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Record W3174579370 · doi:10.1111/ecin.13158

When social assistance meets market power: A mixed duopoly view of health insurance in the United States

2023· article· en· W3174579370 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

VenueEconomic Inquiry · 2023
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHealthcare Policy and Management
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsMedicaidDuopolyInefficiencyMarket powerEconomicsProduct (mathematics)PopulationProduct differentiationPublic economicsQuality (philosophy)MicroeconomicsHealth insuranceBusinessHealth careWelfareSocial WelfareMarket economyMonopolyEconomic growthMedicine

Abstract

fetched live from OpenAlex

Abstract We develop a mixed duopoly model with quality‐differentiated products. The public firm offers its product for free to eligible individuals, while the private firm chooses its product quality and price to maximize profit. We calibrate the model to health insurance for the U.S. working‐age population, with Medicaid being the public firm. We examine distributional implications of policies that expand Medicaid to various degrees. Despite potentially significant inefficiency of Medicaid, its expansion is welfare improving. Central to these findings is the significant market power of the private firm when left unchecked, which is increasingly disciplined as more individuals become Medicaid eligible.

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)0.004
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.506
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.096
GPT teacher head0.315
Teacher spread0.219 · 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