Physician Bias and Racial Disparities in Health: Evidence from Veterans' Pensions
Bibliographic record
Abstract
We estimate racial differences in longevity using records from cohorts of Union Army veterans. Since veterans received pensions based on proof of disability at medical exams, estimates of the causal effect of income on mortality may be biased, as sicker veterans received larger pensions. To circumvent endogeneity bias, we propose an exogenous source of variation in pension income: the judgment of the doctors who certified disability. We find that doctors appeared to discriminate against black veterans. The discrimination we observe is acute-we would not observe any racial mortality differences had physicians not been racially biased in determining pension awards. The effect of income on health was indeed large enough to close the black-white mortality gap in the period. Our work emphasizes that the large effects of physicians' attitudes on racial differentials in health, which persist today amongst both veterans and the civilian population, were equally prominent in the past.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".