MW and Household Consumption: Evidence from High and Low Wage Provinces in Canada
Bibliographic record
Abstract
Minimum wage (MW) has become a common policy instrument in several countries \nand regions across the world. However, researchers are divided regarding whether \nthe MW achieves its intending objectives or not. The current study will examine \nwhether MW enhances household consumption as a sustainable income policy. \nWhile the MW is set under the authority of the provinces, resulting in appealing \ncross-province and time-series diversity, Canada serves as an excellent research \ncenter for this type of inquiry. The fact that we can utilize Canadian data gives us the \nability to examine the impact of the MW by studying cross-province and time-series \nvariation with a high number of MW changes passed in different jurisdictions. \nThis research utilizes a panel-based analysis, trying to compare the four provinces \nthat provide the highest wage levels (Alberta, British Columbia, Ontario ,and \nSaskatchewan) with the other six provinces that provide the lowest wage levels \n(Manitoba, Nova Scotia, New Brunswick, Newfound land/Lab ,Prince Edward \nIsland, and Quebec) for the study period from 1981 to 2019, in Canada. Dynamic \nAutoregressive Distributed Lag Methods (i.e., Pooled Mean Group, Dynamic Fixed Effect ,and Mean Group estimators) are used to examine the effect of minimum wage \non household consumption in the short and long term. The findings demonstrate that \nMW has a favorable long-term impact on household consumption in both low- and \nhigh-wage regions. In both wage categories, the short-term effect is negative, \nalthough not statistically significant in the low-wage group. \nOur findings imply, despite the possible negative effects of MW on employment, \nit can be an effective tool for improving economic growth and welfare. In other \nwords, it might be better to pay greater attention to the spillover impacts of MW \npolicies while designing them.\nKeywords: Panel ARDL; Canada; Minimum Wage; Low-wage province; High wage province; Household consumption
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".