size distribution. For example, if Maoist groups target large firms, then we might set $\alpha = 0.1$ which means that firms above the 90$^{th}$ percentile in the firm size distribution are subject to conflict-induced distortions. If, instead, Maoist groups target small firms, then we might set $\alpha = 0.3$ which means that firms below the 30$^{th}$ percentile of the firm size distribution are subject to conflict-induced distortions. We re-scale each value of the range from which T takes value (Section 5.1) as follows:

$$ 
\begin{aligned}
\hat{T} &= \alpha \times \tilde{T} + (1 - \alpha) \times 0 \\
\Rightarrow \tilde{T} &= \frac{\hat{T}}{\alpha}
\end{aligned}
\qquad (13) $$

For sake of simplicity in the exposure, we assume $T = 0.01$. Figure 4 reports the results. The blue line represents the aggregate output loss as the proportion of impacted firms varies in the case that Maoist groups target small firms, while the yellow line represents the scenario in which large firms are targeted (where each dot represents the loss relative to each value of $\alpha$). Importantly, our results suggest that if Maoist groups target small firms then the average annual loss decreases with $\alpha$. Conversely, if Maoist activity is directed against large firms, then the average annual output loss increases substantially as $\alpha$ decreases (and the extent of firm-specific costs, denoted by $\tilde{T}$, increases according to Equation 13). For example, if Maoist targets their attacks against the 10% largest firms, the resulting average early output loss is more than doubled compared to the scenario in which all firms in conflict-affected districts are impacted. This result is of particular relevance because Maoist groups are more likely to attack capitalistic firms, which are likely to be large firms. This implies that our baseline estimate of the aggregate output loss due to the Maoist insurgency is likely to represent a lower bound of the actual loss.

## 5.4 From cost for conflict-affected firms to overall cost for the economy

To date, we have not quantified the direct distortions suffered by firms located in conflict-affected districts. In this section, based on the model developed in Section 3, we structurally estimate them. This quantification enables to provide precise estimates of the overall cost of conflict to the Indian economy, considering the propagation of these firm-level distortions through the production network.<fnref n="38" /> We use a two-step strategy inspired by Ulyssea (2018) that combines an OLS estimation with a GMM estimator.<fnref n="39" /> We first measure *state-specific conflict-induced distortions* and then *product-specific*

<footnote n="38">³⁸In an earlier version of the paper (Couttenier et al., 2022), we developed in detail the role of direct exposure to violence, and also through the production network, in a reduced-form analysis. In addition, we estimated the mechanisms (*inaction*, *supplier change*, and *input bundle change*) through different empirical exercises. Nevertheless, in view of the potential endogeneity biases discussed in Section 4.4, all reduced-form material is relegated to the Online Appendix.</footnote>

<footnote n="39">³⁹Ulyssea (2018) develops a two-step estimator to study how firms’ behavior is affected by informality. The first step combines direct estimation and calibration. The second step takes the parameters defined in the first step as given and proceed to use a Simulated Minimum Distance estimator to obtain the reminder parameters of the model.</footnote>
<figure><img src="image_9.png" /><figcaption>Figure 4: Maoist strategic targeting.</figcaption></figure>

Note: The blue line plots the average annual loss which varies according to the proportion of impacted firms $\alpha \in [0, 1]$ when Maoist groups target small firms; the yellow line represents the scenario in which violence is directed at large firms. Note that the x-axis is reversed.

conflict-induced distortions (at ASICC 5-digit).

To estimate the state-specific conflict-induced distortions, we focus on firms in conflict-affected states, distinguishing between those located in peaceful districts and those in conflict districts. Specifically, within the 12 states affected by Maoist activity, encompassing a total of 334 districts, there are 213 peaceful districts. In the first step, we derive the optimal price for firms located in peaceful districts, as given by Equation 5 (Section 3), excluding the conflict-dependent term $T_i$ (which by construction affects only the optimal price of firms located in conflict districts). Then, we estimate the following equation on the sub-sample of firms in peaceful districts within conflict-affected states:

$$ \log(P)_{i(d,t)} = \alpha + \eta_1 x_{1,i(d,t)} + \eta_2 x_{2,i(d,t)} + \eta_3 x_{3,i(d,t)} + \gamma \log(TFP)_{i(d,t)} + \theta_d + \theta_t + u_{i(d,t)} \quad (14) $$

where the dependent variable is the (log) price charged by firm $i$, located in district $d$ and observed at time $t$. The constant $\alpha$ represents the first term of the RHS of Equation 5, which is a combination of factor shares. The variables $x_{1,i(d,t)}$, $x_{2,i(d,t)}$, and $x_{3,i(d,t)}$ are the (observed) firm-level prices of capital, labor, and intermediate inputs, respectively. Finally, we control for firm-level (log) total factor

<footnote>Another example is given by Piemontese (2023) in the context of firms’ behavior in markets infiltrated by the mafia. Her two-step strategy combines fixed effects regressions to estimate parameters needed to simulate firm level data and the Simulated Method of Moments to estimate mafia-induced distortions.</footnote>
<figure><img src="image_8.png" /><figcaption>Figure 4: Maoist strategic targeting.</figcaption></figure>

Note: The blue line plots the average annual loss which varies according to the proportion of impacted firms $\alpha \in [0, 1]$ when Maoist groups target small firms; the yellow line represents the scenario in which violence is directed at large firms. Note that the x-axis is reversed.

conflict-induced distortions (at ASICC 5-digit).

To estimate the state-specific conflict-induced distortions, we focus on firms in conflict-affected states, distinguishing between those located in peaceful districts and those in conflict districts. Specifically, within the 12 states affected by Maoist activity, encompassing a total of 334 districts, there are 213 peaceful districts. In the first step, we derive the optimal price for firms located in peaceful districts, as given by Equation 5 (Section 3), excluding the conflict-dependent term $T_i$ (which by construction affects only the optimal price of firms located in conflict districts). Then, we estimate the following equation on the sub-sample of firms in peaceful districts within conflict-affected states:

$$ \log(P)_{i(d,t)} = \alpha + \eta_1 x_{1,i(d,t)} + \eta_2 x_{2,i(d,t)} + \eta_3 x_{3,i(d,t)} + \gamma \log(TFP)_{i(d,t)} + \theta_d + \theta_t + u_{i(d,t)} \quad (14) $$

where the dependent variable is the (log) price charged by firm $i$, located in district $d$ and observed at time $t$. The constant $\alpha$ represents the first term of the RHS of Equation 5, which is a combination of factor shares. The variables $x_{1,i(d,t)}$, $x_{2,i(d,t)}$, and $x_{3,i(d,t)}$ are the (observed) firm-level prices of capital, labor, and intermediate inputs, respectively. Finally, we control for firm-level (log) total factor

<footnote>Another example is given by Piemontese (2023) in the context of firms’ behavior in markets infiltrated by the mafia. Her two-step strategy combines fixed effects regressions to estimate parameters needed to simulate firm level data and the Simulated Method of Moments to estimate mafia-induced distortions.</footnote>
productivity (TFP), district fixed effects ($\theta_d$), and year fixed effects ($\theta_t$).$^{40}$ From this equation, we recover the estimated parameters ($\hat{\eta}_1, \hat{\eta}_2, \hat{\eta}_3, \hat{\gamma}$) and the set of district ($\hat{\theta}_d$) and year ($\hat{\theta}_t$) fixed effects, respectively. Then, we use them to compute the predicted values of firm-specific price, $\widehat{log(P)}_{i(d,t)}$, for each firm located in the 12 states impacted by conflict, i.e., both firms located in peaceful districts and firms located in conflict-affected districts. Based on these predicted values, we define two state-specific groups of moments: (i) average and variance of price of firms located in the 213 peaceful districts; (ii) average and variance of price of firms located in the 121 conflict-affected districts.

In the second step, we rely on these moments to quantify state-specific conflict-induced distortions, $T_s$, experienced by firms affected by conflict within a particular state $s$. Specifically, we use a GMM approach that matches the state-specific average and variance of firm prices in peaceful districts (group (i)) with those in conflict-affected districts within the same state (group (ii)). This strategy assumes that variations in the average and variance of firms’ price between peaceful and conflict-affected districts are solely attributable to conflict, once adjustments have been made for factors such as firms’ input prices and productivity, as well as district- and time-specific unobserved factors. In practice, for each state $s$, $T_s$ is derived according to the following minimization criterion:

$$ \widehat{T}'_s = \arg\min_{T'} [g(T)]'W[g(T)] \quad (15) $$

where $W$ is a positive-semi definite weighting matrix and $[g(T)]$ is the set of moment conditions, i.e., difference between the state-specific average and variance of outputs produced in peaceful and conflict districts.$^{41}$ The estimation of Equation 15 yields 12 state-specific conflict-induced distortions, $T_s$, with an average of 0.087 and a median of 0.061.$^{42}$ By assigning each conflict-affected firm its corresponding distortion term $T_s$, we calculate an annual output loss of 1.18%/year, resulting in a monetary cost of approximately 2.3 billion USD per year.$^{43}$

In a second exercise, we adopt the same approach to estimate product-specific distortions (asicec 5-digit), $T_k$. In the first step, we estimate the parameters of Equation 14 using the sample of firms located in peaceful districts. Then, we compute the predicted value of the price for each firm in the sample. Finally, for each product $k$, we use these predicted values to derive: (i) product-specific average and variance for firms located peaceful districts; (ii) product-specific average and variance

<footnote>$^{40}$Firm-level TFP is computed by applying the proxy method proposed by Wooldridge (2009), which essentially utilizes consistent estimation within a single-step GMM framework to overcome endogeneity issues related to TFP estimation.</footnote>

<footnote>$^{41}$When implementing GMM, we conduct a constrained minimization bounded below at 0. Results are robust to different starting points needed by the minimization routine to work, e.g., we use as starting point 0, or the observed unconditional difference of average price between conflict-affected districts and peaceful districts, or the observed unconditional difference of the variance of price between conflict-affected districts and peaceful districts.</footnote>

<footnote>$^{42}$These estimates are robust to alternative samples in the first step to estimate the parameters of Equation 14, which either include firms located both in peaceful and conflict states, or use only firms in peaceful states.</footnote>

<footnote>$^{43}$By implementing the same strategy to estimate year-state-specific conflict-induced distortions $T_{st}$, we find that the average distortion is 0.092 and the median is 0.024, implying a yearly output loss of 1.85%. Reassuringly, we observe that, in line with the progression of the Maoist insurgency (as depicted in the right panel of Figure 1), our estimates of $T_{st}$ are higher for years post-2004 (average equal to 0.12 and median equal to 0.064) and positively correlated with the logarithm of the number of events observed in state $s$ during year $t$.</footnote>
productivity (TFP), district fixed effects ($\theta_d$), and year fixed effects ($\theta_t$).$^{40}$ From this equation, we recover the estimated parameters ($\hat{\eta}_1, \hat{\eta}_2, \hat{\eta}_3, \hat{\gamma}$) and the set of district ($\hat{\theta}_d$) and year ($\hat{\theta}_t$) fixed effects, respectively. Then, we use them to compute the predicted values of firm-specific price, $\widehat{log(P)}_{i(d,t)}$, for each firm located in the 12 states impacted by conflict, i.e., both firms located in peaceful districts and firms located in conflict-affected districts. Based on these predicted values, we define two state-specific groups of moments: (i) average and variance of price of firms located in the 213 peaceful districts; (ii) average and variance of price of firms located in the 121 conflict-affected districts.

In the second step, we rely on these moments to quantify state-specific conflict-induced distortions, $T_s$, experienced by firms affected by conflict within a particular state $s$. Specifically, we use a GMM approach that matches the state-specific average and variance of firm prices in peaceful districts (group (i)) with those in conflict-affected districts within the same state (group (ii)). This strategy assumes that variations in the average and variance of firms’ price between peaceful and conflict-affected districts are solely attributable to conflict, once adjustments have been made for factors such as firms’ input prices and productivity, as well as district- and time-specific unobserved factors. In practice, for each state $s$, $T_s$ is derived according to the following minimization criterion:

$$ \widehat{T}'_s = \arg\min_{T'} [g(T)]'W[g(T)] \quad (15) $$

where $W$ is a positive-semi definite weighting matrix and $[g(T)]$ is the set of moment conditions, i.e., difference between the state-specific average and variance of outputs produced in peaceful and conflict districts.$^{41}$ The estimation of Equation 15 yields 12 state-specific conflict-induced distortions, $T_s$, with an average of 0.087 and a median of 0.061.$^{42}$ By assigning each conflict-affected firm its corresponding distortion term $T_s$, we calculate an annual output loss of 1.18%/year, resulting in a monetary cost of approximately 2.3 billion USD per year.$^{43}$

In a second exercise, we adopt the same approach to estimate product-specific distortions (asicec 5-digit), $T_k$. In the first step, we estimate the parameters of Equation 14 using the sample of firms located in peaceful districts. Then, we compute the predicted value of the price for each firm in the sample. Finally, for each product $k$, we use these predicted values to derive: (i) product-specific average and variance for firms located peaceful districts; (ii) product-specific average and variance

<footnote>$^{40}$Firm-level TFP is computed by applying the proxy method proposed by Wooldridge (2009), which essentially utilizes consistent estimation within a single-step GMM framework to overcome endogeneity issues related to TFP estimation.</footnote>

<footnote>$^{41}$When implementing GMM, we conduct a constrained minimization bounded below at 0. Results are robust to different starting points needed by the minimization routine to work, e.g., we use as starting point 0, or the observed unconditional difference of average price between conflict-affected districts and peaceful districts, or the observed unconditional difference of the variance of price between conflict-affected districts and peaceful districts.</footnote>

<footnote>$^{42}$These estimates are robust to alternative samples in the first step to estimate the parameters of Equation 14, which either include firms located both in peaceful and conflict states, or use only firms in peaceful states.</footnote>

<footnote>$^{43}$By implementing the same strategy to estimate year-state-specific conflict-induced distortions $T_{st}$, we find that the average distortion is 0.092 and the median is 0.024, implying a yearly output loss of 1.85%. Reassuringly, we observe that, in line with the progression of the Maoist insurgency (as depicted in the right panel of Figure 1), our estimates of $T_{st}$ are higher for years post-2004 (average equal to 0.12 and median equal to 0.064) and positively correlated with the logarithm of the number of events observed in state $s$ during year $t$.</footnote>
of price for firms located in conflict-affected districts. In the second step, we estimate Equation 15 and measure $T_k$ as the parameter that minimize the distance between product-specific average and variance between group (i) and group (ii). This estimation enables us to quantify 4,278 product-specific distortions $T_k$, with an average of 0.079 and a median of 0.063.44,45 Assigning to each conflict-affected firm its corresponding distortion $T_k$, we quantify an average annual output loss of 2.04%, equivalent to a yearly monetary loss of approximately 3.9 billion USD.46

## 5.5 Discussion

The average values of the conflict-induced distortions estimated in Section 5.4 align with the existing literature. For instance, Pinotti (2015) estimates a loss of regional Italian GDP of around 15% during the Mafia violence peak in the 1980's, and Abadie and Gardeazabal (2003) find a 10% regional GDP loss due to terrorism in the Basque Country for the 1980's and 1990's. By considering the propagation of conflict-induced distortions by way of the production network, our estimate of aggregate loss is remarkably higher than those that focus only on directly impacted territories. The share explained by propagation of conflict-related distortions outside conflict-affected districts is almost three times larger than that explained by the direct effect of conflict. This is a key contribution of our study, as it highlights the importance of measuring the cost of conflict also through the lens of network propagation. In fact, our finding suggests that the micro-level disturbances, manifested through conflict-induced distortions, translate into significant aggregate repercussions, providing insights into the broader economic landscape.

# 6 Policy Experiments

In this section, we use the structural model to examine several counterfactuals. The aim is to analyze the potential effectiveness of policies designed either to prevent conflict or to promote interventions, whether military, institutional or diplomatic, that can reduce the costs incurred by impacted firms and facilitate domestic trade. Our main assumption is that the policy maker's objective is to minimize the costs of both conflict and policy interventions. In this context, we evaluate three types of policies: (i) preventing the spread of conflict to neighboring districts, (ii) restraining the aggregate loss, and

<footnote>44These results are robust to an alternative sample in the first step to estimate the parameters of Equation14, where we add firms in conflict-affected districts during years without Maoist activity.</footnote>

<footnote>45When we do not observe product-specific variance of the asicc 5-digit level, we replace it with the asicc 4-digit level. If variance at asicc 4-digit level is unavailable, we use the variance computed at 3-digit level. This method allows use to cover 97.8% of outputs produced in conflict districts, and assign a distortion to 95% of conflict impacted firms. We adopt a conservative approach and assign a $T_k = 0$ to the remaining 5% firms, thus our estimate of aggregate output loss can be interpreted as a lower bound.</footnote>

<footnote>46We assign to multi-product firms a weighted sum of $T_k$, where weights are given by the share of production of each good $k$ over the firm-level total production. Our results do not change when, instead, we assign to multi-product firms the distortion $T_k$ corresponding to its principal product. In fact, in this alternative specification we estimate an average annual output loss equal to 2.00%.</footnote>
of price for firms located in conflict-affected districts. In the second step, we estimate Equation 15 and measure $T_k$ as the parameter that minimize the distance between product-specific average and variance between group (i) and group (ii). This estimation enables us to quantify 4,278 product-specific distortions $T_k$, with an average of 0.079 and a median of 0.063.44,45 Assigning to each conflict-affected firm its corresponding distortion $T_k$, we quantify an average annual output loss of 2.04%, equivalent to a yearly monetary loss of approximately 3.9 billion USD.46

## 5.5 Discussion

The average values of the conflict-induced distortions estimated in Section 5.4 align with the existing literature. For instance, Pinotti (2015) estimates a loss of regional Italian GDP of around 15% during the Mafia violence peak in the 1980's, and Abadie and Gardeazabal (2003) find a 10% regional GDP loss due to terrorism in the Basque Country for the 1980's and 1990's. By considering the propagation of conflict-induced distortions by way of the production network, our estimate of aggregate loss is remarkably higher than those that focus only on directly impacted territories. The share explained by propagation of conflict-related distortions outside conflict-affected districts is almost three times larger than that explained by the direct effect of conflict. This is a key contribution of our study, as it highlights the importance of measuring the cost of conflict also through the lens of network propagation. In fact, our finding suggests that the micro-level disturbances, manifested through conflict-induced distortions, translate into significant aggregate repercussions, providing insights into the broader economic landscape.

# 6 Policy Experiments

In this section, we use the structural model to examine several counterfactuals. The aim is to analyze the potential effectiveness of policies designed either to prevent conflict or to promote interventions, whether military, institutional or diplomatic, that can reduce the costs incurred by impacted firms and facilitate domestic trade. Our main assumption is that the policy maker's objective is to minimize the costs of both conflict and policy interventions. In this context, we evaluate three types of policies: (i) preventing the spread of conflict to neighboring districts, (ii) restraining the aggregate loss, and

<footnote>44These results are robust to an alternative sample in the first step to estimate the parameters of Equation14, where we add firms in conflict-affected districts during years without Maoist activity.</footnote>

<footnote>45When we do not observe product-specific variance of the asicc 5-digit level, we replace it with the asicc 4-digit level. If variance at asicc 4-digit level is unavailable, we use the variance computed at 3-digit level. This method allows use to cover 97.8% of outputs produced in conflict districts, and assign a distortion to 95% of conflict impacted firms. We adopt a conservative approach and assign a $T_k = 0$ to the remaining 5% firms, thus our estimate of aggregate output loss can be interpreted as a lower bound.</footnote>

<footnote>46We assign to multi-product firms a weighted sum of $T_k$, where weights are given by the share of production of each good $k$ over the firm-level total production. Our results do not change when, instead, we assign to multi-product firms the distortion $T_k$ corresponding to its principal product. In fact, in this alternative specification we estimate an average annual output loss equal to 2.00%.</footnote>
(iii) facilitating trade between conflict-affected districts and peaceful districts.

**Preventing the spread of conflict.** We quantify the potential costs that could arise if the conflict spreads to adjacent districts. Assessing the range of global costs that could emerge in the event of contamination is a crucial issue. Since any public intervention to limit the spread of a conflict is inherently costly, having an estimate of the cost of non-intervention allows us to refine the cost-benefit analysis for public authorities. We therefore examine a scenario in which firms located in adjacent districts are also affected by the activity of Maoist groups. In this case, a total of 151 districts (out of 558) would be directly impacted by the insurgency. The results point to a large increase of 55% in the total loss due to the conflict (Table 2, Panel C) relative to the baseline specification: a one percentage point increase in conflict-induced distortion is associated with an average annual decrease of 0.364% in aggregate output, which correspond to a monetary loss of 677 million USD per year. This result suggests that, for each percentage point increase of the conflict-related distortion, any policy to prevent conflict is worthwhile if its cost is less than 239 million USD, which is the difference in annual output loss between the two scenarios.

**Restraining the aggregate loss.** To minimize the costs of the conflict, we identify the firms in conflict-affected districts that would be most beneficial to protect. This approach aims to preserve trade connections, prevent losses to those firms, and avoid the spread of conflict-induced distortions in the rest of the economy. A policy to protect firms can take various forms, such as subsidizing security costs or deploying military intervention in strategic districts. This is particularly relevant when policy makers have limited resources to invest in the protection of these firms and must prioritize among them.

