# The Economic Costs of Conflict: A Production Network Approach

Mathieu Couttenier, Nathalie Monnet, Lavinia Piemontese

<figure><img src="image_1.png" /></figure>

## To cite this version:

Mathieu Couttenier, Nathalie Monnet, Lavinia Piemontese. The Economic Costs of Conflict: A Production Network Approach. 2024. hal-04621803

HAL Id: hal-04621803

https://hal.science/hal-04621803

Preprint submitted on 24 Jun 2024

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_2.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
# The Economic Costs of Conflict: A Production Network Approach

Mathieu Couttenier, Nathalie Monnet, Lavinia Piemontese

► To cite this version:

Mathieu Couttenier, Nathalie Monnet, Lavinia Piemontese. The Economic Costs of Conflict: A Production Network Approach. 2024. hal-04621803

HAL Id: hal-04621803

https://hal.science/hal-04621803v1

Preprint submitted on 24 Jun 2024

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_1.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
CERGIC Working Papers

No. 0001

June 2024

The Economic Costs of Conflict: A Production
Network Approach

Mathieu Couttenier

<figure><img src="image_3.png" /></figure>

Nathalie Monnet

<figure><img src="image_4.png" /></figure>

Lavinia Piemontese

<figure><img src="image_5.png" /></figure>

JEL codes: D22, D74, O12, O47.

Keywords: Conflict, Firms, Production Network, Aggregate Output Loss.
CERGIC Working Papers

No. 0001

June 2024

The Economic Costs of Conflict: A Production
Network Approach

Mathieu Couttenier

Nathalie Monnet

Lavinia Piemontese

<figure><img src="image_2.png" /></figure>

<figure><img src="image_3.png" /></figure>

<figure><img src="image_4.png" /></figure>

JEL codes: D22, D74, O12, O47.

Keywords: Conflict, Firms, Production Network, Aggregate Output Loss.
# The Economic Costs of Conflict: A Production Network Approach*

Mathieu Couttenier†

Nathalie Monnet‡

Lavinia Piemontese§

June 23, 2024

## Abstract

We develop a novel approach for estimating the economic costs of conflict, using the production network as the primary mechanism through which the disruptive effects of localized conflict spread to peaceful areas. Applying this approach to the Maoist insurgency in Eastern India from 2000 to 2009, we quantify the overall impact of conflict, taking network propagation into account. Our key finding reveals that a one percentage point increase in conflict-induced distortion is associated with an average annual decrease of 0.236% in aggregate output. Notably, 73% of the total output loss can be attributed to network propagation.

**Keywords:** Conflict, Firms, Production Network, Aggregate Output Loss.

**JEL classification:** D22, D74, O12, O47.

<footnote>*We are grateful to Pierre-Philippe Combes, Lucie Gadenne, Vasily Korovkin, Alexey Makarin, Julien Martin, Massimo Morelli, Hannes Mueller, Mathieu Parenti, Augustin Tapsoba, Angelos Theodorakopoulos, Mathias Thoenig, Cédric Tille, Farid Toubal, Oliver Vanden Eynde, and participants in the ENS de Lyon, ECARES, CEPR RPN Workshop on preventing conflict, Bristol University, CED, UNSW Australia, CREST Paris, Paris Empirical Political Economy Seminar, IIE, University of Geneva, UAB Barcelona, University of Orleans, CERGE-EI Prague, University of Louvain, University of Montreal, University of Paris 1, University of Bologna, ENS-CEPR Workshop on firms in hostile environments, Paris XI, AFSE Summer Congress, SIOE Summer Congress, University of Montpellier, 2021 Conference on Economic Development, IGIER Bocconi Conference on political economy of power relations for their valuable comments, University of Padova, CERDI, Monash University, Tor Vergata University. Mathieu Couttenier acknowledges financial support from the IDEXLYON from the University of Lyon (French National Research Agency, "Programme Investissements d'Avenir" ANR-16-IDEX-0005). Nathalie Monnet acknowledges financial support from the Swiss National Research Foundation (grant Economics of Conflict and Violence P0GEP1-175125). Lavinia Piemontese acknowledges the financial support from the MSCA Actions (Project FIBRE 101063844). Nathalie Monnet would also like to thank Kritika Saxena for her invaluable assistance in obtaining the ASI data.</footnote>

<footnote>†University of Lyon, Ecole Normale Superieure de Lyon and Center for Economic Research on Governance, Inequality and Conflict, and CEPR. Email: mathieu.couttenier@ens-lyon.fr.</footnote>

<footnote>‡Lausanne University Center for Primary Care and Public Health (unisante). Email: nathalie.monnet@unisante.ch.</footnote>

<footnote>§Bocconi University. Email: lavinia.piemontese@unibocconi.it.</footnote>
# The Economic Costs of Conflict: A Production Network Approach*

Mathieu Couttenier†

Nathalie Monnet‡

Lavinia Piemontese§

June 23, 2024

## Abstract

We develop a novel approach for estimating the economic costs of conflict, using the production network as the primary mechanism through which the disruptive effects of localized conflict spread to peaceful areas. Applying this approach to the Maoist insurgency in Eastern India from 2000 to 2009, we quantify the overall impact of conflict, taking network propagation into account. Our key finding reveals that a one percentage point increase in conflict-induced distortion is associated with an average annual decrease of 0.236% in aggregate output. Notably, 73% of the total output loss can be attributed to network propagation.

**Keywords:** Conflict, Firms, Production Network, Aggregate Output Loss.

**JEL classification:** D22, D74, O12, O47.

<footnote>*We are grateful to Pierre-Philippe Combes, Lucie Gadenne, Vasily Korovkin, Alexey Makarin, Julien Martin, Massimo Morelli, Hannes Mueller, Mathieu Parenti, Augustin Tapsoba, Angelos Theodorakopoulos, Mathias Thoenig, Cédric Tille, Farid Toubal, Oliver Vanden Eynde, and participants in the ENS de Lyon, ECARES, CEPR RPN Workshop on preventing conflict, Bristol University, CED, UNSW Australia, CREST Paris, Paris Empirical Political Economy Seminar, IIE, University of Geneva, UAB Barcelona, University of Orleans, CERGE-EI Prague, University of Louvain, University of Montreal, University of Paris 1, University of Bologna, ENS-CEPR Workshop on firms in hostile environments, Paris XI, AFSE Summer Congress, SIOE Summer Congress, University of Montpellier, 2021 Conference on Economic Development, IGIER Bocconi Conference on political economy of power relations for their valuable comments, University of Padova, CERDI, Monash University, Tor Vergata University. Mathieu Couttenier acknowledges financial support from the IDEXLYON from the University of Lyon (French National Research Agency, "Programme Investissements d'Avenir" ANR-16-IDEX-0005). Nathalie Monnet acknowledges financial support from the Swiss National Research Foundation (grant *Economics of Conflict and Violence* P0GEP1-175125). Lavinia Piemontese acknowledges the financial support from the MSCA Actions (Project FIBRE 101063844). Nathalie Monnet would also like to thank Kritika Saxena for her invaluable assistance in obtaining the ASI data.</footnote>

<footnote>†University of Lyon, Ecole Normale Superieure de Lyon and Center for Economic Research on Governance, Inequality and Conflict, and CEPR. Email: mathieu.couttenier@ens-lyon.fr.</footnote>

<footnote>‡Lausanne University Center for Primary Care and Public Health (unisante). Email: nathalie.monnet@unisante.ch.</footnote>

<footnote>§Bocconi University. Email: lavinia.piemontese@unibocconi.it.</footnote>
# 1 Introduction

The adverse effects of civil conflict hardly require elucidation. Slightly more than one fourth (26%) of the global population was affected by at least one violent political event within a 5km radius of their residence between May 2023 and May 2024 (Armed Conflict Location & Event Data and WorldPop).<fnref n="1" /> The need for a reliable assessment of its economic costs is clear, especially for planning and designing expensive conflict prevention and resolution policies, thereby avoiding the so-called conflict trap.<fnref n="2" /> The standard approach focuses on tangible costs directly observed in the areas of conflict, such as fatalities, displaced persons, or destruction of infrastructure (Mueller et al., 2017). Additionally, there are the costs of direct exposure to the conflict, affecting individuals, political institutions, and economic sectors.<fnref n="3" /> However, this approach overlooks costs that are more difficult to measure, such as the diffusion of the conflict's effects to areas outside the realm of conflict through migration flows, the spread of disease, capital flight, or supply chain disruption.<fnref n="4" /> We address this gap by developing a flexible methodology that considers not only the direct costs of conflict suffered by firms in the conflict zone, but also the propagation effects in peaceful areas. We are therefore able to quantify the total loss incurred by firms due to the conflict.

Adopting this approach is motivated by two main factors. First, the nature of armed conflict has evolved over recent decades towards intra-state violence and armed insurgencies, such as secessionist conflicts or regional insurrections. Second, armed conflicts impact complex economies characterized by dense production networks. Consequently, local armed conflicts, with which small groups of firms or specific economic sectors might coexist, can still disrupt the supply chain and propagate through input-output connections to a wider array of firms and sectors. This leads to an amplification of the conflict's consequences. In such cases, the standard approach underestimates the actual cost of conflict by failing to account for firms indirectly affected by the conflict. This underestimation is particularly significant as it biases the cost-benefit analyses conducted by policymakers.

In this paper, we explore the role of production network in spreading the effect of localized conflict to firms in peaceful areas, using the Maoist insurgency in India as a case study. We quantify the nationwide losses caused by localized conflict by making use of the Indian Annual Survey of Industries,

<footnote n="1">¹Among them, 380 million were impacted by violent events such as battles, explosions, and remote violence, 600 million by violence against civilians, and 1.724 billion by riots and protests. The dataset from the *Conflict Exposure Calculator* was downloaded on May 30th, 2024.</footnote>

<footnote n="2">²The conflict trap relates to the vicious cycles between war and economic decline (Collier and Sambanis, 2002).</footnote>

<footnote n="3">³The relationship between conflict and social topics has been extensively explored in the literature. For example, Blattman (2009) focuses on political participation, Rohner et al. (2013) look at trust and ethnic identity, Cassar et al. (2013) study social and political trust; Grosjean (2014) analyzes trust and preferences for market participation, Voors et al. (2012) consider social behaviors. Finally, Bundervoet et al. (2009), Arcand et al. (2014), and Akresh et al. (2012) study the impact of conflict on health. The impact of conflict on education and, more generally, on human capital has been explored by Akbulut-Yuksel (2014), Shemyakina (2011), Couttenier et al. (2019) and Saing and Kazianga (2020).</footnote>

<footnote n="4">⁴In the recent literature, Hoenig (2021) explores how selective migration is a mechanism through which conflict affects aggregate income. Tapsoba (2023) studies how individuals are affected by the fear of exposure to conflict even prior to the manifestation of violence or in its absence. Spatial spillover of conflict incidence is also studied exploring the role of natural disasters, road network, and geographic proximity (Amarasinghe et al., 2021), and the interdependence between network of military alliances and international trade (Jackson and Nei, 2015).</footnote>
# 1 Introduction

The adverse effects of civil conflict hardly require elucidation. Slightly more than one fourth (26%) of the global population was affected by at least one violent political event within a 5km radius of their residence between May 2023 and May 2024 (Armed Conflict Location & Event Data and WorldPop).<fnref n="1" /> The need for a reliable assessment of its economic costs is clear, especially for planning and designing expensive conflict prevention and resolution policies, thereby avoiding the so-called conflict trap.<fnref n="2" /> The standard approach focuses on tangible costs directly observed in the areas of conflict, such as fatalities, displaced persons, or destruction of infrastructure (Mueller et al., 2017). Additionally, there are the costs of direct exposure to the conflict, affecting individuals, political institutions, and economic sectors.<fnref n="3" /> However, this approach overlooks costs that are more difficult to measure, such as the diffusion of the conflict's effects to areas outside the realm of conflict through migration flows, the spread of disease, capital flight, or supply chain disruption.<fnref n="4" /> We address this gap by developing a flexible methodology that considers not only the direct costs of conflict suffered by firms in the conflict zone, but also the propagation effects in peaceful areas. We are therefore able to quantify the total loss incurred by firms due to the conflict.

Adopting this approach is motivated by two main factors. First, the nature of armed conflict has evolved over recent decades towards intra-state violence and armed insurgencies, such as secessionist conflicts or regional insurrections. Second, armed conflicts impact complex economies characterized by dense production networks. Consequently, local armed conflicts, with which small groups of firms or specific economic sectors might coexist, can still disrupt the supply chain and propagate through input-output connections to a wider array of firms and sectors. This leads to an amplification of the conflict's consequences. In such cases, the standard approach underestimates the actual cost of conflict by failing to account for firms indirectly affected by the conflict. This underestimation is particularly significant as it biases the cost-benefit analyses conducted by policymakers.

In this paper, we explore the role of production network in spreading the effect of localized conflict to firms in peaceful areas, using the Maoist insurgency in India as a case study. We quantify the nationwide losses caused by localized conflict by making use of the Indian Annual Survey of Industries,

<footnote n="1">¹Among them, 380 million were impacted by violent events such as battles, explosions, and remote violence, 600 million by violence against civilians, and 1.724 billion by riots and protests. The dataset from the *Conflict Exposure Calculator* was downloaded on May 30th, 2024.</footnote>

<footnote n="2">²The conflict trap relates to the vicious cycles between war and economic decline (Collier and Sambanis, 2002).</footnote>

<footnote n="3">³The relationship between conflict and social topics has been extensively explored in the literature. For example, Blattman (2009) focuses on political participation, Rohner et al. (2013) look at trust and ethnic identity, Cassar et al. (2013) study social and political trust; Grosjean (2014) analyzes trust and preferences for market participation, Voors et al. (2012) consider social behaviors. Finally, Bundervoet et al. (2009), Arcand et al. (2014), and Akresh et al. (2012) study the impact of conflict on health. The impact of conflict on education and, more generally, on human capital has been explored by Akbulut-Yuksel (2014), Shemyakina (2011), Couttenier et al. (2019) and Saing and Kazianga (2020).</footnote>

<footnote n="4">⁴In the recent literature, Hoenig (2021) explores how selective migration is a mechanism through which conflict affects aggregate income. Tapsoba (2023) studies how individuals are affected by the fear of exposure to conflict even prior to the manifestation of violence or in its absence. Spatial spillover of conflict incidence is also studied exploring the role of natural disasters, road network, and geographic proximity (Amarasinghe et al., 2021), and the interdependence between network of military alliances and international trade (Jackson and Nei, 2015).</footnote>
a firm-level dataset that includes all registered manufacturers with more than 100 employees and a representative sample of smaller manufacturers for the period 2000 to 2009.<fnref n="5" /> By combining information on firm location and acts of violence perpetrated by Maoist groups, we identify the set of firms that are directly exposed to the conflict. Although we lack detailed data on firm-to-firm transactions, we approximate the input-output network of the Indian economy exploiting detailed information on each firm's output and input bundle. Equipped with this information, we apply a well-established model of production networks in the context of local conflict with the goal of quantifying the overall aggregate loss (Acemoglu et al., 2012).

The Maoist insurgency in India is an ideal case for testing the model. First, the activity of Maoist groups is localized in the eastern part of the country (the *Red Corridor*) and therefore a clear distinction can be made between firms in conflict-affected districts and firms outside them. Second, although deadly, the conflict remains at a low level of intensity, impacting but not devastating firms in conflict-affected districts, enabling them to continue production. Conflict-related disruptions can affect firm activity through various mechanisms, such as infrastructure destruction, increased insurance costs, security expenses, protection money payment, and, specific to the Maoist insurgency, extortion.

For each input needed for production, a firm faces a set of potential suppliers and selects the most cost-effective ones based on factors such as size and potential trade costs. A shock, such as a conflict in a supplier's area, leads to an increase in the supplier's output price and a reduction of its output. Therefore, even if a given producer is located outside the affected area, it can incur costs due to the conflict's impact on its suppliers. This can take three forms: (i) *inaction* – the producer continues to purchase inputs from the conflict-affected supplier and absorbs the additional costs in the form of higher input price; (ii) *supplier change* – the producer switches to a different supplier outside the conflict area, incurring adjustment or higher transportation costs; and (iii) *input bundle change* – the producer modifies its bundle of inputs because no other supplier outside the conflict area can provide that input. In summary, we examine the impact of conflict on the behavior of firms located in conflict-affected districts and how these distortions propagate by way of the production network, thereby affecting firms in non-conflict districts. By considering the propagation of these effects through the production network, we quantify the overall cost of the conflict.

In the first part of the paper, we apply the well-established model developed by Acemoglu et al. (2012) into a new context, analyzing the economic consequences of conflict. We use this model to characterize the changes in aggregate output resulting from conflict. This total loss depends on both the number of firms affected and the extent of their impact (*direct component*), and on the network structure of the economy. The network structure determines the degree of propagation of conflict-induced distortions from the affected firms to the rest of the economy (*propagation component*). The innovative aspect of our analysis lies in this approach: while current contributions primarily focus on the *direct component*,

<footnote n="5">⁵This dataset has been used by, among others, Hsieh and Klenow (2009) to study cross-country differences in aggregate productivity; by Martin et al. (2017) to study the relationship between SME and job creation; and most recently by Boehm and Oberfield (2020) to estimate the impact of institutional quality on firms’ output and sourcing decisions.</footnote>
a firm-level dataset that includes all registered manufacturers with more than 100 employees and a representative sample of smaller manufacturers for the period 2000 to 2009.<fnref n="5" /> By combining information on firm location and acts of violence perpetrated by Maoist groups, we identify the set of firms that are directly exposed to the conflict. Although we lack detailed data on firm-to-firm transactions, we approximate the input-output network of the Indian economy exploiting detailed information on each firm's output and input bundle. Equipped with this information, we apply a well-established model of production networks in the context of local conflict with the goal of quantifying the overall aggregate loss (Acemoglu et al., 2012).

The Maoist insurgency in India is an ideal case for testing the model. First, the activity of Maoist groups is localized in the eastern part of the country (the *Red Corridor*) and therefore a clear distinction can be made between firms in conflict-affected districts and firms outside them. Second, although deadly, the conflict remains at a low level of intensity, impacting but not devastating firms in conflict-affected districts, enabling them to continue production. Conflict-related disruptions can affect firm activity through various mechanisms, such as infrastructure destruction, increased insurance costs, security expenses, protection money payment, and, specific to the Maoist insurgency, extortion.

For each input needed for production, a firm faces a set of potential suppliers and selects the most cost-effective ones based on factors such as size and potential trade costs. A shock, such as a conflict in a supplier's area, leads to an increase in the supplier's output price and a reduction of its output. Therefore, even if a given producer is located outside the affected area, it can incur costs due to the conflict's impact on its suppliers. This can take three forms: (i) *inaction* – the producer continues to purchase inputs from the conflict-affected supplier and absorbs the additional costs in the form of higher input price; (ii) *supplier change* – the producer switches to a different supplier outside the conflict area, incurring adjustment or higher transportation costs; and (iii) *input bundle change* – the producer modifies its bundle of inputs because no other supplier outside the conflict area can provide that input. In summary, we examine the impact of conflict on the behavior of firms located in conflict-affected districts and how these distortions propagate by way of the production network, thereby affecting firms in non-conflict districts. By considering the propagation of these effects through the production network, we quantify the overall cost of the conflict.

In the first part of the paper, we apply the well-established model developed by Acemoglu et al. (2012) into a new context, analyzing the economic consequences of conflict. We use this model to characterize the changes in aggregate output resulting from conflict. This total loss depends on both the number of firms affected and the extent of their impact (*direct component*), and on the network structure of the economy. The network structure determines the degree of propagation of conflict-induced distortions from the affected firms to the rest of the economy (*propagation component*). The innovative aspect of our analysis lies in this approach: while current contributions primarily focus on the *direct component*,

<footnote n="5">⁵This dataset has been used by, among others, Hsieh and Klenow (2009) to study cross-country differences in aggregate productivity; by Martin et al. (2017) to study the relationship between SME and job creation; and most recently by Boehm and Oberfield (2020) to estimate the impact of institutional quality on firms’ output and sourcing decisions.</footnote>
our framework allows us to extend beyond the direct costs of conflict and characterize a total loss
that encompasses also the propagation component. Firms located in conflict-affected districts are
subject to distortions that increase their output price. Since every producer in the economy is
a potential input supplier for other firms, conflict-induced distortions propagate throughout the
economy, including firms located in peaceful districts, which in turn increases their optimal price
propagating the distortion to their own buyers, and so on.

