From recession to pandemic: Displacement among workers with disabilities from 2007 through 2021
Bibliographic record
Abstract
BACKGROUND: With at least one-quarter of the U.S. adult population reporting one or more disabilities in 2020, people with disabilities represent a large and diverse group of individuals who often face significant barriers in the labor market, especially job displacement - involuntary job loss due to external factors. OBJECTIVE: We examine how rates of job displacement varied for people with different types of disabilities from 2007–2021, a period that includes the 2008 Great Recession and the COVID-19 pandemic. METHODS: We use data from six waves of Current Population Study Displaced Worker Supplement (CPS DWS, N = 344,729) and a series of logistic regression models to examine differences in displacement by disability status and type. RESULTS: People with disabilities were approximately twice as likely as those without disabilities to experience job displacement, but more during times of economic turmoil. Although displacement disparities by disability status were decreasing from a high of 6.5 percentage points during the Great Recession, the pandemic increased the gap to 5.8 percentage points. CONCLUSION: Involuntary job loss among people with disabilities is exacerbated by exogenous shocks. We extend work on disability and displacement, incorporating the COVID-19 pandemic in our discussion of explanations of both labor market disadvantage and precarity.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".