3.2. We first follow a conventional approach by conducting regression analyses that allow us to highlight key stylized facts on the effect of the RSQ on food consumption. Then, we use those results to guide the implementation of the machine learning method in which we determine an optimal choice of both vulnerability factors and RSQ measures in the context of rural Mauritania.

# **4 Results**

## **4.1 Impact of the rainy season quality on food consumption**

### **4.1.1 Main**

In this section, we present results of pooled OLS estimations for specifications derived from the reduced form given by equation 3. We explore both the impact of RSQ measurement choices on our results and, more importantly, the heterogeneity of the effect of the RSQ on food consumption based on spatial fixed-effects and household characteristics.<sup>22</sup> All standard errors shown in the results are clustered at the department level.<sup>23</sup> As mentioned in section 3, we consider two separate groups of control variables. “Group A” is comprised of household characteristics that are presumably unrelated to the RSQ, while “group B” contains those control variables that may plausibly induce over-controlling issues (job loss, main source of income, livestock ownership, aid received).

We start by estimating a model without interaction, i.e. setting **_Vi,d,r,t_** = 0 in equation 3, to identify the overall marginal effect of the RSQ on food consumption. We show results in Table 1,<sup>24</sup> where we use the 5-month precipitation z-score as a measure of the RSQ.<sup>25</sup> Including both groups of controls (group A and group B) and controlling for 22Although we include year fixed effects in our general specification (equation 3) for reasons explained in section 3, we omit those in our results as they absorb most of the effect of the RSQ on food consumption. The main reason lies in the high spatial correlation in RSQ experienced across departments for each individual year, which causes the inclusion of year fixed-effects to induce an over-controlling problem. We provide maps of the 5-month SPI at the department-level for each year of the studied period (20102014) in appendix (Figure B.5) to illustrate the fact that, for any given year, the rainy season quality is fairly homogeneous across departments. We also use our household-level dataset to regress 5-month SPI values against year fixed-effects and find that the latter explains 82% of the variance in the 5-month SPI in our sample – whereas cross-sectional variation represents only 10%. To confirm that year fixed-effects effectively absorb the effect of the RSQ, we estimate a basic model without interactions and including all controls and year fixed-effects (column (1) of Table C.1 in appendix). We find that year fixed-effects are almost perfectly correlated with a (rural) population-weighted average of department-level 5-month SPI (Pearson coefficient equal to 0.96).

> 23There are only 34 clusters (i.e. departments) so we use a bootstrapping method to calculate our clustered standard errors.

> 24Full results including coefficients associated with all controls are provided in Table C.1 in appendix.

> 25The 5-month precipitation z-score yields the highest goodness-of-fit for this specification although results of estimations using other indices are provided in appendix, more on this below.

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