How do women directors ensure corporate ethics? The role of board tenure
Bibliographic record
Abstract
Women board members have been shown to reduce corporate misconduct. We ask how they accomplish this given that their numbers are small, their voices often go unheard, and they tend to be on committees that have been considered less important. Using social identity theory and the theory of gendered expectations, we hypothesize that long tenure on the board is the characteristic that allows a woman director’s voice to be heard. We test our hypothesis by looking at women directors’ power to reduce stock option backdating. Our hypothesis regarding long tenure is supported. Other sources of power such as critical mass, prestige and ownership power are not significantly related to reduced corporate misconduct. We use logit regression and perform several robustness checks, including instrumental variable techniques to deal with possible endogeneity. We discuss the implications of our findings for management practice and board structure. • Long tenure on the board allows women directors’ voices to be heard • Long-tenured women directors reduce option backdating misconduct • Other forms of power do not help women directors reduce corporate misconduct
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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 | 0.012 | 0.049 |
| 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 | 0.005 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".