The Effect of Government Policy on Pharmaceutical Drug Innovation
Bibliographic record
Abstract
Abstract Drug companies are profit-maximizing entities, and profit is, by definition, revenue less cost. Here we review the impact of government policies that affect sales revenues earned on newly developed drugs and the impact of policies that affect the cost of drug development. The former policies include intellectual property rights, drug price controls, and the extension of public drug coverage to previously underinsured groups. The latter policies include regulations governing drug safety and efficacy, R&D tax credits, publicly funded basic research, and public funding for open drug discovery consortia. The latter policy, public funding of research consortia that seek to better understand the cellular pathways through which new drugs can ameliorate disease, appears very promising. In particular, a better understanding of human pathophysiology may be able to address the high failure rate of drugs undergoing clinical testing. Policies that expand market size by extending drug insurance to previously underinsured groups also appear to be effective at increasing drug R&D. Expansions of pharmaceutical intellectual property rights seem to be less effective, given the countervailing monopsony power of large public drug plans.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".