Structural Influences on Participation Rates: A Canada-U.S. Comparison
Bibliographic record
Abstract
understanding of what drives the participation rate is necessary for the projection of labour force growth, a key input into the determination of the economy’s production potential. 1 The sharp drop in the participation rate in Canada in the 1990s has given rise to considerable debate about its cause and if it was reasonable to have expected it to have returned to its pre-recession peak. If that peak is the appropriate reference point, then the implications are that labour market slack is considerably larger than the present unemployment rate suggests. While the severity of the 1990-91 recession in Canada and the subsequent underachievement of real GDP relative to potential caused much of the decline and subsequent stagnation of the aggregate participation rate, structural developments and compositional shifts among various subgroups of the population of labour force age, which were under way before 1989, also exerted downward pressure. The purpose of this article is to identify those supply side developments that could have accounted for part of the decline in the participation rate since 1989. 2 Although this approach does not include an analysis of demand side factors, cyclical effects are noted. The much worse performance of the Canadian participation rate than its U.S. counterpart in the 1990s, after the similarities of the preceding 15 years, suggests that using the U.S. labour market experience as a benchmark for Canada may shed some light on the situation, especially since the United States has been operating at full capacity for some time. Econometric estimation of participation rates is hampered by the presence of many influences that are difficult to measure, such as changes in the availability of private and public pension plans, family relationships and structures, and the costs of and subsidies to education. 3 If these influences cannot be specifically modelled, projections based on such estimations will be unreliable.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 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".