The effects of sudden health reductions on labor market outcomes: Evidence from incidence of stroke
Bibliographic record
Abstract
While increasing attention is given to how health reductions affect workers, estimating their effects is usually challenging. This paper aims to identify the causal effect of health deterioration on labor market outcomes by exploiting the incidence of stroke. Stroke, which often reduces health suddenly and unexpectedly, allows us to exploit the within-person random variation of the timing and isolate the effects of health reduction. By applying the fixed-effects method to a sample of stroke survivors in the University of Michigan Health and Retirement Study data, I find that stroke immediately halves the employment probability as well as hours and weeks worked 1 year after the occurrence and its effects persist for at least 3 years, while earnings reduction is relatively moderate and gradual. I also find the negative effects of stroke are larger among men with severe stroke and women with longer pre-stroke job tenure, while the effects are mitigated for younger women. These results make a stark contrast with the studies on other health events such as cancer diagnosis, which generally find much smaller effects and significant heterogeneity by education and occupation. This analysis shows that the labor market effects largely differ by types of diseases and calls for disease-specific studies in order to understand the social gradient in health and how workers adjust to work limitations.
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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 | 0.006 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".