A vicious cycle of symbolic tokenism: The gendered effects of external board memberships on chief executive officer compensation
Bibliographic record
Abstract
Abstract Integrating theoretical perspectives on tokenism and perceived preferential selection, we explore whether the relationship between chief executive officers' (CEOs') external board memberships and CEO compensation is gendered. Based on recent pressures to diversify corporate boards, we theorize that female CEOs' memberships on external boards will result in less monetary compensation relative to male CEOs due to concerns of organizational decision‐makers that female CEOs generally inhabit token or “symbolic” positions of limited value. Additionally, we present competing hypotheses (i.e., mitigation vs. exacerbation) regarding how this devaluation will be affected by female representation on the board of directors and compensation committee, respectively. Using a panel sample of 12,464 firm‐year observations comprising of 1,805 unique firms and 2,782 unique CEOs, the relationship between CEO external board memberships and compensation is indeed weaker for female compared to male CEOs. Furthermore, this devaluation primarily occurred in organizations where there was stronger (vs. weaker) female representation on the board of directors or compensation committee. However, supplemental analyses revealed that this differential devaluation was mitigated when female executives on the board held greater power (i.e., chaired important committees), highlighting the importance of moving beyond mere representation to ensuring influence on boards for female directors.
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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 | 0.003 | 0.012 |
| 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 | 0.003 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".