In the second part of the paper, we bring our model to the data. First, we introduce an innovative
methodology to approximate the input-output linkages. Following the well-established literature on
the determinants of domestic sourcing (Bernard et al., 2019), we assign to every potential buyer-
supplier link an index of probability of existence of the link based on geographic proximity and
suppliers’ market power. We validate our methodology using firm-to-firm transactions data for
West Bengal (Gadenne et al., 2019). We find that our measure of input-output linkages is highly
correlated with the observed probability of establishing a buyer-supplier link. Additionally, we test
the validity of our methodology by comparing statistics derived from our imputed network with
observable outcomes and with findings from existing literature (Carvalho, 2014; Acemoglu et al.,
2012). We find a high degree of compatibility. We also address the potential concern that the
Maoist insurgency could influence the input-output network, impacting firms’ locations by either
driving them away or reducing the likelihood of new firms entering districts affected by violence.
Our findings indicate that the impact of the Maoist insurgency on firms’ dynamics is likely minimal.
Furthermore, by constructing an annual representation of the input-output network, our results
should be interpreted as being net of any direct impact of conflict on firm-specific behavior shaping
the production network. Nonetheless, for the sake of completeness, we also examine scenarios where
conflict affects the existence of buyer-supplier links. Even after accounting for network adjustments,
we find significant propagation of conflict-induced distortions and adjustment costs throughout the
network structure.

Second, we quantify the aggregate impact of the Maoist insurgency on the Indian economy. In
our baseline analysis we assume that all firms within an affected district experience the same level
of distortion, and that this distortion is uniform across all affected districts. 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, translating to an average annual GDP loss of 0.035% and a monetary
average annual loss of 438 million USD. Crucially, only 27% of the total loss can be attributed to
the impact on firms located in conflict-affected districts. The remaining 73% depends on network
propagation outside the conflict-affected districts.

These estimates are likely to represent a lower bound of the actual loss due to conflict. In fact, Maoist
groups are committed to extreme-left political ideology and therefore are more likely to organize
attacks against large firms. In this perspective, we consider an alternative specification of the model
in which we allow the level of conflict to be correlated with firm size. We find that the output loss
increases considerably if violence is directed towards firms in the upper part of the distribution by
size. For example, compared to our baseline estimate, if conflict affected only firms belonging to the
our framework allows us to extend beyond the direct costs of conflict and characterize a total loss
that encompasses also the propagation component. Firms located in conflict-affected districts are
subject to distortions that increase their output price. Since every producer in the economy is
a potential input supplier for other firms, conflict-induced distortions propagate throughout the
economy, including firms located in peaceful districts, which in turn increases their optimal price
propagating the distortion to their own buyers, and so on.

In the second part of the paper, we bring our model to the data. First, we introduce an innovative
methodology to approximate the input-output linkages. Following the well-established literature on
the determinants of domestic sourcing (Bernard et al., 2019), we assign to every potential buyer-
supplier link an index of probability of existence of the link based on geographic proximity and
suppliers’ market power. We validate our methodology using firm-to-firm transactions data for
West Bengal (Gadenne et al., 2019). We find that our measure of input-output linkages is highly
correlated with the observed probability of establishing a buyer-supplier link. Additionally, we test
the validity of our methodology by comparing statistics derived from our imputed network with
observable outcomes and with findings from existing literature (Carvalho, 2014; Acemoglu et al.,
2012). We find a high degree of compatibility. We also address the potential concern that the
Maoist insurgency could influence the input-output network, impacting firms’ locations by either
driving them away or reducing the likelihood of new firms entering districts affected by violence.
Our findings indicate that the impact of the Maoist insurgency on firms’ dynamics is likely minimal.
Furthermore, by constructing an annual representation of the input-output network, our results
should be interpreted as being net of any direct impact of conflict on firm-specific behavior shaping
the production network. Nonetheless, for the sake of completeness, we also examine scenarios where
conflict affects the existence of buyer-supplier links. Even after accounting for network adjustments,
we find significant propagation of conflict-induced distortions and adjustment costs throughout the
network structure.

Second, we quantify the aggregate impact of the Maoist insurgency on the Indian economy. In
our baseline analysis we assume that all firms within an affected district experience the same level
of distortion, and that this distortion is uniform across all affected districts. 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, translating to an average annual GDP loss of 0.035% and a monetary
average annual loss of 438 million USD. Crucially, only 27% of the total loss can be attributed to
the impact on firms located in conflict-affected districts. The remaining 73% depends on network
propagation outside the conflict-affected districts.

These estimates are likely to represent a lower bound of the actual loss due to conflict. In fact, Maoist
groups are committed to extreme-left political ideology and therefore are more likely to organize
attacks against large firms. In this perspective, we consider an alternative specification of the model
in which we allow the level of conflict to be correlated with firm size. We find that the output loss
increases considerably if violence is directed towards firms in the upper part of the distribution by
size. For example, compared to our baseline estimate, if conflict affected only firms belonging to the
top 10% of firms’ size distribution, then the average annual loss would more than double.

Our baseline findings include the effects of *inaction, supplier change, and input bundle change*. We then explore the importance of these mechanisms in more detail. First, we assume no network adjustment and find that the average annual output loss of the manufacturing sector is 11% higher than the baseline. Second, we contemplate network reshuffling and find that allowing for the *supplier change* effect would lower the output loss by 10%, whereas combining the *supplier change* and the *input bundle change* effects reduces the output loss substantially, by 25% or 30% whether or not we allow for network adjustment costs.

Finally, we structurally estimate the extent of conflict-induced distortions allowing for different firms to experience varying levels of distortion. We measure both *state-specific* and *product-specific* conflict-induced distortions. In this perspective, we develop a two-step strategy. First, we perform an OLS estimation to estimate the parameters determining firm-level optimal price that do not depend on conflict. Second, we apply a GMM estimator to quantify the magnitude of conflict-induced distortions by minimizing the distance between average and variance of optimal prices between firms located in conflict-affected districts and firms located in peaceful districts. By assigning each conflict-affected firm its corresponding *state-specific* conflict-induced distortions (with an average of 0.087), we calculate an annual output loss of 1.18%/year, resulting in a monetary cost of approximately 2.3 billion USD per year. By assigning each conflict-affected firm its corresponding *product-specific* conflict-induced distortions (with an average of 0.079), we quantify an average annual output loss of 2.04%, equivalent to a yearly monetary loss of approximately 3.9 billion USD.

We conclude the paper performing several policy experiments. First, we estimate the potential loss in the counterfactual scenario in which Maoist activity expands to neighboring districts. We find that the cumulative monetary loss would increase substantially: a one percentage point increase in conflict-induced distortion is associated with an average annual decrease of 0.364% in aggregate output. Second, we explore the effect of various policies in support of firms in conflict-affected districts and firms located elsewhere that are impacted by the conflict by way of the production network. On the one hand, we show that the negative effect of conflict would be mitigated to a large extent by investment in protection for firms that occupy a “central” position in the economy’s production network. For instance, we find that protecting the 4% most central firms would halve the output loss and achieve the same effect as another intervention that protects 50% of randomly chosen conflict-affected firms. On the other hand, we find that policy makers should design interventions that can effectively reduce the trade frictions between states, for example due to low institutional quality, and should implement policies to rapidly restore trade infrastructure damaged by conflict, such as road and railway reconstruction.

Taken together, our approach provides a new lens through which to quantify the cost of conflict. Our findings give substantial evidence for the importance of the production network as a channel of diffusion and a multiplier of the adverse consequences of conflict suffered by firms located in conflict-affected districts. Our approach has the advantage of being easily adapted to different contexts, such as other types of conflict or social unrest, by observing firms’ output and input bundle as well as firms’ location.
top 10% of firms' size distribution, then the average annual loss would more than double.

Our baseline findings include the effects of *inaction, supplier change, and input bundle change*. We then explore the importance of these mechanisms in more detail. First, we assume no network adjustment and find that the average annual output loss of the manufacturing sector is 11% higher than the baseline. Second, we contemplate network reshuffling and find that allowing for the *supplier change* effect would lower the output loss by 10%, whereas combining the *supplier change* and the *input bundle change* effects reduces the output loss substantially, by 25% or 30% whether or not we allow for network adjustment costs.

Finally, we structurally estimate the extent of conflict-induced distortions allowing for different firms to experience varying levels of distortion. We measure both *state-specific* and *product-specific* conflict-induced distortions. In this perspective, we develop a two-step strategy. First, we perform an OLS estimation to estimate the parameters determining firm-level optimal price that do not depend on conflict. Second, we apply a GMM estimator to quantify the magnitude of conflict-induced distortions by minimizing the distance between average and variance of optimal prices between firms located in conflict-affected districts and firms located in peaceful districts. By assigning each conflict-affected firm its corresponding *state-specific* conflict-induced distortions (with an average of 0.087), we calculate an annual output loss of 1.18%/year, resulting in a monetary cost of approximately 2.3 billion USD per year. By assigning each conflict-affected firm its corresponding *product-specific* conflict-induced distortions (with an average of 0.079), we quantify an average annual output loss of 2.04%, equivalent to a yearly monetary loss of approximately 3.9 billion USD.

We conclude the paper performing several policy experiments. First, we estimate the potential loss in the counterfactual scenario in which Maoist activity expands to neighboring districts. We find that the cumulative monetary loss would increase substantially: a one percentage point increase in conflict-induced distortion is associated with an average annual decrease of 0.364% in aggregate output. Second, we explore the effect of various policies in support of firms in conflict-affected districts and firms located elsewhere that are impacted by the conflict by way of the production network. On the one hand, we show that the negative effect of conflict would be mitigated to a large extent by investment in protection for firms that occupy a “central” position in the economy’s production network. For instance, we find that protecting the 4% most central firms would halve the output loss and achieve the same effect as another intervention that protects 50% of randomly chosen conflict-affected firms. On the other hand, we find that policy makers should design interventions that can effectively reduce the trade frictions between states, for example due to low institutional quality, and should implement policies to rapidly restore trade infrastructure damaged by conflict, such as road and railway reconstruction.

Taken together, our approach provides a new lens through which to quantify the cost of conflict. Our findings give substantial evidence for the importance of the production network as a channel of diffusion and a multiplier of the adverse consequences of conflict suffered by firms located in conflict-affected districts. Our approach has the advantage of being easily adapted to different contexts, such as other types of conflict or social unrest, by observing firms' output and input bundle as well as firms' location.
**Related literature and contribution.** The paper contributes to several strands of the literature. The first is a voluminous literature on the economic consequences of conflict. From the macro perspective, the pioneering work of Collier (1999) lays the foundation for the economic consequences of civil war. Cross-country studies find that in countries characterized by high political instability, GDP per-capita growth is significant lower (Alesina et al., 1996), and that trade destruction due to conflict is significant (Martin et al., 2008). From the micro perspective, the literature offers plentiful evidence of the impact of being located in an area of conflict on firm behavior. Several mechanisms for these findings have been explored.

Conflict can curtail a firm's exports, resulting in significant negative labor supply shocks (Ksoll et al., 2023), and it can impact imports by prompting the substitution of domestically produced inputs for imported ones (Amodio and Di Maio, 2018). Additionally, conflict can reduce the availability of production inputs, including intermediates and labor, leading to factors substitution (Del Prete et al., 2023). Furthermore, the threat of predation during conflict can drive firms to reallocate labor from production to protection (Besley and Mueller, 2018). Beyond these factors, conflict has been shown to influence firms' location decisions (Blumenstock et al., 2020), their decisions regarding exit (Camacho et al., 2013; Del Prete et al., 2023), firm productivity (Klapper et al., 2015), and can compel producers to forgo otherwise profitable investments (de Roux and Martinez, 2021).<fnref n="6" /> We attempt to bridge these two strands of the literature by showing that firm-level distortions caused by a localized conflict can propagate by way of the production network and impact the entire economy. In a related study, Korovkin et al. (2024) show that high-intensity warfare leads to a decrease in output and a systematic reorganization of the production network structure even outside the conflict areas. They develop sufficient aggregate statistics to quantify the welfare effects of network disruption and readjustment due to localized conflict. We complement their analysis by studying conflicts that coexist with economic activity, allowing us to focus on network propagation and characterize two distinct components of the total loss: the *direct component* and the *propagation component*.

We also contribute to the literature on the Maoist insurgency. First, Maoist groups concentrate their attacks, which are primarily directed against security forces, in areas rich in raw materials, which are a lucrative source of royalties for the State (Shapiro and Vanden Eynde, 2023). With respect to interventions to resolve conflicts, there is mixed evidence on the effectiveness of development-oriented policies as a counterinsurgency strategy. Khanna and Zimmermann (2017) find that these policies lead to a short-run increase in police-initiated attacks and insurgent attacks on civilians, whereas Fetzer (2020) and Dasgupta et al. (2017) find that public employment programs have helped to reduce Maoist activity. We contribute to this literature by quantifying the long-term aggregate economic cost of the Maoist insurgency.

Furthermore, we contribute to the literature on the role of production networks as a mechanism for the propagation and amplification of shocks. The conditions under which the propagation of

<footnote n="6">⁶Rohner and Thoenig (2021) provide an extensive literature review on the consequences of conflict.</footnote>
**Related literature and contribution.** The paper contributes to several strands of the literature. The first is a voluminous literature on the economic consequences of conflict. From the macro perspective, the pioneering work of Collier (1999) lays the foundation for the economic consequences of civil war. Cross-country studies find that in countries characterized by high political instability, GDP per-capita growth is significant lower (Alesina et al., 1996), and that trade destruction due to conflict is significant (Martin et al., 2008). From the micro perspective, the literature offers plentiful evidence of the impact of being located in an area of conflict on firm behavior. Several mechanisms for these findings have been explored.

Conflict can curtail a firm's exports, resulting in significant negative labor supply shocks (Ksoll et al., 2023), and it can impact imports by prompting the substitution of domestically produced inputs for imported ones (Amodio and Di Maio, 2018). Additionally, conflict can reduce the availability of production inputs, including intermediates and labor, leading to factors substitution (Del Prete et al., 2023). Furthermore, the threat of predation during conflict can drive firms to reallocate labor from production to protection (Besley and Mueller, 2018). Beyond these factors, conflict has been shown to influence firms' location decisions (Blumenstock et al., 2020), their decisions regarding exit (Camacho et al., 2013; Del Prete et al., 2023), firm productivity (Klapper et al., 2015), and can compel producers to forgo otherwise profitable investments (de Roux and Martinez, 2021).<fnref n="6" /> We attempt to bridge these two strands of the literature by showing that firm-level distortions caused by a localized conflict can propagate by way of the production network and impact the entire economy. In a related study, Korovkin et al. (2024) show that high-intensity warfare leads to a decrease in output and a systematic reorganization of the production network structure even outside the conflict areas. They develop sufficient aggregate statistics to quantify the welfare effects of network disruption and readjustment due to localized conflict. We complement their analysis by studying conflicts that coexist with economic activity, allowing us to focus on network propagation and characterize two distinct components of the total loss: the *direct component* and the *propagation component*.

We also contribute to the literature on the Maoist insurgency. First, Maoist groups concentrate their attacks, which are primarily directed against security forces, in areas rich in raw materials, which are a lucrative source of royalties for the State (Shapiro and Vanden Eynde, 2023). With respect to interventions to resolve conflicts, there is mixed evidence on the effectiveness of development-oriented policies as a counterinsurgency strategy. Khanna and Zimmermann (2017) find that these policies lead to a short-run increase in police-initiated attacks and insurgent attacks on civilians, whereas Fetzer (2020) and Dasgupta et al. (2017) find that public employment programs have helped to reduce Maoist activity. We contribute to this literature by quantifying the long-term aggregate economic cost of the Maoist insurgency.

Furthermore, we contribute to the literature on the role of production networks as a mechanism for the propagation and amplification of shocks. The conditions under which the propagation of

<footnote n="6">⁶Rohner and Thoenig (2021) provide an extensive literature review on the consequences of conflict.</footnote>
microeconomic shocks by way of input-output links can translate into sizable aggregate fluctuations have been characterized by Acemoglu et al. (2012) (see Carvalho and Tahbaz-Salehi (2019) for a review of the literature). The pioneering empirical studies that rely on exogenous and well-identified shocks to study the role of firm-level links in propagating input disruptions were provided by Barrot and Sauvagnat (2016) who analyze natural disasters in the US, and by Boehm et al. (2019) and Carvalho et al. (2020) who show that the Great Japanese Earthquake led to cross-country transmission of its consequences and a substantial decline in Japanese real GDP. We contribute to this literature by examining conflict as a micro-disturbance, i.e., a shock that policymakers can actively prevent, mitigate, or resolve. Moreover, we contribute to the empirical aspect of this literature by providing coherent methodology to approximate firm-level production networks. This approach addresses the significant challenges to observe transactions between firms, particularly in low-income countries and regions experiencing violent events or conflicts. Finally, the relationship between production network and firm behavior in the Indian context has been explored by Panigrahi (2021), who shows that the input-output network structure plays a substantial role in explaining variation in firm's sales to other firms.

The remainder of the paper is organized as follows. Section 2 presents a brief overview of the Maoist insurgency. Section 3 presents our conceptual framework. Section 4 describes our methodology to approximate the Indian production network, which is then used in the empirical and counterfactual analysis in Section 5 and Section 6, respectively. Section 7 concludes.

## 2 Firm Activity and Maoist Insurgency

The Maoist insurgency is a long-running and widespread conflict based on Communist ideology which seeks to overthrow the Indian government.<fnref n="7" /> Maoist groups are active in numerous districts located in eastern India which are referred to collectively as the *Red Corridor* (left panel of Figure 1). The insurgency started in the early 1970s as a peasant uprising against landlords. Until the early 2000s, it involved sporadic violent acts carried out by various armed groups. The conflict shifted toward more organized and centralized armed activity in 2004, following the merger of a number of groups. This led to a sharp increase in violent incidents throughout the affected region (right panel of Figure 1).<fnref n="8" /> On average, before 2004, there were 32 events per year resulting in 166 deaths. However, after 2004, the conflict underwent a transformation, becoming more organized and centralized in terms of armed activity. Consequently, there was a notable surge in violent incidents across the affected region from 2004 onward, with an average of 231 events per year and 500 yearly deaths.

<footnote n="7">⁷The movement is also referred to as the *Naxalites* insurgency. The term *Naxalites* is derived from the place of origin of the insurgency, Naxalbari, while the term *Maoists* refers to the movement's communist ideology. The Ministry of Home Affairs prefers the term *Left Wing Extremist Insurgency*.</footnote>

<footnote n="8">⁸In September 2004, the Maoist Communist Center (MCC) and the People's War Group (PWG) merged to form the largest Maoist faction, the Communist Party of India (Maoist), which includes an armed wing, the People's Liberation Guerrilla Army (PLGA).</footnote>
microeconomic shocks by way of input-output links can translate into sizable aggregate fluctuations have been characterized by Acemoglu et al. (2012) (see Carvalho and Tahbaz-Salehi (2019) for a review of the literature). The pioneering empirical studies that rely on exogenous and well-identified shocks to study the role of firm-level links in propagating input disruptions were provided by Barrot and Sauvagnat (2016) who analyze natural disasters in the US, and by Boehm et al. (2019) and Carvalho et al. (2020) who show that the Great Japanese Earthquake led to cross-country transmission of its consequences and a substantial decline in Japanese real GDP. We contribute to this literature by examining conflict as a micro-disturbance, i.e., a shock that policymakers can actively prevent, mitigate, or resolve. Moreover, we contribute to the empirical aspect of this literature by providing coherent methodology to approximate firm-level production networks. This approach addresses the significant challenges to observe transactions between firms, particularly in low-income countries and regions experiencing violent events or conflicts. Finally, the relationship between production network and firm behavior in the Indian context has been explored by Panigrahi (2021), who shows that the input-output network structure plays a substantial role in explaining variation in firm's sales to other firms.

