The Flow Approach to Labor Markets: New Data Sources and Micro-Macro Links
Bibliographic record
Abstract
New data sources and products developed by the Bureau of Labor Statistics and the Bureau of the Census highlight the fluid character of U.S. labor markets. Private-sector job creation and destruction rates average nearly 8% of employment per quarter. Worker flows in the form of hires and separations are more than twice as large. The data also underscore the lumpy nature of micro-level employment adjustments. More than two-thirds of job destruction occurs at establishments that shrink by more than 10% within the quarter, and more than one-fifth occurs at those that shut down. Our study also uncovers highly nonlinear relationships of worker flows to employment growth and job flows at the micro level. These micro relations interact with movements over time in the cross-sectional density of establishment growth rates to produce recurring cyclical patterns in aggregate labor market flows. Cyclical movements in the layoffs-separation ratio, for example, and the propensity of separated workers to become unemployed reflect distinct micro relations for quits and layoffs. A dominant role for the job-finding rate in accounting for unemployment movements in mild downturns and a bigger role for the job-loss rate in severe downturns reflect distinct micro relations for hires and layoffs.
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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 | 0.008 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.019 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.006 |
| 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".