“…Inevitably, this will affect the connectedness of economic agents and thus influence shock propagation mechanisms. For example, studies based on large-scale firm level databases report network density values well below 0.01% (Borsos & Stancsics, 2020;Demir et al, 2018;Huneeus, 2018;Kumar et al, 2021;Peydró et al, 2020;Spray, 2017). We should also mention that estimates of direct shocks to supply and demand are subject to large uncertainties.…”
Natural and anthropogenic disasters frequently affect both the supply and demand sides of an economy. A striking recent example is the Covid-19 pandemic which has created severe disruptions to economic output in most countries. These direct shocks to supply and demand will propagate downstream and upstream through production networks. Given the exogenous shocks, we derive a lower bound on total shock propagation. We find that even in this best case scenario network effects substantially amplify the initial shocks.To obtain more realistic model predictions, we study the propagation of shocks bottom-up by imposing different rationing rules on industries if they are not able to satisfy incoming demand. Our results show that economic impacts depend strongly on the emergence of input bottlenecks, making the rationing assumption a key variable in predicting adverse economic impacts. We further establish that the magnitude of initial shocks and network density heavily influence model predictions.
“…Inevitably, this will affect the connectedness of economic agents and thus influence shock propagation mechanisms. For example, studies based on large-scale firm level databases report network density values well below 0.01% (Borsos & Stancsics, 2020;Demir et al, 2018;Huneeus, 2018;Kumar et al, 2021;Peydró et al, 2020;Spray, 2017). We should also mention that estimates of direct shocks to supply and demand are subject to large uncertainties.…”
Natural and anthropogenic disasters frequently affect both the supply and demand sides of an economy. A striking recent example is the Covid-19 pandemic which has created severe disruptions to economic output in most countries. These direct shocks to supply and demand will propagate downstream and upstream through production networks. Given the exogenous shocks, we derive a lower bound on total shock propagation. We find that even in this best case scenario network effects substantially amplify the initial shocks.To obtain more realistic model predictions, we study the propagation of shocks bottom-up by imposing different rationing rules on industries if they are not able to satisfy incoming demand. Our results show that economic impacts depend strongly on the emergence of input bottlenecks, making the rationing assumption a key variable in predicting adverse economic impacts. We further establish that the magnitude of initial shocks and network density heavily influence model predictions.
“…They are characterized by a high coverage of Japanese firms but with a limited amount of commercial partners. Other domestic datasets contain transaction values among VAT-liable firms: this is the case for countries such as Brazil 14 , Belgium 15 , Hungary 17 , Ecuador 7 , Kenya 19 , Turkey 20 , Spain 21 , Rwanda and Uganda 22 , West Bengal 23 ; or contain transaction values among the totality of registered domestic firms such as in the case of Dominican Republic 18 and Costa Rica 43 . Figure 1 ( a ) Graphical representation of the Dutch multi-layer production network.…”
Triadic motifs are the smallest building blocks of higher-order interactions in complex networks and can be detected as over-occurrences with respect to null models with only pair-wise interactions. Recently, the motif structure of production networks has attracted attention in light of its possible role in the propagation of economic shocks. However, its characterization at the level of individual commodities is still poorly understood. Here we analyze both binary and weighted triadic motifs in the Dutch inter-industry production network disaggregated at the level of 187 commodity groups, which Statistics Netherlands reconstructed from National Accounts registers, surveys and known empirical data. We introduce appropriate null models that filter out node heterogeneity and the strong effects of link reciprocity and find that, while the aggregate network that overlays all products is characterized by a multitude of triadic motifs, most single-product layers feature no significant motif, and roughly 85% of the layers feature only two motifs or less. This result paves the way for identifying a simple ‘triadic fingerprint’ of each commodity and for reconstructing most product-specific networks from partial information in a pairwise fashion by controlling for their reciprocity structure. We discuss how these results can help statistical bureaus identify fine-grained information in structural analyses of interest for policymakers.
Systemic risk represents a critical challenge in modern financial systems characterized by complex interconnections. This chapter comprehensively analyses systemic risk, exploring its measurement, models, determinants, interconnections, and the key variables influencing its dynamics. One of the central focuses of this chapter is to explore the transmission channels through which systemic risk propagates. By analyzing various channels, including contagion risk, credit risk, liquidity risk, market risk, operational risk, and macroeconomic risk, the chapter unveils the mechanisms through which disruptions can spread across financial institutions, markets, and economies. The interconnected nature of these channels is also emphasized to showcase the amplification of systemic risk. The interconnections between financial institutions and markets are crucial factors of systemic risk. We discuss the significance of network analysis and emphasize the importance of considering both visible and hidden (shadow) interconnections when assessing systemic risk. By identifying the vulnerabilities and interdependencies within the financial system, policymakers could then develop targeted measures to mitigate systemic risks. The chapter highlights the need for proactive monitoring, enhanced risk management practices, and coordinated regulatory efforts across jurisdictions. These policy implications could then strengthen the financial system’s resilience and reduce the likelihood of systemic crises.
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