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Record W2799999401 · doi:10.3386/w21783

Externalities and Benefit Design in Health Insurance

2015· report· en· W2799999401 on OpenAlexaff
Amanda Starc, Robert Town

Bibliographic record

VenueNational Bureau of Economic Research · 2015
Typereport
Languageen
FieldEconomics, Econometrics and Finance
TopicHealthcare Policy and Management
Canadian institutionsKellogg's (Canada)
Fundersnot available
KeywordsExternalityEconomicsHealth insuranceActuarial scienceMicroeconomicsPublic economicsHealth careEconomic growth

Abstract

fetched live from OpenAlex

Insurance plan design has important implications for consumer welfare. In this paper, we model insurance design in the Medicare prescription drug coverage market and show that strategic private insurer incentives impose a fiscal externality on the traditional Medicare program. We document that plans covering medical expenses have more generous drug coverage than plans that are only responsible for prescription drug spending, which translates into higher drug utilization by enrollees. The effect is driven by drugs that reduce medical expenditure and treat chronic conditions. Our equilibrium model of plan design endogenizes plan characteristics and accounts for selection; the model estimates confirm that differential incentives to internalize medical care offsets can explain disparities across plans. Counterfactuals show that strategic insurer incentives are as important as selection in determining endogenous plan characteristics.

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 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)0.024
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.933
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0240.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.688
GPT teacher head0.541
Teacher spread0.147 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreOther

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

Citations20
Published2015
Admission routes1
Has abstractyes

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