Thesis (M.S.)--University of Kentucky, 2002. / Title from document title page. Document formatted into pages; contains viii, 51 p. : ill, maps. Includes abstract. Includes bibliographical references (p. 49-50).

Revenue insurance with shallow loss protection for farmers has been introduced recently. A common attribute of most shallow loss proposals is that they would be arearevenue triggered. The impact on optimal hedge ratios of combining these shallow loss insurance proposals with deep loss farm-level insurance is examined. Since crop insurance, commodity programs and forward pricing are commonly used concurrently to manage crop revenue risk, the optimal combinations of these tools are explored. Numerical analysis in the presence of yield, basis and futures price variability is used to find the futures hedge ratio which maximizes the certainty equivalent of a risk averse producer. The results generally reveal a lower optimal hedge ratio with area-insurance than with individual insurance and show that shallow loss revenue insurance tends to slightly increase optimal hedge ratios.

This study is set out to explore how cognitive biases, gambler’s fallacy and hot hand effect, exert an effect on individual crop insurance purchase decision. A laboratory experiment comprised of two separate games was used to establish an insurance purchase environment to induce individual’s behavior. The gambler’s fallacy and hot hand effect failed to be found in the experiment. But the subjects’ perceived probability of loss plays a significant role in determine their purchase decisions—the higher probability they predicted, the more likely to buy insurance they were. It is also fascinating to find that the longer the exposure to random risks the subjects had, the more willing to engage in insurance protection they were.

The STAX and SCO shallow loss crop insurance products were introduced in the 2014 farm bill. This research investigates the farm-level performance of STAX for cotton growers. Using 10 years of actual farm yield data for the period 1999 to 2008, certainty equivalent gains were evaluated under four treatments in Texas, Mississippi and Louisiana for non-irrigated and irrigated cotton production. Following the current practice for STAX, county yield is estimated using yield data from YP, RP, and RP-HPE policies rather than NASS county level yield data. Findings show that, assuming actuariallyair premiums, certainty equivalent gains for RP tend to be higher than for STAX. But with subsidized premiums, the opposite outcome sometimes occurs. Furthermore, with subsidized premiums the findings indicate that almost all farms would benefit from purchasing STAX as a complement to RP. The use of actual farm yield data highlights the heterogeneity of STAX farm-level impacts.

The customizable area whole farm insurance (CAWFI) is proposed and evaluated as a possible wholearm revenue protection design for crop farms. The evaluation included assessing appropriate weight, optimal scale, and optimal coverage level. The optimal CAWFI was tested against no insurance program, 90% farm level whole farm insurance (90% CFWFI), and CAWFI with scale and coverage level as provisioned in GRP product (restricted CAWFI) in representative farm in Kansas, North Dakota, Illinois, and Mississippi. The study finds the optimal CAWFI outperforms no insurance program and restricted CAWFI asserting that CAWFI is a workable insurance model and relaxing restriction on scale and coverage level can increase expected utility of farmers. The optimal CAWFI results in a risk reduction roughly equal with 90% farm-level wholefarm insurance though the expected indemnities in it are at least three fold.

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Weather index insurance has become a popular subject in agricultural risk management. Under these policies farmers receive payments if they experience adverse weather for their crops. Spatial basis risk is the risk that weather observed at stations does not correspond to the weather experienced by the farmer. The objective of this research is to determine to what extent spatial basis risk can be impacted by the interpolation technique used to estimate weather conditions. Using forage crops from Ontario, Canada, as an example, a temperature based insurance index is developed. Seven different interpolation methods are used to estimate indemnities for forage producers. Results show that the number of weather stations in the interpolation area has a larger impact on spatial basis risk than the choice of interpolation technique. For insurers wishing to implement this type of insurance, more focus should be placed on increasing the number of available weather stations. / October 2016

