Who Profits from Occupational Licensing?
Bibliographic record
Abstract
Sociologists have debated intensively how and why occupations matter for economic inequality. I argue that occupational licensing alters wage-setting, depending on the characteristics of the licensing system. Licensing not only restricts market entry, as in the United States; some governments, like that of Germany, also regulate task prices and set occupation-specific wage floors for licensed occupations. I claim that the U.S. system leads to a growing licensing wage advantage across the distribution, and the German system leads to a falling one. Furthermore, I discuss how women may particularly benefit from licensing, as it reduces disadvantages women often face in wage-setting. I present unconditional and gender-specific quantile treatment effects based on CPS-MORG and BIBB/BAuA data from 2018. In the United States, wage premiums are highest for employees in the upper-middle part of the distribution and are small for those in the bottom and the top. In Germany, the wage premium is largest for licensed employees within the lower quarter and reduces significantly toward the top. In both countries, women profit significantly more from licensing. These results challenge claims about the role of licensing for inequality in the top, and suggest licensing reduces penalties faced by disadvantaged groups.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 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".