The access to CETA quotas: Extending CGE models with a market for quota licenses
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Bibliographic record
Abstract
Abstract We analyze the market dynamics that are caused by tariff-rate quotas, particularly the effects of quota license allocation between heterogeneous commodities at the tariff line level. The allocation is endogenously modeled with a mixed complementarity problem approach for the case of the Comprehensive Economic and Trade Agreement between Canada and the European Union. The model results are compared both with alternative models that resemble pre-existing approaches and with the real trade figures that have been collected since the trade agreement's implementation. Our analysis shows a bias toward more expensive commodities if the shadow value of a quota license manifests in a secondary license market. The same quota can thereby be binding to some commodities but not so for others. This feature of quotas can be crucial for policymakers who are concerned about price effects or who want to understand the effects of lumping together commodities of different quality in one quota.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it