Bibliographic record
Abstract
In this paper the author examines whether there is significant evidence of the effect of adjustment costs on Canadian labour demand. This is an important question, as sluggish adjustment of labour demand resulting from significant adjustment costs may be one factor that could help explain some of the unemployment persistence found in Canadian data. The author uses a linear-quadratic model and attempts to estimate the relative adjustment costs of labour demand as well as its rate of adjustment towards long-run equilibrium. In contrast to others who have examined the dynamic behaviour of labour demand, the author estimates the structural parameters using the Euler equation and employs a limited-information approach that does not require an explicit solution for the model's control variables in terms of the forcing processes. The empirical estimates imply that adjustment costs are about four times more important than disequilibrium costs and that it takes over three and a half years for 90 per cent of labour demand adjustment to be completed. Therefore the author concludes that significant adjustment costs are an important feature of Canadian labour demand and that sluggishness due to these costs may be one explanatory factor in unemployment persistence.
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.000 |
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".