For a sake of exposition, we consider $T = 0.01$. Then, we assume that the objective of the policy maker is to halve the average annual loss, i.e., from 0.236% to 0.118%. One option is to randomly select 50% of the firms located in conflict-affected districts. However, this is not an optimal policy as there are alternatives that yield the same benefits while protecting a smaller proportion of firms, thus reducing the cost of the intervention. We consider two alternative interventions.

In the first intervention, resources for protection are allocated to firms that play a more “central” role in the economy. Thus, we estimate the average annual output loss as firms that have to be protected are included progressively, beginning from the most central firm and ending with the least. We approximate the centrality of a firm using the influence vector $v$. The top-panel of Figure 5 displays the simulated average annual loss based on the proportion of firms receiving protection (ranging from 0 to 100%). The results suggest that the greatest benefit is attained when resources for protection are allocated to the most central firms. In particular, the average annual output loss is halved when resources are directed to the top 4% according to the influence vector distribution.

In the second intervention, we assume that the policy maker aims to protect specific clusters of firms. This could be pertinent if a particular good $k$ is predominantly produced by firms located in conflict-affected districts.<fnref n="47" /> We assume that a certain proportion of a good $k$ is produced by at least

<footnote n="47">⁴⁷The data suggest that there are several goods produced entirely by firms located in conflict-affected districts,</footnote>
(iii) facilitating trade between conflict-affected districts and peaceful districts.

**Preventing the spread of conflict.** We quantify the potential costs that could arise if the conflict spreads to adjacent districts. Assessing the range of global costs that could emerge in the event of contamination is a crucial issue. Since any public intervention to limit the spread of a conflict is inherently costly, having an estimate of the cost of non-intervention allows us to refine the cost-benefit analysis for public authorities. We therefore examine a scenario in which firms located in adjacent districts are also affected by the activity of Maoist groups. In this case, a total of 151 districts (out of 558) would be directly impacted by the insurgency. The results point to a large increase of 55% in the total loss due to the conflict (Table 2, Panel C) relative to the baseline specification: a one percentage point increase in conflict-induced distortion is associated with an average annual decrease of 0.364% in aggregate output, which correspond to a monetary loss of 677 million USD per year. This result suggests that, for each percentage point increase of the conflict-related distortion, any policy to prevent conflict is worthwhile if its cost is less than 239 million USD, which is the difference in annual output loss between the two scenarios.

**Restraining the aggregate loss.** To minimize the costs of the conflict, we identify the firms in conflict-affected districts that would be most beneficial to protect. This approach aim to preserve trade connections, prevent losses to those firms, and avoid the spread of conflict-induced distortions in the rest of the economy. A policy to protect firms can take various forms, such as subsidizing security costs or deploying military intervention in strategic districts. This is particularly relevant when policy makers have limited resources to invest in the protection of these firms and must prioritize among them.

For a sake of exposition, we consider $T = 0.01$. Then, we assume that the objective of the policy maker is to halve the average annual loss, i.e., from 0.236% to 0.118%. One option is to randomly select 50% of the firms located in conflict-affected districts. However, this is not an optimal policy as there are alternatives that yield the same benefits while protecting a smaller proportion of firms, thus reducing the cost of the intervention. We consider two alternative interventions.

In the first intervention, resources for protection are allocated to firms that play a more “central” role in the economy. Thus, we estimate the average annual output loss as firms that have to be protected are included progressively, beginning from the most central firm and ending with the least. We approximate the centrality of a firm using the influence vector $v$. The top-panel of Figure 5 displays the simulated average annual loss based on the proportion of firms receiving protection (ranging from 0 to 100%). The results suggest that the greatest benefit is attained when resources for protection are allocated to the most central firms. In particular, the average annual output loss is halved when resources are directed to the top 4% according to the influence vector distribution.

In the second intervention, we assume that the policy maker aims to protect specific clusters of firms. This could be pertinent if a particular good $k$ is predominantly produced by firms located in conflict-affected districts.<fnref n="47" /> We assume that a certain proportion of a good $k$ is produced by at least

<footnote n="47">⁴⁷The data suggest that there are several goods produced entirely by firms located in conflict-affected districts,</footnote>
one firm located in a conflict-affected district and define a *threshold for protection*, above which firms
will be allocated resources for protection. We consider a policy intervention based on this threshold:
the policy maker selects a certain *threshold for protection* and allocates resources to safeguard all
firms in conflict-affected districts producing goods whose production share in conflict-affected districts
equals or exceeds this threshold. We vary the threshold from 0 to 100%, computing the corresponding
average annual losses. Then, we determine the proportion of firms (in conflict-affected districts) that
receive protection for each threshold value.

We find that adopting a threshold of 7% would result in halving the average annual output loss (middle panel of Figure 5). In other words, resources for protection would be allocated to firms in conflict-affected districts when they contribute for at least 7% of total production. The bottom panel of Figure 5 suggests that 42% of the relevant firms would be allocated resources for protection. Therefore, this alternative would be considerably more expensive than the one based on firm-level centrality, which would allocate resources for protection to only 4% of firms in conflict-affected districts.

**Trade facilitation in peaceful districts.** We assess the benefits of facilitating trade among firms located in peaceful districts. For example, policies may focus on subsidizing transportation costs, repairing infrastructure such as roads and railways damaged by conflict, or reducing trade frictions between states due to institutional quality. Since exhaustive time-varying fine-grained data on such policy interventions are lacking, we adopt the following strategy. We measure output loss allowing for network adjustment, accounting for both the supplier change and input bundle change effects. This implies that firms substitute suppliers of inputs located in conflict-affected districts with suppliers of those same inputs located in peaceful districts. We assume that the costs associated with network adjustment accommodate for potential trade frictions between states. To approximate them, we follow Boehm and Oberfield (2020) who show that, in the Indian manufacturing context, firm-level inputs sourcing and production organization are influenced by state-specific characteristics. Specifically, they find that these decisions are distorted in states with weaker enforcement. We approximate the cost through three components. First, we examine the firm-specific change in the share of suppliers located in different states due to network adjustment. The larger is the share the larger is the cost borne by the firm. Second, we take into account state-level quality of institutions, which we proxy as average age of pending civil cases in each State court (Boehm and Oberfield, 2020). Last, as above, we compute the inverse of the difference between the average distance between a buyer *i* and all of its possible suppliers *j* in the input-output matrix with network adjustment and that in the input-output matrix of our baseline model. To approximate these network adjustment costs, we multiply the three components. Once constructed, we add this new vector of firm-specific costs of adjustment to the conflict vector. The expression of aggregate output loss translates into: $\Delta_t \mathbf{Y}_t = v'_t \tilde{\xi}_t$ where $\tilde{\xi}_t = \mathbf{T} + \zeta_t$ and $\zeta_t > 0$ is the cost of network adjustment..$^{48}$

<footnote>including tobacco (Virginia), tobacco oil, natural asphalt, absorbent paper, spun silk yarn, and unblended wool.</footnote>

<footnote>$^{48}$The second component, i.e., proxy for institutional quality, has been re-scaled such it is bounded between zero and one. We are grateful to Johannes Boehm, who kindly shared the data on average age of pending civil cases in</footnote>
one firm located in a conflict-affected district and define a *threshold for protection*, above which firms
will be allocated resources for protection. We consider a policy intervention based on this threshold:
the policy maker selects a certain *threshold for protection* and allocates resources to safeguard all
firms in conflict-affected districts producing goods whose production share in conflict-affected districts
equals or exceeds this threshold. We vary the threshold from 0 to 100%, computing the corresponding
average annual losses. Then, we determine the proportion of firms (in conflict-affected districts) that
receive protection for each threshold value.

We find that adopting a threshold of 7% would result in halving the average annual output loss (middle panel of Figure 5). In other words, resources for protection would be allocated to firms in conflict-affected districts when they contribute for at least 7% of total production. The bottom panel of Figure 5 suggests that 42% of the relevant firms would be allocated resources for protection. Therefore, this alternative would be considerably more expensive than the one based on firm-level centrality, which would allocate resources for protection to only 4% of firms in conflict-affected districts.

**Trade facilitation in peaceful districts.** We assess the benefits of facilitating trade among firms located in peaceful districts. For example, policies may focus on subsidizing transportation costs, repairing infrastructure such as roads and railways damaged by conflict, or reducing trade frictions between states due to institutional quality. Since exhaustive time-varying fine-grained data on such policy interventions are lacking, we adopt the following strategy. We measure output loss allowing for network adjustment, accounting for both the supplier change and input bundle change effects. This implies that firms substitute suppliers of inputs located in conflict-affected districts with suppliers of those same inputs located in peaceful districts. We assume that the costs associated with network adjustment accommodate for potential trade frictions between states. To approximate them, we follow Boehm and Oberfield (2020) who show that, in the Indian manufacturing context, firm-level inputs sourcing and production organization are influenced by state-specific characteristics. Specifically, they find that these decisions are distorted in states with weaker enforcement. We approximate the cost through three components. First, we examine the firm-specific change in the share of suppliers located in different states due to network adjustment. The larger is the share the larger is the cost borne by the firm. Second, we take into account state-level quality of institutions, which we proxy as average age of pending civil cases in each State court (Boehm and Oberfield, 2020). Last, as above, we compute the inverse of the difference between the average distance between a buyer *i* and all of its possible suppliers *j* in the input-output matrix with network adjustment and that in the input-output matrix of our baseline model. To approximate these network adjustment costs, we multiply the three components. Once constructed, we add this new vector of firm-specific costs of adjustment to the conflict vector. The expression of aggregate output loss translates into: $\Delta_t \mathbf{Y}_t = v'_t \tilde{\xi}_t$ where $\tilde{\xi}_t = T + \zeta_t$ and $\zeta_t > 0$ is the cost of network adjustment..$^{48}$

<footnote>including tobacco (Virginia), tobacco oil, natural asphalt, absorbent paper, spun silk yarn, and unblended wool.</footnote>

<footnote>$^{48}$The second component, i.e., proxy for institutional quality, has been re-scaled such it is bounded between zero and one. We are grateful to Johannes Boehm, who kindly shared the data on average age of pending civil cases in</footnote>
<figure><img src="image_10.png" /><figcaption>Figure 5: Alternative protection policies</figcaption></figure>

Note: This figure presents the results of two alternative policies aimed to halve the average loss due to conflict. The first panel reports results of an experiment in which the importance of a firm in the production network, according to the influence vector $v$ (Equation 7) is used as the criterion to allocate resources for protection. We assume that the conflict-induced distortion $T$ equal 0.01. In the first exercise, the policy maker first prioritizes the most important firms and progressively adds less important ones. The blue curve represents the simulated average aggregate loss as the proportion of firms provided with protection varies from 0% to 100%. The second and third panels report results of a policy simulation based on a *threshold for protection* that varies between 0 and 1 (x-axis). For a given good $k$, this threshold is defined as the share of production accounted for by firms located in conflict-affected districts. The mid-panel shows how the aggregate loss varies with the threshold. The bottom panel shows the proportion of firms (within the total number of firms in conflict-affected districts) who will be allocated resources for protection for each possible value of the average annual loss.

Results reported in Table 2 Panel C suggest that, when network adjustment costs include potential trade frictions between states, the output loss approaches the baseline (Table 2, Panel A) and advantages related to network adjustment diminish (Table 2, Panel B last two rows). Specifically, in this scenario, a one percentage point increase in conflict-induced distortion is associated with an average annual decrease of 0.172% in aggregate output. These findings emphasize the significance of

<footnote>Indian courts.</footnote>
<figure><img src="image_9.png" /><figcaption>Figure 5: Alternative protection policies</figcaption></figure>

Note: This figure presents the results of two alternative policies aimed to halve the average loss due to conflict. The first panel reports results of an experiment in which the importance of a firm in the production network, according to the influence vector $v$ (Equation 7) is used as the criterion to allocate resources for protection. We assume that the conflict-induced distortion $T$ equal 0.01. In the first exercise, the policy maker first prioritizes the most important firms and progressively adds less important ones. The blue curve represents the simulated average aggregate loss as the proportion of firms provided with protection varies from 0% to 100%. The second and third panels report results of a policy simulation based on a *threshold for protection* that varies between 0 and 1 (x-axis). For a given good $k$, this threshold is defined as the share of production accounted for by firms located in conflict-affected districts. The mid-panel shows how the aggregate loss varies with the threshold. The bottom panel shows the proportion of firms (within the total number of firms in conflict-affected districts) who will be allocated resources for protection for each possible value of the average annual loss.

Results reported in Table 2 Panel C suggest that, when network adjustment costs include potential trade frictions between states, the output loss approaches the baseline (Table 2, Panel A) and advantages related to network adjustment diminish (Table 2, Panel B last two rows). Specifically, in this scenario, a one percentage point increase in conflict-induced distortion is associated with an average annual decrease of 0.172% in aggregate output. These findings emphasize the significance of

Indian courts.
policy interventions aimed at minimizing trade frictions between firms in non-conflict districts. Such interventions are crucial for retaining the advantages brought about by network adjustments.

# 7 Conclusion

We develop a novel approach to quantifying the economic costs of conflict. The methodology is particularly well-suited to contemporary conflicts, such as intra-state warfare and local insurgencies, and the intricate economic environments of countries impacted by such conflicts. We examine, both theoretically and empirically, the spread of a localized conflict's effects to peaceful districts through the disruption of the supply chain. In the context of the Maoist insurgency in eastern India, we combine a comprehensive firm-level dataset with information regarding the conflict. We exploit the information on each firm's output and input bundle to construct the input-output network that delineates the Indian economy. This enables us to apply a well-established model of production networks in the context of conflict, allowing us to quantify the aggregate loss attributable to the Maoist insurgency. In the first part of the paper, we theoretically outline the mechanism by which distortions caused by a localized conflict can spread to the rest of the economy. To accomplish this, we develop a static model of an input-output network in the spirit of Acemoglu et al. (2012). In the second part, we apply the model to the data to structurally estimate the aggregate loss to the Indian economy due to the Maoist insurgency. We find that a one percentage point increase in conflict-induced distortion is associated with an average annual decrease of 0.236% in aggregate output, which corresponds to a monetary loss of 438 million USD. Importantly, only 27% of the loss can be explained by the direct impact of conflict on firms, while the remaining 73% is due to spread via the supply network to peaceful districts. We proceed by developing a two-step structural estimation strategy to quantify the direct distortions suffered by firms located in conflict-affected districts. This approach allows us to offer a precise estimates of the overall cost of conflict. We measure both *state-specific* and *product-specific* conflict-induced distortions. In the first case, the estimated conflict-induced distortions imply an average yearly output loss equal to 1.18%/year. In the second case, *product-specific* conflict-induced distortions imply an average yearly loss equal to 2.04%/year. Finally, we perform several policy experiments. The findings suggest that policy should focus on: (i) preventing the spread of the conflict to neighboring districts, (ii) allocating resources to protect firms in conflict-affected districts that are pivotal in the supply network of the economy, and (iii) reducing the costs associated with trade frictions between firms and states. Our results support the conclusion reached by Rohner and Thoenig (2021) on a better understanding of these channels of transmission linking war to development slowdown is important to guide policy measures in a post-conflict environment.
policy interventions aimed at minimizing trade frictions between firms in non-conflict districts. Such interventions are crucial for retaining the advantages brought about by network adjustments.

# 7 Conclusion

We develop a novel approach to quantifying the economic costs of conflict. The methodology is particularly well-suited to contemporary conflicts, such as intra-state warfare and local insurgencies, and the intricate economic environments of countries impacted by such conflicts. We examine, both theoretically and empirically, the spread of a localized conflict's effects to peaceful districts through the disruption of the supply chain. In the context of the Maoist insurgency in eastern India, we combine a comprehensive firm-level dataset with information regarding the conflict. We exploit the information on each firm's output and input bundle to construct the input-output network that delineates the Indian economy. This enables us to apply a well-established model of production networks in the context of conflict, allowing us to quantify the aggregate loss attributable to the Maoist insurgency. In the first part of the paper, we theoretically outline the mechanism by which distortions caused by a localized conflict can spread to the rest of the economy. To accomplish this, we develop a static model of an input-output network in the spirit of Acemoglu et al. (2012). In the second part, we apply the model to the data to structurally estimate the aggregate loss to the Indian economy due to the Maoist insurgency. We find that a one percentage point increase in conflict-induced distortion is associated with an average annual decrease of 0.236% in aggregate output, which corresponds to a monetary loss of 438 million USD. Importantly, only 27% of the loss can be explained by the direct impact of conflict on firms, while the remaining 73% is due to spread via the supply network to peaceful districts. We proceed by developing a two-step structural estimation strategy to quantify the direct distortions suffered by firms located in conflict-affected districts. This approach allows us to offer a precise estimates of the overall cost of conflict. We measure both *state-specific* and *product-specific* conflict-induced distortions. In the first case, the estimated conflict-induced distortions imply an average yearly output loss equal to 1.18%/year. In the second case, *product-specific* conflict-induced distortions imply an average yearly loss equal to 2.04%/year. Finally, we perform several policy experiments. The findings suggest that policy should focus on: (i) preventing the spread of the conflict to neighboring districts, (ii) allocating resources to protect firms in conflict-affected districts that are pivotal in the supply network of the economy, and (iii) reducing the costs associated with trade frictions between firms and states. Our results support the conclusion reached by Rohner and Thoenig (2021) on a better understanding of these channels of transmission linking war to development slowdown is important to guide policy measures in a post-conflict environment.
# 8 Appendix

## 8.1 Validation of the approximation of the production network

<table><caption>Table 3: Alternative specifications of $\rho_{ji(k)}$</caption><thead><tr><th>Dep. var.</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><th>(9)</th></tr></thead><tbody><tr><td>ρ<sub>ji(k)</sub></td><td>0.355<sup>a</sup><br>(0.020)</td><td>0.396<sup>a</sup><br>(0.022)</td><td>0.442<sup>a</sup><br>(0.022)</td><td>0.489<sup>a</sup><br>(0.022)</td><td>0.533<sup>a</sup><br>(0.045)</td><td>0.481<sup>a</sup><br>(0.068)</td><td>0.357<sup>a</sup><br>(0.071)</td><td>0.193<sup>a</sup><br>(0.045)</td><td>0.277<sup>a</sup><br>(0.028)</td></tr><tr><td>Shared postcode</td><td>0.003<sup>a</sup><br>(0.001)</td><td>0.004<sup>a</sup><br>(0.001)</td><td>0.004<sup>a</sup><br>(0.001)</td><td>0.004<sup>a</sup><br>(0.001)</td><td>0.004<sup>a</sup><br>(0.001)</td><td>0.005<sup>a</sup><br>(0.001)</td><td>0.005<sup>a</sup><br>(0.001)</td><td>0.006<sup>a</sup><br>(0.001)</td><td>0.004<sup>a</sup><br>(0.001)</td></tr><tr><td>Same district</td><td>0.002<sup>a</sup><br>(0.001)</td><td>0.002<sup>a</sup><br>(0.001)</td><td>0.002<sup>a</sup><br>(0.001)</td><td>0.002<sup>a</sup><br>(0.001)</td><td>0.002<sup>a</sup><br>(0.001)</td><td>0.002<sup>a</sup><br>(0.001)</td><td>0.002<sup>a</sup><br>(0.001)</td><td>0.002<sup>a</sup><br>(0.001)</td><td>0.002<sup>a</sup><br>(0.001)</td></tr><tr><td>Linear combination of<br>relative distance & relative<br>size ρ<sub>ji(k)</sub>, value of λ</td><td>0.1</td><td>0.2</td><td>0.3</td><td>0.4</td><td>0.6</td><td>0.7</td><td>0.8</td><td>0.9</td><td>non-linear</td></tr><tr><td>Seller FE</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>Yes</td></tr><tr><td>Buyer×Sector FE</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>Yes</td></tr><tr><td>Observations</td><td colspan="9">90,438,253</td></tr></tbody></table>

Note: c significant at 10%; b at 5%; a at 1%. Standard errors in parentheses. The dependent variable is an indicator that takes a value of one if firm *i* buys the input *k* (defined at the 4-digit level) from supplier *j*. $\rho_{ji(k)}$ gives the importance of supplier *j* of good *k*, which is measured according to the relative distance between buyer *i* and supplier *j* and relative size of supplier *j* with respect to all the other suppliers of good *k* (see Equation 11 for further details). In columns 1-8 $\rho_{ji(k)}$ is measured by setting the weight to relative buyer-supplier distance equal to 0.1-0.9, and by setting the weight to relative supplier's size equal to 0.9-0.1. In column 9 $\rho_{ji(k)}$ results from a non-linear combination between relative buyer-supplier distance and relative supplier's size (see footnote 21 for further details). Shared postcode is an indicator that equals one if buyer *i* and supplier *j* share the same postcode. Same district is an indicator that equals 1 if buyer *i* and supplier *j* are located in the same district.
# 8 Appendix

