Who Values Human Capitalists' Human Capital? The Earnings and Labor Supply of U.S. Physicians
Bibliographic record
Abstract
Is government guiding the invisible hand at the top of the labor market?We use new administrative data to measure physicians' earnings and estimate the influence of healthcare policies on these earnings, physicians' labor supply, and allocation of talent.Combining the administrative registry of U.S.~physicians with tax data, Medicare billing records, and survey responses, we find that physicians' annual earnings average $350,000 and comprise 8.6% of national healthcare spending.The age-earnings profile is steep; business income comprises onequarter of earnings and is systematically underreported in survey data.There are major differences in earnings across specialties, regions, and firm sizes, with an unusual geographic pattern compared with other workers.We show that health policy has a major impact on the margin: 25% of physician fee revenue driven by Medicare reimbursements accrues to physicians personally.Physicians earn 6% of public money spent on insurance expansions.We find that these policies in turn affect the type and quantity of medical care physicians supply in the short run; retirement timing in the medium run; and earnings affect specialty choice in the long run.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".