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            the effect of conflict exposure on firms&#x2019;
            performance, combining geolocalized longitudinal firm-level
            data and information on political violence events from 91
            countries between 2006 and 2019. Higher conflict exposure
            does not affect firm profits, as it reduces both sales and
            total costs. Sales decline due to the conflict-induced
            reduction in the availability of production inputs and the
            increase in informal competition. Firms react to lower sales
            by reducing labor costs and expenditure on other production
            inputs. The effect of conflict is more detrimental for firms
            in countries with low-quality bureaucracy and that are
            initially at peace.
display_title: Conflict and Firms’ Performance : A
            Global View
listing_relative_url: /research/2024/09/34386562/conflict-firms’-performance-global-view
topicv3: Private Sector Development m1326060 1921,Fragility, Conflict and Violence m1326586 2432
publishtoextweb_dt: 2024-09-05T00:00:00Z
dois: 10.1596/1813-9450-10898
origu: Off of Sr VP Dev Econ/Chief Econ (DECVP)
no_of_pages: 47
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model_extractions: Policy Research Working Paper 10898

# Under Pressure

Global Evidence on Conflict and Firms _Emanuele Brancati Michele Di Maio Roberta Gatti Asif M. Islam_ Middle East and North Africa Region Office of the Chief Economist September 2024
Policy Research Working Paper 10898

# **Abstract**

This paper provides the first global analysis of the impact of conflict exposure on firm performance, combining geolocalized longitudinal firm-level data with information on political violence events across 89 countries between 2006 and 2019. The results show that higher conflict exposure leads to declines in both sales and total costs, resulting in no significant effect on profits for surviving firms. The reduction in sales is driven by lower output, which reflects conflict-induced shortages of raw materials and production inputs, as well as increased informal competition. Firms respond to declining sales by adjusting labor costs, substituting skilled workers with unskilled ones. The negative effects of conflict on firms are more pronounced in countries with high trade openness, low economic complexity, weak bureaucratic quality, involvement in illegal drug production, or an initial state of peace. In these contexts, greater conflict exposure leads to reductions in both sales and profits.

This paper is a product of the Office of the Chief Economist, Middle East and North Africa Region. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at aislam@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# Under pressure: Global evidence on conflict and firms<sup>*</sup>

Emanuele Brancati Michele Di Maio Roberta Gatti Asif M. Islam Originally published in the Policy Research Working Paper Series on September 2024. This version is updated on September 2025. To obtain the originally published version, please email prwp@worldbank.org.

**Keywords:** firm, conflict, political violence, intermediate inputs, labor market, market competition, informality

**JEL codes:** H56, D22, E26, O17

> * Corresponding author: Michele Di Maio, michele.dimaio@uniroma1.it. Emanuele Brancati and Michele Di Maio are affiliated with Sapienza University of Rome (Italy) and IZA Institute of Labor Economics; Roberta Gatti and Asif M. Islam are with The World Bank. The findings, interpretations, and conclusions expressed in this paper do not necessarily reflect the views of the World Bank, its Board of Executive Directors, or the governments they represent. All errors are our own.
# **1 Introduction**

Conflict and political violence have become the _new normal_ in many developing and developed economies, with over 1.7 billion people worldwide living in fragile or conflictaffected countries. In 2024 alone, 167 countries reported incidents of political violence, with 50 facing extreme, high, or turbulent levels of conflict—resulting in more than 233,000 fatalities (ACLED, 2024).<sup>1</sup> Despite the severe suffering and disruption caused by political violence and conflict, economic activity continues. Economists and policymakers are increasingly interested in understanding how firms operate under such challenging conditions. A growing number of studies provide rigorous estimates of the effects of conflict on firm performance and offer in-depth analyses of the underlying mechanisms (see, e.g., Amodio and Di Maio, 2018; Couttenier et al., 2022; Korovkin and Makarin, 2023; Bernal et al., 2024). However, research on firm behavior in settings characterized by political violence and conflict remains limited to a small number of countries, and existing findings are largely countryand conflict-specific.

This paper is the first to provide a global perspective on the impact of conflict on a range of firm-level outcomes and the mechanisms underlying these effects. Moreover, by exploiting differences in cross-country characteristics, it shows how economic, institutional, and political factors shape the way in which firms cope with conflict exposure.

Our analysis relies on the combination of two main data sources. First, we use a confidential version of the global World Bank Enterprise Survey (WBES), which, beyond the rich firm-level data in the public version, includes geolocation information for each firm. We focus on its panel component, covering 89 countries from 2006 to 2019. Second, we draw on the Integrated Crisis Early Warning System (ICEWS) dataset to obtain geolocated data on conflict and political violence events in each country. This allows us to construct a firm-specific, time-varying measure of conflict exposure, which we use in a firm-level fixed-effects model to estimate the impact of conflict on several outcomes.

This study yields a number of empirical findings. We show that higher conflict exposure—as measured by the number of violent events occurring in the neighborhood around a firm—leads to a decline in both sales and spending on production inputs, indicating contradiction in output. These effects result in a null impact on profit for surviving firms. However, the estimated coefficient is consistently negative, and it becomes statistically significant in countries with particular characteristics, as detailed below We address several potential identification threats to the interpretation of our empirical results. First, we tackle the issue of reverse causality by showing that the local

> 1Political violence refers to the intentional use of power or force to achieve political objectives. It includes war and related violent conflicts, state violence, and similar acts carried out by organized groups WHO (2002). Throughout the paper, we use the terms exposure to political violence events and exposure to conflict-related events interchangeably.

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intensity of conflict is not driven by the level or structure of local economic activity, nor is the occurrence or intensity of conflict events systematically related to firm characteristics. We also show that our results remain robust across several alternative specifications. These include controlling for placebo firm exposure to account for potential non-random assignment of conflict events, restricting the estimation sample to clean controls, and incorporating the conflict exposure of suppliers and buyers to address potential violations of the SUTVA. Moreover, we address the possibility that our findings are driven by selection on unobservables. Finally, we show that our conclusions are robust to a range of sample restrictions, alternative definitions of conflict exposure—including variations in buffer size, time window, event type, and data source—as well as to different choices for clustering standard errors.

Next, we explore the mechanisms through which conflict exposure reduces sales and total costs, ultimately leading to a decline in output. We first show that conflict leads to a reduction in firms’ purchases of intermediate inputs and raw materials—both domestic and imported—as well as electricity. Given that prices are unlikely to decrease during periods of conflict, this offers indirect evidence that firms are purchasing fewer production inputs. Furthermore, conflict disrupts access to these inputs by increasing import clearance times and intensifying both the frequency and duration of power outages, further contributing to the decline in output. In addition, conflict heightens competitive pressure from informal firms. Since such competition reduces output, employment, and investment among formal firms, this channel further helps to explain the observed decline in sales.

Faced with these pressures, firms respond by adjusting their cost structures to maintain profitability. While limited access to inputs mechanically lowers expenditures, firms also reduce labor costs by substituting skilled workers with unskilled ones. We also find that conflict reduces sales by decreasing both domestic and foreign demand. In contrast, other potential channels—such as increased credit constraints, corruption, or crime—do not appear to explain our findings. Taken together, these findings indicate that conflict reduces sales through lower output, intensified informal competition, and diminished demand, while profits remain stable due to firms’ adaptive cost-cutting responses.

