Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.012 | 0.004 |
| Scholarly communication | 0.007 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".