Role of Old Boys’ Networks and Regulatory Approaches in Selection Processes for Female Directors
Bibliographic record
Abstract
Abstract This study examines the influence of directors’ and CEOs’ networks in the appointment of female directors. Building on social identity theory and social network theory, we argue that since men and women are members of different networks, recruitment practices based on networks prevent women from accessing board positions. We also examine the role of board gender diversity regulation on the influence of networks, hypothesizing that such regulations help in deinstitutionalizing ‘old boys’ networks’, based on institutional theory. Using a sample of 32,819 new board appointments in the largest listed firms of 17 European countries, the USA and Canada, we determine whether new directors are directly linked through employment, board, charities or club memberships, to the incumbent directors or the CEO. We find that the probability that the new director appointed is a woman decreases by approximately 28% when the new director is associated with one of the incumbent directors. We also find that gender diversity regulation reduces the influence of networks in the appointment of female directors. Our results provide archival evidence that board networks hinder the recruitment of female directors and that gender diversity hard and soft laws deinstitutionalize old boys’ networks.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".