MétaCan
Menu
← Retour à la cohorte
Enregistrement W2910285691 · doi:10.11575/prism/34929

An Examination of Alberta’s Minimum Wage

2018· dissertation· en· W2910285691 sur OpenAlexaboutno aff
Keyli Kosiorek

Notice bibliographique

RevueOpen MIND · 2018
Typedissertation
Langueen
DomaineEconomics, Econometrics and Finance
ThématiqueLabor market dynamics and wage inequality
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésMinimum wageLabour economicsEconomicsPolitical science

Résumé

récupéré en direct d'OpenAlex

Throughout its history, the minimum wage has always been a controversial policy. Politicians, economists, businesses and citizens are continually engaged in debates over its effectiveness and its unintended consequences. Initially designed to protect workers from exploitation, the minimum wage has been gaining popularity as an anti-poverty tool. In fact, in recent history there has been a $15 minimum wage movement taking place across North America that aims to put more money into the pockets of those in need and thus reduce poverty. In 2015, under the newly elected NDP government, the Government of Alberta implemented the $15 minimum wage policy. Their plan to reach $15 consisted of a 47% increase of the minimum wage in just 3 years. Alberta was the first province in Canada to implement such a policy, but since then, Ontario and British Colombia have followed in their footsteps. There is an abundance of research on the minimum wage from both the US and Canada, much of which is focused on the impact of the minimum wage on employment. While researchers have not reached a consensus on the magnitude of the effects, most agree that increasing the minimum wage has a negative impact on employment, especially for low-skilled workers and youth. While the research is plentiful, there have been no studies that have examined the impact of the $15 minimum wage movement on employment. Additionally, there has been no study that has looked at Alberta specifically. This paper adds to the existing literature by analyzing the impact that a rapidly increasing minimum wage has had on employment and unemployment in Alberta. To understand the effects of Alberta’s increasing minimum wage on employment and unemployment, I used a natural/observation study of Alberta and Saskatchewan. Alberta was the treatment group in my study because its minimum wage increased significantly starting in 2015. Saskatchewan was the natural control group in my study because they did not increase their minimum wage arbitrarily and only adjusted it slightly each year for inflation. Because the two provinces are so similar in many ways including industry, political landscape, Saskatchewan’s data became the baseline measure of the experiment. Thus, any changes in Alberta’s employment rates that were not seen in Saskatchewan could attributed to the increase in minimum wage that Alberta experienced. For my study I used pooled time-series data collected from the Canadian Labour Force Survey to compare the two provinces in a regression analysis. The analyses were run on four different age groups: 15+, 15-24, 25-54 and 55+. The results from my empirical analysis were consistent with previous literature with coefficients for the employment rate between -0.068 and -0.434 for the period between 1997 and 2017 in Alberta. I also found coefficients for the unemployment rate between 1.064 and 2.327 in the same time period. These results indicate that the increase in the minimum wage in Alberta resulted in a reduction in the employment rate and an increase in the unemployment rate. In addition to this empirical analysis, I looked at the cost of Alberta’s minimum wage increases since 2015. I calculated that the increase from $10.20 to $15 per hour will cost Albertans over $725 million. This is over four times the cost of tax benefit programs such as Alberta’s Family Employment Tax Credit. Such programs are better targeted towards those who need support. In this section of my paper I compare these two policies and the pros and cons of each. It is important to consider the costs and benefits of each policy prior to implementation to ensure that all objectives are being met and that the policy does not create more harm than good. I conclude my paper with three suggestions for policy makers to consider when developing minimum wage policy: First, I suggest that policy makers should have a clear understanding of who earns the minimum wage prior to making any changes. Secondly, I suggest that policy makers should be clear about their objectives in order to create policies that best target their desired group. Finally, I suggest that all policy decisions should be based on detailed cost-benefit analysis and that all documents should be disclosed to the public for transparent debates.

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)0,002
score de la tête « metaresearch » (Gemma)0,005
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: aucune
GenreSignal candidat: Empirique · Signal consensuel: aucune
Score de désaccord entre enseignants0,081
Score d'incertitude au seuil0,591

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0020,005
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0030,006
Études des sciences et des technologies0,0120,004
Communication savante0,0070,001
Science ouverte0,0030,002
Intégrité de la recherche0,0020,002
Charge utile insuffisante (le modèle a refusé de juger)0,0110,001

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 Opus0,036
Tête enseignante GPT0,290
Écart entre enseignants0,254 · 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
Publié2018
Routes d'admission1
Résumé présentoui

Explorer davantage

Même revueOpen MIND→Même sujetLabor market dynamics and wage inequality→Travaux en français237 207→