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Record W2024556879 · doi:10.1086/467458

Does Regulation Drive Out Competition in Pharmaceutical Markets?

2000· article· en· W2024556879 on OpenAlexaboutno aff
Patricia M. Danzon, Li‐Wei Chao

Bibliographic record

VenueThe Journal of Law and Economics · 2000
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicPharmaceutical Economics and Policy
Canadian institutionsnot available
Fundersnot available
KeywordsCompetitor analysisCompetition (biology)Drug pricesIndustrial organizationBusinessEconomicsPharmacyPublic economicsMarketing

Abstract

fetched live from OpenAlex

Most countries regulate pharmaceutical prices, either directly or indirectly, on the assumption that competition is at best weak in this industry. This paper tests the hypothesis that regulation of manufacturer prices and retail pharmacy margins undermines price competition. We use data from seven countries for 1992 to examine price competition between generic competitors (different manufacturers of the same compound) and therapeutic substitutes (similar compounds) under different regulatory regimes. We find that price competition between generic competitors is significant in unregulated or less regulated markets (United States, United Kingdom, Canada, and Germany) but that regulation undermines generic competition in strict regulatory systems (France, Italy, and Japan). Regulation of retail pharmacy further constrains competition in France, Germany, and Italy. Regulation thus undermines the potential for significant savings on off‐patent drugs, which account for a large and growing share of drug expenditures. Evidence of competition between therapeutic substitutes is less conclusive owing to data limitations.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.010
metaresearch head score (Gemma)0.037
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.016
Threshold uncertainty score0.053

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.037
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0020.003
Science and technology studies0.0020.008
Scholarly communication0.0070.006
Open science0.0010.003
Research integrity0.0040.003
Insufficient payload (model declined to judge)0.0090.001

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.

Opus teacher head0.042
GPT teacher head0.282
Teacher spread0.240 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations242
Published2000
Admission routes1
Has abstractyes

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