Should Global Health be Tailored Toward the Rich? Altruism and Efficient R&D for Neglected Diseases
Bibliographic record
Abstract
We analyze the problem of incentivizing research and development (R&D) into developing world disease from an economic efficiency perspective. We view the problem as how to best promote R&D into goods with positive external effects in the sense that medicines that directly affect the health of the poor also indirectly affect the utility of the altruistic "rich." We demonstrate why existing policy proposals - such as price concessions by manufacturers - adversely impact the poor by placing the burden of R&D only on innovators rather than all altruists in the rich world. We offer policy solutions that are based on economic efficiency and therefore rely on a broad sense of how the world values the treatment of developing world disease. We estimate that global altruism toward those with malaria is, at a minimum, valued between $835 million and $2.4 billion annually and for HIV/AIDS, between $9.1 billion and $26.6 billion annually. We argue that future policies toward neglected diseases need to better incorporate how efficient R&D meets the need of this global altruism.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".