## 8.1 Validation of the approximation of the production network

<table><caption>Table 3: Alternative specifications of $\rho_{ji(k)}$</caption><thead><tr><th>Dep. var.</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><th>(9)</th></tr></thead><tbody><tr><td>ρ<sub>ji(k)</sub></td><td>0.355<sup>a</sup><br>(0.020)</td><td>0.396<sup>a</sup><br>(0.022)</td><td>0.442<sup>a</sup><br>(0.022)</td><td>0.489<sup>a</sup><br>(0.022)</td><td>0.533<sup>a</sup><br>(0.045)</td><td>0.481<sup>a</sup><br>(0.068)</td><td>0.357<sup>a</sup><br>(0.071)</td><td>0.193<sup>a</sup><br>(0.045)</td><td>0.277<sup>a</sup><br>(0.028)</td></tr><tr><td>Shared postcode</td><td>0.003<sup>a</sup><br>(0.001)</td><td>0.004<sup>a</sup><br>(0.001)</td><td>0.004<sup>a</sup><br>(0.001)</td><td>0.004<sup>a</sup><br>(0.001)</td><td>0.004<sup>a</sup><br>(0.001)</td><td>0.005<sup>a</sup><br>(0.001)</td><td>0.005<sup>a</sup><br>(0.001)</td><td>0.006<sup>a</sup><br>(0.001)</td><td>0.004<sup>a</sup><br>(0.001)</td></tr><tr><td>Same district</td><td>0.002<sup>a</sup><br>(0.001)</td><td>0.002<sup>a</sup><br>(0.001)</td><td>0.002<sup>a</sup><br>(0.001)</td><td>0.002<sup>a</sup><br>(0.001)</td><td>0.002<sup>a</sup><br>(0.001)</td><td>0.002<sup>a</sup><br>(0.001)</td><td>0.002<sup>a</sup><br>(0.001)</td><td>0.002<sup>a</sup><br>(0.001)</td><td>0.002<sup>a</sup><br>(0.001)</td></tr><tr><td>Linear combination of<br>relative distance & relative<br>size ρ<sub>ji(k)</sub>, value of λ</td><td>0.1</td><td>0.2</td><td>0.3</td><td>0.4</td><td>0.6</td><td>0.7</td><td>0.8</td><td>0.9</td><td>non-linear</td></tr><tr><td>Seller FE</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>Yes</td></tr><tr><td>Buyer×Sector FE</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>Yes</td></tr><tr><td>Observations</td><td colspan="9">90,438,253</td></tr></tbody></table>

Note: c significant at 10%; b at 5%; a at 1%. Standard errors in parentheses. The dependent variable is an indicator that takes a value of one if firm *i* buys the input *k* (defined at the 4-digit level) from supplier *j*. $\rho_{ji(k)}$ gives the importance of supplier *j* of good *k*, which is measured according to the relative distance between buyer *i* and supplier *j* and relative size of supplier *j* with respect to all the other suppliers of good *k* (see Equation 11 for further details). In columns 1-8 $\rho_{ji(k)}$ is measured by setting the weight to relative buyer-supplier distance equal to 0.1-0.9, and by setting the weight to relative supplier's size equal to 0.9-0.1. In column 9 $\rho_{ji(k)}$ results from a non-linear combination between relative buyer-supplier distance and relative supplier's size (see footnote 21 for further details). Shared postcode is an indicator that equals one if buyer *i* and supplier *j* share the same postcode. Same district is an indicator that equals 1 if buyer *i* and supplier *j* are located in the same district.
## 8.2 Results on the endogeneity of the production network

<table><caption>Table 4: Conflict and number of firms</caption><thead><tr><th rowspan="2">Unit of obs.<br>Dep. var</th><th colspan="4">District</th><th>District × year</th></tr><tr><th colspan="4">— (log-)number of manufacturing firms —</th><th></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>Affected district</td><td>-0.069<br>(0.170)</td><td></td><td></td><td></td><td></td></tr><tr><td>Historically affected district</td><td></td><td>0.088<br>(0.249)</td><td></td><td>0.030<br>(0.267)</td><td></td></tr><tr><td>Newly affected district</td><td></td><td></td><td>-0.111<br>(0.168)</td><td>-0.102<br>(0.179)</td><td></td></tr><tr><td>Incidence (district)</td><td></td><td></td><td></td><td></td><td>-0.049<br>(0.093)</td></tr><tr><td>Incidence (state)</td><td></td><td></td><td></td><td></td><td>0.085<br>(0.065)</td></tr><tr><td>Sample mean</td><td>3.36</td><td>3.36</td><td>3.36</td><td>3.36</td><td>3.35</td></tr><tr><td>Observations</td><td>553</td><td>553</td><td>553</td><td>553</td><td>5280</td></tr><tr><td>District level controls</td><td>Yes</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><td>Yes</td></tr><tr><td>Year FE</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td></tr></tbody></table>

Note: c significant at 10%; b at 5%; a at 1%. Robust standard errors in parenthesis. From columns 1 to 4, the unit of observation is the district. We estimate the following equation: Number of firms<sub>ds</sub> = β<sub>0</sub> + β<sub>1</sub>Affected district<sub>ds</sub> + X'<sub>ds</sub>γ + θ<sub>s</sub> + u<sub>ds</sub>, where Number of firms<sub>ds</sub> measures the (log-)average number of firms observed over the period 2000-2009 in district d in state s. Affected district<sub>ds</sub> corresponds to the indicators of Maoist activity defined as: (i) an indicator that indicates whether the district hosted Maoist violence for at least one year over the period 1989-2009; and (ii) two distinct dummies that mark whether Maoist insurgency impacted the district before 2000 (historically affected districts), or only after (newly affected districts). X'<sub>ds</sub> 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). In column 5, the unit of observation is district × year. We estimate the following equation: Number of firms<sub>dst</sub> = β<sub>0</sub> + β<sub>1</sub>Incidence<sub>dst</sub> + β<sub>2</sub>Incidence<sub>st</sub> + X'<sub>dst</sub>γ + θ<sub>s</sub> + θ<sub>t</sub> + u<sub>dst</sub>, where Number of firms<sub>dst</sub> measures the number of firms (in logs) observed in district d, in state s, at year t. Incidence<sub>dst</sub> measures conflict incidence in district d, in state s at year t. Incidence<sub>st</sub> is a measure of conflict incidence at state level. X'<sub>dst</sub> includes time-varying district-level characteristics, θ<sub>s</sub> are state fixed effects and θ<sub>t</sub> are year fixed effects.
## 8.2 Results on the endogeneity of the production network

<table><caption>Table 4: Conflict and number of firms</caption><thead><tr><th rowspan="2">Unit of obs.<br>Dep. var</th><th colspan="4">District</th><th>District×year</th></tr><tr><th colspan="4">— (log-)number of manufacturing firms —</th><th></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>Affected district</td><td>-0.069<br>(0.170)</td><td></td><td></td><td></td><td></td></tr><tr><td>Historically affected district</td><td></td><td>0.088<br>(0.249)</td><td></td><td>0.030<br>(0.267)</td><td></td></tr><tr><td>Newly affected district</td><td></td><td></td><td>-0.111<br>(0.168)</td><td>-0.102<br>(0.179)</td><td></td></tr><tr><td>Incidence (district)</td><td></td><td></td><td></td><td></td><td>-0.049<br>(0.093)</td></tr><tr><td>Incidence (state)</td><td></td><td></td><td></td><td></td><td>0.085<br>(0.065)</td></tr><tr><td>Sample mean</td><td>3.36</td><td>3.36</td><td>3.36</td><td>3.36</td><td>3.35</td></tr><tr><td>Observations</td><td>553</td><td>553</td><td>553</td><td>553</td><td>5280</td></tr><tr><td>District level controls</td><td>Yes</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><td>Yes</td></tr><tr><td>Year FE</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td></tr></tbody></table>

Note: c significant at 10%; b at 5%; a at 1%. Robust standard errors in parenthesis. From columns 1 to 4, the unit of observation is the district. We estimate the following equation: Number of firms<sub>ds</sub> = β<sub>0</sub> + β<sub>1</sub>Affected district<sub>ds</sub> + X'<sub>ds</sub>γ + θ<sub>s</sub> + u<sub>ds</sub>, where Number of firms<sub>ds</sub> measures the (log-)average number of firms observed over the period 2000-2009 in district d in state s. Affected district<sub>ds</sub> corresponds to the indicators of Maoist activity defined as: (i) an indicator that indicates whether the district hosted Maoist violence for at least one year over the period 1989-2009; and (ii) two distinct dummies that mark whether Maoist insurgency impacted the district before 2000 (historically affected districts), or only after (newly affected districts). X'<sub>ds</sub> 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). In column 5, the unit of observation is district×year. We estimate the following equation: Number of firms<sub>dst</sub> = β<sub>0</sub> + β<sub>1</sub>Incidence<sub>dst</sub> + β<sub>2</sub>Incidence<sub>st</sub> + X'<sub>dst</sub>γ + θ<sub>s</sub> + θ<sub>t</sub> + u<sub>dst</sub>, where Number of firms<sub>dst</sub> measures the number of firms (in logs) observed in district d, in state s, at year t. Incidence<sub>dst</sub> measures conflict incidence in district d, in state s at year t. Incidence<sub>st</sub> is a measure of conflict incidence at state level. X'<sub>dst</sub> includes time-varying district-level characteristics, θ<sub>s</sub> are state fixed effects and θ<sub>t</sub> are year fixed effects.
<table><caption>Table 5: Firms’ age</caption><thead><tr><th rowspan="2">Dep. Var.</th><th colspan="4">— (log-) average firms' age —</th><th colspan="4">— s.d. firms' age —</th></tr><tr><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>Affected district</td><td>-0.125<br>(0.077)</td><td></td><td></td><td></td><td>-2.564<br>(1.584)</td><td></td><td></td><td></td></tr><tr><td>Historically affected district</td><td></td><td>0.007<br>(0.044)</td><td></td><td>-0.082<br>(0.069)</td><td></td><td>-0.009<br>(0.645)</td><td></td><td>-1.811<br>(1.308)</td></tr><tr><td>Newly affected district</td><td></td><td></td><td>-0.109<sup>c</sup><br>(0.065)</td><td>-0.139<sup>c</sup><br>(0.083)</td><td></td><td></td><td>-2.148<br>(1.334)</td><td>-2.798<br>(1.706)</td></tr><tr><td>Sample mean</td><td>2.78</td><td>2.78</td><td>2.78</td><td>2.78</td><td>14.68</td><td>14.68</td><td>14.68</td><td>14.68</td></tr><tr><td>Observations</td><td>550</td><td>550</td><td>550</td><td>550</td><td>550</td><td>550</td><td>550</td><td>550</td></tr><tr><td>District level controls</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</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><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. Estimates are weighted by the number of firms in the district. The unit of observation is the district. We estimate the following equation: $Y_{ds} = \beta_{0} + \beta_{1} \text{Affected district}_{ds} + X'_{ds} \gamma + \theta_{s} + u_{ds}$, where the dependent variable ($Y_{ds}$) measures the average and the standard deviation of firms' age in district $d$ in state $s$. Affected district$_{ds}$ is either an indicator that signales whether district $d$ in state $s$ has been affected by Maoist insurgency (columns 1 and 3), or two distinct dummies that mark whether Maoist insurgency impacted the district before 2000 (historically affected districts), or only after (newly affected districts) (columns 2 and 4). $X'_{ds}$ 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.

<table><caption>Table 6: Firms’ size</caption><thead><tr><th rowspan="2">Dep. Var. :</th><th colspan="4">— (log-) average firms' size —</th><th colspan="4">— s.d. firms' size —</th></tr><tr><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>Affected district</td><td>0.088<br>(0.110)</td><td></td><td></td><td></td><td>0.138<br>(0.142)</td><td></td><td></td><td></td></tr><tr><td>Historically affected district</td><td></td><td>0.252<sup>c</sup><br>(0.151)</td><td></td><td>0.271<sup>c</sup><br>(0.160)</td><td></td><td>0.303<br>(0.200)</td><td></td><td>0.350<br>(0.218)</td></tr><tr><td>Newly affected district</td><td></td><td></td><td>-0.067<br>(0.121)</td><td>0.031<br>(0.121)</td><td></td><td></td><td>-0.053<br>(0.145)</td><td>0.072<br>(0.151)</td></tr><tr><td>Sample mean</td><td>4.44</td><td>4.44</td><td>4.44</td><td>4.44</td><td>5.02</td><td>5.02</td><td>5.02</td><td>5.02</td></tr><tr><td>Observations</td><td>545</td><td>545</td><td>545</td><td>545</td><td>544</td><td>544</td><td>544</td><td>544</td></tr><tr><td>District level controls</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</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><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. Estimates are weighted by the number of firms in the district. The unit of observation is the district. We estimate the following equation: $Y_{ds} = \beta_{0} + \beta_{1} \text{Affected district}_{ds} + X'_{ds} \gamma + \theta_{s} + u_{ds}$, where the dependent variable ($Y_{ds}$) measures the average and the standard deviation of firms' size in district $d$ in state $s$. Affected district$_{ds}$ is either an indicator that signales whether district $d$ in state $s$ has been affected by Maoist insurgency (columns 1 and 3), or two distinct dummies that mark whether Maoist insurgency impacted the district before 2000 (historically affected districts), or only after (newly affected districts) (columns 2 and 4). $X'_{ds}$ 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.
<table><caption>Table 5: Firms’ age</caption><thead><tr><th rowspan="2">Dep. Var.</th><th colspan="4">— (log-) average firms' age —</th><th colspan="4">— s.d. firms' age —</th></tr><tr><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>Affected district</td><td>-0.125<br>(0.077)</td><td></td><td></td><td></td><td>-2.564<br>(1.584)</td><td></td><td></td><td></td></tr><tr><td>Historically affected district</td><td></td><td>0.007<br>(0.044)</td><td></td><td>-0.082<br>(0.069)</td><td></td><td>-0.009<br>(0.645)</td><td></td><td>-1.811<br>(1.308)</td></tr><tr><td>Newly affected district</td><td></td><td></td><td>-0.109<sup>c</sup><br>(0.065)</td><td>-0.139<sup>c</sup><br>(0.083)</td><td></td><td></td><td>-2.148<br>(1.334)</td><td>-2.798<br>(1.706)</td></tr><tr><td>Sample mean</td><td>2.78</td><td>2.78</td><td>2.78</td><td>2.78</td><td>14.68</td><td>14.68</td><td>14.68</td><td>14.68</td></tr><tr><td>Observations</td><td>550</td><td>550</td><td>550</td><td>550</td><td>550</td><td>550</td><td>550</td><td>550</td></tr><tr><td>District level controls</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</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><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. Estimates are weighted by the number of firms in the district. The unit of observation is the district. We estimate the following equation: $Y_{ds} = \beta_{0} + \beta_{1} \text{Affected district}_{ds} + X'_{ds} \gamma + \theta_{s} + u_{ds}$, where the dependent variable ($Y_{ds}$) measures the average and the standard deviation of firms' age in district $d$ in state $s$. Affected district$_{ds}$ is either an indicator that signales whether district $d$ in state $s$ has been affected by Maoist insurgency (columns 1 and 3), or two distinct dummies that mark whether Maoist insurgency impacted the district before 2000 (historically affected districts), or only after (newly affected districts) (columns 2 and 4). $X'_{ds}$ 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.

