The Opportunity Cost of the U.S. Withdrawal from the TPP: A CGE Approach
Bibliographic record
Abstract
The objective of this paper is to quantify the opportunity cost of the U.S. withdrawal from the Trans-Pacific Partnership (TPP) trade deal signed by Australia, Brunei Darussalam, Canada, Chile, Japan, Malaysia, Mexico, New Zealand, Peru, Singapore, the United States and Vietnamy comparing the likely economic effects of the TPP with those of the Comprehensive and Progressive Agreement for the Trans-Pacific Partnership (CPTPP) without the United States, for which a global computable general equilibrium model is used. The opportunity costs of the U.S. withdrawal from the TPP for not only the United States and the CPTPP 11 members but non-members of the TPP are measured in terms of real GDP, equivalent variation (EV) as a measure of welfare, export and import values, and trade balance. In particular, the simulation results reveal that the opportunity costs that the United States has to pay for its withdrawal from the TPP would be a loss of real GDP of 0.76% and a loss of welfare of $107 billion,which is supported by a decrease in its total exports of 8.43% and a decrease in its total imports of 6.31%.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| 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".