The Stalled Jobs Recovery Pushed 1.1 Million Older Workers Out Of The Labor Force
Bibliographic record
Abstract
An examination of the status of older workers in the fourth quarter of 2020 reveals three highlights: After a partial recovery between May and August of 2020, older workers' labor force participation rate fell continuously, reaching its lowest point of the recession in January. Roughly 1.1 million older workers exited the workforce between August and January due to the pandemic recession; older workers' unemployment rate fell in January 2020 by 0.7 percentage points but the decline was driven by unemployed workers leaving the labor force rather than finding jobs; and since October of 2020, the decline in employment for Black, Hispanic, and Asian older workers was more than twice that of white older workers. Policy recommendations include Congress facilitating older workers' return to work with aggressive anti-age discrimination enforcement and expanded unemployment benefits. Congress must also lower the Medicare eligibility age to age 50 and make the program “first payer†to lower the cost of hiring older workers.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".