the parameter 0 _< δ ≤_ 1.<sup>19</sup> Column 1 reports the baseline effects of conflict exposure on sales and total costs (corresponding to columns 4 and 6 in Table 1, respectively). Columns 2–11 show that the estimated coefficient on conflict exposure remains negative across all values of _δ ∈_ (0 _,_ 1). Column 12 reports the value of _δ_<sup>ˆ</sup> required to drive the estimated effect to zero. In both cases, _δ_<sup>ˆ</sup> is negative and large in absolute value, implying that selection on unobservables would need to be substantial and of the opposite sign to the selection on the observables for the effect to be nullified by the bias. These results suggest that our main findings are unlikely to be driven by selection bias from unobserved factors.

One final potential concern with our results is a possible violation of the stable unit treatment value assumption (SUTVA), which requires that a firm’s response depends solely on its own level of conflict exposure, and not on the exposure of other firms. In our setting, the estimated effect of conflict exposure on firm _i_ could be biased if firms in other areas—linked to firm _i_ through supplier or buyer relationships—are also affected by conflict events. In such cases, conflict would exert both a direct effect —through firm _i_ ’s own exposure— and an indirect effect —through network linkages. To address this concern, we check whether our results change when accounting for production and consumption network spillovers in the regression. Ideally, this analysis would require firmto-firm transaction data for each country. However, such data are not available for the vast majority of countries in our sample. As an alternative, we rely on OECD inputoutput tables by country and use the WBES identification of the main product sold (ISIC 4D) to construct a product–country–specific matrix of input-output relationships.<sup>20</sup> For each firm in our sample, we construct two weighting matrices: one reflecting dependence on input purchases and another on output sales. By interacting these matrices with the level of conflict exposure experienced by firms in the supplier and buyer networks, we test whether a firm—beyond being directly affected by conflict—is also indirectly impacted through its production linkages. Table A13 replicates our main results while accounting for potential spatial spillovers of conflict transmitted through the domestic production network. Across all outcomes, the indirect effect of conflict exposure through suppliers

> 19When _δ_ = 1, the selection on unobservables is assumed to be as important as selection on observables.

> 20The OECD does not provide input-output matrices for several countries, including Afghanistan, Angola, Albania, Armenia, Azerbaijan, Benin, Bosnia and Herzegovina, Bolivia, Bhutan, Botswana, Democratic Republic of the Congo, Dominican Republic, Ecuador, Ethiopia, Georgia, Ghana, Guatemala, Honduras, Kenya, Kyrgyzstan, Lebanon, Liberia, Lesotho, Moldova, North Macedonia, Mali, Montenegro, Mongolia, Malawi, Niger, Nicaragua, Nepal, Panama, Paraguay, Palestine, Rwanda, Sierra Leone, El Salvador, Serbia, Suriname, Chad, Togo, Tajikistan, Timor-Leste, Tanzania, Uganda, Uruguay, Uzbekistan, Venezuela, Kosovo, Yemen, Zambia, and Zimbabwe. Therefore, data are unavailable for 53 countries in our sample of 89. For these countries, we substitute missing matrices with an average of input-output matrices from available OECD countries within the same region (Africa; East Asia and Pacific; Europe and Central Asia; Latin America and the Caribbean; Middle East and North Africa; South Asia). Moreover, to ensure consistency with available classifications, input-output tables and the WBES product classification have been aggregated into 15 sectors. To avoid possible endogeneity related to sector reliance potentially driven by conflict exposure, we set the input-output table to reflect data before the beginning of our sample period (year 2000).

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