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Record W3013414029 · doi:10.1787/c8b88d8b-en

Occupational entry regulations and their effects on productivity in services: Firm-level evidence

2020· paratext· en· W3013414029 on OpenAlex

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueOECD Economics Department working papers · 2020
Typeparatext
Languageen
FieldEconomics, Econometrics and Finance
TopicOccupational and Professional Licensing Regulation
Canadian institutions123 Certification (Canada)
Fundersnot available
KeywordsProductivityBusinessLabour economicsChannel (broadcasting)Industrial organizationEconomicsEconomic growthTelecommunications

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.434
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.002

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.

Opus teacher head0.059
GPT teacher head0.255
Teacher spread0.196 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it