Gender wage transparency and the gender pay gap: A survey
Bibliographic record
Abstract
Abstract We survey the literature on the effects of increased transparency of gender segregated wages on the pay gap between men and women in comparable jobs. Pay transparency is promoted by countries and supra‐national institutions and we categorize reforms according to their content and coverage. A growing number of papers have used variations of difference‐in‐difference estimation methods to analyze the impact of reforms on the gender pay gap (GPG), and from these we extract four main findings: First, reform‐based studies find that pay transparency reforms reduce the GPG in all countries but one, which finds no effect. Second, in Canada, Denmark and the UK, the reduction in the GPG from transparency reforms originate from a reduction in the growth rate of male income and less from an increase in women's pay. Third, there is fragmented evidence for the impact of transparency reforms on other labor outcomes and firm productivity. Fourth, the monetary implementation cost of transparency reforms is, in general, small both for individual firms and public administration. These finding are consistent with the notion that gender wage transparency reforms are an effective policy tool to reduce the GPG.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".