Investments in Pharmaceuticals Before and After TRIPS
Bibliographic record
Abstract
This paper addresses the relationship between patent protection and investment in the development of new pharmaceutical treatments. The TRIPS Agreement, which specifies minimum levels of intellectual property protection for countries in the World Trade Organization, has increased levels of patent protection around the world. Since patents also have the potential to reduce access to treatments through higher prices, it is imperative to assess whether wider use of patents has led to off-setting benefits, such as research on diseases that particularly affect the poor. Using variation across countries in the timing of patent laws and the severity of disease, we test the hypothesis that increased patent protection results in greater drug development effort. We find that patent protection in high income countries is associated with increases in research and development (R&D) effort; in other words, patent protection works in high-income countries to induce R&D. However, the introduction of patents in developing countries has not been followed by greater R&D investment in the diseases that are most prevalent there. Our results suggest that alternative mechanisms for inducing R&D may be more appropriate than patents for the "neglected" diseases that are concentrated in low-income countries.
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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 | 0.001 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.022 | 0.002 |
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".