Trade and labour standards: Will there be a race to the bottom?
Bibliographic record
Abstract
Abstract. We investigate whether strategic competition among developing countries for export market shares will lead to a “race‐to‐the‐bottom” (RTB) in labour standards. We consider an environment that is most conducive to an RTB, specifically in a model of strategic trade in which governments have incentives to lower the costs of their domestic firms. Our analysis shows that strategic trade considerations do not lead to an RTB. To the contrary, equilibrium labour standards involve higher levels of labour compensation than those in the absence of government intervention. Binding global trade rules that restrict export subsidies would move the labour standards closer to their efficient level. Résumé. Commerce et normes du travail: y aura‐t‐il un nivellement par le bas? Dans cet article, nous cherchons à savoir si, pour gagner des parts de marché à l’exportation, la compétition stratégique entre les pays en voie de développement engendrera un nivellement par le bas des normes du travail. À cette fin, nous nous appuyons sur un environnement largement propice à cette théorie, notamment dans un modèle commercial stratégique au sein duquel les gouvernements ont intérêt à baisser les coûts de leurs entreprises domestiques. Notre analyse montre que les considérations en matière de commerce stratégique n’engendrent aucun nivellement par le bas. À l’inverse, des normes du travail à l’équilibre impliquent une rémunération de la main d’œ uvre plus élevée qu’en l’absence de toute intervention gouvernementale. Le fait de contraindre les règles du commerce mondial limitant les subventions à l’exportation rapprochera les normes du travail de leur niveau d’efficacité.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".