Can Shareholder Activism Improve Gender Diversity on Corporate Boards?
Bibliographic record
Abstract
Abstract Manuscript type Empirical Research question/issue We empirically examine the antecedents of shareholder activism related to increasing the gender diversity of corporate boards of directors and whether such activism is an effective mechanism for achieving this goal. Because campaigns for increased gender diversity may be driven by either economic efficiency or social legitimacy concerns, we condition our analysis on activists' motivations for achieving their objectives. Research findings/insights Based on a sample of US S&P 1500 firms over 1997–2011, we find that female board representation and board independence are negatively associated with the likelihood of being targeted by a shareholder proposal related to gender diversity. We further document that financially motivated activists are more likely to target firms with extremely low female board representation than are socially motivated activists. Targeted firms significantly increase their female board representation in the two‐year period following proposal initiation, relative to that of a matched sample of non‐targeted firms, with no significant differences observed across activist motivations. Theoretical/academic implications Our findings provide empirical support for the effectiveness of shareholder activism in shaping corporate governance. Our work also suggests that shareholder activists' underlying motivations are an important conditioning variable in governance research, with both agency theory and institutional theory providing insight into differing motivations. Practitioner/policy implications Our findings suggest that shareholder proposals are an effective mechanism for increasing board diversity, irrespective of activist motivations. However, we note that mean female board representation for both targeted and non‐targeted firms remains far below the level of representation sought by various activist groups. For policy‐makers, this suggests that legislative action may be necessary to achieve these corporate board diversity goals in the US.
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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 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 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".