Trade and Investment Flows of Asean Countries in the Context of the Tariff Confrontation between the United States and China
Bibliographic record
Abstract
The article analyzes the new US tariff policy towards Southeast Asian countries and the impact of the trade war between China and the United States on foreign trade and investment flows within the Association of Southeast Asian Nations (ASEAN). The author shows that there is a complex system of tariff rates that applies to goods imported from ASEAN member states (as well as other US trading partners). Thus, the following types of import duty rates are applied: reciprocal tariffs (first introduced on April 2, 2025 and revised during negotiations with some trading partners), which for ASEAN range from 10% (the base rate applicable to Singapore) to 40% (for Myanmar and Laos, which did not participate in the negotiations); tariffs for transshipment of Chinese goods through the territory of ASEAN – 40%; special tariffs for specific goods (steel, aluminum, cars, trucks, etc.) – 25–50%; anti-dumping and countervailing duties (for example, on solar panels from Thailand – 972.23%) and special tariffs in accordance with section 232 of the Trade Expansion Act of 1962. (aimed at protecting national security). An analysis of the ASEAN countries’ foreign trade flows showed that over the periods 2012–2017 and 2018–2024, the average values of China’s shares in the bloc’s total commodity imports increased from 18.1% to 23.1%, in exports – from 12.3% to 15.1%; for the United States, they decreased from 7.5% to 7.4% and increased from 10.1% to 14.3%, respectively. During the study periods, FDI inflows increased from the United States from 15.4% to 16%, and from China from 6.7% to 7.5%. Thus, the ASEAN countries’ dependence on China and the United States in the trade and investment spheres has increased with the escalation of the trade war, but the change in the geopolitical situation is not the only factor that caused shifts in commodity and capital flows. This issue requires further investigation using methods of economic and mathematical modeling
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".