Geopolitical Barriers to Globalization

Mai Wo; Tianyu Fan; Wei Xiang

arxiv: 2509.12084 · v4 · submitted 2025-09-15 · 💰 econ.GN · q-fin.EC

Geopolitical Barriers to Globalization

Tianyu Fan , Mai Wo , Wei Xiang This is my paper

Pith reviewed 2026-05-18 16:42 UTC · model grok-4.3

classification 💰 econ.GN q-fin.EC

keywords geopolitical alignmentbilateral tradetariff liberalizationglobalizationlocal projectionsArmington modelevent data

0 comments

The pith

Deteriorating geopolitical alignment has offset tariff-driven trade gains, reducing 2021 global trade by 5.3 percent.

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

The paper establishes that geopolitical tensions have increasingly counteracted the benefits of tariff cuts in promoting international trade. Since the mid-1990s, worsening political alignments have slowed the pace of trade globalization, particularly after 2007. The authors use large language models to process hundreds of thousands of events into a bilateral alignment measure, then apply local projections to show that better alignment would substantially increase trade between countries. Their model calculations indicate that the negative impact of geopolitical changes is comparable in magnitude to the positive impact from past liberalization, though the effects differ by country.

Core claim

Since the mid-1990s, the trade-promoting effects of tariff liberalization have been increasingly offset by deteriorating geopolitical alignment, slowing trade globalization after 2007. To quantify this barrier, the authors compile 833,485 geopolitical events using large language models and construct a bilateral geopolitical alignment score. Local projections estimate that a one-standard-deviation permanent improvement in alignment raises bilateral trade by 22 percent in the long run. In an Armington framework, tariff reductions raised 2021 global trade by about 7.5 percent, while geopolitical deterioration reduced it by about 5.3 percent, with uneven welfare effects.

What carries the argument

The bilateral geopolitical alignment score, constructed from LLM-compiled events, which is used in local projections to quantify its long-run effect on bilateral trade flows.

If this is right

Geopolitical deterioration has slowed trade globalization since 2007.
A one-standard-deviation permanent improvement in geopolitical alignment raises bilateral trade by 22 percent in the long run.
Tariff reductions have raised 2021 global trade by approximately 7.5 percent.
Geopolitical deterioration has reduced 2021 global trade by approximately 5.3 percent.
The welfare impacts of these geopolitical changes are uneven across countries.

Where Pith is reading between the lines

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

If the alignment measure is accurate, ongoing improvements in relations between specific country pairs could lead to notable increases in their trade volumes.
The framework could be extended to assess how geopolitical factors influence other economic variables such as foreign investment or supply chain decisions.
Policy makers might need to weigh geopolitical alignment alongside traditional trade policies when evaluating globalization strategies.

Load-bearing premise

The LLM-compiled geopolitical events and resulting bilateral alignment score accurately measure the specific tensions that affect trade flows, without substantial measurement error or selection bias in event coverage.

What would settle it

Finding that an alternative geopolitical alignment measure, such as one based solely on formal alliances or UN voting patterns, produces no significant or much weaker estimated effect on trade would falsify the paper's central quantitative claim.

Figures

Figures reproduced from arXiv: 2509.12084 by Mai Wo, Tianyu Fan, Wei Xiang.

read the original abstract

We show that since the mid-1990s, the trade-promoting effects of tariff liberalization have been increasingly offset by deteriorating geopolitical alignment, slowing trade globalization after 2007. To quantify this barrier, we use large language models to compile 833,485 geopolitical events across 193 countries, 1950--2024, and construct a bilateral geopolitical alignment score. Using local projections, we estimate that a one-standard-deviation permanent improvement in alignment raises bilateral trade by 22 percent in the long run. In an Armington framework, tariff reductions raised 2021 global trade by about 7.5 percent, while geopolitical deterioration reduced it by about 5.3 percent, with uneven welfare effects.

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.