A key advantage of using a harmonized global dataset is the ability to compare the effects of conflict on firms across countries with different structural and institutional characteristics. Our results show no meaningful heterogeneity in the effects of conflict exposure across countries with varying levels of per capita income, state fragility, or dependence on agriculture and natural resources. However, we do find substantial variation along other dimensions. Conflict exposure has more negative effects—reducing both sales and profits—for firms operating in countries with lower economic complexity, greater openness to international trade, lower bureaucratic quality, higher levels of corruption, or involvement in the production of illegal drugs. Importantly, the impact of conflict also differs depending on a country’s initial conflict status. Higher conflict exposure reduces both 2
sales and profits for firms located in countries initially at peace, and these effects are significantly larger than for firms in countries already affected by conflict at the beginning of the period. Moreover, we show that the mechanisms through which conflict affects firm performance are significantly different between the two groups of countries. This highlights the importance of accounting for country-specific characteristics when assessing the impact of conflict on firms, as the underlying mechanisms—and the appropriate policy responses—can vary substantially across contexts.

Our paper contributes to the growing literature on the microeconomic consequences of conflict. This strand of research has largely focused on outcomes related to education and health (for reviews, see Verwimp et al., 2019; Rohner, 2023). In contrast, studies examining the economic effects of conflict remain relatively limited—partly due to the difficulty of accessing reliable economic data in conflict-affected settings.<sup>2</sup> While early work explored the macroeconomic effects of conflict (e.g., Abadie and Gardeazabal, 2003; Martin et al., 2008; Glick and Taylor, 2010), more recent empirical studies have shifted attention to its impact on firms.<sup>3</sup> These studies examine various forms of conflict and political violence in specific country contexts—including Afghanistan (Blumenstock et al., 2024; Neri-Lain ́e, 2025), Angola (Guidolin and La Ferrara, 2007), Colombia (Camacho and Rodriguez, 2013; De Roux and Martinez, 2023; Bernal et al., 2024), India (Couttenier et al., 2022), Kenya (Ksoll et al., 2023), Libya (Del Prete et al., 2023), Mozambique (Custodio et al., 2025), Ukraine (Korovkin et al., 2024; Korovkin and Makarin, 2023), Sierra Leone (Collier and Duponchel, 2013), and the West Bank (Amodio and Di Maio, 2018). However, these are all single-country analyses that focus on different outcomes, rely on distinct data sources, and employ different methodological approaches, making their results not directly comparable.

This paper contributes to the literature in three ways. First, it provides the first analysis of the impact of conflict on firms at the global level, offering a more comprehensive perspective than previous single-country studies. Leveraging harmonized firm-level data from both developed and developing countries, we document several empirical regularities that hold across different economic and institutional contexts. Second, we document several mechanisms through which conflict adversely affects firms. Our results confirm that a key mechanism driving the negative impact on firm activity is the conflict-induced reduction in access to both domestic and imported production inputs, consistent with findings from previous studies. In addition, we provide evidence on two novel mechanisms. Conflict increases competitive pressure from informal firms—a well-documented and important obstacle to formal firms’ output and sales We also find suggestive evidence of a conflict-induced substitution effect in the workforce, resulting in a shift toward

> 2A large literature has examined the economic determinants of conflict, including price shocks (Dube and Vargas, 2013), natural resources (Berman et al., 2017), and import restrictions (Amodio et al., 2021).

> 3A related line of research looks at the effects of crime-related violence on economic activity (Pinotti 2015; Rozo 2018; Utar 2024; Piemontese 2023).

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lower-skilled labor. Third, we provide novel evidence on how country-level characteristics shape the impact of conflict on firms. We document that economic, social, and political conditions can either amplify or attenuate the effects of conflict exposure. Furthermore, we show that the specific mechanisms through which conflict affects firm activity vary systematically with these characteristics, suggesting that policy interventions to address the effects of conflict need to be context-specific and tailored to the conditions in which firms operate.

The paper proceeds as follows. Section 2 describes the data. Section 3 outlines the empirical strategy. Section 4 presents the results. Section 5 concludes.

# **2 Data**

## **2.1 Firm data**

Our main data source is the World Bank Enterprise Survey (WBES), a global dataset providing firm-level information for the manufacturing, retail, and other service sectors across 148 countries.<sup>4</sup> Data are collected through face-to-face interviews using a standardized questionnaire and are designed to be representative at the country level. The survey targets privately owned firms with at least five employees operating in the formal (non-agricultural) sector.<sup>5</sup> Our data cover the years 2006-2019 (to exclude the COVID-19 pandemic period).

We use a confidential version of the WBES global dataset, which also provides firms’ geo-localization. This information allows us to match each firm with conflict and political violence events occurring in its geographical neighborhood and is therefore essential for constructing the firm-specific measure of conflict exposure used in our analysis.<sup>6</sup> Another important feature of the WBES is that—although designed as a repeated cross-section—it includes a sizable panel component for a subset of countries, with some firms interviewed in multiple waves. As discussed in Section 3, our estimation strategy relies on firms for which geo-localized data are available and that belong to this panel component. Restricting further to firms with non-missing sales data, our main estimating sample consists of 36,087 firm-year observations across 89 countries.<sup>7</sup> Table A1 provides a detailed list

> 4The WBES dataset is the most comprehensive resource for conducting firm-level studies worldwide, as discussed in Besley and Mueller (2018), Fisman et al. (2024) and Armangu ́e-Jubert et al. (2024).

> 5Firms are selected using random sampling techniques with three stratification levels to ensure national representativeness across firm size (5–19 employees; 20–99 employees; and 100+ employees) and sector (manufacturing, retail, and other services). For details on the WBES sampling methodology, see `https://www.worldbank.org/content/dam/enterprisesurveys/documents/methodology/ Enterprise%20Surveys_Manual%20and%20Guide.pdf` .

> 6The WBES collected information on firm geo-localization in 120 countries. This information is missing for 5% of firm-year observations. For firms with geo-localization available for at least one survey wave, we impute the same location across other waves to increase the sample size. Results are virtually identical when excluding firms with imputed geo-localization.

> 7Of the 120 countries for which there is information on firm geo-localization, 89 have at least two survey

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of the countries and survey waves included in our analysis, while Figure 1 shows their geographical distribution.

As noted by Besley and Mueller (2018), there is a trade-off in using the WBES. While in some countries the information in the WBES dataset is less detailed than that found in national-level manufacturing surveys, the WBES offers several clear advantages. It provides harmonized information across a large and diverse set of countries, covers multiple sectors, and includes data on specific obstacles and market characteristics—such as informal competition—that are typically unavailable in other surveys. Moreover, given the focus of our analysis and the empirical strategy employed, which requires longitudinal firm-level information, the WBES offers better coverage than any other global dataset, including ORBIS Bureau van Dijk.<sup>8</sup> A typical firm in our sample is domestically owned and operates in the private sector. As shown in Table A2, the median firm has 22 employees and has been operating for 16 years. It mainly serves the domestic market—only 16% of firms are exporters—and generates $334,700 in annual sales and $271,800 in annual profits (in 2000 constant USD).<sup>9</sup> All variables are defined in Table C1.

**Selection, attrition, and missing values** As discussed in Section 3, our estimating strategy relies on the subset of firms with multiple observations over time, i.e., those included in the panel component of the WBES. This may raise concerns about potential selection issues affecting our estimating sample. In particular, one might worry about the selfselection of firms that are re-interviewed in subsequent survey waves, vis `a vis firms that, for various reasons, are interviewed only once. If such selection is correlated with our main

waves: 42 have two waves, 45 have three, and two have four. Across these country-waves, the sample includes 40,884 firm-year observations, corresponding to 16,608 firms observed twice and 2,556 observed three times. Dropping firms without sales data yields a final sample of 36,087 firm-year observations across 89 countries.

