Enforcing WTO Obligations: What Can We Learn from Export Subsidies?
Bibliographic record
Abstract
Export subsidies provide a good example for discussing some interesting questions underlying the debate over reforming the current system of remedies for violations of World Trade Organization (WTO) obligations. If the purpose of trade agreements is to maximize economic welfare, discussion of violations of WTO obligations will need to take account of the form of both the requirement and the remedy. The requirement could take the form of a standard or a rule and may be more or less complex. The remedy could take the form of a property rule or a liability rule. Further, both the level and the form of the remedy will be important. Each type of violation needs to be examined separately to determine whether flexibility to adapt to new circumstances should come through the requirement or the remedy. In the case of export subsidies, the current simple rule prohibiting export subsidies is likely optimal but the remedies which support this rule need to be reformed. They are currently both over-inclusive and under-inclusive and do not provide sufficient flexibility or incentive for efficient adjustment. This article considers some alternative remedies for export subsidies and discusses the general lessons for the debate on remedies for violations of WTO obligations.
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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 | 0.014 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.026 |
| Scholarly communication | 0.009 | 0.044 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.015 | 0.022 |
| Insufficient payload (model declined to judge) | 0.008 | 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".