China's Potential Future Growth and Gains from Trade Policy Bargaining: Some Numerical Simulation Results
Bibliographic record
Abstract
Numerical simulation analysis of bargaining solutions is little developed in existing literature.Here we use a multi country, single period numerical general equilibrium model which captures China and her major trading partners and examine the outcomes of trade policy bargaining solutions (bargaining over tariffs and financial transfers) over time as China grows more rapidly than her trade partners.We compute gains relative to non-cooperative Nash equilibria for a range of model parameterizations.This yields a measure of both absolute and relative gain to China from bargaining.We calibrate our model to base case data for 2008 and use a model formulation where there are heterogeneous goods across countries.The gains from trade bargaining accrue more heavily to other countries when we use 2008 data rather than later year data.We then consider the impacts out into the future of different country growth rates which sharply increases China's relative size.Our objective is to assess how China's gains from bargaining change over time; whether they grow at a faster rate than GDP growth and for which parameterizations.Our simulation results indicate that China's welfare gain from trade bargaining will increase over time if countries keep their present GDP growth rates for several decades, but there are major difference when using different bargaining solution concepts.These differences have not been noted in existing literature but have an intuitive explanation.Our results also indicate that if China jointly bargains along with India, Brazil and other developing countries with the OECD, China's gain will further increase.Bargaining gains are also sensitive to country size.When we use PPP to adjust China's relative GDP size; China's trade bargaining welfare gain increases by about 37%.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.005 | 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".