driven by climate change and displacement from the country’s ongoing conflict with Boko Haram in the northeast. Simultaneously, settled communities have expanded, and dry farming techniques have lengthened their land and water use throughout the year. In response to these tensions, three Nigerian states (Benue, Ekiti, and Taraba) passed outright bans on open grazing in 2016 and 2017. These bans exacerbated previous tensions and directly contributed to a peak of violence in the first half of 2018.

We combine detailed panel data of households and individuals from Nigeria’s General Household Survey (GHS) with data on violent events from the Armed Conflict Location and Event Data (ACLED) project (Raleigh _et al._ , 2010). The GHS data contain four survey rounds, each including two seasonal visits—post-planting and post-harvest. This means that individuals can be observed in (up to) eight separate periods between 2010 and 2019. Herder-related violence typically follows a seasonal pattern, with events worsening as herders remain to graze their cattle in areas past May, when they historically moved north.<sup>5</sup> With these data, we leverage variation across time and space using the presence of herder-related violent incidents within a given radius around households (i.e., 10 km) and within a given time frame (i.e., within the last month) as an indicator of exposure to herder-related violence.<sup>6</sup> The granularity of these data allows us to include fixed effects at the level of a comparatively small geographic area, which combined with time and individual-level fixed effects, allows us to estimate changes in economic activities associated with herder-related violence while ruling out confounding variation between narrowly defined locations over time. Therefore, our identification strategy relies upon the fact that exposure to these violent events varies meaningfully, even within narrowly defined geographic areas. This empirical approach helps ensure that we are comparing households with similar agro-ecological and economic conditions. In particular, we include fixed effects by river sub-basins, which are a primary locus of shared space between farmers and herder-grazers. We also differentiate our analysis by the agricultural season, by restricting comparisons within either the post-planting or post-harvest seasons.

This research design presents a challenge in our study context. Recent work demonstrates that twoway fixed effect regressions with staggered treatment timing can be biased in the context of heterogeneous treatment effects (De Chaisemartin and D’Haultfœuille, 2020a; Goodman-Bacon, 2021). In particular, bias can intensify when units treated at the end of the panel use units at the beginning of the panel as a counterfactual (Goodman-Bacon, 2021) and the presence of persistent treatment effects can contaminate estimates from two-way fixed effect regressions (Baker _et al._ , 2022). In our study context, heterogeneity may be driven by prior exposure to violent events. Estimating effects while differentiating by “cohorts” defined based on previous exposure to conflict allows us to implement an estimator akin to those developed by Sun and Abraham (2021) and Wooldridge (2021). To this end, we include an additional series of

> 5This pattern contrasts with more general patterns of conflict following agricultural harvest seasons (Guardado and Pennings, 2020; Ubilava and Atalay, 2021).

> 6Our preferred specification defines our indicator of exposure to herder-farmer conflict in a highly localized way with a radius around households of 10 km. and a time frame of the previous one month. At times, the narrower time window of 1 month (combined with the smaller distance window of 10 km.) leads to having a lower-n sample of treated individuals. To address this, we also include estimates using a larger distance window of 20 km. The results using this distance window are virtually unchanged. We do not have a good prior for the appropriate selected distance window; nor do we have full access to unmasked data to vary the distance window freely (as proposed by, e.g., Butts (2021)). In light of these limitations, we present both the 10 km. and 20 km. window, with a preference for the former, as it is the more localized measure.

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