and buyers is not statistically significant, while the direct effect remains consistent with our baseline findings: negative and significant for sales and total costs, and insignificant for profits. Interestingly, the negative—though statistically insignificant—effect of suppliers’ and buyers’ conflict exposure on sales is consistent with findings from previous studies on conflict and production networks (Couttenier et al., 2022; Korovkin and Makarin, 2023; Korovkin et al., 2024). While these results should be interpreted with caution due to data limitations in the construction of network measures, they suggest that our main estimates are unlikely to be substantially biased by spillovers through input–output linkages.

**Robustness** Our results are robust to a number of checks. First, we examine the estimation sample. As discussed in Section 2.1, the number of observations varies across outcomes due to missing values. All results presented thus far are based on the maximum number of available observations for each outcome in each regression. Table A14 shows that results are unchanged when we restrict the sample to firms with data available for all outcomes. As an additional check, we re-run the main analysis focusing only on manufacturing firms. This ensures that our results are not driven by differences in the construction of _Total costs_ across sectors (see Section 2.1). Table A15 shows that our results continue to hold also in this case. Next, we assess the robustness of our findings to the countries included in the analysis. We re-run our baseline regression 89 times, sequentially excluding one country at a time from the sample. Figure A2 shows that the negative effects on sales and total costs, and the null effect on profits, do not depend on the inclusion or exclusion of any specific country.

Our results are also robust to alternative definitions of the conflict exposure measure. Table A16 shows that the estimated effects of conflict on our main outcomes remain qualitatively unchanged when exposure is defined using: i) smaller buffers around the firm (5 km and 10 km radii in columns 1–2); ii) a larger buffer (40 km radius, column 3); iii) a weighted measure based on the inverse distance of conflict events (column 4); and iv) conflict events occurring within a 20 km radius over the previous two years (column 5). Additionally, we examine how our findings depend on the types of events included in our conflict exposure measure, namely all hostile events in the ICEWS dataset which are defined to be violent in Amodio et al. (2021) (see Table A6). Table A17, column 1, shows the results when we exclude from this list those events that may be less likely to have a direct economic impact on the firm. Column 2 reports estimates obtained by excluding events related to protests and riots. The next two columns show the results when we apply a _quantitative_ rule for event selection using the intensity score (see Section 2.2). Column 3 reports the results when conflict exposure is measured considering only very violent events, i.e. with intensity scores between -8 and -0, while column 4 shows the results when we consider only extremely violent events, i.e. with intensity score -10, such as _killings or suicide attacks._ In all cases, our baseline results are confirmed. Finally, 14