Countries don’t trade, firms do: A firm-level assessment of CETA
Bibliographic record
Abstract
: The impact of free trade agreements (FTAs) has been analysed by numerous empirical studies that focus on their effect on trade values. But what about the number of trading firms? Do FTAs lead to new firms becoming exporters or importers? Using data from the OECD-Eurostat Trade by Enterprise Characteristics dataset and estimating a structural gravity model, this paper examines the effect of the EU-Canada Comprehensive Economic and Trade Agreement (CETA) on the number of EU exporting and importing firms. When debating its future effects during the negotiations, the CETA agreement was the subject of both hope and criticism, including its potential negative effect on small firms. We explore the heterogeneous response of firms to CETA by sector, firm size and EU country. We find a positive but diverse response from EU firms to the opportunities offered by the CETA agreement. On average, CETA increased the number of EU exporting firms by around 11%. The largest increases were found in Spain and Lithuania (over 30%), while the lowest increases were in Italy (8.7%). The increase in the number of trading firms has been higher for small than for large firms. These findings underscore the importance of considering firm-level impacts in trade policy assessments.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".