Navigating turbulent waters: The Philippines' global value chains experience amid trade wars
Bibliographic record
Abstract
In light of recent pronouncements of tariff hikes in the United States (US) and the retaliatory responses of major economies such as China, Canada, and Mexico, this paper revisits the global value chains (GVC) experience of the Philippines amid the trade tensions between China and the US from 2018 to 2019, especially within the context of the intricate web of trade linkages in East and Southeast Asia. The inter-country input-output analysis confirms that the tariff wars generated shocks that adversely affected the GVC and overall trade performance of bystander economies in East and Southeast Asia that are not directly involved in the trade conflicts but are strongly connected to the disputing parties. Sectors directly and indirectly contributing value added to American and Chinese exports hit by tariff hikes expectedly suffered the most. However, the impact was asymmetric. Country-sector pairs with higher contribution to Chinese exports to the US were more likely to experience negative growth of overall exports in 2019. But no similar effect was traced to higher exposure to US exports to China. Moreover, the impact on the Philippines was less severe compared to bigger East and Southeast Asian economies, probably due to the country's relatively weaker direct linkages to Chinese and American GVCs. Given these results, the paper discusses the effects of distortionary tariff wars within the broader context of interconnectedness, multilateralism, and power dynamics in GVC-dominated world trade. The paper argues that restoring the stability of global trade policy is necessary to renew confidence in the world trading system and reduce the lingering uncertainty created by pre-pandemic trade conflicts. The paper also highlights some potential challenges and opportunities for the Philippines amid the resurgence of the tariff wars in 2025.
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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 | 0.001 | 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 | 0.004 | 0.004 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".