Impacts of trade liberalization in Canada's supply managed dairy industry
Bibliographic record
Abstract
Abstract Trade is an integral part of the Canadian economy. The main institutional drivers governing trade are bilateral and multilateral agreements outlining permissible trade distorting measures. Since its inception in 1972, Canada's supply management system has remained protected throughout trade negotiations. The system appears, by any economic measure, to be having an increasingly disproportional influence in recent trade negotiations. However, trade agreements serve not only to maximize social surplus, but also to maximize some measure of political welfare. Canada has recently negotiated three prominent trade agreements: the Canada‐European Union Comprehensive Economic and Trade Agreement (CETA) came into effect in the latter part of 2017; the Comprehensive and Progressive Agreement for Trans‐Pacific Partnership (CPTPP) came into effect at the end of 2018; and the Canada‐United States‐Mexico Agreement (CUSMA) could come into effect in 2020. Collectively, these agreements have guaranteed increased market access for fresh and processed dairy products. We build a spatial partial equilibrium model of the Canadian dairy industry consisting of three regions and 10 commodities to assess the individual and cumulative effect of these trade agreements. We pay particular attention to the institutional drivers within today's dairy sector: milk protein isolates; component pricing, including Class 7; and differential demand growth. We find that the aggregate impacts are: (a) a 1.4% decrease in the marginal retail price; (b) a 4.8% decrease in the blended producer price; and (c) an overall increase in social welfare of 7.8%. Worth noting, the decrease in producer surplus varies from 0.7% in the western region to 1.5% in Ontario. Our results may be relevant to future negotiations as well as the publicly promised compensation package for dairy producers.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".