<table><caption>Table 6: Firms’ size</caption><thead><tr><th rowspan="2">Dep. Var. :</th><th colspan="4">— (log-) average firms' size —</th><th colspan="4">— s.d. firms' size —</th></tr><tr><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>Affected district</td><td>0.088<br>(0.110)</td><td></td><td></td><td></td><td>0.138<br>(0.142)</td><td></td><td></td><td></td></tr><tr><td>Historically affected district</td><td></td><td>0.252<sup>c</sup><br>(0.151)</td><td></td><td>0.271<sup>c</sup><br>(0.160)</td><td></td><td>0.303<br>(0.200)</td><td></td><td>0.350<br>(0.218)</td></tr><tr><td>Newly affected district</td><td></td><td></td><td>-0.067<br>(0.121)</td><td>0.031<br>(0.121)</td><td></td><td></td><td>-0.053<br>(0.145)</td><td>0.072<br>(0.151)</td></tr><tr><td>Sample mean</td><td>4.44</td><td>4.44</td><td>4.44</td><td>4.44</td><td>5.02</td><td>5.02</td><td>5.02</td><td>5.02</td></tr><tr><td>Observations</td><td>545</td><td>545</td><td>545</td><td>545</td><td>544</td><td>544</td><td>544</td><td>544</td></tr><tr><td>District level controls</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</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><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. Estimates are weighted by the number of firms in the district. The unit of observation is the district. We estimate the following equation: $Y_{ds} = \beta_{0} + \beta_{1} \text{Affected district}_{ds} + X'_{ds} \gamma + \theta_{s} + u_{ds}$, where the dependent variable ($Y_{ds}$) measures the average and the standard deviation of firms' size in district $d$ in state $s$. Affected district$_{ds}$ is either an indicator that signales whether district $d$ in state $s$ has been affected by Maoist insurgency (columns 1 and 3), or two distinct dummies that mark whether Maoist insurgency impacted the district before 2000 (historically affected districts), or only after (newly affected districts) (columns 2 and 4). $X'_{ds}$ 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.
<table><caption>Table 7: Firm relocation, exit, entry</caption><thead><tr><th>Dep. var.</th><th>Change district<br>(same state)<br>(1)</th><th>Change district<br>(different state)<br>(2)</th><th>Entry<br>(3)</th><th>Exit<br>(4)</th></tr></thead><tbody><tr><td>Incidence (district)</td><td>0.00015<br>(0.00108)</td><td>-0.00028<br>(0.00079)</td><td>0.00001<br>(0.00005)</td><td>0.00042<br>(0.00278)</td></tr><tr><td>Incidence (state)</td><td>0.00081<br>(0.00070)</td><td>0.00051<br>(0.00050)</td><td>-0.00005<br>(0.00004)</td><td>-0.00090<br>(0.00154)</td></tr><tr><td>Sample mean</td><td>0.008</td><td>0.004</td><td>0.002</td><td>0.024</td></tr><tr><td>Observations</td><td>251521</td><td>251521</td><td>24777568</td><td>152879</td></tr><tr><td>Firm level controls</td><td>Yes</td><td>Yes</td><td>No</td><td>Yes</td></tr><tr><td>District level controls</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>District FE</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Year FE</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Sector 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. The unit of observation is the firm. We estimate the following regression (for columns 1 and 2): Relocation<sub>idst</sub> = β<sub>0</sub> + β<sub>1</sub>Incidence<sub>dst</sub> + β<sub>2</sub>Incidence<sub>st</sub> + X'<sub>idst</sub>γ + Z'<sub>dst</sub>η + θ<sub>d</sub> + θ<sub>t</sub> + θ<sub>c</sub> + u<sub>idst</sub>, where Relocation<sub>idst</sub> is an indicator that takes a value of one if firm i changed district d at time t. We consider two alternative measures: (i) moving to a different district within the same state (column 1), (ii) moving to a different district in a different state (column 2). Incidence<sub>dst</sub> measures conflict incidence in district d, in state s at year t and Incidence<sub>st</sub> is a measure of conflict incidence in state s at year t. X'<sub>idst</sub> is a vector of firm-level characteristics: wages, interest rates, and input prices (all in logarithmic form). Z'<sub>dst</sub> is a vector of time-varying district characteristics: employment (non-farm, manufacturing, services) and nightlight. Additionally, we include fixed effects for district θ<sub>d</sub>, year θ<sub>t</sub> and sector θ<sub>c</sub>. For column 3, we estimate the decision of firm i to enter district d of state s in year t which we express as follows: Entry<sub>idst</sub> = β<sub>0</sub> + β<sub>1</sub>Incidence<sub>dst</sub> + β<sub>2</sub>Incidence<sub>st</sub> + Z'<sub>dst</sub>η + θ<sub>d</sub> + θ<sub>t</sub> + θ<sub>c</sub> + u<sub>idst</sub>, where Entry<sub>idst</sub> an indicator that takes the value of one if district d corresponds to the district in which firm i initiated its production in year t, and zero for all other districts in India. For column 4, we estimate the firm-level decision to exit: Exit<sub>idst</sub> = β<sub>0</sub> + β<sub>1</sub>Incidence<sub>dst</sub> + β<sub>2</sub>Incidence<sub>st</sub> + X'<sub>idst</sub>γ + Z'<sub>dst</sub>η + θ<sub>d</sub> + θ<sub>t</sub> + θ<sub>c</sub> + u<sub>idst</sub>, where Exit<sub>idst</sub> is an indicator that indicates the last year of production of firm i, located in district d of state s. See above for the definition of the covariates.
<table><caption>Table 7: Firm relocation, exit, entry</caption><thead><tr><th>Dep. var.</th><th>Change district<br>(same state)<br>(1)</th><th>Change district<br>(different state)<br>(2)</th><th>Entry<br>(3)</th><th>Exit<br>(4)</th></tr></thead><tbody><tr><td>Incidence (district)</td><td>0.00015<br>(0.00108)</td><td>-0.00028<br>(0.00079)</td><td>0.00001<br>(0.00005)</td><td>0.00042<br>(0.00278)</td></tr><tr><td>Incidence (state)</td><td>0.00081<br>(0.00070)</td><td>0.00051<br>(0.00050)</td><td>-0.00005<br>(0.00004)</td><td>-0.00090<br>(0.00154)</td></tr><tr><td>Sample mean</td><td>0.008</td><td>0.004</td><td>0.002</td><td>0.024</td></tr><tr><td>Observations</td><td>251521</td><td>251521</td><td>24777568</td><td>152879</td></tr><tr><td>Firm level controls</td><td>Yes</td><td>Yes</td><td>No</td><td>Yes</td></tr><tr><td>District level controls</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>District FE</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Year FE</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Sector 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. The unit of observation is the firm. We estimate the following regression (for columns 1 and 2): Relocation$_{idst}$ = β$_{0}$ + β$_{1}$ Incidence$_{dst}$ + β$_{2}$ Incidence$_{st}$ + X'$_{idst}$γ + Z'$_{dst}$η + θ$_{d}$ + θ$_{t}$ + θ$_{c}$ + u$_{idst}$, where Relocation$_{idst}$ is an indicator that takes a value of one if firm i changed district d at time t. We consider two alternative measures: (i) moving to a different district within the same state (column 1), (ii) moving to a different district in a different state (column 2). Incidence$_{dst}$ measures conflict incidence in district d, in state s at year t and Incidence$_{st}$ is a measure of conflict incidence in state s at year t. X'$_{idst}$ is a vector of firm-level characteristics: wages, interest rates, and input prices (all in logarithmic form). Z'$_{dst}$ is a vector of time-varying district characteristics: employment (non-farm, manufacturing, services) and nightlight. Additionally, we include fixed effects for district θ$_{d}$, year θ$_{t}$ and sector θ$_{c}$. For column 3, we estimate the decision of firm i to enter district d of state s in year t which we express as follows: Entry$_{idst}$ = β$_{0}$ + β$_{1}$ Incidence$_{dst}$ + β$_{2}$ Incidence$_{st}$ + Z'$_{dst}$η + θ$_{d}$ + θ$_{t}$ + θ$_{c}$ + u$_{idst}$, where Entry$_{idst}$ an indicator that takes the value of one if district d corresponds to the district in which firm i initiated its production in year t, and zero for all other districts in India. For column 4, we estimate the firm-level decision to exit: Exit$_{idst}$ = β$_{0}$ + β$_{1}$ Incidence$_{dst}$ + β$_{2}$ Incidence$_{st}$ + X'$_{idst}$γ + Z'$_{dst}$η + θ$_{d}$ + θ$_{t}$ + θ$_{c}$ + u$_{idst}$, where Exit$_{idst}$ is an indicator that indicates the last year of production of firm i, located in district d of state s. See above for the definition of the covariates.
<table><caption>Table 8: Market concentration in conflict districts</caption><thead><tr><th rowspan="2">Dep. var</th><th colspan="4">HHI index</th></tr><tr><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th></tr></thead><tbody><tr><td>Affected district</td><td>30.109<br>(84.422)</td><td></td><td></td><td></td></tr><tr><td>Newly affected district: After</td><td></td><td>-24.404<br>(81.330)</td><td></td><td>6.349<br>(89.475)</td></tr><tr><td>Historically affected district</td><td></td><td></td><td>96.641<br>(110.502)</td><td>100.236<br>(121.572)</td></tr><tr><td>Sample mean</td><td>5906.09</td><td>5906.09</td><td>5906.09</td><td>5906.09</td></tr><tr><td>Observations</td><td>17121</td><td>17121</td><td>17121</td><td>17121</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>Industry 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. The unit of observation is industry×district. We estimate the following regression $HHI_{jds} = \beta_0 + \beta_1 Affected district_{ds} + X'_{ds}\gamma + \theta_j + \theta_s + u_{jds}$. The dependent variable is the Herfindahl-Hirschman market concentration index (HHI$_{jds}$) at the industry j district d in state s level. HHI is computed as the sum of the squares of the market share of all the firms in a given market, the larger the index the more concentrated the market is. The index ranges between 10,000 in a monopolistic market and 0 in a perfectly competitive market. Affected district$_{ds}$ is either an indicator that signales whether district d in state s has been affected by Maoist insurgency (columns 1 and 2), or three distinct indicators that mark the time period when district d in state s was affected by conflict, i.e., before 2000, after 2000, both before and after 2000 (columns 3 and 4). $X'_{ds}$ 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 $\theta_j$ are industry fixed effects.
<table><caption>Table 8: Market concentration in conflict districts</caption><thead><tr><th rowspan="2">Dep. var</th><th colspan="4">HHI index</th></tr><tr><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th></tr></thead><tbody><tr><td>Affected district</td><td>30.109<br>(84.422)</td><td></td><td></td><td></td></tr><tr><td>Newly affected district: After</td><td></td><td>-24.404<br>(81.330)</td><td></td><td>6.349<br>(89.475)</td></tr><tr><td>Historically affected district</td><td></td><td></td><td>96.641<br>(110.502)</td><td>100.236<br>(121.572)</td></tr><tr><td>Sample mean</td><td>5906.09</td><td>5906.09</td><td>5906.09</td><td>5906.09</td></tr><tr><td>Observations</td><td>17121</td><td>17121</td><td>17121</td><td>17121</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>Industry 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. The unit of observation is industry×district. We estimate the following regression $HHI_{jds} = \beta_0 + \beta_1 Affected district_{ds} + X'_{ds}\gamma + \theta_j + \theta_s + u_{jds}$. The dependent variable is the Herfindahl-Hirschman market concentration index ($HHI_{jds}$) at the industry $j$ district $d$ in state $s$ level. HHI is computed as the sum of the squares of the market share of all the firms in a given market, the larger the index the more concentrated the market is. The index ranges between 10,000 in a monopolistic market and 0 in a perfectly competitive market. $Affected district_{ds}$ is either an indicator that signales whether district $d$ in state $s$ has been affected by Maoist insurgency (columns 1 and 2), or three distinct indicators that mark the time period when district $d$ in state $s$ was affected by conflict, i.e., before 2000, after 2000, both before and after 2000 (columns 3 and 4). $X'_{ds}$ 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 $\theta_j$ are industry fixed effects.
<table><caption>Table 9: 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>Affected district</td><td>-0.033<sup>a</sup><br>(0.005)</td><td></td><td></td><td></td></tr><tr><td>Historically affected district</td><td></td><td>-0.025<sup>a</sup><br>(0.007)</td><td></td><td>-0.041<sup>a</sup><br>(0.007)</td></tr><tr><td>Newly affected district</td><td></td><td></td><td>-0.018<sup>a</sup><br>(0.005)</td><td>-0.030<sup>a</sup><br>(0.005)</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. The unit of observation is at the product-district level. We estimate the following regression: $Labor\ Intensive_{gds} = \beta_0 + \beta_1 Affected\ district_{ds} + X'_{ds}\gamma + \theta_g + \theta_s + u_{pds}$. 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. Affected district$_{ds}$ is either an indicator that signales whether district d in state s has been affected by Maoist insurgency (columns 1 and 2), or three distinct indicators that mark the time period when district d in state s was affected by conflict, i.e., before 2000, after 2000, both before and after 2000 (columns 3 and 4). $X'_{ds}$ 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.
<table><caption>Table 9: 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>Affected district</td><td>-0.033<sup>a</sup><br>(0.005)</td><td></td><td></td><td></td></tr><tr><td>Historically affected district</td><td></td><td>-0.025<sup>a</sup><br>(0.007)</td><td></td><td>-0.041<sup>a</sup><br>(0.007)</td></tr><tr><td>Newly affected district</td><td></td><td></td><td>-0.018<sup>a</sup><br>(0.005)</td><td>-0.030<sup>a</sup><br>(0.005)</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. The unit of observation is at the product-district level. We estimate the following regression: $Labor\ Intensive_{gds} = \beta_0 + \beta_1 Affected\ district_{ds} + X'_{ds}\gamma + \theta_g + \theta_s + u_{pds}$. 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. Affected district$_{ds}$ is either an indicator that signales whether district d in state s has been affected by Maoist insurgency (columns 1 and 2), or three distinct indicators that mark the time period when district d in state s was affected by conflict, i.e., before 2000, after 2000, both before and after 2000 (columns 3 and 4). $X'_{ds}$ 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.
## 8.3 Alternative specifications of the production network

<table><caption>Table 10: Output Loss in Different Scenarios</caption><thead><tr><th></th><th>(1)<br>Yearly Loss in %</th></tr></thead><tbody><tr><td><strong>Panel A</strong></td><td>Baseline</td></tr><tr><td></td><td>-0.236%</td></tr><tr><td><strong>Panel B</strong></td><td>Alternative specifications</td></tr><tr><td>Non-linear ρ</td><td>-0.236%</td></tr><tr><td>Distance elasticity -2</td><td>-0.230%</td></tr><tr><td>Distance elasticity -3</td><td>-0.228%</td></tr><tr><td>Distance elasticity -4</td><td>-0.227%</td></tr><tr><td>Distance elasticity -5</td><td>-0.227%</td></tr><tr><td><strong>Panel C</strong></td><td>Alternative network</td></tr><tr><td>Time-invariant network</td><td>-1.54%</td></tr><tr><td>Time-varying network</td><td>-1.40%</td></tr></tbody></table>

<footnote>Note: this table reports our estimates of average yearly output loss implied by a one percentage point increase of conflict-induced distortion T computed in the baseline scenario (Panel A); in two alternative ways to construct the production network (Panel B): (i) $\rho_{jit(k)}$ is computed combining the relative inverse distance between each buyer-supplier pair and the relative size of each potential supplier in a non-linear way according to footnote 21; (ii) $\rho_{jit(k)}$ is computed assuming an elasticity of trade with respect to distance from -2 to -5; in two alternative way of constructing the production network (Panel C), the first pulls all the ASI cross-sections together and provides a time-invariant production network and impute the first occurrence value of input share, $\omega_{ij}$, buyer-supplier bilateral distance, and potential supplier size, which give $\rho_{ji(k)}$; the second alternative serves for comparison's sake and reconstruct the baseline time-varying network, using the first-occurrence values of input share, bilateral distance, and supplier size to establish annual buyer-supplier links.</footnote>
## 8.3 Alternative specifications of the production network

<table><caption>Table 10: Output Loss in Different Scenarios</caption><thead><tr><th></th><th>(1)<br>Yearly Loss in %</th></tr></thead><tbody><tr><td><strong>Panel A</strong></td><td>Baseline</td></tr><tr><td></td><td>-0.236%</td></tr><tr><td><strong>Panel B</strong></td><td>Alternative specifications</td></tr><tr><td>Non-linear ρ</td><td>-0.236%</td></tr><tr><td>Distance elasticity -2</td><td>-0.230%</td></tr><tr><td>Distance elasticity -3</td><td>-0.228%</td></tr><tr><td>Distance elasticity -4</td><td>-0.227%</td></tr><tr><td>Distance elasticity -5</td><td>-0.227%</td></tr><tr><td><strong>Panel C</strong></td><td>Alternative network</td></tr><tr><td>Time-invariant network</td><td>-1.54%</td></tr><tr><td>Time-varying network</td><td>-1.40%</td></tr></tbody></table>

<footnote>Note: this table reports our estimates of average yearly output loss implied by a one percentage point increase of conflict-induced distortion T computed in the baseline scenario (Panel A); in two alternative ways to construct the production network (Panel B): (i) $\rho_{jit(k)}$ is computed combining the relative inverse distance between each buyer-supplier pair and the relative size of each potential supplier in a non-linear way according to footnote 21; (ii) $\rho_{jit(k)}$ is computed assuming an elasticity of trade with respect to distance from -2 to -5; in two alternative way of constructing the production network (Panel C), the first pulls all the ASI cross-sections together and provides a time-invariant production network and impute the first occurrence value of input share, $\omega_{ij}$, buyer-supplier bilateral distance, and potential supplier size, which give $\rho_{ji(k)}$; the second alternative serves for comparison's sake and reconstruct the baseline time-varying network, using the first-occurrence values of input share, bilateral distance, and supplier size to establish annual buyer-supplier links.</footnote>
## 8.4 CES aggregation

We assume that each input $k$, produced by a set of $J$ producers, is a CES aggregate with elasticity of substitution $\sigma > 1$:

$$k = \left[ \sum_{j=1}^{J} k_{j}^{\frac{\sigma-1}{\sigma}} \right]^{\frac{\sigma}{\sigma-1}}$$

We simulate what the average output loss would be by letting $\sigma$ vary between $1 + \epsilon$ (with $\epsilon$ close to 0) and 10. Table 6 depicts the results for conflict-induced distortion $T = 0.01$. The average yearly loss decreases as elasticity of substitution $\sigma$ increases. It falls quickly for $1 < \sigma < 3$ (for example, if $\sigma = 2$ the yearly average loss is halved), then it converges to a yearly loss of approximately 0.3% and 0.8%, for $T = 0.03$ and $T = 0.08$ respectively.

<figure><img src="image_11.png" /><figcaption>Figure 6: CES Aggregation of Inputs</figcaption></figure>

Note: This figure depicts the aggregate output loss we obtaining estimating an alternative specification of our model. In this version we assume that each input $k$, produced by a set of $J$ producers, is a CES aggregate with elasticity of substitution $\sigma > 1$. We simulate what the average output loss would be by letting $\sigma$ vary between $1 + \epsilon$ (with $\epsilon$ close to 0) and 7.
## 8.4 CES aggregation

We assume that each input $k$, produced by a set of $J$ producers, is a CES aggregate with elasticity of substitution $\sigma > 1$:

$$k = \left[ \sum_{j=1}^{J} k_{j}^{\frac{\sigma-1}{\sigma}} \right]^{\frac{\sigma}{\sigma-1}}$$

We simulate what the average output loss would be by letting $\sigma$ vary between $1 + \epsilon$ (with $\epsilon$ close to 0) and 10. Table 6 depicts the results for conflict-induced distortion $T = 0.01$. The average yearly loss decreases as elasticity of substitution $\sigma$ increases. It falls quickly for $1 < \sigma < 3$ (for example, if $\sigma = 2$ the yearly average loss is halved), then it converges to a yearly loss of approximately 0.3% and 0.8%, for $T = 0.03$ and $T = 0.08$ respectively.

<figure><img src="image_10.png" /><figcaption>Figure 6: CES Aggregation of Inputs</figcaption></figure>

Note: This figure depicts the aggregate output loss we obtaining estimating an alternative specification of our model. In this version we assume that each input $k$, produced by a set of $J$ producers, is a CES aggregate with elasticity of substitution $\sigma > 1$. We simulate what the average output loss would be by letting $\sigma$ vary between $1 + \epsilon$ (with $\epsilon$ close to 0) and 7.
References

Abadie, A. and J. Gardeazabal (2003). The Economic Costs of Conflict: A Case Study of the Basque Country. *The American Economic Review* 93(1), 113–132.

Acemoglu, D. and P. D. Azar (2020). Endogenous production networks. *Econometrica* 88(1), 33–82.

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

ACLED (2018). While overall violence has declined in 2018, conflict is spreading. *ACLED online press release*.

Akbulut-Yuksel, M. (2014). Children of War The Long-Run Effects of Large-Scale Physical Destruction and Warfare on Children. *Journal of Human resources* 49(3), 634–662.

Akresh, R., L. Lucchetti, and H. Thirumurthy (2012). Wars and child health: Evidence from the Eritrean–Ethiopian conflict. *Journal of development economics* 99(2), 330–340.

Alesina, A., S. Özler, N. Roubini, and P. Swagel (1996). Political instability and economic growth. *Journal of Economic growth* 1(2), 189–211.

Amarasinghe, A., P. Raschky, Y. Zenou, and J. Zhou (2021). Conflicts in Spatial Networks. *Working Paper*.

Amodio, F. and M. Di Maio (2018). Making do with what you have: Conflict, input misallocation and firm performance. *The Economic Journal* 128(615), 2559–2612.

Arcand, J.-L., A.-S. Rodella-Boitreaud, and M. Rieger (2014). The Impact of Land Mines on Child Health: Evidence from Angola. *Economic Development and Cultural Change* 63(2), 249–279.

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).

Baqaee, D. R. and E. Farhi (2019, 09). Productivity and Misallocation in General Equilibrium. *The Quarterly Journal of Economics* 135(1), 105–163.

Barrot, J.-N. and J. Sauvagnat (2016). Input specificity and the propagation of idiosyncratic shocks in production networks. *The Quarterly Journal of Economics* 131(3), 1543–1592.

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.
References

Abadie, A. and J. Gardeazabal (2003). The Economic Costs of Conflict: A Case Study of the Basque Country. *The American Economic Review* 93(1), 113–132.

Acemoglu, D. and P. D. Azar (2020). Endogenous production networks. *Econometrica* 88(1), 33–82.

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

ACLED (2018). While overall violence has declined in 2018, conflict is spreading. *ACLED online press release*.

Akbulut-Yuksel, M. (2014). Children of War The Long-Run Effects of Large-Scale Physical Destruction and Warfare on Children. *Journal of Human resources* 49(3), 634–662.

Akresh, R., L. Lucchetti, and H. Thirumurthy (2012). Wars and child health: Evidence from the Eritrean–Ethiopian conflict. *Journal of development economics* 99(2), 330–340.

Alesina, A., S. Özler, N. Roubini, and P. Swagel (1996). Political instability and economic growth. *Journal of Economic growth* 1(2), 189–211.

Amarasinghe, A., P. Raschky, Y. Zenou, and J. Zhou (2021). Conflicts in Spatial Networks. *Working Paper*.

Amodio, F. and M. Di Maio (2018). Making do with what you have: Conflict, input misallocation and firm performance. *The Economic Journal* 128(615), 2559–2612.

Arcand, J.-L., A.-S. Rodella-Boitreaud, and M. Rieger (2014). The Impact of Land Mines on Child Health: Evidence from Angola. *Economic Development and Cultural Change* 63(2), 249–279.

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).

Baqaee, D. R. and E. Farhi (2019, 09). Productivity and Misallocation in General Equilibrium. *The Quarterly Journal of Economics* 135(1), 105–163.

Barrot, J.-N. and J. Sauvagnat (2016). Input specificity and the propagation of idiosyncratic shocks in production networks. *The Quarterly Journal of Economics* 131(3), 1543–1592.

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.
Bernard, A. B., A. Moxnes, and Y. U. Saito (2019). Production networks, geography, and firm performance. *Journal of Political Economy* 127(2), 639–688.

Besley, T. and H. Mueller (2018). Predation, Protection, and Productivity: A Firm-Level Perspective. *American Economic Journal: Macroeconomics* 10(2), 184–221.

Blattman, C. (2009). From violence to voting: War and political participation in Uganda. *American political Science review* 103(02), 231–247.

Blumenstock, J., T. Ghani, S. Herskowitz, E. Kapstein, T. L. Scherer, and O. Toomet (2022). Insecurity and firm displacement: Evidence from afghan corporate phone records. *working paper*.

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*.

Boehm, C. E., A. Flaaen, and N. Pandalai-Nayar (2019). Input linkages and the transmission of shocks: Firm-level evidence from the 2011 tohoku earthquake. *The Review of Economics and Statistics* 101(1), 60–75.

Boehm, J. and E. Oberfield (2020). Misallocation in the market for inputs: Enforcement and the organization of production. *The Quarterly Journal of Economics* 135(4), 2007–2058.

Bollard, A., P. J. Klenow, and G. Sharma (2013). India’s mysterious manufacturing miracle. *Review of Economic Dynamics* 16(1), 59–85.

Bundervoet, T., P. Verwimp, and R. Akresh (2009). Health and civil war in rural Burundi. *Journal of human Resources* 44(2), 536–563.

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. (2014). From micro to macro via production networks. *Journal of Economic Perspectives* 28(4), 23–48.

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.

Carvalho, V. M. and A. Tahbaz-Salehi (2019, August). Production Networks: A Primer. *Annual Review of Economics* 11(1), 635–663.

Cassar, A., P. Grosjean, and S. Whitt (2013). Legacies of violence: trust and market development. *Journal of Economic Growth* 18(3), 285–318.
Bernard, A. B., A. Moxnes, and Y. U. Saito (2019). Production networks, geography, and firm performance. *Journal of Political Economy* 127(2), 639–688.

Besley, T. and H. Mueller (2018). Predation, Protection, and Productivity: A Firm-Level Perspective. *American Economic Journal: Macroeconomics* 10(2), 184–221.

Blattman, C. (2009). From violence to voting: War and political participation in Uganda. *American political Science review* 103(02), 231–247.

Blumenstock, J., T. Ghani, S. Herskowitz, E. Kapstein, T. L. Scherer, and O. Toomet (2022). Insecurity and firm displacement: Evidence from afghan corporate phone records. *working paper*.

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*.

Boehm, C. E., A. Flaaen, and N. Pandalai-Nayar (2019). Input linkages and the transmission of shocks: Firm-level evidence from the 2011 tohoku earthquake. *The Review of Economics and Statistics* 101(1), 60–75.

Boehm, J. and E. Oberfield (2020). Misallocation in the market for inputs: Enforcement and the organization of production. *The Quarterly Journal of Economics* 135(4), 2007–2058.

Bollard, A., P. J. Klenow, and G. Sharma (2013). India’s mysterious manufacturing miracle. *Review of Economic Dynamics* 16(1), 59–85.

Bundervoet, T., P. Verwimp, and R. Akresh (2009). Health and civil war in rural Burundi. *Journal of human Resources* 44(2), 536–563.

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. (2014). From micro to macro via production networks. *Journal of Economic Perspectives* 28(4), 23–48.

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.

Carvalho, V. M. and A. Tahbaz-Salehi (2019, August). Production Networks: A Primer. *Annual Review of Economics* 11(1), 635–663.

