Probability weighting and insurance demand in a unified framework

We provide a comprehensive analysis of the impact of probability weighting on optimal insurance demand in a unified framework. We identify decreasing relative overweighting as a new local condition on the probability weighting function that is useful for comparative static analysis. We discuss the effects of probability weighting on coinsurance, deductible choice, insurance demand for low-probability, high-impact risks versus high-probability, low-impact risks, and insurance demand in the presence of nonperformance risk. Probability weighting can make better or worse predictions than expected utility depending on the insurance demand problem at hand

Budget-constrained optimal insurance without the nonnegativity constraint on indemnities

The final publication is available at Elsevier via https://doi.org/10.1016/j.insmatheco.2018.10.004 © 2018. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/In a problem of Pareto-efficient insurance contracting (bilateral risk sharing) with expected-utility preferences, Gollier (1987) relaxes the nonnegativity constraint on indemnities and argues that the existence of a deductible is only due to the variability in the cost of insurance, not the nonnegativity constraint itself. In this paper, we find support for a similar statement in problems of budget-constrained optimal insurance (i.e., demand for insurance). Specifically, we consider a setting of ambiguity (unilateral and bilateral) and a setting of belief heterogeneity. We drop the nonnegativity constraint and assume no cost (or a fixed cost) to the insurer, and we derive closed-form solutions to the problems that we formulate. In particular, we show that optimal indemnities no longer include a deductible provision; and they can be negative for small values of the loss, or in case of no loss.Natural Sciences and Engineering Research Council [grant 2018-03961

THEORETICAL AND EMPIRICAL STRATEGIES FOR MANAGING IRRIGATION SUPPLIES RISK: THE CASE OF RIO MAYO IRRIGATION DISTRICT IN SONORA, MEXICO

This dissertation comprises theoretical and empirical models to manage watersupply risk in irrigated agriculture. While irrigation is by itself a strategy to regulate thesupply of water for farm use, water systems that depend on surface water sources are stillsubject to the random inflows that feed their reservoirs. Depending on the size of thereservoir, the demand for irrigation, and the seasonal distribution of inflows, wateravailability may decrease to levels that severely constraint agricultural production. Thisdissertation begins with a theoretical examination of on-farm cropping decisions underwater endowment risk. However, the analysis is extended to the use of a risk-sharinginnovation to transfer the water availability risk outside an irrigation district. Specifically,the design, use, and economic feasibility of an inflow-based derivative are studied for theRio Mayo irrigation district, located in Northwestern Mexico.On the theoretical front, the analysis consists of modeling the on-farm economicsof hedging against uncertain irrigation endowments. The basic model starts by analyzingthe role of crop diversification. As expected, the firm responds to higher degrees of risk,as measured by the variance in the supply of water, by allocating less land towards thewater-intensive crops. The underlying motivation in these strategies is the need to avoidthe relatively larger reductions in productivity sustained by water-intensive cropportfolios. However, crop diversification comes at the cost of reduced profits. As analterative to crop diversification, the model is modified to study the role of an institutionthat transfers water contingent on the states of nature. The extension shows that, undercertain conditions, enrolling in such a scheme produces the same profit as undercertainty.In the empirical component of the dissertation, the economics of an inflow-basedderivative are examined. The modeling strategy consists of simulating the economicenvironment and hydrological profile of the Adolfo Ruiz Cortinez Reservoir on the RioMayo irrigation district. Specifically, a stochastic dynamic simulation model is developedthat captures the intra and inter seasonal risk aspects associated with water risk and wateruse for irrigated agriculture. The results indicate that the inflow-based derivative is aviable instrument in the terms of affordability (i.e. premiums) and yield effective incomeprotection (i.e. risk reduction)

Evaluating Commodity Farm Program Selection and Economic Return Variability on Representative Farms in the Mississippi River Delta Region Using a Risk Return Framework

