Risk Management Agency Research Articles

Financial losses due to fumonisin contamination in the Texas High Plains maize

Fumonisins are one of the main problems affecting maize production in the Texas High Plains (THP), where its agroclimatic conditions make it a perennial hotspot for mycotoxin contamination. In 2017, a fumonisin outbreak in the THP maize motivated stakeholders’ request to repeal a subsection of the Texas Administrative Code, §61.61(a)(7) (Fumonisin Rule), and its related Texas Feed Industry Memorandum (Memo 5–20), which previously permitted the blending of maize containing high fumonisin levels with maize containing ≥ 5 mg/kg under state authority, and pivot to FDA fumonisin guidance. Shortly after, the USDA Risk Management Agency (RMA's) reintroduced Discount Factors (DFs) in annual Special Provisions (SP) that outline price reductions related to fumonisin contamination in maize. In this research, we estimate the potential economic burden posed by these changes through a two-part approach. In part one, we construct a decision model that explores the final disposition of fumonisin-contaminated maize based on blending permissions, fumonisin levels, and crop insurance status. In part two, we estimate the economic impact by inserting output values of the decision model into financial equations that consider testing costs, transportation fees, and discounts from crop insurance and grain elevators when applicable. Our economic analysis projects that the financial losses during a THP crop year with high fumonisin levels could range from $15.1 to $135.5 million without the option to blend under conditions of the revised RMA discount schedule. Findings further highlight crop insurance as the most promising risk management strategy for farmers in areas susceptible to fumonisin contamination.

Feeder and fed cattle purchases of livestock risk protection

PurposePremium subsidy rates were increased in 2019 and 2020 for livestock risk protection (LRP) insurance, which is price insurance for cattle producers. The authors examined if the LRP subsidy rate changes affected the LRP coverage levels purchased by feeder and fed cattle producers.Design/methodology/approachThe authors collected the United States Department of Agriculture Risk Management Agency summary of business sales data for daily LRP purchases from 2015 to 2023. The authors estimated a multinomial logit model to determine if subsidy rate changes were associated with the likelihood of LRP policies being purchased at different coverage levels.FindingsAfter the 2019 and 2020 subsidy rate changes, the likelihood of producers buying LRP-feeder cattle policies with coverage over 95% increased relative to the policies with coverage less than 89.99% but did not influence the likelihood of producers buying LRP-feeder cattle policies with coverage between 90 and 94.99% relative to policies with coverage less than 89.99%. Marginal effects show these subsidy rate changes increased the likelihood of buyers purchasing LRP-feeder cattle policies with greater than 95% coverage. The subsidy change did not affect the purchase of LRP-fed cattle policies.Originality/valueThe results demonstrate the influence of the recent LRP policy adjustments on insurance purchases, which could be important for agency officials and policy makers. This is the first study to explore the LRP policy purchases which provides the United States cattle industry insight into the LRP price insurance take-up, which can guide producer extension education on managing price risk.

Economic benefit of co-regulation to manage aflatoxin risk in maize

Aflatoxins are toxic fungal metabolites that occur naturally in the field among cereals, oilseeds, and nuts that may increase during storage. Texas grown maize, commonly referred as corn, has some of the highest aflatoxin levels in the US. In 2011, the Office of the Texas State Chemist (OTSC) collaborated with the Risk Management Agency (RMA) of the United States Department of Agriculture (USDA) and the Texas grain industry to implement the state's first co-regulation governance option to manage aflatoxin risk. Co-regulation is a form of risk management that relies upon a government-private partnership in regulation; utilizing government-backed codes of practice that result in a more connected and transparent marketplace. To measure the economic benefit of co-regulation to manage aflatoxin risk, interviews were conducted among twenty-seven participants in the OTSC aflatoxin co-regulation program who represented 31% of the grain companies that handled maize contaminated by aflatoxin according to Texas Commercial Feed Rules. A comparative approach was used by gathering evidence from 2010 to 2018, in order to evaluate the results before and after the OTSC implemented its co-regulation strategy. The results were evaluated by using the data gathered from the interviews to measure the specific costs and benefits incurred by producers and grain handlers. The findings were modeled in the form of an income statement. From the income statement, the total economic benefit of the One Sample Strategy in 2018 was $14,572,180. This study provides a more realistic characterization of cost drivers associated with aflatoxin risk management and counters exaggerated economic losses associated with aflatoxin in maize from prior studies.

