Where Have All the Workers Gone? Recalls, Retirements, and Reallocation in the COVID Recovery
Bibliographic record
Abstract
At the onset of the COVID pandemic, the U.S. economy suddenly and swiftly lost 20 million jobs.Over the next two years, the economy has been on the recovery path.We assess the labor market two years into the COVID crisis.We show that early employment dynamics were almost entirely driven by temporary layoffs and later recalls.Taking these into account, we show that the labor market remained surprisingly tight throughout the crisis, despite the dramatic job losses.By spring, 2022, the labor market had largely recovered and was characterized by extremely tight markets and a slightly depressed employment-to-population ratio driven largely by retirements.Finally, we see surprisingly little evidence of excess reallocation, despite predictions that COVID would dramatically and permanently change the way we live and work.We do see that employment has reallocated somewhat away from low-skilled service jobs, and, in light of the job vacancy patterns, conclude that worker preferences or changes in job amenities are driving this shift.In addition, the retirements paved the way for movements up the job ladder, making lowskilled customer-facing jobs even less desirable.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".