<table><caption>Table OA3.10: Intensive margin: good produced in conflict districts</caption><thead><tr><th rowspan="2">Dep. var</th><th colspan="4">Labor-intensive product</th></tr><tr><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th></tr></thead><tbody><tr><td colspan="5"><strong>Panel A: (log-) events per capita</strong></td></tr><tr><td>Intensity - affected districts</td><td>0.002<sup>a</sup><br>(0.000)</td><td></td><td></td><td></td></tr><tr><td>Intensity - historically affected districts</td><td></td><td>0.002<sup>a</sup><br>(0.001)</td><td></td><td>0.003<sup>a</sup><br>(0.001)</td></tr><tr><td>Intensity - newly affected districts</td><td></td><td></td><td>0.001<sup>a</sup><br>(0.000)</td><td>0.002<sup>a</sup><br>(0.000)</td></tr><tr><td colspan="5"><strong>Panel B: (log-) events by area</strong></td></tr><tr><td>Intensity - affected districts</td><td>0.004<sup>a</sup><br>(0.001)</td><td></td><td></td><td></td></tr><tr><td>Intensity - historically affected districts</td><td></td><td>0.003<sup>a</sup><br>(0.001)</td><td></td><td>0.005<sup>a</sup><br>(0.001)</td></tr><tr><td>Intensity - newly affected districts</td><td></td><td></td><td>0.002<sup>a</sup><br>(0.001)</td><td>0.003<sup>a</sup><br>(0.001)</td></tr><tr><td colspan="5"><strong>Panel C: (log-) deaths per capita</strong></td></tr><tr><td>Intensity - affected districts</td><td>0.002<sup>a</sup><br>(0.000)</td><td></td><td></td><td></td></tr><tr><td>Intensity - historically affected districts</td><td></td><td>0.002<sup>a</sup><br>(0.001)</td><td></td><td>0.003<sup>a</sup><br>(0.001)</td></tr><tr><td>Intensity - newly affected districts</td><td></td><td></td><td>0.001<sup>a</sup><br>(0.000)</td><td>0.002<sup>a</sup><br>(0.000)</td></tr><tr><td colspan="5"><strong>Panel D: (log-) deaths by areas</strong></td></tr><tr><td>Intensity - affected districts</td><td>0.004<sup>a</sup><br>(0.001)</td><td></td><td></td><td></td></tr><tr><td>Intensity - historically affected districts</td><td></td><td>0.003<sup>a</sup><br>(0.001)</td><td></td><td>0.005<sup>a</sup><br>(0.001)</td></tr><tr><td>Intensity - newly affected districts</td><td></td><td></td><td>0.002<sup>a</sup><br>(0.001)</td><td>0.003<sup>a</sup><br>(0.001)</td></tr><tr><td>Sample mean</td><td>0.74</td><td>0.74</td><td>0.74</td><td>0.74</td></tr><tr><td>Observations</td><td>125939</td><td>125939</td><td>125939</td><td>125939</td></tr><tr><td>District level controls</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>State FE</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Product FE</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr></tbody></table>

Note: c significant at 10%; b at 5%; a at 1%. Robust standard errors in parenthesis. This table replicates Table 9 using the intensive margin of violence. The unit of observation is at the product-district level. We estimate the following regression: $Labor\ Intensive_{gds} = \beta_0 + \beta_1 Conflict\ intensity_{ds} + X'_{ds}\gamma + \theta_g + \theta_s + u_{pds}$. The dependent variable ($Labor\ Intensive_{gds}$) is a binary indicator that equals one if more than half of the producers of good g located in district d in state s utilize a labor-intensive technology, meaning their production involves higher labor costs than capital costs. $Conflict\ intensity_{ds}$ corresponds to the (log-) violent events per capita (Panel A), (log-) violent events per district area (Panel B), (log-) deaths per capita (Panel C), and (log-) deaths by district area (Panel D) for districts hosting Maoist violence. Columns 2 and 4 separate the intensity of violence in two distinct dummies that mark whether Maoist insurgency impacted the district before 2000 (historically affected districts), or only after (newly affected districts). $X'_{ds}$ stands for district level characteristics: population (Census of India, 2001), sectorial employment (Economic Census of India, 1998), average nighttime light (Henderson et al., 2011), and forest cover (% of the district cover by forest) (Dimiceli et al., 2015; Asher et al., 2021). $\theta_s$ are state fixed effects and $\theta_g$ are product fixed effects.
# OA4 The Impact of Conflict on Firms' Performance

## OA4.1 Empirical strategy

We carry out a reduced-form analysis in order to explore the impact of conflict on firms' performance. We focus on two main channels: (i) distortions experienced by firms located in a conflict-affected district; and (ii) the spread of this effect to other firms by way of the production network both in conflict and peaceful districts. We estimate following equation:

$$Y_{i,k(d,t)} = \beta \text{conflict}_{(d,t)} + \gamma_c \text{PNE}_{i(d,t)}^{\text{conflict districts}} + \gamma_p \text{PNE}_{i(d,t)}^{\text{peace districts}} + \mathbf{D}'_{i(d,t)} \alpha + \theta_k + \theta_d + \theta_{ct} + \varepsilon_{i,k(d,t)} \quad (\text{OA4.2})$$

where firm $i$ produces good $k$ in year $t$ and is located in district $d$ (in state $c$).<fnref n="2" /> The dependent variable $Y_{i,k(d,t)}$ represents the firm's performance measured at the product level $k$ (5-digit) by either (log) price or (log) quantity.<fnref n="3" /> We estimate the direct effect of conflict-induced distortions on firms located in a conflict-affected district by defining the variable conflict$_{(d,t)}$ which equals 1 if firm $i$ is located in district $d$ that is affected by conflict at time $t$.<fnref n="4" /> Variables PNE$_{i(d,t)}^{\text{conflict districts}}$ and PNE$_{i(d,t)}^{\text{peace districts}}$ capture the effect of conflict by way of the production network on firms located in conflict-affected and peaceful districts, respectively. Based on the location of each firm, we are able to distinguish between suppliers located in conflict-affected districts and the rest. We can therefore calculate a yearly measure of exposure to conflict by way of the production network (exposure$_{it}$) for each firm $i$. It is worth mentioning that this index (as well as the index of supplier importance) is calculated for each cross-section in the dataset separately. Thus, each firm's conflict exposure by way of the production network depends on: (i) whether any of the potential suppliers $j$ of its inputs $k \in K$ are located in a conflict-affected district; (ii) the importance of a potential supplier $j$ of input $k$, i.e., $\rho_{jit(k)}$; and (iii) the share of each input $k$ used by firm $i$ ($\omega_{kit}$). We therefore construct exposure$_{it}$ as follows:

$$exposure_{it} = \sum_{k \in K} \left( \omega_{kit} * \sum_{j \in J} (\rho_{jit(k)} \times conflict_{jt}) \right) \quad (\text{OA4.3})$$

where conflict$_{jt}$ is a dummy that equals 1 if firm $j$ is located in a conflict-affected district at year $t$. Since a firm $i$ can be located in a conflict-affected district and be affected by conflict by way of the production network (when a potential supplier of firm $i$ is also located in a conflict-affected district), we disentangle the production network exposure between firms located in conflict-affected districts PNE$_{i(d,t)}^{\text{conflict districts}}$ and firms in peaceful districts PNE$_{i(d,t)}^{\text{peace districts}}$.<fnref n="5" /> In the final sample, there are 8,555 distinct firms that were directly exposed to conflict at least once (about 8% of the sample), 5,266 firms that were exposed at least once by way of the production network in conflict-affected districts, and another 84,688 in peaceful districts. Another 31,924 firms were not exposed through either channel.

We control for time-varying firm-level characteristics $\mathbf{D}'_{i(d,t)}$ which include wage, interest rates, input prices and (time-varying) input bundle fixed effects. Crucially, the richness of our dataset allows for the inclusion

<footnote n="2">²We omit the subscript $c$ because being located in a certain district $d$ means being located in a certain state $c$. We use the subscript $c$ to denote the state × year fixed effects ($\theta_{ct}$).</footnote>

<footnote n="3">³The units of measurement for quantities encompass a diverse range, spanning tons, kilowatt-hours (kWh), bags, item counts, and more. Each product type is consistently associated with a specific unit at the 5-digit product level. The inclusion of a product fixed effect and the logarithmic transformation on output quantities facilitates the interpretation of the direct conflict exposure coefficient as a percentage change in quantities linked to the presence of conflict. This approach enables a comprehensive understanding of the impact conflict has on quantities sold.</footnote>

<footnote n="4">⁴As discussed previously, the data do not provide firm-specific information on the exposure to conflict, and therefore we assume that the effect of conflict on firms located in a conflict-affected district is homogeneous (and consequently, we omit the subscript $i$).</footnote>

<footnote n="5">⁵It is crucial to acknowledge the challenge of disentangling direct exposure to conflict and production network exposure for conflict-affected districts. To address this issue, we have excluded production network exposure within the same district. For instance, if firm A is directly exposed to conflict but also by way of production network, its production network exposure will only account for supplier firms in a different district than firm A.</footnote>
# OA4 The Impact of Conflict on Firms' Performance

## OA4.1 Empirical strategy

We carry out a reduced-form analysis in order to explore the impact of conflict on firms' performance. We focus on two main channels: (i) distortions experienced by firms located in a conflict-affected district; and (ii) the spread of this effect to other firms by way of the production network both in conflict and peaceful districts. We estimate following equation:

$$Y_{i,k(d,t)} = \beta \text{conflict}_{(d,t)} + \gamma_c \text{PNE}_{i(d,t)}^{\text{conflict districts}} + \gamma_p \text{PNE}_{i(d,t)}^{\text{peace districts}} + \mathbf{D}'_{i(d,t)} \alpha + \theta_k + \theta_d + \theta_{ct} + \varepsilon_{i,k(d,t)} \quad (\text{OA4.2})$$

where firm $i$ produces good $k$ in year $t$ and is located in district $d$ (in state $c$).<fnref n="2" /> The dependent variable $Y_{i,k(d,t)}$ represents the firm's performance measured at the product level $k$ (5-digit) by either (log) price or (log) quantity.<fnref n="3" /> We estimate the direct effect of conflict-induced distortions on firms located in a conflict-affected district by defining the variable conflict$_{(d,t)}$ which equals 1 if firm $i$ is located in district $d$ that is affected by conflict at time $t$.<fnref n="4" /> Variables PNE$_{i(d,t)}^{\text{conflict districts}}$ and PNE$_{i(d,t)}^{\text{peace districts}}$ capture the effect of conflict by way of the production network on firms located in conflict-affected and peaceful districts, respectively. Based on the location of each firm, we are able to distinguish between suppliers located in conflict-affected districts and the rest. We can therefore calculate a yearly measure of exposure to conflict by way of the production network (exposure$_{it}$) for each firm $i$. It is worth mentioning that this index (as well as the index of supplier importance) is calculated for each cross-section in the dataset separately. Thus, each firm's conflict exposure by way of the production network depends on: (i) whether any of the potential suppliers $j$ of its inputs $k \in K$ are located in a conflict-affected district; (ii) the importance of a potential supplier $j$ of input $k$, i.e., $\rho_{jit(k)}$; and (iii) the share of each input $k$ used by firm $i$ ($\omega_{kit}$). We therefore construct exposure$_{it}$ as follows:

$$exposure_{it} = \sum_{k \in K} \left( \omega_{kit} * \sum_{j \in J} (\rho_{jit(k)} \times conflict_{jt}) \right) \quad (\text{OA4.3})$$

where conflict$_{jt}$ is a dummy that equals 1 if firm $j$ is located in a conflict-affected district at year $t$. Since a firm $i$ can be located in a conflict-affected district and be affected by conflict by way of the production network (when a potential supplier of firm $i$ is also located in a conflict-affected district), we disentangle the production network exposure between firms located in conflict-affected districts PNE$_{i(d,t)}^{\text{conflict districts}}$ and firms in peaceful districts PNE$_{i(d,t)}^{\text{peace districts}}$.<fnref n="5" /> In the final sample, there are 8,555 distinct firms that were directly exposed to conflict at least once (about 8% of the sample), 5,266 firms that were exposed at least once by way of the production network in conflict-affected districts, and another 84,688 in peaceful districts. Another 31,924 firms were not exposed through either channel.

We control for time-varying firm-level characteristics $\mathbf{D}'_{i(d,t)}$ which include wage, interest rates, input prices and (time-varying) input bundle fixed effects. Crucially, the richness of our dataset allows for the inclusion

<footnote n="2">²We omit the subscript $c$ because being located in a certain district $d$ means being located in a certain state $c$. We use the subscript $c$ to denote the state × year fixed effects ($\theta_{ct}$).</footnote>

<footnote n="3">³The units of measurement for quantities encompass a diverse range, spanning tons, kilowatt-hours (kWh), bags, item counts, and more. Each product type is consistently associated with a specific unit at the 5-digit product level. The inclusion of a product fixed effect and the logarithmic transformation on output quantities facilitates the interpretation of the direct conflict exposure coefficient as a percentage change in quantities linked to the presence of conflict. This approach enables a comprehensive understanding of the impact conflict has on quantities sold.</footnote>

<footnote n="4">⁴As discussed previously, the data do not provide firm-specific information on the exposure to conflict, and therefore we assume that the effect of conflict on firms located in a conflict-affected district is homogeneous (and consequently, we omit the subscript $i$).</footnote>

<footnote n="5">⁵It is crucial to acknowledge the challenge of disentangling direct exposure to conflict and production network exposure for conflict-affected districts. To address this issue, we have excluded production network exposure within the same district. For instance, if firm A is directly exposed to conflict but also by way of production network, its production network exposure will only account for supplier firms in a different district than firm A.</footnote>
of fixed effects that account for observed and unobserved heterogeneity in product ($\theta_k$), district ($\theta_d$) and state × year ($\theta_{ct}$).<fnref n="6" /> Finally, standard errors are clustered at district × year level. Summary statistics on the main variables are detailed in Table OA4.11.

Identification hinges on the assumption that, given product, state × year, and district fixed-effects, conflict in the district where the firm is located is uncorrelated with any underlying determinant of product-level outcomes. If this assumption proves valid, it allows us to pinpoint the causal influence of conflict-related events on the outcomes of firm-product pairs. Yet, various endogeneity issues may contaminate the estimates. First, firm performance may help to determine whether Maoist groups are present (such that the location of conflict will not be random). Second, a firm's decision whether to locate in a particular state or to leave a particular state may be affected by the presence of insurgent groups (Camacho et al. (2013) with respect to Colombia, Blumenstock et al. (2020) with respect to Afghanistan). Thus, while our findings are consistent with the idea that conflict has a causal impact on firm performance, the data do not allow us to rule out all potential confounders. Nevertheless, the endogeneity issues are further discussed in the sensitivity checks.

<table><caption>Table OA4.11: Descriptive Statistics</caption><thead><tr><th></th><th>Obs.</th><th>Mean</th><th>S.D.</th><th>Min</th><th>Max</th></tr></thead><tbody><tr><td colspan="6"><strong>Product and Firm characteristics</strong></td></tr><tr><td>price (log)</td><td>428,410</td><td>7.45</td><td>3.11</td><td>-5</td><td>22</td></tr><tr><td>quantity (log)</td><td>428,410</td><td>8.83</td><td>3.87</td><td>-6</td><td>26</td></tr><tr><td>labor cost (log)</td><td>428,410</td><td>14.73</td><td>2.02</td><td>6</td><td>23</td></tr><tr><td>interest rates (log)</td><td>428,410</td><td>13.91</td><td>2.51</td><td>0</td><td>23</td></tr><tr><td>intermediate inputs price (log)</td><td>428,410</td><td>9.25</td><td>1.90</td><td>-2</td><td>20</td></tr><tr><td>units (log)</td><td>428,413</td><td>0.06</td><td>0.25</td><td>0</td><td>4</td></tr><tr><td>employees (#)</td><td>428,392</td><td>257.32</td><td>937.42</td><td>0</td><td>41,774</td></tr><tr><td>imports (0/1)</td><td>428,410</td><td>0.23</td><td>0.42</td><td>0</td><td>1</td></tr><tr><td>input bundle change</td><td>261,471</td><td>0.33</td><td>0.47</td><td>0</td><td>1</td></tr><tr><td>market share, state-level</td><td>267,038</td><td>22.57</td><td>34.47</td><td>0</td><td>100</td></tr><tr><td>market share, country-level</td><td>267,038</td><td>2.60</td><td>9.40</td><td>0</td><td>100</td></tr><tr><td>market share of competitors, conflict districts</td><td>267,038</td><td>7.57</td><td>12.43</td><td>0</td><td>100</td></tr><tr><td>% of competitor firms, conflict districts</td><td>267,038</td><td>8.19</td><td>8.66</td><td>0</td><td>97</td></tr><tr><td colspan="6"><strong>Conflict-related measures</strong></td></tr><tr><td>direct exposure to conflict</td><td>428,410</td><td>0.06</td><td>0.24</td><td>0</td><td>1</td></tr><tr><td>production network exposure, conflict districts</td><td>428,410</td><td>0.00</td><td>0.03</td><td>0</td><td>1</td></tr><tr><td>production network exposure, peaceful districts</td><td>428,410</td><td>0.06</td><td>0.10</td><td>0</td><td>1</td></tr><tr><td>distance to conflict (log)</td><td>428,410</td><td>5.82</td><td>0.96</td><td>1</td><td>7</td></tr></tbody></table>

Source: authors' computation from ASI and UCDP data. See main text for more details. Note that the variable related to exposure by way of the production network vary to the thousandth decimal units.

## OA4.2 Results

**Baseline estimates.** Table OA4.12 displays the main estimates for two dependent variables: the (log) price of each good produced by firm *i* (columns 1 and 2) and the (log) quantity of output (columns 3 and 4). Following the literature, we start by comparing firms located in conflict-affected districts to firms that are not. Firms in conflict-affected districts display higher prices (column 1) and lower quantities (column 3) but the effects are imprecisely estimated. The implicit reference group composed by firms outside a conflict-affected district is a poor counterfactual because it violates the SUTVA. Firms located in peaceful districts might be exposed to conflict by way of the production network, and thus, their output price and quantity depend on conflict incidence whenever they have suppliers located in a conflict-affected district. We tackle this issue by estimating the performance differential between firms located in conflict-affected districts and firms that are not, which are nonetheless exposed to conflict by way of the production network. In this case the implicit

<footnote n="6">⁶The product fixed effects are defined at the 3-digit level.</footnote>
of fixed effects that account for observed and unobserved heterogeneity in product ($\theta_k$), district ($\theta_d$) and state × year ($\theta_{ct}$).<fnref n="6" /> Finally, standard errors are clustered at district × year level. Summary statistics on the main variables are detailed in Table OA4.11.

Identification hinges on the assumption that, given product, state × year, and district fixed-effects, conflict in the district where the firm is located is uncorrelated with any underlying determinant of product-level outcomes. If this assumption proves valid, it allows us to pinpoint the causal influence of conflict-related events on the outcomes of firm-product pairs. Yet, various endogeneity issues may contaminate the estimates. First, firm performance may help to determine whether Maoist groups are present (such that the location of conflict will not be random). Second, a firm's decision whether to locate in a particular state or to leave a particular state may be affected by the presence of insurgent groups (Camacho et al. (2013) with respect to Colombia, Blumenstock et al. (2020) with respect to Afghanistan). Thus, while our findings are consistent with the idea that conflict has a causal impact on firm performance, the data do not allow us to rule out all potential confounders. Nevertheless, the endogeneity issues are further discussed in the sensitivity checks.

<table><caption>Table OA4.11: Descriptive Statistics</caption><thead><tr><th></th><th>Obs.</th><th>Mean</th><th>S.D.</th><th>Min</th><th>Max</th></tr></thead><tbody><tr><td><strong>Product and Firm characteristics</strong></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>price (log)</td><td>428,410</td><td>7.45</td><td>3.11</td><td>-5</td><td>22</td></tr><tr><td>quantity (log)</td><td>428,410</td><td>8.83</td><td>3.87</td><td>-6</td><td>26</td></tr><tr><td>labor cost (log)</td><td>428,410</td><td>14.73</td><td>2.02</td><td>6</td><td>23</td></tr><tr><td>interest rates (log)</td><td>428,410</td><td>13.91</td><td>2.51</td><td>0</td><td>23</td></tr><tr><td>intermediate inputs price (log)</td><td>428,410</td><td>9.25</td><td>1.90</td><td>-2</td><td>20</td></tr><tr><td>units (log)</td><td>428,413</td><td>0.06</td><td>0.25</td><td>0</td><td>4</td></tr><tr><td>employees (#)</td><td>428,392</td><td>257.32</td><td>937.42</td><td>0</td><td>41,774</td></tr><tr><td>imports (0/1)</td><td>428,410</td><td>0.23</td><td>0.42</td><td>0</td><td>1</td></tr><tr><td>input bundle change</td><td>261,471</td><td>0.33</td><td>0.47</td><td>0</td><td>1</td></tr><tr><td>market share, state-level</td><td>267,038</td><td>22.57</td><td>34.47</td><td>0</td><td>100</td></tr><tr><td>market share, country-level</td><td>267,038</td><td>2.60</td><td>9.40</td><td>0</td><td>100</td></tr><tr><td>market share of competitors, conflict districts</td><td>267,038</td><td>7.57</td><td>12.43</td><td>0</td><td>100</td></tr><tr><td>% of competitor firms, conflict districts</td><td>267,038</td><td>8.19</td><td>8.66</td><td>0</td><td>97</td></tr><tr><td><strong>Conflict-related measures</strong></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>direct exposure to conflict</td><td>428,410</td><td>0.06</td><td>0.24</td><td>0</td><td>1</td></tr><tr><td>production network exposure, conflict districts</td><td>428,410</td><td>0.00</td><td>0.03</td><td>0</td><td>1</td></tr><tr><td>production network exposure, peaceful districts</td><td>428,410</td><td>0.06</td><td>0.10</td><td>0</td><td>1</td></tr><tr><td>distance to conflict (log)</td><td>428,410</td><td>5.82</td><td>0.96</td><td>1</td><td>7</td></tr></tbody></table>

Source: authors' computation from ASI and UCDP data. See main text for more details. Note that the variable related to exposure by way of the production network vary to the thousandth decimal units.

## OA4.2 Results

**Baseline estimates.** Table OA4.12 displays the main estimates for two dependent variables: the (log) price of each good produced by firm *i* (columns 1 and 2) and the (log) quantity of output (columns 3 and 4). Following the literature, we start by comparing firms located in conflict-affected districts to firms that are not. Firms in conflict-affected districts display higher prices (column 1) and lower quantities (column 3) but the effects are imprecisely estimated. The implicit reference group composed by firms outside a conflict-affected district is a poor counterfactual because it violates the SUTVA. Firms located in peaceful districts might be exposed to conflict by way of the production network, and thus, their output price and quantity depend on conflict incidence whenever they have suppliers located in a conflict-affected district. We tackle this issue by estimating the performance differential between firms located in conflict-affected districts and firms that are not, which are nonetheless exposed to conflict by way of the production network. In this case the implicit

<footnote n="6">⁶The product fixed effects are defined at the 3-digit level.</footnote>
reference group consists of firms that are not located in a conflict-affected district nor are they exposed to conflict by way of the production network.<fnref n="7" /> The impact of being located in a conflict-affected district on firm-level price appears to be positive and statistically significant (column 2). The effect of conflict increases prices on average by approximately 8.7%. For these firms, exposure by way of the production network is not significantly different from 0. Interestingly, for firms that are located in a peaceful district but are exposed to conflict by way of the production network, the estimated coefficient is positive and significant. In terms of magnitude, a 10% increase in exposure to conflict by way of the production network (equivalent to a 1 s.d.) leads to a 3.3% increase in price.<fnref n="8" /> Both firms located in conflict-affected districts and firms that are not but are exposed to conflict by way of the production network produce a significantly lower quantity of output relative to the reference group (column 4). The magnitudes are economically significant: being located in a conflict-affected district reduces the quantity produced by 8.9% while a 10% increase in exposure to conflict by way of the production network leads to a 4% decrease in output.