A Bibliography of Key Final Agency Determinations of the United States Department of Agriculture Risk Management Agency

This Article is the first law review article to comprehensively examine Final Agency Determinations (FADs) of the United States Department of Agriculture. A key part of the administrative process within the Risk Management Agency of USDA, FADs contribute to the interpretation and understanding of the Common Crop Insurance Policy, which is the federally-reinsured multi-peril insurance contract. This Article surveys ten of the most significant recent FADs and emphasizes the importance of FADs to litigated disputes between insurance providers and insureds with regard to the federal crop insurance program. Overall, understanding of FADs is critical for stakeholders with the multi-peril crop insurance program.

A Deep Factor Model for Crop Yield Forecasting and Insurance Ratemaking

Effective agricultural insurance and risk management programs rely on accurate crop yield forecasting. In this article, a novel deep factor model for crop yield forecasting and crop insurance ratemaking is proposed. This framework first utilizes a deep autoencoder to extract a latent factor, called the production index, that integrates salient spatial temporal patterns in the original yield data. Then, a concatenated deep learning model is constructed to enhance the modeling of the production index and the reconstruction of crop yields. Convolutional neural networks are employed to capture the high-dimensional and highly nonlinear structure within the crop yield data, as well as its interactions with weather and economic variables. The proposed deep factor framework is applied to the county-level data in the state of Iowa. Empirical results show that the newly proposed deep factor model significantly improves the prediction accuracy, especially in the test set. Based on a retain–cede crop insurance rating game between a private insurer and the government, we show that the proposed deep factor model provides economically and statistically significant improvement over the current Risk Management Agency ratemaking methodology.

Reevaluating the use of volatility factor in crop insurance premium rating

AbstractThis article reevaluates the use of volatility factors in federal crop insurance rate setting. The analysis focuses on price risk for corn and soybeans over the period 1990–2021. The sensitivity of insurance premiums to changes in volatility is comparable to the sensitivity resulting from changes in crop prices. Risk Management Agency's (RMA) volatility factor is generally supported to be an unbiased and efficient estimator of the realized volatility, although this support is found to be marginal in the case of soybeans. The out‐of‐sample forecasting analysis indicates that RMA's current approach can be modified to better reflect price risk over the insurance period.

A Bibliography of Key Final Agency Determinations of the United States Department of Agriculture Risk Management Agency

A Bibliography of Key Final Agency Determinations of the United States Department of Agriculture Risk Management Agency

What Does More Look Like?

