Price Ceilings on Milk Production Quota Values: Future or Folly?
Bibliographic record
Abstract
Since the inception of supply management in Canada during the 1970s, milk production quota has been used to regulate output and participation in the dairy industry. In recent years, milk quota values have increased dramatically, almost tripling in value since the mid 1980s. This led to the Dairy Farmers of Ontario intervening on the milk production quota exchange on two occasions: first, in November 2006 with a progressive transfer assessment and then in July 2009, replacing the former policy with a firm price ceiling—fixing the unit price of quota at $25,000. These policies represent a significant redistribution of economic benefits from milk producers selling their quota to those remaining in the industry. The objective of this study is to first explore the reasons for the increase in production quota values; and second, to assess the welfare and distributional effects of each of the two quota policy schemes. Our results suggest that the increase in quota values were driven by basic economic factors and that the efficiency losses from intervention in the quota exchange are nontrivial. We conclude by suggesting there are several alternative policy options that could minimize efficiency losses while moderating the escalation in quota values. Depuis la mise en place du système de gestion de l’offre au Canada dans les années 1970, les quotas laitiers sont utilisés pour régulariser la production et la participation dans l’industrie laitière. Au cours des dernières années, la valeur des quotas laitiers a fait un bond considérable et a pratiquement triplé depuis le milieu des années 1980. Cette situation a amené la Dairy Farmers of Ontario à intervenir à deux reprises dans le système d’échange de quotas laitiers : en novembre 2006, en imposant l’établissement d’un transfert progressif et en juillet 2009, en remplaçant la politique précédente par l’établissement d’un prix plafond ferme fixéà 25 000 $. Ces politiques permettent une importante redistribution des avantages économiques lorsque des producteurs de lait vendent leurs quotas à des producteurs qui demeurent dans le secteur. La présente étude visait d’abord à examiner les raisons qui sous‐tendent l’augmentation de la valeur des quotas de production et ensuite àévaluer le bien‐être et les effets distributifs de chaque plan de quotas. Les résultats de notre étude autorisent à penser que l’augmentation de la valeur des quotas a été motivée par des facteurs économiques fondamentaux et que les pertes d’efficacité découlant de l’intervention dans les échanges de quotas n’étaient pas sans importance. En conclusion, nous estimons qu’il existe plusieurs politiques de rechange qui pourraient minimiser les pertes d’efficacité tout en modérant l’escalade de la valeur des quotas.
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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 | 0.007 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".