Economic Integration and Regional Industrial Specialization: Evidence from the Canadian- US FTA Experience
Bibliographic record
Abstract
We investigate the impact of Canada–U.S. trade integration on the degree of industrial specialization of the Canadian regions. Trade integration is captured through the decrease of trade-weighted tariffs that were boosted by the implementation of the Canadian–U.S. Free Trade Agreement. We found strong evidence to support integration’s long-run impact on the patterns of absolute industrial diversification. Significantly, this new finding remains robust to the exclusion of the primary sectors and to the potential presence of unit root in the data. Our results lead us to support a positive long-run relationship between trade integration and industrial diversification. / Nous analysons l’incidence de l’intégration économique canado-américaine sur le degré de spécialisation industrielle des régions canadiennes. Le phénomène d’intégration commerciale entre le Canada et les États-Unis est envisagé sous l’angle de la décroissance des tarifs pondérés par le commerce à laquelle a donné lieu l’Accord de libre-échange Canada-États-Unis. Les résultats étayent largement le point de vue selon lequel la libéralisation des échanges aurait favorisé, à long terme, la diversification industrielle des provinces. Ces résultats apparaissent significativement robustes lorsqu’on inclut une racine unitaire dans les données et qu’on exclut le secteur primaire. Nos résultats nous amènent à prévoir une relation positive à long terme entre l’intégration commerciale et la diversification industrielle.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.009 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".