The expected economic impacts of the EU-Canada Comprehensive Economic and Trade Agreement in Finland
Bibliographic record
Abstract
The CETA agreement aims to remove especially various non-tariff measures (NTMs) on trade and investment stemming from regulatory practices, in addition to the nearly full elimination of tariffs between the EU and Canada. This report analyses the expected impacts of the agreement to the Finnish economy with a GTAP CGE model and microdata analyses on the current trade structures. The expected GDP impact of 0.04 percent to Finland is slightly higher than the EU average (0.03 percent). In terms of value added, most sectors in Finland grow minimally as a result of the CETA. The highest bilateral trade effects are found for motor vehicles and transport equipment industries where both bilateral exports and imports are expected to increase by over 100 percent. Further, the extensive liberalisation of services trade is likely to have some positive effects for Finland as some 30-50 percent of the current domestic value added from Finnish exports to Canada originated from service exports. Even nearly total opening of public procurement markets to EU exporters in Canada is not, again, likely to result in very large benefits for EU firms in absolute terms, while some increases are possible. The reduction of fixed and marginal costs of exporting in the CETA agreement is likely to open the Canadian market to Finnish SME exporters, which have not entered the Canadian market as well as other export markets until now.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".