The remainder of the paper is organized as follows. Section 2 presents a brief overview of the Maoist insurgency. Section 3 presents our conceptual framework. Section 4 describes our methodology to approximate the Indian production network, which is then used in the empirical and counterfactual analysis in Section 5 and Section 6, respectively. Section 7 concludes.

## 2 Firm Activity and Maoist Insurgency

The Maoist insurgency is a long-running and widespread conflict based on Communist ideology which seeks to overthrow the Indian government.<fnref n="7" /> Maoist groups are active in numerous districts located in eastern India which are referred to collectively as the *Red Corridor* (left panel of Figure 1). The insurgency started in the early 1970s as a peasant uprising against landlords. Until the early 2000s, it involved sporadic violent acts carried out by various armed groups. The conflict shifted toward more organized and centralized armed activity in 2004, following the merger of a number of groups. This led to a sharp increase in violent incidents throughout the affected region (right panel of Figure 1).<fnref n="8" /> On average, before 2004, there were 32 events per year resulting in 166 deaths. However, after 2004, the conflict underwent a transformation, becoming more organized and centralized in terms of armed activity. Consequently, there was a notable surge in violent incidents across the affected region from 2004 onward, with an average of 231 events per year and 500 yearly deaths.

<footnote n="7">⁷The movement is also referred to as the *Naxalites* insurgency. The term *Naxalites* is derived from the place of origin of the insurgency, Naxalbari, while the term *Maoists* refers to the movement's communist ideology. The Ministry of Home Affairs prefers the term *Left Wing Extremist Insurgency*.</footnote>

<footnote n="8">⁸In September 2004, the Maoist Communist Center (MCC) and the People's War Group (PWG) merged to form the largest Maoist faction, the Communist Party of India (Maoist), which includes an armed wing, the People's Liberation Guerrilla Army (PLGA).</footnote>
The Maoist insurgency consists of the following armed groups: the Maoist Communist Center (MCC), the People's War Group (PWG), the Communist Party of India (CPI-Maoist), the People's Liberation Guerrilla Army (PLGA) and some other minor groups (see Online Appendix Table OA2.3 for an exhaustive list). CPI-Maoist is the dominant group and is responsible for more than 60% of the Maoist-related fatalities. Most of the violence is committed against the Indian government (70.6%), but civilians are not spared and account for about 28.9% of the targets. The remaining 0.5% are the result of clashes between the various Maoist groups.

Several features of the Maoist insurgency make the Indian context an ideal setting for our study. First, although deadly, it remains at the level of low-intensity warfare and economic activity is able to continue despite the violence. According to the Ministry of Home Affairs, 8,197 individuals were killed by the insurgents between 2004 and 2019. While India ranks above the median of the Fragile States Index, it remains below the median of the Ease of Doing Business measure, unlike many fragile economies such as Syria and Yemen (Figure OA2.1).<fnref n="9" /> When compared to other guerrilla warfare conflicts occurring in the same time-frame, the Maoist insurgency resulted in 4,090 deaths during our study period from 2000 to 2009. In contrast, the Taliban's guerrilla warfare against Afghan and international forces caused 26,916 deaths, and the Colombian internal armed conflict led to 12,078 casualties (Sundberg and Erik, 2013). These statistics underscore the unique nature of the Maoist insurgency and highlight the resilience of economic activities despite the challenges posed by the conflict.

Second, there are many actions perpetrated by Maoist groups that hinder business activity including (Online Appendix Table OA2.4 for statistics on events from 2000 to 2009): (i) explosions, which can affect the transportation network, trade infrastructure, and firms' assets, (ii) fatalities/incidents, which impact local demand and employment, and (iii) acts of extortion in order to support the Maoists' activities, such as collection of protection money, direct attacks on firms' assets, etc. (Besley and Mueller, 2018).<fnref n="10" /> Third, the geographical scope of the conflict expanded between 2000 and 2009. In our sample we observe that, in 2000, 29 districts (out of 558) across 8 states (out of 32) were affected by Maoist activities, while in 2009 Maoists were active in 64 districts across 9 states. Overall, during 2000-2009 a total of 121 districts across 12 states were impacted. Furthermore, Maoist activity is mainly concentrated in the eastern part of the country. Therefore, we are able to make a clear-cut distinction between firms located in conflict-affected districts and those located outside it.

In Online Appendix OA1, we provide motivating evidence that firms located in districts impacted by Maoist activity bear additional costs linked to a wide range of constraints. We rely on cross-section

<footnote n="9">⁹India is ranked 74ᵗʰ out of 178 according to the Fragile States Index (The Fund for Peace, 2020) and 63rd out of 190 according to the Ease-of-doing-business measure (World Bank, 2019).</footnote>

<footnote n="10">¹⁰Targets of the extortion are manifold, ranging from individual businesses to large industries and include both private and public companies. Ramana (2018) estimates an annual budget of around Rs. 4.2 billion (around $60 million). For instance, on March 11, 2005, The South Asia Terrorism Portal reports that “In Hyderabad, police arrested a cadre of the Janashakti faction of the Communist Party of India-Marxist-Leninist (CPI-ML), [...], while extorting money from businessmen in Lalaguda”. Similarly, the Hindu, on April 24, 2008, reported that “Communist Party of India-Maoist (CPI-Maoist) cadres set fire to 47 vehicles of a private company, Essar Steels at Korandul in the Dantewada district on April 24 night. [...]”.</footnote>
The Maoist insurgency consists of the following armed groups: the Maoist Communist Center (MCC), the People's War Group (PWG), the Communist Party of India (CPI-Maoist), the People's Liberation Guerrilla Army (PLGA) and some other minor groups (see Online Appendix Table OA2.3 for an exhaustive list). CPI-Maoist is the dominant group and is responsible for more than 60% of the Maoist-related fatalities. Most of the violence is committed against the Indian government (70.6%), but civilians are not spared and account for about 28.9% of the targets. The remaining 0.5% are the result of clashes between the various Maoist groups.

Several features of the Maoist insurgency make the Indian context an ideal setting for our study. First, although deadly, it remains at the level of low-intensity warfare and economic activity is able to continue despite the violence. According to the Ministry of Home Affairs, 8,197 individuals were killed by the insurgents between 2004 and 2019. While India ranks above the median of the Fragile States Index, it remains below the median of the Ease of Doing Business measure, unlike many fragile economies such as Syria and Yemen (Figure OA2.1).<fnref n="9" /> When compared to other guerrilla warfare conflicts occurring in the same time-frame, the Maoist insurgency resulted in 4,090 deaths during our study period from 2000 to 2009. In contrast, the Taliban's guerrilla warfare against Afghan and international forces caused 26,916 deaths, and the Colombian internal armed conflict led to 12,078 casualties (Sundberg and Erik, 2013). These statistics underscore the unique nature of the Maoist insurgency and highlight the resilience of economic activities despite the challenges posed by the conflict.

Second, there are many actions perpetrated by Maoist groups that hinder business activity including (Online Appendix Table OA2.4 for statistics on events from 2000 to 2009): (i) explosions, which can affect the transportation network, trade infrastructure, and firms' assets, (ii) fatalities/incidents, which impact local demand and employment, and (iii) acts of extortion in order to support the Maoists' activities, such as collection of protection money, direct attacks on firms' assets, etc. (Besley and Mueller, 2018).<fnref n="10" /> Third, the geographical scope of the conflict expanded between 2000 and 2009. In our sample we observe that, in 2000, 29 districts (out of 558) across 8 states (out of 32) were affected by Maoist activities, while in 2009 Maoists were active in 64 districts across 9 states. Overall, during 2000-2009 a total of 121 districts across 12 states were impacted. Furthermore, Maoist activity is mainly concentrated in the eastern part of the country. Therefore, we are able to make a clear-cut distinction between firms located in conflict-affected districts and those located outside it.

In Online Appendix OA1, we provide motivating evidence that firms located in districts impacted by Maoist activity bear additional costs linked to a wide range of constraints. We rely on cross-section

<footnote n="9">⁹India is ranked 74ᵗʰ out of 178 according to the Fragile States Index (The Fund for Peace, 2020) and 63rd out of 190 according to the Ease-of-doing-business measure (World Bank, 2019).</footnote>

<footnote n="10">¹⁰Targets of the extortion are manifold, ranging from individual businesses to large industries and include both private and public companies. Ramana (2018) estimates an annual budget of around Rs. 4.2 billion (around $60 million). For instance, on March 11, 2005, The South Asia Terrorism Portal reports that “In Hyderabad, police arrested a cadre of the Janashakti faction of the Communist Party of India-Marxist-Leninist (CPI-ML), [...], while extorting money from businessmen in Lalaguda”. Similarly, the Hindu, on April 24, 2008, reported that “Communist Party of India-Maoist (CPI-Maoist) cadres set fire to 47 vehicles of a private company, Essar Steels at Korandul in the Dantewada district on April 24 night. [...]”.</footnote>
data from the World Bank Enterprise Survey (World Bank, 2014), which provides information on
the business constraints to which Indian firms are subject (access to inputs, crime, corruption). Our
results indicate additional costs borne by firms located in conflict-affected districts. Moreover, they
demonstrate the diversity of costs that can arise from a conflict. This becomes particularly useful in
the conceptual framework derived in Section 3 to accurately model firm behavior in conflict-affected
districts in accordance with the evidence for the existence of both output and input distortions.

<figure><img src="image_6.png" /><figcaption>Figure 1: The Maoist insurgency, 2000-2009</figcaption></figure>

Note: The left panel depicts locations of the Maoist insurgency across districts between 2000 and 2009. The darker the shade of blue, the more intense is the conflict. The right panel presents the number of Maoist-related deadly events per year and per factions. The dotted green line represents the total number of events, the blue line the number of events perpetrated by the CPI-Maoists and the dashed red line the number of events perpetrated by other Maoists outlets.

# 3 Conceptual Framework

In this section, we outline the model on which we establish our empirical analysis. We describe theoretically two crucial aspects of the impact of conflict on firm activity: (i) how the behavior of firms located in conflict-affected districts is affected; and (ii) how the conflict-induced distortions spread by means of the production network, thus affecting firms outside conflict-affected districts. Combining these two effects helps to calculate the loss suffered by the entire economy. In this perspective, we apply into a new context, i.e., the analysis of the economic consequences of conflict, the well established model developed by Acemoglu et al. (2012). They highlight the role of production networks in propagating shocks and transforming microeconomic shocks into macroeconomic fluctuations.<fnref n="11" />

<footnote n="11">¹¹In their review of the literature, Carvalho and Tahbaz-Salehi (2019) refers to this model as the *baseline model*, which laid the foundation for understanding the role of production network as a shock propagation channel throughout the economy. Since then, the literature has developed alternative frameworks to generalize this model. First, some models relax the assumption of Cobb-Douglas technologies and consider more general production functions, such as nested CES production structures (see, for example Carvalho et al. (2020)). This formulation not only encompasses</footnote>
data from the World Bank Enterprise Survey (World Bank, 2014), which provides information on
the business constraints to which Indian firms are subject (access to inputs, crime, corruption). Our
results indicate additional costs borne by firms located in conflict-affected districts. Moreover, they
demonstrate the diversity of costs that can arise from a conflict. This becomes particularly useful in
the conceptual framework derived in Section 3 to accurately model firm behavior in conflict-affected
districts in accordance with the evidence for the existence of both output and input distortions.

<figure><img src="image_5.png" /><figcaption>Figure 1: The Maoist insurgency, 2000-2009</figcaption></figure>

Note: The left panel depicts locations of the Maoist insurgency across districts between 2000 and 2009. The darker the shade of blue, the more intense is the conflict. The right panel presents the number of Maoist-related deadly events per year and per factions. The dotted green line represents the total number of events, the blue line the number of events perpetrated by the CPI-Maoists and the dashed red line the number of events perpetrated by other Maoists outlets.

# 3 Conceptual Framework

In this section, we outline the model on which we establish our empirical analysis. We describe theoretically two crucial aspects of the impact of conflict on firm activity: (i) how the behavior of firms located in conflict-affected districts is affected; and (ii) how the conflict-induced distortions spread by means of the production network, thus affecting firms outside conflict-affected districts. Combining these two effects helps to calculate the loss suffered by the entire economy. In this perspective, we apply into a new context, i.e., the analysis of the economic consequences of conflict, the well established model developed by Acemoglu et al. (2012). They highlight the role of production networks in propagating shocks and transforming microeconomic shocks into macroeconomic fluctuations.<fnref n="11" />

<footnote n="11">¹¹In their review of the literature, Carvalho and Tahbaz-Salehi (2019) refers to this model as the *baseline model*, which laid the foundation for understanding the role of production network as a shock propagation channel throughout the economy. Since then, the literature has developed alternative frameworks to generalize this model. First, some models relax the assumption of Cobb-Douglas technologies and consider more general production functions, such as nested CES production structures (see, for example Carvalho et al. (2020)). This formulation not only encompasses</footnote>
This model reproduces a static perfect competition framework in which price-taker firms acts as producers for both final consumers and other firms that use their outputs as production inputs. Within this framework, firms affected by conflict experience a price increase, negatively affecting all firms that rely on their output as an intermediate input for production. This initial impact leads to further propagation throughout the production network, as the buyers of conflict-affected firms also experience price increases, affecting their own buyers, and so forth. As main prediction, this model characterizes the change of aggregate output due to conflict, which depends not only on the number of firms affected and the extent of their impact (direct component), but also on the network structure of the economy. The network structure determines the degree of propagation of conflict-induced distortions from the affected firms to the rest of the economy (propagation component).

**Model setting.** The economy is populated by a representative household that is endowed with one unit of labor, which is supplied inelastically, and that owns one unit of capital. This household has Cobb-Douglas preferences over $N$ distinct goods:

$$u(c_1, c_2, \dots, c_N) = \prod_{i=1}^{N} (c_i)^{\frac{1}{N}} \qquad (1)$$

where $c_i$ is consumption of good by firm $i$. We assume that the household consumes the same fraction $\frac{1}{N}$ of each good. These $N$ goods, produced by $N$ heterogeneous firms, are either consumed by the representative household or used by other firms as intermediate inputs. Each firm $i$ has a constant returns-to-scale Cobb-Douglas technology whose inputs are capital, labor and intermediate goods:

$$y_i = \tilde{a}_i (k_i^{\gamma} l_i^{1-\gamma})^{\alpha} \mathbf{x}_i^{1-\alpha} \qquad (2)$$

where $\tilde{a}_i$ is firm's productivity, which is the product of two components: (i) a firm-specific component $a_i$, (ii) a common component $\Gamma_d$. The latter is assumed to depend on district-specific characteristics. Firm $i$'s intermediate goods basket is a Cobb-Douglas composite given by:

$$\mathbf{x}_i = \prod_j x_{ji}^{\omega_{ji}}$$

the standard result that a negative shock to a firm increases the production cost of its buyers, but also the possibility that these buyers may decide to readjust their demand for other intermediate inputs as well. Second, another set of models consider deviation from perfect competition by introducing market distortions, which alter network propagation patterns due to changes in the economy's allocative efficiency (Baqaee and Farhi, 2019). Third, an emerging body of literature challenges the assumption that the structure of the production network is exogenous and consider the possibility that firms systematically respond to changes in economic conditions by altering their trading partners (Acemoglu and Azar, 2020; Taschereau-Dumouchel, 2024). These developments provide valuable insights into the significant economic consequences of network propagation of shocks that extend beyond the standard ripple effect caused by price increases highlighted in the baseline model. Quantifying these effects typically requires firm-to-firm transactions data. In our context, the unavailability of such data would necessitate making additional assumptions on crucial elements such as elasticity of substitution parameters, wedges, and network adjustment dynamics, in addition to those we already make to approximate the network (see Section 4). Therefore, we choose to rely on the benchmark model to establish our theoretical framework.
This model reproduces a static perfect competition framework in which price-taker firms acts as producers for both final consumers and other firms that use their outputs as production inputs. Within this framework, firms affected by conflict experience a price increase, negatively affecting all firms that rely on their output as an intermediate input for production. This initial impact leads to further propagation throughout the production network, as the buyers of conflict-affected firms also experience price increases, affecting their own buyers, and so forth. As main prediction, this model characterizes the change of aggregate output due to conflict, which depends not only on the number of firms affected and the extent of their impact (direct component), but also on the network structure of the economy. The network structure determines the degree of propagation of conflict-induced distortions from the affected firms to the rest of the economy (propagation component).

**Model setting.** The economy is populated by a representative household that is endowed with one unit of labor, which is supplied inelastically, and that owns one unit of capital. This household has Cobb-Douglas preferences over $N$ distinct goods:

$$u(c_1, c_2, \dots, c_N) = \prod_{i=1}^{N} (c_i)^{\frac{1}{N}} \qquad (1)$$

where $c_i$ is consumption of good by firm $i$. We assume that the household consumes the same fraction $\frac{1}{N}$ of each good. These $N$ goods, produced by $N$ heterogeneous firms, are either consumed by the representative household or used by other firms as intermediate inputs. Each firm $i$ has a constant returns-to-scale Cobb-Douglas technology whose inputs are capital, labor and intermediate goods:

$$y_i = \tilde{a}_i (k_i^\gamma l_i^{1-\gamma})^\alpha \mathbf{x}_i^{1-\alpha} \qquad (2)$$

where $\tilde{a}_i$ is firm's productivity, which is the product of two components: (i) a firm-specific component $a_i$, (ii) a common component $\Gamma_d$. The latter is assumed to depend on district-specific characteristics. Firm $i$'s intermediate goods basket is a Cobb-Douglas composite given by:

$$\mathbf{x}_i = \prod_j x_{ji}^{\omega_{ji}}$$

the standard result that a negative shock to a firm increases the production cost of its buyers, but also the possibility that these buyers may decide to readjust their demand for other intermediate inputs as well. Second, another set of models consider deviation from perfect competition by introducing market distortions, which alter network propagation patterns due to changes in the economy's allocative efficiency (Baqaee and Farhi, 2019). Third, an emerging body of literature challenges the assumption that the structure of the production network is exogenous and consider the possibility that firms systematically respond to changes in economic conditions by altering their trading partners (Acemoglu and Azar, 2020; Taschereau-Dumouchel, 2024). These developments provide valuable insights into the significant economic consequences of network propagation of shocks that extend beyond the standard ripple effect caused by price increases highlighted in the baseline model. Quantifying these effects typically requires firm-to-firm transactions data. In our context, the unavailability of such data would necessitate making additional assumptions on crucial elements such as elasticity of substitution parameters, wedges, and network adjustment dynamics, in addition to those we already make to approximate the network (see Section 4). Therefore, we choose to rely on the benchmark model to establish our theoretical framework.
where $x_{ij}$ is the amount supplied by firm $j$. The exponent $\omega_{ji} \ge 0$ is the share of good $j$ within firm $i$'s total use of intermediate inputs. In particular, $\omega_{ji} = 0$ if firm $i$ does not use good $j$ as input. We assume that $\sum_{i \in j} \omega_{ji} = 1$ for every $i$.<fnref n="12" />

**The impact on firms located in conflict-affected districts.** As described in Online Appendix OA1, firms located in conflict-affected districts are more likely to incur additional costs, such as loss from theft, payment of protection money, or additional expenditure on security. When modeling the behavior of firms located in conflict-affected districts, we take into account all of the aforementioned distortions, which can be output-specific or input-specific. In particular, if firm $i$ is located in a conflict-affected district, it then maximizes profits according to the following equation:

$$ \max_{k_i, l_i, x_{ji}} \pi_i = (1 - \tau_{y,i})p_i y_i - (1 + \tau_{kl,i})(Rk_i + hl_i) - (1 + \tau_{x,i}) \sum_j p_j x_{ji} \quad (3) $$

where $p_i$ is firm $i$'s output price; $R$, $h$, and $p_j$ are the exogenous input prices of capital, labor and intermediate inputs, respectively; and the different $\tau$'s represent conflict-induced idiosyncratic distortions. As in Hsieh and Klenow (2009), we denote output distortions by $\tau_{y,i}$, and input distortions by $\tau_{kl,i}$ and $\tau_{x,i}$. In this way, we account for multiple ways in which Maoist activity can impact firms located in a conflict-affected district.<fnref n="13" />

