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A DYNAMIC OLIGOPOLY STRUCTURAL MODEL FOR THE PRESCRIPTION DRUG MARKET AFTER PATENT EXPIRATION*

2010· article· en· W2111436115 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueInternational Economic Review · 2010
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicPharmaceutical Economics and Policy
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsOligopolyExpiration dateWelfareNested logitMicroeconomicsExpirationEconomicsPrescription drugConsumer welfareMedical prescriptionMarket structureBusinessIndustrial organizationEconometricsMedicineMarket economyPharmacology

Abstract

fetched live from OpenAlex

This article incorporates consumer learning and heterogeneity into a dynamic oligopoly model for the prescription drug market. In the model, both firms and patients need to learn the generic qualities via patients' experiences, generic firms' entry decisions are endogenous, but their entry timings depend on a random approval process. I apply the model to examine the impact of shortening the expected generic approval time. Although this policy experiment brings generics to the market sooner, it increases a potential entrant's likelihood of entering a crowded market and hence could reduce the total number of generic entrants and consumer 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.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.806
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.001

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 head0.061
GPT teacher head0.313
Teacher spread0.252 · 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