Gains from Trade? The Net Effect of the Trans-Pacific Partnership Agreement on U.S. Wages
Bibliographic record
Abstract
Recent estimates of the U.S. economic gains that would result from the proposed Trans-Pacific Partnership (TPP) are very small -- only 0.13 percent of GDP by 2025. Taking into account the un-equalizing effect of trade on wages, this paper finds the median wage earner will probably lose as a result of any such agreement. In fact, most workers are likely to lose -- the exceptions being some of the bottom quarter or so whose earnings are determined by the minimum wage; and those with the highest wages who are more protected from international competition. Rather, many top incomes will rise as a result of TPP expansion of the terms and enforcement of copyrights and patents. The long-term losses, going forward over the same period (to 2025), from the failure to restore full employment to the United States have been some 25 times greater than the potential gains of the TPP, and more than five times as large as the possible gains resulting from a much broader trade agenda.
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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 | 0.006 |
| 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 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.024 | 0.002 |
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".