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Record W1547900466

Wage Bill Change in Ireland during Recession-How Have Employers Reacted to the Downturn

2012· article· en· W1547900466 on OpenAlexaboutno aff
Kieran James Walsh

Bibliographic record

VenueArrow@dit (Dublin Institute of Technology) · 2012
Typearticle
Languageen
FieldHealth Professions
TopicEmployment and Welfare Studies
Canadian institutionsnot available
Fundersnot available
KeywordsEarningsQuarter (Canadian coin)WageRecessionLabour economicsEconomicsSurvey data collectionDemographic economicsBusinessAccountingStatisticsMacroeconomicsGeography
DOInot available

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.601
Threshold uncertainty score0.791

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.070
GPT teacher head0.374
Teacher spread0.305 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations11
Published2012
Admission routes1
Has abstractyes

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