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Record W2738760258 · doi:10.1111/coep.12241

EMPLOYMENT EFFECTS OF THE MINIMUM WAGE: PANEL DATA EVIDENCE FROM CANADIAN PROVINCES

2017· article· en· W2738760258 on OpenAlexaffabout
Kate Rybczynski, Anindya Sen

Bibliographic record

VenueContemporary Economic Policy · 2017
Typearticle
Languageen
FieldHealth Professions
TopicEmployment and Welfare Studies
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsMinimum wageEconomicsInstrumental variablePanel dataOrdinary least squaresWageLabour economicsDemographic economicsImmigrationPrime (order theory)EconometricsGeographyMathematics

Abstract

fetched live from OpenAlex

Recent U.S. studies offer conflicting evidence on minimum wage impacts. This paper studies the effects of 185 amendments to minimum wage on employment rates using panel data across Canadian provinces from 1981 to 2011. Ordinary least squares and instrumental variables ( IV ) estimates imply a 10% increase in minimum wage is associated with a 1%–4% reduction to employment rates for both male and female teens. We also find that an increase in the minimum wage is associated with lower employment of prime‐aged immigrants. Our results are robust to a wide array of IV and the use of controls for spatial heterogeneity. ( JEL J30, J71, J23)

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.018
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.026
Threshold uncertainty score0.190

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.018
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.010
Science and technology studies0.0030.001
Scholarly communication0.0020.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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.

Opus teacher head0.189
GPT teacher head0.416
Teacher spread0.227 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations17
Published2017
Admission routes2
Has abstractyes

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