Neo-mercantilism in Action - Mexico, Canada, and China under Trump’s 2025 Tariffs: a trade perspective
Bibliographic record
Abstract
Purpose The purpose of this paper is to examine how the 2025 US tariff policy targeting its principal trading partners (China, Canada and Mexico) departs from modern pro-trade economic policy and adopts an unconventional conservative stance on neo-mercantilism. It evaluates how President Trump’s use of an indefinite period of protectionism to maintain trade hegemony departs from the philosophical underpinnings of List and Hamilton. Design/methodology/approach Rather than attempting to perform empirical estimations (which, given the frequent changes in messaging by the USA, would quickly become outdated), the authors engage in a diagrammatic theoretical approach to elucidate the likely trade implications over time. Findings The long-run expectation of the policy is a contraction of domestic and global consumer welfare. The authors argue that if the policy aims are to reduce strategic dependence on China, support US firms to internalise operations, incentivise firms to relocate production within the USA, and insulate trade networks to align with the US policy agenda, then triggering a trade war through tariffs is unlikely to result in the favourable economic outcomes the Trump administration seeks to achieve. Originality/value Drawing on trade theory, the authors provide a critical analysis of the policy implemented by the Trump administration and offer a comprehensive discussion of its potential impacts.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.010 | 0.014 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".