Heterogeneous Labor Market Impacts of the COVID-19 Pandemic
Bibliographic record
Abstract
The authors study the distributional consequences of the COVID-19 pandemic's impact on employment, both during the onset of the pandemic and over subsequent months. Using cross-sectional and matched longitudinal data from the Current Population Survey, they show that the pandemic has exacerbated pre-existing inequalities. Although employment losses have been widespread, they have been substantially larger-and more persistent-in lower-paying occupations and industries. Hispanics and non-White workers suffered larger increases in job losses, not only because of their over-representation in lower-paying jobs but also because of a disproportionate increase in their job displacement probability relative to non-Hispanic White workers with the same job background. Gaps in year-on-year job displacement probabilities between Black and White workers have widened over the course of the pandemic recession, both overall and conditional on pre-displacement occupation and industry. These gaps are not explained by state-level differences in the severity of the pandemic nor by the associated response in terms of mitigation policies. In addition, evidence suggests that older workers have been retiring at faster rates.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".