Profit maximization yields the standard condition that the firm's output price is equal to its marginal cost:

$$ p_i = \left[ \left( \frac{R}{\gamma\alpha} \right)^{\gamma} \left( \frac{h}{(1-\gamma)\alpha} \right)^{1-\gamma} \right]^{\alpha} \left[ \prod_j \left( \frac{p_j}{\omega_{ji}(1-\alpha)} \right)^{\omega_{ji}} \right]^{1-\alpha} \frac{(1+\tau_{kl,i})^{\alpha} (1+\tau_{x,i})^{1-\alpha}}{\tilde{a}_i (1-\tau_{y,i})} \quad (4) $$

In log-form firm-level optimal price is:

$$ \log(p_i) = \log \left( \left[ \left( \frac{1}{\gamma\alpha} \right)^{\gamma} \left( \frac{1}{(1-\gamma)\alpha} \right)^{1-\gamma} \right]^{\alpha} \left[ \left( \frac{1}{1-\alpha} \right)^{1-\alpha} \right] \right) \quad (5) \\ + \alpha\gamma \log(R) + \alpha(1-\gamma)\log(h) + (1-\alpha) \sum_j \omega_{ji} \log\left(\frac{p_j}{\omega_{ji}}\right) - \log(a_i) - \log(\Gamma_{ct}) \\ + \underbrace{\log\left(\frac{(1+\tau_{kl,i})^{\alpha}(1+\tau_{x,i})^{1-\alpha}}{1-\tau_{y,i}}\right)}_{T_i} $$

where $T_i$ represents a comprehensive index of conflict-induced distortions borne by firm $i$. This

<footnote n="12">¹²This condition guarantees that production technology exhibits constant returns to scale.</footnote>

<footnote n="13">¹³The existence of a large variety of output and input distortions is documented in the literature (see, for example, Amodio and Di Maio (2018) and Besley and Mueller (2018)).</footnote>
where $x_{ij}$ is the amount supplied by firm $j$. The exponent $\omega_{ji} \ge 0$ is the share of good $j$ within firm $i$'s total use of intermediate inputs. In particular, $\omega_{ji} = 0$ if firm $i$ does not use good $j$ as input. We assume that $\sum_{i \in j} \omega_{ji} = 1$ for every $i$.<fnref n="12" />

**The impact on firms located in conflict-affected districts.** As described in Online Appendix OA1, firms located in conflict-affected districts are more likely to incur additional costs, such as loss from theft, payment of protection money, or additional expenditure on security. When modeling the behavior of firms located in conflict-affected districts, we take into account all of the aforementioned distortions, which can be output-specific or input-specific. In particular, if firm $i$ is located in a conflict-affected district, it then maximizes profits according to the following equation:

$$ \max_{k_i, l_i, x_{ji}} \pi_i = (1 - \tau_{y,i})p_i y_i - (1 + \tau_{kl,i})(Rk_i + hl_i) - (1 + \tau_{x,i}) \sum_j p_j x_{ji} \quad (3) $$

where $p_i$ is firm $i$'s output price; $R$, $h$, and $p_j$ are the exogenous input prices of capital, labor and intermediate inputs, respectively; and the different $\tau$'s represent conflict-induced idiosyncratic distortions. As in Hsieh and Klenow (2009), we denote output distortions by $\tau_{y,i}$, and input distortions by $\tau_{kl,i}$ and $\tau_{x,i}$. In this way, we account for multiple ways in which Maoist activity can impact firms located in a conflict-affected district.<fnref n="13" />

Profit maximization yields the standard condition that the firm's output price is equal to its marginal cost:

$$ p_i = \left[ \left( \frac{R}{\gamma\alpha} \right)^{\gamma} \left( \frac{h}{(1-\gamma)\alpha} \right)^{1-\gamma} \right]^{\alpha} \left[ \prod_j \left( \frac{p_j}{\omega_{ji}(1-\alpha)} \right)^{\omega_{ji}} \right]^{1-\alpha} \frac{(1+\tau_{kl,i})^{\alpha} (1+\tau_{x,i})^{1-\alpha}}{\tilde{a}_i (1-\tau_{y,i})} \quad (4) $$

In log-form firm-level optimal price is:

$$ \log(p_i) = \log \left( \left[ \left( \frac{1}{\gamma\alpha} \right)^{\gamma} \left( \frac{1}{(1-\gamma)\alpha} \right)^{1-\gamma} \right]^{\alpha} \left[ \left( \frac{1}{1-\alpha} \right)^{1-\alpha} \right] \right) \quad (5) \\ + \alpha\gamma \log(R) + \alpha(1-\gamma)\log(h) + (1-\alpha) \sum_j \omega_{ji} \log\left(\frac{p_j}{\omega_{ji}}\right) - \log(a_i) - \log(\Gamma_{ct}) \\ + \underbrace{\log\left(\frac{(1+\tau_{kl,i})^{\alpha}(1+\tau_{x,i})^{1-\alpha}}{1-\tau_{y,i}}\right)}_{T_i} $$

where $T_i$ represents a comprehensive index of conflict-induced distortions borne by firm $i$. This

<footnote n="12">¹²This condition guarantees that production technology exhibits constant returns to scale.</footnote>

<footnote n="13">¹³The existence of a large variety of output and input distortions is documented in the literature (see, for example, Amodio and Di Maio (2018) and Besley and Mueller (2018)).</footnote>
expression suggests that conflict-induced distortions increase the firm's optimal price. As demonstrated in the upcoming subsection, the rise in $p_i$ attributable to conflict induces each buyer $j$ to decrease its demand for the good $y_i$, consequently leading to a reduction in its production of the good $y_j$.

**The impact on firms not located in conflict-affected districts.** At the core of the analysis is the central role played by the production network in the propagation of the distortions experienced by firms in conflict-affected districts among firms outside those districts. Recall that a firm's output can either be consumed by the representative household or used by other firms as an input for production. For example, consider firm $i$ and firm $j$: if firm $i$'s output appears in firm $j$'s input bundle, then the share of good $i$ within the total intermediate inputs used by firm $j$ is positive and given by $\omega_{ij}$ (Equation 2). Note that at the level of the economy, the parameters $\omega_{ij}$ correspond to the entries of the $N \times N$ input-output matrix $\Omega$, where $N$ is the total number of firms in the economy. The rows of $\Omega$ sum up to one because we assume constant return-to-scale technology. The sum of each column of $\Omega$ represents firm-level weighted outdegree, i.e., the share of firm $i$'s output within the total inputs used by the other firms in the economy (Acemoglu et al., 2012). The increase in firm $i$'s price, $p_i$, due to conflict (Equation 4) implies that firm $j$ will reduce its demand for $x_i$, and will therefore reduce its output accordingly. To see this, consider firm $j$'s output in log form:

$$ \log(y_j) = \log(\tilde{a}_j) + \alpha\gamma\log(k_j) + \alpha(1-\gamma)\log(l_j) + (1-\alpha)(\omega_{1j}\log(x_{1j}) + \dots + \omega_{ij}\log(x_{ij}) + \dots + \omega_{Ni}\log(x_{Ni})) \quad (6) $$

where $x_{ij}$ is the output produced by firm $i$ and $\omega_{ij}$ the share of good $i$ within the total intermediate inputs used by firm $j$. Equation 6 suggests that any distortion experienced by firm $i$ affects firm $j$'s output, which decreases by a proportion of $(1 - \alpha) \omega_{ij}$. This is the first-order propagation effect of conflict through the production network. Specifically, the increase in firm $i$'s output price due to conflict implies that firm $j$, whose input bundle includes firm $i$'s output, will reduce its demand for firm $i$'s output, thus reducing its own output proportionally. We summarize this first-order effect as follows:

$$ (1 - \alpha)[\omega_{i1}, \dots, \omega_{iN}] = (1 - \alpha)\Omega'_{i} $$

where $\Omega'_{i}$ is the $i^{th}$ column of the matrix $\Omega$.

Importantly, this is not the end of the adjustment. All firms whose input bundle includes firm $j$'s output are subject to a second-order effect of distortions experienced by firm $i$. This is captured as follows:

$$ (1 - \alpha)^2[\omega_{i1}, \dots, \omega_{iN}]^2 = (1 - \alpha)\Omega'_{i} $$

Continuing in this fashion with higher-order effects, the impact of conflict-induced distortions
expression suggests that conflict-induced distortions increase the firm's optimal price. As demonstrated in the upcoming subsection, the rise in $p_i$ attributable to conflict induces each buyer $j$ to decrease its demand for the good $y_i$, consequently leading to a reduction in its production of the good $y_j$.

**The impact on firms not located in conflict-affected districts.** At the core of the analysis is the central role played by the production network in the propagation of the distortions experienced by firms in conflict-affected districts among firms outside those districts. Recall that a firm's output can either be consumed by the representative household or used by other firms as an input for production. For example, consider firm $i$ and firm $j$: if firm $i$'s output appears in firm $j$'s input bundle, then the share of good $i$ within the total intermediate inputs used by firm $j$ is positive and given by $\omega_{ij}$ (Equation 2). Note that at the level of the economy, the parameters $\omega_{ij}$ correspond to the entries of the $N \times N$ input-output matrix $\Omega$, where $N$ is the total number of firms in the economy. The rows of $\Omega$ sum up to one because we assume constant return-to-scale technology. The sum of each column of $\Omega$ represents firm-level weighted outdegree, i.e., the share of firm $i$'s output within the total inputs used by the other firms in the economy (Acemoglu et al., 2012). The increase in firm $i$'s price, $p_i$, due to conflict (Equation 4) implies that firm $j$ will reduce its demand for $x_i$, and will therefore reduce its output accordingly. To see this, consider firm $j$'s output in log form:

$$ \log(y_j) = \log(\tilde{a}_j) + \alpha\gamma\log(k_j) + \alpha(1-\gamma)\log(l_j) + (1-\alpha)(\omega_{1j}\log(x_{1j}) + \dots + \omega_{ij}\log(x_{ij}) + \dots + \omega_{Ni}\log(x_{Ni})) \quad (6) $$

where $x_{ij}$ is the output produced by firm $i$ and $\omega_{ij}$ the share of good $i$ within the total intermediate inputs used by firm $j$. Equation 6 suggests that any distortion experienced by firm $i$ affects firm $j$'s output, which decreases by a proportion of $(1 - \alpha) \omega_{ij}$. This is the first-order propagation effect of conflict through the production network. Specifically, the increase in firm $i$'s output price due to conflict implies that firm $j$, whose input bundle includes firm $i$'s output, will reduce its demand for firm $i$'s output, thus reducing its own output proportionally. We summarize this first-order effect as follows:

$$ (1 - \alpha)[\omega_{i1}, \dots, \omega_{iN}] = (1 - \alpha)\Omega'_{i} $$

where $\Omega'_{i}$ is the $i^{th}$ column of the matrix $\Omega$.

Importantly, this is not the end of the adjustment. All firms whose input bundle includes firm $j$'s output are subject to a second-order effect of distortions experienced by firm $i$. This is captured as follows:

$$ (1 - \alpha)^2[\omega_{i1}, \dots, \omega_{iN}]^2 = (1 - \alpha)\Omega'_{i} $$

Continuing in this fashion with higher-order effects, the impact of conflict-induced distortions
experienced by firm i on the entire economy is given by:

$$ \sum_{k}^{\infty} (1 - \alpha)^k (\Omega'_{i})^k = ([I - (1 - \alpha)\Omega']^{-1})'_{i} $$

which is the i-th column of Leontief inverse matrix. Finally, if we consider all firms impacted by conflict-induced distortions, we derive the *influence vector*, which captures the total effect of conflict by way of the production network. Its i-th element represents the cumulative effect of a shock to firm i on the other firms in the economy. The influence vector is expressed as follows:

$$ v = \frac{1}{N} [I - (1 - \alpha)\Omega']^{-1} \mathbf{1} \qquad (7) $$

where **1** is a vector of ones.

**The impact of conflict on aggregate output.** Since we are applying the model proposed by Acemoglu et al. (2012) in the context of civil conflict, we can exploit their results by expressing (log-) aggregate output as a weighted sum of firm-level productivity, in which the weight is given by the influence vector.<fnref n="14" /> (Log-) aggregate output is:

$$ \mathbf{Y} = v'\epsilon + \mu \qquad (8) $$

where $v$ is the influence vector, $\epsilon$ is a vector containing (log) firm-level productivity and conflict-induced distortions (experienced by firms directly exposed to conflict), and $\mu$ is a constant independent of vectors $\epsilon$ and $v$. As noted in Acemoglu et al. (2012), the influence vector is closely related to the *Bonacich centrality vector*. Therefore, firms that occupy more “central” positions in the network play a more important role in determining aggregate output.

Since our goal is to quantify the aggregate output loss due to conflict, our main interest is to calculate the *percentage change* of aggregate output, which is expressed as follows:

$$ \Delta \mathbf{Y} = v'\xi \qquad (9) $$

where vector $\xi$ contains firm-specific conflict-induced distortions defined as follows:

$$ \xi = \begin{cases} 0 & \text{if firm } i \text{ is located in a peaceful district} \\ T_i & \text{if firm } i \text{ is located in a conflict-affected district} \end{cases} \qquad (10) $$

For firms not located in conflict-affected districts the entries of $\xi$ are equal to zero, while the entries for firms located in conflict-affected districts are a function of conflict-induced distortions represented by the last addend of Equation 5, $T_i$.

In what follows, we bring this model to the data. First, in Section 4 we introduce our data, how we

<footnote n="14">¹⁴The proof that Equation 8 characterizes the equilibrium of this economy is developed in Acemoglu et al. (2012) in their Appendix A.</footnote>
experienced by firm i on the entire economy is given by:

$$ \sum_{k}^{\infty} (1 - \alpha)^k (\Omega'_{i})^k = ([I - (1 - \alpha)\Omega']^{-1})'_{i} $$

which is the i-th column of Leontief inverse matrix. Finally, if we consider all firms impacted by conflict-induced distortions, we derive the influence vector, which captures the total effect of conflict by way of the production network. Its i-th element represents the cumulative effect of a shock to firm i on the other firms in the economy. The influence vector is expressed as follows:

$$ v = \frac{1}{N} [I - (1 - \alpha)\Omega']^{-1} \mathbf{1} \qquad (7) $$

where 1 is a vector of ones.

**The impact of conflict on aggregate output.** Since we are applying the model proposed by Acemoglu et al. (2012) in the context of civil conflict, we can exploit their results by expressing (log-) aggregate output as a weighted sum of firm-level productivity, in which the weight is given by the influence vector.<fnref n="14" /> (Log-) aggregate output is:

$$ \mathbf{Y} = v'\epsilon + \mu \qquad (8) $$

where v is the influence vector, ϵ is a vector containing (log) firm-level productivity and conflict-induced distortions (experienced by firms directly exposed to conflict), and μ is a constant independent of vectors ϵ and v. As noted in Acemoglu et al. (2012), the influence vector is closely related to the *Bonacich centrality vector*. Therefore, firms that occupy more “central” positions in the network play a more important role in determining aggregate output.

Since our goal is to quantify the aggregate output loss due to conflict, our main interest is to calculate the *percentage change* of aggregate output, which is expressed as follows:

$$ \Delta \mathbf{Y} = v'\xi \qquad (9) $$

where vector ξ contains firm-specific conflict-induced distortions defined as follows:

$$ \xi = \begin{cases} 0 & \text{if firm } i \text{ is located in a peaceful district} \\ T_i & \text{if firm } i \text{ is located in a conflict-affected district} \end{cases} \qquad (10) $$

For firms not located in conflict-affected districts the entries of ξ are equal to zero, while the entries for firms located in conflict-affected districts are a function of conflict-induced distortions represented by the last addend of Equation 5, Tᵢ.

In what follows, we bring this model to the data. First, in Section 4 we introduce our data, how we

<footnote n="14">¹⁴The proof that Equation 8 characterizes the equilibrium of this economy is developed in Acemoglu et al. (2012) in their Appendix A.</footnote>
construct the production network, and we validate our strategy. Then, in Section 5, we present the
estimates of the output loss due to conflict arising from this model.

# 4 The Production Network

In this section, we present the dataset used in the empirical analysis, which combines firm-level data with information on Maoist activities. We then describe how we construct the production network that characterizes the Indian economy. Next, we present some validation exercises to corroborate that our strategy to build the production network is a reasonable representation of the actual (unobserved) buyer-supplier linkages. Finally, we discuss in detail the potential endogeneity of the production network.

## 4.1 Data

**Firm data.** We use plant-level information on Indian manufacturers from the Annual Survey of Industries (ASI), which was carried out by the Ministry of Statistics and Program Implementation for the period 2000-2001 to 2009-2010.<fnref n="15" /> For simplicity, we use the term *firm* to define a productive unit, i.e., a plant. The ASI provides a panel consisting of all registered manufacturers in India with more than 100 employees plus an annual sample of manufacturers with more than 20 employees (which represents about 20% of the total).

Our identification strategy relies on two unique features of the data. First, they provide rich product-
level information on each firm’s output and intermediate input (i.e., up to 10 products per firm).
Following the 5-digit ASI Commodity Classification (ASICC) codes, we are able to distinguish
between 5,911 different products. This becomes important in the construction of firm-to-firm input-
output links discussed in detail below. Second, we geo-localize firms by district in order to determine
their exposure to the Maoist insurgency using the methodology in Martin et al. (2017).<fnref n="16" /> The final
sample consists of a panel of 187,283 distinct firms observed between 2000 and 2009. About 26% of
the firms were surveyed for at least 7 years, while 38% were surveyed for only 2. More than half of
the firms are single-output producers, while the rest produces between 2 and 10 distinct goods (less
than 0.1% produce up to 23 distinct goods). The data cover 30 different industries (1 and 2-digit

<footnote n="15">¹⁵Accounting years run between April 1 - March 31. For simplicity, herein we refer to these years as 2000 through 2009.</footnote>

<footnote n="16">¹⁶Martin et al. (2017) created the first mapping of the panel dataset (including panel identifiers) to district locations by merging them with annual cross-sectional data (including district identifiers). The cross-sectional information does not include district information from 2009 onward, and therefore our dataset ends with 2009. The Indian Personal Data Protection Bill (approved in 2019) allows the processing of critical personal data, such as localization of firms, only within the territory of India. It also specifies that transfer of critical data outside India is subject to the Data Protection Authority approval in consultation with Government. Therefore, even if questionnaires and data manuals of more recent ASI waves report district information, in such waves the variable that gives district location is always missing from 2010 onward.</footnote>
construct the production network, and we validate our strategy. Then, in Section 5, we present the
estimates of the output loss due to conflict arising from this model.

# 4 The Production Network

In this section, we present the dataset used in the empirical analysis, which combines firm-level data with information on Maoist activities. We then describe how we construct the production network that characterizes the Indian economy. Next, we present some validation exercises to corroborate that our strategy to build the production network is a reasonable representation of the actual (unobserved) buyer-supplier linkages. Finally, we discuss in detail the potential endogeneity of the production network.

## 4.1 Data

**Firm data.** We use plant-level information on Indian manufacturers from the Annual Survey of Industries (ASI), which was carried out by the Ministry of Statistics and Program Implementation for the period 2000-2001 to 2009-2010.<fnref n="15" /> For simplicity, we use the term *firm* to define a productive unit, i.e., a plant. The ASI provides a panel consisting of all registered manufacturers in India with more than 100 employees plus an annual sample of manufacturers with more than 20 employees (which represents about 20% of the total).

