The Impact of Wars, Natural Disasters, and Pandemics on Global Production Networks: Transmission of Monetary and Fiscal Shocks across Multi-Tier Supply Chains

 Case-cum-Research Paper 

The Impact of Wars, Natural Disasters, and Pandemics on Global Production Networks: Transmission of Monetary and Fiscal Shocks across Multi-Tier Supply Chains

Abstract

Global production networks (GPNs) have become increasingly vulnerable to external disruptions such as wars, natural disasters, pandemics, and macroeconomic shocks. This paper examines:

1. How wars, natural disasters, and pandemics disrupt production networks,
2. How monetary and fiscal shocks propagate through multi-tier supply chains, and
3. How shock transmission differs across sectors such as semiconductors, rare earth minerals, automotive, logistics, and pharmaceuticals.

Using case-based evidence (Russia–Ukraine conflict, Hurricane Milton, COVID-19 disruptions) and data from multi-tier mapping platforms (Z2Data, Sourcemap, GSCIP, Resilinc), we analyze network effects using Input–Output (I–O) linkages and Production Network Models (Acemoglu et al., 2012; Baqaee & Farhi, 2020). The study proposes an empirically testable framework using shock simulations, firm-level transactional data, and network centrality metrics. Results highlight the amplification of shocks through highly interconnected hubs, the disproportionate vulnerability of Tier 2–Tier 4 suppliers, and the critical role of liquidity, inventories, and supplier diversification in building resilience.

 Keywords

Global Production Networks, Supply Chain Disruptions, Russia–Ukraine War, Natural Disasters, COVID-19 Pandemic, Monetary Shocks, Fiscal Shocks, Input–Output Networks, Semiconductors, Rare Earth Minerals, Automotive, Multi-Tier Supply Chains.

 

1. Introduction

Modern production networks span thousands of firms across multiple tiers, creating efficient but fragile systems. Over the last decade, shocks from wars, natural disasters, pandemics, and macroeconomic volatility have tested these networks severely.

• Wars disrupt trade corridors, energy flows, and raw materials critical for electronics and automotive industries.
• Natural disasters damage ports, logistics nodes, and transportation pathways, amplifying shortages via JIT (Just-in-Time) dependency.
• Pandemics trigger global shutdowns, workforce shortages, and systemic uncertainties in critical sectors like pharmaceuticals and semiconductors.
• Monetary and fiscal shocks propagate through supply chains, altering production costs, liquidity constraints, and price stickiness at multiple tiers.

This study synthesizes theory and case evidence to understand how these shocks propagate and reshape global production networks.

 

2. Literature Review

2.1 Production Networks and Shock Propagation

• Acemoglu et al. (2012) show that microeconomic shocks can have macroeconomic effects when they hit highly central industries.
• Carvalho (2014) explains amplification through input complementarities.
• Baqaee & Farhi (2020) analyze non-linear propagation and role of bottlenecks.

2.2 Shocks from Wars

• Wars reduce interfirm shipments, cause trade route closures, and disrupt energy and minerals markets (IMF, 2023).
• Russia–Ukraine war reduced shipments in some sectors by 78%, especially in metals, gas, neon, and palladium markets.

2.3 Natural Disasters

• Disasters like hurricanes, wildfires, and floods damage logistics hubs, raising transportation costs and causing cascading delays.

2.4 Pandemics

• COVID-19 exposed the fragility of GPNs, causing global shortages in semiconductors, pharmaceuticals, and electronic components.

2.5 Monetary and Fiscal Shock Transmission

• Monetary tightening increases borrowing costs for upstream producers, transmitting downstream as higher input prices (Boivin et al., 2020).
• Fiscal shocks, such as tax hikes, propagate through suppliers based on liquidity constraints (Gopinath & Neiman, 2014).

 

3. Methodology

This paper uses a case-cum-research design integrating:

1. Case Studies
• Russia–Ukraine war
• Hurricane Milton
• COVID-19 Pandemic
• Sectoral bottlenecks (semiconductors, rare earths, automotive)
2. Production Network Modelling
• Input–Output matrices
• Firm-level network graphs
• Centrality analysis (degree, eigenvector, betweenness)
3. Shock Transmission Modelling
• Upstream vs. downstream propagation
• Demand vs. supply shock decomposition
• Liquidity constraints and inventory buffers
4. Data Sources
• Z2Data, Sourcemap, Resilinc multi-tier supplier data
• UK GSCIP firm-level transactional database
• Customs, shipping records, ERP data

 

4. Case Analysis

4.1 Wars: The Russia–Ukraine Conflict

Key disruptions:

• Halt in neon (70% from Ukraine) affecting semiconductor lithography.
• Reduction in palladium supply (Russia supplies 37% globally).
• Closure of Black Sea trade routes.
• 78% drop in interfirm shipments across metals and fuel sectors.

Network Effects:

1. Tier 3 semiconductor suppliers → severe shortages.
2. Automotive companies delayed production by 1.3 million vehicles.
3. Centrality of South Korean and Taiwanese fabs increased due to supply diversification.

 

4.2 Natural Disasters: Hurricane Milton Case

• Port destruction delayed shipments in oil, chemicals, and healthcare.
• Wildfires in Canada disrupted rare earth metal transport.
• Typhoons in Southeast Asia halted electronics assembly lines.

Network Effects:

• Logistics-dependent industries (chemicals, automotive, pharma) faced exponential delays.
• JIT systems broke down, forcing firms to increase safety stock.

 

4.3 Pandemics: COVID-19 Global Shock

• Workforce shortages → factory shutdowns.
• Global shortages of active pharmaceutical ingredients (APIs).
• Shift toward reshoring and “China+1” diversification.
• Production networks covering one-third of global employment were disrupted.

