The labor market effects of employment -based health insurance: Health-related job-lock and job sorting.
Bibliographic record
Abstract
Three studies of the labor market effects of employment-based health insurance (EBHI) were conducted for workers ages 20--55 years. These studies evaluated job-lock, or reduced worker mobility due to EBHI, and job sorting based on preferences for health benefits. All studies focused on how EBHI affects the job choices of workers with health problems. The first study considered the trend in job-lock for married workers based on the 1987 National Medical Expenditure Survey (MMES) and the 1996--2000 Medical Expenditure Panel Survey (MEPS). Interactions of health insurance with family health measures (difference-in-difference) yielded job-lock estimates ranging from 20--50 percent during 1987. Previous negative findings may be explained by methods used to identify job-lock and estimate models of job turnover. No strong evidence of job-lock was found during 1996--2000. Despite rising health care costs, job-lock did not become more pervasive, and may have been alleviated by a stronger economy and health insurance legislation. This is the first study of the recent trend in job-lock using relatively standardized methods. The second study considered whether key omitted variables might explain job-lock findings by studying Canadian workers, for whom health insurance will not affect job turnover. Data were obtained for single workers in the NMES, the MEPS and the 1996--99 Canadian National Population Health Survey. Based on a relative reduction in mobility due to higher-cost chronic illnesses for U.S. workers with EBHI, there was evidence of job-lock in 1987, but not in 1996--98. A similar reduction in mobility was not observed for Canadian workers, either overall or in relative terms for those predicted to hold EBHI. The third study examined job sorting by predicting job changes involving a gain or loss in health benefits for single workers in the MEPS. Chronic illnesses, especially higher-cost conditions and conditions afflicting uninsured family members (worker or child), were strong predictors of gaining health benefits through a new job. Health-related job sorting may have both positive and negative implications for efficient labor allocation. Relatively few transitions to jobs with health benefits among workers with costly health problems suggests important efficiency and equity issues related to health insurance coverage.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".