Does an Increase in the Minimum Wage\nDecrease Employment? A Meta-Analysis\nof Canadian Studies
Bibliographic record
Abstract
Nous procédons à une méta-analyse de la documentation canadienne sur le salaire minimum à l'aide de méthodes de méta-régression dans le but de déterminer si cette documentation présente un biais de publication et quelle est l'ampleur de l'effet empirique une fois apportés les ajustements nécessaires pour tenir compte de ce biais. Dans le cas des adolescents, le groupe démographique qui réunit le plus grand nombre de travailleurs rémunérés au salaire minimum et, par conséquent, au sujet duquel les constatations qui se dégagent intéressent le plus les décideurs politiques et les législateurs, aucune preuve de biais de publication n'est relevée et l'élasticité du salaire minimum observée avoisine – 0,27. Nous traitons également des conséquences de ces constats pour les chercheurs et les décideurs politiques. Abstract: I conduct a meta-analysis of the Canadian minimum wage literature, using meta-regression methods to determine whether this literature has a publication bias and the size of the empirical effect after adjusting for this bias. For teenagers, the demographic group with the largest share of minimum wage workers and thus providing the findings of most interest to policy-makers and legislators, I find no evidence of a publication bias and a minimum wage elasticity of about –0.27. I also discuss the implications of my findings for researchers and policy-makers.
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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 | 0.027 | 0.064 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.029 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".