LONG‐RUN INEQUALITY AND SHORT‐RUN INSTABILITY OF MEN'S AND WOMEN'S EARNINGS IN CANADA
Why this work is in the frame
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Bibliographic record
Abstract
This paper examines the variability of workers’ earnings in Canada over the period 1982–2006. We decompose the total variance of workers’ earnings into a ‘permanent’ component between workers and a ‘transitory’ earnings instability component over time for given workers. We then investigate the statistical relationships between these components and indicators for the business cycle. The most marked change in earnings variances in Canada since 1982 is the general rise in total earnings variance, which is essentially driven by a quite dramatic rise in long‐run earnings inequality. The patterns across age categories of the two variance components are almost opposite. Long‐run earnings inequality generally rises with age, but earnings instability is seen to generally decline with age, so that earnings instability is markedly highest among entry age workers. Unemployment rate effects are positive on almost all variance measures, while higher unemployment is associated with widened long‐run earnings differentials and greater short‐run earnings instability.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it