Implications of the Comprehensive Economic and Trade Agreement for Processed Food Markets
Bibliographic record
Abstract
Abstract Canada and the European Union (EU) recently completed the Comprehensive Economic and Trade Agreement (CETA) to liberalize bilateral trade. Processed food trade between Canada and the EU is one of the fastest growing markets, in spite of large trade restrictions due to high tariffs and egregious nontariff barriers (NTB). The processed food sector is characterized by firms which differ in size, productivity, produce differentiated products, and engage in monopolistic competition. We implement a four‐region (Canada, the EU, the United States, and the Rest of the World) model of the processed food industry, incorporating these firm characteristics to study the effects of CETA. The results show Canadian and EU bilateral trade flows expand, the number of exporting firms rises, and net welfare in both these countries increases. Though CETA does not liberalize NTBs, we examine the impacts of a 40% cut in NTBs to highlight the benefits that would have accrued had CETA also covered NTBs. Under this scenario, the trade flows would have expanded significantly, and, more importantly, Canadian and EU welfare would have risen by 11.8‐ and 39.4‐fold, respectively. Since CETA excludes the United States, the U.S. processed food industry loses due to greater competition in Canadian and the EU markets, and the net U.S. welfare declines.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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".