Trade Liberalization: Export-market Participation, Productivity Growth, and Innovation
Bibliographic record
Abstract
The paper examines how Canadian manufacturing plants have responded to reductions in tariff barriers between Canada and the rest of world over the past two decades. Three main conclusions emerge from the analysis. First, trade liberalization was a significant factor behind the strong export growth of the Canadian manufacturing sector. As trade barriers fell, more Canadian plants entered the export market and existing exporters increased their share of shipments sold abroad. Second, export-market participation was associated with increases in a plant's productivity growth. Third, our analysis identified the presence of three main mechanisms through which export-market participation raises productivity growth among plants: learning by exporting; exposure to international competition; and increases in product specialization that allowed for exploitation of scale economies. Our evidence also shows that plants that move into export markets increase investments in R&D and training to develop capacities for absorbing foreign technologies and international best practices. Finally, entering export markets leads to increases in the number of advanced technologies being used, increases in foreign sourcing for advanced technologies, and improvements in the information available to firms about advanced technologies. It is also associated with improvements in the novelty of the innovations that are introduced.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 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".