CETA Without Blinders: How Cutting ‘Trade Costs and More’ Will Cause Unemployment, Inequality and Welfare Losses
Bibliographic record
Abstract
Proponents of the Comprehensive Economic and Trade Agreement (CETA) emphasize its prospective economic benefits, with economic growth increasing due to rising trade volumes and investment. Widely cited official projections suggest modest GDP gains after about a decade, varying from between 0.003% to 0.08% in the European Union and between 0.03% to 0.76% in Canada. However, all these quantitative projections stem from the same trade model, which assumes full employment and neutral (if not constant) income distribution in all countries, excluding from the outset any of the major risks of deeper liberalization. This lack of intellectual diversity and of realism shrouding the debate around CETA’s alleged economic benefits calls for an alternative assessment grounded in more realistic modeling premises. In this paper, we provide alternative projections of CETA’s economic effects using the United Nations Global Policy Model (GPM). Allowing for changes in employment and income distribution, we obtain very different results. In contrast to positive outcomes projected with full-employment models, we find CETA will lead to intra-EU trade diversion. More importantly, in the current context of tepid economic growth, competitive pressures induced by CETA will cause unemployment, inequality and welfare losses. At a minimum, this shows that official studies do not offer a solid basis for an informed decision on CETA.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".