in the same year, where some have been randomly assigned to treatment and others not. We thus call equation (2) our RCT specification, where identification fully stems from within-commune variation in exposure to treatment and incidence of conflict events. Robust standard errors are clustered at the commune level.

Table 1 shows that the randomization achieved balance. There is no statistically significant difference in population size between villages that were treated at any point in time and control villages (col. 1). We also perform a placebo test to see if there is any spurious pre-trend by checking whether there is a higher likelihood of conflict in (future) treatment villages one to four years before the start of the program. Results show that there is a common pre-trend before treatment starts (cols. 2-5), using exposure to conflict defined based either on the nearest neighbor village or a 10 km radius. As the treatment is randomized within communes, our estimates from equation (2) capture the causal impact of the unconditional cash transfer program treatment on conflict events.

Table 1: Balance and Placebo Test of Cash Transfer Effects on Conflict Before Treatment (Pre-Trend)

||(1)<br>Village<br>Population|(2)<br>TWFE<br>Nearest neighbor|(3)<br>RCT<br>Nearest neighbor|(4)<br>TWFE<br>10 km radius|(5)<br>RCT<br>10 km radius|
|---|---|---|---|---|---|
|Treated (Any time)|-0.644|||||
||(11.94)|||||
|Pre-trend (1–4 yrs. before treatm.)||0.00230|0.00196|0.00614|-0.000134|
|||(0.00146)|(0.00147)|(0.00951)|(0.00680)|
|Commune and year FE|No|Yes|No|Yes|No|
|Commune x year FE|No|No|Yes|No|Yes|
|Observations|3,891|49,547|49,547|49,547|49,547|
|_R_<sup>2</sup>|0.207|0.021|0.049|0.100|0.273|

Note: Standard errors are in parentheses, clustered at the commune level. The variable in column (1) is village population size according to the 2012 census. The outcome in columns (2)–(5) is a dummy that equals 1 if the village is the closest to a severe conflict event or within 10 km of a severe conflict event. Results in (2) and (4) follow the specification in equation (1), while columns (3) and (5) follow equation (2).<sup>_∗_</sup> _p < ._ 1;<sup>_∗∗_</sup> _p < ._ 05;<sup>_∗∗∗_</sup> _p < ._ 01

# **3 Results**

## **3.1 Main Results**

Table 2 displays our main findings. As mentioned above, the treatment variable takes a value of 1 during the two years when randomized villages received the cash transfer program and 0 otherwise. As far as the dependent variable is concerned, in col. (1) we start with a definition of conflict capturing whether a given village is the closest to a conflict event meeting some minimum severity threshold (i.e., levels 18–21 in CAMEO). While in col. (1) we have the less demanding two-way fixed effects structure (as in equation 1), col. (2) is our preferred RCT specification, including commune times year fixed effects (as in equation 2), which is identified based on the within-commune variation in cash transfer treatment assignment. Cols. (3)–(4) use the same specification as cols. (1)–(2) but apply a broader definition of conflict (taking a value of 1 if a conflict event occurred in a 10 km radius from a village). In all columns, we find that the cash transfer program induces a 10