Overall these results uncover two important facts. First, looking only at conflict affected districts leads to a downward biased estimate of the true cost of conflict by virtue of picking an inappropriate control group that includes firms that are indirectly exposed to conflict and, thus, violates the SUTVA. Second, we show that the production network has a key role in the propagation of the effects of conflict towards peaceful territories. The spillover effects of conflict that we uncover in our reduced-form estimates is confirmed in a paper that is particularly related to ours. Korovkin et al. (2023) provide compelling evidence of how the 2014 Russia-Ukraine conflict disrupted firm activity and trade to and from conflict areas in Ukraine. Their research not only shows adverse impacts on firms in close proximity to violence but also on businesses indirectly connected to conflict regions through a trade network. These results closely parallel our own, positioning our paper as a valuable complement to their estimates on firm performance.

**Historical versus new conflict-affected districts.** Until the early 2000s, Maoist activities were characterized by sporadic violent events. With the switch toward a more organized and centralized armed group, the Maoist activities intensified and spread out of their historical settlements (right panel of Figure 1). We rely on the 2004 merger of the largest Maoist factions and split the districts of conflict into two groups: *historical* conflict-affected districts and *new* conflict-affected districts. The former group represents districts with at least 2 years of conflict before 2004, while the latter includes the remaining districts, which have been impacted by conflict more recently. We find that the effect on price and quantity is mainly driven by firms located in new districts (Table OA4.13). This result suggests that those firms not used to conflict find harder to cope with violence. Consequently, they bear higher costs than firms located in historical districts.

**Intensity of violence.** Indian districts vary in population size. We make use of the number of violent events weighted by district population as an alternative measure of direct exposure to conflict (Table OA4.14). While imprecisely estimated, our results suggest that firms located in conflict-affected districts with more violent events per capita display a larger price and a lower quantity (columns 1 and 4). Next, for a given degree of violence intensity, we explore whether firms located in new conflict-affected districts react differently than firms located in historical conflict-affected districts (columns 2 and 5). Again, while imprecisely estimated, our findings suggest that firms located in new districts seem to suffer more than those located in historical districts. Last, we split the measure of conflict intensity into low and high intensity (columns 3 and 6). The cutoff puts into perspective the highest tale of the distribution of conflict intensity (above the 90th percentile) compared to the lower tale. In new districts, the average effect of conflict in districts affected by high conflict intensity increases prices by 28.7% and decreases quantity produced by 23.9%. Conversely, in historical districts, the effect of high conflict intensity is not statistically different from 0. Finally, in districts with low conflict intensity firms bear the same cost of conflict regardless of whether

<footnote n="7">⁷More precisely, firms belonging to this reference group may be exposed to conflict from the 2nd to the nth degree of the production network. Specifically, firm i is second-degree exposed to conflict if one of its supplier, firm j, is located in a peaceful district and its exposure index defined in Equation OA4.3 is greater than zero.</footnote>

<footnote n="8">⁸In a previous version of the paper (Couttenier et al., 2022), we report slightly different magnitudes. The use of TFP in the previous version explains the difference. We removed TPF as a covariate to gain more firms in our final sample due to missing information necessary in the calculation of the TFP. Yet, magnitudes and statistical significance remain similar.</footnote>
reference group consists of firms that are not located in a conflict-affected district nor are they exposed to conflict by way of the production network.<fnref n="7" /> The impact of being located in a conflict-affected district on firm-level price appears to be positive and statistically significant (column 2). The effect of conflict increases prices on average by approximately 8.7%. For these firms, exposure by way of the production network is not significantly different from 0. Interestingly, for firms that are located in a peaceful district but are exposed to conflict by way of the production network, the estimated coefficient is positive and significant. In terms of magnitude, a 10% increase in exposure to conflict by way of the production network (equivalent to a 1 s.d.) leads to a 3.3% increase in price.<fnref n="8" /> Both firms located in conflict-affected districts and firms that are not but are exposed to conflict by way of the production network produce a significantly lower quantity of output relative to the reference group (column 4). The magnitudes are economically significant: being located in a conflict-affected district reduces the quantity produced by 8.9% while a 10% increase in exposure to conflict by way of the production network leads to a 4% decrease in output.

Overall these results uncover two important facts. First, looking only at conflict affected districts leads to a downward biased estimate of the true cost of conflict by virtue of picking an inappropriate control group that includes firms that are indirectly exposed to conflict and, thus, violates the SUTVA. Second, we show that the production network has a key role in the propagation of the effects of conflict towards peaceful territories. The spillover effects of conflict that we uncover in our reduced-form estimates is confirmed in a paper that is particularly related to ours. Korovkin et al. (2023) provide compelling evidence of how the 2014 Russia-Ukraine conflict disrupted firm activity and trade to and from conflict areas in Ukraine. Their research not only shows adverse impacts on firms in close proximity to violence but also on businesses indirectly connected to conflict regions through a trade network. These results closely parallel our own, positioning our paper as a valuable complement to their estimates on firm performance.

**Historical versus new conflict-affected districts.** Until the early 2000s, Maoist activities were characterized by sporadic violent events. With the switch toward a more organized and centralized armed group, the Maoist activities intensified and spread out of their historical settlements (right panel of Figure 1). We rely on the 2004 merger of the largest Maoist factions and split the districts of conflict into two groups: *historical* conflict-affected districts and *new* conflict-affected districts. The former group represents districts with at least 2 years of conflict before 2004, while the latter includes the remaining districts, which have been impacted by conflict more recently. We find that the effect on price and quantity is mainly driven by firms located in new districts (Table OA4.13). This result suggests that those firms not used to conflict find harder to cope with violence. Consequently, they bear higher costs than firms located in historical districts.

**Intensity of violence.** Indian districts vary in population size. We make use of the number of violent events weighted by district population as an alternative measure of direct exposure to conflict (Table OA4.14). While imprecisely estimated, our results suggest that firms located in conflict-affected districts with more violent events per capita display a larger price and a lower quantity (columns 1 and 4). Next, for a given degree of violence intensity, we explore whether firms located in new conflict-affected districts react differently than firms located in historical conflict-affected districts (columns 2 and 5). Again, while imprecisely estimated, our findings suggest that firms located in new districts seem to suffer more than those located in historical districts. Last, we split the measure of conflict intensity into low and high intensity (columns 3 and 6). The cutoff puts into perspective the highest tale of the distribution of conflict intensity (above the 90th percentile) compared to the lower tale. In new districts, the average effect of conflict in districts affected by high conflict intensity increases prices by 28.7% and decreases quantity produced by 23.9%. Conversely, in historical districts, the effect of high conflict intensity is not statistically different from 0. Finally, in districts with low conflict intensity firms bear the same cost of conflict regardless of whether

<footnote n="7">⁷More precisely, firms belonging to this reference group may be exposed to conflict from the 2nd to the nth degree of the production network. Specifically, firm i is second-degree exposed to conflict if one of its supplier, firm j, is located in a peaceful district and its exposure index defined in Equation OA4.3 is greater than zero.</footnote>

<footnote n="8">⁸In a previous version of the paper (Couttenier et al., 2022), we report slightly different magnitudes. The use of TFP in the previous version explains the difference. We removed TPF as a covariate to gain more firms in our final sample due to missing information necessary in the calculation of the TFP. Yet, magnitudes and statistical significance remain similar.</footnote>
<table><caption>Table OA4.12: Baseline results</caption><thead><tr><th rowspan="2">Dep. var.</th><th colspan="2">Price</th><th colspan="2">Quantity</th></tr><tr><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th></tr></thead><tbody><tr><td>direct exposure to conflict</td><td>0.044<br>(0.030)</td><td>0.087<sup>b</sup><br>(0.034)</td><td>-0.060<sup>c</sup><br>(0.037)</td><td>-0.109<sup>a</sup><br>(0.040)</td></tr><tr><td>production network exposure, conflict districts</td><td></td><td>0.046<br>(0.154)</td><td></td><td>-0.132<br>(0.201)</td></tr><tr><td>production network exposure, peaceful districts</td><td></td><td>0.330<sup>a</sup><br>(0.054)</td><td></td><td>-0.417<sup>a</sup><br>(0.069)</td></tr><tr><td>Firm controls</td><td></td><td></td><td>Yes</td><td></td></tr><tr><td>Product FE</td><td></td><td></td><td>Yes</td><td></td></tr><tr><td>District FE</td><td></td><td></td><td>Yes</td><td></td></tr><tr><td>State&times;year FE</td><td></td><td></td><td>Yes</td><td></td></tr><tr><td>Observations</td><td>428,410</td><td>428,410</td><td>428,410</td><td>428,410</td></tr><tr><td>R-squared</td><td>0.468</td><td>0.468</td><td>0.537</td><td>0.537</td></tr></tbody></table>

Note: <sup>c</sup> significant at 10%; <sup>b</sup> at 5%; <sup>a</sup> at 1%. Standard errors are clustered at the district&times;year level. The dependent variables are the (log) price and the (log) quantity of the firm's production defined at the 5-digit level of the product classification code. *Direct exposure to conflict* is an indicator that equals 1 if the firm is located in a conflict-affected district and *Production network exposure* is a firm-level continuous measure calculated according to Equation OA4.3 and separated into conflict versus peaceful districts. Firm controls include (log) input prices (intermediate inputs, wages, and interest rate), and time-varying input bundle fixed effects (defined at the 2-digit level). Product (defined at the 4-digit level), district, and state&times;year fixed effects are included. See main text for further details and Table OA4.11 for descriptive statistics.

<table><caption>Table OA4.13: Historical *versus* new conflict-affected districts - 2004 merger of Maoist groups</caption><thead><tr><th rowspan="2">Dep. var.</th><th>Price</th><th>Quantity</th></tr><tr><th>(1)</th><th>(2)</th></tr></thead><tbody><tr><td>direct exposure to conflict, new conflict districts</td><td>0.112<sup>a</sup><br>(0.043)</td><td>-0.153<sup>a</sup><br>(0.057)</td></tr><tr><td>direct exposure to conflict, historical conflict districts</td><td>0.068<br>(0.046)</td><td>-0.076<br>(0.052)</td></tr><tr><td>production network exposure, conflict districts</td><td>0.024<br>(0.153)</td><td>-0.092<br>(0.202)</td></tr><tr><td>production network exposure, peaceful districts</td><td>0.330<sup>a</sup><br>(0.054)</td><td>-0.418<sup>a</sup><br>(0.069)</td></tr><tr><td>Firm controls</td><td></td><td>Yes</td></tr><tr><td>Product FE</td><td></td><td>Yes</td></tr><tr><td>District FE</td><td></td><td>Yes</td></tr><tr><td>State&times;year FE</td><td></td><td>Yes</td></tr><tr><td>Observations</td><td>428,410</td><td>428,410</td></tr><tr><td>R-squared</td><td>0.468</td><td>0.537</td></tr></tbody></table>

Note: <sup>c</sup> significant at 10%; <sup>b</sup> at 5%; <sup>a</sup> at 1%. Standard errors are clustered at the district&times;year level. *Historical* conflict-affected districts are defined as districts affected by conflict for at least 2 years prior to 2004 – the merger of Maoist groups – while the remaining districts are classified as *new conflict-affected districts*. *Production network exposure* is a firm-level continuous measure calculated according to Equation OA4.3 and separated into conflict versus peaceful districts. Firm controls include (log) input prices (intermediate inputs, wages, and interest rate), and time-varying input bundle fixed effects (defined at the 2-digit level). Product (defined at the 4-digit level), district, and state&times;year fixed effects are included. See main text for further details and Table OA4.11 for descriptive statistics.

they are located in a new or in a historical district.
<table><caption>Table OA4.12: Baseline results</caption><thead><tr><th rowspan="2">Dep. var.</th><th colspan="2">Price</th><th colspan="2">Quantity</th></tr><tr><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th></tr></thead><tbody><tr><td>direct exposure to conflict</td><td>0.044<br>(0.030)</td><td>0.087<sup>b</sup><br>(0.034)</td><td>-0.060<sup>c</sup><br>(0.037)</td><td>-0.109<sup>a</sup><br>(0.040)</td></tr><tr><td>production network exposure, conflict districts</td><td></td><td>0.046<br>(0.154)</td><td></td><td>-0.132<br>(0.201)</td></tr><tr><td>production network exposure, peaceful districts</td><td></td><td>0.330<sup>a</sup><br>(0.054)</td><td></td><td>-0.417<sup>a</sup><br>(0.069)</td></tr><tr><td>Firm controls</td><td></td><td></td><td>Yes</td><td></td></tr><tr><td>Product FE</td><td></td><td></td><td>Yes</td><td></td></tr><tr><td>District FE</td><td></td><td></td><td>Yes</td><td></td></tr><tr><td>State&times;year FE</td><td></td><td></td><td>Yes</td><td></td></tr><tr><td>Observations</td><td>428,410</td><td>428,410</td><td>428,410</td><td>428,410</td></tr><tr><td>R-squared</td><td>0.468</td><td>0.468</td><td>0.537</td><td>0.537</td></tr></tbody></table>

Note: <sup>c</sup> significant at 10%; <sup>b</sup> at 5%; <sup>a</sup> at 1%. Standard errors are clustered at the district&times;year level. The dependent variables are the (log) price and the (log) quantity of the firm's production defined at the 5-digit level of the product classification code. *Direct exposure to conflict* is an indicator that equals 1 if the firm is located in a conflict-affected district and *Production network exposure* is a firm-level continuous measure calculated according to Equation OA4.3 and separated into conflict versus peaceful districts. Firm controls include (log) input prices (intermediate inputs, wages, and interest rate), and time-varying input bundle fixed effects (defined at the 2-digit level). Product (defined at the 4-digit level), district, and state&times;year fixed effects are included. See main text for further details and Table OA4.11 for descriptive statistics.

<table><caption>Table OA4.13: Historical *versus* new conflict-affected districts - 2004 merger of Maoist groups</caption><thead><tr><th>Dep. var.</th><th>Price<br>(1)</th><th>Quantity<br>(2)</th></tr></thead><tbody><tr><td>direct exposure to conflict, new conflict districts</td><td>0.112<sup>a</sup><br>(0.043)</td><td>-0.153<sup>a</sup><br>(0.057)</td></tr><tr><td>direct exposure to conflict, historical conflict districts</td><td>0.068<br>(0.046)</td><td>-0.076<br>(0.052)</td></tr><tr><td>production network exposure, conflict districts</td><td>0.024<br>(0.153)</td><td>-0.092<br>(0.202)</td></tr><tr><td>production network exposure, peaceful districts</td><td>0.330<sup>a</sup><br>(0.054)</td><td>-0.418<sup>a</sup><br>(0.069)</td></tr><tr><td>Firm controls</td><td></td><td>Yes</td></tr><tr><td>Product FE</td><td></td><td>Yes</td></tr><tr><td>District FE</td><td></td><td>Yes</td></tr><tr><td>State&times;year FE</td><td></td><td>Yes</td></tr><tr><td>Observations</td><td>428,410</td><td>428,410</td></tr><tr><td>R-squared</td><td>0.468</td><td>0.537</td></tr></tbody></table>

Note: <sup>c</sup> significant at 10%; <sup>b</sup> at 5%; <sup>a</sup> at 1%. Standard errors are clustered at the district&times;year level. *Historical* conflict-affected districts are defined as districts affected by conflict for at least 2 years prior to 2004 – the merger of Maoist groups – while the remaining districts are classified as *new* conflict-affected districts. *Production network exposure* is a firm-level continuous measure calculated according to Equation OA4.3 and separated into conflict versus peaceful districts. Firm controls include (log) input prices (intermediate inputs, wages, and interest rate), and time-varying input bundle fixed effects (defined at the 2-digit level). Product (defined at the 4-digit level), district, and state&times;year fixed effects are included. See main text for further details and Table OA4.11 for descriptive statistics.

they are located in a new or in a historical district.
<table><caption>Table OA4.14: Intensity of violence</caption><thead><tr><th>Dep. var.</th><th colspan="3">Price</th><th colspan="3">Quantity</th></tr><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th><th>(6)</th></tr></thead><tbody><tr><td>direct exposure to conflict, # events / pop.</td><td>-0.123<br>(1.293)</td><td></td><td></td><td>-0.101<br>(1.403)</td><td></td><td></td></tr><tr><td>× new conflict districts</td><td></td><td>1.652<br>(1.556)</td><td></td><td></td><td>-3.176<sup>b</sup><br>(1.375)</td><td></td></tr><tr><td>× historical conflict districts</td><td></td><td>-1.462<br>(1.456)</td><td></td><td></td><td>2.219<br>(1.707)</td><td></td></tr><tr><td>direct exposure, high intensity (&gt; 90<sup>th</sup>perc.)</td><td></td><td></td><td>0.287<sup>b</sup></td><td></td><td></td><td>-0.239<sup>c</sup></td></tr><tr><td>× new conflict districts</td><td></td><td></td><td>(0.126)</td><td></td><td></td><td>(0.135)</td></tr><tr><td>direct exposure, high intensity (&gt; 90<sup>th</sup>perc.)</td><td></td><td></td><td>-0.108</td><td></td><td></td><td>0.098</td></tr><tr><td>× historical conflict districts</td><td></td><td></td><td>(0.096)</td><td></td><td></td><td>(0.106)</td></tr><tr><td>direct exposure, low intensity (&lt; 90<sup>th</sup>perc.)</td><td></td><td></td><td>0.104<sup>b</sup></td><td></td><td></td><td>-0.148<sup>a</sup></td></tr><tr><td>× new conflict districts</td><td></td><td></td><td>(0.043)</td><td></td><td></td><td>(0.057)</td></tr><tr><td>direct exposure, low intensity (&lt; 90<sup>th</sup>perc.)</td><td></td><td></td><td>0.077<sup>c</sup></td><td></td><td></td><td>-0.084</td></tr><tr><td>× historical conflict districts</td><td></td><td></td><td>(0.046)</td><td></td><td></td><td>(0.051)</td></tr><tr><td>production network exposure, conflict districts</td><td>0.198<br>(0.147)</td><td>0.183<br>(0.147)</td><td>0.018<br>(0.153)</td><td>-0.318<sup>c</sup><br>(0.191)</td><td>-0.291<br>(0.191)</td><td>-0.089<br>(0.202)</td></tr><tr><td>production network exposure, peaceful districts</td><td>0.305<sup>a</sup><br>(0.052)</td><td>0.306<sup>a</sup><br>(0.052)</td><td>0.331<sup>a</sup><br>(0.054)</td><td>-0.387<sup>a</sup><br>(0.067)</td><td>-0.388<sup>a</sup><br>(0.067)</td><td>-0.419<sup>a</sup><br>(0.069)</td></tr><tr><td>Firm controls</td><td colspan="3">— Yes —</td><td colspan="3"></td></tr><tr><td>Product FE</td><td colspan="3">— Yes —</td><td colspan="3"></td></tr><tr><td>District FE</td><td colspan="3">— Yes —</td><td colspan="3"></td></tr><tr><td>State×year FE</td><td colspan="3">— Yes —</td><td colspan="3"></td></tr><tr><td>Observations</td><td>428,410</td><td>428,410</td><td>428,410</td><td>428,410</td><td>428,410</td><td>428,410</td></tr><tr><td>R-squared</td><td>0.468</td><td>0.468</td><td>0.469</td><td>0.537</td><td>0.537</td><td>0.537</td></tr></tbody></table>

Note: c significant at 10%; b at 5%; a at 1%. Standard errors are clustered at the district×year level. *Historical* conflict-affected districts are defined as districts affected by conflict for at least 2 years prior to 2004 – the merger of Maoist groups – while the remaining districts are classified as *new* conflict-affected districts. Conflict intensity is measured by the number of violent events weighted by population, divided into *high* intensity, i.e., above the 90<sup>th</sup> percentile of the distribution, and *low* intensity, i.e., below the 90<sup>th</sup> percentile of the distribution. *Production network exposure* is a firm-level continuous measure calculated according to Equation OA4.3 and separated into conflict *versus* peaceful districts. Firm controls include (log) input prices (intermediate inputs, wages, and interest rate), and time-varying input bundle fixed effects (defined at the 2-digit level). Product (defined at the 4-digit level), district, and state×year fixed effects are included. See main text for further details and Table OA4.11 for descriptive statistics.

