Numerical General Equilibrium Analysis of China's Impacts from Possible Mega Trade Deals
Bibliographic record
Abstract
This paper explores the potential impacts on both China and other major countries of possible mega trade deals.These include the Trans-Pacific Partnership (TPP), the Regional Comprehensive Economic Partnership (RCEP), and various blocked deals.We use a numerical 13-country global general equilibrium model with trade costs to investigate both tariff and non-tariff effects, and include inside money to endogenously determine imports on the trade imbalance.Trade costs are calculated using a method based on gravity equations.Simulation results reveal that all FTA participation countries will gain but all FTA non-participation countries will lose.If non-tariff barriers are reduced more, the impacts will be larger.All effects to China on welfare, trade, export and import are positive.Comparatively China-TPP and RCEP will yield the highest welfare outcomes for the US in our model, China-Japan-Korea FTA will generate the second highest welfare outcome, and China-US FTA will generate the third highest welfare outcome.For the US, China-TPP FTA will generate the highest welfare outcome.For the EU, all China involved mega deals have negative impacts except China-US FTA.For Japan, RCEP will generate the highest welfare outcome.For both Korea and India, RCEP will generate the highest welfare outcome.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".