Current cybersecurity governance in the water sector is based on a matrix of mandatory and voluntary standards, guidance, and associated legal principles. To date, market and regulatory signals have resulted in an inconsistent application of cybersecurity fundamentals. Technologies that operate critical infrastructure often were developed and installed before the reality of cyber threats created a demand on initial system design. Today, we know the threat posed by cyber bad actors is real and growing. Because the sector collectively is faced with a complex set of information systems and hardware that must operate 24–7 to ensure public health and safety, there is an opportunity and a need to improve the water sector's cybersecurity posture, which will require reconsideration of the sector's current approach to oversight and accountability. Given the number of high-profile cyber incidents that have affected multiple sectors in the past year, Congress has taken action to create mandatory cyber-incident reporting requirements for critical infrastructure systems—this includes water utilities. The Department of Homeland Security will develop the reporting requirements, including how information will be shared with a sector-specific risk management agency; this is the US Environmental Protection Agency (USEPA) for water systems. These same events have led to multiple directives from the White House for enhancing cybersecurity within the US government and the nation's critical infrastructure systems. As part of a general call for implementing best practices, the president and National Security Council mentioned water specifically as one of four sectors in need of focused attention on securing industrial controls systems. The congressional chartered bipartisan Cyberspace Solarium Commission (Solarium) has also focused on the water sector and will be issuing recommendations for improving cybersecurity this fall. It is anticipated that the Solarium report will identify options that Congress may take to stimulate the implementation of cybersecurity best practices, including requiring greater support from USEPA, building capacity, and providing directed funding for implementation of cybersecurity. There is intense federal attention on what various sectors are doing to manage the risk from cyber threats. This raises questions on what more should be done to ensure that water systems are taking appropriate actions to implement cybersecurity controls. We are therefore at a rare inflection point when it comes to informing a new oversight structure for cybersecurity in the water sector. One option is maintaining the voluntary use of existing guidance and standards. That was the approach taken by the pipeline sector until the Colonial Pipeline incident led to rapid implementation of highly prescriptive mandates that are not risk based. Another approach includes USEPA's planned action to incorporate cybersecurity into sanitary surveys. Conceptually, we can agree that cybersecurity is important to operational integrity; however, this state-run program does not have the resources nor cybersecurity expertise to implement appropriately. In April, AWWA's Water Utility Council commissioned a report to examine an approach that puts the water sector in a lead role, following the model used successfully in the electric sector. In particular, the report analyzes the creation of a sector-led process in close partnership with USEPA. This co-regulatory approach would be centered on a new entity, the Water Risk and Resilience Organization (WRRO), that would develop enforceable cyber standards and provide third-party audits. The diversity in utility size would require a tiered level of engagement, most likely starting with the largest drinking water and wastewater systems. Federal oversight would come from USEPA, with support from other federal agencies in the form of reviewing and approving standards developed by the WRRO. This approach provides a process that is capable of quickly adapting to the needs of the sector while ensuring that utility-based expertise guides standard development. In this case, the water sector is the driver rather a passenger. The only remaining question is if the sector is willing to take that responsibility and keep a hand on the wheel of governance or let go and hope for the best. Kevin M. Morley is manager of federal relations at the AWWA Government Affairs Office in Washington, D.C. He can be reached at kmorley@awwa.org. Paul N. Stockton is president of Paul N. Stockton LLC in Sante Fe, N.M.

Utilizing Topographic and Soil Features to Improve Rating for Farm‐Level Insurance Products

Previous studies have shown a strong correlation between topographic/soil features and agricultural production; however, linkages between these features and agricultural insurance products are scarce. Agricultural insurance is an ever‐growing means of governmental support for producers globally. However, failure to set insurance premiums that accurately reflect risk exposure can lead to low participation rates and/or adverse selection. The U.S. federal crop insurance program partly guards against this at the farm level by inducing pricing heterogeneity via a rate multiplier curve, which does not consider topographic/soil information. We develop a method for econometrically incorporating this information into existing rating procedures used by the Risk Management Agency (RMA). The empirical application leverages 149,267 farm‐level observations of Kansas producers across four dryland crops (corn, soybean, sorghum, and wheat), spanning forty‐six years, and matched to fine‐scale topographic/soil features. The results suggest that incorporating these features does improve the prediction accuracy of yield losses and can, in general, improve rating performance. However, these improvements are specific to farms with limited yield histories, as there are no improvements for farms with the commonly used yield history of ten years. This suggests substantial rating improvements for new farms or those with limited histories for a particular crop, but more general improvements for the program are not likely to occur given a large number of current participants with a full ten‐year yield history.

