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Record W7065607900

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

2024· other· en· W7065607900 on OpenAlexaboutno aff

Bibliographic record

VenueSpectrum Research Repository (Concordia University) · 2024
Typeother
Languageen
FieldPhysics and Astronomy
TopicMagnetic confinement fusion research
Canadian institutionsnot available
Fundersnot available
KeywordsMonopsonyMinimum wageEfficiency wageWageProduct (mathematics)Job lossCompensating differential
DOInot available

Abstract

fetched live from 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.

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)0.001
metaresearch head score (Gemma)0.003
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 score0.074
Threshold uncertainty score0.538

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0040.001
Scholarly communication0.0010.000
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.007
GPT teacher head0.230
Teacher spread0.223 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

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