Desk Editor's Note private letter to a colleague

Geopolitics has offset much of the post-1990s tariff-driven trade growth, with a 22 percent long-run bilateral response to alignment shifts and a 5.3 percent global drag in 2021, but the LLM event data is the part that still needs checking.

read the letter

The main thing to know is that this paper quantifies how much geopolitical alignment affects trade and shows it has offset a good chunk of tariff liberalization gains since the mid-1990s. Their estimate is that a permanent one-standard-deviation improvement in alignment raises bilateral trade by 22 percent in the long run. In the counterfactual, geopolitical deterioration cut 2021 global trade by 5.3 percent while tariff reductions boosted it by 7.5 percent. They put together a large database of 833,485 geopolitical events across countries from 1950 to 2024 using LLMs, then build a bilateral alignment score from that. Local projections on trade data give the elasticity, and they run it through an Armington model for the aggregate effects. The scale of the event data and its direct use in this setup is the novel element compared to earlier work on geopolitics and trade. The paper does a solid job on the estimation side by using local projections, which avoids some of the restrictions of standard gravity models and lets the responses build over time. The counterfactual keeps things simple and comparable to existing tariff studies. Where it could be firmer is in validating the alignment score. The stress test raises a fair point about possible selection bias in what the LLM picks up—high-profile events might dominate while quieter disputes get missed, especially outside English sources. If that's happening, the 22 percent figure might overstate the pure geopolitical channel. I'd want to see some cross-checks with existing event datasets or human coding on a subsample. Identification also matters: they need to convince that the alignment changes are not driven by trade itself or other omitted factors. Overall, this is for economists working on international trade and those interested in the role of politics in economic integration. Readers looking for empirical magnitudes on the globalization slowdown will get concrete numbers to think with. The paper shows clear effort on data and modeling, so it deserves a serious referee even if the event data requires more defense. I would send it out for peer review with specific asks around robustness of the LLM pipeline and the identification assumptions.

Referee Report

3 major / 3 minor

Summary. The manuscript argues that since the mid-1990s, the trade-promoting effects of tariff liberalization have been increasingly offset by deteriorating geopolitical alignment, slowing globalization after 2007. The authors use large language models to compile 833,485 geopolitical events across 193 countries from 1950–2024 and construct a bilateral geopolitical alignment score. Local projection estimates show that a one-standard-deviation permanent improvement in alignment raises bilateral trade by 22 percent in the long run. Armington counterfactuals indicate that tariff reductions raised 2021 global trade by about 7.5 percent while geopolitical deterioration reduced it by about 5.3 percent, with uneven welfare effects.

Significance. If the alignment score validly isolates trade-relevant frictions, the paper offers a valuable quantification of non-tariff geopolitical barriers to globalization. The scale of the LLM-compiled event database, the use of local projections to recover dynamic responses, and the subsequent Armington decomposition of tariff versus alignment effects on aggregate trade and welfare are clear strengths that could inform policy discussions on deglobalization.

major comments (3)

[§3] §3 (Alignment Score Construction): The bilateral alignment score is derived from LLM extraction of 833k events with no reported validation against human-coded benchmarks, alternative event databases (e.g., GDELT or ICEWS), or inter-coder reliability checks. This is load-bearing for the central 22 percent claim because systematic under-sampling of low-visibility disputes would induce measurement error correlated with unobserved trade costs, biasing the local-projection impulse responses.
[§4] §4 (Local Projections): The specification treats the alignment score as the key regressor without explicit tests for reverse causality (e.g., whether past trade flows influence future geopolitical events captured by the LLM) or for the exogeneity of the one-SD permanent shock. The long-run 22 percent trade response therefore rests on an untested mapping from the constructed score to trade-cost or preference shifts.
[§5] §5 (Armington Counterfactuals): The decomposition attributes a 5.3 percent reduction in 2021 global trade to geopolitical deterioration, but the paper does not report sensitivity to alternative trade elasticities or to allowing alignment to affect the elasticity itself; this affects the relative magnitude versus the 7.5 percent tariff effect and the welfare conclusions.

minor comments (3)

[Abstract] Abstract: The phrase 'since the mid-1990s' and the exact estimation sample for the local projections should be stated more precisely to allow readers to assess external validity.
[§3] Notation: The normalization of the alignment score (mentioned as a free parameter in the ledger) should be defined explicitly in the main text with the formula and any robustness to alternative scalings.
[Results] Tables/Figures: Standard errors or confidence bands around the 22 percent long-run response and the 7.5/5.3 percent counterfactual changes are not referenced in the abstract and should be highlighted in the results tables.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive and detailed comments, which identify key areas where additional validation and robustness checks can strengthen the paper. We respond to each major comment below and will incorporate the suggested revisions in the next version.

read point-by-point responses

Referee: [§3] §3 (Alignment Score Construction): The bilateral alignment score is derived from LLM extraction of 833k events with no reported validation against human-coded benchmarks, alternative event databases (e.g., GDELT or ICEWS), or inter-coder reliability checks. This is load-bearing for the central 22 percent claim because systematic under-sampling of low-visibility disputes would induce measurement error correlated with unobserved trade costs, biasing the local-projection impulse responses.