Understanding Irrigated Cotton Producer’s Crop Insurance Coverage Level Choices Under the 2014 Farm Bill

The Agricultural Act of 2014 (2014 Farm Bill) significantly changed the Title I commodity programs, and shallow loss insurance programs were added to the federal crop insurance program (Title XI). Stacked Income Protection Plan and Supplemental Coverage Option were new insurance programs enacted in the 2014 Farm Bill. Participation in these programs is influenced by premium subsidies and the type of coverage provided. Relationships between insurance agents and producers also have the potential to influence program participation. These changes could impact the producer’s insurance choice decision. The overall objective of this research is to understand the impact of the new insurance options on Texas cotton producers’ risk management decisions and participation and understand the change in coverage level of primary crop insurance due to availably of other protection. A mail survey was conducted among cotton producers in Texas to collect insurance choices data for 2014 and anticipated choices for 2015. Responses from 42% of Texas counties were received. In 2015, the USDA Risk Management Agency reported 155 counties in Texas produced cotton. Regression analysis conducted on anticipated change in coverage level of crop insurance indicated Common Crop Insurance Policy coverage level choice for larger farm size and higher non-farm income had an increase in coverage level effect; whereas enterprise unit, revenue protection, price, yield, and insurance agents had a decrease in coverage level effect. Results suggest that greater insurance agent involvement in program design and implementation of an insurance program could contribute to program success.

Flexible modelling of multivariate risks in pricing margin protection insurance: modelling portfolio risks with mixtures of mixtures

ABSTRACT Margin Protection Programs (MPPs) are relatively new insurance plans, introduced by USDA’s Risk Management Agency (RMA). The attractiveness of these risk management instruments lies in the fact that the financial stability of agricultural production and farming operations is more dependent on margins than solely revenues, which neglect production costs, as is the case for Revenue Protection Programs (RPPs). This article examines the structure and rating of margin protection insurance policies by considering a broad class of high-dimensional copula models that parameterize the dependence among multivariate sources of risks. A variety of copula methods, including Archimedean Copulas (ACs), Mixture Copulas (MCs), and Vine Copulas (VCs) are used to analyse the dependence structure between revenues and input costs. In terms of methodology, flexible mixtures of parametric distributions are applied to characterize marginal densities, and likewise flexible mixtures of alternative copulas are used to model dependence. This article also argues that the rating methodology that accounts for irregular and anomalous features of dependence such as asymmetry, non-linearity, non-ellipticity, and tail dependence between input prices and output prices can result in more accurate premiums, and therefore, can increase the hedging effectiveness of the MPPs and the market efficiency in the US crop insurance market.

Construction of Financial Risk Management and Control System for Shipping Companies

Bai, N. and He, Z., 2020. Construction of financial risk management and control system for shipping companies. In: Gong, D.; Zhang, M., and Liu, R. (eds.), Advances in Coastal Research: Engineering, Industry, Economy, and Sustainable Development. Journal of Coastal Research, Special Issue No. 106, pp. 490–493. Coconut Creek (Florida), ISSN 0749-0208.This paper attempts to study the financial risk management and control system of shipping companies for improving their operation and investment efficiency. To this end, selecting Z shipping company as an example, this paper analyzes the current status and influencing factors of financial risk management in Z shipping company on the basis of financial risk management-related theories. The financial index analysis was also adopted. The research results show that Z shipping company has different degrees of risk in terms of financing, investment, capital recovery, and income distribution; in the financial risk management of Z company, there exist problems such as the lack of a financial risk management atmosphere and the awareness of financial risk management for all employees, an irrational postsetting of risk management agency, the lack of a scientific financial risk management system, and absence of financial risk professionals. In response to these problems, control measures were proposed from four aspects: financing risk, investment risk, capital operation risk, and establishing a supervisory agency and improving the operating mechanism. The research findings enrich the theoretical research content of financial risk management in shipping companies and play a certain guiding role in the construction of a financial risk management and control system.

