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arxiv: 2606.30685 · v1 · pith:MSRGBAEWnew · submitted 2026-06-28 · 💰 econ.GN · physics.soc-ph· q-fin.EC

Cascading Impacts of the USA--China Trade War on Global Oilseed Supply Chain

Diksha Gupta , Ritwick Mishra , Achla Marathe , Krista Danielle Yu , Anil Vullikanti This is my paper

Pith reviewed 2026-07-01 07:05 UTC · model grok-4.3

classification 💰 econ.GN physics.soc-phq-fin.EC
keywords input-output analysissupply chain disruptionstrade waroilseedslinear programmingtrade reallocationcascading effectsglobal trade networks
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The pith

A 70 percent disruption in U.S. oilseed flows to China produces a 3.27 percent global output loss and a 14.02 percent loss for China.

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

The paper develops a linear programming model of input-output systems to track how trade shocks cascade through supply chains while allowing for reallocation of trade and production expansion. It applies this to the U.S.-China trade war's effects on the global oilseeds network. The model shows that targeted disruptions create uneven losses, with mitigation through reallocation from other suppliers reducing the overall damage. This matters because real supply chains face repeated policy shocks, and understanding propagation helps identify effective responses.

Core claim

The authors formulate an input-output system as a linear program that finds an equilibrium minimizing unmet demand under disruptions, reallocations, and expansions. Applying it to a 70% reduction in U.S. oilseed exports to China yields a 3.27% drop in global output and 14.02% in China. Allowing 20% reallocation from Brazil to China lowers global losses to 1.36%, though final-demand pressures persist, and production expansion creates tradeoffs between global and domestic flows.

What carries the argument

Linear programming formulation of an input-output system that jointly models cascading disruptions, trade reallocation, and production expansion to characterize system-level equilibria.

If this is right

  • China experiences a disproportionate 14.02% output loss compared to the global 3.27%.
  • A 20% reallocation from Brazil reduces global output losses to 1.36%.
  • Production expansion introduces tradeoffs between reducing final-demand losses and protecting domestic flows in Brazil.
  • Domestic reallocation shifts losses toward smaller economies, while global sourcing spreads them more evenly.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The approach could be extended to other commodity supply chains facing tariff shocks.
  • Actual post-2018 trade data on oilseed volumes could test the model's predictions against observed changes.
  • Smaller economies may need targeted policies if reallocations concentrate losses domestically.

Load-bearing premise

The linear programming formulation of the input-output system, with the selected disruption sizes and reallocation rules, produces an equilibrium that matches real-world supply-chain adjustments to tariff shocks.

What would settle it

Compare the model's predicted 3.27% global and 14.02% Chinese output losses to measured changes in oilseed production and trade volumes after the 2018-2019 tariffs.

Figures

Figures reproduced from arXiv: 2606.30685 by Achla Marathe, Anil Vullikanti, Diksha Gupta, Krista Danielle Yu, Ritwick Mishra.

