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Record W3124622972

Investments in Pharmaceuticals Before and After TRIPS

2009· preprint· en· W3124622972 on OpenAlexaff
Margaret Kyle, Anita M. McGahan

Bibliographic record

VenueRePEc: Research Papers in Economics · 2009
Typepreprint
Languageen
FieldBusiness, Management and Accounting
TopicIntellectual Property and Patents
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsIntellectual propertyTRIPS architectureTRIPS AgreementInvestment (military)BusinessAccess to medicinesDeveloping countryInternational tradeDrug pricesPublic economicsDevelopment economicsInternational economicsEconomic growthEconomicsPolitical scienceLawEngineering
DOInot available

Abstract

fetched live from OpenAlex

We examine the relationship between patent protection for pharmaceuticals and investment in development of new drugs. Patent protection has increased around the world as a consequence of the TRIPS Agreement, which specifies minimum levels of intellectual property protection for members of the World Trade Organization. It is generally argued that patents are critical for pharmaceutical research efforts, and so greater patent protection in developing and least-developed countries might result in greater effort by pharmaceutical firms to develop drugs that are especially needed in those countries. Since patents also have the potential to reduce access to treatments through higher prices, it is imperative to assess whether the benefits of increased incentives have materialized in research on diseases that particularly affect the poor. We find that patent protection is associated with increases in research and development (R&D) effort when adopted in high income countries. However, the introduction of patents in developing countries has not been followed by greater investment. Particularly for diseases that primarily affect the poorest countries, our results suggest that alternative mechanisms for inducing R&D may be more appropriate than patents.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.864
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.002
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 head0.131
GPT teacher head0.321
Teacher spread0.189 · 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.

Study designOther design
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

Citations4
Published2009
Admission routes1
Has abstractyes

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