**Firm size.** There are strong reasons to believe that firm size matters to understand the effect of exposure to conflict. On the one hand, since Maoist groups are based on a Communist ideology, we may expect them to target capitalistic producers, which are likely to be large firms. On the other hand, the smallest firm may find harder to cope with the costs implied by conflict. In this perspective, we study whether the effect of conflict is different for firms belonging to different quintiles of size, measured by number of employees. Figure OA4.2 presents the results. First, the direct effect of conflict on price is positive and statistically significant for firms belonging to the first three quintiles of the distribution of size. For the remaining groups of firms, the effect is not statistically different from 0. As for quantity, the effect of conflict is negative and significant only for firms belonging to the first quintile. Second, for both price and quantity, the estimated costs implied by exposure by way of the production network decrease as firm size increases. Overall, these results suggest that firms belonging to the lower tale of the distribution of size are those that find harder to cope with the costs implied by conflict. A more comprehensive analysis of the implications of Maoist's strategic targeting would require data on firms' ideology or information on whether and in what way firms are directly exposed to Maoist activity.<fnref n="9" />

<footnote n="9">⁹In the sensitivity analysis OA4.4, we examine the modulating effects of specific firm characteristics on firm performance. We allow the effect on a firm's price and quantity to vary according to whether the firm relies on imported inputs and the number of</footnote>
<table><caption>Table OA4.14: Intensity of violence</caption><thead><tr><th rowspan="2">Dep. var.</th><th colspan="3">Price</th><th colspan="3">Quantity</th></tr><tr><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th><th>(6)</th></tr></thead><tbody><tr><td>direct exposure to conflict, # events / pop.</td><td>-0.123<br>(1.293)</td><td></td><td></td><td>-0.101<br>(1.403)</td><td></td><td></td></tr><tr><td>× new conflict districts</td><td></td><td>1.652<br>(1.556)</td><td></td><td></td><td>-3.176<sup>b</sup><br>(1.375)</td><td></td></tr><tr><td>× historical conflict districts</td><td></td><td>-1.462<br>(1.456)</td><td></td><td></td><td>2.219<br>(1.707)</td><td></td></tr><tr><td>direct exposure, high intensity (&gt; 90<sup>th</sup>perc.)</td><td></td><td></td><td>0.287<sup>b</sup></td><td></td><td></td><td>-0.239<sup>c</sup></td></tr><tr><td>× new conflict districts</td><td></td><td></td><td>(0.126)</td><td></td><td></td><td>(0.135)</td></tr><tr><td>direct exposure, high intensity (&gt; 90<sup>th</sup>perc.)</td><td></td><td></td><td>-0.108</td><td></td><td></td><td>0.098</td></tr><tr><td>× historical conflict districts</td><td></td><td></td><td>(0.096)</td><td></td><td></td><td>(0.106)</td></tr><tr><td>direct exposure, low intensity (&lt; 90<sup>th</sup>perc.)</td><td></td><td></td><td>0.104<sup>b</sup></td><td></td><td></td><td>-0.148<sup>a</sup></td></tr><tr><td>× new conflict districts</td><td></td><td></td><td>(0.043)</td><td></td><td></td><td>(0.057)</td></tr><tr><td>direct exposure, low intensity (&lt; 90<sup>th</sup>perc.)</td><td></td><td></td><td>0.077<sup>c</sup></td><td></td><td></td><td>-0.084</td></tr><tr><td>× historical conflict districts</td><td></td><td></td><td>(0.046)</td><td></td><td></td><td>(0.051)</td></tr><tr><td>production network exposure, conflict districts</td><td>0.198<br>(0.147)</td><td>0.183<br>(0.147)</td><td>0.018<br>(0.153)</td><td>-0.318<sup>c</sup><br>(0.191)</td><td>-0.291<br>(0.191)</td><td>-0.089<br>(0.202)</td></tr><tr><td>production network exposure, peaceful districts</td><td>0.305<sup>a</sup><br>(0.052)</td><td>0.306<sup>a</sup><br>(0.052)</td><td>0.331<sup>a</sup><br>(0.054)</td><td>-0.387<sup>a</sup><br>(0.067)</td><td>-0.388<sup>a</sup><br>(0.067)</td><td>-0.419<sup>a</sup><br>(0.069)</td></tr><tr><td>Firm controls</td><td></td><td></td><td></td><td>Yes</td><td></td><td></td></tr><tr><td>Product FE</td><td></td><td></td><td></td><td>Yes</td><td></td><td></td></tr><tr><td>District FE</td><td></td><td></td><td></td><td>Yes</td><td></td><td></td></tr><tr><td>State×year FE</td><td></td><td></td><td></td><td>Yes</td><td></td><td></td></tr><tr><td>Observations</td><td>428,410</td><td>428,410</td><td>428,410</td><td>428,410</td><td>428,410</td><td>428,410</td></tr><tr><td>R-squared</td><td>0.468</td><td>0.468</td><td>0.469</td><td>0.537</td><td>0.537</td><td>0.537</td></tr></tbody></table>

Note: c significant at 10%; b at 5%; a at 1%. Standard errors are clustered at the district×year level. *Historical* conflict-affected districts are defined as districts affected by conflict for at least 2 years prior to 2004 – the merger of Maoist groups – while the remaining districts are classified as *new* conflict-affected districts. Conflict intensity is measured by the number of violent events weighted by population, divided into *high* intensity, i.e., above the 90<sup>th</sup> percentile of the distribution, and *low* intensity, i.e., below the 90<sup>th</sup> percentile of the distribution. *Production network exposure* is a firm-level continuous measure calculated according to Equation OA4.3 and separated into conflict *versus* peaceful districts. Firm controls include (log) input prices (intermediate inputs, wages, and interest rate), and time-varying input bundle fixed effects (defined at the 2-digit level). Product (defined at the 4-digit level), district, and state×year fixed effects are included. See main text for further details and Table OA4.11 for descriptive statistics.

**Firm size.** There are strong reasons to believe that firm size matters to understand the effect of exposure to conflict. On the one hand, since Maoist groups are based on a Communist ideology, we may expect them to target capitalistic producers, which are likely to be large firms. On the other hand, the smallest firm may find harder to cope with the costs implied by conflict. In this perspective, we study whether the effect of conflict is different for firms belonging to different quintiles of size, measured by number of employees. Figure OA4.2 presents the results. First, the direct effect of conflict on price is positive and statistically significant for firms belonging to the first three quintiles of the distribution of size. For the remaining groups of firms, the effect is not statistically different from 0. As for quantity, the effect of conflict is negative and significant only for firms belonging to the first quintile. Second, for both price and quantity, the estimated costs implied by exposure by way of the production network decrease as firm size increases. Overall, these results suggest that firms belonging to the lower tale of the distribution of size are those that find harder to cope with the costs implied by conflict. A more comprehensive analysis of the implications of Maoist's strategic targeting would require data on firms' ideology or information on whether and in what way firms are directly exposed to Maoist activity.<fnref n="9" />

<footnote n="9">⁹In the sensitivity analysis OA4.4, we examine the modulating effects of specific firm characteristics on firm performance. We allow the effect on a firm's price and quantity to vary according to whether the firm relies on imported inputs and the number of</footnote>
<figure><img src="image_13.png" /><figcaption>Figure OA4.2: Firm size heterogeneity</figcaption></figure>

Note: c significant at 10%; b at 5%; a at 1%. Standard errors are clustered at the district×year level. The firm size is measured by the (log) number of employees, divided in quintiles. See main text for further details and Table OA4.11 for descriptive statistics.

## OA4.3 Mechanisms

As discussed in Section 4, a firm selects from a set of potential suppliers for each of its inputs. When conflict occurs in a supplier's district, then the firm has three options: (i) inaction; (ii) supplier change; and (iii) input bundle change. In this section, and notwithstanding the data limitations, we estimate the likelihood of choosing options (ii) and (iii). In the absence of information on firm-to-firm transactions, we are unable to directly evaluate the likelihood of the inaction option. However, it is indirectly tested through the two other effects.

**Supplier change.** We overcome the lack of data on firm-to-firm transactions by developing an alternative empirical strategy. The results presented in Section OA4.2 suggest that conflict correlates positively with output price and negatively with output quantity in the case of firms located outside conflict-affected districts but which are exposed to conflict by way of the production network. This implies that there may be firms that gain from conflict. This might occur if a firm replaces a supplier located in a conflict-affected district with one that is not. We therefore estimate whether this substitution of suppliers might be favorable for firms not located in a conflict-affected district. We restrict the sample to suppliers that are not located in a conflict-affected district and which have at least one competitor that is (and which is a firm producing the same good according to the 5-digit product code classification). We estimate the following equation at the product level k:

$$Y_{i,k,t} = \rho \text{competitors-conflict-districts}_{i,k,t} + D'_{i,t}\alpha + \theta_k + \theta_d + \theta_t + \varepsilon_{i,k,t} \quad (\text{OA4.4})$$

where $Y_{i,k,t}$ is firm i's share of total sales of product k at the state or national level in year t. We then calculate two alternative measures of the importance of a competitor (at the product level) located in a conflict-affected district (competitors-conflict-districts$_{i,k,t}$): (i) the percentage of firms producing product k; and (ii) the market share of each firm producing product k. We also include fixed effects for product ($\theta_k$), district ($\theta_d$) and year ($\theta_t$) and firm-specific characteristics $D'_{i,t}$ (wages, interest rate, and input prices, all in log terms), and (time-varying) input bundle fixed effects. Robust standard errors are clustered at the district×year level.<fnref n="10" /> Table OA4.15 presents the results for market share at the state level (columns 1 and 2) and at the national level (columns 3 and 4). In each specification, state- and national-level market shares

affiliates. There is no conclusive evidence that firm-specific characteristics modulate the effect of exposure to conflict on firm performance.

<footnote n="10">¹⁰For the sake of simplicity, the specification's subscripts for district d, state c and industry s are omitted since each firm is nested within a higher category (e.g., a firm is nested within a specific district, state and industry throughout time).</footnote>
<figure><img src="image_12.png" /><figcaption>Figure OA4.2: Firm size heterogeneity</figcaption></figure>

Note: c significant at 10%; b at 5%; a at 1%. Standard errors are clustered at the district×year level. The firm size is measured by the (log) number of employees, divided in quintiles. See main text for further details and Table OA4.11 for descriptive statistics.

## OA4.3 Mechanisms

As discussed in Section 4, a firm selects from a set of potential suppliers for each of its inputs. When conflict occurs in a supplier's district, then the firm has three options: (i) *inaction*; (ii) *supplier change*; and (iii) *input bundle change*. In this section, and notwithstanding the data limitations, we estimate the likelihood of choosing options (ii) and (iii). In the absence of information on firm-to-firm transactions, we are unable to directly evaluate the likelihood of the *inaction* option. However, it is indirectly tested through the two other effects.

**Supplier change.** We overcome the lack of data on firm-to-firm transactions by developing an alternative empirical strategy. The results presented in Section OA4.2 suggest that conflict correlates positively with output price and negatively with output quantity in the case of firms located outside conflict-affected districts but which are exposed to conflict by way of the production network. This implies that there may be firms that gain from conflict. This might occur if a firm replaces a supplier located in a conflict-affected district with one that is not. We therefore estimate whether this substitution of suppliers might be favorable for firms not located in a conflict-affected district. We restrict the sample to suppliers that are not located in a conflict-affected district and which have at least one competitor that is (and which is a firm producing the same good according to the 5-digit product code classification). We estimate the following equation at the product level *k*:

$$Y_{i,k,t} = \rho \text{competitors-conflict-districts}_{i,k,t} + D'_{i,t}\alpha + \theta_k + \theta_d + \theta_t + \varepsilon_{i,k,t} \quad (\text{OA4.4})$$

where $Y_{i,k,t}$ is firm *i*'s share of total sales of product *k* at the state or national level in year *t*. We then calculate two alternative measures of the importance of a competitor (at the product level) located in a conflict-affected district (competitors-conflict-districts$_{i,k,t}$): (i) the percentage of firms producing product *k*; and (ii) the market share of each firm producing product *k*. We also include fixed effects for product ($\theta_k$), district ($\theta_d$) and year ($\theta_t$) and firm-specific characteristics $D'_{i,t}$ (wages, interest rate, and input prices, all in log terms), and (time-varying) input bundle fixed effects. Robust standard errors are clustered at the district×year level.<fnref n="10" /> Table OA4.15 presents the results for market share at the state level (columns 1 and 2) and at the national level (columns 3 and 4). In each specification, state- and national-level market shares

affiliates. There is no conclusive evidence that firm-specific characteristics modulate the effect of exposure to conflict on firm performance.

<footnote n="10">¹⁰For the sake of simplicity, the specification's subscripts for district *d*, state *c* and industry *s* are omitted since each firm is nested within a higher category (e.g., a firm is nested within a specific district, state and industry throughout time).</footnote>
are positively associated with the existence of competitors in conflict-affected districts. An increase in the share of competing firms by 10% leads to an increase in market share of 8.78% at the state level (column 1) and 3.77% at the national level (column 3). Even if precisely estimated, the estimated increase is somewhat smaller for the alternative measure: a 10% increase in the market power of competitors translates into an increase of 2.93% in market share at the state level (column 2) and 1.36% at the national level (column 4).<fnref n="11" /> These results suggest that *supplier change* is indeed the relevant mechanism.

<table><caption>Table OA4.15: Supplier change</caption><thead><tr><th rowspan="3">Dep. var.</th><th colspan="4">Market share</th></tr><tr><th colspan="2">state-level</th><th colspan="2">country-level</th></tr><tr><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th></tr></thead><tbody><tr><td>% of competitor firms, conflict districts</td><td>0.878<sup>a</sup><br>(0.024)</td><td></td><td>0.377<sup>a</sup><br>(0.010)</td><td></td></tr><tr><td>market share of competitors, conflict districts</td><td></td><td>0.293<sup>a</sup><br>(0.010)</td><td></td><td>0.136<sup>a</sup><br>(0.004)</td></tr><tr><td>Firm controls</td><td></td><td></td><td>Yes</td><td></td></tr><tr><td>Product FE</td><td></td><td></td><td>Yes</td><td></td></tr><tr><td>District FE</td><td></td><td></td><td>Yes</td><td></td></tr><tr><td>State&times;year FE</td><td></td><td></td><td>Yes</td><td></td></tr><tr><td>Observations</td><td>267,038</td><td>267,038</td><td>267,038</td><td>267,038</td></tr><tr><td>R-squared</td><td>0.303</td><td>0.281</td><td>0.214</td><td>0.162</td></tr></tbody></table>

Note: c significant at 10%; b at 5%; a at 1%. Standard errors are clustered at district&times;year level. Market share is defined as sales of the firm *i* for product *p* over the total sales at the state&times;year level (columns 1-2) or alternatively at the country &times; year level (columns 3-4). The alternative competitors measures are defined in the main text. Firm controls include (log) input prices (intermediate inputs, wages, and interest rate), and time-varying input bundle fixed effects (defined at the 2-digit level). Product (defined at the 4-digit level), district, and state&times;year fixed effects are included. See main text for more details and Table OA4.11 for descriptive statistics.

**Input bundle change.** Up to this point, our baseline estimates have been based on the assumption that the firm-level input bundle is given, i.e., it is exogenous to exposure to conflict. In this subsection, we provide preliminary evidence that firms do indeed adjust their inputs as a result of exposure to conflict. Thus, we estimate whether contemporaneous and past exposure to conflict leads to a change in input bundle, while restricting the sample to firms not located in a conflict-affected district.<fnref n="12" /> To determine whether a firm changes its input bundle over time, we make use of the ASI data on a firm’s 10 main intermediate inputs.<fnref n="13" /> We therefore estimate the following equation at the firm level:

$$Y_{i,k(d,t)} = \beta \text{PNE}_{i(d,t)}^{\text{peace districts}} + \mathbf{D}'_{i(d,t)}\alpha + \theta_d + \theta_{ct} + \varepsilon_{i,k(d,t)} \quad (\text{OA4.5})$$

where $Y_{i,k(d,t)}$ is equal to 1 if the firm *i* has a different input bundle than in the previous year surveyed and $\text{PNE}_{i(d,t)}^{\text{peace districts}}$ captures the effect of conflict by way of the production network on firms not located in a conflict-affected district. We include district ($\theta_d$) and state&times;year ($\theta_{ct}$) fixed effects, and firm-specific characteristics $\mathbf{D}'_{i,t}$: wages, interest rate, and input prices, all in log-terms, and (time-varying) input bundle

<footnote n="11">¹¹Note that the dependent variable in columns 3 and 4 is the economy *sales vector* as defined in Acemoglu et al. (2012) that coincides with the *influence vector* (introduced in Section 3). This vector tells us which firms play a “central” role in the network representation of the economy and consequently play a more important role in determining aggregate output. The results are thus consistent with the intuition that distortions experienced by certain firms in the economy might mean that other producers are gaining “centrality”.</footnote>

<footnote n="12">¹²As exposure by way of the production network in conflict-affected districts is not precisely estimated due to the high collinearity with the direct exposure measure, we focus only on the peaceful districts.</footnote>

<footnote n="13">¹³Although the data on firm’s products and intermediate inputs is relatively detailed, it does not allow us to calculate a perfect input bundle change measure. This is because the ranking of the 10 most important inputs might vary over time due to unforeseen circumstances, such as an exogenous shock to international price levels.</footnote>
are positively associated with the existence of competitors in conflict-affected districts. An increase in the share of competing firms by 10% leads to an increase in market share of 8.78% at the state level (column 1) and 3.77% at the national level (column 3). Even if precisely estimated, the estimated increase is somewhat smaller for the alternative measure: a 10% increase in the market power of competitors translates into an increase of 2.93% in market share at the state level (column 2) and 1.36% at the national level (column 4).<fnref n="11" /> These results suggest that *supplier change* is indeed the relevant mechanism.

<table><caption>Table OA4.15: Supplier change</caption><thead><tr><th rowspan="3">Dep. var.</th><th colspan="4">Market share</th></tr><tr><th colspan="2">state-level</th><th colspan="2">country-level</th></tr><tr><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th></tr></thead><tbody><tr><td>% of competitor firms, conflict districts</td><td>0.878<sup>a</sup><br>(0.024)</td><td></td><td>0.377<sup>a</sup><br>(0.010)</td><td></td></tr><tr><td>market share of competitors, conflict districts</td><td></td><td>0.293<sup>a</sup><br>(0.010)</td><td></td><td>0.136<sup>a</sup><br>(0.004)</td></tr><tr><td>Firm controls</td><td></td><td></td><td>Yes</td><td></td></tr><tr><td>Product FE</td><td></td><td></td><td>Yes</td><td></td></tr><tr><td>District FE</td><td></td><td></td><td>Yes</td><td></td></tr><tr><td>State&times;year FE</td><td></td><td></td><td>Yes</td><td></td></tr><tr><td>Observations</td><td>267,038</td><td>267,038</td><td>267,038</td><td>267,038</td></tr><tr><td>R-squared</td><td>0.303</td><td>0.281</td><td>0.214</td><td>0.162</td></tr></tbody></table>

Note: c significant at 10%; b at 5%; a at 1%. Standard errors are clustered at district&times;year level. Market share is defined as sales of the firm *i* for product *p* over the total sales at the state&times;year level (columns 1-2) or alternatively at the country &times; year level (columns 3-4). The alternative competitors measures are defined in the main text. Firm controls include (log) input prices (intermediate inputs, wages, and interest rate), and time-varying input bundle fixed effects (defined at the 2-digit level). Product (defined at the 4-digit level), district, and state&times;year fixed effects are included. See main text for more details and Table OA4.11 for descriptive statistics.

**Input bundle change.** Up to this point, our baseline estimates have been based on the assumption that the firm-level input bundle is given, i.e., it is exogenous to exposure to conflict. In this subsection, we provide preliminary evidence that firms do indeed adjust their inputs as a result of exposure to conflict. Thus, we estimate whether contemporaneous and past exposure to conflict leads to a change in input bundle, while restricting the sample to firms not located in a conflict-affected district.<fnref n="12" /> To determine whether a firm changes its input bundle over time, we make use of the ASI data on a firm’s 10 main intermediate inputs.<fnref n="13" /> We therefore estimate the following equation at the firm level:

$$Y_{i,k(d,t)} = \beta \text{PNE}_{i(d,t)}^{\text{peace districts}} + \mathbf{D}'_{i(d,t)}\alpha + \theta_d + \theta_{ct} + \varepsilon_{i,k(d,t)} \quad (\text{OA4.5})$$

where $Y_{i,k(d,t)}$ is equal to 1 if the firm *i* has a different input bundle than in the previous year surveyed and $\text{PNE}_{i(d,t)}^{\text{peace districts}}$ captures the effect of conflict by way of the production network on firms not located in a conflict-affected district. We include district ($\theta_d$) and state&times;year ($\theta_{ct}$) fixed effects, and firm-specific characteristics $\mathbf{D}'_{i,t}$: wages, interest rate, and input prices, all in log-terms, and (time-varying) input bundle

<footnote n="11">¹¹Note that the dependent variable in columns 3 and 4 is the economy *sales vector* as defined in Acemoglu et al. (2012) that coincides with the *influence vector* (introduced in Section 3). This vector tells us which firms play a “central” role in the network representation of the economy and consequently play a more important role in determining aggregate output. The results are thus consistent with the intuition that distortions experienced by certain firms in the economy might mean that other producers are gaining “centrality”.</footnote>

<footnote n="12">¹²As exposure by way of the production network in conflict-affected districts is not precisely estimated due to the high collinearity with the direct exposure measure, we focus only on the peaceful districts.</footnote>

<footnote n="13">¹³Although the data on firm’s products and intermediate inputs is relatively detailed, it does not allow us to calculate a perfect input bundle change measure. This is because the ranking of the 10 most important inputs might vary over time due to unforeseen circumstances, such as an exogenous shock to international price levels.</footnote>
fixed effects. Finally, standard errors are clustered at district × year level. The results point to a positive and significant effect for contemporaneous exposure to conflict by way of the production network on input bundle change in the case of firms not located in a conflict-affected district (Table OA4.16, column 1) and no effect for past exposure (Table OA4.16, column 2).

<table><caption>Table OA4.16: Input bundle change</caption><thead><tr><th rowspan="2">Dep. var.</th><th colspan="2">Input bundle change</th></tr><tr><th>(1)</th><th>(2)</th></tr></thead><tbody><tr><td>production network exposure, peaceful districts <i>t</i></td><td>0.027<sup>a</sup><br>(0.010)</td><td>0.070<sup>a</sup><br>(0.016)</td></tr><tr><td>production network exposure, peaceful districts <i>t</i> &minus; 1</td><td></td><td>0.005<br>(0.017)</td></tr><tr><td>Firm controls</td><td></td><td>Yes</td></tr><tr><td>District FE</td><td></td><td>Yes</td></tr><tr><td>State × year FE</td><td></td><td>Yes</td></tr><tr><td>Observations</td><td>235,518</td><td>106,298</td></tr><tr><td>R-squared</td><td>0.281</td><td>0.368</td></tr></tbody></table>

Note: <sup>c</sup> significant at 10%; <sup>b</sup> at 5%; <sup>a</sup> at 1%. Standard errors are clustered at the district × year level. Input bundle change equals 1 when the firm has changed its inputs bundle composition compared to the previous year according to the 5-digit product classification code. *Production network exposure* is a firm-level continuous measure calculated according to Equation OA4.3. Firm controls include (log) input prices (intermediate inputs, wages, and interest rate), and time-varying input bundle fixed effects (defined at the 3-digit level). Product (defined at the 4-digit level), district, and state × year fixed effects are included. District, and state × year fixed effects are included. See main text for more details and Table OA4.11 for descriptive statistics.

## OA4.4 Sensitivity analysis

In this subsection we show that the baseline estimates of Table OA4.12 are robust to a large battery of sensitivity checks.

**Endogeneity.** The structure of the Indian's input-output network is likely influenced by the Maoist insurgency, and we pinpoint three primary sources of this endogeneity. The first source relates to the geographic locations of firms, the second to the goods they produce, and the last involves the potential correlation between Maoist activity and unobserved characteristics at the firm or district level. The main text explicitly addresses the first two issues. To tackle the final concern of endogeneity, we employ an instrumental variable strategy. We focus on the first channel of distortion by instrumenting our measure of direct exposure to conflict. Regarding the second channel, i.e., the spread of distortions experienced by firms located in a conflict-affected district to other firms by way of the production network, we encounter a limitation. We currently lack a suitable methodology to instrument an endogenous variable used in constructing the production network exposure variables. In what follows, we consider as credible instrumental variables the usual determinants of Maoist activity highlighted in the literature: mineral resources and forest cover.

*Mineral resources.* Natural resources, and in particular mineral resources, are often highlighted as an important driver of conflict (Berman et al., 2017). Iron, bauxite, coal, and copper are minerals that are regularly associated with Maoist activity, as they enable the latter to finance itself, notably through smuggling and firm extortion (Vanden Eynde, 2017). We use data on large-scale mines from the Raw Material Data (IntierraRMG, 2013). This dataset provides worldwide information on the location, production and specific minerals produced by mining companies since 1980 with a focus on large-scale mines operated by State governments or multinational companies.<fnref n="14" /> Following the methodology employed in Berman et al. (2017),

<footnote n="14">¹⁴Note that small-scale mines, and those that are illegally operated, are not included in our dataset. To the best of our knowledge, for India, no comprehensive data exist on the location of artisanal mining activities.</footnote>
fixed effects. Finally, standard errors are clustered at district × year level. The results point to a positive and significant effect for contemporaneous exposure to conflict by way of the production network on input bundle change in the case of firms not located in a conflict-affected district (Table OA4.16, column 1) and no effect for past exposure (Table OA4.16, column 2).

