## **2.1.2 data**

We draw on geo-referenced conflict-event data taken from the GDELT data set (Leetaru and Schrodt 2013). This established data source has been widely used in recent research (see, e.g., Manacorda and Tesei 2020; Armand et al. 2020; Gallea and Rohner 2021; Naidu et al. 2021) and draws on media and NGO reports from a wide variety of international and domestic sources, covering print, broadcast, and web outlets in over 100 languages.

One advantage of this data source for our purpose is that it is probably the most comprehensive database on conflictrelated events available to date, including over a quarter billion geo-referenced records worldwide for the last three decades. The events are geo-coded with a high resolution. It is particularly critical for us that the events are not aggregated at a higher administrative level, as our identification strategy relies on within-commune variation in the incidence of events between villages assigned or not assigned to the cash transfer program. Because of this comprehensiveness and a high degree of resolution, the data set includes a sizeable number of not only large but also small conflict events, allowing us to have enough statistical variation.<sup>13</sup> The GDELT data contain information about the geo-location of a given conflict event (longitude/latitude) as well as the actors involved and the type of event (based on the CAMEO classification of action categories). In some cases, there is also a link to the original news source.

We first build a village-year variable that captures whether a village is the nearest village to a recorded conflict event, using high-intensity events (based on CAMEO categories 18–20 covering assault, fighting, and mass violence events). We build an alternative variable that measures if a village is within a 10 km radius of such a conflict event. Making use of the event typology, actors involved, and original news source, we are also able to code a battery of different conflict variables, such as whether the event involved foreign or terrorist groups (i.e., events in which one of the actors was an insurgent, militant, or extremist group, such as Boko Haram, Al-Qaeda, or the Islamic State).<sup>14</sup> Disposing of such a range of different measures helps us narrow down the mechanisms at work.<sup>15</sup>

# **2.2 Descriptive Statistics**

Figure 1 maps the program communes and indicates the year when villages in each commune were registered to start receiving cash transfers (top panel), as well as the location of conflict events and treated and control villages (bottom panel). To complete the full picture, Table A1 provides a further overview of the structure of our data set. The study considers 3,891 villages, of which 1,190 participated in the cash transfer program at some point in time. Recall that the program lasted two households). This is consistent with the exact program eligibility rules varying by commune. Six percent of localities are trimmed using this procedure. We show in the appendix that results are robust when we include all small localities in the sample or instead trim all localities with fewer than 250 individuals (approximately 25 households). The appendix also shows that the results are robust when we identify treatment villages in the locality registry based on names only, rather than based on their geo-coordinates.

> 13In studies covering whole continents or very populous countries facing major civil wars, such as the Democratic Republic of Congo during the Second Congo War, other datasets with more restrictive inclusion criteria can be suitable, yet for our context in Niger—a country of roughly 24 million people—it is crucial to have the most comprehensive data set possible in order to exploit enough statistical variation to study within-commune variation in the incidence of conflict events.

> 14While GDELT contains “off-the-shelf” information on the country affiliation of groups, the coding of whether a given group has a terrorist background was done by hand, based on background readings on particular armed groups. See the discussion in the next section.

> 15For illustration, we list below some examples of press articles on conflict events contained in the GDELT data: “Witness: Gunmen attack and enter central prison in Niger’s capital,” Fox News, 1 June 2013; “Jihad in Niger: Bad omens,” _The Economist_ , 6 July 2013; “Thousands flee Boko Haram attack on Niger town,” UNHCR, 6 June 2016; “Food aid to double in Niger after fresh Boko Haram attacks,” _Daily Mail_ , 14 June 2016; “5 soldiers, 30 militants die in Nigerien Army clash with Boko Haram,” The News Nigeria, 14 September 2016; “3 US military service members killed in Niger,” ABC News, 5 October 2017.

7