Educational Attainment and Skill Utilisation in the Irish Labour Market: An EU Comparison. Quarterly Economic Commentary Special Article, WINTER 2017
Bibliographic record
Abstract
In recent years the Irish economy has experienced strong economic growth accompanied by significant improvements in the labour market. The unemployment rate in the second quarter of 2017 stands at 6.2 per cent (CSO, 2017), its lowest rate in nine years. In light of these improvements in the labour market, we examine the nature of current employment in Ireland with respect to the intensity of use of certain skills and the mismatch between the skills possessed by employees and those required to do their jobs. Furthermore, we consider the possible future sources of skilled labour supply by examining the characteristics of those currently unemployed and inactive in the labour market, as well as the ability of Ireland to attract high-skilled migrant workers. Our analysis reveals a high degree of skill underutilisation among Irish employees. The percentage of Irish workers reporting education or skill levels in excess of those required to do their job is the third highest of 28 EU countries. Our findings also indicate that, as was the case in recent decades, immigration may play an important role as a source of skilled labour in a tightening labour market.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".