Quota prices as indicators of comparative advantage in supply‐managed industries
Bibliographic record
Abstract
Abstract The Canadian dairy, egg, broiler, and turkey industries operate under supply management, a policy regime that sets product prices and allocates production among provinces and ultimately among farms through quotas. The Canadian Farm Products Agencies Act requires that comparative advantage be used to guide the allocation of new quota when increases in consumer demand necessitate increased production. This requirement, however, has not been met in practice. We develop a proposal by Meilke to use quota prices as measures of comparative advantage. We evaluate the quota price approach and other proposed methods, from a Hayekian and Coasean market process perspective. We conclude that quota prices offer an economically justifiable indicator of provincial comparative advantage. We develop an individual‐level general equilibrium model of quota exchange to illustrate the informational content of quota prices as indicators of comparative advantage. We also discuss potential practical challenges of using quota prices as indicators of comparative advantage.
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How this classification was reachedexpand
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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".