Labour market impacts of occupational licensing and delicensing: New evidence from China
Bibliographic record
Abstract
Abstract We examined the recent occupational regulation changes in China and their labour market impacts. Using data from the China Labor‐Force Dynamic Survey from 2014 to 2018, we found an earning premium of approximately 10 per cent, as well as more employment‐based benefits, for those with an occupational license compared to those without one. Licensed workers reported higher skill‐job task match than unlicensed workers. Our data cover the period of occupational regulation reform in China, when 70 per cent of occupations previously licensed or certified were deregulated. Over this period, the licensing status remained associated with positive earning and employment benefits premiums, and better skill‐job task match at the labour market level. However, delicensing led to a distributional shift in the earning dispersion, especially at the bottom of the earning distribution; earning premiums rose sharply for the 10th to 30th percentiles. Workers directly affected by the licensing reform reported a significant decrease in employment benefits and in subjective job quality measures (i.e. skill‐job task match and voice at work) after delicensing, relative to never‐licensed workers. We suggest that non‐wage compensation is lost in the short term because the signal of competency is no longer valued by employers after delicensing.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".