counteracting the first two effects.

# **3.3 Spillovers**

As part of our analysis of mechanisms, we also consider if there might be local spillovers, whereby cash transfers targeting a village may also affect the likelihood of conflict in neighboring villages. Those spillovers could be negative; for instance, if jihadist groups shift their attacks toward treated villages and away from nearby control villages, thereby reducing conflict events in the latter. They could also be positive if jihadist groups increase the likelihood of attacks in the general vicinity of program villages, thereby increasing the risk of conflict in nearby control villages.<sup>21</sup> We have shown that our results are robust to two alternative definitions of conflict events, including whether a village is the nearest neighbor to a conflict event or is located in a radius around a conflict event. The use of the radius variable may hide the presence of spillovers. Importantly, however, we can use the nearest neighbor conflict variable to test for the existence of local spillovers. Specifically, we build a measure of the distance between control villages and the nearest treated village. Using the median of that variable (approximately 5 km), we define a group of “near” control villages and a group of “far” control villages.

Similar to the approach used by Miguel and Kremer (2004) and Crost et al. (2016) to analyze spillovers, we estimate treatment effects separately by comparing the likelihood of conflict (using the nearest neighbor variable) in treated villages relative to “near” control villages and “far” control villages. Table A9 shows that point estimates are positive and statistically significant using either the near or far control villages for both the TWFE (cols. 1–2) and RCT (cols. 3–4) specifications. The point estimates are also of similar magnitude when we use either the near or far control villages, suggesting no substantial local spillovers. To formally assess the presence of local spillovers, we test whether the likelihood of conflict events differs between near and far control villages. We do not find evidence of local spillovers based on this test (cols. 5–6). The results thus indicate that the cash transfer program does not shift the location of conflict events between villages at the local level or increase the likelihood of conflicts in neighboring villages.

# **3.4 Additional Results from Household-Level Data**

The results disaggregated by type of event in Table 3 highlight that the increase in conflict was mostly driven by foreign terrorist groups. To better understand potential within-village effects, we complement the analysis of the village-level data with secondary household data from a smaller-scale RCT conducted during the first phase of the program (2012–2013).<sup>22</sup> We analyze households’ self-reported information on the incidence of various types of conflict involving community members, based on a follow-up survey collected two years after the start of the program among 3,500 households, of which about two-thirds lived in communities randomly assigned to the cash transfer and one-third in control villages.<sup>23</sup> Table A10 shows that we do not find evidence of a significant decrease or increase in any self-reported conflict, including within households, within the community, or between the community and other nearby communities.<sup>24</sup>

> 21For a discussion of the spatial decay of violence, see Mueller et al. (2022).

> 22This analysis should be considered exploratory as these were not the main outcomes of this smaller-scale RCT, which is discussed in Premand and Barry (2022).

> 23See Premand and Barry (2022) for details on the RCT design, sample, and baseline balance. While the experiment is generally well balanced across a large set of indicators, the variables we study here were not collected at baseline.

> 24The results are similar when focusing only on households eligible for the program rather than a sample of households in the community.

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