(2018) for a review). Attempts to measure vulnerability to poverty in African contexts have highlighted the existence of sizeable welfare fluctuations and fractions of households exposed to poverty risk typically larger than realized poverty rates as measured in traditional cross-sectional poverty surveys (Dercon and Krishnan, 2000; Christiaensen and Subbarao, 2005; Demissie and Kasie, 2017; Hill and Porter, 2017; Skoufias et al., 2021).

In this paper, we develop a new framework inspired from catastrophe risk modeling methods (henceforth referred to as cat risk models or cat models) with the broad objective of improving our understanding of the nature, magnitude and distribution of impacts of weather shocks on household welfare. The primary aim of the proposed methodology is to produce reliable food insecurity forecasts that can support the design and targeting of annual interventions. We focus more specifically on food consumption for at least two reasons. First, ensuring food security in Sub-Saharan Africa remains a prime challenge and food consumption is still an essential welfare dimension considered for the design of anti-poverty programs in the region. Second, poverty surveys traditionally exploited to examine the impact of climate shocks on household welfare often have a comprehensive consumption module but are only carried out every 5 to 10 years. On the other hand, food security surveys primarily focus on food consumption and coping strategies but are generally conducted at least once a year. They therefore provide consumption realizations from a larger set of rainy season conditions scenarios. From an econometric perspective, this simply means that we can rely on relatively more identifying variation to estimate the relation between the rainy season quality (RSQ) and food consumption. We focus first on the estimation of a reduced form that quantifies the impact of the RSQ on food consumption. We investigate the heterogeneity of the RSQ impacts considering both geographic and household characteristics and our findings do reveal marked differences across locations (e.g. regions, livelihood zones...) and according to key household features, such as income sources or livestock ownership. We determine an optimized heterogeneity structure via a machine learning procedure that allows to form distinct groups of households for which food consumption in the lean season reacts to rainy season conditions in comparable ways: those are called “household typologies”. An RSQ-food consumption relationship – called a “vulnerability functions” – is calibrated for each household typology. For any given rainy season conditions scenario, RSQ-induced changes in food consumption relative to a baseline are inferred by evaluating vulnerability functions at RSQ values corresponding to that particular scenario. An exposure component encapsulating the spatial distribution of households and baseline food consumption levels for each household typology then allows to produce food insecurity predictions at sub-national levels. Finally, we incorporate a probabilistic dimension to the hazard component (i.e. the rainy season quality) to build a food insecurity risk profile that allows to appreciate the volatility of food consumption caused by variability in the RSQ.

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