The Evolution of Job Lock in the U.S.: Evidence from the Affordable Care Act
Bibliographic record
Abstract
Since at least the early 1990s, economists have found substantial evidence of “job lock” in the United States: workers who get health insurance from their employer are less likely to switch jobs. Early work showed stronger job lock among groups that place a higher value on health insurance, whereas more recent work has focused on measuring the effect of specific policies on job lock. We combine these approaches by replicating some of the classic group comparisons (job switching among the more versus less healthy, and among those whose spouses do or do not have their own health insurance) over a much longer time period, using data from the Current Population Survey and the Medical Expenditure Panel Survey. This enables us to document the evolution of job lock over time, with a particular focus on how it changed when policies such as the Affordable Care Act (ACA) took effect. Estimates based on a difference-in-differences methodology indicate that job lock remains substantial, and that ACA has not significantly affected job mobility.
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".