> 8The ORBIS dataset lacks meaningful coverage for 63 countries in our sample. Most notably, geolocalization data—a central component of our analysis—is sparsely populated in ORBIS across the majority of countries, significantly limiting the usable sample and raising concerns about sample selection. When we require the availability of both sales, costs, and profits (as needed for the baseline regression) and geolocated information, WBES provides superior coverage in all cases except Kazakhstan, where ORBIS still includes a comparable number of firms relative to WBES. In addition, ORBIS does not collect data on several key variables necessary to investigate the mechanisms underlying our results—such as informality, access to imported inputs, power outages, and other operational constraints. See Appendix B for further discussion.

> 9Profits are not directly provided in the WBES. We construct _Profits_ as _Sales_ minus _Total costs_ , defined as the sum of labor costs and raw materials and intermediate inputs costs, including energy expenditures (for details on the variable definitions, see Table C1). For input costs, the WBES questionnaire administered to service firms only collects information on electricity expenses. As a result, for service firms, the _Total costs_ variable may underestimate overall production expenditures (e.g., by excluding costs related to equipment, telecommunication services, etc.), potentially affecting the comparability of _Profits_ across sectors. To address this, we exclude from the sample all service firms with a _Total costs_ -to- _Sales_ ratio below the fifth percentile of the distribution. This restriction improves the reliability of economic performance comparisons across sectors. In any case, our results are virtually unchanged when focusing only on manufacturing firms (see Section 4.1, Table A15).

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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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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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# **2.3 Additional data**

In our heterogeneity analysis, we draw on a wide range of additional data sources to classify countries across several economic, institutional, and political dimensions. Detailed definitions and data sources are listed in Table C1.

**Income** Countries are grouped by their level of economic development, following the World Bank classification.

**Trade, agriculture, and natural resources shares in GDP** Countries are classified based on three indicators from the World Bank: i) merchandise trade as a share of GDP, capturing countries’ engagement in international trade; ii) the share of agriculture in GDP; and iii) the share of natural resources in GDP.

**Fragility** We use the World Bank classification to identify fragile countries—those experiencing deteriorating governance, prolonged political crises, post-conflict transitions, or gradual but still unstable reform processes. In our sample, 16% of countries fall into this category.

**Economic Complexity** Countries are categorized according to the complexity of their production structure using the Economic Complexity Index (ECI, Hidalgo and Hausmann, 2009). The ECI has been shown to explain income differences across countries and to predict future growth (see, for instance, Hausmann et al., 2014). **Drug producing** Major illicit drug-producing and/or drug-transit countries are identified based on the _2006 International Narcotics Control Strategy Report_ , published by the U.S. Bureau for International Narcotics and Law Enforcement Affairs. **Bureaucracy quality** The quality of a country’s bureaucracy is measured using data from the International Country Risk Guide (ICRG, Political Risk Services Group). This index captures the institutional strength and quality of the civil service, including its ability to function without political interference, ensure continuity in government policy, and effectively implement reforms.

**Corruption** Country-level corruption is measured using an index from the ICRG that captures perceptions of corruption within the political system. This includes the misuse of public office for private gain through practices such as bribery, patronage, and nepotism. **Peace/conflict status** Countries are grouped based on their peace or conflict status at the beginning and throughout the entire period of analysis. A country is classified as _initially at peace_ if, at the time of the first survey in that country, there are no major episodes of political violence (such as civil conflicts, ethnic violence, riots, popular protests, or repression of dissidents). Countries not meeting this criterion are classified as _initially in conflict_ . We also categorize countries based on their conflict status throughout the entire analysis period. A country is classified as _always in conflict_ if major episodes of political violence begin before the first survey wave and persist throughout all subsequent waves. See Table C1 for details.

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# **3 Empirical Strategy**

We estimate the impact of conflict exposure on firms’ economic performance by combining longitudinal firm-level data with geolocalized information on conflict events. Our main specification reads as follows: where _Yit_ represents the outcome of interest for firm _i_ in fiscal year _t_ . The analysis considers several firm-level outcomes as dependent variables, including _Sales_ , _Total Costs_ , and _Profits_ . _Conflict exposure it_ denotes the firm-specific measure of conflict exposure. In our baseline specification, it is computed as the logarithm (log of 1+) of the number of conflict 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.<sup>13</sup> The vector _Xit_ represents a set of time-varying firm characteristics, including size, age, and export status. _μi_ denotes firm fixed effects, accounting for all time-invariant observed and unobserved characteristics of the firm that could potentially influence the outcome of interest. _θt_ represents year fixed effects, controlling for overall trends in economic activity and common shocks. As a robustness check, we also include the full set of area-year interacted fixed effects to account for all time-varying location-specific determinants of firm performance, such as infrastructure availability and local labor market characteristics. Finally, _εit_ is the error term. In all specifications, we report robust standard errors clustered at the firm level, which is the unit of observation used to measure the intensity of conflict exposure. All variables are defined in Table C1.

Our identification strategy relies on the assumption that, conditional on our full set of fixed effects, conflict events occurring near the firm’s location are uncorrelated with any latent determinant of its economic performance. Under this assumption, _β_ captures the reduced-form effect of the firm-specific conflict exposure on economic performance.<sup>14</sup> In what follows, we present our core results and examine in detail potential threats to the validity of our identification strategy.

> 13We also employ alternative measures of conflict exposure constructed using: i) different buffer distances from the firm’s location or weighting by inverse distance; ii) a different time period; iii) different types of conflict events; and iv) the ACLED dataset as an alternative source for conflict events (see Section 2.2).

> 14In our sample, the onset of conflict does not necessarily occur at _t_ 0. As a result, we cannot provide dynamic difference-in-differences estimates of the effect of conflict between _t−_ 1 and _t_ 0.

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# **4 Results**

## **4.1 Conflict exposure, sales, total costs, and profits**

Table 1 presents our main results. Column 1 shows that conflict exposure leads to a large and statistically significant decline in firm sales. This effect remains robust in column 2 after the inclusion of firm-level controls. Based on our estimates, a one-standard-deviation increase in the number of conflict events occurring within a 20 km radius of the firm during the last fiscal year (eight events) leads to a 2.8% reduction in median sales, equivalent to 10,050 USD. Higher conflict exposure also leads to a significant decline in total production costs, measured by expenditures on raw materials, intermediate inputs, and labor (column 3). Results do not change including controls (column 4).

These reductions in both sales and total costs could reflect changes in either prices or quantities. Although the WBES dataset does not allow us to directly test for price changes, it is unlikely that conflict lowers consumer or input prices.<sup>15</sup> This suggests that the observed declines in sales and total production costs are mainly driven by a contraction in output and a reduction in the quantity of inputs and materials used in production.

Finally, our results show that conflict exposure does not affect a firm’s profits (columns 5 and 6).<sup>16</sup> This null effect is consistent with the fact that higher conflict exposure affects both sales and total costs in the same direction, suggesting that firms respond to reduced sales by cutting production costs.<sup>17</sup> As these adjustments result in reduced output, it follows that, while surviving firms’ profitability remains unchanged, overall economic activity is negatively impacted by conflict-related violent events.

**Threats to identification** The firm-level fixed effects in our baseline specification account for all time-invariant firm, sector, and geographical characteristics. However, our estimates may still capture some factors that vary over time at the local level and are simultaneously correlated with the number of conflict events as well as the firm’s economic performance. To address this concern, we augment model 1 with a full set of area-time interacted fixed effects to also control for all time-varying, location-specific determinants of firm performance, such as infrastructure availability and local labor market characteristics.<sup>18</sup>

> 15Previous studies find that conflict typically raises consumer and input prices (Amodio and Di Maio, 2018; Utar, 2024; Del Prete et al., 2023; Couttenier et al., 2022).

