First Fired, Last Hired, and Lower Paid: Re-employment Outcomes Among Displaced Workers With Disabilities, 2007–2021
Bibliographic record
Abstract
Abstract People with disabilities continue to face significant barriers in the labor market. They are also more likely to experience job displacement, involuntary job loss typically resulting from broader exogenous forces (e.g., automation, economic downturns) that make workers no longer needed. What happens to displaced workers with disabilities? Do they find new jobs, and if they do, what jobs are they? We study re-employment outcomes among displaced workers with disabilities using data from the 2010, 2012, 2014, 2018, 2020, and 2022 waves of the Current Population Survey Displaced Worker Supplement. We find that in addition to higher rates of displacement, workers with disabilities took longer to be re-employed than workers without disabilities with a decreased hazard for re-employment of about 30%. For those who found new jobs, earnings losses upon re-employment were 18% greater for people with disabilities when compared to those without disabilities. Although the relationship between disability and time to re-employment did not vary significantly over time, earnings differences between people with and without disabilities were smaller during the pandemic.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".