Exploring Wage Phillips Curves in Advanced Economies
Bibliographic record
Abstract
We investigate the extent to which excess supply (demand) in labour markets contributes to a lower (higher) growth rate of average nominal wages for workers. Using panel methods on data from 10 advanced economies for 1992–2018, we produce reduced-form estimates of a wage Phillips curve specification that is consistent with a New Keynesian framework. We find comparable effects on nominal wage growth from several indicators of “slack” in the labour market: unemployment rates, unemployment rate gaps, the prime-age employment-to-population ratios, a composite labour market indicator constructed using a principal component for a wide range of labour force data, and unemployment rates separated by duration of unemployment. Our results provide evidence that while the slope of the wage Phillips curve seems to have become flatter following the global financial crisis in 2008, the relationship still appears to be highly significant. We find that the long-term unemployment rate (unemployment longer than six months) has had a larger effect on wage growth in the period since 2008. We also investigate the shape of the Phillips curve and find some evidence of a convex relationship between labour market slack and nominal wage growth, particularly for the pre-crisis period. Piecewise regressions suggest some mixed evidence on nominal rigidities in the aggregate data.
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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 | 0.002 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".