Our identification strategy relies on two unique features of the data. First, they provide rich product-
level information on each firm’s output and intermediate input (i.e., up to 10 products per firm).
Following the 5-digit ASI Commodity Classification (ASICC) codes, we are able to distinguish
between 5,911 different products. This becomes important in the construction of firm-to-firm input-
output links discussed in detail below. Second, we geo-localize firms by district in order to determine
their exposure to the Maoist insurgency using the methodology in Martin et al. (2017).<fnref n="16" /> The final
sample consists of a panel of 187,283 distinct firms observed between 2000 and 2009. About 26% of
the firms were surveyed for at least 7 years, while 38% were surveyed for only 2. More than half of
the firms are single-output producers, while the rest produces between 2 and 10 distinct goods (less
than 0.1% produce up to 23 distinct goods). The data cover 30 different industries (1 and 2-digit

<footnote n="15">¹⁵Accounting years run between April 1 - March 31. For simplicity, herein we refer to these years as 2000 through 2009.</footnote>

<footnote n="16">¹⁶Martin et al. (2017) created the first mapping of the panel dataset (including panel identifiers) to district locations by merging them with annual cross-sectional data (including district identifiers). The cross-sectional information does not include district information from 2009 onward, and therefore our dataset ends with 2009. The Indian Personal Data Protection Bill (approved in 2019) allows the processing of critical personal data, such as localization of firms, only within the territory of India. It also specifies that transfer of critical data outside India is subject to the Data Protection Authority approval in consultation with Government. Therefore, even if questionnaires and data manuals of more recent ASI waves report district information, in such waves the variable that gives district location is always missing from 2010 onward.</footnote>
level of NIC) in 558 districts (across 32 states and union territories).

**Conflict data.** We gather data on conflict from the UCDP Georeferenced Event Dataset, which is collected from a wide range of sources, including news media and reports by international organizations and NGOs. It provides daily reports of “incidents where armed force was used by an organized actor against another organized actor, or against civilians, resulting in at least one direct death at a specific location and a specific date” (Sundberg and Erik, 2013). The final dataset covers 1,775 events from 2000 to 2009, which involved 4,737 deaths (based on accounting years).

Although these data are quite rich and, thus, have been widely used in the recent conflict literature (Nunn and Qian, 2014; Berman et al., 2017; König et al., 2017), they do not include information on the specific type of the violent event (e.g., if a given violent event is targeted against public infrastructure, or if insurgents call for *Bandhs*, i.e., strikes). This type of information is reported in ACLED (2018) database, but only from 2016 onward. Another valuable source of information is the *South Asia Terrorism Portal* (SATP), however it does not distinguish further among different categories of violent events. Finally, our firm-level data do not provide information on whether and in what way firms were directly exposed to violent events. Overall, this prevents us from making a precise statement on the heterogeneous intensity of exposure across firms within the same district and on the network implications of different types of violent events. Therefore, we rely on district-level information on the location of firms and of violent events and assume that all firms located in the same district are equally affected by violence in the district. Note that Indian districts are also relatively small territories: the average internal distance is 25.5 km (max. 108 km).<sup>17</sup> In our final sample, which includes 187,283 distinct firms, about 10% of firms were directly exposed to conflict at least once (18,419 firms).

## 4.2 Production network

**Firm-to-firm links.** Ideally, our dataset would include the full input-output network formed by the universe of firm-to-firm transactions. However, since this information is incomplete in the ASI data, we develop a novel method to approximate the input-output network that characterizes the Indian economy.<sup>18</sup> It draws from the literature on the determinants of domestic sourcing, which emphasizes the key role of geographic proximity and suppliers’ market power in establishing buyer-supplier links (Bernard et al., 2019).

This method relies on two main features of the ASI data. First, for every buyer *i*, we observe the

<footnote n="17"><sup>17</sup>To compute the internal distance, we follow Head and Mayer (2010) using the disk approximation: $d_{ii} = (2/3)\sqrt{area/\pi}$.</footnote>

<footnote n="18"><sup>18</sup>Panigrahi (2021) uses data on Indian firm-to-firm transactions. Although the data are quite rich, they are not well-suited to our purpose of examining the propagation effect in peaceful districts and computing the associated total loss for two main reasons: First, “only” five Indian states are covered, of which four are affected by the Maoist insurgency. Second, information on the product sold is missing. Therefore, we would not be able to identify product-level effects on price and quantity.</footnote>
level of NIC) in 558 districts (across 32 states and union territories).

**Conflict data.** We gather data on conflict from the UCDP Georeferenced Event Dataset, which is collected from a wide range of sources, including news media and reports by international organizations and NGOs. It provides daily reports of “incidents where armed force was used by an organized actor against another organized actor, or against civilians, resulting in at least one direct death at a specific location and a specific date” (Sundberg and Erik, 2013). The final dataset covers 1,775 events from 2000 to 2009, which involved 4,737 deaths (based on accounting years).

Although these data are quite rich and, thus, have been widely used in the recent conflict literature (Nunn and Qian, 2014; Berman et al., 2017; König et al., 2017), they do not include information on the specific type of the violent event (e.g., if a given violent event is targeted against public infrastructure, or if insurgents call for *Bandhs*, i.e., strikes). This type of information is reported in ACLED (2018) database, but only from 2016 onward. Another valuable source of information is the *South Asia Terrorism Portal* (SATP), however it does not distinguish further among different categories of violent events. Finally, our firm-level data do not provide information on whether and in what way firms were directly exposed to violent events. Overall, this prevents us from making a precise statement on the heterogeneous intensity of exposure across firms within the same district and on the network implications of different types of violent events. Therefore, we rely on district-level information on the location of firms and of violent events and assume that all firms located in the same district are equally affected by violence in the district. Note that Indian districts are also relatively small territories: the average internal distance is 25.5 km (max. 108 km).<sup>17</sup> In our final sample, which includes 187,283 distinct firms, about 10% of firms were directly exposed to conflict at least once (18,419 firms).

## 4.2 Production network

**Firm-to-firm links.** Ideally, our dataset would include the full input-output network formed by the universe of firm-to-firm transactions. However, since this information is incomplete in the ASI data, we develop a novel method to approximate the input-output network that characterizes the Indian economy.<sup>18</sup> It draws from the literature on the determinants of domestic sourcing, which emphasizes the key role of geographic proximity and suppliers’ market power in establishing buyer-supplier links (Bernard et al., 2019).

This method relies on two main features of the ASI data. First, for every buyer *i*, we observe the

<footnote n="17"><sup>17</sup>To compute the internal distance, we follow Head and Mayer (2010) using the disk approximation: $d_{ii} = (2/3)\sqrt{area/\pi}$.</footnote>

<footnote n="18"><sup>18</sup>Panigrahi (2021) uses data on Indian firm-to-firm transactions. Although the data are quite rich, they are not well-suited to our purpose of examining the propagation effect in peaceful districts and computing the associated total loss for two main reasons: First, “only” five Indian states are covered, of which four are affected by the Maoist insurgency. Second, information on the product sold is missing. Therefore, we would not be able to identify product-level effects on price and quantity.</footnote>
bundle of inputs ($k \in K$) used in production. Second, for every good $k$, we observe its potential producers $j \in J$, their location, and their relative size (compared to other suppliers $r \neq j \in J$ of $k$). As such, for each good $k$ within the input bundle of at least one firm $i$ in year $t$, we can approximate the buyer-supplier links by assigning to each potential supplier $j$ an index of importance, $\rho_{jit(k)}$, such that $0 < \rho_{jit(k)} \le 1$ and $\sum_{j \in J} \rho_{jit(k)} = 1$. This index has two components: the relative inverse distance between buyer $i$ and every supplier $j \in J$ of $k$, and the share of output $k$ produced by each supplier $j \in J$.<fnref n="19" /> For each good $k$ and potential supplier-buyer pair $ji$ in year $t$, our measure is a linear combination of the relative distance between buyer $i$ and supplier $j$, and the relative size of each supplier $j$ of good $k$ in year $t$. For a given good $k$, the index of importance of supplier $j$ with respect to buyer $i$ in year $t$ is given by:

$$ \rho_{jit(k)} = \lambda \frac{D_{ji}}{\sum_{j \in J} D_{ji}} + (1 - \lambda) \frac{k_{jt}}{\sum_{j \in J} k_{jt}} \quad (11) $$

where we set $\lambda = 0.5$ such that the same weight is assigned to every component. $D_{ji}$ measures the inverse bilateral distance between supplier $j$ and buyer $i$, while $k_{jt}$ is the amount of good $k$ produced by $j$ in year $t$.<fnref n="20" /> The index of importance $\rho_{jit(k)}$ assigns to every supplier $j$ the probability of being the actual supplier of good $k$ to firm $i$ at year $t$. By construction, these probabilities sum to one.<fnref n="21" /> The strategy is illustrated in Figure 2, where for a given buyer $i$ and good $k$ there are three suppliers $j = [1, 2, 3]$, i.e., $1(k), 2(k)$ and $3(k)$. As mentioned above, we do not observe which firm is the actual supplier of input $k$. If we were able to, $\rho_{1i(k)}, \rho_{2i(k)}$ and $\rho_{3i(k)}$ would be dummy variables equal to one if supplier $j = [1, 2, 3]$ is the actual supplier and zero otherwise. We approximate these links by assigning to each potential supplier $j$ the probability of being the actual supplier of input $k$ for firm $i$, based on the bilateral distance between buyer $i$ and each supplier $j$ and the size of each supplier $j$ of $k$, represented in Figure 2 by the length of the arrow and the size of the circles.

Note that this index plays a critical role in both our conceptual framework and the structural estimation (Sections 3 and 5), where we model and describe the production network characterizing the Indian economy using the input-output matrix $\Omega$. The entries of this matrix are the observed input

$$ \rho_{jit(k)} = \frac{\frac{D_{ji}}{\sum_{j \in J} D_{ji}} \times \frac{k_{jt}}{\sum_{j \in J} k_{jt}}}{\sum_{j \in J} \left( \frac{D_{ji}}{\sum_{j \in J} D_{ji}} \times \frac{k_{jt}}{\sum_{j \in J} k_{jt}} \right)} $$

This alternative specification is well-defined as it guarantees $0 < \rho_{jit(k)} \le 1$ and $\sum_j \rho_{jit(k)} = 1$. Note that we mechanically attribute more (less) weight to the larger (smaller) and closer (farther) suppliers, compared the index described in Equation 11. Considering this alternative functional form leaves our main estimates essentially unchanged.

<footnote n="19">¹⁹Note that by using inverse distance, we implicitly assume that the elasticity of trade flows with respect to distance between buyers and producers is equal to -1. This is in line with the findings of Panigrahi (2021); however, other studies indicate that the value may be less than -1, especially in contexts related to domestic trade in developing countries (Donaldson, 2018). We therefore also consider alternative formulations of our measure of importance. Specifically, in Section 5.1, we relax this assumption and let the elasticity trade flows with respect to distance to vary between -2 and -5. The estimated aggregate output loss is not particularly sensitive to this value.</footnote>

<footnote n="20">²⁰For suppliers and buyers located in the same district, we set bilateral distance equal to district-specific internal distance (Head and Mayer, 2010).</footnote>

<footnote n="21">²¹Note that we could alternatively construct $\rho_{jit(k)}$ as a non-linear combination of relative distance and relative size:</footnote>
bundle of inputs ($k \in K$) used in production. Second, for every good $k$, we observe its potential producers $j \in J$, their location, and their relative size (compared to other suppliers $r \neq j \in J$ of $k$). As such, for each good $k$ within the input bundle of at least one firm $i$ in year $t$, we can approximate the buyer-supplier links by assigning to each potential supplier $j$ an index of importance, $\rho_{jit(k)}$, such that $0 < \rho_{jit(k)} \le 1$ and $\sum_{j \in J} \rho_{jit(k)} = 1$. This index has two components: the relative inverse distance between buyer $i$ and every supplier $j \in J$ of $k$, and the share of output $k$ produced by each supplier $j \in J$.<fnref n="19" /> For each good $k$ and potential supplier-buyer pair $ji$ in year $t$, our measure is a linear combination of the relative distance between buyer $i$ and supplier $j$, and the relative size of each supplier $j$ of good $k$ in year $t$. For a given good $k$, the index of importance of supplier $j$ with respect to buyer $i$ in year $t$ is given by:

$$ \rho_{jit(k)} = \lambda \frac{D_{ji}}{\sum_{j \in J} D_{ji}} + (1 - \lambda) \frac{k_{jt}}{\sum_{j \in J} k_{jt}} \quad (11) $$

where we set $\lambda = 0.5$ such that the same weight is assigned to every component. $D_{ji}$ measures the inverse bilateral distance between supplier $j$ and buyer $i$, while $k_{jt}$ is the amount of good $k$ produced by $j$ in year $t$.<fnref n="20" /> The index of importance $\rho_{jit(k)}$ assigns to every supplier $j$ the probability of being the actual supplier of good $k$ to firm $i$ at year $t$. By construction, these probabilities sum to one.<fnref n="21" /> The strategy is illustrated in Figure 2, where for a given buyer $i$ and good $k$ there are three suppliers $j = [1, 2, 3]$, i.e., $1(k), 2(k)$ and $3(k)$. As mentioned above, we do not observe which firm is the actual supplier of input $k$. If we were able to, $\rho_{1i(k)}, \rho_{2i(k)}$ and $\rho_{3i(k)}$ would be dummy variables equal to one if supplier $j = [1, 2, 3]$ is the actual supplier and zero otherwise. We approximate these links by assigning to each potential supplier $j$ the probability of being the actual supplier of input $k$ for firm $i$, based on the bilateral distance between buyer $i$ and each supplier $j$ and the size of each supplier $j$ of $k$, represented in Figure 2 by the length of the arrow and the size of the circles.

Note that this index plays a critical role in both our conceptual framework and the structural estimation (Sections 3 and 5), where we model and describe the production network characterizing the Indian economy using the input-output matrix $\Omega$. The entries of this matrix are the observed input

$$ \rho_{jit(k)} = \frac{\frac{D_{ji}}{\sum_{j \in J} D_{ji}} \times \frac{k_{jt}}{\sum_{j \in J} k_{jt}}}{\sum_{j \in J} \left( \frac{D_{ji}}{\sum_{j \in J} D_{ji}} \times \frac{k_{jt}}{\sum_{j \in J} k_{jt}} \right)} $$

<footnote n="19">¹⁹Note that by using inverse distance, we implicitly assume that the elasticity of trade flows with respect to distance between buyers and producers is equal to -1. This is in line with the findings of Panigrahi (2021); however, other studies indicate that the value may be less than -1, especially in contexts related to domestic trade in developing countries (Donaldson, 2018). We therefore also consider alternative formulations of our measure of importance. Specifically, in Section 5.1, we relax this assumption and let the elasticity trade flows with respect to distance to vary between -2 and -5. The estimated aggregate output loss is not particularly sensitive to this value.</footnote>

<footnote n="20">²⁰For suppliers and buyers located in the same district, we set bilateral distance equal to district-specific internal distance (Head and Mayer, 2010).</footnote>

<footnote n="21">²¹Note that we could alternatively construct $\rho_{jit(k)}$ as a non-linear combination of relative distance and relative size:</footnote>

<footnote>This alternative specification is well-defined as it guarantees $0 < \rho_{jit(k)} \le 1$ and $\sum_j \rho_{jit(k)} = 1$. Note that we mechanically attribute more (less) weight to the larger (smaller) and closer (farther) suppliers, compared the index described in Equation 11. Considering this alternative functional form leaves our main estimates essentially unchanged.</footnote>
shares, $\omega_{kit}$, adjusted by the index of supplier importance $\rho_{jit(k)}$. Specifically, the input-output matrix $\Omega$ is the key element to construct the influence vector, which is a crucial component of the Indian aggregate output as it captures how conflict related idiosyncratic distortions propagate downstream to other firms by way of the production network (see Section 3).

<figure><img src="image_7.png" /><figcaption>Figure 2: Buyer-supplier links</figcaption></figure>

Note: An illustration of the procedure we develop to approximate the buyer-supplier links that characterize India's production network.

## 4.3 Validation of our strategy

As we do not observe firm-to-firm transactions, the production network is imputed using parametric assumptions as displayed in Equation 11. The absence of information on firm-to-firm transactions prevents us to perform an in-sample validation of our method to construct the Indian input-output network. Nonetheless, we corroborate our strategy developing several validation exercises.

**Probability of establishing firm-to-firm links.** We leverage administrative data from Gadenne et al. (2019) on firm-to-firm transactions data within West Bengal with product-level information (4-digit level) the postcode of firms, and a proxy of firms' size (turnout).<fnref n="22" />,<fnref n="23" /> Specifically, we construct a cross section of more than 90 million potential buyer-supplier pairs for year 2011, where the unconditional probability of transaction is 0.217% (197,009 distinct transactions). We estimate the following equation:

$$T_{ji(k)} = \beta_0 + \beta_1 \rho_{ji(k)} + D'_{ji(k)} \beta_2 + \varepsilon_{ji(k)} \quad (12)$$

<footnote n="22">²²We are deeply indebted to Lucie Gadenne, who estimated for us the validation exercise as the firm-to-firm data are confidential. Unfortunately, these data do not report firm-to-firm transactions across states.</footnote>

<footnote n="23">²³Panigrahi (2021) uses data on firm-to-firm transactions in five Indian states, which allow to discern whether the buyer and the supplier are located in the same district (58% of transactions corresponding to approximately 42% of total trade value), in different districts (37% of transactions corresponding to 43% of total trade value), or in different states (5% of transactions corresponding to 15% of total trade value). These data prove the existence of sizable inter-district and inter-state trade. Although the data are quite rich, based on our exchange with Piyush Panigrahi, they are not suitable for our purpose because information on the product sold is missing. Therefore, using this data, we would not be able to identify every potential seller j of good k.</footnote>
shares, $\omega_{kit}$, adjusted by the index of supplier importance $\rho_{jit(k)}$. Specifically, the input-output matrix $\Omega$ is the key element to construct the influence vector, which is a crucial component of the Indian aggregate output as it captures how conflict related idiosyncratic distortions propagate downstream to other firms by way of the production network (see Section 3).

<figure><img src="image_6.png" /><figcaption>Figure 2: Buyer-supplier links</figcaption></figure>

Note: An illustration of the procedure we develop to approximate the buyer-supplier links that characterize India's production network.

## 4.3 Validation of our strategy

As we do not observe firm-to-firm transactions, the production network is imputed using parametric assumptions as displayed in Equation 11. The absence of information on firm-to-firm transactions prevents us to perform an in-sample validation of our method to construct the Indian input-output network. Nonetheless, we corroborate our strategy developing several validation exercises.

