Downward Nominal-Wage Rigidity: Micro Evidence from Tobit Models
Bibliographic record
Abstract
This paper uses Tobit models and data for union contracts to examine the extent of downward nominal-wage rigidity in Canada. To be consistent with important stylized facts, the models allow the variance of the notional wage-change distribution to be time-varying and test for menu-cost effects. The empirical results confirm the importance of using a general specification with a time-changing variance and menu-cost effects. The variance of the notional distribution fell as inflation trended downward over the sample period, and there is evidence that menu-cost effects cause some contracts to have wage freezes rather than small wage increases. Each of these features reduces the estimated effect of rigidity on wage growth. The estimated net effect of downward rigidity and menu costs in the 1990s is approximately 0.4 percentage points for the average wage change in the first year of contracts, and less than 0.1 percentage point for the average annual change over the lifetime of contracts. On balance, the evidence suggests that the long-run trade-off between inflation and the unemployment rate is close to vertical at inflation rates of 2 per cent or more if productivity growth is near the average in recent decades.
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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 | 0.009 | 0.070 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.012 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".