Trade and labour standards: theory and new empirical evidence
Bibliographic record
Abstract
Recent trade negotiations, both at the regional and multilateral level, have seen a resurgence of the issue of trade and labour standards. Labour interests in high-standards countries argue that low labour standards are an unfair source of comparative advantage, and that increasing imports from low-standards countries will have an adverse impact on wages and working conditions in high-standards countries, thus leading to a race to the bottom of standards. For low-standards countries, there is the fear that this is just a form of disguised protectionism and that the imposition of high labour standards upon them is equally unfair since it will erode their competitiveness, the latter being largely based on labour costs. Our objective in the present paper is to cast some light on the above debate from both a theoretical and empirical perspective. In particular, we first discuss some possible theoretical links between labour standards and comparative advantage through their effects on the terms of trade. We then investigate empirically the relationship between labour standards, comparative advantage and export performance. Overall, our empirical results suggest that caution should be exercised before drawing broad conclusions on the magnitude and direction of these effects.
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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 | 0.005 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
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".