Premium misrating in federal crop insurance programs: scale, geography, and fiscal impacts

PurposeFederal crop insurance programs are the primary risk management programs of the US farm programs. Currently, these programs have been criticized for being disproportionally in favor of the riskier areas. Despite previous researchers having widely speculated its existence, a formal study of the scale, spatial pattern and fiscal impacts of such misrating phenomenon is still missing in the literature.Design/methodology/approachThis paper first purposes an empirically testable definition of misrating, and then detects the scale of the misrating phenomenon by using over two million actuarial records collected by United States Department of Agriculture (USDA's) risk management agency since 1989. Furthermore, multiple spatial statistics methods have been adopted to study the spatial patterns of the misrating statuses. Finally, the paper builds a simple theoretical model to study the potential fiscal impacts of any policy attempts to mitigate the misrating issue.FindingsThe result reveals that roughly 40% of the counties display some degree of misrating. Furthermore, the distribution of misrating displays a significant pattern of positive global spatial autocorrelation, which reflects the existence of regional clusters of premium rate mispricing. Last but not least, the paper concludes that whether an attempt toward fair rating decreases the total program outlay or not relies on the demand elasticity of crop insurance in both overrated and underrated regions.Originality/valueThis paper offers the first attempt to quantify the scale, identify the spatial pattern and evaluate the fiscal impact of the premium misrating in federal crop insurance programs.

Assessing agricultural risk management using historic crop insurance loss data over the ogallala aquifer

Much of the agricultural production in the Ogallala Aquifer region relies on groundwater for irrigation. In addition to declining water levels, weather and climate-driven events affect crop yields and revenues. Crop insurance serves as a risk management tool to mitigate these perils. Here, we seek to understand what long-term crop insurance loss data can tell us about agricultural risk management in the Ogallala. We assess patterns and trends in crop insurance loss data from the U.S. Department of Agriculture Risk Management Agency. Indemnities, or insurance payments, totaled $22 billion from 1989–2017 for the 161 counties that overlie the Ogallala Aquifer. We focused on the top ten weather and climate-driven causes of crop loss for the Ogallala, which comprised at least 92% of total indemnities. Drought, hail, and heat were the leading causes of crop loss for the region, and varied over space and time. For example, drought is a significant cause of loss across all seasons, while hail is more prevalent in the spring and summer. Spatially heterogeneous patterns emerged showing larger hail indemnities in the northern Ogallala versus larger drought indemnities in the southern portion. We performed a Mann-Kendall trend analysis of county-level annual loss cost values (the ratio of indemnities to liabilities). Drought and excess moisture showed significant increasing loss cost trends in the western counties of the Ogallala. In contrast, hail showed significant decreasing trends in the northern and eastern portions. These results suggest the northern counties of the Ogallala may perceive hail as a greater risk, and may be better equipped to handle drought losses as compared with the southern Ogallala. Crop insurance loss data play a role in integrating long-term trends with near-term management practices, and providing relevant risk information in producers’ operational to tactical decision making processes.

The US federal crop insurance program: a case study in rent seeking

PurposeRent 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.Design/methodology/approachThe 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.FindingsEach 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.Originality/valueThis is the first analysis of the US federal crop insurance program to examine the issue of coalition formation.

Dinamika Pendidikan Tinggi Vokasi Penerbangan Merespon Isu Industri 4.0

Talking about human development, the topic that keeps existing in higher education is skill-gap between university and industry. The challenge is getting harder with shifting conditions in the globe including the issue of industrial revolution 4.0. This challenge has an immediate effect on the existence and the development of vocational academy such as Indonesian Civil Aviation Institute. Therefore, this study explores the changes that occur in as Indonesian Civil Aviation Institute as a result of higher education policy in Indonesia. This study uses SWOT analysis to examine the strategies taken by this university to encounter the issue. According to this research, there are dynamic changes in the relation between this university, government, and industry as the outcome of dissimilar interpretation of the issue.
 
