Bilateral trade and productivity: analysis for trading partners of China and the United States
Bibliographic record
Abstract
Purpose This study investigates the extent to which bilateral trade with China and the United States (US) influences the productivity of trading partners. Design/methodology/approach This study uses panel data estimates to identify the export and import policy channels separately and then their combination with trade integration and trade balance at both the country and sectoral levels between 99 countries and China and the US, incorporating institutional quality and geopolitical risks. The sample period covers the years 2002–2019, and the two-step generalized method of moments (GMM) is employed as the main estimation method. Findings Trade with China boosts total productivity at constant prices through exports and imports, especially in manufacturing, but reduces welfare-relevant total factor productivity through total trade and trade balance, particularly in agriculture. In contrast, trade with the US consistently enhances all productivity across all channels, except for agricultural imports, which lower welfare-relevant total factor productivity. Institutional quality amplifies the positive effects, while trade uncertainty and US–China tensions reduce them. Originality/value This study provides a comparative, channel-specific and sector-sensitive analysis of trade-productivity links with China and the US, offering timely insights for policymakers involved in navigating shifting global trade dynamics.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".