Wage Bill Change in Ireland during Recession-How Have Employers Reacted to the Downturn
Bibliographic record
Abstract
The Earnings, Hours and Employment Costs Survey (EHECS) captures information each quarter on total earnings, paid hours and level of employment from a large representative sample of employers. Responses received typically cover more than 70% of all employees in the state. The main purpose of the survey is to gauge trends in the average level of earnings and hours worked over time across all sectors of the economy. However the presence of the same employers in the sample over time creates a valuable opportunity to undertake longitudinal analysis of the manner in which employers change their wage bill over time. A previously published study from EHECS comparing quarter 3 2008 with quarter 3 2009 showed that for the matched employers, covering over half of all employees in the state, nearly two thirds of those employers had cut their wage bill by more than 2 percent over the year with the primary method of reduction being a reduction in numbers employed, followed by reductions in hours worked and reductions in hourly rates of pay. The level and type of change differed significantly across sectors. This paper will present an update of the findings from that publication for the following years (covering 2009 to 2011) to assess how the behaviour of employers changed as the economic downturn continued.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".