specific local measures due to missing information on household location<sup>12</sup> and we rather focus on RSQ measures based on administrative boundaries.

To allow for comparability across areas with different rainfall regimes, we build RSQ indices as normalized deviations of underlying weather or environmental variables such as precipitations, vegetation greenness and dry matter productivity levels with respect to their historical distributions, which are referred to as “anomalies”. For an underlying variable _x_ , we denote the value of _x_ in year _t_ for a spatial extent _a_ and a time window ∆as _x_ ( _a,_ ∆ _, t_ ). For instance, _x_ can be precipitations, _a_ a department and ∆the JulyAugust two-month time window so that _x_ ( _a,_ ∆ _, t_ ) represents cumulated rains over the July-August period in year _t_ for department _a_ . We employ two standard methods for calculating the anomaly associated with _x_ ( _a,_ ∆ _, t_ ). The first one is a simple normalization that measures a departure from the mean in standard deviations and is called a z-score, that we denote _rsqa,t_<sup>_z_(</sup><sup>_x,_∆).Usingournotations,thiscanbewrittenas:</sup> Where _μx_ ( _a,_ ∆) and _σx_ ( _a,_ ∆) are the long-term mean and standard deviation of _x_ ( _a,_ ∆) respectively. Another way of computing anomalies is based on the methodology developed by McKee et al. (1993) for the calculation of the Standardized Precipitation Index (SPI). They use a normalization procedure that implies the transformation of historical distributions into normal distributions so that the resulting series of SPI have a mean of 0 and a standard deviation of 1.<sup>13</sup> We apply this method to precipitation values to build SPI series but also to our vegetation and dry matter productivity variables to calculate what we call “spi-like” anomalies that we denote _rsqa,t_<sup>_s_(</sup><sup>_x,_∆).Forbothtypesof</sup> anomalies, positive values reflect wetter than usual conditions while negative values indicate drier than usual conditions. However, z-scores are linear in the underlying variable _x_ ( _a,_ ∆ _, t_ ) by construction and are therefore easier to interpret than spi-like anomalies,<sup>14</sup> and for that reason, we mostly prefer z-scores in our study of the impacts of the RSQ on food consumption in section 4. In broad terms, z-scores measure hazard intensity as

> 12 FSMS are geo-referenced only from 2013 onwards.

> 13More formally, a distribution _FX_ is fitted to a long-term time series ( _x_ ( _a,_ ∆ _, t_ )) _t∈_ �1 _...T_ � and transformed to a normal distribution _FN_ via a method of percentile matching, so that the corresponding RSQ index value _rsqa,t_<sup>_s_(</sup><sup>_x,_∆)foranygivenvalue</sup><sup>_x_(</sup><sup>_a,_∆</sup><sup>_, t_)isgivenby:</sup>

> 14For instance, a one unit increase in a precipitation z-score is interpreted as a one standard deviation increase in precipitations in absolute terms and irrespective of the starting point. On the other hand, a one unit increase in SPI is interpreted in probabilistic terms with respect to the normal distribution values. Precipitation values yielding SPI values of 0, 1 and 2 corresponds to probabilities of 0.5, 0.84 and 0.98 of observing rainfall amounts less than or equal to those values respectively, so that increases in the SPI from 0 to 1 and from 1 to 2 do not result in equivalent changes in probability terms.

14