Figure 1
Figure 1. Figure 1: Schematic representation of the input–output structure for a representative firm. [PITH_FULL_IMAGE:figures/full_fig_p005_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Linear programming formulation for modeling cascading supply-chain disruptions [PITH_FULL_IMAGE:figures/full_fig_p007_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Baseline MRIO USA & China Oilseed trade distribution in the Year 2023. [PITH_FULL_IMAGE:figures/full_fig_p009_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Progressive cascading impact of USA–China Oilseed export disruption. While China dominates in output losses, several smaller economies experience disproportion￾ately high final demand losses, indicating heterogeneous exposure to downstream supply-chain effects. 10 [PITH_FULL_IMAGE:figures/full_fig_p010_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Cascading global impacts of USA-China oilseed supply disruption and mitigation scenarios. Country-level impacts are measured as output loss and final demand loss with respect to the baseline values. In Scenario III and IV, Brazil (shaded in gray) experiences a net negative output loss due to increased oilseeds production [PITH_FULL_IMAGE:figures/full_fig_p011_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Percentage country-level impacts across disruption and mitigation sce￾narios. Across scenarios, China consistently experiences the largest impact, while mitigation strategies progressively reduce both the intensity and breadth of global spillovers. 11 [PITH_FULL_IMAGE:figures/full_fig_p011_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Supply-chain flow reconfiguration under USA–China oilseed disruption and mitigation. (A) China’s supply chain flows under the baseline no disruption scenario, the disruption-only case (Scenario I), and mitigation through Brazilian oilseed reallocation to China (Scenario II). (B) Brazil’s Oilseed supply chain flows under the baseline/no-mitigation case, reallocation to China (Scenario II), and domestic prod… view at source ↗
Figure 8
Figure 8. Figure 8: Reallocation of Brazil’s oilseed exports to China under Scenario II rela￾tive to Scenario I. (A) Top 13 sectoral gains in absolute terms and corresponding producer￾region shares induced by reallocation. (B) Distribution of reallocation-induced final demand losses among countries importing oilseeds from Brazil, showing that China absorbs the ma￾jority of the indirect impact, followed by Thailand and Norway.… view at source ↗
Figure 9
Figure 9. Figure 9: Absolute sector-level impact of increasing Brazil’s oilseed production under Scenario III relative to Scenario II. The panels show the top impacted sectors and their corresponding producer region shares. additional pressure on countries indirectly connected to Brazil’s redirected supply flows. In contrast, most large economies show minimal changes in absolute output losses relative to Scenario II. Final-de… view at source ↗
Figure 10
Figure 10. Figure 10: Absolute sector-level impact of diversifying Brazil’s oilseed inputs un￾der Scenario IV relative to Scenario III. The panels report impacted sectors and their corresponding consumer- and producer-country shares. ward larger economies, [PITH_FULL_IMAGE:figures/full_fig_p016_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: Spatial distribution of global Value-Added loss at threshold (Left) [PITH_FULL_IMAGE:figures/full_fig_p023_11.png] view at source ↗
Figure 12
Figure 12. Figure 12: Absolute country-level impacts across disruption and mitigation sce￾narios. Across scenarios, China consistently experiences the largest impact, while mitigation strategies progressively reduce both the intensity and breadth of global spillovers [PITH_FULL_IMAGE:figures/full_fig_p024_12.png] view at source ↗
Figure 13
Figure 13. Figure 13: Absolute global sector-level impacts across disruption and mitigation scenarios. Top panels report output loss (%), and bottom panels report final demand loss (%) across sectors under four scenarios. 24 [PITH_FULL_IMAGE:figures/full_fig_p024_13.png] view at source ↗
Figure 14
Figure 14. Figure 14: Percentage global sector-level impacts across scenarios. Top panels report output loss (%), and bottom panels report final demand loss (%) across sectors under four scenarios. Note that the global sector-level impact remains unchanged for domestic/global partnership-based production increase strategy at a given parameter value, η = 0.10 [PITH_FULL_IMAGE:figures/full_fig_p025_14.png] view at source ↗
Figure 15
Figure 15. Figure 15: Impact of disruption and mitigation scenarios on China’s Supply Chain. We illustrate the top 20 input sector and output sector flow of China, along with final demand. 25 [PITH_FULL_IMAGE:figures/full_fig_p025_15.png] view at source ↗
Figure 16
Figure 16. Figure 16: Impact of disruption and mitigation scenarios on Brazil’s Supply Chain [PITH_FULL_IMAGE:figures/full_fig_p026_16.png] view at source ↗
Figure 17
Figure 17. Figure 17: Impact of disruption and mitigation scenarios on Brazil Oilseed Firm Supply Chain. 26 [PITH_FULL_IMAGE:figures/full_fig_p026_17.png] view at source ↗
Figure 18
Figure 18. Figure 18: Percentage global country-level impacts at USA-China Oilseed flow disruption at [PITH_FULL_IMAGE:figures/full_fig_p027_18.png] view at source ↗
Figure 19
Figure 19. Figure 19: Percentage global country-level impacts at USA-China Oilseed flow disruption at [PITH_FULL_IMAGE:figures/full_fig_p027_19.png] view at source ↗
Figure 20
Figure 20. Figure 20: Percentage global country-level impacts at USA-China Oilseed flow disruption at [PITH_FULL_IMAGE:figures/full_fig_p028_20.png] view at source ↗
read the original abstract

