revenue‐based plans vs yield‐based plans.

Theoretically consistent variables associated with risk are found to significantly influence the choice between crop revenue and yield insurance. Non‐linear parameters identify the region‐specific effects from changes in irrigation, yield price correlation, and the prevalence of corn production on insurance decisions. In addition, spatial components such as the decisions made by nearby producers and marketing drives are also found to influence decisions. These results may demonstrate the relative influence of trusted sources, such as nearby producers and insurance agents, on insurance decisions.

Traditional risk variables are consistently estimated by controlling for spatial heterogeneity. This study also reveals the propensity of producers to rely on the opinions of other producers or agents that they know.

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.

The purpose of this paper is to study the variance risk premium in corn and soybean markets, where the variance risk premium is defined as the difference between the historical realized variance and the corresponding risk-neutral expected variance.

The authors compute variance risk premiums using historical derivatives data. The authors use regression analysis and time series econometrics methods, including EGARCH and the Kalman filter, to analyze variance risk premiums.

There are moderate commonalities in variance within the agricultural sector, but fairly weak commonalities between the agricultural and the equity sectors. Corn and soybean variance risk premia in dollar terms are time-varying and correlated with the risk-neutral expected variance. In contrast, agricultural commodity variance risk premia in log return terms are more likely to be constant and less correlated with the log risk-neutral expected variance. Variance and price (return) risk premia in agricultural markets are weakly correlated, and the correlation depends on the sign of the returns in the underlying commodity.

Commodity variance (i.e. volatility) risk cannot be hedged using futures markets. The results have practical implications for US crop insurance programs because the implied volatilities from the relevant options markets are used to estimate the price volatility factors used to generate premia for revenue insurance products such as “Revenue Protection” and “Revenue Protection with Harvest Price Exclusion.” The variance risk premia found implies that revenue insurance premia are overpriced.

The empirical results suggest that the implied volatilities in corn and soybean futures market overestimate true expected volatility by approximately 15 percent. This has implications for derivative products, such as revenue insurance, that use these implied volatilities to calculate fair premia.

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.

The purpose of this paper is to evaluate the risk management benefits provided by the supplemental coverage option (SCO) insurance plan which was created in the 2014 Farm Bill. Specifically, the marginal expected utility benefits are compared with the potential additional subsidy cost introduced by the new program for a stylized example of a corn producer.

The paper uses a stylized simulation model examines the preferred insurance program choice for a typical Midwestern corn farmer. The expected utility of the farmer is calculated under their preferred insurance program choice both with and without the availability of the SCO program, and compared to the case where crop insurance is not available. Scenarios are examined for a range of farmer risk aversion levels, different levels of correlation between farm-level and county-level corn yields, and case with and without insurance premium subsidies.

The SCO program is found to enter into the preferred insurance program choice for risk averse farmers. As risk aversion increases, farmers are estimated to prefer higher coverage levels for individual products along with SCO coverage. While the availability of existing crop insurance programs are shown to substantially increase the expected utility of farmers, the marginal impact of adding SCO to the crop insurance program is relatively small. Furthermore, the additional expected benefits generated by SCO are shown to include both risk management and expected return components. With subsidies removed, the estimated marginal benefits provided by SCO are reduced significantly.

The findings of this paper can help inform the policy debate for future farm bills as agricultural support programs continue to evolve. The results in this paper can also be used to help explain farm-level decision making related to crop insurance program choices.

This paper contributes to the literature by documenting a new, federally supported risk management programs made available to farmers in the 2014 Farm Bill and evaluates the marginal benefits the SCO program offers US crop producers.

Chester Whitney Wright (1879–1966) received his A.B. in 1901, A.M. in 1902 and Ph.D. in 1906, all from Harvard University. After teaching at Cornell University during 1906–1907, he taught at the University of Chicago from 1907 to 1944. Wright was the author of Economic History of the United States (1941, 1949); editor of Economic Problems of War and Its Aftermath (1942), to which he contributed a chapter on economic lessons from previous wars, and other chapters were authored by John U. Nef (war and the early industrial revolution) and by Frank H. Knight (the war and the crisis of individualism); and co-editor of Materials for the Study of Elementary Economics (1913). Wright’s Wool-Growing and the Tariff received the David Ames Wells Prize for 1907–1908, and was volume 5 in the Harvard Economic Studies. I am indebted to Holly Flynn for assistance in preparing Wright’s biography and in tracking down incomplete references; to Marianne Johnson in preparing many tables and charts; and to F. Taylor Ostrander, as usual, for help in transcribing and proofreading.