The paper first contributes to the climate economy literature by providing new evidence of the distributional impacts of weather conditions on welfare in Africa. However, the most important contribution is methodological with the introduction of a novel framework that incorporates microeconomic estimates into a cat model structure in order to produce food insecurity forecasts that can be used to support the design and targeting of annual interventions and other social protection programs. The methodology exploits the lagged effect of rainy season conditions on food consumption observed during the hunger season to produce sub-national estimates of food insecurity up to 8 months ahead of the hunger season. This represents a clear improvement from food insecurity prediction techniques currently used in Mauritania and other countries in the region – mainly based on qualitative approaches – which can feed into early warning systems with promising prospects to support intervention planning and inform the design and targeting mechanisms of adaptive social protection programs. Our model not only provides spatially disaggregated predictions of food insecurity for specific scenarios, but also allows to produce a probabilistic assessment of the welfare risk induced by weather variability.

We take our model to the data in the context of rural Mauritania. Mauritania is a West African country located in the Sahelian and Saharan zones that is greatly exposed to climate hazards. In particular, the high inter-annual rainfall variability leads to the frequent occurrence of drought conditions that have devastating effects on households’ well-being. In 2011, marked rainfall deficits led to crop failures, water shortages, lack of pasture and livestock losses, which in turn triggered a major food crisis during the 2012 lean season with an estimated one million people (approximately 27% of total population) in a situation of food insecurity.<sup>3</sup> Mauritania has only one rainy season running from June to October and the main harvest takes place between September and November. Following the main harvest, food stocks gradually decline until the lean season (MayAugust) that is typically characterized by lower food reserves, low demand for agricultural labor and rising food insecurity. The magnitude of these seasonal patterns is largely determined by the quality of the rainy season and drought conditions therefore exacerbate lean season food insecurity peaks. The lagged effect of the rainy season quality on food consumption is a feature of interest because it implies some degree of predictability of the forthcoming impact on households as soon as the quality of the RSQ is observable, which can be exploited to support early action.

When tested against historical data, the prediction model produces national food insecurity estimates with a mean absolute error of 1.2 p.p., with historical rates ranging between 29% and 42% over the period 2011-2015. At the regional-level, model predictions yield a mean absolute error of 4.9 p.p. and a correlation coefficient of 0.84. More importantly, the methodology allows to uncover the fact that inter-year variations in rural

> 3Source: authors calculations based on the 2012 Food Security Monitoring Survey.

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