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Participation patterns of the rainfall index insurance for pasture, rangeland and forage programme

Using a novel policyholder-level data set, we analyse participants’ choices of 2-month index intervals in the Rainfall Index for Pasture, Rangeland and Forage (RI-PRF) insurance programme. We first provide a conceptual model that illustrates participation patterns of the rainfall index insurance. We then connect these predicted patterns to some empirical evidence from the policyholder-level data set, which is a subset of data provided by the USDA Risk Management Agency for all RI-PRF participants in Nebraska and Kansas during the years 2013–2017. Because the correlations between forage yield and precipitation and the expected premium subsidy vary by month, different degrees of risk aversion may predict distinctively different choices of the 2-month intervals. Using cluster analysis, we group the participants with similar allocation patterns across the 2-month intervals. We observe that the number of participants displaying relatively low levels of risk aversion increase over time. We connect this to the fact that premium subsidies and producer returns associated with non-growing season (risk-increasing) months are often greater than those for growing season (risk-reducing) months, and this has important implications for policy design. Our findings suggest that more research in this area could assist policymakers in keeping the RI-PRF programme in line with its objective of reducing risk for livestock producers.

Comparing survey-based and programme-based yield data: implications for the U.S. Agricultural Risk Coverage-County programme

One of the changes introduced by the U.S. 2018 Farm Bill in the Agricultural Risk Coverage-County (ARC-CO) programme is the requirement to use Risk Management Agency (RMA) programme-based data rather than National Agricultural Statistics Service (NASS) survey-based data. This study compares the NASS and RMA county yield data for the period 1991–2015 and finds no systematic differences between the two data sets. Additionally, using RMA or NASS yield data results in relatively small and statistically insignificant differences in the ARC-CO payments. The spatial disparities across neighbouring counties in ARC-CO payments are also similar, no matter whether NASS or RMA data are used.

Spatio-temporal variation of crop loss in the United States from 2001 to 2016

Crop insurance loss data can illuminate variations in agricultural impacts from exposure to weather and climate-driven events, and can improve our understanding of agricultural vulnerabilities. Here we perform a retrospective analysis of weather and climate-driven reasons for crop loss (i.e. cause of loss) obtained from the Risk Management Agency of the United States Department of Agriculture. The federal crop insurance program has insured over $440 billion in liabilities representing farmers’ crops from 2001 to 2016. Specifically, we examine the top ten weather and climate-driven causes of loss from 2001 to 2016 across the nation comprising at least 83% of total indemnities (i.e. insurance payouts provided to farmers after crop loss events). First, we analyzed the relative fraction of indemnities by causes of loss, over different spatial and temporal resolutions. We found that drought and excess precipitation comprised the largest sources of crop loss across the nation. However, these causes varied strongly over space and time. We applied two additional normalization techniques to indemnities using (1) insurance premia and the gross domestic product implicit price deflator, and (2) liabilities to calculate the loss cost. We conducted trend analyses using the Mann–Kendall statistical test on loss cost over time. Differential trends and patterns in loss cost demonstrated the importance of spatio-temporal resolution in assessing causes of loss. The majority of monthly significant trends (p < 0.05) showed increasing loss cost (i.e. increasing indemnities or decreasing liabilities) in response to weather events. Finally, we briefly discuss an online portal (AgRisk Viewer) to make these data accessible at multiple spatial scales and sub-annual time steps to support both research and outreach efforts promoting adaptation and resilience in agricultural systems.

Are Actuarial Crop Insurance Rates Fair?: An Analysis Using a Penalized BivariateB-Spline Method

SummaryIn this paper, we investigate whether the yield insurance premium rates given by the US Department of Agriculture's Risk Management Agency are actuarially fair by comparing the conditional yield density inferred from premium data with the conditional yield density inferred from yield data. A procedure is developed to estimate the conditional yield density by using premium data through partial derivatives of the premium rate function, as fitted by penalized bivariate tensor product B-splines. We study the asymptotic properties of partial derivatives of a penalized bivariate tensor product B-spline estimator and provide variance estimates. The conditional yield density inferred from premium data and its variance estimator are evaluated through simulation studies. The procedure is also applied to a crop insurance data set from the state of Iowa to examine the actuarial fairness of the premium rates. On average, premium rates are close to our estimates and this is true for each coverage level. However, premiums for low productivity land are generally too low whereas those for high productivity land are generally too high. Even after subsidies, premiums for the more productive land are generally substantially higher than are our estimates of the corresponding actuarially fair rates.