Benchmarks for assessing labour market health: 2024 update
Bibliographic record
Abstract
This staff analytical note builds on Ens et al. (2021); Ens et al. (2022); and Ens, See and Luu (2023) to assess the health of the Canadian labour market. These earlier works established a more granular framework for assessing the labour market given its diverse and segmented nature. In this note we do three things: We update the range of benchmarks in our dashboard of indicators, to ensure they remain relevant. We add an additional year of data, adjust for population aging and improve our filter estimates. We also estimate a series of Phillips curve equations using each indicator of the dashboard to understand how much inflationary pressure is coming from the labour market. We do a deep dive to better understand structural changes in the Canadian labour market that could cloud assessments of labour slack. This includes a closer look at the muted recoveries in self-employment and low-wage jobs. And finally, we discuss whether the labour market has moved into balance and how much it is contributing to inflation. We find that the labour market has likely entered into balance from overheated levels, and we also see some signs of structural changes: Overall, the labour market appears to have moved into balance. Notably, we see a large reduction in job openings and other demand indicators but no significant rise in layoffs. Moreover, Phillips curve estimates suggest that the labour market was not a large source of inflationary pressure in the first quarter of 2024. Self-employment and low-wage employment appear to be permanently lower than in the past. For low-wage workers, this reflects lower levels of demand coming out of the COVID-19 pandemic, though impacted workers were largely able to reallocate to other sectors given the tight labour market following the reopening of the economy. Workers who are self-employed due to weak job prospects also make up a smaller share of the labour market than in the past, and this appears to be a long-term trend. As a result, weakness in indicators of self-employment and low-wage employment in the dashboard is less likely to represent labour market slack.
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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 | 0.022 | 0.079 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.018 | 0.021 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.008 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.028 | 0.017 |
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".