The Effects of NAFTA/CUSFTA on Agricultural Trade Flows: An Empirical Investigation
Bibliographic record
Abstract
This paper examines the effects of the North American Free Trade Agreement (NAFTA) and its predecessor, the Canada‐United States Free Trade Agreement (CUSFTA), on agricultural trade flows at disaggregated product categories. The empirical analysis is implemented through gravity models using different econometric methods. It accounts for the baseline NAFTA/CUSFTA‐unrelated magnitudes of trade between member countries throughout the assessment of the NAFTA/CUSFTA trade effects. The benchmark estimates show considerable differences across agricultural product categories. The net post‐NAFTA/CUSFTA magnitudes of trade between member countries are found to be markedly low in some cases, underlining missed regional trade opportunities. The empirical analysis proceeds to estimate the NAFTA/CUSFTA trade effects by time period and by bilateral trading partnership, revealing important variations. Cet article examine les effets de l'Accord de Libre‐Échange Nord‐Américain (ALÉNA) et de son prédécesseur, l'Accord de Libre‐Échange entre le Canada et les États‐Unis (ALÉCÉU), sur les flux commerciaux agricoles au niveau des catégories ventilées. L'analyse empirique est mise en œuvre grâce à des modèles de gravité en utilisant une variété de méthodes économétriques. Elle compte pour la ligne de base des magnitudes non‐reliées à l'ALÉNA/ALÉCÉU du commerce entre les pays membres à travers l′évaluation des effets de l'ALÉNA/ALÉCÉU sur le commerce. Les estimations de référence montrent des différences considérables entre les catégories de produits agricoles. Les magnitudes post‐ALÉNA/ALÉCÉU nettes du commerce entre les pays membres se trouvent d′être considérablement faibles dans certains cas. Ces résultats soulignent des opportunités commerciales régionales manquées. L'analyse empirique procède à estimer les effets de l'ALÉNA/ALÉCÉU sur le commerce par période et par partenariat commercial bilatéral, et elle montre des variations importantes.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 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".