statistically significant increase in the likelihood of conflict events. Quantitatively, the program increases the likelihood of conflict occurring closest to the village by 0.63 percentage point (col. 2) and the likelihood of a conflict occurring within 10 km of the village by 1.68 percentage points (col. 4).<sup>19</sup> While the incidence of severe conflict events remains rare in the sample (see Table A1, cols. (5)-(6)), the likelihood of an event occurring in the immediate vicinity of the village approximately doubles. The likelihood of an event occurring within 10 km of the village increases by approximately 25 percent.

Table 2: Effect of Cash Transfers on Severe Conflict Events

||(1)<br>TWFE|(2)<br>RCT|(3)<br>TWFE|(4)<br>RCT|
|---|---|---|---|---|
||Nearest neighbor|Nearest neighbor|10 km radius|10 km radius|
|Treated (Last 2 years)|0.00511**|0.00631**|0.0385***|0.0168*|
||(0.00214)|(0.00255)|(0.0143)|(0.00883)|
|Commune and year FE|Yes|No|Yes|No|
|Commune x year FE|No|Yes|No|Yes|
|Observations|61,198|61,198|61,198|61,198|
|_R_<sup>2</sup>|0.019|0.045|0.108|0.278|

Note: Standard errors are in parentheses, clustered at the commune level. The outcome variable is a dummy that equals 1 if there has been a severe conflict event nearest to the village or within a 10 km radius of the village. Results in columns (1) and (3) are based on the specification in equation (1), while columns (2) and (4) are based on equation (2).<sup>_∗_</sup> _p < ._ 1;<sup>_∗∗_</sup> _p < ._ 05;<sup>_∗∗∗_</sup> _p < ._ 01

# **3.2 Mechanisms**

Table 3 is dedicated to better understanding what types of conflict events drive the main results and what underlying mechanisms may be at work. Cols. (1)–(4) show that the cash transfer program results in a statistically significant surge in conflict events involving terrorism, while conflicts unrelated to terrorism are not affected. Conflict events involving terrorism increase by 0.41 percentage point (significant at the 5 percent level in the RCT specification), while the coefficient for conflict events that do not involve terrorism equals 0.22 and is not statistically significant. Cols. (5)–(8) further distinguish between terrorist conflict events involving foreign actors and terrorist conflict events only involving domestic actors. Importantly, the cash transfer program only fuels terrorism perpetrated by foreign actors (which increases by 0.34 percentage point and is significant at the 5 percent level), with no impact on domestic terrorism (with a point estimate of 0.07 that is not statistically significant).

In a nutshell, these findings are in line with the notion that the surge in violence in areas treated by the program is substantially driven by the activities of international terrorist groups such as Boko Haram. To the extent that multiple media reports have shown that jihadist groups have deliberately targeted aid workers and government services such as schools or

> 19The appendix contains a series of robustness checks. It shows that the results are robust with respect to alternative definitions of conflict in the GDELT data set (Table A2), alternative coding of small villages (Tables A3–A4), and alternative geographical matching (Table A5). In Tables A6–A7, we also reproduce our results using an alternative data source, the Armed Conflict Location Event Data Project (ACLED) data set (Raleigh et al. 2010). We include severe conflict events involving battles, explosions/remote violence, protests, riots and violence against civilians (ACLED 2019). ACLED is less fine grained and contains fewer events than GDELT. For instance, 6 percent of communities are within 10km from a conflict event reported by GDELT, but 2.7 percent of communities are within 10km from a conflict event reported by ACLED. While in Table A6—which draws exclusively on ACLED—the coefficients of interest maintain the expected sign but lose statistical significance, in the following Table A7—which pools ACLED with GDELT data—results remain statistically significant.

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