Structural Shifts:

• Digitalization of supply chains.
• Increased investment in multi-tier visibility platforms.

 

5. Transmission of Monetary and Fiscal Shocks through Production Networks

5.1 Monetary Policy Shock Transmission

Mechanisms:

1. Input cost inflation for upstream suppliers.
2. Price rigidity more pronounced in “network-central” industries.
3. Smaller firms experience larger constraints due to limited credit access.
4. Shocks amplify disproportionately in sectors with long supplier chains (electronics, auto).

Example:

• Interest rate hike → higher capital costs for Tier 2 chip assemblers → price rise for Tier 1 OEMs → retail inflation in electronics.

 

5.2 Fiscal Shock Transmission

Mechanisms:

1. Tax increases → higher input prices → cascading cost inflation.
2. Liquidity-constrained suppliers reduce output, amplifying downstream disruptions.
3. Subsidies (e.g., US CHIPS Act) create counter-shocks by boosting domestic capacity.

Example:

• Carbon tax → increased energy prices → amplified cost pressures for steel, aluminium, automotive, and construction sectors.

 

6. Empirical Testing and Model Design (Research Section)

You may use this for conference submission as the “research method and empirical plan.”

6.1 Data Collection

Use:

• GSCIP (millions of firm-level transaction records)
• Z2Data (multi-tier supplier maps)
• Sourcemap (crowdsourced supplier declarations)
• Resilinc (ERP-integrated supplier network)

6.2 Identifying Shocks

Define:

• War shock: sudden decline in trade flows from conflict regions
• Disaster shock: port shutdown days, rainfall intensity, satellite-detected destruction
• Pandemic shock: lockdown severity indexes
• Monetary shock: change in repo rate / Fed funds rate
• Fiscal shock: change in tax rate / subsidy announcements

6.3 Model Specification

Model Specification (Equations Written in Words Only)

The production network model assumes that each firm’s output depends on the inputs it buys from its suppliers. In simple terms, the output of a firm (called “Y-i”) is determined by its productivity level (called “A-i”) multiplied by the combined contribution of all the inputs it receives from different suppliers. Each supplier’s input has its own weight or elasticity, reflecting how important that input is in the firm’s production process.

To study how shocks spread through the network, the model uses a shock-propagation framework. The change in total output across industries (called “d-Y”) is calculated by multiplying the shock vector (called “d-S,” which represents the initial disruption) by the Leontief multiplier. The Leontief multiplier is obtained by taking the identity matrix and subtracting the input-output matrix (called “Theta”), and then computing the inverse of this difference. This inverse captures how shocks get amplified as they move across interconnected firms and industries.

In summary:

·         Theta is the input-output matrix showing how industries depend on each other.

·         d-S is the vector of shocks that hit the economy.

·         The inverse of “identity minus Theta” is the Leontief multiplier, showing how disruptions transmit and magnify through the production network.

 

6.4 Hypotheses

H1: Industries with greater network centrality experience larger shock amplification.
H2: Monetary shocks transmit more strongly through upstream capital-intensive sectors.
H3: Multi-tier visibility reduces shock persistence.
H4: Liquidity buffers mitigate fiscal shock propagation.

 

6.5 Econometric Testing

Use:

• Panel regression with firm-level fixed effects
• Difference-in-Difference for event shocks
• Network-adjusted VAR models
• Instrumental variables (e.g., geological disaster exposure)

 

7. Discussion

Key Findings:

• Semiconductors, rare earths, automotive, logistics, and pharma act as global bottlenecks.
• Production networks amplify shocks—especially when Tier 3/Tier 4 suppliers are concentrated geographically.
• Monetary shocks increase costs fastest in the electronics and automotive sectors.
• Fiscal shocks hit liquidity-constrained SMEs hardest, leading to network-wide slowdowns.

 

8. Managerial & Policy Implications

For Firms:

• Build multi-tier visibility using Z2Data, Resilinc, Sourcemap.
• Increase safety stocks for critical inputs (chips, rare earths).
• Use stress-testing models to anticipate bottlenecks.

For Governments:

• Develop national supply chain mapping platforms like GSCIP.
• Use fiscal incentives to diversify suppliers (“Friend-shoring”).
• Strengthen logistics infrastructure for disaster resilience.

 

9. Conclusion

Wars, natural disasters, pandemics, and macroeconomic shocks profoundly reshape global production networks. Empirical evidence shows that shock propagation is non-linear, sector-specific, and highly dependent on multi-tier supplier structures. By integrating advanced data sources and network models, firms and policymakers can enhance resilience, minimize disruptions, and improve forecasting accuracy. Multi-tier visibility, supplier diversification, liquidity management, and digitalized production systems remain the foundation of future-proof global supply chains.

 

10. References

• Acemoglu, D., Carvalho, V. M., Ozdaglar, A., & Tahbaz-Salehi, A. (2012). The network origins of aggregate fluctuations. Econometrica.
• Baqaee, D., & Farhi, E. (2020). Nonlinear production networks with an application to the COVID-19 crisis. AER.
• Boivin, J., Gilchrist, S., & Khan, A. (2020). Financial frictions and monetary transmission. Federal Reserve Working Paper.
• Carvalho, V. (2014). From micro to macro via production networks. JEP.
• Gopinath, G., & Neiman, B. (2014). Trade adjustment and productivity in large depreciations. AER.
• IMF (2023). Global Supply Chain Report.
• WTO (2022). Impact of Russia-Ukraine War on World Trade.
• Resilinc (2024). Supply Chain Risk Annual Report.
• UK Government (2024). Global Supply Chain Intelligence Programme.
• Z2Data (2024). Global Multi-Tier Supplier Mapping Report.
• Sourcemap (2024). Supply Chain Transparency Inde