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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.023 | 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; both teacher heads agree on what is shown here.
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".