Authors: We agree that formal validation of the LLM-extracted events is important for addressing potential measurement error. The manuscript currently relies on the scale of the 833,485 events and the systematic prompting procedure but does not report external benchmarks. In the revision we will add a new subsection in §3 that reports (i) accuracy and completeness metrics from human annotation of a stratified random sample of LLM outputs, (ii) overlap and correlation statistics with GDELT for the post-2000 period, and (iii) inter-annotator agreement rates. These additions will directly mitigate concerns about correlated measurement error and support the credibility of the 22 percent long-run estimate. revision: yes
Referee: [§4] §4 (Local Projections): The specification treats the alignment score as the key regressor without explicit tests for reverse causality (e.g., whether past trade flows influence future geopolitical events captured by the LLM) or for the exogeneity of the one-SD permanent shock. The long-run 22 percent trade response therefore rests on an untested mapping from the constructed score to trade-cost or preference shifts.

Authors: We acknowledge that reverse causality and the mapping from alignment to trade costs require explicit discussion. The local-projection specification already includes lagged alignment to reduce contemporaneous feedback, but we did not report formal tests. In the revised manuscript we will add Granger causality tests between bilateral trade and alignment, robustness checks that replace contemporaneous alignment with predetermined lags or historical alliance instruments, and an expanded section clarifying the identifying assumptions under which a one-SD alignment improvement corresponds to a reduction in bilateral trade costs. These changes will make the 22 percent long-run response more transparent. revision: yes
Referee: [§5] §5 (Armington Counterfactuals): The decomposition attributes a 5.3 percent reduction in 2021 global trade to geopolitical deterioration, but the paper does not report sensitivity to alternative trade elasticities or to allowing alignment to affect the elasticity itself; this affects the relative magnitude versus the 7.5 percent tariff effect and the welfare conclusions.

Authors: We agree that sensitivity to the trade elasticity is a standard robustness check that affects the relative size of the tariff and geopolitical effects. The current counterfactuals use a baseline elasticity drawn from the literature but do not report alternatives. In the revision we will add tables in §5 that recompute the 2021 trade and welfare decompositions under a range of elasticities (4, 5, 7, and 10) and under an extension in which alignment enters the effective elasticity. These results will show whether the 5.3 percent geopolitical reduction remains smaller than the 7.5 percent tariff gain and will clarify the robustness of the welfare conclusions. revision: yes

Circularity Check

0 steps flagged

No significant circularity in derivation chain

full rationale

The paper constructs a bilateral geopolitical alignment score from 833,485 LLM-extracted events (1950-2024), estimates the long-run trade response via local projections treating the score as an exogenous regressor, and then applies a standard Armington framework for counterfactual quantification of tariff vs. alignment effects. None of these steps reduces a claimed prediction or first-principles result to its own inputs by construction; the alignment measure is built from external event data rather than fitted to trade flows, the 22% elasticity is identified separately, and the Armington counterfactual uses conventional model structure without self-referential loops or load-bearing self-citations.

Axiom & Free-Parameter Ledger

1 free parameters · 1 axioms · 0 invented entities

The central estimates rest on the validity of LLM event extraction for geopolitical alignment and on the identifying assumptions of local projections; the Armington counterfactual inherits standard CES demand and iceberg-cost assumptions from prior trade literature.

free parameters (1)

alignment score normalization
The bilateral alignment score is scaled to have a one-standard-deviation interpretation whose exact construction depends on how events are aggregated and weighted.

axioms (1)

domain assumption Local projections recover the causal response of trade to a permanent alignment shock
Invoked when translating the estimated impulse responses into a 22 percent long-run level effect.

pith-pipeline@v0.9.0 · 5637 in / 1292 out tokens · 31958 ms · 2026-05-18T16:42:48.656346+00:00 · methodology

discussion (0)

Lean theorems connected to this paper

Citations machine-checked in the Pith Canon. Every link opens the source theorem in the public Lean library.

