The Impact of Market Intervention on Quota Mobility: The Case of the Ontario Dairy Industry
Bibliographic record
Abstract
We examine the relationship between farm‐level scale efficiency and quota purchases in the Ontario dairy quota market before and after the implementation of a progressive transfer assessment policy and a price cap policy. We find that scale efficiency has a positive effect on net quota purchases and that the two regulations slowed down this effect. The largest deterrent in the movement of quota from less efficient to more efficient producers occurred after the implementation of the capital asset pricing policy. If the capital asset pricing policy remains in effect, it will likely take a longer time to achieve an efficient allocation of quota across producers in the industry. Nous examinons la relation entre l'efficience d'échelle agricole et les achats de quota au sein du marché des quotas laitiers ontariens, avant et après l'implantation d'une politique d'évaluation progressive de transfert, et d'une autre de plafonnement des prix. Nous avançons que l'efficience d'échelle a un effet positif sur les achats nets de quota et que les deux politiques ont freiné cet effet. La plus grande dissuasion au sein du mouvement des quotas de producteurs moins efficients vers ceux qui le sont plus, survient après le plafonnement des prix. Si la politique de plafonnement des prix demeure en vigueur, il faudra attendre encore longtemps avant de réussir une allocation efficiente des quotas parmi les producteurs de l'industrie.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".