Downward Nominal Wage Rigidity, Inflation and Unemployment: New Evidence Using Micro‐Level Data
Bibliographic record
Abstract
Recent evidence suggests that the extent of downward nominal wage rigidity (DNWR) in the Canadian labour market has risen following the 2008–09 recession (see Brouillette, Kostyshyna and Kyui 2016). This note studies whether DNWR can lead to a long‐run trade‐off between inflation and unemployment, especially at lower rates of inflation—a question that has important implications for the optimal level of inflation in the long run. The results suggest that the trade‐off between unemployment and inflation remains weak despite the estimated increase in DNWR. In particular, the long‐run Phillips curve is close to vertical at inflation rates of 2 per cent or more, in line with earlier findings (Crawford and Wright 2001). As a result, an increase in long‐term inflation from 2 to 3 per cent would lower unemployment by about 0.1–0.2 percentage points. Overall, our results suggest that the benefits of raising the inflation target to attain a lower long‐term unemployment level seem rather weak.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".