Intellectual Property & External Consumption Effects: Generalizations from Pharmaceutical Markets
Bibliographic record
Abstract
There is a long-standing literature that recognizes that an efficient solution in correcting a consumption externality is through applying subsidies and taxes that line up private incentives with social ones.An equally long-standing literature tackles the appropriate methods of generating the efficient amount of R&D into goods that only have private consumption effects, e.g. the analysis of the welfare effects of patent regulations.This paper analyzes the joint problem of the optimal provision of R&D and consumption incentives for goods that at the same time undergo technological change and have external consumption effects.For good with external effects, just as is the case for goods with only private effects, ex-post static efficiency may have to be sacrificed for dynamic efficiency.For goods with only private consumption effects, it is well-understood that efficient competition ex-post leads to insufficient R&D incentives ex-ante, which is of course the common rationale for patents.For external effects, this analogy has the important and unrecognized implication that classic interventions to solve externality problems, such as Pigouvian taxes and subsidies, may often be inefficient under technological change.In many cases, arguing for Pigouvian solutions in presence of technological change is analogous to arguing for competitive markets for new inventions (!), as both argue for ex-post efficiency rather than dynamic efficiency.The results are discussed in the context of the pharmaceutical industry which simultaneously is one of the most R&D-intensive industries and one for which consumption of its output often seems to involve external effects, e.g. through human rights-based access issues.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.025 | 0.001 |
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".