Cassar, A., P. Grosjean, and S. Whitt (2013). Legacies of violence: trust and market development. *Journal of Economic Growth* 18(3), 285–318.
Census of India (2001). Office of the registrar general and census commissioner india. https://censusindia.gov.in/census website/data/census-tables.

Collier, P. (1999). On the Economic Consequences of Civil War. *Oxford Economic Papers* 51(1), 168–183.

Collier, P. and N. Sambanis (2002). Understanding civil war: A new agenda. *Journal of Conflict Resolution* 46(1), 3–12.

Conroy, T., S. Deller, and A. Tsvetkova (2016). Regional business climate and interstate manufacturing relocation decisions. *Regional science and urban economics* 60, 155–168.

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.

Couttenier, M., V. Petrencu, D. Rohner, and M. Thoenig (2019, December). The Violent Legacy of Conflict: Evidence on Asylum Seekers, Crime, and Public Policy in Switzerland. *American Economic Review* 109(12), 4378–4425.

Dasgupta, A., K. Gawande, and D. Kapur (2017). (when) do antipoverty programs reduce violence? india’s rural employment guarantee and maoist conflict. *International organization* 71(3), 605–632.

de Roux, N. and L. Martinez (2021). Forgone Investment: Civil Conflict and Agricultural Credit in Colombia. *Working Paper*.

Del Prete, D., M. Di Maio, and A. Rahman (2023). Firms amid conflict: Performance, production inputs, and market competition. *Journal of Development Economics* 164, 103143.

Dimiceli, C., M. Carroll, R. Sohlberg, D. Kim, M. Kelly, and J. Townshend (2015). Mod44b modis/terra vegetation continuous fields yearly 13 global 250 m sin grid v006 [data set]. NASA *EOSDIS Land Process*.

Donaldson, D. (2018). Railroads of the raj: Estimating the impact of transportation infrastructure. *American Economic Review* 108(4-5), 899–934.

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.

Fetzer, T. (2020). Can workfare programs moderate conflict? Evidence from India. *Journal of the European Economic Association* 18(6), 3337–3375.

Gadenne, L., T. Nandi, and R. Rathelot (2019, August). Taxation and Supplier Networks: Evidence from India. *CEPR Discussion Papers* 13971.
Census of India (2001). Office of the registrar general and census commissioner india. https://censusindia.gov.in/census website/data/census-tables.

Collier, P. (1999). On the Economic Consequences of Civil War. *Oxford Economic Papers* **51**(1), 168–183.

Collier, P. and N. Sambanis (2002). Understanding civil war: A new agenda. *Journal of Conflict Resolution* **46**(1), 3–12.

Conroy, T., S. Deller, and A. Tsvetkova (2016). Regional business climate and interstate manufacturing relocation decisions. *Regional science and urban economics* **60**, 155–168.

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.

Couttenier, M., V. Petrencu, D. Rohner, and M. Thoenig (2019, December). The Violent Legacy of Conflict: Evidence on Asylum Seekers, Crime, and Public Policy in Switzerland. *American Economic Review* **109**(12), 4378–4425.

Dasgupta, A., K. Gawande, and D. Kapur (2017). (when) do antipoverty programs reduce violence? india's rural employment guarantee and maoist conflict. *International organization* **71**(3), 605–632.

de Roux, N. and L. Martinez (2021). Forgone Investment: Civil Conflict and Agricultural Credit in Colombia. *Working Paper*.

Del Prete, D., M. Di Maio, and A. Rahman (2023). Firms amid conflict: Performance, production inputs, and market competition. *Journal of Development Economics* **164**, 103143.

Dimiceli, C., M. Carroll, R. Sohlberg, D. Kim, M. Kelly, and J. Townshend (2015). Mod44b modis/terra vegetation continuous fields yearly 13 global 250 m sin grid v006 [data set]. NASA *EOSDIS Land Process*.

Donaldson, D. (2018). Railroads of the raj: Estimating the impact of transportation infrastructure. *American Economic Review* **108**(4-5), 899–934.

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.

Fetzer, T. (2020). Can workfare programs moderate conflict? Evidence from India. *Journal of the European Economic Association* **18**(6), 3337–3375.

Gadenne, L., T. Nandi, and R. Rathelot (2019, August). Taxation and Supplier Networks: Evidence from India. *CEPR Discussion Papers* 13971.
Grosjean, P. (2014). Conflict and social and political preferences: Evidence from World War II and civil conflict in 35 European countries. *Comparative Economic Studies* 56(3), 424–451.

Head, K. and T. Mayer (2010). Illusory border effects: distance mismeasurement inflates estimates of home bias in trade. In *Brakman, Steven and Peter van Bergeijk eds. The Gravity Model in International Trade: Advances and Applications*. Cambridge University Press.

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

Hoenig, T. (2021). The legacy of conflict: aggregate evidence from Sierra Leone. *WIDER Working Paper 2021/104*.

Hospers, G.-J. (2011). Place marketing in shrinking europe: some geographical notes. *Tijdschrift voor economische en sociale geografie* 102(3), 369–375.

Hsieh, C.-T. and P. J. Klenow (2009). Misallocation and manufacturing TFP in China and India. *The Quarterly journal of economics* 124(4), 1403–1448.

Jackson, M. O. and S. Nei (2015). Networks of military alliances, wars, and international trade. *Proceedings of the National Academy of Sciences* 112(50), 15277–15284.

Kapoor, R. (2018). Understanding the Performance of India's Manufacturing Sector: Evidence from Firm Level Data. *CSE Working Paper*.

Khanna, G. and L. Zimmermann (2017). Guns and butter? Fighting violence with the promise of development. *Journal of Development Economics* 124, 120–141.

Klapper, L., C. Richmond, and T. Trang (2015). Civil Conflict and Firm Performance: Evidence from Côte d'Ivoire. World Bank Policy Working Paper No. 6640.

König, M. D., D. Rohner, M. Thoenig, and F. Zilibotti (2017). Networks in conflict: Theory and evidence from the great war of Africa. *Econometrica* 85(4), 1093–1132.

Korovkin, V., A. Makarin, and Y. Miyauchi (2024). Supply chain disruption and reorganization: Theory and evidence from ukraine’s war. Available at SSRN 4825542.

Ksoll, C., R. Macchiavello, and A. Morjaria (2023). Electoral violence and supply chain disruptions in kenya’s floriculture industry. *Review of economics and statistics*, 1–17.

Martin, L. A., S. Nataraj, and A. E. Harrison (2017). In with the big, out with the small: Removing small-scale reservations in India. *American Economic Review* 107(2), 354–86.

Martin, P., T. Mayer, and M. Thoenig (2008). Civil Wars and International Trade. *Journal of the European Economic Association* 6(2-3), 541–550.
Grosjean, P. (2014). Conflict and social and political preferences: Evidence from World War II and civil conflict in 35 European countries. *Comparative Economic Studies* 56(3), 424–451.

Head, K. and T. Mayer (2010). Illusory border effects: distance mismeasurement inflates estimates of home bias in trade. In *Brakman, Steven and Peter van Bergeijk eds. The Gravity Model in International Trade: Advances and Applications*. Cambridge University Press.

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

Hoenig, T. (2021). The legacy of conflict: aggregate evidence from Sierra Leone. *WIDER Working Paper 2021/104*.

Hospers, G.-J. (2011). Place marketing in shrinking europe: some geographical notes. *Tijdschrift voor economische en sociale geografie* 102(3), 369–375.

Hsieh, C.-T. and P. J. Klenow (2009). Misallocation and manufacturing TFP in China and India. *The Quarterly journal of economics* 124(4), 1403–1448.

Jackson, M. O. and S. Nei (2015). Networks of military alliances, wars, and international trade. *Proceedings of the National Academy of Sciences* 112(50), 15277–15284.

Kapoor, R. (2018). Understanding the Performance of India's Manufacturing Sector: Evidence from Firm Level Data. *CSE Working Paper*.

Khanna, G. and L. Zimmermann (2017). Guns and butter? Fighting violence with the promise of development. *Journal of Development Economics* 124, 120–141.

Klapper, L., C. Richmond, and T. Trang (2015). Civil Conflict and Firm Performance: Evidence from Côte d'Ivoire. World Bank Policy Working Paper No. 6640.

König, M. D., D. Rohner, M. Thoenig, and F. Zilibotti (2017). Networks in conflict: Theory and evidence from the great war of Africa. *Econometrica* 85(4), 1093–1132.

Korovkin, V., A. Makarin, and Y. Miyauchi (2024). Supply chain disruption and reorganization: Theory and evidence from ukraine’s war. Available at SSRN 4825542.

Ksoll, C., R. Macchiavello, and A. Morjaria (2023). Electoral violence and supply chain disruptions in kenya’s floriculture industry. *Review of economics and statistics*, 1–17.

Martin, L. A., S. Nataraj, and A. E. Harrison (2017). In with the big, out with the small: Removing small-scale reservations in India. *American Economic Review* 107(2), 354–86.

Martin, P., T. Mayer, and M. Thoenig (2008). Civil Wars and International Trade. *Journal of the European Economic Association* 6(2-3), 541–550.
Mueller, H., L. Piemontese, and A. Tapsoba (2017). *Recovery from conflict: lessons of success*. The World Bank.

Nunn, N. and N. Qian (2014). Us food aid and civil conflict. *American Economic Review* 104(6), 1630–66.

Panigrahi, P. (2021). Endogenous spatial production networks: Quantitative implications for trade and productivity. *Working Paper*.

Pellenbarg, P. H., L. J. Van Wissen, and J. Van Dijk (2002). Firm relocation: state of the art and research prospects.

Piemontese, L. (2023). Uncovering illegal and underground economies: The case of mafia extortion racketeering. *Journal of Public Economics* 227, 104997.

Pinotti, P. (2015). The Economic Costs of Organized Crime: Evidence from Southern Italy. *The Economic Journal* 125(586), F203–F232.

Ramana, P. V. (2018). Maoist Finances. *Journal of Defence Studies* 12(2), 59–75.

Rohner, D. and M. Thoenig (2021). The elusive peace dividend of development policy: From war traps to macro complementarities. *Annual Review of Economics* 13(1), 111–131.

Rohner, D., M. Thoenig, and F. Zilibotti (2013). Seeds of distrust: Conflict in Uganda. *Journal of Economic Growth* 18(3), 217–252.

Saing, C. H. and H. Kazianga (2020). The long-term impacts of violent conflicts on human capital: US bombing and, education, earnings, health, fertility and marriage in Cambodia. *The Journal of Development Studies* 56(5), 874–889.

Shapiro, J. N. and O. Vanden Eynde (2023). Fiscal incentives for conflict: Evidence from india’s red corridor. *Review of Economics and Statistics* 105(1), 217–225.

Shemyakina, O. (2011). The effect of armed conflict on accumulation of schooling: Results from Tajikistan. *Journal of Development Economics* 95(2), 186–200.

Sundberg, R. and M. Erik (2013). Introducing the UCDP Georeferenced Event Dataset. *Journal of Peace Research* 50(4), 523–532.

Tapsoba, A. (2023). The cost of fear: Impact of violence risk on child health during conflict. *Journal of Development Economics* 160, 102975.

Taschereau-Dumouchel, M. (2024). Cascades and Fluctuations in an Economy with an Endogenous Production Network. *R&R Review of Economic Studies*.

The Fund for Peace (2020). Fragile States Index 2020 – Annual Report. *Report*.
Mueller, H., L. Piemontese, and A. Tapsoba (2017). *Recovery from conflict: lessons of success*. The World Bank.

Nunn, N. and N. Qian (2014). Us food aid and civil conflict. *American Economic Review* 104(6), 1630–66.

Panigrahi, P. (2021). Endogenous spatial production networks: Quantitative implications for trade and productivity. *Working Paper*.

Pellenbarg, P. H., L. J. Van Wissen, and J. Van Dijk (2002). Firm relocation: state of the art and research prospects.

Piemontese, L. (2023). Uncovering illegal and underground economies: The case of mafia extortion racketeering. *Journal of Public Economics* 227, 104997.

Pinotti, P. (2015). The Economic Costs of Organized Crime: Evidence from Southern Italy. *The Economic Journal* 125(586), F203–F232.

Ramana, P. V. (2018). Maoist Finances. *Journal of Defence Studies* 12(2), 59–75.

Rohner, D. and M. Thoenig (2021). The elusive peace dividend of development policy: From war traps to macro complementarities. *Annual Review of Economics* 13(1), 111–131.

Rohner, D., M. Thoenig, and F. Zilibotti (2013). Seeds of distrust: Conflict in Uganda. *Journal of Economic Growth* 18(3), 217–252.

Saing, C. H. and H. Kazianga (2020). The long-term impacts of violent conflicts on human capital: US bombing and, education, earnings, health, fertility and marriage in Cambodia. *The Journal of Development Studies* 56(5), 874–889.

Shapiro, J. N. and O. Vanden Eynde (2023). Fiscal incentives for conflict: Evidence from india’s red corridor. *Review of Economics and Statistics* 105(1), 217–225.

Shemyakina, O. (2011). The effect of armed conflict on accumulation of schooling: Results from Tajikistan. *Journal of Development Economics* 95(2), 186–200.

Sundberg, R. and M. Erik (2013). Introducing the UCDP Georeferenced Event Dataset. *Journal of Peace Research* 50(4), 523–532.

Tapsoba, A. (2023). The cost of fear: Impact of violence risk on child health during conflict. *Journal of Development Economics* 160, 102975.

Taschereau-Dumouchel, M. (2024). Cascades and Fluctuations in an Economy with an Endogenous Production Network. *R&R Review of Economic Studies*.

The Fund for Peace (2020). Fragile States Index 2020 – Annual Report. *Report*.
Ulyssea, G. (2018, August). Firms, informality, and development: Theory and evidence from brazil. *American Economic Review* 108(8), 2015–47.

Voors, M. J., E. E. Nillesen, P. Verwimp, E. H. Bulte, R. Lensink, and D. P. Van Soest (2012). Violent conflict and behavior: a field experiment in Burundi. *The American Economic Review* 102(2), 941–964.

Wooldridge, J. M. (2009). On estimating firm-level production functions using proxy variables to control for unobservables. *Economics letters* 104(3), 112–114.

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

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

World Bank (2021). World bank development indicators. Washington, DC: World Bank.
Ulyssea, G. (2018, August). Firms, informality, and development: Theory and evidence from brazil. *American Economic Review* 108(8), 2015–47.

Voors, M. J., E. E. Nillesen, P. Verwimp, E. H. Bulte, R. Lensink, and D. P. Van Soest (2012). Violent conflict and behavior: a field experiment in Burundi. *The American Economic Review* 102(2), 941–964.

Wooldridge, J. M. (2009). On estimating firm-level production functions using proxy variables to control for unobservables. *Economics letters* 104(3), 112–114.

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

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

World Bank (2021). World bank development indicators. Washington, DC: World Bank.
The Economic Costs of Conflict: A Production Network
Approach
Online Appendix
The Economic Costs of Conflict: A Production Network
Approach
Online Appendix
# OA1 Evidence of additional costs imposed by the conflict

We provide an initial exploration of the costs borne by firms located in conflict-affected districts. We rely on cross-section data from the World Bank Enterprise Survey (World Bank, 2014), which provides information on the business constraints to which Indian firms are subject.<fnref n="1" /> This data is particularly well-suited for our purpose because the survey covers a broad range of business environment, crime, corruption, infrastructure and firm-level performance measures. To study the extra costs suffered by firms located in conflict-affected districts, we focus on five outcomes:

1. an indicator that equals 1 if the firm reports any losses due to theft (as percentage of the value of their products), built from the question: “losses due to theft, robbery, vandalism or arson experienced in last fiscal year? 1 if yes, 0 if no.”

2. an indicator that equals 1 if the firm declares to provide informal payments or gifts to other actors, built from the question: “percent of total annual sales paid in informal payments? 1 if percentage is >0, and 0 if they report no payment or gifts are paid.”

3. an indicator that equals to 1 if the firm allocates resources to pay for security (e.g. expenses on equipment, personnel, professional security services), built from the question: “how much of an obstacle: electricity to operations of this establishment? 1 if moderate, major or very severe obstacle; 0 if no obstacle or minor.”

4. an indicator that equals 1 if the firm reports access to electricity as an obstacle in her business environment, built from the question: “how much of an obstacle: electricity to operations of this establishment? 1 if moderate, major or very severe obstacle; 0 if no obstacle or minor.”

5. the total cost of production includes: total labor cost (including wages, salaries and bonuses), total annual costs of electricity, total annual costs of fuel, total annual cost of finished goods/materials bought to resell, other costs of production not included, total rental cost of machinery, vehicles and equipment, total rental cost of land and buildings

A preliminary balancing test suggests striking differences across firms: firms located in a conflict-affected district report having more facilities, managers with more experience and more working capital financed from internal funds; however, they have a lower number of employees and a lower level of sales (Table OA1.1).

To examine the additional costs borne by firms located in conflict-affected districts, we estimate an equation of the following form:

$$Y_{id} = \beta \text{Exposure-conflict-districts}_d + \mathbf{D}'_{id}\alpha + \theta_s + \varepsilon_{id} \quad (OA1.1)$$

where $Y_{id}$ are the various firm-level outcomes related to business constraints of firm $i$ in district $d$: (i) an indicator that equals 1 if the firm reports losses due to theft; (ii) an indicator that equals 1 if the firm declares that it pays protection money; (iii) an indicator that equals 1 if the firm incurs expenses for security (such as expenditure on equipment, personnel, professional security services, etc.); (iv) an indicator that equals 1 if the firm reports problems of access to electricity; and (v) the total cost of production. The main explanatory variable, *Exposure-conflict-districts*<sub>d</sub>, indicates whether the firm is located in a district affected by the insurgency since 2000 (see Section 4 for details on conflict data). In the sample, approximately 23% firms are located in a conflict-affected district. We also include (log) firm characteristics ($\mathbf{D}'_{id}$): number of facilities, facility type, manager's experience, value of sales, number of employees, percentage of working capital financed from internal funds, sector of activity, legal status and main product. Lastly, we include state fixed effects ($\theta_s$) to capture state-level policies that may have an effect on the distortions experienced by a firm as a result of the conflict.

<footnote n="1">¹Data for India are only available for the year 2014. The WBES is a firm-level survey conducted on a representative sample of the private sector in 149 different countries. The dataset has been used in various contexts and to investigate various research questions, ranging from informal enterprises and female employment in developing countries to estimation of productivity. See De Haas and Poelhekke (2019) for a detailed literature review.</footnote>
# OA1 Evidence of additional costs imposed by the conflict

We provide an initial exploration of the costs borne by firms located in conflict-affected districts. We rely on cross-section data from the World Bank Enterprise Survey (World Bank, 2014), which provides information on the business constraints to which Indian firms are subject.<fnref n="1" /> This data is particularly well-suited for our purpose because the survey covers a broad range of business environment, crime, corruption, infrastructure and firm-level performance measures. To study the extra costs suffered by firms located in conflict-affected districts, we focus on five outcomes:

1. an indicator that equals 1 if the firm reports any losses due to theft (as percentage of the value of their products), built from the question: “losses due to theft, robbery, vandalism or arson experienced in last fiscal year? 1 if yes, 0 if no.”

2. an indicator that equals 1 if the firm declares to provide informal payments or gifts to other actors, built from the question: “percent of total annual sales paid in informal payments? 1 if percentage is >0, and 0 if they report no payment or gifts are paid.”

3. an indicator that equals to 1 if the firm allocates resources to pay for security (e.g. expenses on equipment, personnel, professional security services), built from the question: “how much of an obstacle: electricity to operations of this establishment? 1 if moderate, major or very severe obstacle; 0 if no obstacle or minor.”

4. an indicator that equals 1 if the firm reports access to electricity as an obstacle in her business environment, built from the question: “how much of an obstacle: electricity to operations of this establishment? 1 if moderate, major or very severe obstacle; 0 if no obstacle or minor.”

5. the total cost of production includes: total labor cost (including wages, salaries and bonuses), total annual costs of electricity, total annual costs of fuel, total annual cost of finished goods/materials bought to resell, other costs of production not included, total rental cost of machinery, vehicles and equipment, total rental cost of land and buildings

A preliminary balancing test suggests striking differences across firms: firms located in a conflict-affected district report having more facilities, managers with more experience and more working capital financed from internal funds; however, they have a lower number of employees and a lower level of sales (Table OA1.1).

To examine the additional costs borne by firms located in conflict-affected districts, we estimate an equation of the following form:

$$Y_{id} = \beta \text{Exposure-conflict-districts}_d + \mathbf{D}'_{id}\alpha + \theta_s + \varepsilon_{id} \quad (\text{OA1.1})$$

where $Y_{id}$ are the various firm-level outcomes related to business constraints of firm $i$ in district $d$: (i) an indicator that equals 1 if the firm reports losses due to theft; (ii) an indicator that equals 1 if the firm declares that it pays protection money; (iii) an indicator that equals 1 if the firm incurs expenses for security (such as expenditure on equipment, personnel, professional security services, etc.); (iv) an indicator that equals 1 if the firm reports problems of access to electricity; and (v) the total cost of production. The main explanatory variable, *Exposure-conflict-districts*<sub>d</sub>, indicates whether the firm is located in a district affected by the insurgency since 2000 (see Section 4 for details on conflict data). In the sample, approximately 23% firms are located in a conflict-affected district. We also include (log) firm characteristics ($\mathbf{D}'_{id}$): number of facilities, facility type, manager's experience, value of sales, number of employees, percentage of working capital financed from internal funds, sector of activity, legal status and main product. Lastly, we include state fixed effects ($\theta_s$) to capture state-level policies that may have an effect on the distortions experienced by a firm as a result of the conflict.

