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Weather index insurance underwrites a weather risk, typically highly correlated with agricultural production losses, as a proxy for economic loss and is gaining popularity in lower income countries. This instrument, although subject to basis risk and high start-up costs, should reduce costs over traditional agricultural insurance. Multilateral institutions have suggested that weather index insurance could enhance the ability of stakeholders in lower income countries to adapt to climate change. While weather index insurance could have several benefits in this context (e.g. providing a safety net to vulnerable households and price signals regarding the weather risk), climate change impacts increase the price of insurance due to increasing weather risk. Uncertainty about the extent of regional impacts compounds pricing difficulties. Policy recommendations for insurance market development include funding risk assessments, start-up costs and the extreme layer of risk. General premium subsidies are cautioned against as they may actually slow household adaptation. The Geneva Papers (2009) 34, 401–424. doi:10.1057/gpp.2009.11

An important source of risk in agricultural production is the variability to crop yields refl ecting irregularly changing weather. This variability may be described as a stochastic process that has a function of density. Analyses of historical data on crop yields reveal that the function of density changed from right-skewed to left-skewed, along with increasing mean yields. All examined yields of crops cultivated recently in Poland demonstrate the left skew, which does not diminish with the aggregation of acreage. A fairly good approximation of the probability distribution for actual yields may be obtained using the log-normal distribution with an inverted abscissa.

Recent incidences of mass livestock mortality, known as dzud, have called into question the sustainability of pastoral nomadic herding, the cornerstone of Mongolian culture. A total of 20 million head of livestock perished in the mortality events of 2000-2002, and 2009-2010. To mitigate the effects of such events on the lives of herders, international agencies such as the World Bank are taking increasing interest in developing tailored market-based solutions like index-insurance. Their ultimate success depends on understanding the historical context and underlying causes of mortality. In this paper we examine mortality in 21 Mongolian aimags (provinces) between 1955 and 2013 in order to explain its density independent cause(s) related to climate variability. We show that livestock mortality is most strongly linked to winter (November-February) temperatures, with incidences of mass mortality being most likely to occur because of an anomalously cold winter. Additionally, we find prior summer (July-September) drought and precipitation deficit to be important triggers for mortality that intensifies the effect of upcoming winter temperatures on livestock. Our density independent mortality model based on winter temperature, summer drought, summer precipitation, and summer potential evaporanspiration explains 48.4% of the total variability in the mortality dataset. The Mongolian index based livestock insurance program uses a threshold of 6% mortality to trigger payouts. We find that on average for Mongolia, the probability of exceedance of 6% mortality in any given year is 26% over the 59 year period between 1955 and 2013.

This article focuses on innovation in weather insurance designed to fit the special circutnstances of the poor in lower income countries where rural and agricultural financial markets are largely underdeveloped. Index insurance is an innovation that circumvents many of the fundamental problems that hamper the development of insurance for weather risks in lower income countries. With index insurance, payments are made based upon an objective and independent index that serves as a proxy for significant losses to crops, livestock, or other property. For example, the index can be based upon extreme rainfall measures that create either drought or flooding. Weather stations or even satellite imagery coupled with computer models can be used to create reliable "indexes" as the basis of payments. This article reviews this innovation by providing the background for its development and the motivation for using the innovation for the poor.

This research identifies two problems in the new Federal Crop Insurance that may cause adverse selection: (a) the relationship between rate making and expected yields for individual farmers, and (b) the bias introduced in coverage protection when trends are not used to establish expected yields. A theoretical investigation using the normality assumption demonstrates the potential severity of these problems, and empirical results from farm‐level data lend further support. As crop insurance changes to individualized methods of protection, these issues will be particularly important for developing rates.

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