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

There is a strong link between weather and the welfare of poor populations. Low-frequency, short-term, but catastrophic weather shocks can trigger destructive coping responses to disaster--for example, withdrawal of children from school, distress sale of assets, refugee migration, crime, and severe human suffering. Moreover, these adverse impacts often persist as children's physical growth falters, and household productivity, asset accumulation, and income growth are dampened (Dercon and Krishnan 2000; Hoddinott and Kinsey 2001; Hoddinott 2006). The prospect of such shocks may also induce underinvestment in assets at risk, limiting poor households' ability to grow their way out of poverty over time (Carter and Barrett 2006).

The problem originates with the difficulty poor households face in insuring covariate risk. While informal social insurance arrangements and flexible credit contracts often provide the poor with significant insurance against household-specific, idiosyncratic risk, when entire communities or social networks confront the same biophysical shock, their capacity to buffer members' welfare may be insufficient to prevent severe and widespread human suffering. The magnitude and intensity of such suffering sometimes merits the label "famine" (Howe and Devereux 2004). External (domestic and international) relief organizations and governments commonly step in to provide emergency assistance in the wake of catastrophic covariate shocks such as drought, especially when the specter of famine looms. Operational agencies and the donor community are thereby financially exposed to catastrophic weather risks in developing countries via their humanitarian commitment to emergency response.

In addition to their potential for other purposes (Barnett, Barrett and Skees forthcoming; Alderman and Haque 2007), recent innovations in index insurance show promise as a means to facilitate improved emergency response to weather-related catastrophic shocks that threaten famine. Just as improved early warning systems and emergency needs assessment practices have used timely monitoring and analysis of vulnerable areas to significantly improve humanitarian response in recent decades (Barrett and Maxwell 2005), so too can weather index insurance facilitate further improvement by addressing several key remaining weaknesses in global famine prevention efforts. This paper briefly outlines how donors and operational agencies might use weather index insurance for famine prevention, enumerates key prospective benefits from such products, and then illustrates the possibilities with an application to the arid lands of northern Kenya, an area of recurring severe droughts that elicit massive international humanitarian responses.

How to Use Weather Index Insurance for Famine Prevention

Weather index insurance pays claims based on realizations of a weather index that is highly correlated with an outcome variable of interest. The insurance policy specifies an event or threshold at which payments are triggered and a payment schedule as either a lump sum or a function of index values beyond that threshold. The pricing of the product is based on the underlying payment schedule and the probability of realizations of the index that might trigger indemnity payments. Those probabilities are typically derived from historical rainfall records (Turvey 2001).

In slightly more formal terms, the key to designing a weather index insurance product is the existence of some observable relationship, y = f(W, X) + [epsilon], where y is some outcome variable of interest, W represents one or more weather variable of interest (e.g., rainfall), X are other covariates that condition changes in y and that may be correlated with W, f([??]) is a general function, and e is a standard mean zero disturbance term. One will typically use time series observations on the variables to estimate some parametric relation that may involve multiple lags of the independent variables, polynomials in those lags to allow for non-linearities, etc. The key is that the specified relationship explains much of the variation in y and successfully forecasts out-of-sample.

Assuming f([??]) is invertible, and given a threshold level of y at which one wants to trigger a response, [y.bar], and observable X, one can specify and estimate a version of the previous equation and then recover a trigger level for W, [W.sup.*] (Turvey 2001) at which E[f(W, X)] = [y.bar]. Thus [f.sup.-1]([y.bar], X) = [W.sup.*]. It is also possible to estimate the pure reduced form relation y = h(W) + [psi] and similarly derive a threshold value for the weather index 1 if one cannot observe X or if the cost of making such observations exceeds the marginal gains in predictive accuracy. The value of the pure reduced form is that the forecasted human impact conditional on observed weather h(W) depends solely on observed weather, and thus it is objective, verifiable and independent from human manipulation. Therefore, f(W, X) and h(W) offer two alternative forms for a parametric index that proxies the risk associated with observed weather events.

Most commonly, the outcome variable reflects economic losses. In the present case, however, we are interested in measures of severe, widespread human suffering, that is, of famine. The dependent variable we use is the proportion of children aged 6-59 months in a community who suffer a mid-upper arm circumference (MUAC) z-score [less than or equal to] -2. (1) As a measure of wasting, MUAC reflects short-term fluctuations in nutritional stress and is typically easier and less costly to collect than weight-for-height, the most commonly used anthropometric measure of wasting. Furthermore, several studies have found MUAC a far better predictor of child mortality than weight-for-height (Alam, Wojtyniac, and Rahaman 1989; Vella et al. 1994). We follow Howe and Devereux's (2004) definition of famine as a condition where 20% or more of children in a specified area are severely wasted (z [less than or equal to] -2).

Historically, "most famines in poor economies are associated with the impact of extreme weather ... [and] the worst famines have been the product of back-to-back shortfalls of the staple crop" (O Grada 2007, p. 7). While weather shocks are neither necessary nor sufficient to induce famine, there is a strong historical correlation that can potentially be exploited. Our preliminary work with detailed data from three districts in northern Kenya finds a strong historical relationship between community-level MUAC indicators--in particular, the proportion of a community's children with MUAC z-score [less than or equal to] -2--and lagged rainfall indicators, with considerable out-of-sample forecast accuracy (Mude et al. 2006). This offers a promising platform on which to build weather insurance for drought response.

The Potential Gains of Weather Index Insurance for Drought Response

There have been a number of recent experiments with weather index insurance programs for protection against disasters. The best-known example is the Mexican public reinsurance program, Agroasemex, which has marketed weather index insurance policies to state governments to insure against drought, and which has links to the National Fund for Natural Disasters, FONDEN (Alderman and Haque 2007).

Weather insurance offers several different, potentially major improvements to the global response to climate-related, slow-onset emergencies such as drought. First, insurance by its nature enables the insured to smooth its stream of payments. Rather than incurring irregular, massive expenses for emergency response, one pays a far smaller amount regularly in the form of insurance premium, but receives large indemnity payments when resources are needed. Given liquidity constraints and the value to expenditure smoothing, such smoothing should be advantageous to operational agencies and donors if such insurance can be reasonably priced in the market.

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Second, the irregularity of need for famine prevention resources underscores the value of insurance for low-probability, high-impact events as part of an effective risk-layering strategy. Communities can easily absorb modest variability in rainfall. In our setting, pastoralists in northern Kenya have developed highly adaptive livelihood strategies over many centuries of coping with volatile rainfall patterns. So a layer of individual and community-level self-insurance is feasible. Bigger covariate shocks commonly demand some outside interventions. Agencies and donors can readily handle small-scale crises within their usual budgets and operational mandates. The problem emerges when rare, widespread and devastating shocks occur and probabilistically threaten famine. With insurance in place to provide resources necessary for such low frequency but potentially catastrophic weather events, other actors can focus better on insuring the range of commonplace risks over which they possess comparative advantage.