<footnote n="1">¹Data for India are only available for the year 2014. The WBES is a firm-level survey conducted on a representative sample of the private sector in 149 different countries. The dataset has been used in various contexts and to investigate various research questions, ranging from informal enterprises and female employment in developing countries to estimation of productivity. See De Haas and Poelhekke (2019) for a detailed literature review.</footnote>
<table><caption>Table OA1.1: World Bank Economic Survey Data: balancing test</caption><thead><tr><th></th><th>(1)<br>peaceful districts</th><th>(2)<br>conflict districts</th><th>(3)<br>difference</th></tr></thead><tbody><tr><td>Loss due to theft</td><td>0.032<br>(0.176)</td><td>0.074<br>(0.262)</td><td>0.042<sup>a</sup><br>(0.005)</td></tr><tr><td>Loss due to payment</td><td>0.054<br>(0.227)</td><td>0.080<br>(0.272)</td><td>0.026<sup>a</sup><br>(0.007)</td></tr><tr><td>Pay for security</td><td>0.662<br>(0.473)</td><td>0.767<br>(0.423)</td><td>0.105<sup>a</sup><br>(0.012)</td></tr><tr><td>Access to electricity</td><td>0.448<br>(0.497)</td><td>0.561<br>(0.496)</td><td>0.113<sup>a</sup><br>(0.012)</td></tr><tr><td>Total cost of production</td><td>17.074<br>(2.022)</td><td>16.790<br>(2.136)</td><td>-0.284<sup>a</sup><br>(0.052)</td></tr><tr><td>(log) establishments (#)</td><td>0.228<br>(0.510)</td><td>0.271<br>(0.562)</td><td>0.043<sup>a</sup><br>(0.013)</td></tr><tr><td>(log) manager experience</td><td>2.340<br>(0.773)</td><td>2.399<br>(0.631)</td><td>0.059<sup>a</sup><br>(0.019)</td></tr><tr><td>Manufacturing</td><td>0.778<br>(0.416)</td><td>0.760<br>(0.427)</td><td>-0.018<sup>c</sup><br>(0.010)</td></tr><tr><td>(log) sales</td><td>17.663<br>(1.872)</td><td>17.262<br>(2.078)</td><td>-0.401<sup>a</sup><br>(0.048)</td></tr><tr><td>(log) employees (#)</td><td>3.738<br>(1.259)</td><td>3.505<br>(1.230)</td><td>-0.233<sup>a</sup><br>(0.031)</td></tr><tr><td>(log) capital (#)</td><td>3.914<br>(1.026)</td><td>4.045<br>(0.881)</td><td>0.131<sup>a</sup><br>(0.025)</td></tr><tr><td>Observations</td><td>7,131</td><td>2,059</td><td>9,190</td></tr></tbody></table>

Table OA1.2 displays the results. In columns 1-5 we control only for firm characteristics, whereas in columns 6-10 we add state fixed effects. Firms located in conflict-affected districts are 92% more likely to report losses due to theft (column 1), 66% more likely to pay protection money (column 2), 17% more likely to pay for security (column 3), and 19% more likely to report access to electricity as an obstacle in business activity (column 4), relative to the sample mean. Finally, firms located in conflict-affected districts have, on average, 5% higher costs of production (column 5). By adding state fixed effects, firms located in a conflict-affected district can be compared to other firms outside conflict-affected districts within the same state. With the notable exception of losses due to theft, firms located in a conflict-affected district are even more likely to bear some additional costs. While the magnitude is slightly lower for payment of protection money (column 7), expenditure on security (column 8), and access to electricity (column 9), firms located in a conflict-affected district report a total cost of production that is on average 13% higher than other firms. This effect is almost three times larger than in the model without state fixed effects (column 5). Due to endogeneity concerns, this exercise cannot extend beyond correlation only. Even if somewhat limited, this preliminary evidence is nonetheless relevant for two main reasons. First, it indicates the large magnitude of additional costs borne by firms located in conflict-affected districts. Second, it demonstrates the diversity of costs that can arise from a conflict. This becomes particularly useful in the conceptual framework derived in Section 3. Indeed, we are able to accurately model firm behavior in conflict-affected districts in accordance with the evidence for the existence of both output and input distortions.
<table><caption>Table OA1.1: World Bank Economic Survey Data: balancing test</caption><thead><tr><th></th><th>(1)<br>peaceful districts</th><th>(2)<br>conflict districts</th><th>(3)<br>difference</th></tr></thead><tbody><tr><td>Loss due to theft</td><td>0.032<br>(0.176)</td><td>0.074<br>(0.262)</td><td>0.042<sup>a</sup><br>(0.005)</td></tr><tr><td>Loss due to payment</td><td>0.054<br>(0.227)</td><td>0.080<br>(0.272)</td><td>0.026<sup>a</sup><br>(0.007)</td></tr><tr><td>Pay for security</td><td>0.662<br>(0.473)</td><td>0.767<br>(0.423)</td><td>0.105<sup>a</sup><br>(0.012)</td></tr><tr><td>Access to electricity</td><td>0.448<br>(0.497)</td><td>0.561<br>(0.496)</td><td>0.113<sup>a</sup><br>(0.012)</td></tr><tr><td>Total cost of production</td><td>17.074<br>(2.022)</td><td>16.790<br>(2.136)</td><td>-0.284<sup>a</sup><br>(0.052)</td></tr><tr><td>(log) establishments (#)</td><td>0.228<br>(0.510)</td><td>0.271<br>(0.562)</td><td>0.043<sup>a</sup><br>(0.013)</td></tr><tr><td>(log) manager experience</td><td>2.340<br>(0.773)</td><td>2.399<br>(0.631)</td><td>0.059<sup>a</sup><br>(0.019)</td></tr><tr><td>Manufacturing</td><td>0.778<br>(0.416)</td><td>0.760<br>(0.427)</td><td>-0.018<sup>c</sup><br>(0.010)</td></tr><tr><td>(log) sales</td><td>17.663<br>(1.872)</td><td>17.262<br>(2.078)</td><td>-0.401<sup>a</sup><br>(0.048)</td></tr><tr><td>(log) employees (#)</td><td>3.738<br>(1.259)</td><td>3.505<br>(1.230)</td><td>-0.233<sup>a</sup><br>(0.031)</td></tr><tr><td>(log) capital (#)</td><td>3.914<br>(1.026)</td><td>4.045<br>(0.881)</td><td>0.131<sup>a</sup><br>(0.025)</td></tr><tr><td>Observations</td><td>7,131</td><td>2,059</td><td>9,190</td></tr></tbody></table>

Table OA1.2 displays the results. In columns 1-5 we control only for firm characteristics, whereas in columns 6-10 we add state fixed effects. Firms located in conflict-affected districts are 92% more likely to report losses due to theft (column 1), 66% more likely to pay protection money (column 2), 17% more likely to pay for security (column 3), and 19% more likely to report access to electricity as an obstacle in business activity (column 4), relative to the sample mean. Finally, firms located in conflict-affected districts have, on average, 5% higher costs of production (column 5). By adding state fixed effects, firms located in a conflict-affected district can be compared to other firms outside conflict-affected districts within the same state. With the notable exception of losses due to theft, firms located in a conflict-affected district are even more likely to bear some additional costs. While the magnitude is slightly lower for payment of protection money (column 7), expenditure on security (column 8), and access to electricity (column 9), firms located in a conflict-affected district report a total cost of production that is on average 13% higher than other firms. This effect is almost three times larger than in the model without state fixed effects (column 5). Due to endogeneity concerns, this exercise cannot extend beyond correlation only. Even if somewhat limited, this preliminary evidence is nonetheless relevant for two main reasons. First, it indicates the large magnitude of additional costs borne by firms located in conflict-affected districts. Second, it demonstrates the diversity of costs that can arise from a conflict. This becomes particularly useful in the conceptual framework derived in Section 3. Indeed, we are able to accurately model firm behavior in conflict-affected districts in accordance with the evidence for the existence of both output and input distortions.
<table><caption>Table OA1.2: Additional costs due to conflict</caption><thead><tr><th>Dep. var.</th><th>Loss due to theft (1)</th><th>Protection money (2)</th><th>Expenditure on security (3)</th><th>Access to electricity (4)</th><th>Total cost of production (5)</th></tr></thead><tbody><tr><td>Exposure, conflict districts</td><td>0.039<sup>a</sup><br>(0.006)</td><td>0.040<sup>a</sup><br>(0.008)</td><td>0.118<sup>a</sup><br>(0.044)</td><td>0.093<sup>a</sup><br>(0.013)</td><td>0.046<sup>a</sup><br>(0.016)</td></tr><tr><td>Firm's characteristics</td><td></td><td></td><td>Yes</td><td></td><td></td></tr><tr><td>State FE</td><td></td><td></td><td>No</td><td></td><td></td></tr><tr><td></td><td>(6)</td><td>(7)</td><td>(8)</td><td>(9)</td><td>(10)</td></tr><tr><td>Exposure, conflict districts</td><td>-0.015<br>(0.012)</td><td>0.037<sup>c</sup><br>(0.019)</td><td>0.070<sup>c</sup><br>(0.038)</td><td>0.051<sup>b</sup><br>(0.022)</td><td>0.123<sup>a</sup><br>(0.035)</td></tr><tr><td>Firm's characteristics</td><td></td><td></td><td>Yes</td><td></td><td></td></tr><tr><td>State FE</td><td></td><td></td><td>Yes</td><td></td><td></td></tr><tr><td>Observations</td><td>8769</td><td>7159</td><td>8791</td><td>8801</td><td>8648</td></tr><tr><td>% exposed firms</td><td>23.06</td><td>23.91</td><td>23</td><td>22.97</td><td>23.08</td></tr><tr><td>Sample mean</td><td>0.04</td><td>0.06</td><td>0.69</td><td>0.48</td><td>17.04</td></tr></tbody></table>

Note: c significant at 10%; b at 5%; a at 1%. Robust standard errors are reported in parentheses. The alternative dependent variables are: (i) an indicator that equals 1 if the firm reports any losses due to theft (as a percentage of the value of their production) (columns 1 and 6); (ii) an indicator that equals 1 if the firm pays protection money (columns 2 and 7); (iii) an indicator that equals 1 if the firm allocates resources to security (e.g., expenses on equipment, personnel, professional security services, etc.) (columns 3 and 8); (iv) an indicator that equals 1 if the firm reports access to electricity as an obstacle to business activity (columns 4 and 9); and (v) the cost of production (columns 5 and 10). Exposure-conflict-zone is an indicator that equals 1 if the firm is located in a conflict-affected district. Additional controls include (log) firm characteristics and state fixed effects (second panel).
<table><caption>Table OA1.2: Additional costs due to conflict</caption><thead><tr><th>Dep. var.</th><th>Loss due<br>to theft<br>(1)</th><th>Protection<br>money<br>(2)</th><th>Expenditure<br>on security<br>(3)</th><th>Access to<br>electricity<br>(4)</th><th>Total cost<br>of production<br>(5)</th></tr></thead><tbody><tr><td>Exposure, conflict districts</td><td>0.039<sup>a</sup><br>(0.006)</td><td>0.040<sup>a</sup><br>(0.008)</td><td>0.118<sup>a</sup><br>(0.044)</td><td>0.093<sup>a</sup><br>(0.013)</td><td>0.046<sup>a</sup><br>(0.016)</td></tr><tr><td>Firm's characteristics</td><td></td><td></td><td>Yes</td><td></td><td></td></tr><tr><td>State FE</td><td></td><td></td><td>No</td><td></td><td></td></tr><tr><td></td><td>(6)</td><td>(7)</td><td>(8)</td><td>(9)</td><td>(10)</td></tr><tr><td>Exposure, conflict districts</td><td>-0.015<br>(0.012)</td><td>0.037<sup>c</sup><br>(0.019)</td><td>0.070<sup>c</sup><br>(0.038)</td><td>0.051<sup>b</sup><br>(0.022)</td><td>0.123<sup>a</sup><br>(0.035)</td></tr><tr><td>Firm's characteristics</td><td></td><td></td><td>Yes</td><td></td><td></td></tr><tr><td>State FE</td><td></td><td></td><td>Yes</td><td></td><td></td></tr><tr><td>Observations</td><td>8769</td><td>7159</td><td>8791</td><td>8801</td><td>8648</td></tr><tr><td>% exposed firms</td><td>23.06</td><td>23.91</td><td>23</td><td>22.97</td><td>23.08</td></tr><tr><td>Sample mean</td><td>0.04</td><td>0.06</td><td>0.69</td><td>0.48</td><td>17.04</td></tr></tbody></table>

Note: c significant at 10%; b at 5%; a at 1%. Robust standard errors are reported in parentheses. The alternative dependent variables are: (i) an indicator that equals 1 if the firm reports any losses due to theft (as a percentage of the value of their production) (columns 1 and 6); (ii) an indicator that equals 1 if the firm pays protection money (columns 2 and 7); (iii) an indicator that equals 1 if the firm allocates resources to security (e.g., expenses on equipment, personnel, professional security services, etc.) (columns 3 and 8); (iv) an indicator that equals 1 if the firm reports access to electricity as an obstacle to business activity (columns 4 and 9); and (v) the cost of production (columns 5 and 10). Exposure-conflict-zone is an indicator that equals 1 if the firm is located in a conflict-affected district. Additional controls include (log) firm characteristics and state fixed effects (second panel).
OA2 Additional information on the Maoist Insurgency

Figure OA2.1 plots the 2019 country-level indexes for the Fragile States (x-axis, hereafter FSI) and the Ease-of-doing-business (y-axis, hereafter EDB), in percentiles, using information from (The Fund for Peace, 2020) and (World Bank, 2019). The higher is the FSI index, the more fragile is the state. On the other hand, the EDB classifies the countries from the easiest business environment (low index) to the hardest business environment (high index). Countries are classified in four categories with respect to their sample median: (i) low FSI, low EDB; (ii) low FSI, high EDB; (iii) high FSI, low EDB and (iv) high FSO, high EDB. India is at the 74th position of the most Fragile States out of 178 countries and at the 63rd position of the Ease-of-doing-business measure out of 190 States.

<figure><img src="image_12.png" /><figcaption>Figure OA2.1: Ease-of-Doing-Business and Fragile States</figcaption></figure>

Note: This figure plots the 2019 country-level indexes for the Fragile States (x-axis, hereafter FSI) and the Ease-of-doing-business (y-axis, hereafter EDB), in percentiles. Countries are classified in four categories with respect to their sample median: (i) low FSI, low EDB; (ii) low FSI, high EDB; (iii) high FSI, low EDB and (iv) high FSO, high EDB.
OA2 Additional information on the Maoist Insurgency

Figure OA2.1 plots the 2019 country-level indexes for the Fragile States (x-axis, hereafter FSI) and the Ease-of-doing-business (y-axis, hereafter EDB), in percentiles, using information from (The Fund for Peace, 2020) and (World Bank, 2019). The higher is the FSI index, the more fragile is the state. On the other hand, the EDB classifies the countries from the easiest business environment (low index) to the hardest business environment (high index). Countries are classified in four categories with respect to their sample median: (i) low FSI, low EDB; (ii) low FSI, high EDB; (iii) high FSI, low EDB and (iv) high FSO, high EDB. India is at the 74th position of the most Fragile States out of 178 countries and at the 63rd position of the Ease-of-doing-business measure out of 190 States.

<figure><img src="image_11.png" /><figcaption>Figure OA2.1: Ease-of-Doing-Business and Fragile States</figcaption></figure>

Note: This figure plots the 2019 country-level indexes for the Fragile States (x-axis, hereafter FSI) and the Ease-of-doing-business (y-axis, hereafter EDB), in percentiles. Countries are classified in four categories with respect to their sample median: (i) low FSI, low EDB; (ii) low FSI, high EDB; (iii) high FSI, low EDB and (iv) high FSO, high EDB.
Table OA2.3 and Table OA2.4 presents Maoist-related descriptive statistics based on information from South Asia Terrorism Portal and UCDP data.

<table><caption>Table OA2.3: List of Maoist-related Armed Groups</caption><thead><tr><td>Maoist Outfit</td><td>Abbreviation</td><td>Share of fatalities</td></tr></thead><tbody><tr><td>Communist Party of India (Maoist)</td><td>CPI-Maoist</td><td>60.37</td></tr><tr><td>Communist Party of India (Marxist-Leninist) People's War</td><td>PWG</td><td>12.86</td></tr><tr><td>Maoist Communist Centre of India</td><td>MCC</td><td>7.75</td></tr><tr><td>United National Liberation Front</td><td>UNLF</td><td>6.88</td></tr><tr><td>People's Liberation Army</td><td>PLA</td><td>4.67</td></tr><tr><td>Kangleipak Communist Party</td><td>KPC</td><td>2.88</td></tr><tr><td>The People's Revolutionary Party of Kangleipak</td><td>PREPAK</td><td>2.68</td></tr><tr><td>Communist Party of India (Marxist-Leninist) Janashakti</td><td>CPI-ML-J</td><td>1.15</td></tr><tr><td>Communist Party of India (Marxist-Leninist)</td><td>CPI-ML-WM</td><td>0.76</td></tr><tr><td>People's Liberation Front of India</td><td>PLFI</td><td>0</td></tr></tbody></table>

Note: This table presents a simplified list of Maoist-related armed groups between 2000 and 2009, for which UCDP data provides information on conflictual events and related fatalities. Source: Authors' computation using information from South Asia Terrorism Portal and UCDP data.

<table><caption>Table OA2.4: Maoist insurgency</caption><thead><tr><th></th><th>2000-2009</th></tr></thead><tbody><tr><td>Violent events</td><td>1775</td></tr><tr><td>Fatalities</td><td>4737</td></tr><tr><td>Major incidents</td><td>563</td></tr><tr><td>Explosions</td><td>750</td></tr></tbody></table>

Note: This table gives the number of maoist-related violent event or fatalities between 2000 and 2009 (based on accounting years). Source: authors' computation using information from South Asia Terrorism Portal and UCDP data.
Table OA2.3 and Table OA2.4 presents Maoist-related descriptive statistics based on information from South Asia Terrorism Portal and UCDP data.

<table><caption>Table OA2.3: List of Maoist-related Armed Groups</caption><thead><tr><td>Maoist Outfit</td><td>Abbreviation</td><td>Share of fatalities</td></tr></thead><tbody><tr><td>Communist Party of India (Maoist)</td><td>CPI-Maoist</td><td>60.37</td></tr><tr><td>Communist Party of India (Marxist-Leninist) People's War</td><td>PWG</td><td>12.86</td></tr><tr><td>Maoist Communist Centre of India</td><td>MCC</td><td>7.75</td></tr><tr><td>United National Liberation Front</td><td>UNLF</td><td>6.88</td></tr><tr><td>People's Liberation Army</td><td>PLA</td><td>4.67</td></tr><tr><td>Kangleipak Communist Party</td><td>KPC</td><td>2.88</td></tr><tr><td>The People's Revolutionary Party of Kangleipak</td><td>PREPAK</td><td>2.68</td></tr><tr><td>Communist Party of India (Marxist-Leninist) Janashakti</td><td>CPI-ML-J</td><td>1.15</td></tr><tr><td>Communist Party of India (Marxist-Leninist)</td><td>CPI-ML-WM</td><td>0.76</td></tr><tr><td>People's Liberation Front of India</td><td>PLFI</td><td>0</td></tr></tbody></table>

Note: This table presents a simplified list of Maoist-related armed groups between 2000 and 2009, for which UCDP data provides information on conflictual events and related fatalities. Source: Authors' computation using information from South Asia Terrorism Portal and UCDP data.

<table><caption>Table OA2.4: Maoist insurgency</caption><thead><tr><th></th><th>2000-2009</th></tr></thead><tbody><tr><td>Violent events</td><td>1775</td></tr><tr><td>Fatalities</td><td>4737</td></tr><tr><td>Major incidents</td><td>563</td></tr><tr><td>Explosions</td><td>750</td></tr></tbody></table>

Note: This table gives the number of maoist-related violent event or fatalities between 2000 and 2009 (based on accounting years). Source: authors' computation using information from South Asia Terrorism Portal and UCDP data.
OA3 Results on the endogeneity of the production network: intensive margin

In this section, we replicate the results on the endogeneity of the production network (section 4.4) by focusing on the intensive margin of the conflict rather than the extensive margin. We focus on four different measures of conflict intensity: 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. Our conclusions remain unchanged when using measures of the intensity of violence.

**Conflict & number of firms.** Table OA3.5 shows that the number of firms is not significantly associated with the intensity of conflict. Intensity at the state-level slightly decreases the number of firms, but the effect is imprecisely estimated (only for panels A and C and at 10% level) and the magnitude remain very small: a 1% increase in events per capita within the state leads to a 0.006% decrease in the number of firms.

