Bibliographic record
Abstract
Issues regarding the effects of supply management systems, seem to attract special attention from the industry, policy makers, and academic environments. The Canadian dairy industry is no exception. In addition to higher milk product prices for the consumer, the milk marketing quota is perhaps the most debated side of the dairy supply management regime. The milk quotas were initially allocated to each farmer, and are now traded openly in most provinces through a milk quota exchange. Substantial variation in milk quota prices can be observed in the last 15 years as compared to the TSE 300 Stock Price Index. The objective of this research is to analyze and explore why the large variation in observed milk quota prices in the 1980's and 1990's occurred, and to reveal the factors that are important for the formation of milk quota prices. Two factors are the focus of this thesis, one is the uncertainty regarding the future of the supply management system, especially during the two major trade negotiations, GATT and CUSTA, that took place in the late 1980's and early 1990's. The second is the expectations of future returns from holding milk production quotas that were formed in the presence of this uncertainty. Based on a standard capitalization model, three price functions are derived. Using an adaptive expectation framework, and one of the most complete data sets collected for the purpose of analyzing quota prices and quota issues in Ontario, Quebec, and Alberta, the estimated results suggest that, in general, unit changes in the net profit variable are important in MSQ pricing, more so for Used MSQ prices and fluid milk quota prices, than Unused MSQ prices. This supports the impression that fluctuations in Unused MSQ prices are partly driven by short-run considerations to avoid over-quota and maintenance penalties. The adaptive expectation model provides better results when explaining the formation of MSQ prices than fluid milk quota prices. This analysis also concludes that the milk quota auction is not a perfectly understood marketplace, and that several puzzles remain to be explained in future work.
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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 | 0.000 |
| 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".