An Empirical Evaluation of the Effect of Covid-19 Travel Restrictions on Canadians' Cross Border Travel and Canadian Retailers
Bibliographic record
Abstract
This paper estimates the impact on Canadian retailers' revenues of significantly decreased international travel by Canadians in response to the Covid-19 related travel restrictions imposed on the US-Canada border. We use detailed data from 1991 to 2020 on Canadians' travel to the United States to estimate the monthly fraction of a community's residents who cross the border for 237 communities within 150 kilometers of the border. We estimate the model of cross-border travel from Baggs, Fung and Lapham (2018) and use those estimates to establish community-level counterfactual crossing rates had the pandemic not occurred. We then combine those rates with actual crossing rates to estimate the revenue losses that small Canadian retailers' avoided due to the near cessation of cross-border travel by Canadians as a result of the pandemic. Our results suggest that, on average, the border closure prevented a 1.7% decrease in revenues for a small Canadian retailer located within 150 kilometers of the border. However, we document considerable variation in the magnitude of this decrease across communities and retail sectors, with estimates ranging from approximately 0% to 234%. Specifically, retailers that are located in less affluent communities near sizeable US shopping opportunities and that are in sub-sectors that cater to travelers experienced the largest foregone revenue losses due to border closures in 2020.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".