**Age & size of firms.** Firms' size is also largely not correlated with conflict intensity (Table OA3.7) except in historically affected districts, i.e., both affected by conflict before and after 2000. In those, the districts are between 0.023-0.065% smaller than in other districts, i.e., districts affected by violence only after 2000 and peaceful districts (column 2). Similar results hold for the standard deviation of firm's size which is negatively associated with conflict intensity (column 4).

**Relocation, entry & exit.** Relocation, entry and exit of firms is not significantly associated with any conflict intensity measures (Table OA3.8).

**Market structure.** Turning to market concentration, we do not find significant differences in market concentration between conflict and non-conflict districts (Table OA3.9).

**Labor vs. capital intensive technologies.** Consistent with our extensive margin estimation, we show that in conflict-affected districts, it is less likely for a certain good to be produced using labor-intensive technology compared to non-conflict districts (Table OA3.10).
OA3 Results on the endogeneity of the production network: intensive margin

In this section, we replicate the results on the endogeneity of the production network (section 4.4) by focusing on the intensive margin of the conflict rather than the extensive margin. We focus on four different measures of conflict intensity: 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. Our conclusions remain unchanged when using measures of the intensity of violence.

**Conflict & number of firms.** Table OA3.5 shows that the number of firms is not significantly associated with the intensity of conflict. Intensity at the state-level slightly decreases the number of firms, but the effect is imprecisely estimated (only for panels A and C and at 10% level) and the magnitude remain very small: a 1% increase in events per capita within the state leads to a 0.006% decrease in the number of firms.

**Age & size of firms.** Firms' size is also largely not correlated with conflict intensity (Table OA3.7) except in historically affected districts, i.e., both affected by conflict before and after 2000. In those, the districts are between 0.023-0.065% smaller than in other districts, i.e., districts affected by violence only after 2000 and peaceful districts (column 2). Similar results hold for the standard deviation of firm's size which is negatively associated with conflict intensity (column 4).

**Relocation, entry & exit.** Relocation, entry and exit of firms is not significantly associated with any conflict intensity measures (Table OA3.8).

**Market structure.** Turning to market concentration, we do not find significant differences in market concentration between conflict and non-conflict districts (Table OA3.9).

**Labor vs. capital intensive technologies.** Consistent with our extensive margin estimation, we show that in conflict-affected districts, it is less likely for a certain good to be produced using labor-intensive technology compared to non-conflict districts (Table OA3.10).
<table><caption>Table OA3.5: Intensive margin: number of firms</caption><thead><tr><th>Unit of obs.</th><th colspan="4">District</th><th>District × year</th></tr><tr><th>Dep. var</th><th colspan="4">(log-)number of manufacturing firms</th><th></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 colspan="6"><strong>Panel A: (log-) events per capita</strong></td></tr><tr><td>Intensity - affected districts</td><td>0.000<br>(0.012)</td><td></td><td></td><td></td><td></td></tr><tr><td>Intensity - historically affected districts</td><td></td><td>-0.009<br>(0.021)</td><td></td><td>-0.008<br>(0.022)</td><td></td></tr><tr><td>Intensity: newly affected districts</td><td></td><td></td><td>0.004<br>(0.012)</td><td>0.002<br>(0.013)</td><td></td></tr><tr><td>Intensity (district)</td><td></td><td></td><td></td><td></td><td>-0.000<br>(0.006)</td></tr><tr><td>Intensity (state)</td><td></td><td></td><td></td><td></td><td>-0.006<sup>c</sup><br>(0.004)</td></tr><tr><td colspan="6"><strong>Panel B: (log-) events by area</strong></td></tr><tr><td>Intensity - affected districts</td><td>0.001<br>(0.022)</td><td></td><td></td><td></td><td></td></tr><tr><td>Intensity - historically affected districts</td><td></td><td>-0.018<br>(0.037)</td><td></td><td>-0.015<br>(0.039)</td><td></td></tr><tr><td>Intensity: newly affected districts</td><td></td><td></td><td>0.008<br>(0.022)</td><td>0.004<br>(0.023)</td><td></td></tr><tr><td>Intensity (district)</td><td></td><td></td><td></td><td></td><td>0.006<br>(0.010)</td></tr><tr><td>Intensity (state)</td><td></td><td></td><td></td><td></td><td>-0.008<br>(0.005)</td></tr><tr><td colspan="6"><strong>Panel C: (log-) deaths per capita</strong></td></tr><tr><td>Intensity - affected districts</td><td>0.001<br>(0.013)</td><td></td><td></td><td></td><td></td></tr><tr><td>Intensity - historically affected districts</td><td></td><td>-0.015<br>(0.023)</td><td></td><td>-0.013<br>(0.024)</td><td></td></tr><tr><td>Intensity: newly affected districts</td><td></td><td></td><td>0.006<br>(0.013)</td><td>0.003<br>(0.013)</td><td></td></tr><tr><td>Intensity (district)</td><td></td><td></td><td></td><td></td><td>-0.001<br>(0.006)</td></tr><tr><td>Intensity (state)</td><td></td><td></td><td></td><td></td><td>-0.006<sup>c</sup><br>(0.004)</td></tr><tr><td colspan="6"><strong>Panel D: (log-) deaths by area</strong></td></tr><tr><td>Intensity - affected districts</td><td>0.007<br>(0.023)</td><td></td><td></td><td></td><td></td></tr><tr><td>Intensity - historically affected districts</td><td></td><td>-0.035<br>(0.043)</td><td></td><td>-0.027<br>(0.045)</td><td></td></tr><tr><td>Intensity: newly affected districts</td><td></td><td></td><td>0.017<br>(0.023)</td><td>0.012<br>(0.024)</td><td></td></tr><tr><td>Intensity (district)</td><td></td><td></td><td></td><td></td><td>0.006<br>(0.011)</td></tr><tr><td>Intensity (state)</td><td></td><td></td><td></td><td></td><td>-0.008<br>(0.005)</td></tr><tr><td>Sample mean</td><td>3.36</td><td>3.36</td><td>3.36</td><td>3.36</td><td>3.35</td></tr><tr><td>Observations</td><td>553</td><td>553</td><td>553</td><td>553</td><td>5280</td></tr><tr><td>Firm- and district-level controls</td><td>No</td><td>Yes</td><td>No</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><td>Yes</td></tr><tr><td>Year FE</td><td>No</td><td>No</td><td>No</td><td>No</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 4 using the intensive margin of violence. From columns 1 to 4, the unit of observation is the district. We estimate the following equation: Number of firms<sub>ds</sub> = β<sub>0</sub> + β<sub>1</sub> Conflict intensity<sub>ds</sub> + X'<sub>ds</sub>γ + θ<sub>s</sub> + u<sub>ds</sub>, where Number of firms<sub>ds</sub> measures the (log-)average number of firms observed over the period 2000-2009 in district d in state s. Conflict intensity<sub>ds</sub> 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 and mutually exclusive types of districts that mark whether Maoist insurgency impacted the district both before and after 2000 (historically affected districts), or only after (newly affected districts). X'<sub>ds</sub> 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). In column 5, the unit of observation is district × year. We estimate the following equation: Number of firms<sub>dst</sub> = β<sub>0</sub> + β<sub>1</sub> Intensity<sub>dst</sub> + β<sub>2</sub> Intensity<sub>st</sub> + X'<sub>dst</sub>γ + θ<sub>s</sub> + θ<sub>t</sub> + u<sub>dst</sub>, where Number of firms<sub>dst</sub> measures the number of firms (in logs) observed in district d, in state s, at year t. Intensity<sub>dst</sub> measures conflict intensity in district d, in state s at year t. Intensity<sub>st</sub> is a measure of conflict intensity at state level. Both measures are defined according to their respective panel. X'<sub>dst</sub> includes time-varying district-level characteristics, θ<sub>s</sub> are state fixed effects and θ<sub>t</sub> are year fixed effects.
<table><caption>Table OA3.5: Intensive margin: number of firms</caption><thead><tr><th>Unit of obs.</th><th colspan="4">District</th><th>District × year</th></tr><tr><th>Dep. var</th><th colspan="4">(log-)number of manufacturing firms</th><th></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 colspan="6"><strong>Panel A: (log-) events per capita</strong></td></tr><tr><td>Intensity - affected districts</td><td>0.000<br>(0.012)</td><td></td><td></td><td></td><td></td></tr><tr><td>Intensity - historically affected districts</td><td></td><td>-0.009<br>(0.021)</td><td></td><td>-0.008<br>(0.022)</td><td></td></tr><tr><td>Intensity: newly affected districts</td><td></td><td></td><td>0.004<br>(0.012)</td><td>0.002<br>(0.013)</td><td></td></tr><tr><td>Intensity (district)</td><td></td><td></td><td></td><td></td><td>-0.000<br>(0.006)</td></tr><tr><td>Intensity (state)</td><td></td><td></td><td></td><td></td><td>-0.006<sup>c</sup><br>(0.004)</td></tr><tr><td colspan="6"><strong>Panel B: (log-) events by area</strong></td></tr><tr><td>Intensity - affected districts</td><td>0.001<br>(0.022)</td><td></td><td></td><td></td><td></td></tr><tr><td>Intensity - historically affected districts</td><td></td><td>-0.018<br>(0.037)</td><td></td><td>-0.015<br>(0.039)</td><td></td></tr><tr><td>Intensity: newly affected districts</td><td></td><td></td><td>0.008<br>(0.022)</td><td>0.004<br>(0.023)</td><td></td></tr><tr><td>Intensity (district)</td><td></td><td></td><td></td><td></td><td>0.006<br>(0.010)</td></tr><tr><td>Intensity (state)</td><td></td><td></td><td></td><td></td><td>-0.008<br>(0.005)</td></tr><tr><td colspan="6"><strong>Panel C: (log-) deaths per capita</strong></td></tr><tr><td>Intensity - affected districts</td><td>0.001<br>(0.013)</td><td></td><td></td><td></td><td></td></tr><tr><td>Intensity - historically affected districts</td><td></td><td>-0.015<br>(0.023)</td><td></td><td>-0.013<br>(0.024)</td><td></td></tr><tr><td>Intensity: newly affected districts</td><td></td><td></td><td>0.006<br>(0.013)</td><td>0.003<br>(0.013)</td><td></td></tr><tr><td>Intensity (district)</td><td></td><td></td><td></td><td></td><td>-0.001<br>(0.006)</td></tr><tr><td>Intensity (state)</td><td></td><td></td><td></td><td></td><td>-0.006<sup>c</sup><br>(0.004)</td></tr><tr><td colspan="6"><strong>Panel D: (log-) deaths by area</strong></td></tr><tr><td>Intensity - affected districts</td><td>0.007<br>(0.023)</td><td></td><td></td><td></td><td></td></tr><tr><td>Intensity - historically affected districts</td><td></td><td>-0.035<br>(0.043)</td><td></td><td>-0.027<br>(0.045)</td><td></td></tr><tr><td>Intensity: newly affected districts</td><td></td><td></td><td>0.017<br>(0.023)</td><td>0.012<br>(0.024)</td><td></td></tr><tr><td>Intensity (district)</td><td></td><td></td><td></td><td></td><td>0.006<br>(0.011)</td></tr><tr><td>Intensity (state)</td><td></td><td></td><td></td><td></td><td>-0.008<br>(0.005)</td></tr><tr><td>Sample mean</td><td>3.36</td><td>3.36</td><td>3.36</td><td>3.36</td><td>3.35</td></tr><tr><td>Observations</td><td>553</td><td>553</td><td>553</td><td>553</td><td>5280</td></tr><tr><td>Firm- and district-level controls</td><td>No</td><td>Yes</td><td>No</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><td>Yes</td></tr><tr><td>Year FE</td><td>No</td><td>No</td><td>No</td><td>No</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 4 using the intensive margin of violence. From columns 1 to 4, the unit of observation is the district. We estimate the following equation: Number of firms<sub>ds</sub> = β<sub>0</sub> + β<sub>1</sub> Conflict intensity<sub>ds</sub> + X'<sub>ds</sub>γ + θ<sub>s</sub> + u<sub>ds</sub>, where Number of firms<sub>ds</sub> measures the (log-)average number of firms observed over the period 2000-2009 in district d in state s. Conflict intensity<sub>ds</sub> 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 and mutually exclusive types of districts that mark whether Maoist insurgency impacted the district both before and after 2000 (historically affected districts), or only after (newly affected districts). X'<sub>ds</sub> 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). In column 5, the unit of observation is district × year. We estimate the following equation: Number of firms<sub>dst</sub> = β<sub>0</sub> + β<sub>1</sub> Intensity<sub>dst</sub> + β<sub>2</sub> Intensity<sub>st</sub> + X'<sub>dst</sub>γ + θ<sub>s</sub> + θ<sub>t</sub> + u<sub>dst</sub>, where Number of firms<sub>dst</sub> measures the number of firms (in logs) observed in district d, in state s, at year t. Intensity<sub>dst</sub> measures conflict intensity in district d, in state s at year t. Intensity<sub>st</sub> is a measure of conflict intensity at state level. Both measures are defined according to their respective panel. X'<sub>dst</sub> includes time-varying district-level characteristics, θ<sub>s</sub> are state fixed effects and θ<sub>t</sub> are year fixed effects.
<table><caption>Table OA3.6: Intensive margin: firms' age</caption><thead><tr><th>Dep. Var.</th><th colspan="2">— (log-) average firms' age —</th><th colspan="2">— s.d. firms' age —</th></tr><tr><th></th><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>0.007<br>(0.005)</td><td></td><td>0.157<br>(0.108)</td><td></td></tr><tr><td>Intensity - historically affected districts</td><td></td><td>0.002<br>(0.004)</td><td></td><td>0.092<br>(0.085)</td></tr><tr><td>Intensity: newly affected districts</td><td></td><td>0.008<br>(0.006)</td><td></td><td>0.176<br>(0.118)</td></tr><tr><td colspan="5"><strong>Panel B: (log-) events by area</strong></td></tr><tr><td>Intensity - affected districts</td><td>0.012<br>(0.009)</td><td></td><td>0.281<br>(0.186)</td><td></td></tr><tr><td>Intensity - historically affected districts</td><td></td><td>0.003<br>(0.007)</td><td></td><td>0.161<br>(0.146)</td></tr><tr><td>Intensity: newly affected districts</td><td></td><td>0.014<br>(0.010)</td><td></td><td>0.318<br>(0.204)</td></tr><tr><td colspan="5"><strong>Panel C: (log-) deaths per capita</strong></td></tr><tr><td>Intensity - affected districts</td><td>0.006<br>(0.005)</td><td></td><td>0.144<br>(0.105)</td><td></td></tr><tr><td>Intensity - historically affected districts</td><td></td><td>0.002<br>(0.005)</td><td></td><td>0.086<br>(0.089)</td></tr><tr><td>Intensity: newly affected districts</td><td></td><td>0.007<br>(0.006)</td><td></td><td>0.159<br>(0.112)</td></tr><tr><td colspan="5"><strong>Panel D: (log-) deaths by area</strong></td></tr><tr><td>Intensity - affected districts</td><td>0.009<br>(0.009)</td><td></td><td>0.232<br>(0.169)</td><td></td></tr><tr><td>Intensity - historically affected districts</td><td></td><td>0.001<br>(0.008)</td><td></td><td>0.136<br>(0.157)</td></tr><tr><td>Intensity: newly affected districts</td><td></td><td>0.011<br>(0.009)</td><td></td><td>0.255<br>(0.180)</td></tr><tr><td>Sample mean</td><td>2.78</td><td>2.78</td><td>14.68</td><td>14.68</td></tr><tr><td>Observations</td><td>550</td><td>550</td><td>550</td><td>550</td></tr><tr><td>State 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%. Standard errors in parenthesis. This table replicates Table 5 using the intensive margin of violence. Estimates are weighted by the number of firms in the district. The unit of observation is the district. We estimate the following equation: $Y_{ds} = \beta_{0} + \beta_{1}\text{Conflict intensity}_{ds} + \theta_{s} + u_{ds}$, where the dependent variable ($Y_{ds}$) measures the average and the standard deviation of firms' age in district $d$ in state $s$. 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 and mutually exclusive types of districts that mark whether Maoist insurgency impacted the district both before and after 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.
<table><caption>Table OA3.6: Intensive margin: firms' age</caption><thead><tr><th>Dep. Var.</th><th colspan="2">— (log-) average firms' age —</th><th colspan="2">— s.d. firms' age —</th></tr><tr><th></th><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>0.007<br>(0.005)</td><td></td><td>0.157<br>(0.108)</td><td></td></tr><tr><td>Intensity - historically affected districts</td><td></td><td>0.002<br>(0.004)</td><td></td><td>0.092<br>(0.085)</td></tr><tr><td>Intensity: newly affected districts</td><td></td><td>0.008<br>(0.006)</td><td></td><td>0.176<br>(0.118)</td></tr><tr><td colspan="5"><strong>Panel B: (log-) events by area</strong></td></tr><tr><td>Intensity - affected districts</td><td>0.012<br>(0.009)</td><td></td><td>0.281<br>(0.186)</td><td></td></tr><tr><td>Intensity - historically affected districts</td><td></td><td>0.003<br>(0.007)</td><td></td><td>0.161<br>(0.146)</td></tr><tr><td>Intensity: newly affected districts</td><td></td><td>0.014<br>(0.010)</td><td></td><td>0.318<br>(0.204)</td></tr><tr><td colspan="5"><strong>Panel C: (log-) deaths per capita</strong></td></tr><tr><td>Intensity - affected districts</td><td>0.006<br>(0.005)</td><td></td><td>0.144<br>(0.105)</td><td></td></tr><tr><td>Intensity - historically affected districts</td><td></td><td>0.002<br>(0.005)</td><td></td><td>0.086<br>(0.089)</td></tr><tr><td>Intensity: newly affected districts</td><td></td><td>0.007<br>(0.006)</td><td></td><td>0.159<br>(0.112)</td></tr><tr><td colspan="5"><strong>Panel D: (log-) deaths by area</strong></td></tr><tr><td>Intensity - affected districts</td><td>0.009<br>(0.009)</td><td></td><td>0.232<br>(0.169)</td><td></td></tr><tr><td>Intensity - historically affected districts</td><td></td><td>0.001<br>(0.008)</td><td></td><td>0.136<br>(0.157)</td></tr><tr><td>Intensity: newly affected districts</td><td></td><td>0.011<br>(0.009)</td><td></td><td>0.255<br>(0.180)</td></tr><tr><td>Sample mean</td><td>2.78</td><td>2.78</td><td>14.68</td><td>14.68</td></tr><tr><td>Observations</td><td>550</td><td>550</td><td>550</td><td>550</td></tr><tr><td>State 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%. Standard errors in parenthesis. This table replicates Table 5 using the intensive margin of violence. Estimates are weighted by the number of firms in the district. The unit of observation is the district. We estimate the following equation: $Y_{ds} = \beta_{0} + \beta_{1}\text{Conflict intensity}_{ds} + \theta_{s} + u_{ds}$, where the dependent variable ($Y_{ds}$) measures the average and the standard deviation of firms' age in district $d$ in state $s$. 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 and mutually exclusive types of districts that mark whether Maoist insurgency impacted the district both before and after 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.
<table><caption>Table OA3.7: Intensive margin: firms' size</caption><thead><tr><th>Dep. Var.</th><th>(1)<br>— (log-) average firms' size —</th><th>(2)</th><th>(3)<br>— s.d. firms' size —</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>-0.008<br>(0.008)</td><td></td><td>-0.012<br>(0.010)</td><td></td></tr><tr><td>Intensity - historically affected districts</td><td></td><td>-0.023<sup>b</sup><br>(0.011)</td><td></td><td>-0.031<sup>c</sup><br>(0.016)</td></tr><tr><td>Intensity: newly affected districts</td><td></td><td>-0.004<br>(0.009)</td><td></td><td>-0.007<br>(0.011)</td></tr><tr><td colspan="5"><strong>Panel B: (log-) events by area</strong></td></tr><tr><td>Intensity - affected districts</td><td>-0.013<br>(0.014)</td><td></td><td>-0.020<br>(0.018)</td><td></td></tr><tr><td>Intensity - historically affected districts</td><td></td><td>-0.042<sup>b</sup><br>(0.020)</td><td></td><td>-0.055<sup>c</sup><br>(0.028)</td></tr><tr><td>Intensity: newly affected districts</td><td></td><td>-0.004<br>(0.016)</td><td></td><td>-0.009<br>(0.020)</td></tr><tr><td colspan="5"><strong>Panel C: (log-) deaths per capita</strong></td></tr><tr><td>Intensity - affected districts</td><td>-0.012<br>(0.008)</td><td></td><td>-0.016<br>(0.011)</td><td></td></tr><tr><td>Intensity - historically affected districts</td><td></td><td>-0.030<sup>b</sup><br>(0.012)</td><td></td><td>-0.039<sup>b</sup><br>(0.017)</td></tr><tr><td>Intensity: newly affected districts</td><td></td><td>-0.007<br>(0.009)</td><td></td><td>-0.010<br>(0.012)</td></tr><tr><td colspan="5"><strong>Panel D: (log-) deaths by areas</strong></td></tr><tr><td>Intensity - affected districts</td><td>-0.024<br>(0.015)</td><td></td><td>-0.030<br>(0.022)</td><td></td></tr><tr><td>Intensity - historically affected districts</td><td></td><td>-0.065<sup>a</sup><br>(0.024)</td><td></td><td>-0.083<sup>b</sup><br>(0.032)</td></tr><tr><td>Intensity: newly affected districts</td><td></td><td>-0.015<br>(0.016)</td><td></td><td>-0.018<br>(0.022)</td></tr><tr><td>Sample mean</td><td>4.44</td><td>4.44</td><td>5.02</td><td>5.02</td></tr><tr><td>Observations</td><td>545</td><td>545</td><td>544</td><td>544</td></tr><tr><td>State 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%. Standard errors in parenthesis. This table replicates Table 5 using the intensive margin of violence. Estimates are weighted by the number of firms in the district. The unit of observation is the district. We estimate the following equation: $Y_{ds} = \beta_{0} + \beta_{1}\text{Conflict intensity}_{ds} + \theta_{s} + u_{ds}$, where the dependent variable ($Y_{ds}$) measures the average and the standard deviation of firms' size in district $d$ in state $s$. 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 and mutually exclusive types of districts that mark whether Maoist insurgency impacted the district both before and after 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.
<table><caption>Table OA3.7: Intensive margin: firms' size</caption><thead><tr><th>Dep. Var.</th><th>(1)<br>— (log-) average firms' size —</th><th>(2)</th><th>(3)<br>— s.d. firms' size —</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>-0.008<br>(0.008)</td><td></td><td>-0.012<br>(0.010)</td><td></td></tr><tr><td>Intensity - historically affected districts</td><td></td><td>-0.023<sup>b</sup><br>(0.011)</td><td></td><td>-0.031<sup>c</sup><br>(0.016)</td></tr><tr><td>Intensity: newly affected districts</td><td></td><td>-0.004<br>(0.009)</td><td></td><td>-0.007<br>(0.011)</td></tr><tr><td colspan="5"><strong>Panel B: (log-) events by area</strong></td></tr><tr><td>Intensity - affected districts</td><td>-0.013<br>(0.014)</td><td></td><td>-0.020<br>(0.018)</td><td></td></tr><tr><td>Intensity - historically affected districts</td><td></td><td>-0.042<sup>b</sup><br>(0.020)</td><td></td><td>-0.055<sup>c</sup><br>(0.028)</td></tr><tr><td>Intensity: newly affected districts</td><td></td><td>-0.004<br>(0.016)</td><td></td><td>-0.009<br>(0.020)</td></tr><tr><td colspan="5"><strong>Panel C: (log-) deaths per capita</strong></td></tr><tr><td>Intensity - affected districts</td><td>-0.012<br>(0.008)</td><td></td><td>-0.016<br>(0.011)</td><td></td></tr><tr><td>Intensity - historically affected districts</td><td></td><td>-0.030<sup>b</sup><br>(0.012)</td><td></td><td>-0.039<sup>b</sup><br>(0.017)</td></tr><tr><td>Intensity: newly affected districts</td><td></td><td>-0.007<br>(0.009)</td><td></td><td>-0.010<br>(0.012)</td></tr><tr><td colspan="5"><strong>Panel D: (log-) deaths by areas</strong></td></tr><tr><td>Intensity - affected districts</td><td>-0.024<br>(0.015)</td><td></td><td>-0.030<br>(0.022)</td><td></td></tr><tr><td>Intensity - historically affected districts</td><td></td><td>-0.065<sup>a</sup><br>(0.024)</td><td></td><td>-0.083<sup>b</sup><br>(0.032)</td></tr><tr><td>Intensity: newly affected districts</td><td></td><td>-0.015<br>(0.016)</td><td></td><td>-0.018<br>(0.022)</td></tr><tr><td>Sample mean</td><td>4.44</td><td>4.44</td><td>5.02</td><td>5.02</td></tr><tr><td>Observations</td><td>545</td><td>545</td><td>544</td><td>544</td></tr><tr><td>State 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%. Standard errors in parenthesis. This table replicates Table 5 using the intensive margin of violence. Estimates are weighted by the number of firms in the district. The unit of observation is the district. We estimate the following equation: $Y_{ds} = \beta_{0} + \beta_{1}\text{Conflict intensity}_{ds} + \theta_{s} + u_{ds}$, where the dependent variable ($Y_{ds}$) measures the average and the standard deviation of firms' size in district $d$ in state $s$. 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 and mutually exclusive types of districts that mark whether Maoist insurgency impacted the district both before and after 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.
<table><caption>Table OA3.8: Intensive margin: firm relocation, exit, entry</caption><thead><tr><th>Dep. var.</th><th>Change district (same state) (1)</th><th>Change district (different state) (2)</th><th>Entry (3)</th><th>Exit (4)</th></tr></thead><tbody><tr><td colspan="5"><strong>Panel A: (log-) events per capita</strong></td></tr><tr><td>Intensity (district)</td><td>-0.00001<br>(0.00009)</td><td>0.00001<br>(0.00006)</td><td>-0.00000<br>(0.00000)</td><td>-0.00002<br>(0.00019)</td></tr><tr><td>Intensity (state)</td><td>-0.00005<br>(0.00004)</td><td>-0.00003<br>(0.00003)</td><td>0.00000<br>(0.00000)</td><td>0.00007<br>(0.00008)</td></tr><tr><td colspan="5"><strong>Panel B: (log-) events by area</strong></td></tr><tr><td>Intensity (district)</td><td>-0.00000<br>(0.00014)</td><td>0.00003<br>(0.00010)</td><td>-0.00000<br>(0.00001)</td><td>0.00002<br>(0.00032)</td></tr><tr><td>Intensity (state)</td><td>-0.00007<br>(0.00005)</td><td>-0.00005<br>(0.00004)</td><td>0.00000<br>(0.00000)</td><td>0.00011<br>(0.00012)</td></tr><tr><td colspan="5"><strong>Panel C: (log-) deaths per capita</strong></td></tr><tr><td>Intensity (district)</td><td>-0.00003<br>(0.00009)</td><td>-0.00000<br>(0.00006)</td><td>-0.00000<br>(0.00000)</td><td>-0.00007<br>(0.00020)</td></tr><tr><td>Intensity (state)</td><td>-0.00005<br>(0.00004)</td><td>-0.00003<br>(0.00003)</td><td>0.00000<br>(0.00000)</td><td>0.00003<br>(0.00008)</td></tr><tr><td colspan="5"><strong>Panel D: (log-) deaths by areas</strong></td></tr><tr><td>Intensity (district)</td><td>-0.00003<br>(0.00015)</td><td>0.00000<br>(0.00011)</td><td>-0.00001<br>(0.00001)</td><td>-0.00009<br>(0.00034)</td></tr><tr><td>Intensity (state)</td><td>-0.00006<br>(0.00005)</td><td>-0.00004<br>(0.00004)</td><td>0.00000<br>(0.00000)</td><td>0.00005<br>(0.00011)</td></tr><tr><td>Sample mean</td><td>0.008</td><td>0.004</td><td>0.002</td><td>0.024</td></tr><tr><td>Observations</td><td>251521</td><td>251521</td><td>24777568</td><td>152879</td></tr><tr><td>Firm level controls</td><td>Yes</td><td>Yes</td><td>No</td><td>Yes</td></tr><tr><td>District level controls</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>District FE</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Year FE</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Sector FE</td><td>Yes</td><td>Yes</td><td>No</td><td>Yes</td></tr></tbody></table>

