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The effects of minimum wage increases on employment and average wages of affected workers in Canada

2024· other· en· W7065607900 sur OpenAlexaboutno aff

Notice bibliographique

RevueSpectrum Research Repository (Concordia University) · 2024
Typeother
Langueen
DomainePhysics and Astronomy
ThématiqueMagnetic confinement fusion research
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésMonopsonyMinimum wageEfficiency wageWageProduct (mathematics)Job lossCompensating differential
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

The employment effects of the minimum wage are debated among economists, with traditional competitive models suggesting potential job losses for low-wage workers, while alternative models like institutional and dynamic monopsony suggest potential positive impacts. The institutional model posits that raising the minimum wage could boost employment if wages are below the marginal product of labor, and the dynamic monopsony model suggests that higher minimum wages could reduce turnover costs in low-wage labor markets, mitigating predicted job losses. Empirical studies, even within the competitive model, show mixed results, with some indicating significant disemployment effects and others not. This thesis provides a comprehensive analysis of the impacts of minimum wage increases on employment and wages across Canada, utilizing a robust methodological framework including the bunching approach, event study analysis, and difference-in-difference methods to examine the effects of varying provincial minimum wages over time. To accomplish this, I first employ the bunching approach to detect any concentration of wages just above the minimum wage threshold, providing insights into employer behavior in response to wage regulations. This technique identifies subtle adjustments in wage distribution that might not be apparent through other methods. Next, I utilize event study analysis to explore the immediate and long-term effects of minimum wage hikes, comparing employment and wage data from periods before and after the increases. This method captures both short-term disruptions and long-term adjustments in the labor market. Additionally, the difference-in-difference method is employed to compare outcomes between provinces with and without minimum wage increases, isolating the specific effects of these policies by controlling for other influencing variables. This approach underscores the importance of regional economic conditions in shaping the effectiveness of wage regulations. The empirical analysis begins with Labor Force Survey data from Ontario, which experienced significant real minimum wage increases in January 2018. The study estimates the counterfactual frequency distribution of hourly wages in Ontario for three years before and two years after the minimum wage increase. The findings show a significant decrease in jobs paying below the new minimum wage and a proportional increase in jobs paying up to $4 above the real minimum wage, indicating no significant overall employment impact. Contrary to anticipated employment effects, the average wage of affected workers increased significantly by 22.7% over the two years following the minimum wage shock. The only exception in the Ontario study was for the teen group; contrary to much of the existing Canadian literature such as Fossati and Marchand (2024), I found a significant positive employment effect for teenagers. However, overall, I did not observe a significant negative employment effect for young adults. Further analysis includes other provinces, such as Alberta, and cities such as Gatineau versus Ottawa, which also experienced substantial nominal and real minimum wage increases. The study applies the same methodology to assess the employment and wage effects, finding similar results to the Ontario study. Contrary to the Ontario study, in the Alberta study, I found a significant negative employment effect for the teen group, but overall, I did not find a significant negative employment effect for young adults. Since referring to a single minimum wage is inherently problematic, I also investigated a pooled analysis of hourly wage data from all Canadian provinces from 1999 to 2019. This analysis, covering 56 minimum wage increases, reveals no significant employment effect (-2%) over six months following minimum wage increases but a significant average wage increase (6.4%) for affected workers. The study also investigates potential employment shifts from low-skilled to high-skilled workers, finding no indication of such shifts. Subgroup analyses by education level, age, and other demographics show approximately similar employment and wage effects, suggesting that the consequences of minimum wage policies are shared among different worker groups. Additionally, sectoral analyses show no negative employment effect in the food industry, aligning with Card and Krueger (1993)’s findings on the impact of minimum wage increases in the fast-food industry. However, a significant negative employment effect (-4%) was observed in the retail sector, highlighting the influence of local industry composition on minimum wage impacts. Finally, in the Canada study (pooling all 56 minimum wage increase across all provinces), I assess the size of wage spillovers, finding that only 7% of the impact on average wages of affected workers comes from wage spillovers at the lower part of the wage distribution, which was statistically insignificant. This aligns with Canadian literature, such as Campolieti (2015), which found modest wage spillovers based on Canadian data compared to American data. Overall, this thesis provides robust evidence on the employment and wage effects of minimum wage increases in Canada. The findings suggest that, contrary to the traditional competitive model, minimum wage increases do not significantly reduce overall employment of low-wage workers and can lead to substantial wage gains for them. This has important implications for policymakers considering minimum wage adjustments to improve labor market outcomes for low-wage workers.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,003
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,074
Score d'incertitude au seuil0,538

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0010,003
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0010,002
Études des sciences et des technologies0,0040,001
Communication savante0,0010,000
Science ouverte0,0020,001
Intégrité de la recherche0,0010,001
Charge utile insuffisante (le modèle a refusé de juger)0,0020,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,007
Tête enseignante GPT0,230
Écart entre enseignants0,223 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2024
Routes d'admission1
Résumé présentoui

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