Propagation of negative shocks across nation-wide firm networks

Abstract

This study examines how negative shocks due to, for example, natural disasters propagate through supply chains. We apply a simulation technique to actual supply chain data covering most Japanese firms. To investigate the property of the propagation in the network, we test different types of artificial negative shocks. We find that, first, network structures severely affect the speed of propagation in the short run, and the total loss in the long run. The scale-free nature of the actual supply-chain network—that is, the power-law degree distribution—leads to faster propagation. Second, more intensive damages—that is, more damages suffered by fewer firms—result in faster propagation than extensive damages of the same total size. Third, the actual supply-chain network has innate robustness that comes from substitutability of supplies. If the supply-chain network has severe substitutability, the propagation of negative shocks becomes substantially large. Fourth, direct damages in urban regions promote faster propagation than those in rural regions. Fifth, different sectoral damages show significant differences in the speed of propagation. Finally, we check the indirect damage triggered by a single firm’s loss: 9.7% of all firms contribute to significant loss, and this loss accounts for more than 10% of the damage to the entire production. The simulations conspicuously show that different direct damages, even if they have the same total magnitude of damages, can generate considerably different damages because of the structure of the supply-chain network.

Global economic footprint of the COVID-19 pandemic

“…(•) is the production function for firms. There are a wide range of functional forms, such as Leontief 28 , Cobb-Douglas (C-D) and Constant Elasticity of Substitution (CES) production function 29 . Different functional forms reflect the possibility for firms to substitute an input for another.…”

A model of the indirect losses from negative shocks in production and finance

“…Otherwise, if Q r i;t > P i;t , supplier i needs to ration goods. We follow the algorithm introduced by [13,14].…”

“…Payment process and inventory dynamics. Following the improvement of the ARIO model described in [11,13,14], we propose including additional indirect economic losses due to the demand shortage effect. If the total deposits of customer i, D i,t , are greater than its total realized goods, Q � i;t ¼ P j Q � ij;t , trading is completed.…”

“…However, the IO approach suffers from rigidity, as it does not allow for the possibility of recovery or an adaptive strategy by agents, which leads to overestimated indirect losses, as explained in [12]. To overcome this wellknown limitation of IO models, [13,14] introduced an autonomous recovery mechanism for firms in the production network after a disaster. These authors create a simulation with their recovery-based ARIO model using the real production network of Japan (2011 data) and successfully reproduce the dynamics of the value added (VA) of the Japanese economy in the aftermath of the 2011 Great Earthquake.…”

“…Therefore, the main contribution of this work is that it builds a framework able to measure the indirect economic losses due to financial crises and natural disasters. This paper proposes a framework based on the ARIO model discussed in [11,13,14]. First, we add the modelling of the Japanese credit market based on real bank-firm credit network data, which allows the credit-based ARIO to measure the effect of an exogenous shock to real production on the banking system.…”