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Calum G. Turvey and Megan K. Mclaurin

1. Introduction Index-based agricultural insurance is gaining increasing popularity because of its ease of implementation, its safeguards against the moral hazard and adverse selection problems frequently found in traditional agricultural indemnity insurance, and its resulting affordability. 1 In the developing world, index-based insurance is seen as a method of providing risk protection to communities previously thought to be uninsurable ( Barrett et al. 2009 ), while Brown and de Beurs

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Daniel Leppert, Tobias Dalhaus, and Carl-Johan Lagerkvist

1. Introduction Extreme heat events can cause substantial losses in U.S. corn production, and climate change is expected to further exacerbate heat stress ( Schlenker and Roberts 2009 ). Therefore, efficient risk management is crucial in order to protect farmers’ incomes when extreme weather conditions occur. Traditional indemnity-based crop insurance comes at the cost of adverse selection and moral hazard ( Glauber 2013 ; Goodwin and Smith 2013 ), which puts an additional burden on insurers

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Travis M. Williams and William R. Travis

1. Introduction Weather index insurance instruments are spreading around the world ( Greatrex et al. 2015 ), offering ease of application and a relatively simple actuarial design. Index insurance uses a single indicator, like rainfall, to stand for losses caused by extreme conditions ( Conradt et al. 2015 ). An index insures only one cause of loss and, because it is not influenced by policyholder decision-making, managerial strategies cannot increase the chance of payment, reducing costs

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Tanya M. Brown, William H. Pogorzelski, and Ian M. Giammanco

1. Introduction Since the 1940s, considerable knowledge about damaging hailstorms has been gleaned from crop-hail insurance data. Hail-related losses were generally thought to be better documented by crop-hail insurance companies than by property insurers, who did not distinguish between hail, wind, tornado, rain, or lightning losses ( Changnon 1972 , 1977 ; Changnon et al. 2009 ). Historically, crop-hail loss data had been used to understand the spatial and temporal aspects of economic

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Markus Enenkel, Daniel Osgood, Martha Anderson, Bristol Powell, Jessica McCarty, Christopher Neigh, Mark Carroll, Margaret Wooten, Greg Husak, Christopher Hain, and Molly Brown

communities. Thus, they tend to avoid taking agricultural risks that could increase their production ( Rosenzweig and Wolpin 1993 ) in average and high-yield seasons that could serve as a buffer for drought years. The goal of weather index insurance (WII) is to limit agricultural losses caused by adverse weather events while simultaneously allowing farmers to take certain risks that aim at increasing their production. While agricultural insurance covered 40% of the total costs of weather-related disasters

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Tobias Dalhaus and Robert Finger

1. Introduction Crop production is exposed to a variety of risks that increase the volatility of farm profits (e.g., Hardaker et al. 2004 ). In particular, the high variability of weather is of relevance for agricultural producers, encouraging the development of efficient risk management tools. In this context agricultural insurance schemes traditionally play a major role in many countries ( Glauber 2013 ). To overcome problems caused by asymmetric information associated with indemnity

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Asher Siebert

challenges is index insurance. Index insurance differs from loss and verification insurance in that index insurance contracts are written against geophysical indices (which are intended to be well correlated to a livelihood risk). When the geophysical index reaches a certain critical value, a payout is triggered, regardless of whether the losses in the insured region were particularly severe. This structure has the potential benefit of lowering transaction costs, reducing or eliminating moral hazard, and

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Calum G. Turvey, Apurba Shee, and Ana Marr

1. Introduction All too often, scholars and practitioners examining weather data for the purpose of developing weather index insurance (WII) make the assumption, or presumption, that the data are Gaussian and Markovian, meaning that day-over-day or season-over-season measures are independent and uncorrelated. For example, the index insurance designs presented in Mahul and Skees (2007) for livestock insurance in Mongolia, in Khalil et al. (2007) for El Niño insurance in Peru, in Makaudze

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E. Ray Fosse

876 JOURNAL OF APPLIED METEOROLOGY VOLUMSI?What Weather Modification Needs: An Insurance Perspective E. RAY Foss]~Crop-Hail Insurance Actuarial Association, Chicago, Ill. 60606(Manuscript received 19 October 1977, in final form 30 November 1977) Individuals and businesses use insurance as one important means of dealing with uncertainty. Whilethe insurance industry does not require the

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Stanley A. Changnon and Joyce M. Changnon

568 JOURNAL OF CLIMATE -OLOME3Use of Climatological Data in Weather Insurance STANLEY A. CHANGNON AND JOYCE M. CHANGNONMidwestern Climate Center. Climate and Meteorology Section, lllinois State Water Survey, Champaign, Illinois(Manuscript received 13 June 1989, in final form 8 December 1989)ABSTRACT There are three major types of crop-related weather insurance: hail, all perils, and

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