Breaking the Glass Ceiling or Reinforcing It? Hedge Fund Activism and Board Gender Diversity
Bibliographic record
Abstract
The composition of corporate boards, particularly the inclusion of women, has gained significant scholarly attention and public debate in recent years. This study investigates whether and how activist hedge funds—prominent value-driven shareholders—impact board gender diversity in both target and non-targeted companies. Drawing on female leadership research, we theorize that hedge fund activism negatively influences board gender diversity. Additionally, we explore whether the presence of celebrity CEOs moderates this effect, leading to differentiated firm outcomes. To test our hypotheses, we analyze data on hedge fund activism incidents in S&P 500 firms from 2000 to 2020. Findings offer partial support for our arguments. Specifically, hedge fund activism is associated with declines in board gender diversity in target firms. However, this negative impact is attenuated in firms led by celebrity CEOs, suggesting that high-profile leadership may mitigate pressures that undermine board diversity. These findings contribute to the literature on shareholder activism and corporate governance by highlighting the nuanced relationship between value-oriented shareholder activism, leadership dynamics, and board diversity initiatives.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".