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Systemic risk mitigation in supply chains through network rewiring

T0 review · 3 major / 5 minor · reviewed 2026-08-16 · deepseek-v4-flash

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Pith's one-line read Rewiring supplier–customer links in real supply networks can cut systemic risk by 16–50 percent while leaving firm production intact.

desk verdict A clever optimization study whose headline claim about output-neutral risk reduction is undermined by the unenforced 20% output tolerance. read the letter →

arxiv 2504.12955 v2 pith:4Y3RPVOE submitted 2025-04-17 econ.GN physics.soc-phq-fin.EC

Giacomo Zelbi , Leonardo Niccolò Ialongo , Stefan Thurner This is my paper · ORCID
classification econ.GNphysics.soc-phq-fin.EC
keywords supplychainnetworkssystemicriskESRInetworkrewiringmitigationsimulatedannealingfirm-levelproductionNACEclassification
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper asks whether the systemic risk carried by real supply chain networks can be reduced by rewiring supplier–customer links instead of by holding inventories or adding redundant suppliers. The authors claim it can: a constrained Monte Carlo link-swapping algorithm applied to six national subnetworks lowers the average Economic Systemic Risk Index by 16–50% while preserving each firm's input quantities per product and keeping output close to its empirical level. If the claim is right, the topology of observed supply chains is not forced by production technology, and a meaningful share of systemic risk could be removed through targeted rewiring incentives rather than costly redundancy.

What carries the argument

The key machinery is the Economic Systemic Risk Index (ESRI) combined with a constrained link-swapping Metropolis-Hastings algorithm. ESRI assigns each firm a value equal to the total production lost when that firm fails, using generalized Leontief production functions with essential and non-essential inputs; the network-level risk is the average $\langle \mathrm{ESRI} \rangle$ over all firms. The rewiring step swaps the endpoints of two directed links that share the same ordered pair of NACE 3-digit activities, preserving the per-sector input and output structure; weighted links are swapped when similar or split when very different, and each firm's out-strength is kept within 80–120% of its empirical value. Simulated annealing raises the acceptance temperature parameter $\beta$ so the system accepts higher-risk moves less often, driving the network toward configurations with lower average risk while escaping local minima.

What would settle it

Re-run the rewiring on a supply-chain dataset with actual product-level transaction records, replacing NACE 3-digit codes with the real products exchanged; if the 16–50% risk reduction shrinks or vanishes, the sector-substitutability proxy is the source of the result. A cheaper check is to repeat the analysis using NACE 4-digit or product-category constraints and compare the achievable reduction.

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Extended reading notes

Core claim

The paper's central claim is that the topology of real supply chain networks carries a large, removable component of systemic risk. Using the average Economic Systemic Risk Index (ESRI)—the expected fraction of economy-wide output lost when a firm fails—as the objective, the authors show that a Metropolis-Hastings link-swapping algorithm, run under constraints that preserve each firm's input quantities per product and keep output within 20% of its empirical value, lowers average ESRI by 16–50% in six national subnetworks (Ecuador's crustacean and soft-drinks chains, weighted and unweighted, and Hungary's food and automotive chains, unweighted). Unbiased rewiring leaves risk close to the empirical level, while risk-biased rewiring converges to lower plateaus; the empirical networks therefore resemble random configurations rather than risk-minimizing ones. The authors read this as evidence that observed supply-chain topologies are suboptimal with respect to systemic risk and that the margin could be captured through market-based incentives.

Load-bearing premise

The result rests on the assumption that firms with the same NACE 3-digit code are interchangeable suppliers and customers for a given sector pair, and that the ESRI model's essentiality and substitutability parameters faithfully describe how failures propagate; if real products within a sector are specialized, the feasible rewiring set and the computed risk reductions are partly artifacts.

