Occupational entry regulations and their effects on productivity in services: Firm-level evidence
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This paper assesses the possible dynamic effects of occupational entry regulations (OER) on productivity. It combines firm-level productivity data with a new cross-country policy indicator measuring the stringency of OER by the presence of administrative burdens, qualifications requirements, and mobility restrictions, for five professional and ten personal services. The evidence suggests that bold reforms easing OER, especially those concerning qualification requirements, could help increase the contribution of personal and professional services to aggregate productivity growth via two channels: the acceleration of their catch up to best global practices (within-firm channel), where firms in regulated sectors could gain up to 2.5 percentage points of productivity on average; and a higher contribution of labour reallocation to firms’ employment growth (between-firm channel), which could increase by up to 10 percent for the most productive firms.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 0.002 |
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 it