Quantifying Firm-Level Economic Systemic Risk from Nation-Wide Supply Networks

Diem, Christian; Borsos, András; Reisch, Tobias; Kertész, János; Thurner, Stefan

doi:10.2139/ssrn.3826514

Cited by 7 publications

(8 citation statements)

References 2 publications

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“…Buiten et al [39] have improved this method by making use of firm-level commodity supply and use, available from surveys asking individual firms to report their production and use of each of 10-20 commodities. Firms within an industry may have fairly different sets of inputs and outputs [49], so determining the input and output vector of each firm can potentially improve the quality of firm-to-firm matching. A further enhancement of the new study is the approach to handling trade from and to retail and wholesale firms.…”

Section: Matching Algorithmsmentioning

confidence: 99%

Reconstructing supply networks

Mungo,

Brintrup,

Garlaschelli

et al. 2024

J. Phys. Complex.

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Network reconstruction is a well-developed sub-field of network science, but it has only recently been applied to production networks, where nodes are firms and edges represent customer-supplier relationships. We review the literature that has flourished to infer the topology of these networks by partial, aggregate, or indirect observation of the data. We discuss why this is an important endeavour, what needs to be reconstructed, what makes it different from other network reconstruction problems, and how different researchers have approached the problem. We conclude with a research agenda.

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Section: Matching Algorithmsmentioning

confidence: 99%

Reconstructing supply networks

Mungo,

Brintrup,

Garlaschelli

et al. 2024

J. Phys. Complex.

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show abstract

“…The field of complexity science has long studied the vulnerability of interconnected systems. Studies of cascading failures in financial networks [11], [12], supply chains [13], power distribution networks [14], regional economies [15], and healthcare systems [16] all highlight that a few key nodes in a system can play a systemically important role that is not obvious from their local topology or individual size. The spread of errors in coupled networks, and thus the identity of the most important nodes vis-a-vis such spreading, is even less predictable [17], [18].…”

Section: A Resilience and Vulnerability Of Complex Systemsmentioning

confidence: 99%

“…In these simulations, the functions governing how errors spread are tailored to the specific situation. For instance, recent work on the resilience of supply chains has used production functions such as the Cobb-Douglas and Leontief functions to model the effect of upstream failures on a node's production [13]. The choice of a specific production function allows us to consider whether inputs are complementary or whether they can be substituted for each other.…”

Section: A Resilience and Vulnerability Of Complex Systemsmentioning

confidence: 99%

Modeling Interconnected Social and Technical Risks in Open Source Software Ecosystems

Schueller¹,

Wachs²

2022

Preprint

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Open source software ecosystems consist of thousands of interdependent libraries, which users can combine to great effect. Recent work has pointed out two kinds of risks in these systems: that technical problems like bugs and vulnerabilities can spread through dependency links, and that relatively few developers are responsible for maintaining even the most widely used libraries. However, a more holistic diagnosis of systemic risk in software ecosystem should consider how these social and technical sources of risk interact and amplify one another. Motivated by the observation that the same individuals maintain several libraries within dependency networks, we present a methodological framework to measure risk in software ecosystems as a function of both dependencies and developers. In our models, a library's chance of failure increases as its developers leave and as its upstream dependencies fail. We apply our method to data from the Rust ecosystem, highlighting several systemically important libraries that are overlooked when only considering technical dependencies. We compare potential interventions, seeking better ways to deploy limited developer resources with a view to improving overall ecosystem health and software supply chain resilience.

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“…The computation of an economic systemic risk index for individual companies in a country allows analysts to identify the systemic weak spots of an economy. This has demonstrated that default by a small core of fewer than 50 companies can pose a substantial threat to a whole economy (Diem et al, 2021).…”

Section: Financial Systemic Risk Modellingmentioning

confidence: 99%

Addressing biases to increase investment in risk reduction

2022

Global Assessment Report on Disaster Risk Reduction (GAR)

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To download the full report, visit: www.undrr.org/GAR2022To share your comments and news on GAR on Twitter and Facebook, please use #GAR2022The designations employed and the presentation of the material in this publication do not imply the expression of any opinion whatsoever on the part of the Secretariat of the United Nations concerning the legal status of any country or territory or of its authorities or concerning the delimitations of its frontiers or boundaries. The designations of country groups in the text and the tables are intended solely for statistical or analytical convenience and do not necessarily express a judgment about the stage reached by a particular country or area in the development process. Mention of the names of firms and commercial products does not imply the endorsement of the United Nations. Some rights reserved. This work is made available under the Creative Commons Attribution-NonCommercial 3.0 IGO licence (CC BY-NC IGO); creativecommons.org/licenses/by-nc/3.0/igo/legalcode Under the terms of this licence, this work may be copied, redistributed and adapted for non-commercial purposes, provided that the work is appropriately cited. In any use of this work, there should be no suggestion that UNDRR endorses any specific organization, products or services.The use of the UNDRR logo is not permitted. If a translation of this work is created, it must include the following disclaimer along with the required citation below: "This translation was not created by the United Nations Office for Disaster Risk Reduction (UNDRR). UNDRR is not responsible for the content or accuracy of this translation." UNDRR information products are available for non-commercial use. Requests for commercial use, rights and licensing should be submitted via: www.undrr.org/contact-us This publication may be freely quoted but acknowledgement of the source is requested.

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Quantifying Firm-Level Economic Systemic Risk from Nation-Wide Supply Networks

Cited by 7 publications

References 2 publications

Reconstructing supply networks

Reconstructing supply networks

Modeling Interconnected Social and Technical Risks in Open Source Software Ecosystems

Addressing biases to increase investment in risk reduction

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