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An Economic Justification for Open Access to Essential Medicine Patents in Developing Countries

2009· article· en· W2103664762 on OpenAlexaff
Sean Flynn, Aidan Hollis

Bibliographic record

VenueThe Journal of Law Medicine & Ethics · 2009
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicIntellectual Property and Patents
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsMonopolyIncentiveDeveloping countryDeadweight lossMarket powerProfit (economics)EconomicsPrice discriminationBusinessPublic economicsWelfareInternational economicsIndustrial organizationMarket economyMicroeconomicsEconomic growth

Abstract

fetched live from OpenAlex

This paper offers an economic rationale for compulsory licensing of needed medicines in developing countries. The patent system is based on a trade-off between the "deadweight losses" caused by market power and the incentive to innovate created by increased profits from monopoly pricing during the period of the patent. However, markets for essential medicines under patent in developing countries with high income inequality are characterized by highly convex demand curves, producing large deadweight losses relative to potential profits when monopoly firms exercise profit-maximizing pricing strategies. As a result, these markets are systematically ill-suited to exclusive marketing rights, a problem which can be corrected through compulsory licensing. Open licenses that permit any qualified firm to supply the market on the same terms, such as may be available under licenses of right or essential facility legal standards, can be used to mitigate the negative effects of government-granted patents, thereby increasing overall social welfare.

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 imitation

Not 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.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.684
Threshold uncertainty score0.326

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.002
Open science0.0020.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.449
GPT teacher head0.432
Teacher spread0.017 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations16
Published2009
Admission routes1
Has abstractyes

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