The Effects of Employer-Sponsored Health Insurance Premiums on Employment and Wages: Evidence from US Longitudinal Data.
Bibliographic record
Abstract
We analyze the effect of employer-sponsored health insurance premiums on employment and annual wages in the US using a county-level panel dataset for the period 2005-2010. Using variation in medical malpractice payments and variation in medical malpractice legislation over time and within states as the source of identifying variation in the health insurance premiums, we estimate the causal effects of rising health insurance premiums on employment and annual wages. We find that a 10% increase in premiums reduces employment by 1.1 percentage points, and leads to a statistically insignificant reduction of annual wages. Since US counties are characterized by a varying degree of private health insurance coverage, we also test whether the private health insurance coverage is a moderating variable for the relationship between the health insurance premiums and the labor market outcomes analyzed in this study. We find that rising premiums negatively affect the labor market conditions faced by US workers, especially in areas that are characterized by high private health insurance coverage.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".