If the US-China trade war is here to stay, what are the risks and opportunities for other GVC economies outside the war zone?
Bibliographic record
Abstract
Over the last three years, trade tensions between the United States (US) and China have transformed a fairly open bilateral trading environment into a rather protectionist one. The new administration of the United States has maintained most of the bilateral tariffs and non-tariff barriers put in place by the previous administration. Moreover, incentives to diversify trade partners and localisation have been intensified following COVID-19-induced global supply-chain disruptions. Continuing bilateral trade tensions between the world's largest economic powerhouse scan be expected to have significant impacts on the rest of the world. While it is intuitive to conclude that bilateral trade restrictions create new opportunities for others, deep economic interdependence through Global Value Chains (GVCs) complicate matters considerably. Impact assessment needs to consider supply-side relationships between each economy and the two GVC giants. Therefore, in this paper, we develop an analytical framework based on input-output data to assess how existing trade flows will be affected by tariff escalation between the US and China. The aim is to identify potential winners and losers among third-party economies. More specifically, we used product-level data and a new UN-ESCAP WWZ decomposition technique of MRIO trade in value-added data to (1) model tariff's impacts on US-China bilateral trade and then (2) track how third-countries might be impacted via international supply-chains: both negatively, via lower exports in affected (tariffed) links, as well as positively via enhanced exports demand in alternative ones. We find that the current trade war has been overall positive for third-party economies, shifting US$ 61.1 billion in net exports away from the US-China link towards other economies. Due to export asymmetries in the US-China bilateral trade relationship, opportunities for third-party economies emerge more from the US tariff imposition than a vice versa. Indeed, our model indicates that the supply-side reconfiguration to avoid US tariffs accounted for more than 80% of third-party economies' gains, equivalent to US$ 49.5 billion in net exports coming from China to the US diverted. Mexico, Canada, Republic of Korea, Germany and Japan are among the top beneficiaries. Ultimately, lingering trade tensions between the world's two largest economies will continue to pressure international trade downwards, while accelerating pre-existing trends, such as diversification of supply chains.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".