The Ethical Role of Pro-Equality Laws in Reducing Executive Gender Pay Gaps under Cultural Resistance
Bibliographic record
Abstract
Abstract This study investigates how informal cultural norms and formal pro-equality legislation shape the executive gender pay gap (GPG), and whether legal interventions can ethically substitute for weak cultural support for gender equity. We integrate insights from role congruity theory, institutional theory, and feminist ethics to explain the phenomena. Pro-equality legislation is measured using the World Bank’s Women, Business, and the Law (WBL) Index, while gender egalitarianism is derived from the World Values Surveys. We find that executive pay disparities are most pronounced in less gender-egalitarian societies, especially among non-CEO top management team members and in salary-based compensation. Pro-equality laws—particularly those targeting pay rights, asset ownership, and entrepreneurship—significantly reduce these disparities, with the strongest effects observed in countries with lower cultural egalitarianism. These findings suggest that formal legal reforms can act as ethical correctives where informal norms fail, advancing care-based principles of justice and accountability at the highest organizational levels. Our study contributes to feminist ethics by showing how legal structures can institutionalize equity in the face of cultural resistance.
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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 | 0.025 | 0.085 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.014 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| 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".