c c i i i i i<sup>heell</sup> Where c c i is a dummy variable indicating the occurrence of any conflict event in cell ii ii ii ii<sup>GG5 S</sup> ii ii ii in country cciicctt = αα+ ββ S SSII + γγ S SSII × DD + νν XX + ηη + ττ + εε in year , S i is the value taken by the SPEI in that cell in the given year, is a dummy equal to ii 1 if the grid cell falls within one of the G5 Sahel countries, and cciicctt and are a full set of country- and year-<sup>heell</sup> ii cc ii ii<sup>GG5 S</sup> tt SSII DD specific fixed effects. Additionally, i contains a set of controls for time-invariant cell characteristics (also ii ii ηη ττ used by HF in their specification) that may be correlated with both climate and conflict: terrain, elevation, XX cell area, presence of roads, distance to river, presence of a country border, presence of any mineral resources and a measure of ethno-linguistic fractionalization. The regression allows for the examination of _contemporaneous_ (as opposed to mean) correlation between conflict incidence and variations in the SPEI, after accounting for these time-invariant cell-specific cofactors that may be correlated with both conflict incidence and the SPEI.

The results suggest that for the base group (i.e., the rest of Africa), there appears to be no relationship between the SPEI and conflict incidence.<sup>5</sup> However for the G5 Sahel countries, this relationship is negative as indicated by the statistically significant coefficient of the interaction term that is also large in magnitude. The results (Table A1 - column 5) suggest that a 1 unit increase in the SPEI reduces conflict in the Sahel by 3 percentage points, but has no effect in the rest of Africa. That is, when rainfall is more abundant, conflict incidence in reduced for the G5 Sahel countries, unlike in the rest of Africa.<sup>6</sup> Next, building on the finding from HF that it is variation in the growing-season-specific SPEI that induces conflict, we present estimates of the following regression specifically for the subsample of G5 Sahel countries: c c i i i i i where i is a measure of the growing-season-specific SPEI, and the remaining terms are as ii ii ii ii ii ii cciicctt = αα+ ββ S SSII + γγ GGSS_S SSII + νν XX + ηη + ττ + εε described above (Table A2). This specification allows for the effect of growing-season SPEI measure on ii GGSS_S SSII conflict to be estimated while controlling for overall SPEI for the year. If conflict is driven by climatevariation through a livelihoods channel, we would expect the growing-season-specific SPEI to have an effect on conflict. The results from Table A2 confirm this. After growing-season-SPEI is included in the specification, the coefficient on overall SPEI is small in magnitude and statistically insignificant, whereas the growing-season SPEI has a large and statistically significant coefficient. The results suggest that conflict occurrence reduces by 7.3 percentage points with a 1 unit increase in the growing-season-specific SPEI (Table A2 – column 5). Which in turn correlates with agricultural output.

These results underline that climate-induced variation in income/ food security is an important driver of conflict incidence, particularly for the countries in the Sahel region. Specifically, the incidence of conflict is found to be lower when there are favorable conditions for agricultural crops due to increased soil-moisture as measured by the growing-season SPEI.

_5.2.1 The Spark? Conflict over Irrigated Land in the Sahel in the Aftermath of the Arab Spring_

> 5 The regression results are presented in Table A1. Column 1 contains the baseline estimates without any controls, with columns 2 – 4 sequentially adding the controls listed above. Column 5 additionally controls for common trends at the country-year level.

> 6 A fan-regression plot of the partial relationship between SPEI and conflict from the specification in column 5 is plotted in Figure A3.

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