from administrative centers, markets, and population centers.<sup>31</sup>

# **3.3 Estimation Strategy**

As noted above, our data comes from four separate GHS rounds, run approximately every two years, with an observation for both the post-planting and post-harvest periods (Figure 2). While these data allow us to observe many of the same individuals at different time periods, the irregularity of the timing of the GHS rounds presents a few challenges for analysis. First, we expect individual and household labor decisions to be different in post-planting and post-harvest seasons (the patterns in Table 1 suggest this expectation has merit). Secondly, since exposure to violent events (as measured by the ACLED data) can happen at any time relative to GHS data collection, we must decide on a reasonable time window for that exposure. However, any choice of a time window for such exposure will require an assumption about the expected duration and form of any effect on labor-related outcomes. To clarify this point: if the effects of violence exposure are relatively long-lasting—that is, if exposure to violence has effects on individual work several months afterward—then a time window that is too short will be contaminated by prior exposure. For instance, if we were to choose a time window for exposure equal to one month, when the true effect of exposure to violence had effects that lasted over six months, then an individual coded as _not_ being exposed within the last month (but who _was_ exposed in the last six months) would be coded as not treated, even though such an individual would be experiencing the effects of prior violence exposure. Just the same, if we were to choose a time window that was too long (e.g., a time window of six months when the true effect lasted one month), any employment effects would be attenuated.

To more formally discuss our approach, we first use the baseline specification given by: Where _yiet_ is one of the seven labor-related outcome variables shown in Table 1: three dummy variables if there was informal, agricultural, or out-of-the-home work reported for individual _i_ in the last seven days, and three variables for the total hours worked, as well as in each respective type of work, over the same period.<sup>32</sup> Enumeration areas are indexed by _e_ and the month-year period of the interview is indicated by _t_ ; _αi_ is an individual-level fixed effect and _γt_ is a month-year fixed effect. The main treatment variable, _Viet_<sup>_′_</sup> , is a dummy variable that takes a value of one if a herder-related violent event occurred within an indicated distance window and a given time window, _t_<sup>_′_</sup> _∈_ 1 _,_ 3 _,_ 6 months.

We include two vectors of additional controls, **_J_**<sup>**_′_**</sup> and **_X_**<sup>**_′_**</sup> . The vector **_J_**<sup>**_′_**</sup> is a series of additional fixed effects that are likely to be pertinent. Unsurprisingly, many accounts of the herder-farmer conflict noted that incidents tended to occur in heavily agricultural areas and disproportionately involve households working in agriculture. And, so, we include a fixed effect if anyone in person _i_ ’s household had engaged in any agricultural cultivation over the period covered by a given GHS round. A concern may be that

> 31Although the magnitudes of these differences are small, they are significant at a level of p _<_ 0.05.

> 32The outcomes for hours worked are only available in the 2015-2016 and 2018-2019 data.

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