Assessing Labour Market Slack for Monetary Policy
Bibliographic record
Abstract
The large and uneven impact of COVID-19 has emphasized the complexity and diversity of the labour market. This complexity implies that traditional headline measures may be inadequate at providing a comprehensive picture of labour market health. We address this concern in two ways. First, we construct a summary indicator of the labour market and use it to evaluate the level of disagreement between various labour market measures. Our exercise reveals that the COVID-19 shock has heightened the degree of disagreement between different indicators. The unprecedented level of disagreement across individual measures implies that a single measure cannot fully account for differences in labour market slack across various groups, or for the quality of job gains. Second, we propose a comprehensive framework to assess the labour market recovery along three dimensions, all of which capture different elements of slack: overall labour market conditions, labour market inclusiveness and job characteristics. As an example of how to apply this approach, we identify areas of concern in the ongoing recovery of the Canadian labour market.
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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 | 0.017 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".