Labour Cost Adjustment during the Crisis: Firm-level Evidence
Bibliographic record
Abstract
This paper introduces a new firm-level dataset, based on the results from a survey on the wage-setting practices of Irish firms in the second half of 2014. These survey results represent a useful resource for policy makers, allowing for firm-level analysis of the approach to the adjustment of labour demand and wages in the face of a large negative shock. A number of findings are worth highlighting in relation to these results: in terms of the labour cost cutting approach, firms relied upon both reductions in the quantity (employment and hours) and the price of labour (wages); employee numbers was the most widely used margin of adjustment, followed by wage cuts and hours. While the majority of Irish firms opted to freeze base wages, in the region of 60 per cent, there is strong evidence of downward wage flexibility, with almost one quarter of firms surveyed cutting wages. A comparison with previous findings in relation to Ireland and other euro area countries points to a dramatic increase in the incidence of wage freezes and wage cuts amongst Irish firms during the 2008-2013 period.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".