Note: c significant at 10%; b at 5%; a at 1%. Standard errors in parenthesis. This table replicates Table 7 using the intensive margin of violence. The unit of observation is the firm. We estimate the following regression (for columns 1 and 2): Relocation<sub>idst</sub> = β<sub>0</sub> + β<sub>1</sub>Intensity<sub>dst</sub> + β<sub>2</sub>Intensity<sub>st</sub> + X'<sub>idst</sub>γ + Z'<sub>dst</sub>η + θ<sub>d</sub> + θ<sub>t</sub> + θ<sub>c</sub> + u<sub>idst</sub>, where Relocation<sub>idst</sub> is an indicator that takes a value of one if firm *i* changed district *d* at time *t*. We consider two alternative measures: (i) moving to a different district within the same state (column 1), (ii) moving to a different district in a different state (column 2). Intensity<sub>dst</sub> measures conflict intensity in district *d*, in state *s* at year *t* and Intensity<sub>st</sub> is a measure of conflict intensity in state *s* at year *t*. Both measures are defined according to their respective panel. X'<sub>idst</sub> is a vector of firm-level characteristics: wages, interest rates, and input prices (all in logarithmic form). Z'<sub>dst</sub> is a vector of time-varying district characteristics: employment (non-farm, manufacturing, services) and nightlight. Additionally, we include fixed effects for district θ<sub>d</sub>, year θ<sub>t</sub> and sector θ<sub>c</sub>. For column 3, we estimate the decision of firm *i* to enter district *d* of state *s* in year *t* which we express as follows: Entry<sub>idst</sub> = β<sub>0</sub> + β<sub>1</sub>Intensity<sub>dst</sub> + β<sub>2</sub>Intensity<sub>st</sub> + Z'<sub>dst</sub>η + θ<sub>d</sub> + θ<sub>t</sub> + θ<sub>c</sub> + u<sub>idst</sub>, where Entry<sub>idst</sub> an indicator that takes the value of one if district *d* corresponds to the district in which firm *i* initiated its production in year *t*, and zero for all other districts in India. For column 4, we estimate the firm-level decision to exit: Exit<sub>idst</sub> = β<sub>0</sub> + β<sub>1</sub>Intensity<sub>dst</sub> + β<sub>2</sub>Intensity<sub>st</sub> + X'<sub>idst</sub>γ + Z'<sub>dst</sub>η + θ<sub>d</sub> + θ<sub>t</sub> + θ<sub>c</sub> + u<sub>idst</sub>, where Exit<sub>idst</sub> is an indicator that indicates the last year of production of firm *i*, located in district *d* of state *s*. See above for the definition of the covariates.
<table><caption>Table OA3.8: Intensive margin: firm relocation, exit, entry</caption><thead><tr><th>Dep. var.</th><th>Change district (same state) (1)</th><th>Change district (different state) (2)</th><th>Entry (3)</th><th>Exit (4)</th></tr></thead><tbody><tr><td colspan="5"><strong>Panel A: (log-) events per capita</strong></td></tr><tr><td>Intensity (district)</td><td>-0.00001<br>(0.00009)</td><td>0.00001<br>(0.00006)</td><td>-0.00000<br>(0.00000)</td><td>-0.00002<br>(0.00019)</td></tr><tr><td>Intensity (state)</td><td>-0.00005<br>(0.00004)</td><td>-0.00003<br>(0.00003)</td><td>0.00000<br>(0.00000)</td><td>0.00007<br>(0.00008)</td></tr><tr><td colspan="5"><strong>Panel B: (log-) events by area</strong></td></tr><tr><td>Intensity (district)</td><td>-0.00000<br>(0.00014)</td><td>0.00003<br>(0.00010)</td><td>-0.00000<br>(0.00001)</td><td>0.00002<br>(0.00032)</td></tr><tr><td>Intensity (state)</td><td>-0.00007<br>(0.00005)</td><td>-0.00005<br>(0.00004)</td><td>0.00000<br>(0.00000)</td><td>0.00011<br>(0.00012)</td></tr><tr><td colspan="5"><strong>Panel C: (log-) deaths per capita</strong></td></tr><tr><td>Intensity (district)</td><td>-0.00003<br>(0.00009)</td><td>-0.00000<br>(0.00006)</td><td>-0.00000<br>(0.00000)</td><td>-0.00007<br>(0.00020)</td></tr><tr><td>Intensity (state)</td><td>-0.00005<br>(0.00004)</td><td>-0.00003<br>(0.00003)</td><td>0.00000<br>(0.00000)</td><td>0.00003<br>(0.00008)</td></tr><tr><td colspan="5"><strong>Panel D: (log-) deaths by areas</strong></td></tr><tr><td>Intensity (district)</td><td>-0.00003<br>(0.00015)</td><td>0.00000<br>(0.00011)</td><td>-0.00001<br>(0.00001)</td><td>-0.00009<br>(0.00034)</td></tr><tr><td>Intensity (state)</td><td>-0.00006<br>(0.00005)</td><td>-0.00004<br>(0.00004)</td><td>0.00000<br>(0.00000)</td><td>0.00005<br>(0.00011)</td></tr><tr><td>Sample mean</td><td>0.008</td><td>0.004</td><td>0.002</td><td>0.024</td></tr><tr><td>Observations</td><td>251521</td><td>251521</td><td>24777568</td><td>152879</td></tr><tr><td>Firm level controls</td><td>Yes</td><td>Yes</td><td>No</td><td>Yes</td></tr><tr><td>District level controls</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>District FE</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Year FE</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Sector FE</td><td>Yes</td><td>Yes</td><td>No</td><td>Yes</td></tr></tbody></table>

Note: c significant at 10%; b at 5%; a at 1%. Standard errors in parenthesis. This table replicates Table 7 using the intensive margin of violence. The unit of observation is the firm. We estimate the following regression (for columns 1 and 2): Relocation<sub>idst</sub> = β<sub>0</sub> + β<sub>1</sub>Intensity<sub>dst</sub> + β<sub>2</sub>Intensity<sub>st</sub> + X'<sub>idst</sub>γ + Z'<sub>dst</sub>η + θ<sub>d</sub> + θ<sub>t</sub> + θ<sub>c</sub> + u<sub>idst</sub>, where Relocation<sub>idst</sub> is an indicator that takes a value of one if firm *i* changed district *d* at time *t*. We consider two alternative measures: (i) moving to a different district within the same state (column 1), (ii) moving to a different district in a different state (column 2). Intensity<sub>dst</sub> measures conflict intensity in district *d*, in state *s* at year *t* and Intensity<sub>st</sub> is a measure of conflict intensity in state *s* at year *t*. Both measures are defined according to their respective panel. X'<sub>idst</sub> is a vector of firm-level characteristics: wages, interest rates, and input prices (all in logarithmic form). Z'<sub>dst</sub> is a vector of time-varying district characteristics: employment (non-farm, manufacturing, services) and nightlight. Additionally, we include fixed effects for district θ<sub>d</sub>, year θ<sub>t</sub> and sector θ<sub>c</sub>. For column 3, we estimate the decision of firm *i* to enter district *d* of state *s* in year *t* which we express as follows: Entry<sub>idst</sub> = β<sub>0</sub> + β<sub>1</sub>Intensity<sub>dst</sub> + β<sub>2</sub>Intensity<sub>st</sub> + Z'<sub>dst</sub>η + θ<sub>d</sub> + θ<sub>t</sub> + θ<sub>c</sub> + u<sub>idst</sub>, where Entry<sub>idst</sub> an indicator that takes the value of one if district *d* corresponds to the district in which firm *i* initiated its production in year *t*, and zero for all other districts in India. For column 4, we estimate the firm-level decision to exit: Exit<sub>idst</sub> = β<sub>0</sub> + β<sub>1</sub>Intensity<sub>dst</sub> + β<sub>2</sub>Intensity<sub>st</sub> + X'<sub>idst</sub>γ + Z'<sub>dst</sub>η + θ<sub>d</sub> + θ<sub>t</sub> + θ<sub>c</sub> + u<sub>idst</sub>, where Exit<sub>idst</sub> is an indicator that indicates the last year of production of firm *i*, located in district *d* of state *s*. See above for the definition of the covariates.
<table><caption>Table OA3.9: Intensive margin: market concentration in conflict districts</caption><thead><tr><th rowspan="2">Dep. var</th><th colspan="4">HHI index</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>2.933<br>(5.956)</td><td></td><td></td><td></td></tr><tr><td>Intensity - historically affected districts</td><td></td><td>2.607<br>(8.723)</td><td></td><td>4.089<br>(9.402)</td></tr><tr><td>Intensity - newly affected districts</td><td></td><td></td><td>1.623<br>(5.791)</td><td>2.636<br>(6.242)</td></tr><tr><td colspan="5"><strong>Panel B: (log-) events by area</strong></td></tr><tr><td>Intensity - affected districts</td><td>2.184<br>(10.390)</td><td></td><td></td><td></td></tr><tr><td>Intensity - historically affected districts</td><td></td><td>0.640<br>(15.560)</td><td></td><td>1.843<br>(16.604)</td></tr><tr><td>Intensity - newly affected districts</td><td></td><td></td><td>1.851<br>(10.258)</td><td>2.275<br>(10.946)</td></tr><tr><td colspan="5"><strong>Panel C: (log-) deaths per capita</strong></td></tr><tr><td>Intensity - affected districts</td><td>2.294<br>(6.386)</td><td></td><td></td><td></td></tr><tr><td>Intensity - historically affected districts</td><td></td><td>2.843<br>(9.553)</td><td></td><td>3.981<br>(10.332)</td></tr><tr><td>Intensity - newly affected districts</td><td></td><td></td><td>0.944<br>(6.136)</td><td>1.919<br>(6.636)</td></tr><tr><td colspan="5"><strong>Panel D: (log-) deaths by areas</strong></td></tr><tr><td>Intensity - affected districts</td><td>-0.761<br>(11.638)</td><td></td><td></td><td></td></tr><tr><td>Intensity - historically affected districts</td><td></td><td>0.038<br>(18.214)</td><td></td><td>-0.437<br>(19.485)</td></tr><tr><td>Intensity - newly affected districts</td><td></td><td></td><td>-0.734<br>(11.310)</td><td>-0.830<br>(12.099)</td></tr><tr><td>Sample mean</td><td>5906.09</td><td>5906.09</td><td>5906.09</td><td>5906.09</td></tr><tr><td>Observations</td><td>17121</td><td>17121</td><td>17121</td><td>17121</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>Industry FE</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr></tbody></table>

<footnote>Note: c significant at 10%; b at 5%; a at 1%. Robust standard errors in parenthesis. The unit of observation is industry×district. We estimate the following regression $HHI_{jds} = \beta_0 + \beta_1$ Affected district$_{ds} + X'_{ds}\gamma + \theta_j + \theta_s + u_{jds}$. The dependent variable is the Herfindahl-Hirschman market concentration index ($HHI_{jds}$) at the industry j district d in state s level. HHI is computed as the sum of the squares of the market share of all the firms in a given market, the larger the index the more concentrated the market is. The index ranges between 10,000 in a monopolistic market and 0 in a perfectly competitive market. 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 $\theta_j$ are industry fixed effects.</footnote>
<table><caption>Table OA3.9: Intensive margin: market concentration in conflict districts</caption><thead><tr><th rowspan="2">Dep. var</th><th colspan="4">HHI index</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>2.933<br>(5.956)</td><td></td><td></td><td></td></tr><tr><td>Intensity - historically affected districts</td><td></td><td>2.607<br>(8.723)</td><td></td><td>4.089<br>(9.402)</td></tr><tr><td>Intensity - newly affected districts</td><td></td><td></td><td>1.623<br>(5.791)</td><td>2.636<br>(6.242)</td></tr><tr><td colspan="5"><strong>Panel B: (log-) events by area</strong></td></tr><tr><td>Intensity - affected districts</td><td>2.184<br>(10.390)</td><td></td><td></td><td></td></tr><tr><td>Intensity - historically affected districts</td><td></td><td>0.640<br>(15.560)</td><td></td><td>1.843<br>(16.604)</td></tr><tr><td>Intensity - newly affected districts</td><td></td><td></td><td>1.851<br>(10.258)</td><td>2.275<br>(10.946)</td></tr><tr><td colspan="5"><strong>Panel C: (log-) deaths per capita</strong></td></tr><tr><td>Intensity - affected districts</td><td>2.294<br>(6.386)</td><td></td><td></td><td></td></tr><tr><td>Intensity - historically affected districts</td><td></td><td>2.843<br>(9.553)</td><td></td><td>3.981<br>(10.332)</td></tr><tr><td>Intensity - newly affected districts</td><td></td><td></td><td>0.944<br>(6.136)</td><td>1.919<br>(6.636)</td></tr><tr><td colspan="5"><strong>Panel D: (log-) deaths by areas</strong></td></tr><tr><td>Intensity - affected districts</td><td>-0.761<br>(11.638)</td><td></td><td></td><td></td></tr><tr><td>Intensity - historically affected districts</td><td></td><td>0.038<br>(18.214)</td><td></td><td>-0.437<br>(19.485)</td></tr><tr><td>Intensity - newly affected districts</td><td></td><td></td><td>-0.734<br>(11.310)</td><td>-0.830<br>(12.099)</td></tr><tr><td>Sample mean</td><td>5906.09</td><td>5906.09</td><td>5906.09</td><td>5906.09</td></tr><tr><td>Observations</td><td>17121</td><td>17121</td><td>17121</td><td>17121</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>Industry FE</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr></tbody></table>

<footnote>Note: c significant at 10%; b at 5%; a at 1%. Robust standard errors in parenthesis. The unit of observation is industry×district. We estimate the following regression $HHI_{jds} = \beta_0 + \beta_1 Affected district_{ds} + X'_{ds}\gamma + \theta_j + \theta_s + u_{jds}$. The dependent variable is the Herfindahl-Hirschman market concentration index ($HHI_{jds}$) at the industry j district d in state s level. HHI is computed as the sum of the squares of the market share of all the firms in a given market, the larger the index the more concentrated the market is. The index ranges between 10,000 in a monopolistic market and 0 in a perfectly competitive market. 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 $\theta_j$ are industry fixed effects.</footnote>
<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.