Health Insurance and the Labor Supply Decisions of Older Workers: Evidence from the U.S. Department of Veterans Affairs
Bibliographic record
Abstract
This paper exploits a major mid-1990s expansion in the U.S. Department of Veterans Affairs health care system to provide evidence on the labor market effects of expanding health insurance availability. Using data from the Current Population Survey, we compare the labor market behavior of older veterans and non-veterans before and after the VA health benefits expansion to test the impact of public health insurance on labor supply. We find that older workers are significantly more likely to decrease work both on the extensive and intensive margins after receiving access to non-employer based insurance. Older workers are also more likely to leave self-employment, a result inconsistent with "job-lock" effects of employer-based insurance, but consistent with a positive income effect from new access to public insurance. Some relatively disadvantaged subpopulations, however, may increase their labor supply after gaining greater access to public insurance, consistent with complementary positive health effects of health care access for these groups. We conclude that this reform has affected employment and retirement decisions, and suggest that future moves toward universal coverage or expansions of Medicare are likely to have significant labor market effects. To illustrate, we calculate that as much as 10% of the difference in retirement rates in the US and Canada may be due to Canada's provision of universal health care.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".