The Effect of CUSFTA and NAFTA on Canada’s Export Composition
Bibliographic record
Abstract
This research analyzes the effect of the North American Free Trade Agreement (NAFTA) and its predecessor, the Canada-USA Free Trade Agreement (CUSFTA), on the change in the export composition of Canada using a multivariate time series model. Export composition is measured using 11 different indexes from diversification, extensive margin, intensive margin, product sophistication, and economic complexity. These measures are important as they represent the degree of risk a country faces in its international trade and show the potential for economic development. We use a dummy variable to represent participation in the trade agreement. This study extends the literature by computing and assessing a comprehensive set of measures of export performance (eleven measures), and using a long time series of 50 years of data from 1970 to 2019. This study accounts for the stationarity of all variables and calculates the product diversification index in three scenarios: Canada’s exports to the USA and Mexico, Canada’s exports to all countries, and Canada’s exports to all countries except the USA and Mexico. While most previous studies have been conducted using panel data, this study uses a time series approach to focus on how the trade performance of a developed country such as Canada changed over time due to participation in a free trade agreement. Consistent with previous studies, this study shows that greater economic integration is associated with positive change in export diversity estimated by the Herfindahl-Hirschman index for Canada. The other major result of this study shows that the effect of CUSFTA-NAFTA is negative on the changes in the intensive margin concentration of Canada's exports to the USA and Mexico. Of these 11 measures of trade performance, only four measures show a statistically significant relationship with trade agreements. These four statistically significant measures are the product diversification of Canada’s exports to the USA and Mexico, product diversification of exports to all countries, the intensive margin of export to the USA and Mexico, and the intensive margin of exports to all countries. The other seven trade performance measures, including product sophistication, economic complexity, and extensive margin, are not statistically significant in this study.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".