Does board gender diversity reduce workplace sexual harassment?
Bibliographic record
Abstract
Abstract Research Question/Issue We contrast the predictions of gender socialization theory and “fem‐power washing” (deceptively positioning as a firm promoting female empowerment without any tangible actions) to investigate whether promoting female directors on the board of directors associates with a reduction in the prevalence of firm‐level workplace sexual harassment (SH). Research Findings/Insights We estimate the incidence rate of SH through textual analysis of US employees' job reviews published online during the period 2011–2021. We find that an increase of one female director is associated with a 21.81% decrease in workplace SH and that firms with high board gender diversity synchronize the reduction in SH with improved social policies (e.g., policies to better employee relations, health and safety, or diversity challenges). Our results do not support the fem‐power washing theory but rather imply that nominating female directors may have a profound impact on the firm's ethical culture. Theoretical/Academic Implications This study validates the ethical dimension of corporate governance: Nominating female directors impacts a firm harassment culture and, by extension, a firm's ethical and corporate culture. This study adds to the governance literature that debates the merits of board gender diversity by highlighting an oft‐ignored channel through which board diversity affects firm value: ethics and corporate culture. Practitioner/Policy Implications For boards of directors, having more female directors can curb workplace SH, a behavior that is associated with a severe and lasting negative effect on firm value. For practitioners, regulators, and the business community, this study reinforces the merits of aiming towards more gender‐balanced boards.
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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 | 0.005 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".