<table><caption>Table OA4.16: Input bundle change</caption><thead><tr><th rowspan="2">Dep. var.</th><th colspan="2">Input bundle change</th></tr><tr><th>(1)</th><th>(2)</th></tr></thead><tbody><tr><td>production network exposure, peaceful districts <i>t</i></td><td>0.027<sup>a</sup><br>(0.010)</td><td>0.070<sup>a</sup><br>(0.016)</td></tr><tr><td>production network exposure, peaceful districts <i>t</i> &minus; 1</td><td></td><td>0.005<br>(0.017)</td></tr><tr><td>Firm controls</td><td colspan="2">Yes</td></tr><tr><td>District FE</td><td colspan="2">Yes</td></tr><tr><td>State × year FE</td><td colspan="2">Yes</td></tr><tr><td>Observations</td><td>235,518</td><td>106,298</td></tr><tr><td>R-squared</td><td>0.281</td><td>0.368</td></tr></tbody></table>

Note: <sup>c</sup> significant at 10%; <sup>b</sup> at 5%; <sup>a</sup> at 1%. Standard errors are clustered at the district × year level. Input bundle change equals 1 when the firm has changed its inputs bundle composition compared to the previous year according to the 5-digit product classification code. *Production network exposure* is a firm-level continuous measure calculated according to Equation OA4.3. Firm controls include (log) input prices (intermediate inputs, wages, and interest rate), and time-varying input bundle fixed effects (defined at the 3-digit level). Product (defined at the 4-digit level), district, and state × year fixed effects are included. District, and state × year fixed effects are included. See main text for more details and Table OA4.11 for descriptive statistics.

## OA4.4 Sensitivity analysis

In this subsection we show that the baseline estimates of Table OA4.12 are robust to a large battery of sensitivity checks.

**Endogeneity.** The structure of the Indian's input-output network is likely influenced by the Maoist insurgency, and we pinpoint three primary sources of this endogeneity. The first source relates to the geographic locations of firms, the second to the goods they produce, and the last involves the potential correlation between Maoist activity and unobserved characteristics at the firm or district level. The main text explicitly addresses the first two issues. To tackle the final concern of endogeneity, we employ an instrumental variable strategy. We focus on the first channel of distortion by instrumenting our measure of direct exposure to conflict. Regarding the second channel, i.e., the spread of distortions experienced by firms located in a conflict-affected district to other firms by way of the production network, we encounter a limitation. We currently lack a suitable methodology to instrument an endogenous variable used in constructing the production network exposure variables. In what follows, we consider as credible instrumental variables the usual determinants of Maoist activity highlighted in the literature: mineral resources and forest cover.

*Mineral resources.* Natural resources, and in particular mineral resources, are often highlighted as an important driver of conflict (Berman et al., 2017). Iron, bauxite, coal, and copper are minerals that are regularly associated with Maoist activity, as they enable the latter to finance itself, notably through smuggling and firm extortion (Vanden Eynde, 2017). We use data on large-scale mines from the Raw Material Data (IntierraRMG, 2013). This dataset provides worldwide information on the location, production and specific minerals produced by mining companies since 1980 with a focus on large-scale mines operated by State governments or multinational companies.<fnref n="14" /> Following the methodology employed in Berman et al. (2017),

<footnote n="14">¹⁴Note that small-scale mines, and those that are illegally operated, are not included in our dataset. To the best of our knowledge, for India, no comprehensive data exist on the location of artisanal mining activities.</footnote>
we create a measure of large-scale mines by identifying active mines per district and per mineral for the year 2000. This measure is then interacted with world prices (in log) of the relevant minerals. The real-world prices of these minerals, measured in 2010 USD, are obtained from the World Bank Commodities prices dataset. To ensure accuracy and avoid any confounding effects, we exclude all firms falling under the mining and quarrying industry classification (national industrial classification code 14). For three out of the four minerals (copper, coal, bauxite) the interaction term between world price and district mining activity is positive and significant (Tables OA4.17, OA4.18, and OA4.19). Note that the first-stage F-test is relatively low, especially for coal and bauxite, which might explain why, even if the effect of exposure is positively correlated with price (and negatively with quantities) it is imprecisely estimated. In the case of iron mine, we fail identifying something conclusive (to ease the reading, the table is not reproduced here). Given that the first-stage does not necessarily show a strong correlation between the instruments and conflict, we also report Anderson-Rubin confidence sets with correction for weak instrument problem (Anderson and Rubin, 1949; Mikusheva and Poi, 2006). In all cases, the confidence sets are not too large, allowing for meaningful inferences.

**Forest cover.** Forest resources play a significant role in Maoist activity (Borooah, 2008; Gomes, 2015; Iyer, 2009; Gawande et al., 2017). To measure forest cover, we rely on data from Vegetation Continuous Fields (VCF), a satellite-based measure of tree cover (Dimiceli et al., 2015; Asher et al., 2021). We focus on the maximum forest cover value in each geographic unit and aggregate it at the district-level for the year 2000. The measure is then interacted with the international price of timber (index based on 2010 USD from the World Bank Commodities prices dataset). To eliminate potential interference from firms linked to the forest industry, we exclude those involved in the manufacture of wood (national industrial classification code 20). Table OA4.20 displays the results. The effect of the interaction term between world price of timber and the forest cover is positive and significant. The second-stage results confirms our reduced-form findings: firms in conflict-affected districts display higher prices and lower quantities.

**Spatial correlation.** An important element of our strategy relies on how we define the exposure by way of the production network (Equation 11). The use of buyer-supplier inverse distance may introduce mechanically some spatial correlation. For instance, any spillover effects of conflict through increased uncertainty or demand shocks may affect firms close to conflict. In Table OA4.21, we investigate how our baseline estimates would misinterpret such spillovers as effects by way of the production network. First, we add a dummy that equals 1 if the firm is located in an adjacent conflict-affected district (column 1). Note that the coefficient related to conflict is around 10% larger than column 2 of Table OA4.12 that confirms the importance of carefully considering the SUTVA violation. Alternatively, we include the (log) distance between the firm and its closest conflict-affected district (column 2). In both cases, it does not affect the magnitude nor the precision of the effect of the production network. Last, we perform a more demanding exercise by adding district×year fixed effects (column 3). Doing so, we compare two firms in the same district - thus with the same distance to a conflict-affected district - observed in the same year but with different intensity of exposure by way of the production network. The point estimate barely changes in spite of the demanding exercise.

**Supply chain.** Based on the intra-state firm-to-firm transactions data in West Bengal, Gadenne et al. (2019) find that the median distance between a supplier and a buyer is around 30 km, similar to Carvalho et al. (2020) for Japan. This suggests that the supply chain in India is mainly local. Therefore, one can argue that the propagation effect by way of the production network might be mostly driven by firms located in peaceful districts *within* a conflict-affected state. In column 4 of Table OA4.21, we exclude firms located in conflict-affected state. The identifying variation relies only firms located in peaceful states but with different level of exposure by way of the production network. The results point out that while a significant part of the supply chain is local, our baseline estimates are not fully driven by a handful of firms located in peaceful districts *within* a conflict-affected state. We also test different definitions of the supply chain that do not affect our baseline estimates. First, we modify firm-to-firm linkages by using alternative measures of production network exposure in which $ρ_{jit(k)}$, our measure of supplier's importance, is estimated changing the elasticity of bilateral trade with respect to distance (from -2 to -5) and with respect to supplier size (from 2
we create a measure of large-scale mines by identifying active mines per district and per mineral for the year 2000. This measure is then interacted with world prices (in log) of the relevant minerals. The real-world prices of these minerals, measured in 2010 USD, are obtained from the World Bank Commodities prices dataset. To ensure accuracy and avoid any confounding effects, we exclude all firms falling under the mining and quarrying industry classification (national industrial classification code 14). For three out of the four minerals (copper, coal, bauxite) the interaction term between world price and district mining activity is positive and significant (Tables OA4.17, OA4.18, and OA4.19). Note that the first-stage F-test is relatively low, especially for coal and bauxite, which might explain why, even if the effect of exposure is positively correlated with price (and negatively with quantities) it is imprecisely estimated. In the case of iron mine, we fail identifying something conclusive (to ease the reading, the table is not reproduced here). Given that the first-stage does not necessarily show a strong correlation between the instruments and conflict, we also report Anderson-Rubin confidence sets with correction for weak instrument problem (Anderson and Rubin, 1949; Mikusheva and Poi, 2006). In all cases, the confidence sets are not too large, allowing for meaningful inferences.

**Forest cover.** Forest resources play a significant role in Maoist activity (Borooah, 2008; Gomes, 2015; Iyer, 2009; Gawande et al., 2017). To measure forest cover, we rely on data from Vegetation Continuous Fields (VCF), a satellite-based measure of tree cover (Dimiceli et al., 2015; Asher et al., 2021). We focus on the maximum forest cover value in each geographic unit and aggregate it at the district-level for the year 2000. The measure is then interacted with the international price of timber (index based on 2010 USD from the World Bank Commodities prices dataset). To eliminate potential interference from firms linked to the forest industry, we exclude those involved in the manufacture of wood (national industrial classification code 20). Table OA4.20 displays the results. The effect of the interaction term between world price of timber and the forest cover is positive and significant. The second-stage results confirms our reduced-form findings: firms in conflict-affected districts display higher prices and lower quantities.

**Spatial correlation.** An important element of our strategy relies on how we define the exposure by way of the production network (Equation 11). The use of buyer-supplier inverse distance may introduce mechanically some spatial correlation. For instance, any spillover effects of conflict through increased uncertainty or demand shocks may affect firms close to conflict. In Table OA4.21, we investigate how our baseline estimates would misinterpret such spillovers as effects by way of the production network. First, we add a dummy that equals 1 if the firm is located in an adjacent conflict-affected district (column 1). Note that the coefficient related to conflict is around 10% larger than column 2 of Table OA4.12 that confirms the importance of carefully considering the SUTVA violation. Alternatively, we include the (log) distance between the firm and its closest conflict-affected district (column 2). In both cases, it does not affect the magnitude nor the precision of the effect of the production network. Last, we perform a more demanding exercise by adding district×year fixed effects (column 3). Doing so, we compare two firms in the same district - thus with the same distance to a conflict-affected district - observed in the same year but with different intensity of exposure by way of the production network. The point estimate barely changes in spite of the demanding exercise.

**Supply chain.** Based on the intra-state firm-to-firm transactions data in West Bengal, Gadenne et al. (2019) find that the median distance between a supplier and a buyer is around 30 km, similar to Carvalho et al. (2020) for Japan. This suggests that the supply chain in India is mainly local. Therefore, one can argue that the propagation effect by way of the production network might be mostly driven by firms located in peaceful districts *within* a conflict-affected state. In column 4 of Table OA4.21, we exclude firms located in conflict-affected state. The identifying variation relies only firms located in peaceful states but with different level of exposure by way of the production network. The results point out that while a significant part of the supply chain is local, our baseline estimates are not fully driven by a handful of firms located in peaceful districts *within* a conflict-affected state. We also test different definitions of the supply chain that do not affect our baseline estimates. First, we modify firm-to-firm linkages by using alternative measures of production network exposure in which $ρ_{jit(k)}$, our measure of supplier's importance, is estimated changing the elasticity of bilateral trade with respect to distance (from -2 to -5) and with respect to supplier size (from 2
<table><caption>Table OA4.17: Instrumental variable: copper mines</caption><thead><tr><th>Model</th><th>reduced-form</th><th>reduced-form</th><th>first-stage</th><th>2SLS</th><th>2SLS</th></tr></thead><tbody><tr><td><strong>Panel A</strong><br>Dep. var.</td><td>(1)<br>price</td><td>(2)<br>quantity</td><td>(3)<br>direct exposure<br>to conflict</td><td>(4)<br>price</td><td>(5)<br>quantity</td></tr><tr><td>copper mines<sub>2000</sub> × price<sub>t</sub> (log)</td><td>0.522<sup>a</sup><br>(0.150)</td><td>-0.438<sup>a</sup><br>(0.157)</td><td>0.489<sup>a</sup><br>(0.113)</td><td></td><td></td></tr><tr><td>direct exposure to conflict</td><td></td><td></td><td></td><td>1.068<sup>b</sup><br>(0.447)</td><td>-0.896<sup>b</sup><br>(0.453)</td></tr><tr><td>R-squared</td><td>0.468</td><td>0.537</td><td>0.652</td><td></td><td></td></tr><tr><td>First-stage F-test</td><td></td><td></td><td>18.75</td><td></td><td></td></tr><tr><td>Anderson-Rubin CI</td><td></td><td></td><td></td><td>[0.737;1.399]</td><td>[-1.287;-0.506]</td></tr><tr><td><strong>Panel B</strong><br>Dep. var.</td><td>(6)<br>price</td><td>(7)<br>quantity</td><td>(8)<br>direct exposure<br>to conflict</td><td>(9)<br>price</td><td>(10)<br>quantity</td></tr><tr><td>copper mines<sub>2000</sub> × price<sub>t</sub> (log)</td><td>0.529<sup>a</sup><br>(0.148)</td><td>-0.443<sup>a</sup><br>(0.156)</td><td>0.396<sup>a</sup><br>(0.097)</td><td></td><td></td></tr><tr><td>direct exposure to conflict</td><td></td><td></td><td></td><td>1.336<sup>b</sup><br>(0.563)</td><td>-1.119<sup>b</sup><br>(0.570)</td></tr><tr><td>production network exposure, conflict districts</td><td>0.179<br>(0.147)</td><td>-0.304<br>(0.189)</td><td>1.713<sup>a</sup><br>(0.141)</td><td>-2.109<sup>b</sup><br>(0.989)</td><td>1.613<br>(1.024)</td></tr><tr><td>production network exposure, peaceful districts</td><td>0.306<sup>a</sup><br>(0.052)</td><td>-0.388<sup>a</sup><br>(0.067)</td><td>-0.276<sup>a</sup><br>(0.023)</td><td>0.675<sup>a</sup><br>(0.166)</td><td>-0.697<sup>a</sup><br>(0.172)</td></tr><tr><td>R-squared</td><td>0.468</td><td>0.537</td><td>0.707</td><td></td><td></td></tr><tr><td>First-stage F-test</td><td></td><td></td><td>16.80</td><td></td><td></td></tr><tr><td>Anderson-Rubin CI</td><td></td><td></td><td></td><td>[0.928;1.744]</td><td>[-1.601;-0.637]</td></tr><tr><td colspan="6"><strong>All panels</strong></td></tr><tr><td>Firm controls</td><td></td><td></td><td></td><td>yes</td><td></td></tr><tr><td>Product FE</td><td></td><td></td><td></td><td>yes</td><td></td></tr><tr><td>District FE</td><td></td><td></td><td></td><td>yes</td><td></td></tr><tr><td>State × Year FE</td><td></td><td></td><td></td><td>yes</td><td></td></tr><tr><td>Observations</td><td></td><td></td><td></td><td>428,197</td><td></td></tr></tbody></table>

Note: c significant at 10%; b at 5%; a at 1%. Standard errors are clustered at the district × year level. Panel A reproduces Table OA4.12 columns 1 and 3, while Panel B replicates columns 2 and 4 using an instrumental variable strategy. The dependent variables are the (log) price and the (log) quantity of the firm's production defined at the 5-digit level of the product classification code (columns 1, 2, 4 and 5). *Direct exposure to conflict* is an indicator that equals 1 if the firm is located in a conflict-affected district. It is instrumented by the interaction between an indicator signaling whether copper mines were present in a district in 2000 and the (log) international price of copper between 2000 and 2009. *Production network exposure* is a firm-level continuous measure calculated according to Equation OA4.3 and separated into conflict versus peaceful districts. Firm controls include (log) input prices (intermediate inputs, wages, and interest rate), and time-varying input bundle fixed effects (defined at the 2-digit level). Product (defined at the 4-digit level), district, and state × year fixed effects are included. See main text for further details and Table OA4.11 for descriptive statistics.

to 5) (Table OA4.22). Second, we restrict trade between firms belonging to the same state, tribe, caste or religion (Table OA4.23).

**Alternative Specifications.** We assess whether our baseline estimates in Table OA4.12 are robust to various alternative specifications. First, we use an alternative definition of conflict based on the incidence of at least one fatality (Table OA4.24). Second, we restrict the sample to firms surveyed for at least 1, 3, 5, 7 years throughout the sample and firms always surveyed (Table OA4.25). Third, we use an alternative measure of exposure by way of the production network in which $\rho_{jit}(k)$, our measure of supplier's importance,
<table><caption>Table OA4.17: Instrumental variable: copper mines</caption><thead><tr><th>Model</th><th>reduced-form</th><th>reduced-form</th><th>first-stage</th><th>2SLS</th><th>2SLS</th></tr></thead><tbody><tr><td><strong>Panel A</strong></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Dep. var.</td><td>(1)<br>price</td><td>(2)<br>quantity</td><td>(3)<br>direct exposure<br>to conflict</td><td>(4)<br>price</td><td>(5)<br>quantity</td></tr><tr><td>copper mines<sub>2000</sub> × price<sub>t</sub> (log)</td><td>0.522<sup>a</sup><br>(0.150)</td><td>-0.438<sup>a</sup><br>(0.157)</td><td>0.489<sup>a</sup><br>(0.113)</td><td></td><td></td></tr><tr><td>direct exposure to conflict</td><td></td><td></td><td></td><td>1.068<sup>b</sup><br>(0.447)</td><td>-0.896<sup>b</sup><br>(0.453)</td></tr><tr><td>R-squared</td><td>0.468</td><td>0.537</td><td>0.652</td><td></td><td></td></tr><tr><td>First-stage F-test</td><td></td><td></td><td>18.75</td><td></td><td></td></tr><tr><td>Anderson-Rubin CI</td><td></td><td></td><td></td><td>[0.737;1.399]</td><td>[-1.287;-0.506]</td></tr><tr><td><strong>Panel B</strong></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Dep. var.</td><td>(6)<br>price</td><td>(7)<br>quantity</td><td>(8)<br>direct exposure<br>to conflict</td><td>(9)<br>price</td><td>(10)<br>quantity</td></tr><tr><td>copper mines<sub>2000</sub> × price<sub>t</sub> (log)</td><td>0.529<sup>a</sup><br>(0.148)</td><td>-0.443<sup>a</sup><br>(0.156)</td><td>0.396<sup>a</sup><br>(0.097)</td><td></td><td></td></tr><tr><td>direct exposure to conflict</td><td></td><td></td><td></td><td>1.336<sup>b</sup><br>(0.563)</td><td>-1.119<sup>b</sup><br>(0.570)</td></tr><tr><td>production network exposure, conflict districts</td><td>0.179<br>(0.147)</td><td>-0.304<br>(0.189)</td><td>1.713<sup>a</sup><br>(0.141)</td><td>-2.109<sup>b</sup><br>(0.989)</td><td>1.613<br>(1.024)</td></tr><tr><td>production network exposure, peaceful districts</td><td>0.306<sup>a</sup><br>(0.052)</td><td>-0.388<sup>a</sup><br>(0.067)</td><td>-0.276<sup>a</sup><br>(0.023)</td><td>0.675<sup>a</sup><br>(0.166)</td><td>-0.697<sup>a</sup><br>(0.172)</td></tr><tr><td>R-squared</td><td>0.468</td><td>0.537</td><td>0.707</td><td></td><td></td></tr><tr><td>First-stage F-test</td><td></td><td></td><td>16.80</td><td></td><td></td></tr><tr><td>Anderson-Rubin CI</td><td></td><td></td><td></td><td>[0.928;1.744]</td><td>[-1.601;-0.637]</td></tr><tr><td colspan="6"><strong>All panels</strong></td></tr><tr><td>Firm controls</td><td></td><td></td><td></td><td>yes</td><td></td></tr><tr><td>Product FE</td><td></td><td></td><td></td><td>yes</td><td></td></tr><tr><td>District FE</td><td></td><td></td><td></td><td>yes</td><td></td></tr><tr><td>State × Year FE</td><td></td><td></td><td></td><td>yes</td><td></td></tr><tr><td>Observations</td><td></td><td></td><td></td><td>428,197</td><td></td></tr></tbody></table>

Note: c significant at 10%; b at 5%; a at 1%. Standard errors are clustered at the district × year level. Panel A reproduces Table OA4.12 columns 1 and 3, while Panel B replicates columns 2 and 4 using an instrumental variable strategy. The dependent variables are the (log) price and the (log) quantity of the firm's production defined at the 5-digit level of the product classification code (columns 1, 2, 4 and 5). *Direct exposure to conflict* is an indicator that equals 1 if the firm is located in a conflict-affected district. It is instrumented by the interaction between an indicator signaling whether copper mines were present in a district in 2000 and the (log) international price of copper between 2000 and 2009. *Production network exposure* is a firm-level continuous measure calculated according to Equation OA4.3 and separated into conflict versus peaceful districts. Firm controls include (log) input prices (intermediate inputs, wages, and interest rate), and time-varying input bundle fixed effects (defined at the 2-digit level). Product (defined at the 4-digit level), district, and state × year fixed effects are included. See main text for further details and Table OA4.11 for descriptive statistics.

to 5) (Table OA4.22). Second, we restrict trade between firms belonging to the same state, tribe, caste or religion (Table OA4.23).

