Closing pay gaps through transparent compensation
Bibliographic record
Abstract
Income inequalities and pay gaps are persistent issues around the world, affecting individuals across various sociodemographic groups. Natural experiments from the US, the UK, Canada, and Denmark show that pay transparency can mitigate pay inequity. Yet, little is known about why pay transparency works. What perpetuates pay inequity when pay gaps are hidden, and through which causal mechanisms can pay transparency alleviate inequity when pay gaps can no longer be ignored? This study examines one causal mechanism through which pay transparency may mitigate pay inequity, focusing on the role of deliberate ignorance in self-serving resource allocations. We developed an experimental game paradigm in which employers, acting as third parties, can seek or deliberately ignore information on pay discrimination between first and second parties—who perform the same work for different pay—before making resource allocation decisions between themselves and the disadvantaged first parties. We plan to test our formally derived predictions in an incentivised online experiment by comparing the effects of hidden and transparent pay discrimination on pay inequity for high and low costs. By examining a causal mechanism through which pay transparency may mitigate pay inequity, the study will contribute to the existing literature on the effectiveness of pay transparency policies. The findings of this study could inform policymakers and organisations in designing and implementing effective strategies to address pay discrimination and improve workplace equity.
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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 | 0.020 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.021 | 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".