> 16Due to the coverage of the WBES, the null effect on profits applies only to surviving firms that remain formal—that is, those observable in subsequent survey waves. However, not all firms exiting the sample necessarily go bankrupt. As discussed in Section 4.2.3, conflict may also induce firms to shift to the informal sector, causing them to disappear from our sample (which includes only formal firms) while continuing to operate in the economy.

> 17While the average effect of conflict exposure on profits is null, in Section 4.3, we discuss the firm and country characteristics for which profits decrease with conflict exposure.

> 18 _Area_ is the most granular geographical unit of observation in the WBES and represents the sampling region. There are 314 areas, and they are typically larger than the 20 km radius buffer used to construct our conflict exposure measure.

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Table A7, columns 1 and 2, shows that the coefficient for _Sales_ remains negative, sizable, and significant (though somewhat smaller than in the baseline), while the coefficient for _Profits_ stays null.

Another potential threat to our identification is reverse causality. To address this, we start including the lead number of conflict events in our baseline regression for both _Sales_ and _Profits_ (see Table A7, columns 3-4, respectively). Reassuringly, for both outcomes, the results are consistent with the baseline, and the coefficients for the lead variables are centered around zero. This pattern indicates that future changes in conflict are not driven by current firm performance, which helps rule out reverse causality and reassures about parallel trends in firm outcomes. A related concern is that the negative relationship between sales and conflict might be due to low-performing firms being targeted because they cannot afford protection. However, this does not seem to be the case: as shown in Table A25 (columns 5-7), conflict exposure does not increase firms’ losses from theft or their security expenditures.

We also consider the possibility that poor local economic conditions may themselves trigger violent events. To address this concern, we conduct two additional tests. Table A8 shows that local conflict intensity—defined at the second-level administrative unit (i.e., GID2)—is not predicted by the level of economic activity. Specifically, we find no correlation between conflict intensity in a given area and various proxies for economic activity measured in the preceding period—namely, total sales, total employment, total profits, sales growth, and employment growth (columns 1–5, respectively). This result holds whether conflict intensity is measured in the following year, over the next two years, or over the next three years (reported in panels A, B, and C, respectively). At the same time, results in Table A9 rule out the possibility that conflict intensity is driven by the local composition of economic activity. There is no significant correlation between the economic size of any sector and subsequent conflict intensity in the same area—whether measured one, two, or three years later (columns 1-3).

Another concern is that the location and intensity of conflict events may instead be driven by the presence of firms with better economic performance, making them valuable targets for conflict fighters. As an additional indirect test for reverse causality, we reestimate our model by excluding large firms, those with high sales and profits, and those located in capital cities. The results, reported in Table A10, are virtually identical to those obtained using the full sample. This suggests that our findings are not driven by the location and intensity of conflict events being influenced by the presence of high-value firms or by the activity of firms located in political centers.

Finally, we address the concern that firms may be nonrandomly exposed to conflict events by implementing a test in the spirit of Borusyak and Hull (2023). We construct two different placebo controls designed to account for the possibility that firms are exposed to conflict in a systematic, predictable way. In the first exercise, we hold the firm’s location 11
fixed but randomize the timing of conflict events by assigning each firm its actual conflict exposure from previous years ( _t −_ 2 to _t −_ 5), drawn from a uniform distribution. This placebo measure is then included as an additional control in the baseline sales regression to capture any predictable component of conflict exposure driven by past patterns. In the second exercise, we construct a placebo measure based on the number of conflict events affecting other firms operating in the same year and geographical area. In both cases, we repeat the randomization process 5,000 times and estimate the marginal effect of actual conflict exposure, controlling for the placebo measure. Figure A1 shows that the distribution of our estimates remains centered around our main coefficient, suggesting that the results are not driven by nonrandom assignment of conflict exposure.

Conflict exposure is heterogeneous across firms and varies over time. In a two-way fixed-effects model, this type of treatment may lead to the negative weighting problem (De Chaisemartin and d’Haultfoeuille, 2020; Goodman-Bacon, 2021). To check whether this is an issue in our case, we perform a clean control analysis through sample restriction in the spirit of De Chaisemartin et al. (2022). Table A11 presents the results. As a preliminary step for this analysis, in column 1, we replicate Table 1 using a dummy variable as an alternative measure of conflict. The indicator I(Conflict exposure _≥_ p(25)) equals one if the firm is exposed to a number of conflict events above the 25th percentile of the country-year distribution, and zero otherwise. The results indicate that the estimated effect is negative and highly significant. Once confirmed that our results continue to hold using a dummy treatment variable, in columns 2 and 3, we refine the analysis using a “clean” sample of movers and stayers. In the spirit of the methodology proposed by De Chaisemartin et al. (2022), we classify movers as treated units and quasi-stayers as controls. In this framework, we define movers as firms with substantial treatment intensity—those at or above the 25th percentile of the treatment distribution—and quasistayers as firms with negligible exposure, falling below that threshold. In each survey wave, we exclude firms that had already been significantly exposed to conflict. This allows us to compare outcomes between firms experiencing conflict for the first time and those that remain untreated or have not yet been treated. To further refine the sample, we exclude firms with significant conflict exposure in the two years prior to the survey wave. Results in Table A11, columns 2 and 3, show that our findings remain robust across all specifications in this clean controls analysis, using the same dummy treatment variable as in column 1: conflict exposure reduces sales and has no effect on profits. Taken together, this analysis suggests that the observed effects are not driven by heterogeneous treatment responses within our empirical framework.

To support our interpretation of the main results, we also conduct a formal check for selection on unobservables using the test proposed by Altonji et al. (2005) and further refined by Oster (2019). Table A12 presents the estimated impact of conflict exposure under varying assumptions about the relative importance of unobservables, captured by 12
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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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
column 5 shows that the results for both sales and total costs remain consistent also when using the ACLED dataset to measure conflict exposure, with the effect on profits being negative and marginally significant. These findings are reassuring, given that ACLED covers a substantially different—and smaller—set of countries, as well as a distinct set of conflict-related events compared to ICEWS. While all our results are consistent across these alternative definitions of conflict exposure, they also suggest that firms respond differently depending on the type of event, with the negative effects of conflict being more pronounced when the events are more violent.

Finally, Table A18 shows that our results remain unchanged when accounting for spatial correlation in the error terms, using the arbitrary clustering correction for standard errors proposed by Colella et al. (2023) to re-estimate our baseline model.

# **4.2 Mechanisms**

Our results show that conflict exposure reduces a firm’s sales and total costs. These effects are likely driven by a combination of production-side and market-level channels. One mechanism relates to the availability of production inputs. Higher conflict exposure may constrain access to domestic and foreign inputs—such as raw materials, intermediate goods, labor, or electricity—forcing firms to adopt a sub-optimal input mix. This reduction in input quantity and quality lowers overall output and, in turn, depresses sales. At the same time, conflict may reduce sales by changing the level and type of competition faced by firms. In particular, it is possible that conflict increases competition from informal firms, a phenomenon that is well-known to have negative consequences for formal ones. Finally, conflict may lower sales by decreasing demand or increasing the severity of the obstacles to a firm’s activity. In what follows, we take these hypotheses to the data to examine the mechanisms driving the negative effect of conflict on firms’ sales and production input expenditures.

## **4.2.1 Raw materials and intermediate inputs**

Table 2, column 1, shows that conflict exposure leads to a decline in firms’ expenditures on raw materials and intermediate inputs. This reduction could reflect either lower input prices or a decrease in the quantity of inputs purchased. As the WBES does not report prices and quantities separately, we cannot test this directly. However, given that conflict is unlikely to lower the unit cost of inputs, we read these findings as evidence of a conflict-induced reduction in the quantity of raw materials and intermediate inputs used in production.<sup>21</sup> Consistent with this interpretation, conflict also appears to reduce electricity use, another key production input. Firms more exposed to conflict events report

> 21This is line with evidence from previous studies for West Bank, India, Libya, and Ukraine (Amodio and Di Maio, 2018; Couttenier et al., 2022; Del Prete et al., 2023; Korovkin et al., 2024).