**Alternative Specifications.** We assess whether our baseline estimates in Table OA4.12 are robust to various alternative specifications. First, we use an alternative definition of conflict based on the incidence of at least one fatality (Table OA4.24). Second, we restrict the sample to firms surveyed for at least 1, 3, 5, 7 years throughout the sample and firms always surveyed (Table OA4.25). Third, we use an alternative measure of exposure by way of the production network in which $\rho_{jit}(k)$, our measure of supplier's importance,
<table><caption>Table OA4.18: Instrumental variable: coal mines</caption><thead><tr><th>Model</th><th>reduced-form</th><th>reduced-form</th><th>first-stage</th><th>2SLS</th><th>2SLS</th></tr></thead><tbody><tr><td><strong>Panel A</strong><br>Dep. var.</td><td>(1)<br>price</td><td>(2)<br>quantity</td><td>(3)<br>direct exposure<br>to conflict</td><td>(4)<br>price</td><td>(5)<br>quantity</td></tr><tr><td>coal mines<sub>2000</sub> × price<sub>t</sub> (log)</td><td>0.069<br>(0.051)</td><td>-0.052<br>(0.060)</td><td>0.127<sup>b</sup><br>(0.054)</td><td></td><td></td></tr><tr><td>direct exposure to conflict</td><td></td><td></td><td></td><td>0.539<br>(0.434)</td><td>-0.406<br>(0.492)</td></tr><tr><td>R-squared</td><td>0.468</td><td>0.537</td><td>0.651</td><td></td><td></td></tr><tr><td>First-stage F-test</td><td></td><td></td><td>5.462</td><td></td><td></td></tr><tr><td>Anderson-Rubin CI</td><td></td><td></td><td></td><td>[0.209;0.869]</td><td>[-0.806;-0.005]</td></tr><tr><td><strong>Panel B</strong><br>Dep. var.</td><td>(6)<br>price</td><td>(7)<br>quantity</td><td>(8)<br>direct exposure<br>to conflict</td><td>(9)<br>price</td><td>(10)<br>quantity</td></tr><tr><td>coal mines<sub>2000</sub> × price<sub>t</sub> (log)</td><td>0.066<br>(0.051)</td><td>-0.046<br>(0.059)</td><td>0.076<sup>c</sup><br>(0.043)</td><td></td><td></td></tr><tr><td>direct exposure to conflict</td><td></td><td></td><td></td><td>0.870<br>(0.782)</td><td>-0.610<br>(0.842)</td></tr><tr><td>production network exposure, conflict districts</td><td>0.180<br>(0.146)</td><td>-0.308<br>(0.189)</td><td>1.708<sup>a</sup><br>(0.141)</td><td>-1.306<br>(1.357)</td><td>0.734<br>(1.468)</td></tr><tr><td>production network exposure, peaceful districts</td><td>0.305<sup>a</sup><br>(0.052)</td><td>-0.387<sup>a</sup><br>(0.067)</td><td>-0.277<sup>a</sup><br>(0.023)</td><td>0.546<sup>b</sup><br>(0.221)</td><td>-0.556<sup>b</sup><br>(0.245)</td></tr><tr><td>R-squared</td><td></td><td></td><td></td><td>0.014</td><td>0.145</td></tr><tr><td>First-stage F-test</td><td></td><td></td><td>3.118</td><td></td><td></td></tr><tr><td>Anderson-Rubin CI</td><td></td><td></td><td></td><td>[0.316;1.424]</td><td>[-1.282;0.062]</td></tr><tr><td colspan="6"><strong>All panels</strong></td></tr><tr><td>Firm controls</td><td></td><td></td><td></td><td>yes</td><td></td></tr><tr><td>Product FE</td><td></td><td></td><td></td><td>yes</td><td></td></tr><tr><td>District FE</td><td></td><td></td><td></td><td>yes</td><td></td></tr><tr><td>State × Year FE</td><td></td><td></td><td></td><td>yes</td><td></td></tr><tr><td>Observations</td><td></td><td></td><td></td><td>428,197</td><td></td></tr></tbody></table>

Note: c significant at 10%; b at 5%; a at 1%. Standard errors are clustered at the district × year level. Panel A reproduces Table OA4.12 columns 1 and 3, while Panel B replicates columns 2 and 4 using an instrumental variable strategy. The dependent variables are the (log) price and the (log) quantity of the firm's production defined at the 5-digit level of the product classification code (columns 1, 2, 4 and 5). *Direct exposure to conflict* is an indicator that equals 1 if the firm is located in a conflict-affected district. It is instrumented by the interaction between an indicator signaling whether coal mines were present in a district in 2000 and the (log) international price of coal between 2000 and 2009. *Production network exposure* is a firm-level continuous measure calculated according to Equation OA4.3 and separated into conflict versus peaceful districts. Firm controls include (log) input prices (intermediate inputs, wages, and interest rate), and time-varying input bundle fixed effects (defined at the 2-digit level). Product (defined at the 4-digit level), district, and state × year fixed effects are included. See main text for further details and Table OA4.11 for descriptive statistics.

is estimated in a non-linear fashion as detailed in footnote 21 (Table OA4.26). Fourth, we show that the baseline effects are robust to the inclusion of lagged effects in Table OA4.27. Finally, we reproduce the baseline estimates by varying the clustering of the standard errors, including Conley (1999) standard errors allowing for spatial correlation within a 500 km radius and for infinite serial correlation. Results are stable across all of these exercises (Table OA4.28).
<table><caption>Table OA4.18: Instrumental variable: coal mines</caption><thead><tr><th>Model</th><th>reduced-form</th><th>reduced-form</th><th>first-stage</th><th>2SLS</th><th>2SLS</th></tr></thead><tbody><tr><td><strong>Panel A</strong><br>Dep. var.</td><td>(1)<br>price</td><td>(2)<br>quantity</td><td>(3)<br>direct exposure<br>to conflict</td><td>(4)<br>price</td><td>(5)<br>quantity</td></tr><tr><td>coal mines<sub>2000</sub> × price<sub>t</sub> (log)</td><td>0.069<br>(0.051)</td><td>-0.052<br>(0.060)</td><td>0.127<sup>b</sup><br>(0.054)</td><td></td><td></td></tr><tr><td>direct exposure to conflict</td><td></td><td></td><td></td><td>0.539<br>(0.434)</td><td>-0.406<br>(0.492)</td></tr><tr><td>R-squared</td><td>0.468</td><td>0.537</td><td>0.651</td><td></td><td></td></tr><tr><td>First-stage F-test</td><td></td><td></td><td>5.462</td><td></td><td></td></tr><tr><td>Anderson-Rubin CI</td><td></td><td></td><td></td><td>[0.209;0.869]</td><td>[-0.806;-0.005]</td></tr><tr><td><strong>Panel B</strong><br>Dep. var.</td><td>(6)<br>price</td><td>(7)<br>quantity</td><td>(8)<br>direct exposure<br>to conflict</td><td>(9)<br>price</td><td>(10)<br>quantity</td></tr><tr><td>coal mines<sub>2000</sub> × price<sub>t</sub> (log)</td><td>0.066<br>(0.051)</td><td>-0.046<br>(0.059)</td><td>0.076<sup>c</sup><br>(0.043)</td><td></td><td></td></tr><tr><td>direct exposure to conflict</td><td></td><td></td><td></td><td>0.870<br>(0.782)</td><td>-0.610<br>(0.842)</td></tr><tr><td>production network exposure, conflict districts</td><td>0.180<br>(0.146)</td><td>-0.308<br>(0.189)</td><td>1.708<sup>a</sup><br>(0.141)</td><td>-1.306<br>(1.357)</td><td>0.734<br>(1.468)</td></tr><tr><td>production network exposure, peaceful districts</td><td>0.305<sup>a</sup><br>(0.052)</td><td>-0.387<sup>a</sup><br>(0.067)</td><td>-0.277<sup>a</sup><br>(0.023)</td><td>0.546<sup>b</sup><br>(0.221)</td><td>-0.556<sup>b</sup><br>(0.245)</td></tr><tr><td>R-squared</td><td></td><td></td><td></td><td>0.014</td><td>0.145</td></tr><tr><td>First-stage F-test</td><td></td><td></td><td>3.118</td><td></td><td></td></tr><tr><td>Anderson-Rubin CI</td><td></td><td></td><td></td><td>[0.316;1.424]</td><td>[-1.282;0.062]</td></tr><tr><td colspan="6"><strong>All panels</strong></td></tr><tr><td>Firm controls</td><td></td><td></td><td></td><td>yes</td><td></td></tr><tr><td>Product FE</td><td></td><td></td><td></td><td>yes</td><td></td></tr><tr><td>District FE</td><td></td><td></td><td></td><td>yes</td><td></td></tr><tr><td>State × Year FE</td><td></td><td></td><td></td><td>yes</td><td></td></tr><tr><td>Observations</td><td></td><td></td><td></td><td>428,197</td><td></td></tr></tbody></table>

Note: c significant at 10%; b at 5%; a at 1%. Standard errors are clustered at the district × year level. Panel A reproduces Table OA4.12 columns 1 and 3, while Panel B replicates columns 2 and 4 using an instrumental variable strategy. The dependent variables are the (log) price and the (log) quantity of the firm's production defined at the 5-digit level of the product classification code (columns 1, 2, 4 and 5). *Direct exposure to conflict* is an indicator that equals 1 if the firm is located in a conflict-affected district. It is instrumented by the interaction between an indicator signaling whether coal mines were present in a district in 2000 and the (log) international price of coal between 2000 and 2009. *Production network exposure* is a firm-level continuous measure calculated according to Equation OA4.3 and separated into conflict versus peaceful districts. Firm controls include (log) input prices (intermediate inputs, wages, and interest rate), and time-varying input bundle fixed effects (defined at the 2-digit level). Product (defined at the 4-digit level), district, and state × year fixed effects are included. See main text for further details and Table OA4.11 for descriptive statistics.

is estimated in a non-linear fashion as detailed in footnote 21 (Table OA4.26). Fourth, we show that the baseline effects are robust to the inclusion of lagged effects in Table OA4.27. Finally, we reproduce the baseline estimates by varying the clustering of the standard errors, including Conley (1999) standard errors allowing for spatial correlation within a 500 km radius and for infinite serial correlation. Results are stable across all of these exercises (Table OA4.28).
<table><caption>Table OA4.19: Instrumental variable: bauxite mines</caption><thead><tr><th>Model</th><th>reduced-form</th><th>reduced-form</th><th>first-stage</th><th>2SLS</th><th>2SLS</th></tr></thead><tbody><tr><td><strong>Panel A</strong></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Dep. var.</td><td>(1)<br>price</td><td>(2)<br>quantity</td><td>(3)<br>direct exposure<br>to conflict</td><td>(4)<br>price</td><td>(5)<br>quantity</td></tr><tr><td>bauxite mines<sub>2000</sub> × price<sub>t</sub> (log)</td><td>-0.105<br>(0.203)</td><td>0.220<sup>c</sup><br>(0.119)</td><td>0.371<sup>a</sup><br>(0.136)</td><td></td><td></td></tr><tr><td>direct exposure to conflict</td><td></td><td></td><td></td><td>-0.282<br>(0.594)</td><td>0.592<br>(0.447)</td></tr><tr><td>R-squared</td><td>0.468</td><td>0.537</td><td>0.653</td><td></td><td></td></tr><tr><td>First-stage F-test</td><td></td><td></td><td>7.474</td><td></td><td></td></tr><tr><td>Anderson-Rubin CI</td><td></td><td></td><td></td><td>[-0.564;-<br>0.0004]</td><td>[0.265; 0.920]</td></tr><tr><td><strong>Panel B</strong></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Dep. var.</td><td>(6)<br>price</td><td>(7)<br>quantity</td><td>(8)<br>direct exposure<br>to conflict</td><td>(9)<br>price</td><td>(10)<br>quantity</td></tr><tr><td>bauxite mines<sub>2000</sub> × price<sub>t</sub> (log)</td><td>-0.092<br>(0.206)</td><td>0.205<sup>c</sup><br>(0.123)</td><td>0.305<sup>a</sup><br>(0.116)</td><td></td><td></td></tr><tr><td>direct exposure to conflict</td><td></td><td></td><td></td><td>-0.301<br>(0.728)</td><td>0.674<br>(0.560)</td></tr><tr><td>production network exposure, conflict districts</td><td>0.201<br>(0.148)</td><td>-0.329<sup>c</sup><br>(0.189)</td><td>1.713<sup>a</sup><br>(0.142)</td><td>0.716<br>(1.272)</td><td>-1.483<br>(0.988)</td></tr><tr><td>production network exposure, peaceful districts</td><td>0.304<sup>a</sup><br>(0.052)</td><td>-0.386<sup>a</sup><br>(0.067)</td><td>-0.275<sup>a</sup><br>(0.022)</td><td>0.222<br>(0.202)</td><td>-0.201<br>(0.164)</td></tr><tr><td>R-squared</td><td>0.468</td><td>0.537</td><td>0.708</td><td></td><td></td></tr><tr><td>First-stage F-test</td><td></td><td></td><td>6.886</td><td></td><td></td></tr><tr><td>Anderson-Rubin CI</td><td></td><td></td><td></td><td>[-0.644;0.042]</td><td>[0.275;1.072]</td></tr><tr><td colspan="6"><strong>All panels</strong></td></tr><tr><td>Firm controls</td><td></td><td></td><td></td><td>yes</td><td></td></tr><tr><td>Product FE</td><td></td><td></td><td></td><td>yes</td><td></td></tr><tr><td>District FE</td><td></td><td></td><td></td><td>yes</td><td></td></tr><tr><td>State × Year FE</td><td></td><td></td><td></td><td>yes</td><td></td></tr><tr><td>Observations</td><td colspan="4">428,197</td><td></td></tr></tbody></table>

Note: c significant at 10%; b at 5%; a at 1%. Standard errors are clustered at the district × year level. Panel A reproduces Table OA4.12 columns 1 and 3, while Panel B replicates columns 2 and 4 using an instrumental variable strategy. The dependent variables are the (log) price and the (log) quantity of the firm's production defined at the 5-digit level of the product classification code (columns 1, 2, 4 and 5). *Direct exposure to conflict* is an indicator that equals 1 if the firm is located in a conflict-affected district. It is instrumented by the interaction between an indicator signaling whether bauxite mines were present in a district in 2000 and the (log) international price of bauxite between 2000 and 2009. *Production network exposure* is a firm-level continuous measure calculated according to Equation OA4.3 and separated into conflict versus peaceful districts. Firm controls include (log) input prices (intermediate inputs, wages, and interest rate), and time-varying input bundle fixed effects (defined at the 2-digit level). Product (defined at the 4-digit level), district, and state × year fixed effects are included. See main text for further details and Table OA4.11 for descriptive statistics.
<table><caption>Table OA4.19: Instrumental variable: bauxite mines</caption><thead><tr><th>Model</th><th>reduced-form</th><th>reduced-form</th><th>first-stage</th><th>2SLS</th><th>2SLS</th></tr></thead><tbody><tr><td><strong>Panel A</strong></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Dep. var.</td><td>(1)<br>price</td><td>(2)<br>quantity</td><td>(3)<br>direct exposure<br>to conflict</td><td>(4)<br>price</td><td>(5)<br>quantity</td></tr><tr><td>bauxite mines<sub>2000</sub> × price<sub>t</sub> (log)</td><td>-0.105<br>(0.203)</td><td>0.220<sup>c</sup><br>(0.119)</td><td>0.371<sup>a</sup><br>(0.136)</td><td></td><td></td></tr><tr><td>direct exposure to conflict</td><td></td><td></td><td></td><td>-0.282<br>(0.594)</td><td>0.592<br>(0.447)</td></tr><tr><td>R-squared</td><td>0.468</td><td>0.537</td><td>0.653</td><td></td><td></td></tr><tr><td>First-stage F-test</td><td></td><td></td><td>7.474</td><td></td><td></td></tr><tr><td>Anderson-Rubin CI</td><td></td><td></td><td></td><td>[-0.564;-<br>0.0004]</td><td>[0.265; 0.920]</td></tr><tr><td><strong>Panel B</strong></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Dep. var.</td><td>(6)<br>price</td><td>(7)<br>quantity</td><td>(8)<br>direct exposure<br>to conflict</td><td>(9)<br>price</td><td>(10)<br>quantity</td></tr><tr><td>bauxite mines<sub>2000</sub> × price<sub>t</sub> (log)</td><td>-0.092<br>(0.206)</td><td>0.205<sup>c</sup><br>(0.123)</td><td>0.305<sup>a</sup><br>(0.116)</td><td></td><td></td></tr><tr><td>direct exposure to conflict</td><td></td><td></td><td></td><td>-0.301<br>(0.728)</td><td>0.674<br>(0.560)</td></tr><tr><td>production network exposure, conflict districts</td><td>0.201<br>(0.148)</td><td>-0.329<sup>c</sup><br>(0.189)</td><td>1.713<sup>a</sup><br>(0.142)</td><td>0.716<br>(1.272)</td><td>-1.483<br>(0.988)</td></tr><tr><td>production network exposure, peaceful districts</td><td>0.304<sup>a</sup><br>(0.052)</td><td>-0.386<sup>a</sup><br>(0.067)</td><td>-0.275<sup>a</sup><br>(0.022)</td><td>0.222<br>(0.202)</td><td>-0.201<br>(0.164)</td></tr><tr><td>R-squared</td><td>0.468</td><td>0.537</td><td>0.708</td><td></td><td></td></tr><tr><td>First-stage F-test</td><td></td><td></td><td>6.886</td><td></td><td></td></tr><tr><td>Anderson-Rubin CI</td><td></td><td></td><td></td><td>[-0.644;0.042]</td><td>[0.275;1.072]</td></tr><tr><td colspan="6"><strong>All panels</strong></td></tr><tr><td>Firm controls</td><td></td><td></td><td></td><td>yes</td><td></td></tr><tr><td>Product FE</td><td></td><td></td><td></td><td>yes</td><td></td></tr><tr><td>District FE</td><td></td><td></td><td></td><td>yes</td><td></td></tr><tr><td>State × Year FE</td><td></td><td></td><td></td><td>yes</td><td></td></tr><tr><td>Observations</td><td></td><td></td><td></td><td>428,197</td><td></td></tr></tbody></table>

Note: c significant at 10%; b at 5%; a at 1%. Standard errors are clustered at the district × year level. Panel A reproduces Table OA4.12 columns 1 and 3, while Panel B replicates columns 2 and 4 using an instrumental variable strategy. The dependent variables are the (log) price and the (log) quantity of the firm's production defined at the 5-digit level of the product classification code (columns 1, 2, 4 and 5). *Direct exposure to conflict* is an indicator that equals 1 if the firm is located in a conflict-affected district. It is instrumented by the interaction between an indicator signaling whether bauxite mines were present in a district in 2000 and the (log) international price of bauxite between 2000 and 2009. *Production network exposure* is a firm-level continuous measure calculated according to Equation OA4.3 and separated into conflict versus peaceful districts. Firm controls include (log) input prices (intermediate inputs, wages, and interest rate), and time-varying input bundle fixed effects (defined at the 2-digit level). Product (defined at the 4-digit level), district, and state × year fixed effects are included. See main text for further details and Table OA4.11 for descriptive statistics.
<table><caption>Table OA4.20: Instrumental variable: forest cover</caption><thead><tr><th>Model</th><th>reduced-form</th><th>reduced-form</th><th>first-stage</th><th>2SLS</th><th>2SLS</th></tr></thead><tbody><tr><td><strong>Panel A</strong><br>Dep. var.</td><td>(1)<br>price</td><td>(2)<br>quantity</td><td>(3)<br>direct exposure<br>to conflict</td><td>(4)<br>price</td><td>(5)<br>quantity</td></tr><tr><td>forest cover<sub>2000</sub> × price timber<sub>t</sub> (index)</td><td>0.009<sup>c</sup><br>(0.005)</td><td>-0.014<sup>a</sup><br>(0.005)</td><td>0.007<sup>a</sup><br>(0.003)</td><td></td><td></td></tr><tr><td>direct exposure to conflict</td><td></td><td></td><td></td><td>1.269<sup>c</sup><br>(0.753)</td><td>-1.926<sup>c</sup><br>(0.987)</td></tr><tr><td>R-squared</td><td>0.473</td><td>0.540</td><td>0.654</td><td></td><td></td></tr><tr><td>First-stage F-test</td><td></td><td></td><td>6.798</td><td></td><td></td></tr><tr><td>Anderson-Rubin CI</td><td></td><td></td><td></td><td>[0.744; 1.794]</td><td>[-2.564;-1.289]</td></tr><tr><td><strong>Panel B</strong><br>Dep. var.</td><td>(6)<br>price</td><td>(7)<br>quantity</td><td>(8)<br>direct exposure<br>to conflict</td><td>(9)<br>price</td><td>(10)<br>quantity</td></tr><tr><td>forest cover<sub>2000</sub> × price timber<sub>t</sub> (index)</td><td>0.010<sup>b</sup><br>(0.005)</td><td>-0.015<sup>a</sup><br>(0.005)</td><td>0.006<sup>b</sup><br>(0.003)</td><td></td><td></td></tr><tr><td>direct exposure to conflict</td><td></td><td></td><td></td><td>1.606<sup>c</sup><br>(0.962)</td><td>-2.424<sup>c</sup><br>(1.291)</td></tr><tr><td>production network exposure, conflict districts</td><td>0.172<br>(0.144)</td><td>-0.323<sup>c</sup><br>(0.189)</td><td>1.715<sup>a</sup><br>(0.141)</td><td>-2.583<br>(1.666)</td><td>3.833<sup>c</sup><br>(2.253)</td></tr><tr><td>production network exposure, peaceful districts</td><td>0.283<sup>a</sup><br>(0.052)</td><td>-0.395<sup>a</sup><br>(0.069)</td><td>-0.278<sup>a</sup><br>(0.023)</td><td>0.730<sup>a</sup><br>(0.272)</td><td>-1.069<sup>a</sup><br>(0.362)</td></tr><tr><td>R-squared</td><td>0.473</td><td>0.540</td><td>0.710</td><td></td><td></td></tr><tr><td>First-stage F-test</td><td></td><td></td><td>5.424</td><td></td><td></td></tr><tr><td>Anderson-Rubin CI</td><td></td><td></td><td></td><td>[0.971;2.242]</td><td>[-3.195;-1.652]</td></tr><tr><td colspan="6"><strong>All panels</strong></td></tr><tr><td>Firm controls</td><td></td><td></td><td></td><td>yes</td><td></td></tr><tr><td>Product FE</td><td></td><td></td><td></td><td>yes</td><td></td></tr><tr><td>District FE</td><td></td><td></td><td></td><td>yes</td><td></td></tr><tr><td>State × Year FE</td><td></td><td></td><td></td><td>yes</td><td></td></tr><tr><td>Observations</td><td></td><td></td><td></td><td>428,197</td><td></td></tr></tbody></table>

