recent violence.<sup>34</sup> Lastly, as an implied mechanism in this specification is that violence exposure raises or lowers the relative opportunity cost certain types of work, we also divide our results into the full, pooled sample (“All”) as well as by “Men” and “Women”. In particular, reports of the conflict noted that women were at particular risk of attack, including sexual assault.<sup>35</sup> Such a division also comports with other work, which has found differential labor responses for women versus men in the face of escalated violence (Vel ́asquez, 2020; Tsaneva _et al._ , 2019).

# **3.4 Sample Attrition**

Endogenous sample attrition represents a further threat to credible identification. This could well be the case if prior violent events or unobserved shocks affect the likelihood that an individual remains in the panel sample. Indeed, in our study context this is a sizeable threat. There are 12,918 individuals in the GHS data who were first interviewed in the 2010 and who are also located in enumeration areas that were part of the coverage of all four rounds. Of these, 3,732 (or 29 percent) are observed all the way through wave 4 of the GHS, including those who moved and were tracked. This gives a back-of-the-envelope, cumulative attrition rate of approximately 16 percent for each period. While this is not worrisome for each individual period, it does present problems cumulatively. Although a relatively long household-level panel dataset provides the ability to track individuals and households over time and account for timeinvariant individual or household characteristics, they also inherently lead to a greater risk of attrition bias. To address non-ignorable attrition, we use the following probit estimation to estimate attritioncorrected sampling weights: Equation (5) includes the enumeration area herder-related violent events, a state-time fixed effect and the full list of time-interacted co-variates as described above. From equation (5) we calculate a predicted probability of survival (attrit=0) for each individual and time period. We then average these probabilities and invert them to produce inverse probability weights (IPWs). We use these weights throughout our analysis of panel-level data, which provide a correction for potential attrition bias by up-weighting those individuals with a higher probability of attriting but who remain in the sample. The mean value for our IPW is 3.2, with a standard deviation of 1.8, a minimum value of 1.3, and a maximum value 12.8. This adjustment relies on the assumption that attrition is random, conditional on the factors included in equation (5). We include individual-level fixed effects in all specifications to adjust for any unobserved time-invariant factors that could affect selection into exposure to herder-related violence.

> 34Note then, as well, that the interaction terms give a cleaner cohort for comparison to determine the marginal effect of recent violence (and as such in the spirit of recent papers, such as Wooldridge (2021), to isolate relevant treatment cohorts to address the problem of contaminated effects in two-way fixed effects regressions (Baker _et al._ , 2022; Goodman-Bacon, 2021; De Chaisemartin and D’Haultfœuille, 2020b)).

> 35As noted by the International Crisis Group in a 2018 report.

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