Editorial extensions

If this is right

  • Real supply networks sit near the risk level of unbiased random rewiring, so the systemic risk they carry is not a fundamental property of production alone; it can be lowered by choosing different links.
  • Risk can be cut by 16–50% across six subnetworks while keeping each firm's input mix per product fixed and its total output within 20% of the empirical value.
  • The reduction is concentrated among the riskiest firms: the top of the ESRI profile shrinks by half or more, with only a few firms becoming riskier.
  • Standard network metrics—degree, clustering, reciprocity, diameter, and component sizes—do not explain the improvement, pointing to meso-scale structures such as a 'systemic risk core'.
  • Because observed supply-link turnover is already high, modest market-based incentives could push networks toward lower-risk configurations without forcing firms to change technology or output.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • If the NACE substitutability premise holds, comparable mitigation margins should appear in other national supply networks; that is testable wherever firm-level transaction data exist.
  • The sector-pair swap constraint is a double-edged proxy: real products may be specialized within a NACE class (over-estimating feasible swaps) or may cross sector boundaries (under-estimating them), so the 16–50% range is an estimate, not a fixed bound.
  • The paper's own observation that a small set of swaps likely produces most of the reduction suggests the rewiring result could be turned into a targeted intervention: identify the few critical links and adjust incentives around them.
  • The similar reductions found in unweighted networks imply that countries with only presence/absence transaction data can still pursue topology-based mitigation without waiting for transaction volumes.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 5 minor

Summary. The paper asks whether systemic risk in supply chain networks can be substantially reduced by rewiring supplier-customer links while preserving firms' production outputs. Using the ESRI measure of firm-level systemic risk, the authors apply a Metropolis-Hastings simulated-annealing algorithm to rewire links within six subnetworks of Ecuadorian and Hungarian supply chains, constrained to preserve firms' in- and out-degrees per industry sector and to keep out-strength within 80-120% of its empirical value. They report average ESRI reductions of 16-50%, attribute the reductions to meso-scale topological features, and argue that real supply chain networks carry unnecessarily high systemic risk. A beta=0 control shows that unbiased random rewiring does not reduce average ESRI, supporting the role of the risk bias.

Significance. If the result holds, the paper provides a novel and important demonstration that supply chain topology alone has a large effect on systemic risk and that network rewiring, which occurs naturally at high rates, could be steered by policy. The optimization methodology is coherent, and the beta=0 control is a good check that the reduction is not simply an artifact of any random rewiring. The paper also makes a useful step by applying the approach to both weighted and unweighted networks and to two countries. However, the central claim of output neutrality is currently not empirically verified, and the feasibility of the rewiring is grounded in a coarse industry classification. If these issues are resolved, the paper would be a valuable contribution to the literature on supply chain resilience and systemic risk.

