How Parallel Trade Affects Drug Policies and Prices in Canada and the United States
Bibliographic record
Abstract
U.S. consumers and Canadian pharmacies have rushed to take advantage of the opportunity presented by price differences in patented pharmaceuticals. This rapidly growing parallel trade has brought the Canadian and U.S. systems for determining pharmaceutical pricing under increased scrutiny, and the pressure for change seems to be building. This paper examines why parallel trade in pharmaceuticals has grown and considers some of the policy options confronting both countries. To do this we begin by identifying similarities and differences in the Canadian and U.S. regulatory frameworks governing trade in pharmaceuticals. While the differences are considerable, we show that they are not the only reason for the emergence of the price disparity that has fuelled the growth in parallel trade. In particular, we argue that the price discrimination strategies of pharmaceutical manufacturers and exchange rate fluctuations have played an underappreciated role. Following this, we examine the claims that this trade represents a threat to American and Canadian interests, and find that there are good reasons for policy makers on both sides of the border to be concerned. Unchecked, this rising trade presents a threat to R&D funding and continued Canadian consumer access to pharmaceutical products.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".