The Impact of U.S. Free Trade Agreements on Calorie Availability and Obesity: A Natural Experiment in Canada
Bibliographic record
Abstract
INTRODUCTION: Globalization via free trade and investment agreements is often implicated in the obesity pandemic. Concerns center on how free trade and investment agreements increase population exposure to unhealthy, high-calorie diets, but existing studies preclude causal conclusions. Few studies of free trade and investment agreements and diets isolated their impact from confounding changes, and none examined any effect on caloric intake, despite its critical role in the etiology of obesity. This study addresses these limitations by analyzing a unique natural experiment arising from the exceptional circumstances surrounding the implementation of the 1989 Canada-U.S. Free Trade Agreement. METHODS: Data from the UN (2017) were analyzed using fixed-effects regression models and the synthetic control method to estimate the impact of the Canada-U.S. Free Trade Agreement on calorie availability in Canada, 1978-2006, and coinciding increases in U.S. exports and investment in Canada's food and beverage sector. The impact of changes to calorie availability on body weights was then modeled. RESULTS: Calorie availability increased by ≅170 kilocalories per capita per day in Canada after the Canada-U.S. Free Trade Agreement. There was a coinciding rise in U.S. trade and investment in the Canadian food and beverage sector. This rise in calorie availability is estimated to account for an average weight gain of between 1.8 kg and 12.2 kg in the Canadian population, depending on sex and physical activity levels. CONCLUSIONS: The Canada-U.S. Free Trade Agreement was associated with a substantial rise in calorie availability in Canada. U.S. free trade and investment agreements can contribute to rising obesity and related diseases by pushing up caloric intake.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".