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Record W2883811532 · doi:10.1257/pol.20180489

Preferred Pharmacy Networks and Drug Costs

2021· article· en· W2883811532 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

VenueAmerican Economic Journal Economic Policy · 2021
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicPharmaceutical Economics and Policy
Canadian institutionsKellogg's (Canada)
Fundersnot available
KeywordsPharmacyBusinessCost sharingSubsidyMedical prescriptionPrescription drugHealth careMedicare Part DDrugMarketingActuarial scienceFamily medicineMedicineEconomicsPharmacologyNursing

Abstract

fetched live from OpenAlex

Selective contracting is an increasingly popular tool for reducing health care costs, but any savings must be weighed against consumer surplus losses from restricted access. Recently, many prescription drug plans (PDPs) utilize preferred pharmacy networks to reduce drug prices. Our results suggest that Medicare Part D plans with preferred pharmacy networks pay lower retail drug prices, while subsidized enrollees’ insensitivity to preferred pharmacy cost-sharing discounts reduces these savings. We then estimate pharmacy demand models to quantify the costs and benefits of preferred pharmacy networks, finding that the average enrollee benefits from preferred pharmacy contracting due to reduced out-of-pocket (OOP) costs at preferred pharmacies. (JEL G22, H51, I13, I18, L65, L81)

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.821
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.002

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 head0.032
GPT teacher head0.307
Teacher spread0.275 · 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