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Record W3024333332 · doi:10.3386/w21513

Does Health Plan Generosity Enhance Hospital Market Power?

2015· preprint· en· W3024333332 on OpenAlexaff
Laurence C. Baker, M. Kate Bundorf, Daniel P. Kessler

Bibliographic record

VenueNational Bureau of Economic Research · 2015
Typepreprint
Languageen
FieldEconomics, Econometrics and Finance
TopicHealthcare Systems and Reforms
Canadian institutionsInstitute of Health Services and Policy Research
FundersStanford Graduate School of Business
KeywordsGenerosityPlan (archaeology)Health planBusinessPower (physics)EconomicsHealth careGeographyEconomic growthPolitical science

Abstract

fetched live from OpenAlex

We test whether the generosity of employer-sponsored health insurance facilitates the exercise of market power by hospitals.We construct indices of health plan generosity and the price and volume of hospital services using data from Truven MarketScan for 601 counties from 2001-2007.We use variation in the industry and union status of covered workers within a county over time to identify the causal effects of generosity.Although OLS estimates fail to reject the hypothesis that generosity facilitates the exercise of hospital market power, IV estimates show a statistically significant and economically important positive effect of plan generosity on hospital prices in uncompetitive markets, but not in competitive markets.Our results suggest that most of the aggregate effect of hospital market structure on prices found in previous work may be coming from areas with generous plans.

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)0.003
metaresearch head score (Gemma)0.030
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 score0.012
Threshold uncertainty score0.039

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.030
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.002
Scholarly communication0.0020.003
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0120.000

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 head0.277
GPT teacher head0.466
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 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

Citations0
Published2015
Admission routes1
Has abstractyes

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