Global supply chains are highly interconnected, making them vulnerable to cascading disruptions induced by trade policy shocks. Understanding how such disruptions propagate through production networks, and how mitigation mechanisms such as trade reallocation and production adjustment can alleviate their impacts, remains a central challenge. In this work, we develop a linear programming formulation of an Input-Output (IO) system that captures cascading supply-chain disruptions together with trade reallocation and production expansion. Our formulation yields a system-level equilibrium characterization that enables the joint analysis of disruption propagation and mitigation within a unified framework. We propose an efficient algorithm for computing approximate equilibrium solutions by minimizing total unmet demand in large IO systems. We apply our approach to tariff-induced disruptions in the global oilseeds supply chain arising from the U.S.-China trade war. Our results show that a localized 70% disruption to flows from the U.S. oilseeds sector to China leads to a 3.27% loss in global output, with China experiencing a disproportionate loss of 14.02%. As a counterfactual mitigation strategy, allowing a 20% reallocation from Brazil's oilseed sector to China significantly reduces global output losses to 1.36%, although pressure remains high on final-demand flows. We further investigate production expansion as an additional mitigation mechanism and show that it introduces tradeoffs between reducing global final-demand losses and protecting Brazil's domestic flows. Domestic reallocation disproportionately shifts losses toward smaller economies, while globally sourced expansion redistributes losses more broadly across the network.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

3 major / 2 minor

Summary. The paper develops a linear programming formulation of an input-output system to capture cascading supply-chain disruptions from tariff shocks, along with mitigation via trade reallocation and production expansion. Applied to the US-China trade war in the global oilseeds sector, it reports that a 70% localized disruption in US-to-China flows produces a 3.27% global output loss (14.02% for China) while a counterfactual 20% reallocation from Brazil reduces the global loss to 1.36%.

Significance. If the LP formulation and its equilibrium characterization prove robust, the work supplies a unified computational framework for joint analysis of disruption propagation and mitigation in large production networks, with direct relevance to agricultural trade policy and supply-chain resilience.

major comments (3)
  1. [Abstract] Abstract and model section: the reported loss figures (3.27%, 14.02%, 1.36%) are direct outputs of an LP whose disruption magnitude (70%) and reallocation magnitude (20%) are free parameters chosen by the authors; without external validation against observed trade-war data or sensitivity checks on these parameters, the quantitative claims remain internal counterfactual simulations rather than independently tested predictions.
  2. [Results] Results and discussion: the manuscript presents no comparison of model outputs to historical oilseed trade or production data from 2018–2020, nor any out-of-sample test of the LP equilibrium against actual reallocation patterns, leaving the claim that the formulation 'accurately reflects real-world supply-chain responses' unsupported.
  3. [Model Formulation] Model formulation: the linear-programming relaxation of the IO system assumes fixed technical coefficients and instantaneous reallocation/expansion; this assumption is load-bearing for the reported global and country-level percentages yet receives no robustness analysis under alternative functional forms or capacity constraints.
minor comments (2)
  1. [Abstract] The description of the 'efficient algorithm for computing approximate equilibrium solutions' would benefit from explicit pseudocode or convergence guarantees.
  2. [Results] Clarify the precise definition of 'final-demand flows' versus 'domestic flows' when discussing tradeoffs under production expansion.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the detailed and constructive report. We agree that the analysis consists of counterfactual simulations and will revise the manuscript to clarify this scope, add sensitivity checks, and strengthen the discussion of limitations and assumptions. Below we respond point by point.

read point-by-point responses

  1. Referee: [Abstract] Abstract and model section: the reported loss figures (3.27%, 14.02%, 1.36%) are direct outputs of an LP whose disruption magnitude (70%) and reallocation magnitude (20%) are free parameters chosen by the authors; without external validation against observed trade-war data or sensitivity checks on these parameters, the quantitative claims remain internal counterfactual simulations rather than independently tested predictions.

