Globally Trading Firms in Canada: Productivity and Global Value Chains
Bibliographic record
Abstract
Using firm-level data in Canada from 2002 to 2008, I compare the economic performance of three types of firms: those that both export and import (called globally trading firms—GTFs), exporters-only, and importers-only. The results show that GTFs are more productive, larger, more capital intensive, pay higher wages, trade more goods, and trade with more countries than both types of one-way traders. These premia for GTFs were found even before they turned into GTFs (self-selection). Moreover, even after turning into GTFs, the productivity growth of a subset of them was faster than that of one-way traders. The higher the involvement in global value chains (GVCs), the higher was the performance of the “learning-by-turning GTFs”. The GTFs with higher productivity growth were the ones that imported from multiple countries, not those that imported only from China. By another measure, they were both-in-both GTFs—those that traded both final and intermediate goods, and in both directions (exports and imports). Even though they employed only 10% of Canada’s business sector workforce, they contributed 60% of its labour productivity growth.
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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 | 0.005 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".