The Health Wedge and Labor Market Inequality
Bibliographic record
Abstract
ABSTRACT: Over half of the US population receives health insurance through an employer with premium contributions creating a flat "head tax" per worker, independent of their earnings. This paper develops and calibrates a stylized model of the labor market to explore how this uniquely American approach to financing health insurance contributes to labor market inequality. We consider a partial-equilibrium counterfactual in which employer-provided health insurance is instead financed by a statutory payroll tax on firms. We find that, under this counterfactual financing, in 2019 the college wage premium would have been 11 percent lower, noncollege annual earnings would have been $1,700 (3 percent) higher, and noncollege employment would have been nearly 500,000 higher. These calibrated labor market effects of switching from head tax to payroll tax financing are in the same ballpark as estimates of the impact of other leading drivers of labor market inequality, including changes in outsourcing, robot adoption, rising trade, unionization, and the real minimum wage. We also consider a separate partial-equilibrium counterfactual in which the current head tax financing is maintained, but 2019 US health care spending as a share of GDP is reduced to the Canadian share; here, we estimate that the 2019 college wage premium would have been 5 percent lower and noncollege annual earnings would have been 5 percent higher. These findings suggest that health care costs and the financing of health insurance warrant greater attention in both public policy and research on US labor market inequality.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".