The Agricultural Act of 2014, signed February 7, 2014, introduces a new era of federal support in the production of major agricultural commodities in the United States for the 2014 through 2018 crop years. The ultimate result of the Act was a 954-page piece of legislation that represented market-oriented policies such as the creation of an area-wide shallow loss revenue support program for covered commodities and a greater reliance on crop insurance products offered as a suite of risk management tools available to producers. The impact that this law has on agricultural producers in the Mississippi River delta region of the Mid-south is not yet fully known. Moving forward, the elimination of the direct payment program is likely to have an impact on farm income, as these payments were made annually and were decoupled from actual market prices. Various combinations of federal farm programs, chosen irrevocable, paired with multiple crop insurance products, that are purchased annually, will act to mitigate the risks of production. Simulation analysis provides a basis for evaluating the variability associated with production systems in the Mississippi River delta region. Three representative rice and soybean farms and six corn, cotton, and soybean farms were modeled as to determine the five year net returns resulting from price and yield risk as well as to evaluate alternative farm program and crop insurance selection. Financial performance of these farms is measured for varying levels of risk using a stochastic efficiency criteria. Results are presented for multiple combinations of the agriculture risk coverage and price loss coverage programs of the commodity title and revenue protection, supplemental coverage option endorsement, and the stacked income protection plan for producers of upland cotton contained in crop insurance title of the current farm law. For each farm at each location, an estimate to the net present value of the cumulative net returns above variable costs to the producer for the five year life of the farm bill is provided. Results from different farming operations suggest the preferred pairing of farm programs and crop insurance policies does vary across locale and crops

Advances in Cost-Effectiveness Analysis of Health Interventions

The growing application of cost-effectiveness (CE) analysis and controversies about its methods has led to a need to explore its welfare economic foundations. Examination of its welfare theoretic foundations can provide a rationale for selecting specific standards for the application of CE analysis while deepening our understanding of the implications of alternative methodological approaches. In this paper, I explore conditions under which decision making based on CE analysis, carried out a specific way, leads to a distribution of resources that has desirable social welfare properties. The first section describes the basics of CE analysis and how it can be applied to aid decisions about the allocation of health resources. The paper then turns to the potential welfare economic foundations of CE analysis, and addresses specific issues in carrying out CE analysis, such as which costs to include, whose perspective matters in the analysis, and how health outcomes are measured. It demonstrates how a welfare economic foundation can help resolve ambiguities and uncertainties about the application of CE analysis. The paper also discusses the limitations of such an approach, which indeed reflect limitations of CE analysis as an analytic framework. Finally, it addresses unresolved issues such as the difficulties in using the results of CE analysis to make health policy at the societal or group level.

Pandemics: Insurance and Social Protection

This open access book collects expert contributions on actuarial modelling and related topics, from machine learning to legal aspects, and reflects on possible insurance designs during an epidemic/pandemic. Starting by considering the impulse given by COVID-19 to the insurance industry and to actuarial research, the text covers compartment models, mortality changes during a pandemic, risk-sharing in the presence of low probability events, group testing, compositional data analysis for detecting data inconsistencies, behaviouristic aspects in fighting a pandemic, and insurers’ legal problems, amongst others. Concluding with an essay by a practicing actuary on the applicability of the methods proposed, this interdisciplinary book is aimed at actuaries as well as readers with a background in mathematics, economics, statistics, finance, epidemiology, or sociology

Drought climate adaptation program: producing enhanced agricultural crop insurance systems: final report

Queensland farmers are subject to highly variable climatic conditions, including drought and floods, which can undermine production. Insurance could play an important role in helping Queensland farmers manage their climate risk. However, currently the use of insurance to manage climate related production risk is poorly understood and utilised by farmers. This project aims to address this gap by providing information on climate risks and the role of insurance for managing these. This project conducted focussed reviews on climate risk in agriculture and on how insurance products could be used to address these risks. The project also carried out on-ground surveys from cotton and sugar industry and conducted modelling to assess risks and the role of insurance for cotton and sugar cane farmers in Queensland. Prototype climate assessment risk and reporting tools were also developed. The reviews carried out in this project identified that Queensland’s agricultural sector is highly exposed to production volatility as a result of weather risks. It is our view that the Queensland agricultural sector has an excellent opportunity to provide its farmers with protection against uninsured seasonal risks to crop production. Key climate and farming systems risks were identified by interviewing a total of 55 farmers (23 cotton growers and 32 sugar cane growers) across Queensland. Key climate risks to the cotton industry include hail, drought/dry years (lack of rainfall during planting and season), quality downgrade (discolouration), excessive heat, floods and wet weather (during season and especially during harvest). Similarly, for the sugar industry, key climate risks include, drought, flood, excessive rainfall during harvest, cyclone, pests and disease. Key messages from farmer surveys are that current insurance products available to Queensland farmers (specifically, cotton and sugar cane farmers) may not address critical risks to the production and/or profitability of these systems and that farmers would prefer to have comprehensive insurance products available that cover them against profitability losses across multiple risk factors. A ‘climate and agricultural risk assessment and reporting tool’ (prototype) was developed as part of the project. This ‘tool’ allows quantification of key climate risks, initially for the sugar and cotton industry. The tool provides an option to generate a detail climate risk report based on historical data and a future seasonal climate forecast for an individual location. The tool data also serves as a dataset portal, allowing for the download of data in a required template. Cotton and sugarcane crop models APSIM and DSSAT were employed to simulate the growth and yield for 10 and 12 sites, respectively, across Queensland over the period 1940-2017 for various crop management factors. Comparing the simulated yields (from each model or the mean simulated value from ensemble models) to the observed yield (available at regional scale) the trend in year to year variability is satisfactorily captured for cotton on average, whereas for sugarcane there is a trend to overestimate or underestimate the yield depending on the site. Based on survey findings three prototype insurance products were developed for the cotton industry Insurance products developed were Drought Cover: insufficient rainfall during the planting season – August to November; Drought Cover: insufficient rainfall during growing season – November to February; and Wet Harvest Cover: excessive rainfall during harvest season – March to June. Two prototype insurance products were developed for sugar industry. They include; Cyclone Cover: crop damage during cyclone season – November to April; and Wet Harvest Cover: excessive rainfall during harvest season – June to December. Rainfall-indexed based worked examples were also developed for sugar and cotton industry growers to better appreciate the insurance mechanisms

