Using weather index insurance to improve drought response for famine prevention.

Figure 2 plots mean monthly rainfall volumes across these three districts along with the percentage of the 21 sites in which the short and/or long rains failed, where "failure" reflects cumulative rainfall more than one standard deviation below its long-term, site-specific mean. Three major recent droughts had dire humanitarian consequences: 1997/8, 2000/1, and 2005/6. Aggregate rainfall was low in all of these years, and the drought conditions were spatially widespread and continued across multiple seasons. Mude et al. (2006) show that drought episodes are strongly associated with dramatic herd losses due to mortality, lower livestock lactation rates, and a sharply higher prevalence of severe child wasting. Intriguingly, they also find that forecasts of severe wasting prevalence generated from a relatively simple model based on a small set of variables that ALRMP regularly monitors yields highly accurate out-of-sample forecasts with a lead of three months. Rainfall is the key explanatory variable. It seems that observed rainfall patterns may be useful in predicting and insuring against famine.

In this setting, designing weather index insurance to facilitate financing of drought-related humanitarian response appears attractive. We conceptualize two ways in which weather insurance can be effectively designed to serve this goal. The first is a simple put option based on cumulative long rains (March-May) and/or cumulative short rains (October-December)--appropriately weighted across rainfall sites--as a weather index. This might pay out some pre-determined sum per mm shortfall of seasonal cumulative rainfall relative to a contractually established threshold at the end of the contract term for each season. To take into account the intensity of droughts in cases of severe rainfall deficit, the option payout could be a convex function of the seasonal cumulative rain shortfall. Payout could be even simpler, a lump sum payment at the end of the contract term if seasonal cumulative rainfall fell below the threshold. As historical data show that seasonal rain shortfalls are strongly associated with the emergence of famine conditions, even such simple insurance seems to offer a reasonable hedging tool for organizations committed to humanitarian drought response. The simple nature of such contracts can potentially increase reinsurance opportunities and thus lower the prospective price of such insurance in international markets. As local droughts within districts can effectively be handled by traditional means, it might also be more cost effective to write a single contract for the whole area rather than for each district separately.

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The second weather index insurance design exploits the apparent ability to forecast famine based on rainfall several months ahead. Specifically, one could use a validated forecasting model to establish the rainfall level below which the expected future prevalence of child wasting equals or exceeds 20%, thereby triggering indemnity payments under the insurance contract. The model would be specified in the contract and new forecasts generated in near real-time based on the arrival of weather data. The weather index evolves continuously and can therefore better capture not only the impact of shortfalls in rainfall quantity but also the timing and distribution within a season as well. The forecast model can readily incorporate monthly or seasonal dummy variables and location-specific dummies, in short, any other covariates that affect the dependent variable of interest that can be objectively verified and cannot be manipulated by parties to the contract. The nonstandard nature of this contract might make it somewhat harder to price and sell in financial markets. Weather-based famine index insurance of this sort could complement existing appeals-based systems based more on realizations of human suffering, thereby facilitating faster, lower-cost intervention based more directly on anticipated need and less on supply-side conditions in food aid donor countries.

The famine insurance we envision, especially the second variant, differs in a few key ways from the well-publicized drought insurance contract that WFP took out for Ethiopia with AXA Re in 2006. First, that contract did not use any weather stations from the country's pastoral regions, on which we focus. Second, the weather risks were quantified in terms of expected income loss by at-risk populations based on estimates of the elasticity of crop production to rainfall at different stages of crop growth. Crop- and area-specific estimates were aggregated, mapped to income via price estimates, and then converted into a livelihood loss index. Our design is to tie rainfall directly to a human outcome of interest rather than to indirect measures and to use the commonplace superiority of reduced form forecasting over those based on structural models. Third, the 2006 Ethiopia drought insurance contract covered the entire agricultural season, consisting of two rainy seasons, from March to October, and triggered payment by the end of the contract (in October) when data gathered throughout the contract period indicated that rainfall was significantly below historic averages, pointing to the likelihood of widespread crop failure. The product we envision would pay out at any time within the contract period once the model predicts a prevalence of severe child wasting that meets or exceeds the pre-specified trigger level. Thus, if the seasonal rains failed badly and widely, this might trigger indemnity payments well before the end of the contract so as to allow more effective and lower cost intervention. In parallel work, we explore the framework for pricing such contracts (Chantarat et al. 2007).

We gratefully acknowledge financial support from the Global Livestock Collaborative Research Support Program (GL CRSP), funded by the Office of Agriculture and Food Security, Global Bureau, USAID, under grant number DAN-1328-G-00-0046-00. The views expressed here are those of the authors and do not represent any official agency. Any remaining errors are our own.

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(1) MUAC data are standardized using the international standard 1978 CDC/WHO growth chart. The threshold z [less than or equal to] 2 is consistent with the famine benchmark often employed by emergency relief agencies (Howe and Devereux 2004; World Food Programme 2000).

(2) The food aid figures, obtained from WFP annual reports, reflect deliveries into the whole of Kenya, not just the northern three districts we study. Unfortunately, we could not obtain district-level disaggregated figures. However, these three districts were among the leading recipients of food aid within the country over this period, thus we are confident that the basic patterns are satisfactorily reflected in these data.