Salary transparency and gender pay inequality: Evidence from Canadian universities
Bibliographic record
Abstract
Abstract Research Abstract We examine whether salary transparency influences gender pays inequality in the context of Canadian universities by exploiting a policy change enacted in one Canadian province that required salary disclosure through a publicly searchable database, thus lowering the cost of monitoring the gender pay gap. We find that, on average, salary disclosure improves gender pay equality but institutions respond in different ways. Despite little media attention around gender equality at the time of the policy, institutions most likely to anticipate higher scrutiny, such as top ranked institutions, respond more aggressively to improve gender pay equality—both in terms of the magnitude and type of response. Combined, our findings suggest that the extent of change from salary transparency depends on the reduction in monitoring costs and organizational characteristics. Managerial Abstract Salary transparency has been implemented in various ways around the world as a strategy by firms and policy makers to reduce the gender pay gap. However, whether and how it can achieve this in practice is unclear. We examine a salary transparency policy that mandated disclosure to the public through an online database in one Canadian province by comparing the change in gender pay inequality in that province relative to the change in the gender pay gap in provinces without disclosure. We find that salary transparency improves average gender pay equality primarily within the most visible organizations that likely anticipate high levels of public scrutiny. Our findings imply that facilitating low‐cost public monitoring of gender inequalities can motivate organizations to enact change.
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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 | 0.006 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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.
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".