intensity score ranging from _−_ 10 to +10, ranking events from the most hostile to the most cooperative.

We construct our panel dataset of events by selecting all hostile entries in the ICEWS dataset—i.e., those with negative intensity values—that are classified as violent according to Amodio et al. (2021).<sup>11</sup> This includes a broad spectrum of political violence and conflict episodes, even those that did not result in fatalities. Our final dataset comprises a total of 1.1 million political violence events occurring between 2006 and 2019. A detailed breakdown of event types and their frequency over the period of analysis is provided in Table A6, while Figure 2 displays their geographical distribution.

We exploit this data to construct our firm-specific measure of exposure to political violence, labeled _Conflict Exposure_ . This variable is defined as the logarithm (log of 1+) of the number of political violence events that occurred within a 20 km radius of the firm’s location during the 12 months preceding the end of the last fiscal year in each country. In absolute terms, the mean is 13, the standard deviation is 7.3, and the median and third quartile are 13 and 79, respectively.

As robustness checks, we construct alternative measures of conflict exposure: i) by using different radii for the buffer around each firm or weighting by inverse distance; ii) by exploring different types of conflict events; and iii) by employing alternative data from the PRIO/Uppsala Armed Conflict and Location Event Dataset (ACLED) (Raleigh et al., 2010). Like the ICEWS dataset, ACLED provides the date and geolocation of several types of conflict-related events. However, ACLED’s country coverage during our period of analysis is significantly smaller.<sup>12</sup> The bottom panel of Table A6 reports the number of conflict events by type over our period of analysis.

Our measures of conflict exposure may be subject to measurement error, as the ICEWS dataset shares common limitations of large-scale datasets compiled from multiple sources. One such issue is the presence of duplicated events, which we address by excluding entries with identical characteristics (e.g., date, location, actors). Another potential source of measurement error is related to possible biases in event reporting, which may favor certain locations. To mitigate this concern, we account for potential (time-invariant) geographical differences in reporting by including firm and area fixed effects in our analysis. Additionally, we cross-validate ICEWS conflict event data with information from the ACLED dataset. The correlation between the number of conflict events reported by the two datasets is very high (0.77) within the same sample. Robustness checks further confirm that our main results hold when using ACLED as a dataset for conflict events.

> 11See Table A6 in the Online Appendix for details on this classification.

> 12Until 2016, ACLED collected data only for African countries and a limited number of Asian countries. For details on the ACLED coverage see: `https://acleddata.com/acleddatanew/wp-content/ uploads/dlm_uploads/2019/07/Country-and-Time-Period-coverage_updOct2019.pdf` .

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