Trends in Low-Wage Employment in Canada: Incidence, Gap and Intensity, 1997-2014
Bibliographic record
Abstract
This paper introduces two new concepts to the debate on job quality: the low-wage gap and low-wage intensity. These two measures provide information on the depth and severity of low wages. Using Labour Force Survey microdata, we discuss trends in these two measures, along with trends in the incidence of low wages over the 1997-2014 period. For example, in 2014, 27.6 per cent of all employees aged 20 to 64 years earned less than two-thirds of median hourly wages for full-time workers aged 20 to 64 years (or $16.01 per hour), our low-wage cutoff. In this same year, the low-wage gap was 21.0 per cent, which means that the average low-wage employee earned approximately 79.0 per cent of the low-wage cutoff (or $12.66 per hour). Consequently, low-wage intensity, defined as the product of the incidence and the gap (scaled by 100) was 5.8. This is down from an intensity of 6.3 in 1997, which was the result of a slightly higher incidence (27.9 per cent) and a higher gap (22.7 per cent). This paper also provides these results by gender, age, educational attainment, industry, occupation, employment status and province. These detailed results help identify which groups face the highest rates, greatest depths, and largest intensities of low-wage employment in Canada. Furthermore, this paper explores the implications of a $15 minimum wage on the low-wage gap in 2014. Finally, to provide a brief sensitivity analysis, we discuss (1) the results for low-wage employment in Canada using a different cutoff (two-thirds mean hourly wages for full-time employees aged 25 to 54 years) and (2) comparisons of our results to those of CIBC’s Employment Quality Index and the OECD’s low-pay data.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.017 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".