major comments (3)
  1. [Constraints for the rewiring algorithm and SI S2, S3.A] The abstract's claim 'without reducing the production output of firms' is not established by the reported analysis. The algorithm explicitly allows out-strength (identified with production output in SI S2) to deviate by up to 20%, and the manuscript never reports the realized out-strength changes in the risk-mitigated configurations. Because average ESRI is a production-weighted loss, a configuration that reduces the out-strength of high-ESRI firms can lower average ESRI mechanically, independently of topology. The beta=0 control does not resolve this because it also operates under the same 20% tolerance; it only shows that unbiased rewiring does not reduce ESRI. Please report the actual out-strength distributions after rewiring and, ideally, recompute ESRI with out-strengths fixed at their empirical values to verify that the reduction is topological.
  2. [SI S3.A and Discussion (NACE proxy limitation)] The rewiring space is defined by NACE 3-digit substitutability: firms in the same NACE group are assumed to be freely interchangeable suppliers or customers. The authors correctly acknowledge the limitation, noting the error could go in either direction. However, the central policy conclusion that real topologies 'carry unnecessarily high levels of systemic risk' rests on this assumption. Without a sensitivity analysis at a coarser or finer industry classification, or a more detailed justification of within-NACE substitutability, the magnitude of the mitigation (16-50%) cannot be interpreted as an achievable reduction in a real economy. Please qualify the abstract's claim to 'under the NACE proxy' or provide additional validation.
  3. [Figure 3, Figure S4, and Table S2] Each beta value and the simulated annealing run are reported as a single trajectory. Since the algorithm is stochastic and the authors themselves note the risk of getting trapped in local minima (e.g., beta=3200 in Fig. 3a), the reported reductions may depend on initialization and random seed. Please report results across multiple independent runs and state the number of seeds and dispersion (e.g., min/max or interquartile range) for the final average ESRI values. This is important for interpreting the 16-50% range as a robust property of the rewiring process.
minor comments (5)
  1. [Results (rewiring steps interpretation)] The statement that '10,000 steps roughly correspond to one update for each supply link, equivalent to less than two years of real-world rewiring rates' assumes the algorithm's swap moves are comparable to observed annual link turnover. Please clarify how the two quantities are mapped.
  2. [Table I and main text] The paper calls the beta=0 rewiring a 'configuration model', which is not the standard configuration model (which randomizes edges preserving the degree sequence). Here the rewiring preserves degrees per NACE sector pair and avoids multi-edges in unweighted networks; consider using a term like 'random link-swap model' to avoid confusion.
  3. [Abstract and Results] The abstract reports '16-50%' but the exact reductions in Table I are 16.1%, 33.3%, 42.4%, 43.7%, 50.0%, and 18.3%. Please state the range consistently, e.g., '16-50%' with a footnote or table reference.
  4. [Introduction and Materials and Methods (ESRI)] The Introduction describes ESRI as 'the fraction of the total production of the economy affected by the failure of that firm', while the Methods defines it as the fraction of total production lost. Please align the wording to avoid ambiguity about whether the contribution is a conditional loss or an effect measure.
  5. [Figure 1 caption] The caption says 'the crustaceans and soft drinks subnetworks are highlighted by red and green nodes'. Given the complexity of the network plot, consider a color-blind-accessible palette or a separate panel with the subnetworks isolated for visibility.

Circularity Check

1 steps flagged · score 6.0 of 10

The output-neutrality claim is self-definitional: the paper defines production output as out-strength, then lets the rewiring algorithm reduce out-strength by up to 20%, so the reported ESRI mitigation may partly reflect permitted output shrinkage rather than topology.

  1. self definitional [Abstract; Main text 'Constraints for the rewiring algorithm'; SI S2, Eq. (5); SI S3 A]
    "'we demonstrate that systemic risk can be considerably mitigated by 16-50% without reducing the production output of firms' (Abstract). 'we impose a constraint that ensures that a firm’s total out-strength does not deviate by more than 20% from its original value' (Main). 'The production output that each firm i is able to sustain ... is exactly equal to the summed volume of its sales transactions, the node outstrength' (SI S2)."

    By the paper's own calibration, production output is the out-strength s_out, and ESRI is computed from that same s_out both as per-firm loss weight and as normalization (SI S2, Eq. 5). The rewiring constraints explicitly allow each firm's out-strength, hence its measured output, to fall to 80% of the empirical value. Because the objective ⟨ESRI⟩ is a function of these out-strengths, a configuration that exploits the allowed downward slack will reduce ⟨ESRI⟩ mechanically, even if the link topology is not the cause. The abstract's claim 'without reducing the production output of firms' is therefore not a derived consequence of the rewiring; it is an unverified restatement of an input tolerance.

full rationale

Most of the numerical optimization chain is self-contained. The Metropolis-Hastings procedure minimizes ⟨ESRI⟩ and then reports the decrease in the same quantity, which is the normal structure of an optimization study rather than circularity; the β=0 configuration-model control adds independent evidence that unbiased rewiring under identical constraints does not lower ⟨ESRI⟩. The load-bearing circularity is confined to the output-neutrality claim. The paper defines production output as out-strength and then explicitly permits out-strength to deviate by up to 20%, while the abstract asserts mitigation occurs 'without reducing the production output of firms.' Since ESRI is computed from the same out-strengths, any exploitation of that slack lowers the measured risk by construction. The paper's Discussion honestly acknowledges related data limitations (NACE substitutability, lack of product information), but it does not address this output-slack inconsistency. Because the disputed claim is central to the policy-relevant headline, the score is 6; the minimization itself is not circular.

