Bibliographic record
Abstract
Although the vast majority of Americans have private health insurance, researchers focus almost exclusively on public provision.Data on the private insurance sector is extremely difficult to obtain because health insurance contracts are complex, renegotiated annually, and not subject to reporting requirements.This study makes use of a privately-gathered national database of insurance contracts agreed upon by a sample of large, multisite employers between 1998 and 2005.To gauge the competitiveness of the group health insurance industry, I investigate whether health insurers charge higher premiums, ceteris paribus, to more profitable firms.I find they do, and this result is not driven by cross-sectional differences across firms or plans: firms with positive profit shocks subsequently face higher premium growth, even for the same healthplans.Moreover, this relationship is strongest in geographic markets served by a small number of insurance carriers.Further analysis suggests profits act to increase employers' switching costs, and insurers exploit this inelasticity where they have sufficient bargaining power.Given the rapid industry consolidation during the study period, these findings suggest healthcare insurers possess and exercise market power in an increasing number of geographic markets.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.032 | 0.002 |
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".