Health Care Reform Big Benefits for Small Businesses
Bibliographic record
Abstract
Employer-sponsored insurance (ESI) is the dominant source of health insurance for the non-elderly population in the United States, still covering 62.9 % of this group despite consistent erosion over the past eight years (Gould 2008). Furthermore, employer contributions to ESI premiums ($532 billion in 2008) account for almost a quarter of all health spending in the United States, and for roughly one-third of health spending when Medicare (whose spending accrues overwhelmingly to the over-65 population) is excluded. Although increased health care costs have made it challenging for all firms to offer coverage, small businesses are at a particular disadvantage: declines in offers by firms with less than 10 workers have driven much of the overall decline in ESI offers. The implications for small businesses often carry much (perhaps even outsized, given its importance) weight in debates over health reform. This brief highlights the challenges faced by small businesses and the potential for fundamental health reform to greatly improve their ability to offer quality, affordable health insurance to their workers. Its key findings are: Small employers offer health insurance to their workers at much lower rates than other employers, and it is this decline that explains much of the erosion in ESI coverage since 2000. These low offer rates are due to a number of factors (e.g., insufficient size to offer attractive pools to potential insurers; high administrative costs; and little competition in the insurers ’ markets) that make purchasing insurance particularly expensive for small firms.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.018 | 0.001 |
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".