Downward nominal wage rigidity in Canada: Evidence against a “greasing effect”
Bibliographic record
Abstract
Abstract The existence of downward nominal wage rigidity (DNWR) has often been used to justify a positive inflation target. It is traditionally assumed that positive inflation could “grease the wheels” of the labour market by putting downward pressure on real wages, easing labour market adjustments during a recession. A rise in the inflation target would attenuate the long‐run level of unemployment and hasten economic recovery after an adverse shock. Following Daly and Hobijn (2014), we re‐examine these issues in a model that accounts for precautionary motives in wage‐setting behaviour. We confirm that DNWR generates a long‐run negative relationship between inflation and unemployment, in line with previous contributions to the literature. However, we also find that the increase in the number of people bound by DNWR following a negative demand shock rises with the inflation target, offsetting the beneficial effects a higher inflation target has on closing the unemployment gap. As an implication, contrary to previous contributions that neglected precautionary behaviour, the speed at which unemployment returns back to pre‐crisis levels during recessions is relatively unaffected by variations in the inflation target.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".