Bibliographic record
Abstract
We show that occupational licensing has significant negative effects on labor market fluidity defined as cross-occupation mobility.Using a balanced panel of workers constructed from the CPS and SIPP data, we analyze the link between occupational licensing and labor market outcomes.We find that workers with a government-issued occupational license experience churn rates significantly lower than those of non-licensed workers.Specifically, licensed workers are 24% less likely to switch occupations and 3% less likely to become unemployed in the following year.Moreover, occupational licensing represents barriers to entry for both non-employed workers and employed ones.The effect is more prominent for employed workers relative to those entering from nonemployment, because the opportunity cost of acquiring a license is much higher for employed individuals.Lastly, we find that average wage growth is higher for licensed workers than non-licensed workers, whether they stay in the same occupation in the next year or switch occupations.We find significant heterogeneity in the licensing effect across different occupation groups.These results hold across various data sources, time spans, and indicators of being licensed.Overall, licensing could account for almost 8% of the total decline in monthly occupational mobility over the past two decades.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".