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Record W3170046144 · doi:10.3390/su13126540

Minimum Wage and Household Consumption in Canada: Evidence from High and Low Wage Provinces

2021· article· en· W3170046144 on OpenAlexaffabout
Leila Sabokkhiz, Fatma Güven Lisaniler, Ikechukwu Darlington Nwaka

Bibliographic record

VenueSustainability · 2021
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEnergy, Environment, Economic Growth
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsWageConsumption (sociology)EconomicsMinimum wageSustainable developmentDistributed lagPanel dataTerm (time)Demographic economicsLabour economicsEconometricsPolitical science

Abstract

fetched live from OpenAlex

The minimum wage is a major factor for the successful implementation of much of the sustainable development goals (SDGs). The present research will investigate whether minimum wage (MW) as a sustainable wage policy improves household consumption. Thus, a panel-based analysis comparing high wage (Alberta, British Columbia, Ontario, and Saskatchewan) and low wage provinces (Manitoba, New Brunswick, Newfound land/Lab, Nova Scotia, Prince Edward Island, and Quebec) is employed for the Canadian case within the study period from 1981 to 2019. We analyze the long-term and short-term effects of MW on household consumption using the Dynamic Autoregressive Distributed Lag techniques of the Pooled Mean Group, Dynamic Fixed Effects, and Mean Group estimators. Results show that the long-term impact of MW on household consumption is positive in both the low- and high-wage provinces. The short-term effect is negative in both wage groups, but not significant for the low-wage group. This offers significant debate on the relevance of the MW towards economic stabilization and consumption-led growth.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.030
Threshold uncertainty score0.852

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.019
GPT teacher head0.194
Teacher spread0.175 · 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 teacher head, 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

Citations6
Published2021
Admission routes2
Has abstractyes

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