No abstract

This study quantifies the economic effect of a possible lockdown of Tokyo to prevent the spread of COVID-19. The negative effect of such a lockdown may propagate to other regions through supply chains because of supply and demand shortages. Applying an agent-based model to the actual supply chains of nearly 1.6 million firms in Japan, we simulate what would happen to production activities outside Tokyo if production activities that are not essential to citizens' survival in Tokyo were shut down for a certain period. We find that if Tokyo were locked down for a month, the indirect effect on other regions would be twice as large as the direct effect on Tokyo, leading to a total production loss of 27 trillion yen in Japan or 5.2% of the country's annual GDP. Although the production that would be shut down in Tokyo accounts for 21% of the total production in Japan, the lockdown would result in an 86% reduction of the daily production in Japan after one month.

This study quantifies the economic effect of a possible lockdown of Tokyo to prevent spread of COVID-19. The negative effect of the lockdown may propagate to other regions through supply chains because of shortage of supply and demand. Applying an agent-based model to the actual supply chains of nearly 1.6 million firms in Japan, we simulate what would happen to production activities outside Tokyo when production activities that are not essential to citizens' survival in Tokyo were shut down for a certain period. We find that when Tokyo is locked down for a month, the indirect effect on other regions would be twice as large as the direct effect on Tokyo, leading to a total production loss of 27 trillion yen in Japan, or 5.3% of its annual GDP. Although the production shut down in Tokyo accounts for 21% of the total production in Japan, the lockdown would result in a reduction of the daily production in Japan by 86% in a month.

No abstract

This paper examines how negative shocks due to, for example, natural disasters, propagate through supply chains, applying a simulation technique to actual data on supply chains of Japanese firms. We obtained the following five results. (1) Network structures severely affect the speed of propagation in the medium run and total loss in the long run. The scale-free nature of the actual supply chain network, i.e., the power law degree distribution, leads to faster propagation, while dense links between firms within the community in the actual network slow propagation. (2) More intensive damages, i.e., larger damages to fewer firms, result in faster propagation than extensive damages of the same total size. (3) When substitution of undamaged suppliers for damaged suppliers is more difficult to achieve, propagation of negative shocks becomes substantially fast. (4) Direct damages in industrial regions promote faster propagation than those in rural regions. (5) Different sectoral damages cause large differences in the speed of propagation and the long-run loss. All of these results imply that the same size of direct damages by disasters can generate considerably different damages, depending on the structure of the supply chain network in the economy.

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.

The objective of this study is to shed new light on the industrial flow structure embedded in microscopic supplier-buyer relations. We first construct directed networks from actual data from interfirm transaction relations in Japan; as one example, the dataset compiled by the Tokyo Shoko Research, Ltd. in 2016 contains five million links between one million firms. Then, we analyze the industrial flow structure of such a large-scale network with a special emphasis on its hierarchy and circularity. The Helmholtz-Hodge decomposition enables us to break down the flow on a directed network into two flow components: gradient flow and circular flow. The gradient flow between a pair of nodes is given by the difference of their potentials obtained by the Helmholtz-Hodge decomposition. The gradient flow runs from a node with higher potential to a node with lower potential; hence, the potential of a node shows its hierarchical position in a network. On the other hand, the circular flow component illuminates feedback loops built in a network. The potential values averaged over firms classified by the major industrial category describe hierarchical characteristics of sectors. The ordering of sectors according to the potential agrees well with the general idea of the supply chain. We also identify industrially integrated clusters of firms by applying a flow-based community detection method to the extracted circular flow network. We then find that each of the major communities is characterized by its main industry, forming a hierarchical supply chain with feedback loops by complementary industries such as transport and services.

This paper studies the structure of the Japanese production network, which includes one million firms and five million supplier-customer links. This study finds that this network forms a tightly-knit structure with a core giant strongly connected component (GSCC) surrounded by IN and OUT components constituting two half-shells of the GSCC, which we call awalnut structure because of its shape. The hierarchical structure of the communities is studied by the Infomap method, and most of the irreducible communities are found to be at the second level. The composition of some of the major communities, including overexpressions regarding their industrial or regional nature, and the connections that exist between the communities are studied in detail. The findings obtained here cause us to question the validity and accuracy of using the conventional input-output analysis, which is expected to be useful when firms in the same sectors are highly connected to each other.