The Magnitude and Nature of Risk Selection in Employer-Sponsored Health Plans
Bibliographic record
Abstract
Most existing studies of risk selection in the employer-sponsored health insurance market are case studies of a single employer or of an employer coalition in a single market.We examine risk selection in the employer-sponsored market by applying a "switcher" methodology to a national, panel data set of enrollees in employer-sponsored health plans.We find that people who switched from a non-HMO to an HMO plan used 11 percent fewer medical services in the period prior to switching than people who remained in the non-HMO plan, and that this relatively low use persists once they enroll in an HMO.Furthermore, people who switch from an HMO to a non-HMO plan used 18 percent more medical services in the period prior to switching than those who remained in an HMO plan.HMOs would most likely continue to experience favorable risk selection if employers adjusted health plan payments based on enrollees' gender and age because the selection appears to occur based on enrollee characteristics that are difficult to observe, such as preferences for medical care and health status.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.018 | 0.061 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".