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lower expenditures on electricity (column 2) and a higher likelihood of experiencing power outages (column 3), suggesting that conflict constrains the availability of electricity and forces firms to cut its usage. Furthermore, conflict decreases the share of imported intermediate inputs used in production (column 4) and, for the same argument above, likely reduces their quantities as well. Importantly, the results in Table A19 suggest that the decline is driven by reduced availability of imported inputs, rather than by a decrease in firms’ demand. Conflict appears to increase the difficulty of importing, thereby limiting access to foreign production inputs. Column 1 shows that, among firms using imported inputs, those more exposed to conflict are more likely to report customs procedures as a significant obstacle to their operations. Column 2 further indicates that higher conflict exposure is associated with a longer number of days required to clear customs for imported goods. These findings align with previous single-country studies indicating that conflict hampers firms’ access to imported inputs due to increased trade costs or uncertainty.<sup>22</sup> Given that imported inputs are crucial for firm productivity (see, e.g., Topalova and Khandelwal, 2011; Halpern et al., 2015), a negative shock to their availability likely leads to a significant reduction in firms’ economic activity (Amodio and Di Maio, 2018; Boehm et al., 2019; Carvalho et al., 2021). Taken together, these findings suggest that conflict reduces the availability of both raw materials and intermediate inputs, especially imported ones, resulting in a decline in the firm’s overall output, and consequently, sales.<sup>23</sup>

# **4.2.2 Labor**

Table 2, column 5, shows that conflict exposure leads to a decline in total labor costs. To shed light on the mechanisms behind this effect, columns 6-8 explore changes in employment levels, workforce composition, and average wages. Conflict exposure does not appear to affect the total number of workers employed by the average firm (column 6), where employment includes both full-time permanent employees and temporary workers expressed in full-time equivalents. However, conflict does have a significant impact on the composition of the workforce, resulting in a sizable increase in the share of unskilled workers (column 7). Consistent with this substitution effect, higher conflict exposure also leads to a reduction in the average wage (column 8), which helps explain the observed decrease in total labor costs.<sup>24</sup>

> 22Firms exporting to firms in conflict-affected countries may react to higher local violence by decreasing volume or raising prices to cover export risk (Amodio and Di Maio, 2018; Korovkin et al., 2024). 23Table 2 reports the regression results obtained using the maximum number of observation available for each outcome. Due to missing values, the number of observations differ across outcomes. As a robustness check, we replicate our analysis using the sample of firms for which information is available for all the outcomes related to the _raw materials and intermediate inputs mechanism_ . Table A20 show that results are unchanged when we restrict the sample to firms for which information is available for all the outcomes related to the _Raw materials and intermediate inputs_ mechanism.

> 24Table A21 shows that results are unchanged when we restrict the sample to firms for which information is available for all the outcomes related to the _labour mechanism._

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Table A22 provides suggestive evidence supporting the idea that firms reduce total labor costs by substituting skilled labor with less expensive unskilled workers. As shown in column 1, the substitution effect is more pronounced among firms producing non-differentiated products, which are typically more reliant on cost efficiency and less dependent on specialized labor.<sup>25</sup> This is expected, as producing differentiated goods relies more heavily on higher-skilled labor, limiting firms’ ability to substitute for lowerskilled workers. Additionally, column 2 indicates that the magnitude of the substitution effect varies with a country’s labor legislation. In contexts where firing costs are higher (proxied by severance pay levels), the substitution of skilled workers for unskilled ones due to conflict exposure is smaller. These results align with the idea that worker substitution is a defensive strategy employed by firms to reduce labor costs when affected by conflict: when sales decrease due to lower output from reduced availability of production inputs, firms adjust their workforce to lower costs.

The remaining columns of Table A22 help rule out alternative explanations for the observed reduction in total labor costs. Column 3 shows that the decline is not driven by an increased reliance on temporary workers, suggesting that changes in contract structure are not a key mechanism. Column 4 further indicates that the substitution away from skilled labor is not due to supply-side constraints—such as a conflict-induced decline in the availability of skilled workers. Specifically, firms do not report a worsening of the skilled labor supply as a more serious obstacle to their operations. Taken together, these results provide suggestive evidence that the substitution of skilled with unskilled workers is driven primarily by demand-side adjustments—namely, firms responding to conflictinduced reductions in their production possibilities.

# **4.2.3 Market competition**

An important determinant of firm sales is the level of market competition. In Table 2, columns 9–10, we examine how conflict affects market competition. Results indicate that conflict increases competition faced by formal firms from informal ones.<sup>26</sup> Column 9 shows that conflict exposure increases the likelihood that firms report informal competition as an obstacle and column 10 that conflict heightens the perceived intensity of the threat posed by informal competitors.

The conflict-induced increase in informal competition is consistent with two different dynamics: new informal firms entry the market or formal firms shift into the informal 25Drawing from the Rauch (1999) classification, we define a product as differentiated if its trade neither occurs on an organized exchange nor relies on a reference price. As Rauch and Trindade (2002) highlights, goods with a reference price are sufficiently homogeneous, as prices convey all relevant information for international trade; this is not the case for more differentiated goods lacking reference prices. The original classification is available at: `https://econweb.ucsd.edu/~jrauch/rauch_classification.html` .

> 26A large informal sector is a common feature of developing economies, where formal and informal firms often operate in the same sectors and produce similar goods (Ulyssea, 2020).

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sector. Since the WBES includes only formal firms, we cannot directly test for these two alternative explanations. Yet, suggestive evidence indicates that our effect is driven by formal firms becoming informal. First, Table A23 columns 1 and 3 show that conflict intensity does not change the (self-reported) total number of competitors of the firm. At the same time, conflict is associated with higher exit rates among formal firms (see Table A5). In sum, the overall level of competitive pressure faced by the firm remains unchanged, conflict makes some formal firms to exit the market, and competition from informal firms increases. These three effect are consistent with conflict inducing some of the previously formal firms to become informal.

These results align with prior studies showing that informality tends to rise in response to negative shocks, as firms seek to exploit the cost advantages of non-compliance with formal regulations (Ulyssea, 2018). This is also the case for an increase in conflict exposure. For instance, Neri-Lain ́e (2025) documents that —in the context of Afghanistan— an increase in insecurity reduces formal firms’ existence, increasing informality. These results documenting the shock-induced increase in informality are relevant for our analysis because of the evidence showing that more competition of informal firms can have negative consequences for formal ones. An increase in informal competition reduces the output of formal firms (Rozo and Winkler, 2021), lowers their employment (Amin, 2023), weakens innovation incentives (Avenyo et al., 2021), and dampens demand for credit, thereby curbing investment (Brancati et al., 2024). More broadly, the expansion of informality poses challenges for the functioning of the overall economy. It contributes to resource misallocation, reduces total factor productivity, and is often associated with broader welfare losses (Ulyssea, 2018). Taken together, this evidence suggests that the conflict-induced increase in informal competition contributes to explaining the reduction in firms’ sales documented in Table 1.

# **4.2.4 Demand**

An alternative explanation for the observed reduction in sales and total costs is that conflict reduces consumer demand. In this case, firms would respond to falling consumer expenditure by scaling back production and, consequently, reducing input purchases—leading to lower sales. While we cannot directly test this channel due to the lack of consumer demand data in the WBES, Table A24 provides suggestive evidence that this mechanism alone is unlikely to fully account for our results.