Drought climate adaptation program: producing enhanced agricultural crop insurance systems: final report

Queensland farmers are subject to highly variable climatic conditions, including drought and floods, which can undermine production. Insurance could play an important role in helping Queensland farmers manage their climate risk. However, currently the use of insurance to manage climate related production risk is poorly understood and utilised by farmers. This project aims to address this gap by providing information on climate risks and the role of insurance for managing these. This project conducted focussed reviews on climate risk in agriculture and on how insurance products could be used to address these risks. The project also carried out on-ground surveys from cotton and sugar industry and conducted modelling to assess risks and the role of insurance for cotton and sugar cane farmers in Queensland. Prototype climate assessment risk and reporting tools were also developed. The reviews carried out in this project identified that Queensland’s agricultural sector is highly exposed to production volatility as a result of weather risks. It is our view that the Queensland agricultural sector has an excellent opportunity to provide its farmers with protection against uninsured seasonal risks to crop production. Key climate and farming systems risks were identified by interviewing a total of 55 farmers (23 cotton growers and 32 sugar cane growers) across Queensland. Key climate risks to the cotton industry include hail, drought/dry years (lack of rainfall during planting and season), quality downgrade (discolouration), excessive heat, floods and wet weather (during season and especially during harvest). Similarly, for the sugar industry, key climate risks include, drought, flood, excessive rainfall during harvest, cyclone, pests and disease. Key messages from farmer surveys are that current insurance products available to Queensland farmers (specifically, cotton and sugar cane farmers) may not address critical risks to the production and/or profitability of these systems and that farmers would prefer to have comprehensive insurance products available that cover them against profitability losses across multiple risk factors. A ‘climate and agricultural risk assessment and reporting tool’ (prototype) was developed as part of the project. This ‘tool’ allows quantification of key climate risks, initially for the sugar and cotton industry. The tool provides an option to generate a detail climate risk report based on historical data and a future seasonal climate forecast for an individual location. The tool data also serves as a dataset portal, allowing for the download of data in a required template. Cotton and sugarcane crop models APSIM and DSSAT were employed to simulate the growth and yield for 10 and 12 sites, respectively, across Queensland over the period 1940-2017 for various crop management factors. Comparing the simulated yields (from each model or the mean simulated value from ensemble models) to the observed yield (available at regional scale) the trend in year to year variability is satisfactorily captured for cotton on average, whereas for sugarcane there is a trend to overestimate or underestimate the yield depending on the site. Based on survey findings three prototype insurance products were developed for the cotton industry Insurance products developed were Drought Cover: insufficient rainfall during the planting season – August to November; Drought Cover: insufficient rainfall during growing season – November to February; and Wet Harvest Cover: excessive rainfall during harvest season – March to June. Two prototype insurance products were developed for sugar industry. They include; Cyclone Cover: crop damage during cyclone season – November to April; and Wet Harvest Cover: excessive rainfall during harvest season – June to December. Rainfall-indexed based worked examples were also developed for sugar and cotton industry growers to better appreciate the insurance mechanisms