IndisputableMonolith/Foundation/RealityFromDistinction.lean reality_from_one_distinction unclear

?

unclear
Relation between the paper passage and the cited Recognition theorem.

We use large language models to compile 833,485 geopolitical events... construct a bilateral geopolitical alignment score. Using local projections, we estimate that a one-standard-deviation permanent improvement in alignment raises bilateral trade by 22 percent in the long run.
IndisputableMonolith/Cost/FunctionalEquation.lean washburn_uniqueness_aczel unclear

?

unclear
Relation between the paper passage and the cited Recognition theorem.

dynamic bilateral geopolitical scores through weighted moving averages... Si j,t =(1−ϕ i j,t)⋅S i j,t−1+ϕ i j,t ⋅˜Si j,t

What do these tags mean?

matches: The paper's claim is directly supported by a theorem in the formal canon.
supports: The theorem supports part of the paper's argument, but the paper may add assumptions or extra steps.
extends: The paper goes beyond the formal theorem; the theorem is a base layer rather than the whole result.
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contradicts: The paper's claim conflicts with a theorem or certificate in the canon.
unclear: Pith found a possible connection, but the passage is too broad, indirect, or ambiguous to say the theorem truly supports the claim.

Reference graph

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, " * write output.state after.block = add.period write newline

ENTRY address author booktitle chapter doi edition editor eid howpublished institution journal key month note number organization pages publisher school series title type url volume year label extra.label sort.label short.list INTEGERS output.state before.all mid.sentence after.sentence after.block FUNCTION init.state.consts #0 'before.all := #1 'mid.sent...

work page
[69]

write newline

" write newline "" before.all 'output.state := FUNCTION n.dashify 't := "" t empty not t #1 #1 substring "-" = t #1 #2 substring "--" = not "--" * t #2 global.max substring 't := t #1 #1 substring "-" = "-" * t #2 global.max substring 't := while if t #1 #1 substring * t #2 global.max substring 't := if while FUNCTION word.in bbl.in capitalize " " * FUNCT...

work page

[1] [1]

Aghion, Philippe, Xavier Jaravel, Torsten Persson, and Dorothée Rouzet. 2019. Education and military rivalry. Journal of the European Economic Association 17 (2): 376--412

work page 2019

[2] [2]

Ahn, Daniel P and Rodney D Ludema. 2020. The sword and the shield: The economics of targeted sanctions. European Economic Review 130 (103587): 103587

work page 2020

[3] [3]

Airaudo, Florencia, Francois De Soyres, Keith Richards, and Ana Maria Santacreu. 2025. Measuring Geopolitical Fragmentation: Implications for Trade, Financial Flows, and Economic Policy. Federal Reserve Bank of St. Louis Review

work page 2025

[4] [4]

Connector

Aiyar, Shekhar and Franziska Ohnsorge. 2024. Geoeconomic Fragmentation and" Connector" Countries. Working Paper

work page 2024

[5] [5]

Alesina, Alberto, Enrico Spolaore, and Romain Wacziarg. 2000. Economic integration and political disintegration. American Economic Review 90 (5): 1276--1296

work page 2000

[6] [6]

Alfaro, Laura, Harald Fadinger, Jan Schymik, and Virananda Gede. 2025. Trade and Industrial Policy in Supply Chains: Directed Technological Change in Rare Earths

work page 2025

[7] [7]

Anderson, James E and Eric Van Wincoop. 2003. Gravity with gravitas: A solution to the border puzzle. American economic review 93 (1): 170--192

work page 2003

[8] [8]

Anderson, James E and Eric Van Wincoop. 2004. Trade costs. Journal of Economic literature 42 (3): 691--751

work page 2004

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ENTRY address author booktitle chapter doi edition editor eid howpublished institution journal key month note number organization pages publisher school series title type url volume year label extra.label sort.label short.list INTEGERS output.state before.all mid.sentence after.sentence after.block FUNCTION init.state.consts #0 'before.all := #1 'mid.sent...

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write newline

" write newline "" before.all 'output.state := FUNCTION n.dashify 't := "" t empty not t #1 #1 substring "-" = t #1 #2 substring "--" = not "--" * t #2 global.max substring 't := t #1 #1 substring "-" = "-" * t #2 global.max substring 't := while if t #1 #1 substring * t #2 global.max substring 't := if while FUNCTION word.in bbl.in capitalize " " * FUNCT...

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