mitigate this, we also code incidents involving the term “Fulani” (e.g., the tribe most identified with the nomadic herding) as herder-related. Thus, these data include violent events that occur between herders and civilians, clashing militias, and/or reprisals or actions taken against herdsmen or Fulani tribes. Due to this, we prefer to use the general term herder-related violence. In total, we code 1,564 incidents between 2009 and 2019 as both violent and herder-related.

We combine the GHS and ACLED datasets using GPS coordinates, which are available in both datasets. To construct our main treatment variable of exposure to a herder-related violent event, we create a series of binary variables that take a value of one if a household was within a given distance from any herder-related violence in the month prior to the start of the GHS interview. The exposure measures are calculated for distance windows of 10, 20, and 30 kilometers (km.). We are able to calculate exposure measures that are as precise as 10 km. because we were able to use the restricted-use unmasked GPS coordinates from the GHS data.<sup>26</sup> Our preferred specifications use the 10 km. exposure radius as it is the most precise available in our measures, while allowing for potential mis-measurement of the exact location of violent events in the ACLED data.

# **3.2 Descriptive Statistics**

Table 1 shows basic descriptive statistics for our set of key, labor-related outcomes at the individual level. We focus on seven labor outcomes. The first three are binary values that take a value of one if an individual reports to have worked in own-account or household-enterprise work, agricultural work, or work outside the home, respectively, in the last week. These variables are available in all four rounds of the GHS and provide information on the extensive margin of changes in labor supply. Starting in the 2015-2016 round, the GHS began recording the number of hours individuals worked in each of these activities as well as total hours worked.

Descriptive statistics on these seven outcomes are found in Table 1 in six panels (for the pooled sample, men, and women, separately, each for both the post-planting and post-harvest period). One important thing made clear by the table is that, while the mean values of hours worked appear in a reasonable range (the mean total hours worked is 18 hours in the post-planting season and 12 in the post-harvest period), there is substantial variation in all measures of the hours worked (the standard deviation for total hours, e.g., is 22 and 20 hours in both periods, respectively). As shown in Appendix Figures A2–A4, these moments of the data are the result of bunching at zero values, with a large rightward skew, though for work outside the home, there is visual evidence of bunching at 40 hours, which makes intuitive sense under many ideas of a “full” work week. Due to the rightward skew and the incidence of zero-value responses, we also consider results using the inverse hyperbolic sine (IHS). However, the use of IHStransformed measures as outcome variables can be problematic at the point of interpretation, especially

> 26We are indebted to the World Bank’s LSMS team for producing these measures using the unmasked GPS coordinates. Note that this process involved, first, calculating exposure variables using publicly available GPS data; this process was replicated by the LSMS team using the unmasked data. To assure household anonymity, we never gained access to linked data between individual households and specific violent events.

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