Assumptions & free parameters 4 free parameters · 5 assumptions · 0 invented entities

The central claim rests on the ESRI cascade model from [21] with overlapping authorship, the NACE 3-digit product proxy, the market-share substitutability assumption, and hand-chosen rewiring constraints such as the 20 percent output tolerance, the 3000 USD swap threshold, and per-network annealing schedules. These choices define what counts as a valid swap and what counts as risk, so the 16 to 50 percent reduction is conditional on them.

free parameters (4)
  • Simulated annealing beta curves = Per-network curves in Table S2, e.g., beta(step) = 12800 * step / 50000 for food production
    Annealing schedules were calibrated after fixed-beta runs, so the reported mitigation percentages depend on these hand-chosen temperature curves.
  • Weight tolerance threshold for full swaps = 3000 USD
    The threshold for treating two link weights as equal is set to the Ecuadorian data minimum transaction value, which controls how often full versus partial swaps occur.
  • Maximum out-strength deviation = 20 percent
    Firms' total output is allowed to change by up to 20 percent during rewiring, meaning the claim of 'no production loss' is approximate and not exact.
  • Subnetwork extraction thresholds = Top 16 supplier and 8 customer NACE groups for crustaceans; top 23 and 20 for soft drinks; minimum 5 firms per group
    These hand-picked thresholds were designed to produce manageable subnetworks with enough redundancy for rewiring, which may bias the estimated mitigation potential upward.
assumptions (5)
  • domain assumption Generalized Leontief production function with essential and non-essential inputs, Eq. (1) and SI Eq. (4), calibrated as in the ESRI model of [21].
    The cascade risk measure assumes this production function and the essentiality survey classify how missing inputs reduce output; the survey reference is left unresolved as '[?]' in the text.
  • domain assumption Supplier substitutability is proxied by market share: firms with higher market share in a product are assumed harder to replace.
    Stated in SI S2; this directly shapes how large a cascade a failed firm triggers.
  • domain assumption The NACE 3-digit classification of the selling firm proxies the product exchanged on every link.
    Explicitly stated in the main text and SI S1/S2; the paper acknowledges this can introduce errors in both ESRI and feasible rewiring sets.
  • domain assumption Firms in the same NACE 3-digit source and target sectors are mutually substitutable as suppliers and customers in the rewiring algorithm.
    SI S3 A states that firms in the same NACE group can freely change suppliers within the allowed sector pairs; if false, many proposed swaps are not realistic.
  • domain assumption During an ESRI cascade, firms do not rewire or adapt their suppliers, and the shock propagates iteratively until convergence.
    SI S2 defines ESRI this way and explicitly notes it is not designed to simulate real dynamic adaptation, only potential susceptibility.

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Cite this review

Pith. "Pith review of Systemic risk mitigation in supply chains through network rewiring." pith.science (2026). https://pith.science/paper/4Y3RPVOE

@misc{pith2026250412955,
  author       = {Pith},
  title        = {Pith review of: Systemic risk mitigation in supply chains through network rewiring},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/4Y3RPVOE}},
  note         = {Machine review of arXiv:2504.12955}
}
read the original abstract

The networked nature of supply chains makes them susceptible to systemic risk, where local firm failures can propagate through firm interdependencies that can lead to cascading supply chain disruptions. The systemic risk of supply chains can be quantified and is closely related to the topology and dynamics of supply chain networks (SCN). How different network properties contribute to this risk remains unclear. Here, we ask whether systemic risk can be significantly reduced by strategically rewiring supplier-customer links. In doing so, we understand the role of specific endogenously emerged network structures and to what extent the observed systemic risk is a result of fundamental properties of the dynamical system. We minimize systemic risk through rewiring by employing a method from statistical physics that respects firm-level constraints to production. Analyzing six specific subnetworks of the national SCNs of Ecuador and Hungary, we demonstrate that systemic risk can be considerably mitigated by 16-50% without reducing the production output of firms. A comparison of network properties before and after rewiring reveals that this risk reduction is achieved by changing the connectivity in non-trivial ways. These results suggest that actual SCN topologies carry unnecessarily high levels of systemic risk. We discuss the possibility of devising policies to reduce systemic risk through minimal, targeted interventions in supply chain networks through market-based incentives.

