Impacts of the COVID-19 Pandemic and the CARES Act on Earnings and Inequality
Bibliographic record
Abstract
Using data from the Current Population Survey (CPS), we show that the Covid-19 pandemic led to a loss of aggregate real labor earnings of more than $250 billion between March and July 2020. By exploiting the panel structure of the CPS, we show that the decline in aggregate earnings was entirely driven by declines in employment; individuals who remained employed did not experience any atypical earnings changes. We find that job losses were substantially larger among workers in low-paying jobs. This led to a dramatic increase in inequality in labor earnings during the pandemic. Simulating standard unemployment benefits and Unemployment Insurance (UI) provisions in the Coronavirus Aid, Relief, and Economic Security (CARES) Act, we estimate that UI payments exceeded total pandemic earnings losses between March and July 2020 by $9 billion. Workers who were previously in the bottom third of the earnings distribution received 49% of the pandemic-associated UI and CARES benefits, reversing the increases in labor earnings inequality. These lower-income individuals are likely to have a high fiscal multiplier, suggesting these extra payments may have helped stimulate aggregate demand.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".