The Short-Term Economic Consequences of COVID-19: Exposure to Disease, Remote Work and Government Response
Bibliographic record
Abstract
In this ongoing project, we examine the short-term consequences of COVID-19 on employment and wages in the United States. Guided by a pre-analysis plan, we document the impact of COVID-19 at the national-level using a simple difference and test whether states with relatively more confirmed cases/deaths were more affected. Our findings suggest that COVID-19 increased the unemployment rate, decreased hours of work and labor force participation and had no significant impacts on wages. The negative impacts on labor market outcomes are larger for men, younger workers, Hispanics and less-educated workers. This suggest that COVID-19 increases labor market inequalities. We also investigate whether the economic consequences of this pandemic were larger for certain occupations. We built three indexes using ACS and O*NET data: workers relatively more exposed to disease, workers that work with proximity to coworkers and workers who can easily work remotely. Our estimates suggest that individuals in occupations working in proximity to others are more affected while occupations able to work remotely are less affected. We also find that occupations classified as more exposed to disease are less affected, possibly due to the large number of essential workers in these occupations.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".