Figures

Figures reproduced from arXiv: 2504.12955 by the authors.

Figure 1
Figure 1. FIG. 1. (a) Ecuadorian supply chain network comprised of 65’614 firms and 650’931 supply links. The crustaceans and soft [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. FIG. 2. Schematic example of a constrained network rewiring [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. FIG. 3. Decrease of systemic risk, [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figures from the paper (2 more)
Figure 4
Figure 4. Figure 4: FIG. 4. Systemic risk profile of the empirical (light green bars) and risk-mitigated (black bars) soft drinks network. Firms are [PITH_FULL_IMAGE:figures/full_fig_p006_4.png]
Figure 4
Figure 4. Figure 4: Many of the firms within the core are mutually [PITH_FULL_IMAGE:figures/full_fig_p007_4.png]

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Forward citations

Cited by 1 Pith paper

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Reference graph

Works this paper leans on

44 extracted references · 40 canonical work pages · cited by 1 Pith paper

  1. [1]

    C. W. Craighead, J. Blackhurst, M. J. Rungtusanatham, and R. B. Handfield, The severity of supply chain disrup- tions: design characteristics and mitigation capabilities, Decision sciences 38, 131 (2007)

    work page 2007

  2. [2]

    T. Y. Choi, T. H. Netland, N. Sanders, M. S. Sodhi, and S. M. Wagner, Just-in-time for supply chains in turbulent times, Production and Operations Management 32, 2331 (2023)

    work page 2023

  3. [3]

    Haraguchi and U

    M. Haraguchi and U. Lall, Flood risks and impacts: A case study of thailand’s floods in 2011 and research questions for supply chain decision making, International Journal of Disaster Risk Reduction 14, 256 (2015)

    work page 2015

  4. [4]

    V. M. Carvalho, M. Nirei, Y. U. Saito, and A. Tahbaz-Salehi, Supply Chain Disruptions: Ev- idence from the Great East Japan Earthquake*, The Quarterly Journal of Economics 136, 1255 (2020), https://academic.oup.com/qje/article- pdf/136/2/1255/36725306/qjaa044.pdf

    work page 2020

  5. [5]

    Inoue and Y

    H. Inoue and Y. Todo, Firm-level propagation of shocks through supply-chain networks, Nature Sustainability 2, 841 (2019)

    work page 2019

  6. [6]

    Pichler, M

    A. Pichler, M. Pangallo, R. M. del Rio-Chanona, F. La- fond, and J. D. Farmer, Forecasting the propagation of pandemic shocks with a dynamic input-output model, Journal of Economic Dynamics and Control 144, 104527 (2022)

    work page 2022

  7. [7]

    R. M. del Rio-Chanona, P. Mealy, A. Pichler, F. La- fond, and J. D. Farmer, Supply and demand shocks in the covid-19 pandemic: An industry and occupation perspec- tive, Oxford Review of Economic Policy 36, S94 (2020)

    work page 2020

  8. [8]

    Bonadio, Z

    B. Bonadio, Z. Huo, A. A. Levchenko, and N. Pandalai- Nayar, Global supply chains in the pandemic, Journal of international economics 133, 103534 (2021)

    work page 2021

Show all 44 references
  1. [9]

    Ascari, D

    G. Ascari, D. Bonam, and A. Smadu, Global supply chain pressures, inflation, and implications for monetary policy, Journal of International Money and Finance 142, 103029 (2024)

    2024

  2. [10]

    Kent and H

    P. Kent and H. Haralambides, A perfect storm or an im- perfect supply chain? the us supply chain crisis, Mar- itime Economics & Logistics 24, 1 (2022)

    2022

  3. [11]

    Ramani, D

    V. Ramani, D. Ghosh, and M. S. Sodhi, Understand- ing systemic disruption from the covid-19-induced semi- conductor shortage for the auto industry, Omega 113, 102720 (2022)

    2022

  4. [12]