Note: c significant at 10%; b at 5%; a at 1%. Standard errors are clustered at the district × year level. Panel A reproduces Table OA4.12 columns 1 and 3, while Panel B replicates columns 2 and 4 using an instrumental variable strategy. The dependent variables are the (log) price and the (log) quantity of the firm's production defined at the 5-digit level of the product classification code (columns 1, 2, 4 and 5). *Direct exposure to conflict* is an indicator that equals 1 if the firm is located in a conflict-affected district. It is instrumented by the interaction between district-level forest cover in 2000 and the (log) international price of timber between 2000 and 2009. *Production network exposure* is a firm-level continuous measure calculated according to Equation OA4.3 and separated into conflict versus peaceful districts. Firm controls include (log) input prices (intermediate inputs, wages, and interest rate), and time-varying input bundle fixed effects (defined at the 2-digit level). Product (defined at the 4-digit level), district, and state × year fixed effects are included. See main text for further details and Table OA4.11 for descriptive statistics.
<table><caption>Table OA4.20: Instrumental variable: forest cover</caption><thead><tr><th>Model</th><th>reduced-form</th><th>reduced-form</th><th>first-stage</th><th>2SLS</th><th>2SLS</th></tr></thead><tbody><tr><td><strong>Panel A</strong><br>Dep. var.</td><td>(1)<br>price</td><td>(2)<br>quantity</td><td>(3)<br>direct exposure<br>to conflict</td><td>(4)<br>price</td><td>(5)<br>quantity</td></tr><tr><td>forest cover<sub>2000</sub> × price timber<sub>t</sub> (index)</td><td>0.009<sup>c</sup><br>(0.005)</td><td>-0.014<sup>a</sup><br>(0.005)</td><td>0.007<sup>a</sup><br>(0.003)</td><td></td><td></td></tr><tr><td>direct exposure to conflict</td><td></td><td></td><td></td><td>1.269<sup>c</sup><br>(0.753)</td><td>-1.926<sup>c</sup><br>(0.987)</td></tr><tr><td>R-squared</td><td>0.473</td><td>0.540</td><td>0.654</td><td></td><td></td></tr><tr><td>First-stage F-test</td><td></td><td></td><td>6.798</td><td></td><td></td></tr><tr><td>Anderson-Rubin CI</td><td></td><td></td><td></td><td>[0.744; 1.794]</td><td>[-2.564;-1.289]</td></tr><tr><td><strong>Panel B</strong><br>Dep. var.</td><td>(6)<br>price</td><td>(7)<br>quantity</td><td>(8)<br>direct exposure<br>to conflict</td><td>(9)<br>price</td><td>(10)<br>quantity</td></tr><tr><td>forest cover<sub>2000</sub> × price timber<sub>t</sub> (index)</td><td>0.010<sup>b</sup><br>(0.005)</td><td>-0.015<sup>a</sup><br>(0.005)</td><td>0.006<sup>b</sup><br>(0.003)</td><td></td><td></td></tr><tr><td>direct exposure to conflict</td><td></td><td></td><td></td><td>1.606<sup>c</sup><br>(0.962)</td><td>-2.424<sup>c</sup><br>(1.291)</td></tr><tr><td>production network exposure, conflict districts</td><td>0.172<br>(0.144)</td><td>-0.323<sup>c</sup><br>(0.189)</td><td>1.715<sup>a</sup><br>(0.141)</td><td>-2.583<br>(1.666)</td><td>3.833<sup>c</sup><br>(2.253)</td></tr><tr><td>production network exposure, peaceful districts</td><td>0.283<sup>a</sup><br>(0.052)</td><td>-0.395<sup>a</sup><br>(0.069)</td><td>-0.278<sup>a</sup><br>(0.023)</td><td>0.730<sup>a</sup><br>(0.272)</td><td>-1.069<sup>a</sup><br>(0.362)</td></tr><tr><td>R-squared</td><td>0.473</td><td>0.540</td><td>0.710</td><td></td><td></td></tr><tr><td>First-stage F-test</td><td></td><td></td><td>5.424</td><td></td><td></td></tr><tr><td>Anderson-Rubin CI</td><td></td><td></td><td></td><td>[0.971;2.242]</td><td>[-3.195;-1.652]</td></tr><tr><td colspan="6"><strong>All panels</strong></td></tr><tr><td>Firm controls</td><td></td><td></td><td></td><td>yes</td><td></td></tr><tr><td>Product FE</td><td></td><td></td><td></td><td>yes</td><td></td></tr><tr><td>District FE</td><td></td><td></td><td></td><td>yes</td><td></td></tr><tr><td>State × Year FE</td><td></td><td></td><td></td><td>yes</td><td></td></tr><tr><td>Observations</td><td></td><td></td><td></td><td>428,197</td><td></td></tr></tbody></table>

Note: c significant at 10%; b at 5%; a at 1%. Standard errors are clustered at the district × year level. Panel A reproduces Table OA4.12 columns 1 and 3, while Panel B replicates columns 2 and 4 using an instrumental variable strategy. The dependent variables are the (log) price and the (log) quantity of the firm's production defined at the 5-digit level of the product classification code (columns 1, 2, 4 and 5). *Direct exposure to conflict* is an indicator that equals 1 if the firm is located in a conflict-affected district. It is instrumented by the interaction between district-level forest cover in 2000 and the (log) international price of timber between 2000 and 2009. *Production network exposure* is a firm-level continuous measure calculated according to Equation OA4.3 and separated into conflict versus peaceful districts. Firm controls include (log) input prices (intermediate inputs, wages, and interest rate), and time-varying input bundle fixed effects (defined at the 2-digit level). Product (defined at the 4-digit level), district, and state × year fixed effects are included. See main text for further details and Table OA4.11 for descriptive statistics.
<table><caption>Table OA4.21: Spillover effects of conflict</caption><thead><tr><th rowspan="2">Dep. var.</th><th colspan="4">Price</th></tr><tr><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th></tr></thead><tbody><tr><td>direct exposure to conflict</td><td>0.115<sup>a</sup><br>(0.040)</td><td>0.071<br>(0.045)</td><td></td><td></td></tr><tr><td>production network exposure, conflict districts</td><td>0.042<br>(0.154)</td><td>0.048<br>(0.154)</td><td>-0.104<br>(0.170)</td><td></td></tr><tr><td>production network exposure, peaceful districts</td><td>0.329<sup>a</sup><br>(0.054)</td><td>0.329<sup>a</sup><br>(0.054)</td><td>0.359<sup>a</sup><br>(0.056)</td><td>0.346<sup>a</sup><br>(0.067)</td></tr><tr><td>adjacent districts to conflict districts</td><td>0.049<br>(0.036)</td><td></td><td></td><td></td></tr><tr><td>(log) distance to conflict</td><td></td><td>-0.015<br>(0.028)</td><td></td><td></td></tr></tbody><thead><tr><th rowspan="2">Dep. var.</th><th colspan="4">Quantity</th></tr><tr><th>(5)</th><th>(6)</th><th>(7)</th><th>(8)</th></tr></thead><tbody><tr><td>direct exposure to conflict</td><td>-0.147<sup>a</sup><br>(0.047)</td><td>-0.054<br>(0.051)</td><td></td><td></td></tr><tr><td>production network exposure, conflict districts</td><td>-0.125<br>(0.200)</td><td>-0.138<br>(0.201)</td><td>0.070<br>(0.233)</td><td></td></tr><tr><td>production network exposure, peaceful districts</td><td>-0.417<sup>a</sup><br>(0.069)</td><td>-0.415<sup>a</sup><br>(0.069)</td><td>-0.446<sup>a</sup><br>(0.072)</td><td>-0.489<sup>a</sup><br>(0.095)</td></tr><tr><td>(log) distance to conflict</td><td></td><td>0.051<sup>c</sup><br>(0.029)</td><td></td><td></td></tr><tr><td>adjacent districts to conflict districts</td><td>-0.066<sup>c</sup><br>(0.038)</td><td></td><td></td><td></td></tr><tr><td>Firm controls</td><td colspan="4">yes</td></tr><tr><td>Product FE</td><td colspan="4">yes</td></tr><tr><td>District FE</td><td>yes</td><td>yes</td><td>yes</td><td>no</td></tr><tr><td>District×year FE</td><td>no</td><td>no</td><td>no</td><td>yes</td></tr><tr><td>State×year FE</td><td colspan="4">yes</td></tr><tr><td>Observations</td><td>428,410</td><td>428,410</td><td>428,410</td><td>258,308</td></tr><tr><td>R-squared (price)</td><td>0.468</td><td>0.468</td><td>0.476</td><td>0.479</td></tr><tr><td>R-squared (quantity)</td><td>0.537</td><td>0.537</td><td>0.543</td><td>0.554</td></tr></tbody></table>

Note: c significant at 10%; b at 5%; a at 1%. Standard errors are clustered at the district×year level. The dependent variables are the (log) price and the (log) quantity of the firm's production defined at the 5-digit level of the product classification code. Direct exposure to conflict is an indicator that equals 1 if the firm is located in a conflict-affected district and Production network exposure is a firm-level continuous measure calculated according to Equation OA4.3 and separated into conflict versus peaceful districts. Column 1 includes an indicator that equals 1 if the firm is located in an adjacent conflict-affected district. Column 2 includes (log) distance between the firm and its closest conflict-affected district. Column 3 controls for district×year fixed effects. Column 4 excludes firms located in conflict-affected state. Firm controls include (log) input prices (intermediate inputs, wages, and interest rate), and time-varying input bundle fixed effects (defined at the 2-digit level). Product (defined at the 4-digit level), district, and state×year fixed effects are included. See main text for further details and Table OA4.11 for descriptive statistics.
<table><caption>Table OA4.21: Spillover effects of conflict</caption><thead><tr><th rowspan="2">Dep. var.</th><th colspan="4">Price</th></tr><tr><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th></tr></thead><tbody><tr><td>direct exposure to conflict</td><td>0.115<sup>a</sup><br>(0.040)</td><td>0.071<br>(0.045)</td><td></td><td></td></tr><tr><td>production network exposure, conflict districts</td><td>0.042<br>(0.154)</td><td>0.048<br>(0.154)</td><td>-0.104<br>(0.170)</td><td></td></tr><tr><td>production network exposure, peaceful districts</td><td>0.329<sup>a</sup><br>(0.054)</td><td>0.329<sup>a</sup><br>(0.054)</td><td>0.359<sup>a</sup><br>(0.056)</td><td>0.346<sup>a</sup><br>(0.067)</td></tr><tr><td>adjacent districts to conflict districts</td><td>0.049<br>(0.036)</td><td></td><td></td><td></td></tr><tr><td>(log) distance to conflict</td><td></td><td>-0.015<br>(0.028)</td><td></td><td></td></tr></tbody><thead><tr><th rowspan="2">Dep. var.</th><th colspan="4">Quantity</th></tr><tr><th>(5)</th><th>(6)</th><th>(7)</th><th>(8)</th></tr></thead><tbody><tr><td>direct exposure to conflict</td><td>-0.147<sup>a</sup><br>(0.047)</td><td>-0.054<br>(0.051)</td><td></td><td></td></tr><tr><td>production network exposure, conflict districts</td><td>-0.125<br>(0.200)</td><td>-0.138<br>(0.201)</td><td>0.070<br>(0.233)</td><td></td></tr><tr><td>production network exposure, peaceful districts</td><td>-0.417<sup>a</sup><br>(0.069)</td><td>-0.415<sup>a</sup><br>(0.069)</td><td>-0.446<sup>a</sup><br>(0.072)</td><td>-0.489<sup>a</sup><br>(0.095)</td></tr><tr><td>(log) distance to conflict</td><td></td><td>0.051<sup>c</sup><br>(0.029)</td><td></td><td></td></tr><tr><td>adjacent districts to conflict districts</td><td>-0.066<sup>c</sup><br>(0.038)</td><td></td><td></td><td></td></tr><tr><td>Firm controls</td><td colspan="4">yes</td></tr><tr><td>Product FE</td><td colspan="4">yes</td></tr><tr><td>District FE</td><td>yes</td><td>yes</td><td>yes</td><td>no</td></tr><tr><td>District×year FE</td><td>no</td><td>no</td><td>no</td><td>yes</td></tr><tr><td>State×year FE</td><td colspan="4">yes</td></tr><tr><td>Observations</td><td>428,410</td><td>428,410</td><td>428,410</td><td>258,308</td></tr><tr><td>R-squared (price)</td><td>0.468</td><td>0.468</td><td>0.476</td><td>0.479</td></tr><tr><td>R-squared (quantity)</td><td>0.537</td><td>0.537</td><td>0.543</td><td>0.554</td></tr></tbody></table>

Note: c significant at 10%; b at 5%; a at 1%. Standard errors are clustered at the district×year level. The dependent variables are the (log) price and the (log) quantity of the firm's production defined at the 5-digit level of the product classification code. Direct exposure to conflict is an indicator that equals 1 if the firm is located in a conflict-affected district and Production network exposure is a firm-level continuous measure calculated according to Equation OA4.3 and separated into conflict versus peaceful districts. Column 1 includes an indicator that equals 1 if the firm is located in an adjacent conflict-affected district. Column 2 includes (log) distance between the firm and its closest conflict-affected district. Column 3 controls for district×year fixed effects. Column 4 excludes firms located in conflict-affected state. Firm controls include (log) input prices (intermediate inputs, wages, and interest rate), and time-varying input bundle fixed effects (defined at the 2-digit level). Product (defined at the 4-digit level), district, and state×year fixed effects are included. See main text for further details and Table OA4.11 for descriptive statistics.
<table><caption>Table OA4.22: Elasticities</caption><thead><tr><th rowspan="3">Elasticity</th><th colspan="8">Distance</th><th colspan="5">Supplier size</th></tr><tr><th colspan="8">Price</th><th colspan="5">Quantity</th></tr><tr><th>-2</th><th>-3</th><th>-4</th><th>-5</th><th>2</th><th>3</th><th>4</th><th>5</th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th><th>(6)</th><th>(7)</th><th>(8)</th></tr></thead><tbody><tr><td>Dep. var.</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>direct exposure to conflict</td><td>0.086<sup>b</sup><br>(0.034)</td><td>0.087<sup>b</sup><br>(0.034)</td><td>0.087<sup>a</sup><br>(0.034)</td><td>0.087<sup>a</sup><br>(0.034)</td><td>0.081<sup>b</sup><br>(0.034)</td><td>0.078<sup>b</sup><br>(0.033)</td><td>0.076<sup>b</sup><br>(0.033)</td><td>0.075<sup>b</sup><br>(0.033)</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>production network exposure, conflict districts</td><td>0.049<br>(0.149)</td><td>0.029<br>(0.143)</td><td>0.017<br>(0.137)</td><td>0.010<br>(0.133)</td><td>0.001<br>(0.153)</td><td>-0.016<br>(0.148)</td><td>-0.018<br>(0.146)</td><td>-0.017<br>(0.144)</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>production network exposure, peaceful districts</td><td>0.295<sup>a</sup><br>(0.052)</td><td>0.284<sup>a</sup><br>(0.050)</td><td>0.276<sup>a</sup><br>(0.049)</td><td>0.267<sup>a</sup><br>(0.048)</td><td>0.273<sup>a</sup><br>(0.048)</td><td>0.243<sup>a</sup><br>(0.045)</td><td>0.233<sup>a</sup><br>(0.044)</td><td>0.228<sup>a</sup><br>(0.043)</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Dep. var.</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>direct exposure to conflict</td><td>-0.107<sup>a</sup><br>(0.040)</td><td>-0.108<sup>a</sup><br>(0.039)</td><td>-0.107<sup>a</sup><br>(0.039)</td><td>-0.105<sup>a</sup><br>(0.039)</td><td>-0.099<sup>b</sup><br>(0.039)</td><td>-0.094<sup>b</sup><br>(0.039)</td><td>-0.093<sup>b</sup><br>(0.039)</td><td>-0.092<sup>b</sup><br>(0.039)</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>production network exposure, conflict districts</td><td>-0.199<br>(0.190)</td><td>-0.200<br>(0.180)</td><td>-0.187<br>(0.174)</td><td>-0.180<br>(0.170)</td><td>-0.090<br>(0.188)</td><td>-0.087<br>(0.180)</td><td>-0.088<br>(0.175)</td><td>-0.087<br>(0.173)</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>production network exposure, peaceful districts</td><td>-0.402<sup>a</sup><br>(0.067)</td><td>-0.391<sup>a</sup><br>(0.064)</td><td>-0.373<sup>a</sup><br>(0.061)</td><td>-0.356<sup>a</sup><br>(0.059)</td><td>-0.337<sup>a</sup><br>(0.061)</td><td>-0.304<sup>a</sup><br>(0.057)</td><td>-0.294<sup>a</sup><br>(0.055)</td><td>-0.289<sup>a</sup><br>(0.054)</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Firm controls</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Product FE</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>District FE</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>State × Year FE</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Observations</td><td colspan="8">428,410</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>R-squared (price)</td><td>0.468</td><td>0.468</td><td>0.468</td><td>0.468</td><td>0.468</td><td>0.468</td><td>0.468</td><td>0.468</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>R-squared (quantity)</td><td>0.537</td><td>0.537</td><td>0.537</td><td>0.537</td><td>0.537</td><td>0.537</td><td>0.537</td><td>0.537</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr></tbody></table>

Note: c significant at 10%; b at 5%; a at 1%. Standard errors are clustered at the district × year level. The dependent variables are the (log) price and the (log) quantity of the firm's production defined at the 5-digit level of the product classification code. Direct exposure to conflict is an indicator that equals 1 if the firm is located in a conflict-affected district. Production network exposure is a firm-level continuous measure calculated according to Equation OA4.3 in which $\rho_{jit(k)}$, our measure of supplier's importance, is estimated changing the elasticity of bilateral trade with respect to distance (from -2 to -5, columns 1 to 4) and with respect to supplier size (from 2 to 5, columns 5 to 8). The measure is then separated into conflict versus peaceful districts. Firm controls include (log) input prices (intermediate inputs, wages, and interest rate), and time-varying input bundle fixed effects (defined at the 2-digit level). Product (defined at the 4-digit level), district, and state × year fixed effects are included. See main text for further details and Table OA4.11 for descriptive statistics.
<table><caption>Table OA4.22: Elasticities</caption><thead><tr><th rowspan="3">Elasticity</th><th colspan="8">Distance</th><th colspan="5">Supplier size</th></tr><tr><th colspan="8">Price</th><th colspan="5">Quantity</th></tr><tr><th>-2</th><th>-3</th><th>-4</th><th>-5</th><th>2</th><th>3</th><th>4</th><th>5</th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th><th>(6)</th><th>(7)</th><th>(8)</th></tr></thead><tbody><tr><td>Dep. var.</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>direct exposure to conflict</td><td>0.086<sup>b</sup><br>(0.034)</td><td>0.087<sup>b</sup><br>(0.034)</td><td>0.087<sup>a</sup><br>(0.034)</td><td>0.087<sup>a</sup><br>(0.034)</td><td>0.081<sup>b</sup><br>(0.034)</td><td>0.078<sup>b</sup><br>(0.033)</td><td>0.076<sup>b</sup><br>(0.033)</td><td>0.075<sup>b</sup><br>(0.033)</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>production network exposure, conflict districts</td><td>0.049<br>(0.149)</td><td>0.029<br>(0.143)</td><td>0.017<br>(0.137)</td><td>0.010<br>(0.133)</td><td>0.001<br>(0.153)</td><td>-0.016<br>(0.148)</td><td>-0.018<br>(0.146)</td><td>-0.017<br>(0.144)</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>production network exposure, peaceful districts</td><td>0.295<sup>a</sup><br>(0.052)</td><td>0.284<sup>a</sup><br>(0.050)</td><td>0.276<sup>a</sup><br>(0.049)</td><td>0.267<sup>a</sup><br>(0.048)</td><td>0.273<sup>a</sup><br>(0.048)</td><td>0.243<sup>a</sup><br>(0.045)</td><td>0.233<sup>a</sup><br>(0.044)</td><td>0.228<sup>a</sup><br>(0.043)</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Dep. var.</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>direct exposure to conflict</td><td>-0.107<sup>a</sup><br>(0.040)</td><td>-0.108<sup>a</sup><br>(0.039)</td><td>-0.107<sup>a</sup><br>(0.039)</td><td>-0.105<sup>a</sup><br>(0.039)</td><td>-0.099<sup>b</sup><br>(0.039)</td><td>-0.094<sup>b</sup><br>(0.039)</td><td>-0.093<sup>b</sup><br>(0.039)</td><td>-0.092<sup>b</sup><br>(0.039)</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>production network exposure, conflict districts</td><td>-0.199<br>(0.190)</td><td>-0.200<br>(0.180)</td><td>-0.187<br>(0.174)</td><td>-0.180<br>(0.170)</td><td>-0.090<br>(0.188)</td><td>-0.087<br>(0.180)</td><td>-0.088<br>(0.175)</td><td>-0.087<br>(0.173)</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>production network exposure, peaceful districts</td><td>-0.402<sup>a</sup><br>(0.067)</td><td>-0.391<sup>a</sup><br>(0.064)</td><td>-0.373<sup>a</sup><br>(0.061)</td><td>-0.356<sup>a</sup><br>(0.059)</td><td>-0.337<sup>a</sup><br>(0.061)</td><td>-0.304<sup>a</sup><br>(0.057)</td><td>-0.294<sup>a</sup><br>(0.055)</td><td>-0.289<sup>a</sup><br>(0.054)</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Firm controls</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Product FE</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>District FE</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>State × Year FE</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Observations</td><td colspan="8">428,410</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>R-squared (price)</td><td>0.468</td><td>0.468</td><td>0.468</td><td>0.468</td><td>0.468</td><td>0.468</td><td>0.468</td><td>0.468</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>R-squared (quantity)</td><td>0.537</td><td>0.537</td><td>0.537</td><td>0.537</td><td>0.537</td><td>0.537</td><td>0.537</td><td>0.537</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr></tbody></table>

Note: c significant at 10%; b at 5%; a at 1%. Standard errors are clustered at the district × year level. The dependent variables are the (log) price and the (log) quantity of the firm's production defined at the 5-digit level of the product classification code. Direct exposure to conflict is an indicator that equals 1 if the firm is located in a conflict-affected district. Production network exposure is a firm-level continuous measure calculated according to Equation OA4.3 in which $\rho_{jit(k)}$, our measure of supplier's importance, is estimated changing the elasticity of bilateral trade with respect to distance (from -2 to -5, columns 1 to 4) and with respect to supplier size (from 2 to 5, columns 5 to 8). The measure is then separated into conflict versus peaceful districts. Firm controls include (log) input prices (intermediate inputs, wages, and interest rate), and time-varying input bundle fixed effects (defined at the 2-digit level). Product (defined at the 4-digit level), district, and state × year fixed effects are included. See main text for further details and Table OA4.11 for descriptive statistics.
<table><caption>Table OA4.23: Trade Restrictions</caption><thead><tr><th>Trade Restriction</th><th>intra-state</th><th>intra-caste</th><th>intra-tribe</th><th>intra-religion</th></tr></thead><tbody><tr><td>Dep. var.</td><td></td><td></td><td>Price</td><td></td></tr><tr><td></td><td>(1)</td><td>(2)</td><td>(3)</td><td>(4)</td></tr><tr><td>direct exposure to conflict</td><td>0.066<sup>b</sup><br>(0.032)</td><td>0.084<sup>a</sup><br>(0.032)</td><td>0.073<sup>b</sup><br>(0.032)</td><td>0.080<sup>b</sup><br>(0.032)</td></tr><tr><td>production network exposure, conflict districts</td><td>-0.269<br>(0.181)</td><td>-0.148<br>(0.125)</td><td>0.027<br>(0.124)</td><td>-0.034<br>(0.115)</td></tr><tr><td>production network exposure, peaceful districts</td><td>0.182<sup>a</sup><br>(0.057)</td><td>0.221<sup>a</sup><br>(0.038)</td><td>0.204<sup>a</sup><br>(0.043)</td><td>0.251<sup>a</sup><br>(0.039)</td></tr><tr><td>Dep. var.</td><td></td><td></td><td>Quantity</td><td></td></tr><tr><td></td><td>(5)</td><td>(6)</td><td>(7)</td><td>(8)</td></tr><tr><td>direct exposure to conflict</td><td>-0.089<sup>b</sup><br>(0.038)</td><td>-0.125<sup>a</sup><br>(0.038)</td><td>-0.096<sup>b</sup><br>(0.038)</td><td>-0.089<sup>b</sup><br>(0.039)</td></tr><tr><td>production network exposure, conflict districts</td><td>-0.012<br>(0.274)</td><td>0.396<sup>b</sup><br>(0.177)</td><td>-0.059<br>(0.184)</td><td>-0.095<br>(0.177)</td></tr><tr><td>production network exposure, peaceful districts</td><td>-0.321<sup>a</sup><br>(0.093)</td><td>-0.317<sup>a</sup><br>(0.064)</td><td>-0.263<sup>a</sup><br>(0.064)</td><td>-0.252<sup>a</sup><br>(0.053)</td></tr><tr><td>Firm controls</td><td></td><td></td><td>yes</td><td></td></tr><tr><td>Product FE</td><td></td><td></td><td>yes</td><td></td></tr><tr><td>District FE</td><td></td><td></td><td>yes</td><td></td></tr><tr><td>State × Year FE</td><td></td><td></td><td>yes</td><td></td></tr><tr><td>Observations</td><td colspan="4">428,410</td></tr><tr><td>R-squared (price)</td><td>0.468</td><td>0.468</td><td>0.468</td><td>0.468</td></tr><tr><td>R-squared (quantity)</td><td>0.537</td><td>0.537</td><td>0.537</td><td>0.537</td></tr></tbody></table>

