Conference Draft – Comments Welcome Labor Market Fluidity and Economic Performance
Bibliographic record
Abstract
Abstract: U.S. labor markets became much less fluid in recent decades. Job reallocation rates fell more than a quarter after 1990, and worker reallocation rates fell more than a quarter after 2000. The declines cut across states, industries and demographic groups defined by age, gender and education. Younger and less educated workers had especially large declines, as did the retail sector. A shift to older businesses, an aging workforce, and policy developments that suppress reallocation all contributed to fluidity declines. Drawing on previous work, we argue that reduced fluidity has harmful consequences for productivity, real wages and employment. To quantify the effects of reallocation intensity on employment, we estimate regression models that exploit low frequency variation over time within states, using state-level changes in the population composition as instruments. We find large positive effects of worker reallocation rates on employment, especially for men, young workers, and the less educated. Similar estimates obtain when dropping data from the Great Recession and its aftermath. These results suggest the U.S. economy faced serious impediments to high employment rates well before the Great Recession, and that sustained high employment is unlikely to return without restoring labor market fluidity.
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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 | 0.007 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.010 | 0.008 |
| Insufficient payload (model declined to judge) | 0.413 | 0.228 |
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".