Teleworking and lost work during the pandemic: new evidence from the CPS
Bibliographic record
Abstract
To measure the effects of the coronavirus disease 2019 pandemic, the U.S. Bureau of Labor Statistics added questions to the Current Population Survey, the main U.S. labor force survey, starting in May 2020. This article analyzes the results from questions asking people (1) whether they teleworked because of the pandemic and (2) whether they were unable to work because their employers closed or lost business because of the pandemic. We use the data on telework to refine work completed earlier in the pandemic that classified occupations on their suitability for telework. We then apply the revised classification to examine trends in telework and the extent to which working in an occupation suitable for telework shields workers from unemployment. Our results show that the pandemic resulted in a large increase in teleworking, with 33 percent of U.S. workers reporting teleworking because of the coronavirus in the period May-June 2020, before declining to a still substantial 22 percent in the fourth quarter. Rates of lost work varied widely both by an occupation’s suitability for telework and by demographic category.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".