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Record W1568302852 · doi:10.1002/hec.2942

PUBLIC AND PRIVATE HEALTH INSURANCE IN GERMANY: THE IGNORED RISK SELECTION PROBLEM

2013· article· en· W1568302852 on OpenAlex
Martina Grunow, Robert Nuscheler

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.

fundA Canadian funder is recorded on the work.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueHealth Economics · 2013
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHealthcare Policy and Management
Canadian institutionsnot available
FundersCanadian Institutes of Health Research
KeywordsPrivate insuranceActuarial scienceHealth insuranceGermanBusinessAdverse selectionGroup insuranceHealth careCompetition (biology)Public healthShock (circulatory)General insuranceEconomicsFinancePublic economicsInsurance policyIncome protection insuranceEconomic growthMedicine

Abstract

fetched live from OpenAlex

We investigate risk selection between public and private health insurance in Germany. With risk-rated premiums in the private system and community-rated premiums in the public system, advantageous selection in favor of private insurers is expected. Using 2000 to 2007 data from the German Socio-Economic Panel Study (SOEP), we find such selection. While private insurers are unable to select the healthy upon enrollment, they profit from an increase in the probability to switch from private to public health insurance of those individuals who have experienced a negative health shock. To avoid distorted competition between the two branches of health care financing, risk-adjusted transfers from private to public insurers should be instituted.

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)0.002
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.481
Threshold uncertainty score0.951

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 science0.0000.000
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 head0.066
GPT teacher head0.260
Teacher spread0.194 · 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