**Probability of establishing firm-to-firm links.** We leverage administrative data from Gadenne et al. (2019) on firm-to-firm transactions data within West Bengal with product-level information (4-digit level) the postcode of firms, and a proxy of firms' size (turnout).<fnref n="22" />,<fnref n="23" /> Specifically, we construct a cross section of more than 90 million potential buyer-supplier pairs for year 2011, where the unconditional probability of transaction is 0.217% (197,009 distinct transactions). We estimate the following equation:

$$T_{ji(k)} = \beta_0 + \beta_1 \rho_{ji(k)} + D'_{ji(k)} \beta_2 + \varepsilon_{ji(k)} \quad (12)$$

<footnote n="22">²²We are deeply indebted to Lucie Gadenne, who estimated for us the validation exercise as the firm-to-firm data are confidential. Unfortunately, these data do not report firm-to-firm transactions across states.</footnote>

<footnote n="23">²³Panigrahi (2021) uses data on firm-to-firm transactions in five Indian states, which allow to discern whether the buyer and the supplier are located in the same district (58% of transactions corresponding to approximately 42% of total trade value), in different districts (37% of transactions corresponding to 43% of total trade value), or in different states (5% of transactions corresponding to 15% of total trade value). These data prove the existence of sizable inter-district and inter-state trade. Although the data are quite rich, based on our exchange with Piyush Panigrahi, they are not suitable for our purpose because information on the product sold is missing. Therefore, using this data, we would not be able to identify every potential seller j of good k.</footnote>
where $T_{ji(k)}$ is a dummy equal to 1 if firm $i$ buys from supplier $j$ good $k$ and 0 otherwise, and $\rho_{ji(k)}$ is our index of supplier's importance defined in Equation 11. $D'_{ji(k)}$ is a set of fixed effects (sector, district of the seller, district of the buyer) according to the estimates. The results suggest that the index of supplier importance, $\rho_{ji(k)}$, correlates positively with the probability of firm-to-firm transaction (Table 1, columns 1 to 3). In columns 4 and 5 we add progressively dummies that mark whether the buyer and the seller share the same postcode and whether the seller and the buyer are located in the same district. The inclusion of these controls imply a modest decrease of our coefficient of interest: compared to column 3, the coefficient in column 5 is 15% smaller. In terms of magnitude, our results confirm the key role that proximity plays in determining domestic firm-to-firm transactions (Donaldson, 2018; Bernard et al., 2019). Column 5 suggests that having the same postcode is correlated with a 0.005 increase of the probability of firm-to-firm transaction, which is two times larger than the observed unconditional probability of trade. More importantly, our analysis suggests that our index has a considerable effect on the probability of firm-to-firm transactions: a 1 s.d. increase of $\rho_{ji(k)}$ (s.d. = 0.0039) doubles the observed average probability of trade.<fnref n="24" />

<table><caption>Table 1: The role of $\rho_{ji(k)}$ in predicting buyer-supplier links</caption><thead><tr><th>Dep. var.</th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th></tr></thead><tbody><tr><td>ρ<sub>ji(k)</sub></td><td>0.684<sup>a</sup><br>(0.049)</td><td>0.684<sup>a</sup><br>(0.047)</td><td>0.615<sup>a</sup><br>(0.038)</td><td>0.537<sup>a</sup><br>(0.029)</td><td>0.526<sup>a</sup><br>(0.027)</td></tr><tr><td>Shared postcode</td><td></td><td></td><td></td><td>0.005<sup>a</sup><br>(0.001)</td><td>0.004<sup>a</sup><br>(0.001)</td></tr><tr><td>Same district</td><td></td><td></td><td></td><td></td><td>0.002<sup>a</sup><br>(0.001)</td></tr><tr><td>Sector (4-digit) FE</td><td>Yes</td><td>Yes</td><td>—</td><td>No</td><td>—</td></tr><tr><td>District supplier FE</td><td>No</td><td>Yes</td><td>—</td><td>No</td><td>—</td></tr><tr><td>District buyer FE</td><td>No</td><td>Yes</td><td>—</td><td>No</td><td>—</td></tr><tr><td>Supplier FE</td><td>No</td><td>No</td><td>—</td><td>Yes</td><td>—</td></tr><tr><td>Buyer×Sector FE</td><td>No</td><td>No</td><td>—</td><td>Yes</td><td>—</td></tr><tr><td>Observations</td><td colspan="5">90,438,253</td></tr><tr><td>Sample mean</td><td colspan="5">0.00217</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 $k$. These two components are combined linearly and have the same weight (see Equation 11 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.

<footnote n="24">²⁴In Appendix, Table 3 displays estimates with alternative formulations of our index of supplier's importance. Specifically, we compute $\rho_{ji(k)}$ by setting the weight to relative buyer-supplier distance component, i.e., $\lambda$ in Equation 11, equal to 0.1 – 0.9, and the weight to relative supplier size component, i.e., $1 - \lambda$ in Equation 11, equal to 0.9 – 0.1. We also propose an alternative specification of $\rho_{ji(k)}$ where we combine relative buyer-supplier distance and relative supplier size in a non-linear way (see footnote 21). In each of these alternative specifications, there is a positive and statistically significant correlation between $\rho_{ji(k)}$ and the probability of establishing a buyer-supplier link. Moreover, the magnitude of the estimated coefficients are relatively stable.</footnote>
where $T_{ji(k)}$ is a dummy equal to 1 if firm $i$ buys from supplier $j$ good $k$ and 0 otherwise, and $\rho_{ji(k)}$ is our index of supplier's importance defined in Equation 11. $D'_{ji(k)}$ is a set of fixed effects (sector, district of the seller, district of the buyer) according to the estimates. The results suggest that the index of supplier importance, $\rho_{ji(k)}$, correlates positively with the probability of firm-to-firm transaction (Table 1, columns 1 to 3). In columns 4 and 5 we add progressively dummies that mark whether the buyer and the seller share the same postcode and whether the seller and the buyer are located in the same district. The inclusion of these controls imply a modest decrease of our coefficient of interest: compared to column 3, the coefficient in column 5 is 15% smaller. In terms of magnitude, our results confirm the key role that proximity plays in determining domestic firm-to-firm transactions (Donaldson, 2018; Bernard et al., 2019). Column 5 suggests that having the same postcode is correlated with a 0.005 increase of the probability of firm-to-firm transaction, which is two times larger than the observed unconditional probability of trade. More importantly, our analysis suggests that our index has a considerable effect on the probability of firm-to-firm transactions: a 1 s.d. increase of $\rho_{ji(k)}$ (s.d. = 0.0039) doubles the observed average probability of trade.<fnref n="24" />

<table><caption>Table 1: The role of $\rho_{ji(k)}$ in predicting buyer-supplier links</caption><thead><tr><th>Dep. var.</th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th></tr></thead><tbody><tr><td>ρ<sub>ji(k)</sub></td><td>0.684<sup>a</sup><br>(0.049)</td><td>0.684<sup>a</sup><br>(0.047)</td><td>0.615<sup>a</sup><br>(0.038)</td><td>0.537<sup>a</sup><br>(0.029)</td><td>0.526<sup>a</sup><br>(0.027)</td></tr><tr><td>Shared postcode</td><td></td><td></td><td></td><td>0.005<sup>a</sup><br>(0.001)</td><td>0.004<sup>a</sup><br>(0.001)</td></tr><tr><td>Same district</td><td></td><td></td><td></td><td></td><td>0.002<sup>a</sup><br>(0.001)</td></tr><tr><td>Sector (4-digit) FE</td><td>Yes</td><td>Yes</td><td></td><td>No</td><td></td></tr><tr><td>District supplier FE</td><td>No</td><td>Yes</td><td></td><td>No</td><td></td></tr><tr><td>District buyer FE</td><td>No</td><td>Yes</td><td></td><td>No</td><td></td></tr><tr><td>Supplier FE</td><td>No</td><td>No</td><td></td><td>Yes</td><td></td></tr><tr><td>Buyer×Sector FE</td><td>No</td><td>No</td><td></td><td>Yes</td><td></td></tr><tr><td>Observations</td><td colspan="5">90,438,253</td></tr><tr><td>Sample mean</td><td colspan="5">0.00217</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 $k$. These two components are combined linearly and have the same weight (see Equation 11 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.

<footnote n="24">²⁴In Appendix, Table 3 displays estimates with alternative formulations of our index of supplier's importance. Specifically, we compute $\rho_{ji(k)}$ by setting the weight to relative buyer-supplier distance component, i.e., $\lambda$ in Equation 11, equal to 0.1 – 0.9, and the weight to relative supplier size component, i.e., $1 - \lambda$ in Equation 11, equal to 0.9 – 0.1. We also propose an alternative specification of $\rho_{ji(k)}$ where we combine relative buyer-supplier distance and relative supplier size in a non-linear way (see footnote 21). In each of these alternative specifications, there is a positive and statistically significant correlation between $\rho_{ji(k)}$ and the probability of establishing a buyer-supplier link. Moreover, the magnitude of the estimated coefficients are relatively stable.</footnote>
**Relate network to observable statistics.** We rely on the existing literature and use a “bottom-up” perspective by linking the imputed (not observed) production network to observable outcomes. First, we rely on Acemoglu et al. (2012) who show that the influence vector, which measures how “central” each firm is in the network representation of the economy, coincides with the *sales vector*, i.e., the vector containing firm-specific share of sales over the whole economy. In this perspective, we correlate the influence vector that arises from our measure of the production network and the observed sales vector. For each year, the coefficient of correlation is positive and fairly high, i.e., around 0.5. Moreover, using the Spearman correlation we get a coefficient equal to 0.53 and we reject the null-hypothesis that influence and sales vectors are independent (p-value equal to 0). Finally, regressing the sales vector on the influence vector for a given year (without controlling for any other covariate) yields a $R^2$ that is firmly in the vicinity of 0.24. These findings are consistent with applications based on observed networks. In fact, when we apply this methodology to firm-to-firm transaction data from Gadenne et al. (2019), we obtain similar results: a correlation coefficient of 0.45 and an $R^2$ value of 0.20.

Second, we validate our construction on the input-output network comparing the weighted outdegree distribution associated with 2002 Indian firm level data to that computed with 2002 US sector level shown in Carvalho (2014) (Figure 3). The weighted outdegree of a given firm *i* is defined as the sum over all the weights of the network in which firm *i* appears as an input-supplying firm. This measure ranges from 0 if a firm does not supply inputs to any other firm, to *N* if a single firm is the sole input supplier of every firm in the economy. The shape of the weighted outdegree distribution is comparable to that presented in Carvalho (2014): (i) nearly every firm has an outdegree greater than 0.01, (ii) one-tenth of firms have an outdegree greater than 1, and (iii) about 1 percent of all sectors have an outdegree measure greater than 10, these are producers of “general purpose” manufactured goods like iron and steel or oil.

## 4.4 Discussion on the endogeneity of the production network

One potential concern is that the Maoist insurgency may play a role in shaping the input-output network influencing firms’ location, either by pushing them away or by reducing the likelihood that new firms enter in districts affected by violence. Additionally, there may be a self-selection process occurring in conflict-affected districts. Yet, it is likely that our results are not significantly driven by this possibility. First, as discussed in the Section 2, the Maoist insurgency is a longstanding conflict that has persisted alongside with firms’ activity. Its intensity is relatively low, even following the notable increase of Maoist-related events observed starting from 2004 (right panel of Figure 1). Therefore, compared to the more intense conflicts highlighted in the literature, we expect that, if anything, the impact of conflict on firms’ dynamics will be relatively minor. Second, for each year in our analysis (2000-2009), we observe firm-level location and sales. This allows us to compute a yearly measure at the firm-level of *supplier importance*, $\rho_{jit(k)}$, and subsequently, to construct a yearly representation of the input-output network characterizing the Indian economy, summarized
**Relate network to observable statistics.** We rely on the existing literature and use a “bottom-up” perspective by linking the imputed (not observed) production network to observable outcomes. First, we rely on Acemoglu et al. (2012) who show that the influence vector, which measures how “central” each firm is in the network representation of the economy, coincides with the *sales vector*, i.e., the vector containing firm-specific share of sales over the whole economy. In this perspective, we correlate the influence vector that arises from our measure of the production network and the observed sales vector. For each year, the coefficient of correlation is positive and fairly high, i.e., around 0.5. Moreover, using the Spearman correlation we get a coefficient equal to 0.53 and we reject the null-hypothesis that influence and sales vectors are independent (p-value equal to 0). Finally, regressing the sales vector on the influence vector for a given year (without controlling for any other covariate) yields a $R^2$ that is firmly in the vicinity of 0.24. These findings are consistent with applications based on observed networks. In fact, when we apply this methodology to firm-to-firm transaction data from Gadenne et al. (2019), we obtain similar results: a correlation coefficient of 0.45 and an $R^2$ value of 0.20.

Second, we validate our construction on the input-output network comparing the weighted outdegree distribution associated with 2002 Indian firm level data to that computed with 2002 US sector level shown in Carvalho (2014) (Figure 3). The weighted outdegree of a given firm *i* is defined as the sum over all the weights of the network in which firm *i* appears as an input-supplying firm. This measure ranges from 0 if a firm does not supply inputs to any other firm, to *N* if a single firm is the sole input supplier of every firm in the economy. The shape of the weighted outdegree distribution is comparable to that presented in Carvalho (2014): (i) nearly every firm has an outdegree greater than 0.01, (ii) one-tenth of firms have an outdegree greater than 1, and (iii) about 1 percent of all sectors have an outdegree measure greater than 10, these are producers of “general purpose” manufactured goods like iron and steel or oil.

## 4.4 Discussion on the endogeneity of the production network

One potential concern is that the Maoist insurgency may play a role in shaping the input-output network influencing firms’ location, either by pushing them away or by reducing the likelihood that new firms enter in districts affected by violence. Additionally, there may be a self-selection process occurring in conflict-affected districts. Yet, it is likely that our results are not significantly driven by this possibility. First, as discussed in the Section 2, the Maoist insurgency is a longstanding conflict that has persisted alongside with firms’ activity. Its intensity is relatively low, even following the notable increase of Maoist-related events observed starting from 2004 (right panel of Figure 1). Therefore, compared to the more intense conflicts highlighted in the literature, we expect that, if anything, the impact of conflict on firms’ dynamics will be relatively minor. Second, for each year in our analysis (2000-2009), we observe firm-level location and sales. This allows us to compute a yearly measure at the firm-level of *supplier importance*, $\rho_{jit(k)}$, and subsequently, to construct a yearly representation of the input-output network characterizing the Indian economy, summarized
<figure><img src="image_8.png" /><figcaption>Figure 3: The weighted outdegree distribution associated with 2002 Indian input-output data</figcaption></figure>

Note: This figure replicates Figure 3 in Carvalho (2014) with Indian data. The x-axis gives the weighted outdegree for each sector, presented on a log scale. The y-axis, also in log scale, gives the probability of finding a sector with weighted outdegree larger than or equal to x, that is the empirical counter-cumulative distribution (CCDF). We use 2002 as representative year as Carvalho (2014) (Figure 3). Distributions computed for years 2000, 2001, 2003-2009 are perfectly compatible with this figure.

by the influence vector $v_t$. This implies that, for each year, the configuration of the production network accounts for the potential effects of Maoist activity. Consequently, our results should be interpreted as being *net* of any potential direct impact of conflict on the firm-specific behavior defining the structure of the production network. Nevertheless, we empirically validate the hypothesis that Maoist insurgency is not a first-order determinant of the production network (Tables are relegated to Appendix 8.2).

To capture different district-specific exposure to violence, we define alternative measures for the presence of Maoists. First, we use a dummy that indicates whether a district hosted Maoist violence for at least one year between 1989 and 2009 (labeled as *affected districts*). This definition encompasses 124 out of 553 districts. Second, we refine this measure by distinguishing between districts that hosted Maoist activities both before and after the period in which we observe firms (i.e., before 2000, labeled as *historically affected districts*, accounting for 45 districts) and those that only hosted activities after 2000 (labeled as *newly affected districts*, representing 79 districts). In Online Appendix OA3, we supplement our analysis using measures of intensity of violence, rather than its incidence.

**Conflict & number of firms.** The literature suggests that firms respond to violent shocks by reducing their presence in the affected areas. This is driven both by an increase in firm exit and a
<figure><img src="image_7.png" /><figcaption>Figure 3: The weighted outdegree distribution associated with 2002 Indian input-output data</figcaption></figure>

Note: This figure replicates Figure 3 in Carvalho (2014) with Indian data. The x-axis gives the weighted outdegree for each sector, presented on a log scale. The y-axis, also in log scale, gives the probability of finding a sector with weighted outdegree larger than or equal to x, that is the empirical counter-cumulative distribution (CCDF). We use 2002 as representative year as Carvalho (2014) (Figure 3). Distributions computed for years 2000, 2001, 2003-2009 are perfectly compatible with this figure.

by the influence vector $v_t$. This implies that, for each year, the configuration of the production network accounts for the potential effects of Maoist activity. Consequently, our results should be interpreted as being *net* of any potential direct impact of conflict on the firm-specific behavior defining the structure of the production network. Nevertheless, we empirically validate the hypothesis that Maoist insurgency is not a first-order determinant of the production network (Tables are relegated to Appendix 8.2).

To capture different district-specific exposure to violence, we define alternative measures for the presence of Maoists. First, we use a dummy that indicates whether a district hosted Maoist violence for at least one year between 1989 and 2009 (labeled as *affected districts*). This definition encompasses 124 out of 553 districts. Second, we refine this measure by distinguishing between districts that hosted Maoist activities both before and after the period in which we observe firms (i.e., before 2000, labeled as *historically affected districts*, accounting for 45 districts) and those that only hosted activities after 2000 (labeled as *newly affected districts*, representing 79 districts). In Online Appendix OA3, we supplement our analysis using measures of intensity of violence, rather than its incidence.

**Conflict & number of firms.** The literature suggests that firms respond to violent shocks by reducing their presence in the affected areas. This is driven both by an increase in firm exit and a
decrease in firm entry.<fnref n="25" /> Through different sources of variation (e.g., cross-sectional data at district level, firm-level panel data, etc.), we estimate the impact of Maoist insurgency on the number of firms. Our findings suggest that there are no significant differences in terms of the number of firms between conflict and non-conflict districts, nor conflict incidence in the state where the district is located (Table 4).<fnref n="26" /> These results present a *net effect* and do not preclude for any potential *compensation effect* that may arise, such as if higher exit rates are compensated by higher entry rates.

**Age & size of firms.** If Maoist activity does affect firms’ dynamics, we should observe significant differences in terms of firms’ age and size when comparing firms in conflict and non-conflict districts. Over the 2000-2009 period, at the district level, we compute the yearly (log-) average age (size) and standard deviation of age (size) for firms (Tables 5 and 6). First, we do not find a significant difference in the average age of firms between affected and unaffected districts, but we do observe that firms in newly affected districts tend to be younger. Quantitatively, firms in those districts are, on average, 4% younger than firms in peaceful districts, translating broadly in a difference of less than one year (average age is 16 years). Second, the dispersion of firm’s age is not significantly different across affected and unaffected districts. Regarding firm size, we do not find any significant differences between conflict and peaceful districts.<fnref n="27" />

**Relocation, entry & exit.** Finally, we disentangle the impact of conflict on firms’ dynamics, such as firms’ relocation, entry, and exit. Note, as discussed in Section 4.1, the original ASI data lack both information on district location (that we impute following Martin et al. (2017)), and firm-level panel identifiers that prevents to follow firm overtime as soon as location changes. To overcome this limitation, we follow a six-step procedure to assign every firm a panel-identifier that takes into account their possible mobility, and their actual entry or exit (Bollard et al., 2013).<fnref n="28" /> This procedure

<footnote n="25">²⁵In the short run, Blumenstock et al. (2022) finds that Afghan firms are 5-23% more likely to leave a district in the month after violence, and are 7-16% less likely to enter. Concerning long-term conflict, Camacho et al. (2013) focuses on the Colombian manufacturing sectors and estimate that a one-standard deviation increase of local armed groups attacks increases the probability of firm exit of 0.28 standard deviation. This effect is stronger for younger and smaller firms.</footnote>

<footnote n="26">²⁶Reassuringly, in terms of data quality, the covariates have an expected effect on the number of firms: there are fewer firms in rural districts, and the higher the proportion of employment in the manufacturing sector and the level of development, the higher the number of firms.</footnote>

<footnote n="27">²⁷Alternatively, we consider firm-level as unit of observations and estimate a standard sample mean comparison of firms’ age and size between conflict and non-conflict districts: firms unaffected by Maoist activity are not significantly older (16.89 years vs. 16.53 years), yet significantly smaller (129 employees vs. 165 employees).</footnote>

<footnote n="28">²⁸The six-step procedure is the following: i) we identify the least recent and the most recent year in which a given firm identifier appears in the panel; ii) we assume that firms that are not observed every year between their least and most recent observations did not change location in the unobserved years; iii) for years outside of this range, we match observations based on year-to-year transitions; iv) To account for issues resulting from misreporting of closing or opening values, we create a tolerance interval based on the observed differences between year-to-year closing and opening values of firms that we observe for subsequent years. We compute the difference between closing and opening values for each of the three variables at the firm-level, remove outliers by trimming the top and bottom 1% of the three distributions, and consider two potential intervals for each variable (±1 and ±3 standard deviations); v) we pair observations that match uniquely on all three matching variables, selecting the pair with the smallest bilateral difference on the three matching variables for each set of potential matches; and vi) we assign a unique panel identifier to the matched firm, corresponding to its least recent observation.</footnote>
decrease in firm entry.<fnref n="25" /> Through different sources of variation (e.g., cross-sectional data at district level, firm-level panel data, etc.), we estimate the impact of Maoist insurgency on the number of firms. Our findings suggest that there are no significant differences in terms of the number of firms between conflict and non-conflict districts, nor conflict incidence in the state where the district is located (Table 4).<fnref n="26" /> These results present a *net effect* and do not preclude for any potential *compensation effect* that may arise, such as if higher exit rates are compensated by higher entry rates.