Crop insurance is recognized as one of the most efficient mechanisms of income protection in agriculture, transferring risk from agriculture to other agents and economic sectors. Insurance tends to stimulate the increase of cultivated area and the use of technology, especially as it acts as an additional guarantee for access to credit. In Brazil, however, the massification of rural insurance is limited due to the restricted budget to fund government subsidization. Also, the lack of predictability and guarantee of resources prevents the long-term planning of investments by the private sector, imposes costs on the beneficiaries and generates dissatisfaction of the target public. This thesis aims to contribute to the expansion of crop insurance in Brazil through the research of index insurance, which has lower administrative and claim adjustment costs when compared to traditional insurance. The absence of in situ claim adjustment and moral hazard monitoring reduces the administrative costs of this type of insurance, permitting a subsidy free crop insurance. In the first of two articles, we explore the availability and quality of public databases for soybean yields and daily rainfall in the state of Paraná in Brazil in order to verify the feasibility of an index insurance product. We use multiple imputation by chained equations (MICE) to fill missing values in the rainfall dataset and study the existence of spatial and temporal patterns in the data by means of hierarchical clustering. Our results indicate that Paraná fulfills data requirements for a scalable weather index insurance with MICE and hierarchical clustering being effective tools in the pre-processing of data. The second article studies the efficiency of a novel regression approach, the geographically weighted quantile LASSO (GWQLASSO) in the modelling of yield-index relationship for weather index insurance products. GWQLASSO allows regression coefficients to vary spatially, while using the information from neighboring locations to derive robust estimates. The LASSO component of the model facilitates the selection of relevant explanatory variables. A weather index insurance (WII) product is developed based on 1-month SPI derived from a daily precipitation dataset for 41 weather stations in the State of Paraná (Brazil) for the period of 1979 through 2015. Soybean yield data are also used for the 41 municipalities from 1980 through 2015. The effectiveness of the GWQLASSO product is evaluated against a classic quantile regression approach and a traditional yield insurance product using the Spectral Risk Measure (SRM) and the Mean Semi-deviation. While GWQLASSO proved as effective as quantile regression it outperformed the yield insurance product, thus proving an alternative to the crop insurance market in Brazil and other locations with limited data. / O seguro agrícola é reconhecido como um dos mecanismos mais eficientes de proteção de renda na agricultura, transferindo o risco da fazenda para outros agentes e setores econômicos. O seguro tende a estimular o aumento da área cultivada e o uso de tecnologia, principalmente por atuar como garantia adicional de acesso ao crédito. No Brasil, no entanto, a massificação do seguro rural é limitada devido ao orçamento restrito para financiar o programa de subvenção governamental. Além disso, a falta de previsibilidade e garantia de recursos impede o planejamento de investimentos de longo prazo pelo setor privado, impõe custos aos beneficiários e gera insatisfação do público alvo. Esta tese visa contribuir para a expansão do seguro agrícola no Brasil por meio da pesquisa de seguro de índice climático, que possui menores custos administrativos e regulatórios quando comparado ao seguro tradicional. A ausência de validação de sinistro in loco e monitoramento de risco moral reduz os custos administrativos desse tipo de seguro, permitindo um seguro agrícola sem subsídio. No primeiro de dois artigos, exploramos a disponibilidade e a qualidade de bancos de dados públicos para produtividade de soja e precipitação diária no estado do Paraná, no Brasil, a fim de verificar a viabilidade de um produto de seguro de índice climático. Usamos a imputação múltipla por equações encadeadas (MICE) para preencher valores ausentes no conjunto de dados de precipitação e estudar a existência de padrões espaciais e temporais nos dados por meio de agrupamento hierárquico. Nossos resultados indicam que o Paraná preenche os requisitos de dados para um seguro de índice climático escalável com o uso do método MICE, e o agrupamento hierárquico é uma ferramenta eficaz no pré-processamento de dados. O segundo artigo estuda a eficiência de uma nova abordagem de regressão, a regressão quantílica LASSO ponderada geograficamente (GWQLASSO) na modelagem da relação entre o índice climático e a produtividade de soja. O GWQLASSO permite que os coeficientes de regressão variem espacialmente, enquanto utiliza a informação proveniente dos locais vizinhos de modo a obter estimativas robustas. O componente LASSO do modelo facilita a seleção de variáveis explicativas relevantes. Um produto de seguro de índice climático (WII) é desenvolvido com base em um índice de precipitação normalizado (intervalo de 1 mês) derivado de um conjunto de dados diários de precipitação para 41 estações meteorológicas (uma por município) no Estado do Paraná no período de 1979 a 2015. Os dados de rendimento da soja também são obtidos para estes 41 municípios de 1980 a 2015. A eficácia do produto GWQLASSO é avaliada em comparação com uma abordagem de regressão quantílica clássica e um produto tradicional de seguro de rendimento utilizando-se a medida de risco espectral (SRM) e o semi-desvio médio. Embora o GWQLASSO tenha se mostrado tão eficaz quanto a regressão quantílica, ele superou o produto de seguro de rendimento, provando assim ser uma alternativa ao mercado de seguro agrícola no Brasil e em outros locais com dados limitados.