    Authors: We agree that the reported percentages are outputs of the LP for author-selected parameter values (70% disruption and 20% reallocation) chosen to represent plausible scales from the US-China trade war. The work is framed as a counterfactual exercise to illustrate the unified framework rather than as tested predictions. We will revise the abstract and model section to state this explicitly and add a new sensitivity subsection that varies disruption from 50% to 90% and reallocation from 10% to 30%, reporting how the qualitative conclusions hold or change. revision: partial

  2. Referee: [Results] Results and discussion: the manuscript presents no comparison of model outputs to historical oilseed trade or production data from 2018–2020, nor any out-of-sample test of the LP equilibrium against actual reallocation patterns, leaving the claim that the formulation 'accurately reflects real-world supply-chain responses' unsupported.

    Authors: The manuscript does not contain historical comparisons to 2018-2020 data or out-of-sample tests, and the abstract does not assert that the formulation 'accurately reflects real-world supply-chain responses.' The contribution is the LP formulation and its application to scenarios. We will insert a limitations paragraph in the discussion section that explicitly notes the absence of empirical validation and the data challenges involved in such tests for this network. revision: partial

  3. Referee: [Model Formulation] Model formulation: the linear-programming relaxation of the IO system assumes fixed technical coefficients and instantaneous reallocation/expansion; this assumption is load-bearing for the reported global and country-level percentages yet receives no robustness analysis under alternative functional forms or capacity constraints.

    Authors: Fixed technical coefficients and instantaneous adjustment are standard in input-output models to maintain tractability for large networks. We acknowledge that no robustness checks under nonlinear forms or explicit capacity constraints are provided. We will expand the model section to justify these choices with reference to the IO literature, add a paragraph discussing the implications and possible extensions (including capacity constraints), and note this as a limitation without implementing a full alternative specification in the current revision. revision: partial

Circularity Check

0 steps flagged

No significant circularity detected

full rationale

The paper introduces an LP formulation for IO systems to analyze cascading disruptions and mitigation via reallocation/expansion, then computes specific numerical outcomes (3.27% global loss, 14.02% China loss, 1.36% mitigated loss) under author-chosen inputs (70% US-China disruption, 20% Brazil reallocation). These are standard counterfactual model outputs, not reductions by construction, fitted parameters renamed as predictions, or self-citation chains. No equations, uniqueness theorems, or ansatzes are shown reducing to inputs tautologically. The derivation is self-contained as a modeling exercise; results follow directly from the stated LP and scenarios without circular equivalence.

Axiom & Free-Parameter Ledger

2 free parameters · 2 axioms · 0 invented entities

The model rests on standard input-output accounting identities and linear optimization assumptions drawn from prior economic literature. The shock sizes are scenario parameters rather than fitted values. No new physical or economic entities are postulated.

free parameters (2)
  • disruption magnitude (70%)
    Chosen input representing the tariff shock size.
  • reallocation magnitude (20%)
    Chosen counterfactual mitigation level.
axioms (2)
  • domain assumption Global production and trade can be represented by a linear input-output system whose equilibrium is found by minimizing total unmet demand.
    Core modeling choice stated in the abstract.
  • domain assumption Trade reallocation and production expansion can be incorporated as decision variables within the same linear program.
    Enables the joint analysis of disruption and mitigation.

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discussion (0)

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