**Age & size of firms.** If Maoist activity does affect firms’ dynamics, we should observe significant differences in terms of firms’ age and size when comparing firms in conflict and non-conflict districts. Over the 2000-2009 period, at the district level, we compute the yearly (log-) average age (size) and standard deviation of age (size) for firms (Tables 5 and 6). First, we do not find a significant difference in the average age of firms between affected and unaffected districts, but we do observe that firms in newly affected districts tend to be younger. Quantitatively, firms in those districts are, on average, 4% younger than firms in peaceful districts, translating broadly in a difference of less than one year (average age is 16 years). Second, the dispersion of firm’s age is not significantly different across affected and unaffected districts. Regarding firm size, we do not find any significant differences between conflict and peaceful districts.<fnref n="27" />

**Relocation, entry & exit.** Finally, we disentangle the impact of conflict on firms’ dynamics, such as firms’ relocation, entry, and exit. Note, as discussed in Section 4.1, the original ASI data lack both information on district location (that we impute following Martin et al. (2017)), and firm-level panel identifiers that prevents to follow firm overtime as soon as location changes. To overcome this limitation, we follow a six-step procedure to assign every firm a panel-identifier that takes into account their possible mobility, and their actual entry or exit (Bollard et al., 2013).<fnref n="28" /> This procedure

<footnote n="25">²⁵In the short run, Blumenstock et al. (2022) finds that Afghan firms are 5-23% more likely to leave a district in the month after violence, and are 7-16% less likely to enter. Concerning long-term conflict, Camacho et al. (2013) focuses on the Colombian manufacturing sectors and estimate that a one-standard deviation increase of local armed groups attacks increases the probability of firm exit of 0.28 standard deviation. This effect is stronger for younger and smaller firms.</footnote>

<footnote n="26">²⁶Reassuringly, in terms of data quality, the covariates have an expected effect on the number of firms: there are fewer firms in rural districts, and the higher the proportion of employment in the manufacturing sector and the level of development, the higher the number of firms.</footnote>

<footnote n="27">²⁷Alternatively, we consider firm-level as unit of observations and estimate a standard sample mean comparison of firms’ age and size between conflict and non-conflict districts: firms unaffected by Maoist activity are not significantly older (16.89 years vs. 16.53 years), yet significantly smaller (129 employees vs. 165 employees).</footnote>

<footnote n="28">²⁸The six-step procedure is the following: i) we identify the least recent and the most recent year in which a given firm identifier appears in the panel; ii) we assume that firms that are not observed every year between their least and most recent observations did not change location in the unobserved years; iii) for years outside of this range, we match observations based on year-to-year transitions; iv) To account for issues resulting from misreporting of closing or opening values, we create a tolerance interval based on the observed differences between year-to-year closing and opening values of firms that we observe for subsequent years. We compute the difference between closing and opening values for each of the three variables at the firm-level, remove outliers by trimming the top and bottom 1% of the three distributions, and consider two potential intervals for each variable (±1 and ±3 standard deviations); v) we pair observations that match uniquely on all three matching variables, selecting the pair with the smallest bilateral difference on the three matching variables for each set of potential matches; and vi) we assign a unique panel identifier to the matched firm, corresponding to its least recent observation.</footnote>
comes at the cost of a reduction in the number of distinct firms we have in the data.<fnref n="29" /> Concerning manufacturing firms’ relocation from 2000 to 2009, we find that very few did: 0.39% of firms located in peaceful districts changed district between 2000 and 2009, and 0.25% moved to another district located in a different state. Concerning firms located in conflict-districts, 0.29% of them moved to another district and 0.19% moved to another district located in a different state.<fnref n="30" /> These magnitudes are in line with the existent, though restricted, literature on firm relocation.<fnref n="31" /> Going further, we estimate a model of relocation choices at the firm level controlling for firm-level costs (wages, interest rates, and input prices), time-varying district characteristics (sector employment and district-level wealth), and district, year and sector fixed effects, respectively. We find that conflict incidence (at the district nor state level) does not affect significantly the decision to change district (Table 7). We also investigate the impact of conflict on a firm’s choice to initiate production in a particular district by constructing an entry-decision model that concentrates on a subset of firms that started operations between 2000 and 2009. None of our conflict incidence indicators show a statistically significant impact on a firm’s entry (Table 7). Finally, we focus on a sub-sample of firms that we observe for at least two subsequent years and we model firm-level decision to exit as a function of conflict incidence both at district and state level. We find no significant relationship between conflict incidence, both at district and state level, and the firm-level decision to exit (Table 7).

**Market structure.** Recent literature suggests that violence diminishes market competition by prompting firm exits (Del Prete et al., 2023). Note that, if conflict-affected firms face fewer competitors, their transmission of conflict-induced distortions to other firms in the production network might be more pronounced than in a more competitive market. We analyze this potential concern in a cross-sectional analysis aimed to compare markets in conflict and non-conflict districts in terms of their level of concentration, by computing a Herfindahl-Hirschman market concentration index at the industry and district level. Overall, we do not find significant differences in market concentration between conflict and non-conflict districts (Table 8).

**Labor vs. capital intensive technologies.** As armed group activity can damage firms’ physical assets, firms located in conflict areas might choose labor-intensive production technologies. Consequently, a certain good produced by firms located in conflict districts is more likely to be produced in a labor-intensive manner compared to if it were produced by firms located in peaceful districts.

<footnote n="29">²⁹Specifically, the dataset we use in this paper includes 187,283 firms. Instead, when we account for relocation number of firms decreases to 177,758, whereas when we account for exit and entry we are left with 60,931 and 41,854 firms, respectively.</footnote>

<footnote n="30">³⁰Looking at the history of Maoist activity, a similar pattern emerges: 0.27% of firms located at least one year in districts affected by Maoist activity before our time-span of interest moved, 0.24% for firms located at least one year in districts affected by Maoist activity before 2000 and between 2000 and 2009, and 0.26% for firms located at least one year in districts affected by Maoist activity between 2000 and 2009.</footnote>

<footnote n="31">³¹For OECD countries, Hospers (2011) and Conroy et al. (2016) find a low propensity of firms to relocate, which is even lower for firms belonging to the manufacturing sector (Pellenbarg et al., 2002). Concerning developing countries, the effects of conflict on firm performance identified by Del Prete et al. (2023) in the Libyan context remain unchanged if they exclude movers from their sample. We are grateful to Michele Di Maio for sharing the following information: In their sample of almost 390 Libyan firms, 66 of them have relocated due to conflict. Among the movers, only 5 firms belong to the manufacturing sector. Moreover, nearly 80% of the movers relocated within the same town.</footnote>
comes at the cost of a reduction in the number of distinct firms we have in the data.<fnref n="29" /> Concerning manufacturing firms’ relocation from 2000 to 2009, we find that very few did: 0.39% of firms located in peaceful districts changed district between 2000 and 2009, and 0.25% moved to another district located in a different state. Concerning firms located in conflict-districts, 0.29% of them moved to another district and 0.19% moved to another district located in a different state.<fnref n="30" /> These magnitudes are in line with the existent, though restricted, literature on firm relocation.<fnref n="31" /> Going further, we estimate a model of relocation choices at the firm level controlling for firm-level costs (wages, interest rates, and input prices), time-varying district characteristics (sector employment and district-level wealth), and district, year and sector fixed effects, respectively. We find that conflict incidence (at the district nor state level) does not affect significantly the decision to change district (Table 7). We also investigate the impact of conflict on a firm’s choice to initiate production in a particular district by constructing an entry-decision model that concentrates on a subset of firms that started operations between 2000 and 2009. None of our conflict incidence indicators show a statistically significant impact on a firm’s entry (Table 7). Finally, we focus on a sub-sample of firms that we observe for at least two subsequent years and we model firm-level decision to exit as a function of conflict incidence both at district and state level. We find no significant relationship between conflict incidence, both at district and state level, and the firm-level decision to exit (Table 7).

**Market structure.** Recent literature suggests that violence diminishes market competition by prompting firm exits (Del Prete et al., 2023). Note that, if conflict-affected firms face fewer competitors, their transmission of conflict-induced distortions to other firms in the production network might be more pronounced than in a more competitive market. We analyze this potential concern in a cross-sectional analysis aimed to compare markets in conflict and non-conflict districts in terms of their level of concentration, by computing a Herfindahl-Hirschman market concentration index at the industry and district level. Overall, we do not find significant differences in market concentration between conflict and non-conflict districts (Table 8).

**Labor vs. capital intensive technologies.** As armed group activity can damage firms’ physical assets, firms located in conflict areas might choose labor-intensive production technologies. Consequently, a certain good produced by firms located in conflict districts is more likely to be produced in a labor-intensive manner compared to if it were produced by firms located in peaceful districts.

<footnote n="29">²⁹Specifically, the dataset we use in this paper includes 187,283 firms. Instead, when we account for relocation number of firms decreases to 177,758, whereas when we account for exit and entry we are left with 60,931 and 41,854 firms, respectively.</footnote>

<footnote n="30">³⁰Looking at the history of Maoist activity, a similar pattern emerges: 0.27% of firms located at least one year in districts affected by Maoist activity before our time-span of interest moved, 0.24% for firms located at least one year in districts affected by Maoist activity before 2000 and between 2000 and 2009, and 0.26% for firms located at least one year in districts affected by Maoist activity between 2000 and 2009.</footnote>

<footnote n="31">³¹For OECD countries, Hospers (2011) and Conroy et al. (2016) find a low propensity of firms to relocate, which is even lower for firms belonging to the manufacturing sector (Pellenbarg et al., 2002). Concerning developing countries, the effects of conflict on firm performance identified by Del Prete et al. (2023) in the Libyan context remain unchanged if they exclude movers from their sample. We are grateful to Michele Di Maio for sharing the following information: In their sample of almost 390 Libyan firms, 66 of them have relocated due to conflict. Among the movers, only 5 firms belong to the manufacturing sector. Moreover, nearly 80% of the movers relocated within the same town.</footnote>
We perform a cross-sectional analysis to compare conflict and non-conflict districts in terms of the adoption of labor or capital intensive technologies. We aggregate our data at product level (5-digit of ASICC classification) and district level to compute the share of the producers for which the production involves higher labor costs than capital costs. 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 9). If anything, these findings suggest that the Maoist insurgency has not compelled firms to adopt less sophisticated production processes.

In the Online Appendix OA3, we replicate these results by focusing on several measures of violence intensity. Our conclusions remain unchanged. Overall, this battery of exercises provides evidence that firms’ characteristics and dynamics, together with market structure do not significantly correlate with conflict incidence. When they do, the effects are quantitatively small. Nonetheless, in Section 5.2, we explore empirically the implications that network adjustment due to conflict would have on the total output loss. We still find evidence of significant propagation effects due to conflict-induced distortions and network-adjustment costs.

# 5 The Aggregate Output Loss due to Conflict

In this section, we apply the theoretical model presented in Section 3 to the data. Our goal is to measure the output loss of the entire Indian economy due to the Maoist insurgency. We consider in turn three possible mechanisms through which localized conflict can affect the performance of firms not located in conflict-affected districts. In particular, buyers that purchase inputs from suppliers in conflict-affected districts have three alternatives: (i) to continue buying inputs from the supplier affected by conflict (*inaction*), (ii) to buy the same inputs from a supplier located in a peaceful district (*supplier change*), or (iii) to modify their input bundle in order to purchase different inputs from suppliers in peaceful districts (*input bundle change*). Moreover, we examine a scenario in which Maoist groups target their attacks against firms according to their size. Finally, we develop a structural estimation strategy aimed to provide explicit estimates of conflict-induced distortions and, in turn, the corresponding loss for the Indian economy. We consider both *state-specific* and *product-specific* conflict-induced distortions.

## 5.1 Aggregate output loss: Baseline results

The aim of our analysis is to measure the loss in aggregate output of the manufacturing sector suffered by the Indian economy due to the Maoist insurgency between 2000 and 2009. For each year, we quantify the loss as the product of the annual conflict cost vector ($\xi_t$) and the annual influence
We perform a cross-sectional analysis to compare conflict and non-conflict districts in terms of the adoption of labor or capital intensive technologies. We aggregate our data at product level (5-digit of ASICC classification) and district level to compute the share of the producers for which the production involves higher labor costs than capital costs. 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 9). If anything, these findings suggest that the Maoist insurgency has not compelled firms to adopt less sophisticated production processes.

In the Online Appendix OA3, we replicate these results by focusing on several measures of violence intensity. Our conclusions remain unchanged. Overall, this battery of exercises provides evidence that firms’ characteristics and dynamics, together with market structure do not significantly correlate with conflict incidence. When they do, the effects are quantitatively small. Nonetheless, in Section 5.2, we explore empirically the implications that network adjustment due to conflict would have on the total output loss. We still find evidence of significant propagation effects due to conflict-induced distortions and network-adjustment costs.

# 5 The Aggregate Output Loss due to Conflict

In this section, we apply the theoretical model presented in Section 3 to the data. Our goal is to measure the output loss of the entire Indian economy due to the Maoist insurgency. We consider in turn three possible mechanisms through which localized conflict can affect the performance of firms not located in conflict-affected districts. In particular, buyers that purchase inputs from suppliers in conflict-affected districts have three alternatives: (i) to continue buying inputs from the supplier affected by conflict (*inaction*), (ii) to buy the same inputs from a supplier located in a peaceful district (*supplier change*), or (iii) to modify their input bundle in order to purchase different inputs from suppliers in peaceful districts (*input bundle change*). Moreover, we examine a scenario in which Maoist groups target their attacks against firms according to their size. Finally, we develop a structural estimation strategy aimed to provide explicit estimates of conflict-induced distortions and, in turn, the corresponding loss for the Indian economy. We consider both *state-specific* and *product-specific* conflict-induced distortions.

## 5.1 Aggregate output loss: Baseline results

The aim of our analysis is to measure the loss in aggregate output of the manufacturing sector suffered by the Indian economy due to the Maoist insurgency between 2000 and 2009. For each year, we quantify the loss as the product of the annual conflict cost vector ($\xi_t$) and the annual influence
vector ($v_t$), which is derived from the matrix defining the production network ($\Omega_t$, Equation 7).<fnref n="32" /> Unless otherwise stated, to ensure consistency across our exercises, we assume that all firms within an affected district experience the same level of distortion, and that this distortion is uniform across all affected districts. This assumption implies linearity in the corresponding loss in aggregate output; for instance, if the imputed conflict-induced distortion doubles, the loss in aggregate output will also double. We proceed by setting the conflict-induced distortion (T) to 0.01. This allows us to present the results as the change of total output (in percent) associated with a one percentage point increase in conflict-induced distortion.<fnref n="33" />

In Panel A of Table 2 we present the results of our baseline specification. Our findings indicate that a one percentage point increase in conflict-induced distortion is associated with an average annual decrease of 0.236% in aggregate output, translating to an average annual GDP loss of 0.035% (the manufacturing sector contributes 15% to India's GDP (Kapoor, 2018)). This represents a monetary average yearly loss of 438 million USD.<fnref n="34" /> Remarkably, our central finding, independent of the estimation of the conflict-induced distortion, is that only 27% of the loss can be attributed to the direct impact of conflict in the affected districts, while the remaining 73% of the loss depends on the spread of a conflict's effects to peaceful districts by way of the production network. By omitting the propagation mechanism, the aggregate impact of conflict would be significantly underestimated: a one percentage point increase of T would correspond to an average decrease of aggregate output equal to 0.064%.

**Alternative production function.** Our baseline results rely on the assumption that firm-level intermediate inputs enter into firm-level production functions as a Cobb-Douglas aggregate of good $k$, $k = N$. This implies that the elasticity of substitution between any version of input $k$ (produced by different suppliers $j \in J$) is equal to 1. We relax this assumption and assume that each input $k$, produced by a set of $J$ producers, is a CES aggregate with elasticity of substitution $\sigma > 1$. We simulate the average output loss by letting $\sigma$ vary from $1 + \epsilon$ (with $\epsilon$ close to 0 up to 7). We find that the average annual loss decreases as the elasticity of substitution $\sigma$ increases (Appendix 8.4). Regardless the value of conflict induced distortion $T$, the loss declines rapidly for $1 < \sigma < 3$ (for example, if $\sigma = 2$ the average annual loss is halved). Then, it converges to a constant where it remains stable.

<footnote n="32">³²The inverse of the Leontief matrix, $[I - (1 - \alpha)\Omega_t']^{-1}$, is calculated using the estimated weight $\alpha$ ($\hat{\alpha} = 0.28$) obtained from the estimation of the firm's production function with firm-level data following Wooldridge (2009).</footnote>

<footnote n="33">³³Note that, as explained in Section 4, in each year $t$, the entries of the input-output matrix $\Omega_t$, which is used to quantify the aggregate loss, are the observed input shares of firm $i$, i.e., $\omega_{kit}$, weighted by $\rho_{jit(k)}$, the importance of every possible supplier $j$ of $k$. The calculation of the former component, $\rho_{jit(k)}$, assumes that the elasticity of bilateral trade with respect to distance is equal to -1. However, its actual value might of course be less than that. Therefore, we calculate four versions of input-output matrix $\Omega_t$, in which $\rho_{jit(k)}$ is measured assuming an elasticity of trade with respect to distance equal to -2, -3, -4, and -5, respectively. The corresponding estimates of aggregate output loss are not substantially different from our baseline results (Panel B of Table 10 Appendix 8.3). Moreover, we estimate the aggregate output loss using an alternative specification of $\rho_{jit(k)}$, which combines the relative inverse distance between each buyer-supplier pair and the relative size of each potential supplier in a non-linear way as detailed in footnote 21. The estimated average output loss does not differ from the baseline ones (Panel B of Table 10 Appendix 8.3).</footnote>

<footnote n="34">³⁴These results are derived from data on value added in Indian manufacturing (constant 2010 USD) obtained from the World Bank Development Indicators (World Bank, 2021).</footnote>
vector ($v_t$), which is derived from the matrix defining the production network ($\Omega_t$, Equation 7).<fnref n="32" /> Unless otherwise stated, to ensure consistency across our exercises, we assume that all firms within an affected district experience the same level of distortion, and that this distortion is uniform across all affected districts. This assumption implies linearity in the corresponding loss in aggregate output; for instance, if the imputed conflict-induced distortion doubles, the loss in aggregate output will also double. We proceed by setting the conflict-induced distortion (T) to 0.01. This allows us to present the results as the change of total output (in percent) associated with a one percentage point increase in conflict-induced distortion.<fnref n="33" />

In Panel A of Table 2 we present the results of our baseline specification. Our findings indicate that a one percentage point increase in conflict-induced distortion is associated with an average annual decrease of 0.236% in aggregate output, translating to an average annual GDP loss of 0.035% (the manufacturing sector contributes 15% to India's GDP (Kapoor, 2018)). This represents a monetary average yearly loss of 438 million USD.<fnref n="34" /> Remarkably, our central finding, independent of the estimation of the conflict-induced distortion, is that only 27% of the loss can be attributed to the direct impact of conflict in the affected districts, while the remaining 73% of the loss depends on the spread of a conflict's effects to peaceful districts by way of the production network. By omitting the propagation mechanism, the aggregate impact of conflict would be significantly underestimated: a one percentage point increase of T would correspond to an average decrease of aggregate output equal to 0.064%.

**Alternative production function.** Our baseline results rely on the assumption that firm-level intermediate inputs enter into firm-level production functions as a Cobb-Douglas aggregate of good $k$, $k = N$. This implies that the elasticity of substitution between any version of input $k$ (produced by different suppliers $j \in J$) is equal to 1. We relax this assumption and assume that each input $k$, produced by a set of $J$ producers, is a CES aggregate with elasticity of substitution $\sigma > 1$. We simulate the average output loss by letting $\sigma$ vary from $1 + \epsilon$ (with $\epsilon$ close to 0 up to 7). We find that the average annual loss decreases as the elasticity of substitution $\sigma$ increases (Appendix 8.4). Regardless the value of conflict induced distortion $T$, the loss declines rapidly for $1 < \sigma < 3$ (for example, if $\sigma = 2$ the average annual loss is halved). Then, it converges to a constant where it remains stable.