Note: c significant at 10%; b at 5%; a at 1%. Standard errors are clustered at the district × year level. The dependent variables are the (log) price and the (log) quantity of the firm's production defined at the 5-digit level of the product classification code. Direct exposure to conflict is an indicator that equals 1 if the firm is located in a conflict-affected district. Production network exposure is a firm-level continuous measure calculated according to Equation OA4.3 in which trade is restricted within firms belonging to the same state (column 1), caste (column 2), tribe (column 3), and religion (column 4). The measure is then separated into conflict versus peaceful districts. Firm controls include (log) input prices (intermediate inputs, wages, and interest rate), and time-varying input bundle fixed effects (defined at the 2-digit level). Product (defined at the 4-digit level), district, and state × year fixed effects are included. See main text for further details and Table OA4.11 for descriptive statistics.
<table><caption>Table OA4.23: Trade Restrictions</caption><thead><tr><th>Trade Restriction</th><th>intra-state</th><th>intra-caste</th><th>intra-tribe</th><th>intra-religion</th></tr></thead><tbody><tr><td>Dep. var.</td><td></td><td></td><td>Price</td><td></td></tr><tr><td></td><td>(1)</td><td>(2)</td><td>(3)</td><td>(4)</td></tr><tr><td>direct exposure to conflict</td><td>0.066<sup>b</sup><br>(0.032)</td><td>0.084<sup>a</sup><br>(0.032)</td><td>0.073<sup>b</sup><br>(0.032)</td><td>0.080<sup>b</sup><br>(0.032)</td></tr><tr><td>production network exposure, conflict districts</td><td>-0.269<br>(0.181)</td><td>-0.148<br>(0.125)</td><td>0.027<br>(0.124)</td><td>-0.034<br>(0.115)</td></tr><tr><td>production network exposure, peaceful districts</td><td>0.182<sup>a</sup><br>(0.057)</td><td>0.221<sup>a</sup><br>(0.038)</td><td>0.204<sup>a</sup><br>(0.043)</td><td>0.251<sup>a</sup><br>(0.039)</td></tr><tr><td>Dep. var.</td><td></td><td></td><td>Quantity</td><td></td></tr><tr><td></td><td>(5)</td><td>(6)</td><td>(7)</td><td>(8)</td></tr><tr><td>direct exposure to conflict</td><td>-0.089<sup>b</sup><br>(0.038)</td><td>-0.125<sup>a</sup><br>(0.038)</td><td>-0.096<sup>b</sup><br>(0.038)</td><td>-0.089<sup>b</sup><br>(0.039)</td></tr><tr><td>production network exposure, conflict districts</td><td>-0.012<br>(0.274)</td><td>0.396<sup>b</sup><br>(0.177)</td><td>-0.059<br>(0.184)</td><td>-0.095<br>(0.177)</td></tr><tr><td>production network exposure, peaceful districts</td><td>-0.321<sup>a</sup><br>(0.093)</td><td>-0.317<sup>a</sup><br>(0.064)</td><td>-0.263<sup>a</sup><br>(0.064)</td><td>-0.252<sup>a</sup><br>(0.053)</td></tr><tr><td>Firm controls</td><td></td><td></td><td>yes</td><td></td></tr><tr><td>Product FE</td><td></td><td></td><td>yes</td><td></td></tr><tr><td>District FE</td><td></td><td></td><td>yes</td><td></td></tr><tr><td>State × Year FE</td><td></td><td></td><td>yes</td><td></td></tr><tr><td>Observations</td><td colspan="4">428,410</td></tr><tr><td>R-squared (price)</td><td>0.468</td><td>0.468</td><td>0.468</td><td>0.468</td></tr><tr><td>R-squared (quantity)</td><td>0.537</td><td>0.537</td><td>0.537</td><td>0.537</td></tr></tbody></table>

Note: c significant at 10%; b at 5%; a at 1%. Standard errors are clustered at the district × year level. The dependent variables are the (log) price and the (log) quantity of the firm's production defined at the 5-digit level of the product classification code. *Direct exposure to conflict* is an indicator that equals 1 if the firm is located in a conflict-affected district. *Production network exposure* is a firm-level continuous measure calculated according to Equation OA4.3 in which trade is restricted within firms belonging to the same state (column 1), caste (column 2), tribe (column 3), and religion (column 4). The measure is then separated into conflict versus peaceful districts. Firm controls include (log) input prices (intermediate inputs, wages, and interest rate), and time-varying input bundle fixed effects (defined at the 2-digit level). Product (defined at the 4-digit level), district, and state × year fixed effects are included. See main text for further details and Table OA4.11 for descriptive statistics.
<table><caption>Table OA4.24: Alternative conflict measure</caption><thead><tr><th>Dep. var.</th><th>Price<br>(1)</th><th>Quantity<br>(2)</th></tr></thead><tbody><tr><td>direct exposure to conflict (fat.)</td><td>0.083<sup>b</sup><br>(0.033)</td><td>-0.106<sup>a</sup><br>(0.041)</td></tr><tr><td>production network exposure, conflict districts (fat.)</td><td>0.060<br>(0.153)</td><td>-0.140<br>(0.204)</td></tr><tr><td>production network exposure, peaceful districts (fat.)</td><td>0.309<sup>a</sup><br>(0.053)</td><td>-0.422<sup>a</sup><br>(0.069)</td></tr><tr><td>Firm controls</td><td>— yes —</td><td></td></tr><tr><td>Product FE</td><td>— yes —</td><td></td></tr><tr><td>District FE</td><td>— yes —</td><td></td></tr><tr><td>State × Year FE</td><td>— yes —</td><td></td></tr><tr><td>Observations</td><td>428,410</td><td>428,410</td></tr><tr><td>R-squared</td><td>0.468</td><td>0.537</td></tr></tbody></table>

Note: <sup>c</sup> significant at 10%; <sup>b</sup> at 5%; <sup>a</sup> at 1%. Standard errors are clustered at the district×year level. The dependent variables are the (log) price and the (log) quantity of the firm's production defined at the 5-digit level of the product classification code. *Direct exposure to conflict* is an indicator that equals 1 if the firm is located in a conflict-affected district and *Production network exposure* is a firm-level continuous measure calculated according to Equation OA4.3 and separated into conflict versus peaceful districts. Conflict-affected districts are defined based on the incidence of at least one fatality (instead of one violent event). Firm controls include (log) input prices (intermediate inputs, wages, and interest rate), and time-varying input bundle fixed effects (defined at the 2-digit level). Product (defined at the 4-digit level), district, and state×year fixed effects are included. See main text for further details and Table OA4.11 for descriptive statistics.
<table><caption>Table OA4.24: Alternative conflict measure</caption><thead><tr><th>Dep. var.</th><th>Price<br>(1)</th><th>Quantity<br>(2)</th></tr></thead><tbody><tr><td>direct exposure to conflict (fat.)</td><td>0.083<sup>b</sup><br>(0.033)</td><td>-0.106<sup>a</sup><br>(0.041)</td></tr><tr><td>production network exposure, conflict districts (fat.)</td><td>0.060<br>(0.153)</td><td>-0.140<br>(0.204)</td></tr><tr><td>production network exposure, peaceful districts (fat.)</td><td>0.309<sup>a</sup><br>(0.053)</td><td>-0.422<sup>a</sup><br>(0.069)</td></tr><tr><td>Firm controls</td><td>— yes —</td><td>—</td></tr><tr><td>Product FE</td><td>— yes —</td><td>—</td></tr><tr><td>District FE</td><td>— yes —</td><td>—</td></tr><tr><td>State × Year FE</td><td>— yes —</td><td>—</td></tr><tr><td>Observations</td><td>428,410</td><td>428,410</td></tr><tr><td>R-squared</td><td>0.468</td><td>0.537</td></tr></tbody></table>

Note: c significant at 10%; b at 5%; a at 1%. Standard errors are clustered at the district × year level. The dependent variables are the (log) price and the (log) quantity of the firm's production defined at the 5-digit level of the product classification code. *Direct exposure to conflict* is an indicator that equals 1 if the firm is located in a conflict-affected district and *Production network exposure* is a firm-level continuous measure calculated according to Equation OA4.3 and separated into conflict versus peaceful districts. Conflict-affected districts are defined based on the incidence of at least one fatality (instead of one violent event). Firm controls include (log) input prices (intermediate inputs, wages, and interest rate), and time-varying input bundle fixed effects (defined at the 2-digit level). Product (defined at the 4-digit level), district, and state × year fixed effects are included. See main text for further details and Table OA4.11 for descriptive statistics.
<table><caption>Table OA4.25: Subsample of firms</caption><thead><tr><th>Years surveyed</th><th>&gt; 1</th><th>&gt; 3</th><th>&gt; 5</th><th>&gt; 7</th><th>census</th></tr><tr><th>Dep. var.</th><th colspan="5">Price</th></tr><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th></tr></thead><tbody><tr><td>direct exposure to conflict</td><td>0.071<sup>b</sup><br>(0.034)</td><td>0.088<sup>b</sup><br>(0.043)</td><td>0.118<sup>b</sup><br>(0.048)</td><td>0.136<sup>a</sup><br>(0.050)</td><td>0.089<sup>b</sup><br>(0.041)</td></tr><tr><td>production network exposure, conflict districts</td><td>0.016<br>(0.154)</td><td>-0.036<br>(0.197)</td><td>-0.166<br>(0.253)</td><td>-0.185<br>(0.296)</td><td>-0.048<br>(0.201)</td></tr><tr><td>production network exposure, peaceful districts</td><td>0.324<sup>a</sup><br>(0.056)</td><td>0.337<sup>a</sup><br>(0.070)</td><td>0.231<sup>a</sup><br>(0.083)</td><td>0.228<sup>b</sup><br>(0.097)</td><td>0.340<sup>a</sup><br>(0.066)</td></tr><tr><td>Dep. var.</td><td colspan="5">Quantity</td></tr><tr><td></td><td>(6)</td><td>(7)</td><td>(8)</td><td>(9)</td><td>(10)</td></tr><tr><td>direct exposure to conflict</td><td>-0.096<sup>b</sup><br>(0.038)</td><td>-0.095<sup>b</sup><br>(0.045)</td><td>-0.076<br>(0.048)</td><td>-0.103<sup>c</sup><br>(0.058)</td><td>-0.109<sup>b</sup><br>(0.044)</td></tr><tr><td>production network exposure, conflict districts</td><td>-0.032<br>(0.202)</td><td>0.182<br>(0.241)</td><td>0.132<br>(0.268)</td><td>0.003<br>(0.313)</td><td>0.147<br>(0.221)</td></tr><tr><td>production network exposure, peaceful districts</td><td>-0.382<sup>a</sup><br>(0.071)</td><td>-0.281<sup>a</sup><br>(0.081)</td><td>-0.080<br>(0.093)</td><td>-0.135<br>(0.109)</td><td>-0.310<sup>a</sup><br>(0.083)</td></tr><tr><td>Firm controls</td><td colspan="5">yes</td></tr><tr><td>Product FE</td><td colspan="5">yes</td></tr><tr><td>District FE</td><td colspan="5">yes</td></tr><tr><td>State &times; Year FE</td><td colspan="5">yes</td></tr><tr><td>Observations</td><td>360,279</td><td>219,331</td><td>152,621</td><td>111,816</td><td>270,914</td></tr><tr><td>R-squared (price)</td><td>0.474</td><td>0.492</td><td>0.513</td><td>0.533</td><td>0.476</td></tr><tr><td>R-squared (quantity)</td><td>0.534</td><td>0.523</td><td>0.522</td><td>0.527</td><td>0.506</td></tr></tbody></table>

Note: c significant at 10%; b at 5%; a at 1%. Standard errors are clustered at the district&times;year level. The sample of firms varies according to the number of years surveyed or whether the firm was part of the census (see column's header for details). The dependent variables are the (log) price and the (log) quantity of the firm's production defined at the 5-digit level of the product classification code. *Direct exposure to conflict* is an indicator that equals 1 if the firm is located in a conflict-affected district and *Production network exposure* is a firm-level continuous measure calculated according to Equation OA4.3 and separated into conflict versus peaceful districts. Firm controls include (log) input prices (intermediate inputs, wages, and interest rate), and time-varying input bundle fixed effects (defined at the 2-digit level). Product (defined at the 4-digit level), district, and state&times;year fixed effects are included. See main text for further details and Table OA4.11 for descriptive statistics.
<table><caption>Table OA4.25: Subsample of firms</caption><thead><tr><th>Years surveyed</th><th>&gt; 1</th><th>&gt; 3</th><th>&gt; 5</th><th>&gt; 7</th><th>census</th></tr><tr><th>Dep. var.</th><th>(1)</th><th>(2)</th><th>Price<br>(3)</th><th>(4)</th><th>(5)</th></tr></thead><tbody><tr><td>direct exposure to conflict</td><td>0.071<sup>b</sup><br>(0.034)</td><td>0.088<sup>b</sup><br>(0.043)</td><td>0.118<sup>b</sup><br>(0.048)</td><td>0.136<sup>a</sup><br>(0.050)</td><td>0.089<sup>b</sup><br>(0.041)</td></tr><tr><td>production network exposure, conflict districts</td><td>0.016<br>(0.154)</td><td>-0.036<br>(0.197)</td><td>-0.166<br>(0.253)</td><td>-0.185<br>(0.296)</td><td>-0.048<br>(0.201)</td></tr><tr><td>production network exposure, peaceful districts</td><td>0.324<sup>a</sup><br>(0.056)</td><td>0.337<sup>a</sup><br>(0.070)</td><td>0.231<sup>a</sup><br>(0.083)</td><td>0.228<sup>b</sup><br>(0.097)</td><td>0.340<sup>a</sup><br>(0.066)</td></tr><tr><td>Dep. var.</td><td>(6)</td><td>(7)</td><td>Quantity<br>(8)</td><td>(9)</td><td>(10)</td></tr><tr><td>direct exposure to conflict</td><td>-0.096<sup>b</sup><br>(0.038)</td><td>-0.095<sup>b</sup><br>(0.045)</td><td>-0.076<br>(0.048)</td><td>-0.103<sup>c</sup><br>(0.058)</td><td>-0.109<sup>b</sup><br>(0.044)</td></tr><tr><td>production network exposure, conflict districts</td><td>-0.032<br>(0.202)</td><td>0.182<br>(0.241)</td><td>0.132<br>(0.268)</td><td>0.003<br>(0.313)</td><td>0.147<br>(0.221)</td></tr><tr><td>production network exposure, peaceful districts</td><td>-0.382<sup>a</sup><br>(0.071)</td><td>-0.281<sup>a</sup><br>(0.081)</td><td>-0.080<br>(0.093)</td><td>-0.135<br>(0.109)</td><td>-0.310<sup>a</sup><br>(0.083)</td></tr><tr><td>Firm controls</td><td></td><td></td><td>yes</td><td></td><td></td></tr><tr><td>Product FE</td><td></td><td></td><td>yes</td><td></td><td></td></tr><tr><td>District FE</td><td></td><td></td><td>yes</td><td></td><td></td></tr><tr><td>State &times; Year FE</td><td></td><td></td><td>yes</td><td></td><td></td></tr><tr><td>Observations</td><td>360,279</td><td>219,331</td><td>152,621</td><td>111,816</td><td>270,914</td></tr><tr><td>R-squared (price)</td><td>0.474</td><td>0.492</td><td>0.513</td><td>0.533</td><td>0.476</td></tr><tr><td>R-squared (quantity)</td><td>0.534</td><td>0.523</td><td>0.522</td><td>0.527</td><td>0.506</td></tr></tbody></table>

Note: c significant at 10%; b at 5%; a at 1%. Standard errors are clustered at the district&times;year level. The sample of firms varies according to the number of years surveyed or whether the firm was part of the census (see column's header for details). The dependent variables are the (log) price and the (log) quantity of the firm's production defined at the 5-digit level of the product classification code. *Direct exposure to conflict* is an indicator that equals 1 if the firm is located in a conflict-affected district and *Production network exposure* is a firm-level continuous measure calculated according to Equation OA4.3 and separated into conflict versus peaceful districts. Firm controls include (log) input prices (intermediate inputs, wages, and interest rate), and time-varying input bundle fixed effects (defined at the 2-digit level). Product (defined at the 4-digit level), district, and state&times;year fixed effects are included. See main text for further details and Table OA4.11 for descriptive statistics.
<table><caption>Table OA4.26: Alternative production network exposure measure</caption><thead><tr><th>Dep. var.</th><th>Price<br>(1)</th><th>Quantity<br>(2)</th></tr></thead><tbody><tr><td>direct exposure to conflict</td><td>0.076<sup>b</sup><br>(0.033)</td><td>-0.095<sup>b</sup><br>(0.039)</td></tr><tr><td>production network exposure, conflict districts (non-linear)</td><td>0.099<br>(0.129)</td><td>-0.105<br>(0.163)</td></tr><tr><td>production network exposure, peaceful districts (non-linear)</td><td>0.266<sup>a</sup><br>(0.043)</td><td>-0.295<sup>a</sup><br>(0.052)</td></tr><tr><td>Firm controls</td><td>—</td><td>yes</td></tr><tr><td>Product FE</td><td>—</td><td>yes</td></tr><tr><td>District FE</td><td>—</td><td>yes</td></tr><tr><td>State × Year FE</td><td>—</td><td>yes</td></tr><tr><td>Observations</td><td>428,410</td><td>428,410</td></tr><tr><td>R-squared</td><td>0.468</td><td>0.537</td></tr></tbody></table>

Note: <sup>c</sup> significant at 10%; <sup>b</sup> at 5%; <sup>a</sup> at 1%. Standard errors are clustered at the district × year level. The dependent variables are the (log) price and the (log) quantity of the firm's production defined at the 5-digit level of the product classification code. Direct exposure to conflict is an indicator that equals 1 if the firm is located in a conflict-affected district and Production network exposure is a firm-level continuous measure calculated according to footnote 21 (alternative non-linear measure) and separated into conflict versus peaceful districts. Firm controls include (log) input prices (intermediate inputs, wages, and interest rate), and time-varying input bundle fixed effects (defined at the 2-digit level). Product (defined at the 4-digit level), district, and state × year fixed effects are included. See main text for further details and Table OA4.11 for descriptive statistics.

<table><caption>Table OA4.27: Lagged effects</caption><thead><tr><th>Dep. var.</th><th>Price<br>(1)</th><th>Quantity<br>(2)</th></tr></thead><tbody><tr><td>direct exposure to conflict</td><td>0.080<sup>c</sup><br>(0.046)</td><td>-0.083<sup>c</sup><br>(0.047)</td></tr><tr><td>direct exposure to conflict (t − 1)</td><td>0.065<br>(0.043)</td><td>-0.063<br>(0.046)</td></tr><tr><td>production network exposure, conflict districts</td><td>-0.067<br>(0.213)</td><td>-0.017<br>(0.270)</td></tr><tr><td>production network exposure, conflict districts (t − 1)</td><td>0.166<br>(0.231)</td><td>-0.220<br>(0.253)</td></tr><tr><td>production network exposure, peaceful districts</td><td>0.349<sup>a</sup><br>(0.077)</td><td>-0.296<sup>a</sup><br>(0.090)</td></tr><tr><td>production network exposure, peaceful districts (t − 1)</td><td>0.383<sup>a</sup><br>(0.076)</td><td>-0.293<sup>a</sup><br>(0.096)</td></tr><tr><td>Firm controls</td><td>—</td><td>yes</td></tr><tr><td>Product FE</td><td>—</td><td>yes</td></tr><tr><td>District FE</td><td>—</td><td>yes</td></tr><tr><td>State × Year FE</td><td>—</td><td>yes</td></tr><tr><td>Observations</td><td>199,345</td><td>199,345</td></tr><tr><td>R-squared</td><td>0.486</td><td>0.520</td></tr></tbody></table>

Note: <sup>c</sup> significant at 10%; <sup>b</sup> at 5%; <sup>a</sup> at 1%. Standard errors are clustered at the district × year level. The dependent variables are the (log) price and the (log) quantity of the firm's production defined at the 5-digit level of the product classification code. Direct exposure to conflict is an indicator that equals 1 if the firm is located in a conflict-affected district and Production network exposure is a firm-level continuous measure calculated according to Equation OA4.3 and separated into conflict versus peaceful areas. Lagged measures are included for each of these variables. Firm controls include (log) input prices (intermediate inputs, wages, and interest rate), (log) and time-varying input bundle fixed effects (defined at the 2-digit level). Product (defined at the 4-digit level), district, and state × year fixed effects are included. See main text for further details and Table OA4.11 for descriptive statistics.
<table><caption>Table OA4.26: Alternative production network exposure measure</caption><thead><tr><th>Dep. var.</th><th>Price<br>(1)</th><th>Quantity<br>(2)</th></tr></thead><tbody><tr><td>direct exposure to conflict</td><td>0.076<sup>b</sup><br>(0.033)</td><td>-0.095<sup>b</sup><br>(0.039)</td></tr><tr><td>production network exposure, conflict districts (non-linear)</td><td>0.099<br>(0.129)</td><td>-0.105<br>(0.163)</td></tr><tr><td>production network exposure, peaceful districts (non-linear)</td><td>0.266<sup>a</sup><br>(0.043)</td><td>-0.295<sup>a</sup><br>(0.052)</td></tr><tr><td>Firm controls</td><td>—</td><td>yes</td></tr><tr><td>Product FE</td><td>—</td><td>yes</td></tr><tr><td>District FE</td><td>—</td><td>yes</td></tr><tr><td>State × Year FE</td><td>—</td><td>yes</td></tr><tr><td>Observations</td><td>428,410</td><td>428,410</td></tr><tr><td>R-squared</td><td>0.468</td><td>0.537</td></tr></tbody></table>

Note: <sup>c</sup> significant at 10%; <sup>b</sup> at 5%; <sup>a</sup> at 1%. Standard errors are clustered at the district × year level. The dependent variables are the (log) price and the (log) quantity of the firm's production defined at the 5-digit level of the product classification code. *Direct exposure to conflict* is an indicator that equals 1 if the firm is located in a conflict-affected district and *Production network exposure* is a firm-level continuous measure calculated according to footnote 21 (alternative non-linear measure) and separated into conflict versus peaceful districts. Firm controls include (log) input prices (intermediate inputs, wages, and interest rate), and time-varying input bundle fixed effects (defined at the 2-digit level). Product (defined at the 4-digit level), district, and state × year fixed effects are included. See main text for further details and Table OA4.11 for descriptive statistics.

