High Drug Prices, Big R&D Spenders and “Free Riders”: Canada in the Topsy Turvy World of Pharmaceuticals
Bibliographic record
Abstract
The main reason why health and general living standards in the world’s developed countries are so much better than in earlier eras is that today’s technology is much more advanced. But new technology does not come for free. Most of it, in healthcare and elsewhere, comes about because large amounts of resources are spent on R&D. All countries, especially those with high per-capita incomes, face an inevitable tension between their obligation to contribute their fair share to global pharmaceutical R&D financing and their desire to save money for the taxpayers, private insurers and patients who pay for drugs. In this Commentary, we compare how patent law and pharmaceutical regulation help determine drug prices in Canada, the US, and major countries in Europe and Australasia. Different countries respond in different ways to balancing the need to contain drug spending with contributing to the development of new pharmaceutical technologies that improve our ability to treat previously untreatable conditions. Government policy in many other countries plays a more comprehensive role than it does in Canada, either in the form of direct regulation of drug prices or via the government’s role, direct or indirect, in the process under which insurance plans negotiate with pharmaceutical companies about drug purchasing and pricing. Specifically, we examine what policies Canada should pursue to help overcome criticism that it is a free rider while avoiding paying more than its fair share. With complex interactions between regulations, patent laws, and R&D tax incentives and subsidies, it is difficult to determine whether Canada’s contributions to global pharmaceutical R&D are “optimal.” It is clear, however, that Canada is less of a free-rider than some other countries that employ restrictive drug pricing policies. Conversely, evidence suggests that US consumers pay more than their fair share towards pharmaceutical R&D due to high prices. Though lower than in the US, published prices of patented pharmaceuticals in Canada are comparable to or higher than in many other developed nations, as are our contributions to business R&D through direct funding and tax expenditures. We recommend that Canada pursue a two-track strategy. In the short run, we benefit from and, therefore, should aim for the lowest drug prices that we can get without inviting opposition from our main trading partners. But we should simultaneously work with our trading partners and international agencies toward a model of global R&D funding that overcomes the free-rider problem and moves us closer to a more efficient management of this aspect of the global commons.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.023 | 0.016 |
| Scholarly communication | 0.012 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.016 | 0.016 |
| 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".