the household-level. We use repeated cross-sectional data compiled from our five FSMS in lean season that provide household-level information on food consumption along with key socio-economic characteristics. We thus propose the following general reduced form that relates the FCS to an RSQ index: _yi,d,r,t_ is the FCS in lean season of year _t_ for household _i_ , living in department _d_ and region _r_ . _rsqd,t_ is an RSQ index measuring the quality of the previous rainy season in department _d_ .<sup>19</sup> **_Xi,d,r,t_** is a vector of controls and **_Vi,d,r,t_** is a broadly defined vector of vulnerability factors such that the marginal effect of the RSQ on individual _i_ ’s food consumption is given by _β_ 1 + **_β_ 2**<sup>**_′_**</sup><sup>_._</sup><sup>**_Vi,d,r,t_**.Ofcourse,theproposedmodelassumesalinear</sup> relationship between food consumption and the RSQ and the vulnerability function for household _i_ is therefore given by _vi_ ( _rsq_ ) = ( _β_ 1 + **_β_ 2**<sup>**_′_**</sup><sup>_._</sup><sup>**_Vi,d,r,t_**)</sup><sup>_× rsq_,althoughourresults</sup> also include estimations with more complex polynomial functional forms.

Regional fixed-effects _γr_ absorb time-invariant unobserved spatial characteristics at the regional level that are not captured in **_Xi,d,r,t_** . Those include, for instance, access to food markets, to urban markets for trade or non-agricultural work, climatic and geographic factors, institutions and cultural characteristics. Year fixed-effects capture yearly variations at the country level such as those due to exogenous price shocks on food commodities or other goods typically consumed by households and affecting their real disposable income. In fact, exogenous food price shocks can significantly compound the food security situation in Mauritania<sup>20</sup> where more than half of cereal needs are typically covered with imports.<sup>21</sup> For practical reasons, we exclusively use categorical variables for vulnerability factors where each value defines a household typology. For instance, assuming there is only one vulnerability factor _Vi,d,r,t_ that takes on 5 values _k ∈_ �1 _,_ 5�, we have 5 household typologies ( _Hk_ ) _k∈_ �1 _,_ 5� defined by _Hk_ = _{i ∈H|Vi,d,r,t_ = _k}_ , where _H_ is the full set of households.

> 19The choice of spatial resolution for the hazard component is another dimension that we only briefly explore in this paper. We ultimately use department-level RSQ indices because they consistently yielded better results compared to indices at regional and communal levels. We could also have tested householdlevel indices considering buffers of different sizes (e.g. 5km, 10km, 20km...) around household locations, although only three (2013-2015) out of our five FSMS rounds include households’ GPS coordinates which would lead to a substantial reduction in the sample size.

> 20For instance, in 2008, Mauritania was hard hit by a global food price shock that deteriorated a structurally fragile food situation.

> 21Cereal imports (rice, wheat, millet, sorghum and corn) represent a yearly average of 62.5% of cereal consumption in the country, although we do not have information on how much this fraction differ between rural and urban markets. Source: authors’ calculation based on data from the _Office National de la Statistique de la R ́epublique Islamique de Mauritanie_ (Statistical Office of Mauritania).

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