    Ben Hassen and H

    T. Ben Hassen and H. El Bilali, Impacts of the russia- ukraine war on global food security: towards more sus- tainable and resilient food systems?, Foods 11, 2301 (2022)

    2022

  5. [13]

    Laber, P

    M. Laber, P. Klimek, M. Bruckner, L. Yang, and S. Thurner, Shock propagation from the russia–ukraine conflict on international multilayer food production net- work determines global food availability, Nature Food 4, 508 (2023)

    2023

  6. [14]

    The White House, FACT SHEET: President Biden Takes Action to Protect American Workers and Businesses from China’s Unfair Trade Practices (2024)

    2024

  7. [15]

    European Commission, Definitive Duties on BEV Im- ports from China (2024)

    2024

  8. [16]

    Draghi, The Future of European Competitiveness – A Competitiveness Strategy for Europe (2024)

    M. Draghi, The Future of European Competitiveness – A Competitiveness Strategy for Europe (2024)

    2024

  9. [17]

    Lafond, P

    F. Lafond, P. Astudillo-Est´ evez, A. Bacilieri, and A. Bor- sos, Firm-level production networks: what do we (really) know?, INET Oxford Working Papers 2023-08 (Institute for New Economic Thinking at the Oxford Martin School, University of Oxford, 2023)

    2023

  10. [18]

    Pichler, C

    A. Pichler, C. Diem, A. Brintrup, F. Lafond, G. Mager- man, G. Buiten, T. Y. Choi, V. M. Carvalho, J. D. Farmer, and S. Thurner, Building an alliance to map global supply networks, Science 382, 270 (2023), https://www.science.org/doi/pdf/10.1126/science.adi7521

    2023 doi

  11. [19]

    T. Y. Choi and D. R. Krause, The supply base and its complexity: Implications for transaction costs, risks, re- sponsiveness, and innovation, Journal of operations man- agement 24, 637 (2006)

    2006

  12. [20]

    C. Diem, A. Borsos, T. Reisch, J. Kert´ esz, and S. Thurner, Estimating the loss of eco- nomic predictability from aggregating firm-level production networks, PNAS Nexus 3, pgae064 (2024), https://academic.oup.com/pnasnexus/article- pdf/3/3/pgae064/57092508/pgae064.pdf

    2024

  13. [21]

    C. Diem, A. Borsos, T. Reisch, J. Kert´ esz, and S. Thurner, Quantifying firm-level economic systemic risk from nation-wide supply networks, Scientific Reports 12, 7719 (2022)

    2022

  14. [22]

    Acemoglu and A

    D. Acemoglu and A. Tahbaz-Salehi, The macroeconomics of supply chain disruptions, The Review of Economic Studies 92, 656 (2024), https://academic.oup.com/restud/article- pdf/92/2/656/57400561/rdae038.pdf

    2024

  15. [23]

    M. Boss, H. Elsinger, M. Summer, and S. Thurner 4, Network topology of the interbank market, Quantitative Finance 4, 677 (2004)

    2004

  16. [24]

    G. Iori, S. Jafarey, and F. G. Padilla, Systemic risk on the interbank market, Journal of Economic Behavior & Organization 61, 525 (2006)

    2006

  17. [25]

    Battiston, M

    S. Battiston, M. Puliga, R. Kaushik, P. Tasca, and G. Caldarelli, DebtRank: Too Central to Fail? Financial Networks, the FED and Systemic Risk, Scientific Reports 2, 1 (2012)

    2012

  18. [26]

    Poledna, J

    S. Poledna, J. L. Molina-Borboa, S. Mart´ ınez-Jaramillo, M. van der Leij, and S. Thurner, The multi-layer network nature of systemic risk and its implications for the costs of financial crises, Journal of Financial Stability 20, 70 (2015)

    2015

  19. [27]

    Reisch, A

    T. Reisch, A. Borsos, and S. Thurner, Supply chain network rewiring dynamics at the firm-level (2025), arXiv:2503.20594 [econ.GN]

    2025 arXiv

  20. [28]

