regressors of interest and outcome variables, it could bias our results. In Table A3, we test whether firms included in the panel (and thus in our estimation sample) differ systematically from those in the cross-section. Specifically, we consider all firms in countries with at least two survey waves and for which geo-localization data are available. We then examine whether the likelihood of being interviewed a second time—i.e., of being included in the panel—is correlated with key variables used in the analysis: _Sales_ , _Total costs_ , _Profits_ , and _Conflict exposure_ (defined below in Section 2.2). Results in columns 1-5 show that the probability of inclusion in the estimation sample is not driven by these characteristics, allaying concerns about systematic differences between panel and non-panel firms.

Another potential concern with our data is that missing values may be correlated with conflict exposure, possibly biasing the estimation sample.<sup>10</sup> Results reported in Table A4 suggest that this is not the case. Column 1 shows that, in the full sample, our measure of conflict exposure (see Section 2.2) is not correlated with firms _not reporting_ sales. Column 2 confirms that this result also holds in the subsample of firms for which baseline control variables are available. Moreover, in the estimation sample, conflict exposure is not correlated with non-reporting of raw material and intermediate input costs (column 3) or labor costs (column 4). Taken together, these results indicate that, for the firm outcome variables collected in the WBES, non-reporting is not systematically associated with conflict exposure.

Finally, we explore potential issues related to attrition. In Table A5, we test whether conflict exposure is correlated with firms exiting our sample. As expected, the results show that firms are more likely to exit when they are exposed to a higher number of conflict events between two survey waves, and when their pre-exit sales levels are lower. This pattern suggests that, if anything, our estimates of the negative effect of conflict on sales may be downward biased—potentially underestimating the broader impact of conflict on economic activity.

# **2.2 Conflict data**

Our main source of data on political violence events is the Integrated Crisis Early Warning System (ICEWS, Shilliday and Lautenschlager, 2012). For each event, the ICEWS dataset provides information on the date, source and target actors, and precise location (geospatial coordinates). It captures all types of interactions—whether cooperative or hostile—among sociopolitical entities, including individuals, groups, and nation-states. These events are categorized according to the Conflict and Mediation Event Observations (CAMEO) classification (Schrodt and Yilmaz, 2012). Each category is then assigned an

> 10A related concern is that firms may strategically choose not to report their performance to avoid being targeted by fighters. However, it is worth noting that participation in the WBES is voluntary, and firms are under no obligation to respond to the questionnaire. Moreover, the number of missing values is higher for _Total Costs_ than for _Sales_ (see the number of observations in Table 1), which suggests that non-reporting is unlikely to be a strategic choice of firms trying to hide their economic performance.

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