Bibliographic record
Abstract
Abstract. This paper contains the first cross‐country comparison of the cyclical behaviour of real wages using microdata. After controlling for changes in labour quality, I find that real wages are strongly procyclical in Canada, the United Kingdom, and the United States. In contrast, the cyclicality of government‐published real aggregate hourly wages varies substantially across these three countries. The disparity suggests that a direct comparison of the cyclical behaviour of real aggregate wages is misleading. Finally, I show that variations in labour quality also bias the cross‐country correlation of several key labour market variables. JEL classification: J3, E3 Une comparaison transversale entre pays du caractère cyclique des salaires réels. Ce mémoire présente la première comparaison entre pays du comportement cyclique des salaires réels à l’aide de micro‐données. Après normalisation pour tenir compte des changements dans la qualité du travail, cette étude montre que les salaires réels sont pro‐cycliques au Canada, au Royaume‐Uni et aux Etats Unis. Au contraire, le caractère cyclique des données sur les salaires horaires agrégés varie substantiellement entre ces trois pays. Cette disparité suggère qu’une comparaison directe du comportement cyclique des salaires réels agrégés peut être trompeuse. Finalement, cette étude montre que les variations dans la qualité du travail biaisent aussi les corrélations entre pays de plusieurs variables du marché du travail.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".