predictions. Interpreting those differences based on regional characteristics remains difficult at this point, although allowing for more complex functional forms provides some – insights into this issue more on this below. Second, we try to explain differences in the marginal effect of the RSQ on food consumption based on relevant household characteristics. We estimate equation 4 considering a range of household-level interactions: age, sex, education level and marital status of household head, dependency ratio, household size, livestock ownership<sup>30</sup> and primary income source.<sup>31</sup> Only livestock ownership and primary income source yield statistically significant coefficients and we report the corresponding results in Table 3.<sup>32</sup> The magnitude of the marginal effect of the RSQ on the FCS shows a clear increasing trend with livestock ownership, from 1.9 for households without any form of livestock (base category) to 4.1 for those in the top quartile. This tends to suggest that livestock ownership increases the sensitivity of livelihood means to climate variability. Further analyses will be needed to clearly identify the channel through which this results in a more elastic food consumption.<sup>33</sup> Column 2 in Table 3 shows the same results replacing livestock ownership by the reported primary income source. Consistent with our previous finding, households relying mainly on livestock activities showcase the highest marginal effect of the RSQ on food consumption. Somehow surprisingly, those living from agriculture have the lowest marginal effect (1.8). In fact, only 12% of households report agriculture as their primary source of income whereas 64% declare possessing farmland so the sample of households generating agricultural income is most likely biased towards a subset of agricultural households farming larger plots,<sup>34</sup> perhaps with higher rates of technology adoption and irrigation access, making them relatively more climate-resilient. Overall, our findings point to the existence of important disparities in the marginal effect of the RSQ on food consumption across regions and

> 30Livestock ownership is measured in Tropical Livestock Units (TLU), which allows to aggregate livestock numbers to a common unit by assigning different weights to livestock types: 0.7 for camels, 0.5 for cattle and 0.1 for sheep and goats. We create a categorical variable that takes on 5 values: 1 if the household has no livestock, and 2, 3, 4, 5 if the TLU value is in the first, second, third and top quartile of the rural TLU distribution respectively.

> 31Income source categories are not consistent across our five surveys but we harmonize them to a common set of 6 categories: agriculture, livestock, fishing, small business/informal, formal sector and remittances. Households relying primarily on fishing are excluded as they represent only 1% of the original sample.

> 32Results for other household interactions available upon request to the authors.

33 However, one can hypothesize a direct effect on the self-consumption of meat due to livestock losses and distress sales, as well as an income effect also caused by livestock losses but also by a potential drop in livestock selling prices. On the other hand, a closer look at livestock ownership as a control variable (Table C.1 in appendix C.1) reveals that it is, all else equal, a significant determinant of the FCS, with households in the top quartile having on average an additional 13.3 points compared to those with no livestock. The overall effect on food insecurity is therefore ambiguous since households owning more livestock are more sensitive to climate variability but are also farther away from the food insecurity line. Note that our prediction model resolves this ambiguity by explicitly quantifying the distance away from the food insecurity line through the baseline food consumption distributions for each typology.

> 34We find disproportionately more households owning more than 0.5 ha of farmland in the subset of households reporting agricultural as a primary income source (41%), compared to the full sample (28%).

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