Agricultural producers face uncertain agricultural production and market conditions. Much of the uncertainty faced by agricultural producers cannot be controlled by the producer, but can be managed. Several risk management programs are available in the U.S. to help manage uncertainties in agricultural production, marketing, and finance. This study focuses on the farm level economic implications of the federal risk management programs. In particular, the effects of the federal risk management programs on investment, production, and contract design are investigated. The dissertation is comprised of three essays. The unifying theme of these essays is the economic analysis of crop insurance programs. The first essay examines the effects of revenue insurance on the entry and exit thresholds of table grape producers using a real option approach. The results show that revenue insurance decreases the entry and exit thresholds compared with no revenue insurance, thus increasing the investment and current farming operation. If the policy goal is to induce more farmers in grape farming, the insurance policy with a high coverage level and high subsidy rate is effective. In the second essay, a mathematical programming model is used to examine the effects of federal risk management programs on optimal nitrogen fertilizer use and land allocation simultaneously. Current insurance programs and the Marketing Loan Program increase the optimal fertilizer rate 2% and increase the optimal cotton acreage 119-130% in a Texas cotton-sorghum system. Assuming nitrogen is harmful to the environment and cotton requires higher nitrogen use, these risk management programs counteract federal environmental programs. The third essay uses a principal-agent model to examine the optimal contract design that induces the best effort from the farmer when crop insurance is purchased. With the introduction of crop insurance, the investor’s optimal equity financing contract requires that the farmer bear more risk in order to have the incentive to work hard, which is achieved by increasing variable compensation and decreasing fixed compensation.

Some insurance firms are faced with the unique challenge of managing risks that are large, infrequent, and potentially highly correlated within geographic regions and/or across product lines. An example of this is crop insurance, which includes weather risk, and leads to a portfolio of risks with high variance. A solution to this problem is undertaken in this study, through using a combination of pooling and private reinsurance in a portfolio approach. This approach takes advantage of offsetting risks across regions, in order to reduce risk in a cost effective manner. An asset liability management (ALM) approach is used to examine the entire crop insurance sector for Canada. This is the first study to focus on pooling for an entire insurance sector in a country, and it uses all major crops from 1978-2009, across 10 regions (provinces). Chapter two develops an innovative insurance portfolio under a full premium pool, combining a self managed insurance pool and private reinsurance using the coefficient of variation (CV) of the loss coverage ratio (LCR), Model 3. Results show that this portfolio approach reduces risk across regions. Chapter three, in contrast to chapter two, uses a reinsurance premium pool, where regions contribute only a portion of their risk to a reinsurance pool. An improved insurance portfolio model is developed in chapter three, using combinatorial optimization with a genetic algorithm to combine a self managed reinsurance pool and private reinsurance, Model C. Results show that this reinsurance portfolio model efficiently reduces risk. Chapter four uses a similar approach to chapter three, except that it allows for dependence (correlation) across regions. Results for this model (Model CC) are consistent with those of chapter three, indicating the effectiveness of the portfolio approach when correlation is present across regions. Overall, the portfolio models developed in each of the three chapters (Model 1, Model C, and Model CC), produce acceptable surplus, survival probability, and deficit at ruin, indicating that the portfolio approach using pooling is efficient for reducing risk. Beyond crop insurance, the portfolio models can be applied to other large natural disaster and weather related insurance, and other portfolio applications.