<footnote n="32">³²The inverse of the Leontief matrix, $[I - (1 - \alpha)\Omega_t']^{-1}$, is calculated using the estimated weight $\alpha$ ($\hat{\alpha} = 0.28$) obtained from the estimation of the firm's production function with firm-level data following Wooldridge (2009).</footnote>

<footnote n="33">³³Note that, as explained in Section 4, in each year $t$, the entries of the input-output matrix $\Omega_t$, which is used to quantify the aggregate loss, are the observed input shares of firm $i$, i.e., $\omega_{kit}$, weighted by $\rho_{jit(k)}$, the importance of every possible supplier $j$ of $k$. The calculation of the former component, $\rho_{jit(k)}$, assumes that the elasticity of bilateral trade with respect to distance is equal to -1. However, its actual value might of course be less than that. Therefore, we calculate four versions of input-output matrix $\Omega_t$, in which $\rho_{jit(k)}$ is measured assuming an elasticity of trade with respect to distance equal to -2, -3, -4, and -5, respectively. The corresponding estimates of aggregate output loss are not substantially different from our baseline results (Panel B of Table 10 Appendix 8.3). Moreover, we estimate the aggregate output loss using an alternative specification of $\rho_{jit(k)}$, which combines the relative inverse distance between each buyer-supplier pair and the relative size of each potential supplier in a non-linear way as detailed in footnote 21. The estimated average output loss does not differ from the baseline ones (Panel B of Table 10 Appendix 8.3).</footnote>

<footnote n="34">³⁴These results are derived from data on value added in Indian manufacturing (constant 2010 USD) obtained from the World Bank Development Indicators (World Bank, 2021).</footnote>
**Alternative network construction method.** The methodology we develop to construct the yearly production network relies on the universe of producers within the sample of firms that appear in the ASI database. This sampling may omit some medium-sized firms and, in turn, might make our method overlook potential buyer-supplier links. Given the data available, we make an effort to gauge both the direction and magnitude of this potential bias. We devise an alternative methodology to construct the production network, aiming to maximize the number of firms observed annually. This alternative specification pulls all the ASI cross-sections together and provides a *time-invariant* production network. Specifically, as described in Section 4.2, the components needed to set up the production network are input share ($\omega_{ji}$), buyer-supplier bilateral distance, and potential supplier size, which in turn are needed to compute supplier importance ($\rho_{ji(k)}$). As this production network is *time-invariant*, we impute the values of input share related to the first occurrence of each potential buyer-supplier link. For comparison's sake, we 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.

The countervailing mechanisms that are in play depend on where the higher number of firms captured by the *time-invariant* network is located: (i) these firms are mainly located in conflict-affected districts, thus more firms are spreading conflict-induced distortions; and (ii) these firms are located mainly in peaceful districts, are linked to conflict-affected firms, and the value of their influence vector is high. When comparing the *time-invariant* and *time-varying* networks, the estimation implies that incorporating more firms into the network results in an estimated loss that is 10% higher (Table 10, Panel C). The result suggests that mechanism (i) is more pronounced — with the *time-invariant* network, on average, we observe 12% more firms in conflict zones compared to the alternative scenario. This indicates that our baseline results are somewhat biased downward representing a conservative estimate of the true output loss.

## 5.2 Mechanisms

**Inaction effect.** We compute the yearly output loss in the scenario in which conflict does not lead to any network adjustment. In this case, producers do not change their suppliers and thus bear the indirect costs of conflict. To quantify the annual output loss, we assume that the initial network structure (i.e., the input-output matrix for the year 2000) does not change over time, while allowing the direct exposure to conflict to change over time according to the data. In practice, we estimate the annual output loss as follows (Equation 9): $\Delta\mathbf{Y}_t = v'_{2000} \times \xi_t$ with $t = [2000, ..., 2009]$ where $v_{2000}$ is the influence vector calculated for 2000 and $\xi_t$ contains the conflict-induced distortions for the period 2000-2009.<fnref n="35" />

The average annual loss is calculated to be approximately 11% higher than the baseline one. Specifically, a one percentage point increase in the conflict-induced distortion $T$ implies an average annual decrease

<footnote n="35">³⁵For the sake of precision, we add firms observed starting from year 2001 to $v_{2000}$. We adopt this strategy in order to maximize the number of firms tracked over time.</footnote>
**Alternative network construction method.** The methodology we develop to construct the yearly production network relies on the universe of producers within the sample of firms that appear in the ASI database. This sampling may omit some medium-sized firms and, in turn, might make our method overlook potential buyer-supplier links. Given the data available, we make an effort to gauge both the direction and magnitude of this potential bias. We devise an alternative methodology to construct the production network, aiming to maximize the number of firms observed annually. This alternative specification pulls all the ASI cross-sections together and provides a *time-invariant* production network. Specifically, as described in Section 4.2, the components needed to set up the production network are input share ($\omega_{ji}$), buyer-supplier bilateral distance, and potential supplier size, which in turn are needed to compute supplier importance ($\rho_{ji(k)}$). As this production network is *time-invariant*, we impute the values of input share related to the first occurrence of each potential buyer-supplier link. For comparison's sake, we 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.

The countervailing mechanisms that are in play depend on where the higher number of firms captured by the *time-invariant* network is located: (i) these firms are mainly located in conflict-affected districts, thus more firms are spreading conflict-induced distortions; and (ii) these firms are located mainly in peaceful districts, are linked to conflict-affected firms, and the value of their influence vector is high. When comparing the *time-invariant* and *time-varying* networks, the estimation implies that incorporating more firms into the network results in an estimated loss that is 10% higher (Table 10, Panel C). The result suggests that mechanism (i) is more pronounced — with the *time-invariant* network, on average, we observe 12% more firms in conflict zones compared to the alternative scenario. This indicates that our baseline results are somewhat biased downward representing a conservative estimate of the true output loss.

## 5.2 Mechanisms

**Inaction effect.** We compute the yearly output loss in the scenario in which conflict does not lead to any network adjustment. In this case, producers do not change their suppliers and thus bear the indirect costs of conflict. To quantify the annual output loss, we assume that the initial network structure (i.e., the input-output matrix for the year 2000) does not change over time, while allowing the direct exposure to conflict to change over time according to the data. In practice, we estimate the annual output loss as follows (Equation 9): $\Delta\mathbf{Y}_t = v'_{2000} \times \xi_t$ with $t = [2000, ..., 2009]$ where $v_{2000}$ is the influence vector calculated for 2000 and $\xi_t$ contains the conflict-induced distortions for the period 2000-2009.<fnref n="35" />

The average annual loss is calculated to be approximately 11% higher than the baseline one. Specifically, a one percentage point increase in the conflict-induced distortion $T$ implies an average annual decrease

<footnote n="35">³⁵For the sake of precision, we add firms observed starting from year 2001 to $v_{2000}$. We adopt this strategy in order to maximize the number of firms tracked over time.</footnote>
<table><caption>Table 2: Output Loss in Different Scenarios</caption><thead><tr><th></th><th>(1)<br>Loss (in %)</th><th>(2)<br>GDP decline (in %)</th><th>(3)<br>Loss (mln. USD)</th></tr></thead><tbody><tr><td colspan="4"><strong>Panel A</strong></td></tr><tr><td></td><td>-0.236%</td><td>-0.035%</td><td>438 mln. USD</td></tr><tr><td colspan="4"><strong>Panel B</strong></td></tr><tr><td></td><td>-0.263%</td><td>-0.039%</td><td>489 mln. USD</td></tr><tr><td>Inaction</td><td>-0.212%</td><td>-0.032%</td><td>394 mln. USD</td></tr><tr><td>Supplier change</td><td>-0.163%</td><td>-0.024%</td><td>304 mln. USD</td></tr><tr><td>Supplier and Input Bundle Change (no costs)</td><td>-0.165%</td><td>-0.025%</td><td>307 mln. USD</td></tr><tr><td colspan="4"><strong>Panel C</strong></td></tr><tr><td></td><td>-0.364%</td><td>-0.055%</td><td>677 mln. USD</td></tr><tr><td>Spread to adjacent districts</td><td>-0.172%</td><td>-0.026%</td><td>320 mln. USD</td></tr></tbody></table>

Note: This table reports our estimates of output loss computed in for different scenarios: (i) baseline (Panel A); (ii) network mechanism such as inaction effect, supplier change effect, supplier change and input bundle change effects (Panel B); (iii) policy experiments that include conflict spread to adjacent districts, supplier change and input bundle change effects with costly network adjustment (Panel C). For each specification, in column (1) we report the average yearly output loss in percent associated with a one percentage point increase in the conflict-induced distortion T, column (2) gives the corresponding annual GDP decline, column (3) translates the loss million USD.

in aggregate output of 0.263%. This is equivalent to an average yearly monetary loss of 489 million USD (Table 2, Panel B). These results suggest that allowing for network adjustment (even if only partial) through supplier change and input bundle change reduces output loss.

**Supplier change & input bundle change effects.** Relaxing the assumption of the absence of reshuffling of the production network, we now allow firms to substitute suppliers of inputs located in conflict-affected districts with suppliers of those same inputs located in peaceful districts (the *supplier change effect*). To simulate this network adjustment, we construct an alternative input-output matrix $\tilde{\Omega}_t$ where buyers do not purchase inputs from suppliers located in districts that have been affected by conflict for two or more years. In other words, allowing for network adjustment implies that, at $t+1$, we force every buyer to change its supplier as long as that supplier has been impacted by conflict at both $t+1$ and $t$. We then consider three alternative scenarios: (i) the network adjustment involves only the *supplier change effect*; (ii) the network adjustment involves both the *supplier change effect* and the *input bundle change effect*; and (iii) the network adjustment involves some costs.

In the first scenario, as we want to rule out the *input bundle change effect*, we calculate the annual output loss as the product of the influence vector derived from the adjusted input-output matrix for 2000, i.e., $\tilde{v}_{2000}$ from $\tilde{\Omega}_{2000}$ (which we construct using conflict data prior to 2000), and the conflict-cost vector $\xi_t$ for the period 2000-2009. We rely on the assumption that the input-output matrix characterizing the Indian economy remains unchanged from 2000 until 2009. Unfortunately, we are not able to fully rule out the *inaction effect* because if all the suppliers of a given input are located in
<table><caption>Table 2: Output Loss in Different Scenarios</caption><thead><tr><th></th><th>(1)<br>Loss (in %)</th><th>(2)<br>GDP decline (in %)</th><th>(3)<br>Loss (mln. USD)</th></tr></thead><tbody><tr><td colspan="4"><strong>Panel A</strong></td></tr><tr><td></td><td>-0.236%</td><td>-0.035%</td><td>438 mln. USD</td></tr><tr><td colspan="4"><strong>Panel B</strong></td></tr><tr><td></td><td>-0.263%</td><td>-0.039%</td><td>489 mln. USD</td></tr><tr><td>Inaction</td><td>-0.212%</td><td>-0.032%</td><td>394 mln. USD</td></tr><tr><td>Supplier change</td><td>-0.163%</td><td>-0.024%</td><td>304 mln. USD</td></tr><tr><td>Supplier and Input Bundle Change (no costs)</td><td>-0.165%</td><td>-0.025%</td><td>307 mln. USD</td></tr><tr><td>Supplier and Input Bundle Change (with costs)</td><td>-0.165%</td><td>-0.025%</td><td>307 mln. USD</td></tr><tr><td colspan="4"><strong>Panel C</strong></td></tr><tr><td></td><td>-0.364%</td><td>-0.055%</td><td>677 mln. USD</td></tr><tr><td>Spread to adjacent districts</td><td>-0.172%</td><td>-0.026%</td><td>320 mln. USD</td></tr><tr><td>Trade facilitation peaceful districts</td><td>-0.172%</td><td>-0.026%</td><td>320 mln. USD</td></tr></tbody></table>

Note: This table reports our estimates of output loss computed in for different scenarios: (i) baseline (Panel A); (ii) network mechanism such as inaction effect, supplier change effect, supplier change and input bundle change effects (Panel B); (iii) policy experiments that include conflict spread to adjacent districts, supplier change and input bundle change effects with costly network adjustment (Panel C). For each specification, in column (1) we report the average yearly output loss in percent associated with a one percentage point increase in the conflict-induced distortion T, column (2) gives the corresponding annual GDP decline, column (3) translates the loss million USD.

in aggregate output of 0.263%. This is equivalent to an average yearly monetary loss of 489 million USD (Table 2, Panel B). These results suggest that allowing for network adjustment (even if only partial) through supplier change and input bundle change reduces output loss.

**Supplier change & input bundle change effects.** Relaxing the assumption of the absence of reshuffling of the production network, we now allow firms to substitute suppliers of inputs located in conflict-affected districts with suppliers of those same inputs located in peaceful districts (the *supplier change effect*). To simulate this network adjustment, we construct an alternative input-output matrix $\tilde{\Omega}_t$ where buyers do not purchase inputs from suppliers located in districts that have been affected by conflict for two or more years. In other words, allowing for network adjustment implies that, at $t+1$, we force every buyer to change its supplier as long as that supplier has been impacted by conflict at both $t+1$ and $t$. We then consider three alternative scenarios: (i) the network adjustment involves only the *supplier change effect*; (ii) the network adjustment involves both the *supplier change effect* and the *input bundle change effect*; and (iii) the network adjustment involves some costs.

In the first scenario, as we want to rule out the *input bundle change effect*, we calculate the annual output loss as the product of the influence vector derived from the adjusted input-output matrix for 2000, i.e., $\tilde{v}_{2000}$ from $\tilde{\Omega}_{2000}$ (which we construct using conflict data prior to 2000), and the conflict-cost vector $\xi_t$ for the period 2000-2009. We rely on the assumption that the input-output matrix characterizing the Indian economy remains unchanged from 2000 until 2009. Unfortunately, we are not able to fully rule out the *inaction effect* because if all the suppliers of a given input are located in
conflict-affected districts, then there is no possibility for adjustment. The resulting average annual loss is approximately 10% lower than the baseline: a one percentage point increase in T implies an average annual decrease of total output of 0.0212%, which corresponds to a monetary loss of 394 million USD (Table 2, Panel B).

The second scenario of network adjustment encompasses both the *supplier change* effect and *input bundle change* effect. We calculate the annual loss as the product of the annual adjusted influence vector ($\tilde{v}_t$) and the annual cost-of-conflict vector ($\xi_t$). The results suggest that allowing buyers to switch suppliers or to alter their input bundles, and assuming that these adjustments do not involve any costs, reduces the output loss substantially. Indeed, the average annual loss is now 30% lower than the baseline: a one percentage point increase in T corresponds to an average output loss of 0.163%, equivalent to 304 million USD (Table 2, Panel B). Finally, we consider the scenario in which network adjustment involves some costs, which depend, for example, on firms having to establish new trade relationships with new suppliers, i.e., the annual cost-of-conflict vector translates into $\xi_t + \lambda_t$ where $\lambda_t$ represents the adjustment costs. We assume that these costs increase with the distance between every firm and the new pool of potential suppliers, and with the importance that the supplier that was previously discarded had for the firm. In this perspective, we build a cost vector multiplying two components: (i) a distance component measured as the inverse of the difference between the change in average distance between a buyer $i$ and all of its possible suppliers $j$ occurred because of network adjustment; (ii) a supplier-importance component measured with the value of the parameter $\rho_{jit(k)}$ corresponding to the suppliers whose link have been removed because of conflict adjustment.<fnref n="36" /> Our results suggest that even allowing for potential costs, network adjustment helps significantly reducing the output loss. Indeed, the cumulative loss is now 25% lower than the baseline (Table 2, Panel B). These results suggest that encouraging network adjustment is key to alleviate the output loss due to conflict.

## 5.3 Strategic targeting of Maoist attacks: the role of firms’ size

So far, we have assumed that all firms in a district are equally affected by the conflict. We depart from this hypothesis considering a scenario where conflict-induced distortions depend on firm-specific characteristics. In practice, we assume that Maoist groups target their attacks against specific groups of firms based on firms’ size.<fnref n="37" /> We let the proportion of firms located in a conflict-affected district that are affected by the conflict to vary between 10% and 90% (i.e., we allow for $\alpha \in [0.1, 0.9]$, whereas in Section 5.1 we assume that $\alpha = 1$). We also assume the Maoist targeting rule is correlated with firm size (measured by number of employees) and divide firms into two categories according to the

<footnote n="36">³⁶Concerning the second component of the cost, for firms that have discarded more than one supplier, we take the maximum value of $\rho_{jit(k)}$.</footnote>

<footnote n="37">³⁷Yet, Maoist groups are likely to target composite groups of firms that are not strictly based on their size, but also on other characteristics, e.g., ideology (Ramana, 2018). This is the best we can do with our data, as we do not observe whether and in what way firms are exposed to Maoist activity.</footnote>
conflict-affected districts, then there is no possibility for adjustment. The resulting average annual loss is approximately 10% lower than the baseline: a one percentage point increase in T implies an average annual decrease of total output of 0.0212%, which corresponds to a monetary loss of 394 million USD (Table 2, Panel B).

The second scenario of network adjustment encompasses both the *supplier change* effect and *input bundle change* effect. We calculate the annual loss as the product of the annual adjusted influence vector ($\tilde{v}_t$) and the annual cost-of-conflict vector ($\xi_t$). The results suggest that allowing buyers to switch suppliers or to alter their input bundles, and assuming that these adjustments do not involve any costs, reduces the output loss substantially. Indeed, the average annual loss is now 30% lower than the baseline: a one percentage point increase in T corresponds to an average output loss of 0.163%, equivalent to 304 million USD (Table 2, Panel B). Finally, we consider the scenario in which network adjustment involves some costs, which depend, for example, on firms having to establish new trade relationships with new suppliers, i.e., the annual cost-of-conflict vector translates into $\xi_t + \lambda_t$ where $\lambda_t$ represents the adjustment costs. We assume that these costs increase with the distance between every firm and the new pool of potential suppliers, and with the importance that the supplier that was previously discarded had for the firm. In this perspective, we build a cost vector multiplying two components: (i) a distance component measured as the inverse of the difference between the change in average distance between a buyer $i$ and all of its possible suppliers $j$ occurred because of network adjustment; (ii) a supplier-importance component measured with the value of the parameter $\rho_{jit(k)}$ corresponding to the suppliers whose link have been removed because of conflict adjustment.<fnref n="36" /> Our results suggest that even allowing for potential costs, network adjustment helps significantly reducing the output loss. Indeed, the cumulative loss is now 25% lower than the baseline (Table 2, Panel B). These results suggest that encouraging network adjustment is key to alleviate the output loss due to conflict.

## 5.3 Strategic targeting of Maoist attacks: the role of firms’ size

So far, we have assumed that all firms in a district are equally affected by the conflict. We depart from this hypothesis considering a scenario where conflict-induced distortions depend on firm-specific characteristics. In practice, we assume that Maoist groups target their attacks against specific groups of firms based on firms’ size.<fnref n="37" /> We let the proportion of firms located in a conflict-affected district that are affected by the conflict to vary between 10% and 90% (i.e., we allow for $\alpha \in [0.1, 0.9]$, whereas in Section 5.1 we assume that $\alpha = 1$). We also assume the Maoist targeting rule is correlated with firm size (measured by number of employees) and divide firms into two categories according to the

<footnote n="36">³⁶Concerning the second component of the cost, for firms that have discarded more than one supplier, we take the maximum value of $\rho_{jit(k)}$.</footnote>

<footnote n="37">³⁷Yet, Maoist groups are likely to target composite groups of firms that are not strictly based on their size, but also on other characteristics, e.g., ideology (Ramana, 2018). This is the best we can do with our data, as we do not observe whether and in what way firms are exposed to Maoist activity.</footnote>
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 conflict-induced distortions*.

<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>