How Has the Gender Earnings Gap in Ireland Changed in Thirty Years?
Bibliographic record
Abstract
Since 1987, the wages of women in Ireland have been growing faster than those of men. This, coupled with a decrease in the average hours worked by men, has resulted in a reduction in the gender earnings gap in Ireland, most notably at the bottom of the earnings distribution. This paper provides a descriptive analysis of the growth of male and female wages, weekly earnings, and differences in working patterns across the wage and earnings distribution in Ireland over the last three decades, using detailed microdata covering the period 1987–2019. Using a Oaxaca–Blinder decomposition approach, based on unconditional quantile regressions for each time period, we also show how the explained and unexplained components of the gender wage gap have changed across the wage distribution. We find that the mean and median gender gap in earnings fell by one-sixth and one-quarter, respectively, between 1987 and 2019. This change is attributable to the faster growth of women’s wages compared to men’s and some convergence in the average hours worked by men and women. However, there has been relatively stable structural inequality at the top of the wage and earnings distribution over the past three decades, which points towards a persistent glass ceiling in Ireland.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".