Health Care, Technological Change, and Altruistic Consumption Externalities
Bibliographic record
Abstract
Traditional economic analysis has proposed well known remedies to deal with consumption externalities and inefficient technological change in isolation, but lacks a general framework for addressing them jointly.We argue that the joint determination of R&D and consumption externalities is central to health care industries around the world generally, and for the pharmaceutical industry in particular.This is because technological change drives the expansion of the health care sector and altruism seems to motivate many public subsidies such as Medicaid in the US.We stress that standard remedies to the two problems in isolation are inefficient -Pigouvian corrections to consumption externalities are inefficient under technological change and standard R&D stimuli are inefficient because they focus only on consumer and producer surplus, not the altruistic surplus accruing to non-consumers.We provide illustrative calculations of the dynamic inefficiency in the level of US R&D spending due to the inability of innovators to appropriate the altruistic surplus.We find that altruistic gains amount to about a quarter of consumer surplus in the baseline scenario.Our analysis implies that total R&D could be under-provided by as much as 60 percent.
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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 | 0.006 |
| 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 | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".