    Poledna and S

    S. Poledna and S. Thurner, Elimination of systemic risk in financial networks by means of a systemic risk transaction tax, Quantitative Finance 16, 1599 (2016), https://doi.org/10.1080/14697688.2016.1156146

    2016

  21. [29]

    M. V. Leduc and S. Thurner, Incentivizing resilience in financial networks, Journal of Economic Dynamics and Control 82, 44 (2017)

    2017

  22. [30]

    C. Diem, A. Pichler, and S. Thurner, What is the minimal systemic risk in financial exposure networks?, Journal of Economic Dynamics and Control 116, 103900 (2020)

    2020

  23. [31]

    Pichler, S

    A. Pichler, S. Poledna, and S. Thurner, Systemic risk- efficient asset allocations: Minimization of systemic risk as a network optimization problem, Journal of Financial Stability 52, 100809 (2021), network models and stress 11 testing for financial stability: the conference

    2021

  24. [32]

    Regulation (EC) No 1893/2006 of the European Parlia- ment and of the Council of 20 December 2006 establishing the statistical classification of economic activities NACE Revision 2 and amending Council Regulation (EEC) No 3037/90 as well as certain EC Regulations on specific st...

    2006

  25. [33]

    W. Ho, T. Zheng, H. Yildiz, and S. Talluri, Supply chain risk management: a literature review, International jour- nal of production research 53, 5031 (2015)

    2015

  26. [34]

    Baldwin and R

    R. Baldwin and R. Freeman, Risks and global supply chains: What we know and what we need to know, An- nual Review of Economics 14, 153 (2022)

    2022

  27. [35]

    M. D. K¨ onig, A. Levchenko, T. Rogers, and F. Zilibotti, Aggregate fluctuations in adaptive production networks, Proceedings of the National Academy of Sciences 119, e2203730119 (2022), https://www.pnas.org/doi/pdf/10.1073/pnas.2203730119

    2022 doi

  28. [36]

    Sprecher, I

    B. Sprecher, I. Daigo, S. Murakami, R. Kleijn, M. Vos, and G. J. Kramer, Framework for resilience in mate- rial supply chains, with a case study from the 2010 rare earth crisis, Environmental science & technology49, 6740 (2015)

    2015

  29. [37]

    Kamalahmadi and M

    M. Kamalahmadi and M. M. Parast, A review of the lit- erature on the principles of enterprise and supply chain resilience: Major findings and directions for future re- search, International journal of production economics 171, 116 (2016)

    2016

  30. [38]

    Thurner and S

    S. Thurner and S. Poledna, Debtrank-transparency: Controlling systemic risk in financial networks, Scientific Reports 3, 1888 (2013)

    2013

  31. [39]

    Demir, A

    B. Demir, A. C. Fieler, D. Y. Xu, and K. K. Yang, O-ring production networks, Journal of Political Economy 132, 200 (2024)

    2024

  32. [40]

    A. B. Bernard, A. Moxnes, and Y. U. Saito, Production networks, geography, and firm performance, Journal of Political Economy 127, 639 (2019)

    2019

  33. [41]

    Juh´ asz, Z

    S. Juh´ asz, Z. Elekes, V. Ily´ es, and F. Neffke, Colo- cation of skill related suppliers–revisiting coagglomer- ation using firm-to-firm network data, arXiv preprint arXiv:2405.07071 (2024)

    2024 arXiv

  34. [42]

    International Standard Industrial Classification of All Economic Activities Revision 4, Series M: Miscellaneous Statistical Papers, No. 4 Rev. 4, New York: United Na- tions. ST/ESA/STAT/SER.M/4/REV.4 (2008)

    2008

  35. [43]

    Ecuador’s basket export in 2015: atlas.cid.harvard.edu

    2015

  36. [44]

    Crustaceans

    The Growth Lab at Harvard University, The Atlas of Economic Complexity (2013), http://www.atlas.cid. harvard.edu. ACKNOWLEDGEMENTS We are indebted to Tobias Reisch and Andras Borsos for making the Hungarian data available and to Pablo Astudillo-Estevez for the Ecuadorian data....

    2013

Pith tools

Reviewed August 16, 2026 · model on record in the stance chip above.