Column 1 examines whether the effect of conflict exposure on sales varies by the firm’s main destination market—local, national, or international. If the decline in sales were driven solely by a conflict-induced drop in domestic demand, we would expect firms selling primarily to international markets to be less affected. However, this is not what we find: the effect of conflict exposure on sales does not differ significantly across the three market categories. This suggests that the negative impact on sales is not confined 18
to firms serving domestic customers, making it unlikely that a reduction in consumer demand alone explains our results.

Next, we explicitly examine heterogeneities based on firms’ export status. Results in column 2 show that the negative impact of conflict on sales is smaller for exporting firms, and column 3 indicates that this mitigating effect increases with the export share in total sales. However, even for firms that export 100% of their output, the effect remains economically and statistically significant. Our estimates suggest that a one-standarddeviation increase in nearby conflict events reduces median sales by approximately 2.7% for a firm exporting _all_ of its output. This implies that even firms with no domestic customer base are substantially affected by conflict occurring close to their location.<sup>27</sup> Finally, we analyze the effect of conflict on capacity utilization to further assess whether changes in demand could explain our findings. The WBES defines _Capacity utilization_ as the firm’s actual output expressed as a percentage of its maximum potential output using all available inputs, providing a useful proxy for identifying demand-driven fluctuations in production. Since a firm’s production capacity is directly tied to input availability, a drop in capacity utilization would suggest that firms are producing less in response to weaker demand. However, the results in Table A24, column 4, show that capacity utilization is not significantly affected by conflict exposure. This suggests that lower sales are unlikely to be the results of a limited use of installed production capacity, which would be the case if the firm is responding to a conflict induced reduction in demand.<sup>28</sup> Taken together, these results indicate that while demand-side factors may contribute in some cases, they are unlikely to be the main driver of the observed decline in firm performance.

# **4.2.5 Other obstacles to firms’ activity**

Next, we explore whether the decline in firms’ sales could be explained by a conflictinduced worsening of various obstacles to business activity. Table A25 provides little support for this explanation. Columns 1-4 report results for several potential barriers, including access to finance, corruption, transportation, and land availability. None of these are more likely to be reported as severe obstacles by firms more exposed to conflict, suggesting they do not significantly contribute to the observed decline in performance. Similarly, columns 5-7 show that the reduction in sales is not driven by conflict-induced increased exposure to crime (measured by losses due to theft), or higher costs or spending in security measures. Overall, these results indicate that the worsening of the business

> 27While it is possible that foreign buyers reallocate their sourcing away from firms located in conflictaffected countries (Korn and Stemmler, 2025), the finding that the negative effect on sales diminishes with the export share suggests that this type of substitution likely plays only a limited role in explaining the overall decline in sales among exporting firms.

> 28While it is theoretically possible that firms fully anticipate conflict-induced demand shocks and adjust input purchases accordingly, our findings—particularly the sharp (forced) decline in imported input use and electricity consumption (see Section 4.2.1)—suggest that conflict directly restricts firms’ ability to procure essential inputs.

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environment constraints are unlikely to be the main channel through which conflict affects firm outcomes.

**Summing up** Taken together, our results suggest that conflict restricts firms’ access to key intermediate inputs—particularly imported goods and electricity—leading to a reduction in output. Conflict also increases the pressure from informal competition. In response, firms adjust by cutting production costs, including substituting skilled labor with unskilled workers. As a result, sales decline due to both lower output and heightened informal competition, while profits remain stable thanks to firms’ cost-cutting adjustments.

# **4.3 Heterogeneity**

In this section, we test the heterogeneous effect of conflict exposure on our main outcomes across several firm-level and country-level characteristics.

**Firm-level characteristics** Table A26 presents heterogeneity tests based on several firm characteristics, including size, age, government ownership, and foreign ownership. The results show no significant differences in the effect of conflict exposure on sales or profits across these characteristics, except for firm size. The negative impact on sales is more pronounced for smaller firms and gradually diminishes as firm size increases. Overall, our findings suggest that larger firms are more resilient to conflict, likely due to greater resources or operational flexibility, while other firm-level attributes play a limited role in mediating the effects of conflict.

We also examine whether the effect of conflict exposure varies across sectors. Table A27 shows that the impact on sales is negative across all sectors, with the exception of _Other manufacturing_ and _Other services_ , where the effect is statistically insignificant. Conflict exposure significantly reduces profits in only two sectors— _Furniture_ and _Transportation_ —where the negative effect is also the largest among all industries.<sup>29</sup>

**Country-level characteristics** To explore how the effects of conflict on firms vary across institutional and economic environments, we examine heterogeneity along a set of countrylevel characteristics. These are selected to capture key dimensions that are likely to shape firms’ ability to cope with conflict-related disruptions. Specifically, we consider indicators of economic development, trade integration, sectoral composition, state capacity, governance quality, and exposure to organized crime. We also account for countries’ prior

> 29This analysis is constrained by the level of sectoral detail available in the WBES. The sector classifications do not allow us to isolate firms that might benefit from conflict—such as those involved in arms production, supplying intermediates to the defense industry, offering security services, or natural resources and precious materials (e.g., Guidolin and La Ferrara, 2007). These firms are grouped with others less likely to benefit from conflict, potentially masking meaningful variation across more narrowly defined activities.

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conflict status, as historical exposure may influence both firm behavior and institutional adaptation. This approach allows us to test whether the severity of conflict’s impact is systematically related to structural features of the economy and the broader institutional context in which firms operate.

Figure 3 presents heterogeneity results, using country-level characteristics measured at the beginning of the first survey wave in each country. The figure displays the estimated coefficients and 95% confidence intervals for the interaction terms between conflict exposure and each country characteristic. Results are reported separately for both sales and profits.<sup>30</sup> We begin by examining characteristics that do not appear to shape the effect of conflict exposure on firms. As shown in Figure 3, being located in a high-income country does not, in itself, mitigate the negative effect of conflict on firm outcomes. Likewise, firms in fragile countries are not more vulnerable to conflict events. This suggests that, although the fragility index is often used to assess the risk of conflict (see, for example, Chami et al., 2021; Verwimp et al., 2019), it is not a strong predictor of the severity of conflict’s effects on firms in a given country. Similarly, the adverse effects of conflict do not differ between countries with high dependence on agriculture or natural resources. This is consistent with the idea that the mechanisms identified in Section 4.2 operate largely independently of a country’s sectoral specialization.

Figure 3 also highlights several country-level characteristics that significantly influence the effect of conflict exposure on firm performance. First, the negative effect of conflict is more pronounced in countries with greater exposure to international trade. Two complementary mechanisms help explain this result. As discussed in Section 4.2.1, conflict disrupts access to imported inputs. Firms in countries that rely heavily on foreign inputs are therefore more exposed to the conflict-induced breakdown of international supply chains (Del Prete et al., 2023). In addition, as shown in Section 4.2.4, the export activity of firms in conflict-affected areas declines with conflict intensity (Chauvin and Rohner, 2009). Since firms in more trade-open countries often depend heavily on foreign demand, they are particularly vulnerable to conflict-related disruptions in export markets.<sup>31</sup> In sum, greater dependence on international trade—on both the supply and demand sides—amplifies firms’ vulnerability to conflict.

Second, the effect of conflict is also more severe in countries with low economic complexity. Because economic complexity reflects the number and strength of domestic pro-

> 30Definitions for all the country-level variables are provided in Table C1. Table A28 reports correlations among the country-level characteristics used in the analysis, confirming that they capture distinct dimensions rather than a single underlying factor. Table A29 further shows no meaningful correlation between these characteristics and either the average attrition observed in the panel (Panel A) or the average non-response rate for key firm-level outcomes (Panels B–D). These patterns suggest that sample attrition and item non-response are unlikely to be driving the heterogeneity observed in our results.

