Searching, Recalls, and Tightness: An Interim Report on the COVID Labor Market
Bibliographic record
Abstract
We report on the state of the labor market midway through the COVID recession, focusing particularly on measuring market tightness.As we show using a simple model, tightness is crucial for understanding the relative importance of labor supply or demand side factors in job creation.In tight markets, worker search effort has a relatively larger impact on job creation, while employer profitability looms larger in slack markets.We measure tightness combining job seeker information from the CPS and vacancy postings from Burning Glass Technologies.To parse the former, we develop a taxonomy of the non-employed that identifies job seekers and excludes the large number of those on temporary layoff who are waiting to be recalled.With this taxonomy, we find that effective tightness has declined about 50% since the onset of the epidemic to levels last seen in 2016, when labor markets generally appeared to be tight.Disaggregating market tightness, we find mismatch has surprisingly declined in the COVID recession.Further, while markets still appear to be tight relative to other recessionary periods, this could change quickly if the large group of those who lost their jobs but are not currently searching for a range of COVIDrelated reasons reenter the search market.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".