Impact of the First Wave of the COVID-19 Pandemic on Trade between Canada and the United States
Bibliographic record
Abstract
We examine how the coronavirus disease 2019 (COVID-19) pandemic has affected trade between Canada and the United States, using a novel dataset on monthly bilateral trade flows between Canadian provinces and US states merged with COVID-19 health data. Our results show that a one-standard-deviation increase in COVID-19 severity (case levels, hospitalizations, deaths) in a Canadian province leads to a 3.1 percent to 4.9 percent fall in exports and a 6.7 percent to 9.1 percent fall in imports. Decomposing our analysis by industry, we determine that trade in the manufacturing industry was most negatively affected by the pandemic, and the agriculture industry had the least disruption to trade flows. Our descriptive evidence suggests that lockdowns may also have reduced Canadian exports and imports. However, although our regression coefficients are consistent with that finding, they are not statistically significant, perhaps because of the lack of variation as a result of similar timing in the imposition of restrictions across provinces.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".