> 31In some contexts, foreign demand may substitute for weakened domestic demand, as observed during the Mexican Drug War (Utar, 2024).

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duction linkages (Hausmann and Hidalgo, 2011; Gloria et al., 2024), this result suggests that firms operating in less complex economies face greater difficulty in substituting disrupted inputs or reorganizing supply chains in response to conflict. As a result, they experience sharper declines in output and performance.

The quality of institutions also plays a critical role in shaping how firms are affected by conflict. We find that conflict exposure has a more detrimental impact in countries with low-quality bureaucracy. This supports the idea that, _ceteris paribus_ , more efficient governments are better positioned to help firms absorb and respond to negative shocks. Similarly, firms in countries with higher levels of corruption are more negatively impacted by conflict events. As discussed in Section 4.2.1, conflict reduces access to essential inputs and services, such as imported goods and electricity. In settings where access to these resources is already mediated by corruption, the disruption caused by conflict is likely to be magnified.<sup>32</sup> The presence of organized criminal activity further compounds these effects. Our results show that firms located in countries characterized by the production and trafficking of illegal drugs are more adversely affected by conflict and political violence, suggesting that the presence of criminal networks can amplify the economic consequences of such events.

Overall, these results indicate that the economic consequences of conflict for firms are largely shaped by the structural and institutional features of the environments in which they operate. Countries with weaker state capacity, lower economic complexity, and greater exposure to corruption or organized crime provide fewer buffers against the disruptions caused by conflict, underscoring the importance of country-level factors in shaping firms’ ability to cope with political violence.

Finally, we explore whether the effects of conflict events differ depending on a country’s prior conflict status. Specifically, we test whether firms in countries that were initially at peace respond differently to conflict events compared to those in countries already marked by ongoing political violence The sign of this heterogeneity is a priori ambiguous. On the one hand, firms in persistently violent environments may have developed coping strategies that reduce the marginal negative impact of additional conflict exposure. On the other hand, prolonged exposure to violence may exacerbate existing vulnerabilities, thereby amplifying the negative economic consequences of conflict events. Our results show that the economic consequences of conflict are substantially larger for firms in countries that were initially at peace. In these settings, increased conflict exposure leads to a significantly larger decline in sales, and the effect on profits is also considerable: a one-standard-deviation increase in nearby political violence events reduces median profits by approximately 3.9%. We find similar patterns in the comparison between countries with continuous versus intermittent conflict. Taken together, these findings suggest that

> 32Fisman et al. (2024) argue that in highly corrupt environments, officials have greater discretionary power over which firms gain access to key inputs and services.

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cross-country heterogeneity in the effects of conflict exposure likely reflects differences in firms’ adjustment capacity and institutional resilience. Firms in countries with ongoing or repeated exposure to conflict may already be operating with adaptive strategies suited to unstable environments, whereas firms in previously peaceful settings are more vulnerable to sudden shocks. Taken together, these findings suggest that the cross-country heterogeneity in the effects of conflict exposure likely reflects differences in firms’ adjustment costs across varying institutional and economic environments. Firms in countries already experiencing conflict—or consistently exposed to it—are more likely to have already adapted to operating in a conflict-ridden environment. By contrast, firms in countries with no prior exposure may be less prepared and, therefore, more vulnerable to a sudden increase in conflict intensity.

Country-level characteristics also shape the relative importance of the mechanisms through which conflict affects firm performance. To illustrate this, we examine how these mechanisms differ depending on a country’s conflict status at the beginning of the period. Table 3 presents the results. Columns 1-4 show that conflict exposure reduces spending on intermediate inputs and electricity, and increases the likelihood of power outages in both groups. However, with the exception of the imported input share, these effects are consistently more severe in countries initially at peace. Columns 5-8 consider labor market responses. In countries initially at peace, firms tend to substitute skilled workers with unskilled labor, leading to a decline in average wages and overall labor costs. On the other hand, in countries initially in conflict, an increase in conflict exposure leads to a reduction in the number of workers. Finally, columns 9-10 examine the impact on informal competition. Also in this case, the differences across countries are striking. While conflict intensity does not affect market competition in countries in conflict at the beginning of the period, it increases both the probability for formal firms of facing informal competitors and the perceived threat posed by such competition (columns 9-10) in countries initially at peace. This sharp difference across countries is consistent with our interpretation of the informal market competition mechanism as a conflict-induced adjustment process. Specifically, conflict may push firms to shift into informality, a response likely to have already occurred—at least partially—in countries with pre-existing conflict. In contrast, this adjustment is less likely in countries initially at peace, where a new increase in conflict exposure has a more pronounced effect. In general, the differences in underlying mechanisms we have discussed are consistent with the sharper declines in sales and profits observed among firms in countries initially at peace (see Figure 3), reinforcing the idea that the effects of conflict exposure—and the channels through which they operate—vary significantly with broader country characteristics.

23
# **5 Conclusions**

Our analysis, covering 89 developed and developing countries from 2006 to 2019, offers a unique perspective on the effects of conflict on firm activity. By leveraging harmonized firm-level data, we document several empirical regularities that hold across different settings and contexts. The global scope of our analysis enables cross-country comparisons, providing novel insights and a more comprehensive understanding of how conflict affects firm performance across the world.

Our results show that higher exposure to conflict reduces both firm sales and production costs. Conflict disrupts access to domestic and imported intermediate inputs and raw materials, forcing firms to scale down production, which in turn lowers sales. Sales are further depressed by intensified competition from informal firms. While reduced output drives down production costs through lower input purchases, firms also respond to conflict by cutting labor costs—substituting skilled workers with unskilled ones. As a result of these adjustments in both sales and costs, the average effect of conflict exposure on profits is null. However, the estimated effect on profits is consistently negative and statistically significant in countries with specific characteristics.

A key advantage of our global analysis is that it allows us to provide novel evidence on how economic, social, and political country-level characteristics shape the effect of conflict on firm behavior. We find that the impact of conflict exposure is more severe for firms operating in countries with high trade openness, low economic complexity, weak bureaucratic quality, illicit drug production, and those initially at peace. In these contexts, conflict leads to substantial declines in both sales and profits. Finally, we show that the relative importance of the mechanisms through which conflict affects firm performance also varies systematically with country characteristics.

Three policy implications emerge from our analysis. First, the quality of government institutions plays a critical role in helping firms cope with the adverse effects of conflict. Our findings highlight the importance of investing in state capacity and bureaucratic capabilities, particularly in settings characterized by high conflict risk. Second, economies with low complexity and high trade openness—typical of many developing countries reliant on a few primary sectors—are more severely affected by conflict. Policies that promote economic diversification, local input sourcing, and infrastructure development can enhance resilience to external shocks, including political violence. Third, the economic costs of conflict are disproportionately high for firms in countries that are initially at peace. This implies that, although early interventions to preserve peace—such as targeted diplomacy, inclusive political institutions, or conflict-sensitive development assistance—may be costly and complex, they can yield substantial returns. Our results suggest that both domestic policymakers and the international community should invest in conflict prevention, as the economic value of maintaining peace is significant, both within and across countries.

