Tariff‐Rate Quota Liberalization: The Case of World Price Uncertainty and Supply Management
Bibliographic record
Abstract
This paper examines the options for liberalizing tariff‐rate quotas when a marketing board controls domestic production and international prices are stochastic. Lowering over‐quota tariff and increasing import quota volumes are not equivalent. The trade‐offs between the two types of trade reforms are examined through numerical simulations for the Canadian chicken industry. Introducing world price variability results in a distribution of welfare impacts for the different groups in the industry. Consumers and processors gain more with tariff liberalization than with increased import quotas. The difference in producer surplus between liberalization regimes is truncated so that producers only see down‐side risk under the tariff liberalization regime. Le présent article a examiné les options concernant la libéralisation des contingents tarifaires lorsqu'un office de commercialisation encadre la production nationale et que les prix internationaux sont stochastiques. Diminuer le taux de droit hors contingent et augmenter les volumes de contingent d'importation ne sont pas des options équivalentes. Nous avons examiné les deux types de réforme commerciale en effectuant des simulations numériques pour le secteur du poulet au Canada. Incorporer la variabilité du prix mondial provoque une distribution de répercussions sur le bien‐être des divers groupes du secteur. Les consommateurs et les transformateurs gagnent davantage en appliquant une libéralisation des droits hors contingent qu'en appliquant une augmentation des contingents d'importation. La différence quant au surplus des producteurs entre les régimes de libéralisation est tronquée de sorte que les producteurs ne voient que le risque de perte sous un régime de libéralisation tarifaire.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".