<table><caption>Table OA4.27: Lagged effects</caption><thead><tr><th>Dep. var.</th><th>Price<br>(1)</th><th>Quantity<br>(2)</th></tr></thead><tbody><tr><td>direct exposure to conflict</td><td>0.080<sup>c</sup><br>(0.046)</td><td>-0.083<sup>c</sup><br>(0.047)</td></tr><tr><td>direct exposure to conflict (t − 1)</td><td>0.065<br>(0.043)</td><td>-0.063<br>(0.046)</td></tr><tr><td>production network exposure, conflict districts</td><td>-0.067<br>(0.213)</td><td>-0.017<br>(0.270)</td></tr><tr><td>production network exposure, conflict districts (t − 1)</td><td>0.166<br>(0.231)</td><td>-0.220<br>(0.253)</td></tr><tr><td>production network exposure, peaceful districts</td><td>0.349<sup>a</sup><br>(0.077)</td><td>-0.296<sup>a</sup><br>(0.090)</td></tr><tr><td>production network exposure, peaceful districts (t − 1)</td><td>0.383<sup>a</sup><br>(0.076)</td><td>-0.293<sup>a</sup><br>(0.096)</td></tr><tr><td>Firm controls</td><td>—</td><td>yes</td></tr><tr><td>Product FE</td><td>—</td><td>yes</td></tr><tr><td>District FE</td><td>—</td><td>yes</td></tr><tr><td>State × Year FE</td><td>—</td><td>yes</td></tr><tr><td>Observations</td><td>199,345</td><td>199,345</td></tr><tr><td>R-squared</td><td>0.486</td><td>0.520</td></tr></tbody></table>

Note: <sup>c</sup> significant at 10%; <sup>b</sup> at 5%; <sup>a</sup> at 1%. Standard errors are clustered at the district × year level. The dependent variables are the (log) price and the (log) quantity of the firm's production defined at the 5-digit level of the product classification code. *Direct exposure to conflict* is an indicator that equals 1 if the firm is located in a conflict-affected district and *Production network exposure* is a firm-level continuous measure calculated according to Equation OA4.3 and separated into conflict versus peaceful areas. Lagged measures are included for each of these variables. Firm controls include (log) input prices (intermediate inputs, wages, and interest rate), (log) and time-varying input bundle fixed effects (defined at the 2-digit level). Product (defined at the 4-digit level), district, and state × year fixed effects are included. See main text for further details and Table OA4.11 for descriptive statistics.
<table><caption>Table OA4.28: Clustering</caption><thead><tr><th>Clustering</th><th>district</th><th>state</th><th>state × year</th><th>Conley SE</th></tr></thead><tbody><tr><td>Dep. var.</td><td></td><td></td><td>Price</td><td></td></tr><tr><td></td><td>(1)</td><td>(2)</td><td>(3)</td><td>(4)</td></tr><tr><td>direct exposure to conflict</td><td>0.087<sup>b</sup><br>(0.042)</td><td>0.087<sup>a</sup><br>(0.028)</td><td>0.087<sup>b</sup><br>(0.036)</td><td>0.087<sup>b</sup><br>(0.046)</td></tr><tr><td>production network exposure, conflict districts</td><td>0.046<br>(0.219)</td><td>0.046<br>(0.157)</td><td>0.046<br>(0.137)</td><td>0.046<br>(0.207)</td></tr><tr><td>production network exposure, peaceful districts</td><td>0.330<sup>a</sup><br>(0.057)</td><td>0.330<sup>a</sup><br>(0.075)</td><td>0.330<sup>a</sup><br>(0.071)</td><td>0.330<sup>a</sup><br>(0.085)</td></tr><tr><td>Dep. var.</td><td></td><td></td><td>Quantity</td><td></td></tr><tr><td></td><td>(5)</td><td>(6)</td><td>(7)</td><td>(8)</td></tr><tr><td>direct exposure to conflict</td><td>-0.109<sup>b</sup><br>(0.051)</td><td>-0.109<sup>a</sup><br>(0.029)</td><td>-0.109<sup>b</sup><br>(0.045)</td><td>-0.109<sup>b</sup><br>(0.055)</td></tr><tr><td>production network exposure, conflict districts</td><td>-0.131<br>(0.199)</td><td>-0.131<br>(0.185)</td><td>-0.131<br>(0.230)</td><td>0.131<br>(0.232)</td></tr><tr><td>production network exposure, peaceful districts</td><td>-0.418<sup>a</sup><br>(0.062)</td><td>-0.418<sup>a</sup><br>(0.070)</td><td>-0.418<sup>a</sup><br>(0.103)</td><td>-0.418<sup>a</sup><br>(0.104)</td></tr><tr><td>Firm controls</td><td></td><td></td><td>yes</td><td></td></tr><tr><td>Product FE</td><td></td><td></td><td>yes</td><td></td></tr><tr><td>District FE</td><td></td><td></td><td>yes</td><td></td></tr><tr><td>State × Year FE</td><td></td><td></td><td>yes</td><td></td></tr><tr><td>Observations</td><td>428,410</td><td>428,410</td><td>428,410</td><td>428,410</td></tr><tr><td>R-squared (price)</td><td>0.468</td><td>0.468</td><td>0.468</td><td>0.468</td></tr><tr><td>R-squared (quantity)</td><td>0.537</td><td>0.537</td><td>0.537</td><td>0.537</td></tr></tbody></table>

Note: c significant at 10%; b at 5%; a at 1%. Standard errors are clustered at different levels according to each column's header. The dependent variables are the (log) price and the (log) quantity of the firm's production defined at the 5-digit level of the product classification code. *Direct exposure to conflict* is an indicator that equals 1 if the firm is located in a conflict-affected district and *Production network exposure* is a firm-level continuous measure calculated according to Equation OA4.3 and separated into conflict versus peaceful areas. Firm controls include (log) input prices (intermediate inputs, wages, and interest rate), (log) and time-varying input bundle fixed effects (defined at the 2-digit level). Product (defined at the 4-digit level), district, and state × year fixed effects are included. See main text for further details and Table OA4.11 for descriptive statistics.
<table><caption>Table OA4.28: Clustering</caption><thead><tr><th>Clustering</th><th>district</th><th>state</th><th>state × year</th><th>Conley SE</th></tr></thead><tbody><tr><td>Dep. var.</td><td></td><td></td><td>Price</td><td></td></tr><tr><td></td><td>(1)</td><td>(2)</td><td>(3)</td><td>(4)</td></tr><tr><td>direct exposure to conflict</td><td>0.087<sup>b</sup><br>(0.042)</td><td>0.087<sup>a</sup><br>(0.028)</td><td>0.087<sup>b</sup><br>(0.036)</td><td>0.087<sup>b</sup><br>(0.046)</td></tr><tr><td>production network exposure, conflict districts</td><td>0.046<br>(0.219)</td><td>0.046<br>(0.157)</td><td>0.046<br>(0.137)</td><td>0.046<br>(0.207)</td></tr><tr><td>production network exposure, peaceful districts</td><td>0.330<sup>a</sup><br>(0.057)</td><td>0.330<sup>a</sup><br>(0.075)</td><td>0.330<sup>a</sup><br>(0.071)</td><td>0.330<sup>a</sup><br>(0.085)</td></tr><tr><td>Dep. var.</td><td></td><td></td><td>Quantity</td><td></td></tr><tr><td></td><td>(5)</td><td>(6)</td><td>(7)</td><td>(8)</td></tr><tr><td>direct exposure to conflict</td><td>-0.109<sup>b</sup><br>(0.051)</td><td>-0.109<sup>a</sup><br>(0.029)</td><td>-0.109<sup>b</sup><br>(0.045)</td><td>-0.109<sup>b</sup><br>(0.055)</td></tr><tr><td>production network exposure, conflict districts</td><td>-0.131<br>(0.199)</td><td>-0.131<br>(0.185)</td><td>-0.131<br>(0.230)</td><td>0.131<br>(0.232)</td></tr><tr><td>production network exposure, peaceful districts</td><td>-0.418<sup>a</sup><br>(0.062)</td><td>-0.418<sup>a</sup><br>(0.070)</td><td>-0.418<sup>a</sup><br>(0.103)</td><td>-0.418<sup>a</sup><br>(0.104)</td></tr><tr><td>Firm controls</td><td></td><td></td><td>yes</td><td></td></tr><tr><td>Product FE</td><td></td><td></td><td>yes</td><td></td></tr><tr><td>District FE</td><td></td><td></td><td>yes</td><td></td></tr><tr><td>State × Year FE</td><td></td><td></td><td>yes</td><td></td></tr><tr><td>Observations</td><td>428,410</td><td>428,410</td><td>428,410</td><td>428,410</td></tr><tr><td>R-squared (price)</td><td>0.468</td><td>0.468</td><td>0.468</td><td>0.468</td></tr><tr><td>R-squared (quantity)</td><td>0.537</td><td>0.537</td><td>0.537</td><td>0.537</td></tr></tbody></table>

Note: c significant at 10%; b at 5%; a at 1%. Standard errors are clustered at different levels according to each column's header. The dependent variables are the (log) price and the (log) quantity of the firm's production defined at the 5-digit level of the product classification code. *Direct exposure to conflict* is an indicator that equals 1 if the firm is located in a conflict-affected district and *Production network exposure* is a firm-level continuous measure calculated according to Equation OA4.3 and separated into conflict versus peaceful areas. Firm controls include (log) input prices (intermediate inputs, wages, and interest rate), (log) and time-varying input bundle fixed effects (defined at the 2-digit level). Product (defined at the 4-digit level), district, and state × year fixed effects are included. See main text for further details and Table OA4.11 for descriptive statistics.
<table><caption>Table OA4.29: Firms Heterogeneity</caption><thead><tr><th>Dep. var.</th><th>Price<br>(1)</th><th>Quantity<br>(2)</th><th>Price<br>(3)</th><th>Quantity<br>(4)</th></tr></thead><tbody><tr><td>direct exposure to conflict</td><td>0.087<sup>b</sup><br>(0.037)</td><td>-0.107<sup>b</sup><br>(0.043)</td><td>0.085<sup>b</sup><br>(0.034)</td><td>-0.113<sup>a</sup><br>(0.041)</td></tr><tr><td>× imports (0/1)</td><td>0.014<br>(0.078)</td><td>-0.027<br>(0.086)</td><td></td><td></td></tr><tr><td>× units (log)</td><td></td><td></td><td>0.042<br>(0.137)</td><td>0.057<br>(0.129)</td></tr><tr><td>production network exposure, conflict districts</td><td>0.096<br>(0.169)</td><td>-0.156<br>(0.225)</td><td>0.069<br>(0.158)</td><td>-0.092<br>(0.209)</td></tr><tr><td>× imports (0/1)</td><td>-0.129<br>(0.132)</td><td>0.258<sup>c</sup><br>(0.146)</td><td></td><td></td></tr><tr><td>× units (log)</td><td></td><td></td><td>-0.128<br>(0.147)</td><td>0.319<sup>c</sup><br>(0.175)</td></tr><tr><td>production network exposure, peaceful districts</td><td>0.367<sup>a</sup><br>(0.058)</td><td>-0.465<sup>a</sup><br>(0.076)</td><td>0.338<sup>a</sup><br>(0.054)</td><td>-0.435<sup>a</sup><br>(0.070)</td></tr><tr><td>× imports (0/1)</td><td>-0.272<br>(0.390)</td><td>0.165<br>(0.456)</td><td></td><td></td></tr><tr><td>× units (log)</td><td></td><td></td><td>-0.299<br>(0.306)</td><td>-0.486<br>(0.354)</td></tr><tr><td>imports (0/1)</td><td>0.298<sup>a</sup><br>(0.020)</td><td>0.044<sup>b</sup><br>(0.020)</td><td></td><td></td></tr><tr><td>units (log)</td><td></td><td></td><td>-0.054<sup>b</sup><br>(0.024)</td><td>0.001<br>(0.030)</td></tr><tr><td>Firm controls</td><td></td><td></td><td>yes</td><td></td></tr><tr><td>Product FE</td><td></td><td></td><td>yes</td><td></td></tr><tr><td>District FE</td><td></td><td></td><td>yes</td><td></td></tr><tr><td>State × Year FE</td><td></td><td></td><td>yes</td><td></td></tr><tr><td>Observations</td><td>428,410</td><td>428,410</td><td>428,410</td><td>428,410</td></tr><tr><td>R-squared</td><td>0.469</td><td>0.537</td><td>0.468</td><td>0.537</td></tr></tbody></table>

Note: c significant at 10%; b at 5%; a at 1%. Standard errors are clustered at the district×year level. The dependent variables are the (log) price and the (log) quantity of the firm's production defined at the 5-digit level of the product classification code. *Direct exposure to conflict* is an indicator that equals 1 if the firm is located in a conflict-affected district and *Production network exposure* is a firm-level continuous measure calculated according to Equation OA4.3 and separated into conflict versus peaceful districts. Each exposure variable is interacted with firm-level characteristics: an indicator signaling whether a firm has imports and the (log) number of unit that each firm has. Firm controls include (log) input prices (intermediate inputs, wages, and interest rate), and time-varying input bundle fixed effects (defined at the 2-digit level). Product (defined at the 4-digit level), district, and state×year fixed effects are included. See main text for further details and Table OA4.11 for descriptive statistics.
<table><caption>Table OA4.29: Firms Heterogeneity</caption><thead><tr><th>Dep. var.</th><th>Price<br>(1)</th><th>Quantity<br>(2)</th><th>Price<br>(3)</th><th>Quantity<br>(4)</th></tr></thead><tbody><tr><td>direct exposure to conflict</td><td>0.087<sup>b</sup><br>(0.037)</td><td>-0.107<sup>b</sup><br>(0.043)</td><td>0.085<sup>b</sup><br>(0.034)</td><td>-0.113<sup>a</sup><br>(0.041)</td></tr><tr><td>× imports (0/1)</td><td>0.014<br>(0.078)</td><td>-0.027<br>(0.086)</td><td></td><td></td></tr><tr><td>× units (log)</td><td></td><td></td><td>0.042<br>(0.137)</td><td>0.057<br>(0.129)</td></tr><tr><td>production network exposure, conflict districts</td><td>0.096<br>(0.169)</td><td>-0.156<br>(0.225)</td><td>0.069<br>(0.158)</td><td>-0.092<br>(0.209)</td></tr><tr><td>× imports (0/1)</td><td>-0.129<br>(0.132)</td><td>0.258<sup>c</sup><br>(0.146)</td><td></td><td></td></tr><tr><td>× units (log)</td><td></td><td></td><td>-0.128<br>(0.147)</td><td>0.319<sup>c</sup><br>(0.175)</td></tr><tr><td>production network exposure, peaceful districts</td><td>0.367<sup>a</sup><br>(0.058)</td><td>-0.465<sup>a</sup><br>(0.076)</td><td>0.338<sup>a</sup><br>(0.054)</td><td>-0.435<sup>a</sup><br>(0.070)</td></tr><tr><td>× imports (0/1)</td><td>-0.272<br>(0.390)</td><td>0.165<br>(0.456)</td><td></td><td></td></tr><tr><td>× units (log)</td><td></td><td></td><td>-0.299<br>(0.306)</td><td>-0.486<br>(0.354)</td></tr><tr><td>imports (0/1)</td><td>0.298<sup>a</sup><br>(0.020)</td><td>0.044<sup>b</sup><br>(0.020)</td><td></td><td></td></tr><tr><td>units (log)</td><td></td><td></td><td>-0.054<sup>b</sup><br>(0.024)</td><td>0.001<br>(0.030)</td></tr><tr><td>Firm controls</td><td></td><td></td><td>yes</td><td></td></tr><tr><td>Product FE</td><td></td><td></td><td>yes</td><td></td></tr><tr><td>District FE</td><td></td><td></td><td>yes</td><td></td></tr><tr><td>State × Year FE</td><td></td><td></td><td>yes</td><td></td></tr><tr><td>Observations</td><td>428,410</td><td>428,410</td><td>428,410</td><td>428,410</td></tr><tr><td>R-squared</td><td>0.469</td><td>0.537</td><td>0.468</td><td>0.537</td></tr></tbody></table>

Note: c significant at 10%; b at 5%; a at 1%. Standard errors are clustered at the district×year level. The dependent variables are the (log) price and the (log) quantity of the firm's production defined at the 5-digit level of the product classification code. *Direct exposure to conflict* is an indicator that equals 1 if the firm is located in a conflict-affected district and *Production network exposure* is a firm-level continuous measure calculated according to Equation OA4.3 and separated into conflict versus peaceful districts. Each exposure variable is interacted with firm-level characteristics: an indicator signaling whether a firm has imports and the (log) number of unit that each firm has. Firm controls include (log) input prices (intermediate inputs, wages, and interest rate), and time-varying input bundle fixed effects (defined at the 2-digit level). Product (defined at the 4-digit level), district, and state×year fixed effects are included. See main text for further details and Table OA4.11 for descriptive statistics.
References

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Anderson, T. W. and H. Rubin (1949). Estimation of the parameters of a single equation in a complete system of stochastic equations. *The Annals of mathematical statistics* 20(1), 46–63.

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Camacho, A., C. Rodriguez, et al. (2013). Firm Exit and Armed Conflict in Colombia. *Journal of Conflict Resolution* 57(1), 89–116.

Carvalho, V. M., M. Nirei, Y. U. Saito, and A. Tahbaz-Salehi (2020). Supply chain disruptions: Evidence from the great east japan earthquake. *The Quarterly Journal of Economics* 136(2), 1255–1321.

Census of India (2001). Office of the registrar general and census commissioner india. https://censusindia.gov.in/census website/data/census-tables.

Conley, T. G. (1999). GMM estimation with cross sectional dependence. *Journal of econometrics* 92(1), 1–45.

Couttenier, M., N. Monnet, and L. Piemontese (2022, January). The Economic Costs of Conflict: A Production Network Approach. CEPR Discussion Papers 16984, C.E.P.R. Discussion Papers.

De Haas, R. and S. Poelhekke (2019). Mining matters: Natural resource extraction and firm-level constraints. *Journal of International Economics* 117, 109–124.

Dimiceli, C., M. Carroll, R. Sohlberg, D. Kim, M. Kelly, and J. Townshend (2015). Mod44b modis/terra vegetation continuous fields yearly l3 global 250 m sin grid v006 [data set]. NASA EOSDIS Land Process.

Economic Census of India (1998). Central statistics organisation, ministry of statistics and programme implementation, government of india. http://microdata.gov.in/nada43/index.php/catalog/ECO.

Gadenne, L., T. Nandi, and R. Rathelot (2019, August). Taxation and Supplier Networks: Evidence from India. *CEPR Discussion Papers* 13971.

Gawande, K., D. Kapur, and S. Satyanath (2017). Renewable natural resource shocks and conflict intensity: findings from india’s ongoing maoist insurgency. *Journal of Conflict Resolution* 61(1), 140–172.

Gomes, J. F. (2015). The political economy of the Maoist conflict in India: an empirical analysis. *World Development* 68, 96–123.

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References

Acemoglu, D., V. M. Carvalho, A. Ozdaglar, and A. Tahbaz-Salehi (2012). The network origins of aggregate fluctuations. *Econometrica* 80(5), 1977–2016.

Anderson, T. W. and H. Rubin (1949). Estimation of the parameters of a single equation in a complete system of stochastic equations. *The Annals of mathematical statistics* 20(1), 46–63.

Asher, S., T. Lunt, R. Matsuura, and P. Novosad (2021). Development research at high geographic resolution: an analysis of night-lights, firms, and poverty in india using the shrug open data platform. *The World Bank Economic Review* 35(4).

Berman, N., M. Couttenier, D. Rohner, and M. Thoenig (2017). This Mine is mine! How Minerals fuel Conflicts in Africa. *American Economic Review* 107(6), 1564–1610.

Blumenstock, J., T. Ghani, S. Herskowitz, E. B. Kapstein, T. L. Scherer, and O. Toomet (2020). How Do Firms Respond to Insecurity? Evidence from Afghan Corporate Phone Records. *Working Paper*.

Borooah, V. K. (2008). Deprivation, violence, and conflict: An analysis of Naxalite activity in the districts of India. *International Journal of Conflict and Violence (IJCV)* 2(2), 317–333.

Camacho, A., C. Rodriguez, et al. (2013). Firm Exit and Armed Conflict in Colombia. *Journal of Conflict Resolution* 57(1), 89–116.

Carvalho, V. M., M. Nirei, Y. U. Saito, and A. Tahbaz-Salehi (2020). Supply chain disruptions: Evidence from the great east japan earthquake. *The Quarterly Journal of Economics* 136(2), 1255–1321.

Census of India (2001). Office of the registrar general and census commissioner india. https://censusindia.gov.in/census website/data/census-tables.

Conley, T. G. (1999). GMM estimation with cross sectional dependence. *Journal of econometrics* 92(1), 1–45.

Couttenier, M., N. Monnet, and L. Piemontese (2022, January). The Economic Costs of Conflict: A Production Network Approach. CEPR Discussion Papers 16984, C.E.P.R. Discussion Papers.

De Haas, R. and S. Poelhekke (2019). Mining matters: Natural resource extraction and firm-level constraints. *Journal of International Economics* 117, 109–124.

Dimiceli, C., M. Carroll, R. Sohlberg, D. Kim, M. Kelly, and J. Townshend (2015). Mod44b modis/terra vegetation continuous fields yearly l3 global 250 m sin grid v006 [data set]. *NASA EOSDIS Land Process*.

Economic Census of India (1998). Central statistics organisation, ministry of statistics and programme implementation, government of india. http://microdata.gov.in/nada43/index.php/catalog/ECO.

Gadenne, L., T. Nandi, and R. Rathelot (2019, August). Taxation and Supplier Networks: Evidence from India. *CEPR Discussion Papers* 13971.

Gawande, K., D. Kapur, and S. Satyanath (2017). Renewable natural resource shocks and conflict intensity: findings from india’s ongoing maoist insurgency. *Journal of Conflict Resolution* 61(1), 140–172.

Gomes, J. F. (2015). The political economy of the Maoist conflict in India: an empirical analysis. *World Development* 68, 96–123.

Henderson, J. V., A. Storeygard, and D. N. Weil (2011). A Bright Idea for Measuring Economic Growth. *American Economic Review*.

IntierraRMG (2013). Snl metals & mining. Dataset.
Iyer, L. (2009). The bloody millennium: Internal conflict in south asia. *Harvard Business School BGIE Unit Working Paper* (09-086).

Korovkin, V., A. Makarin, and Y. Miyauchi (2023). A sufficient statistics approach for endogenous production networks: Theory and evidence from ukraine?s war.

Mikusheva, A. and B. P. Poi (2006). Tests and confidence sets with correct size when instruments are potentially weak. *The Stata Journal* 6(3), 335–347.

The Fund for Peace (2020). Fragile States Index 2020 – Annual Report. Report.

Vanden Eynde, O. (2017). Targets of violence: Evidence from India’s Naxalite conflict. *The Economic Journal* 128(609), 887–916.

World Bank (2014). Enterprise surveys. Washington, DC: World Bank.

World Bank (2019). Doing business 2020. Washington, DC: World Bank.
Iyer, L. (2009). The bloody millennium: Internal conflict in south asia. *Harvard Business School BGIE Unit Working Paper* (09-086).

Korovkin, V., A. Makarin, and Y. Miyauchi (2023). A sufficient statistics approach for endogenous production networks: Theory and evidence from ukraine?s war.

Mikusheva, A. and B. P. Poi (2006). Tests and confidence sets with correct size when instruments are potentially weak. *The Stata Journal* 6(3), 335-347.

The Fund for Peace (2020). Fragile States Index 2020 – Annual Report. Report.

Vanden Eynde, O. (2017). Targets of violence: Evidence from India's Naxalite conflict. *The Economic Journal* 128(609), 887-916.

World Bank (2014). Enterprise surveys. Washington, DC: World Bank.

World Bank (2019). Doing business 2020. Washington, DC: World Bank.