24
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29
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— Figure 3: Main results Heterogeneity by country characteristics <!-- Start of picture text --> Sales<br>Low income<br>High trade<br>High agriculture<br>High natural resources<br>Low economic complexity<br>Low bureaucracy quality<br>High corruption<br>Fragile<br>Illicit drug producing<br>Initially at peace<br>-.2 -.15 -.1 -.05 0 .05<br>Profits<br>Low income<br>High trade<br>High agriculture<br>High natural resources<br>Low economic complexity<br>Low bureaucracy quality<br>High corruption<br>Fragile<br>Illicit drug producing<br>Initially at peace<br>-.15 -.1 -.05 0 .05<br><!-- End of picture text --> _Notes:_ This figure shows heterogeneity in the effects of conflict exposure across a range of country-level characteristics. The estimating equation is the same as the baseline specification, augmented with an interaction term between conflict exposure and a single country characteristic at a time. Each bar represents the estimated coefficient on this interaction term, with horizontal lines indicating 95% confidence intervals. The top panel presents results for _Sales_ , while the bottom panel shows results for _Profits_ . All variables are defined in Table C1.

31
# **Tables**

— Table 1: Conflict exposure Sales, total costs, and profits

|Dependent variable:|Sa|les|Tota|l costs|Pr|ofts|
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
|Confict exposure|-0.167***|-0.171***|-0.122***|-0.136***|-0.0853|-0.0999|
||[0.0175]|[0.0178]|[0.0197]|[0.0194]|[0.0645]|[0.0682]|
|Size||0.654***||0.688***||0.635***|
|||[0.0241]||[0.0254]||[0.0814]|
|Age||0.0753**<br>[0.0329]||0.0651*<br>[0.0339]||0.178<br>[0.109]|
|Export||0.000848||0.00158***||-0.00123|
|||[0.000532]||[0.000574]||[0.00216]|
|Firm FE|yes|yes|yes|yes|yes|yes|
|Time FE|yes|yes|yes|yes|yes|yes|
|Adj R-squared|0.683|0.707|0.759|0.783|0.217|0.222|
|Sample|36,087|33,402|27,554|25,852|27,557|25,855|

_Notes:_ Fixed effects OLS estimates. _Sales_ is the logarithm of the firm’s total annual sales across all products and services. _Total costs_ is the logarithm of total production costs, defined as the sum of raw material and intermediate input costs, labor costs, and energy expenditures. _Profits_ is the inverse hyperbolic sine transformation of the difference between sales and total costs. All monetary values are expressed in constant 2000 USD. _Conflict exposure_ is the logarithm of (1+) the number of conflict 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. Robust standard errors in brackets. *, **, and *** denote statistical significance at the 10%, 5%, and 1% levels, respectively. All variables are defined in Table C1.

32
— Table 2: Mechanisms Raw materials and intermediate inputs, labor, and market competition

||Raw mat|erials and inter|mediate inpu|ts||L|abor||Informal|competition|
|---|---|---|---|---|---|---|---|---|---|---|
|Dependent variable:|Raw materials and<br>intermediate costs|Electricity<br>expenditure|Power<br>outages|Imported<br>inputs (%)|Labor<br>cost|Number<br>workers|Unskilled<br>workers (%)|Unit<br>wage|Informality<br>(dummy)|Informality<br>(intensity)|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|
|Confict exposure|-0.209***<br>[0.0309]|-0.204***<br>[0.0219]|0.0161***<br>[0.00478]|-1.151**<br>[0.471]|-0.186***<br>[0.0176]|-0.00426<br>[0.00431]|1.285***<br>[0.458]|-0.182***<br>[0.0176]|0.00862*<br>[0.00484]|0.0293**<br>[0.0146]|
|Firm controls|yes|yes|yes|yes|yes|yes|yes|yes|yes|yes|
|Firm FE|yes|yes|yes|yes|yes|yes|yes|yes|yes|yes|
|Time FE|yes|yes|yes|yes|yes|yes|yes|yes|yes|yes|
|Adj R-squared|0.720|0.669|0.243|0.495|0.720|0.927|0.221|0.579|0.207|0.269|
|Sample|15,742|28,275|33,256|17,071|30,899|33,072|15,920|30,601|31,976|31,976|

_Notes:_ Fixed effects OLS estimates. _Raw materials and intermediate costs_ is the total annual cost of raw materials and intermediate goods used in production (in log). _Electricity expenditure_ is the total annual cost of electricity (in log). _Power outages_ is a dummy that takes the value 1 if the firm experienced power outages during the last fiscal year. _Imported inputs (%)_ is the share of material inputs or supplies of foreign origin. _Labor costs_ is the total annual cost of labor, including wages, salaries, bonuses, and social security payments (in log). _Number of workers_ is the sum of full-time permanent employees and temporary workers, expressed in full-time equivalents (in log). Temporary workers are defined as “full-time seasonal or temporary employees,” referring to individuals employed for short periods (i.e., less than a year), without a guarantee of contract renewal, and working full-time hours. _Unskilled workers (%)_ is the share of workers in unskilled production jobs, whose tasks involve no specialized knowledge. As individual wage data at the firm level is not reported by the WBES, _Unit wage_ is computed as the log average wage, defined as the ratio of total labor costs to the total number of employees (in full-time equivalents). _Informality (dummy)_ is a dummy that takes the value 1 if the firm reports facing competition from informal firms. _Informality (intensity)_ measures the perceived severity of obstacles posed by informal competitors. _Conflict exposure_ is the logarithm of (1+) the number of conflict 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. Robust standard errors in brackets. *, **, and *** indicate statistical significance at the 10%, 5%, and 1% levels, respectively. Details on variable definitions are provided in Table C1.
— Table 3: Mechanisms Heterogeneity by initial conflict status

||Raw mat|erials and inter|mediate inpu|ts||L|abor||Informal|competition|
|---|---|---|---|---|---|---|---|---|---|---|
|Dependent variable:|Raw materials and<br>intermediate costs|Electricity<br>expenditure|Power<br>outages|Imported<br>inputs (%)|Labor<br>cost|Number<br>workers|Unskilled<br>workers (%)|Unit<br>wage|Informality<br>(dummy)|Informality<br>(intensity)|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|
|Confict exposure _×_ Initially in confict|-0.0468<br>[0.0378]|-0.0694***<br>[0.0261]|0.00650<br>[0.00624]|-1.580**<br>[0.640]|-0.00862<br>[0.0211]|-0.0138**<br>[0.00593]|0.205<br>[0.562]|0.0166<br>[0.0209]|-0.00310<br>[0.00701]|-0.000584<br>[0.0218]|
|Confict exposure _×_ Initially at peace|-0.394***<br>[0.0458]|-0.343***<br>[0.0336]|0.0245***<br>[0.00664]|-0.696<br>[0.618]|-0.351***<br>[0.0267]|0.00405<br>[0.00582]|2.741***<br>[0.727]|-0.363***<br>[0.0267]|0.0176***<br>[0.00617]|0.0523***<br>[0.0180]|
|Firm controls|yes|yes|yes|yes|yes|yes|yes|yes|yes|yes|
|Firm FE|yes|yes|yes|yes|yes|yes|yes|yes|yes|yes|
|Time FE|yes|yes|yes|yes|yes|yes|yes|yes|yes|yes|
|Adj R-squared|0.722|0.671|0.244|0.495|0.723|0.927|0.221|0.584|0.207|0.270|
|Sample|15,742|28,275|33,256|17,071|30,899|33,072|15,920|30,601|31,976|31,976|

_Notes:_ Fixed effects OLS estimates. Dependent variables are defined as in Table 2. _Initially at peace_ is a dummy equal to 1 for firms located in countries not experiencing major episodes of political violence at the time of the first survey wave. _Initially at conflict_ is a dummy equal to 1 for firms located in countries experiencing major episodes of political violence at the time of the first survey wave. The set of countries in the _Initially at conflict_ group is the complement of those in the _Initially at peace_ group. Robust standard errors in brackets. All variables are defined in Table C1. *, **, and *** indicate statistical significance at the 10%, 5%, and 1% levels, respectively.