Re‐examining the Glass Cliff Hypothesis using Survival Analysis: The Case of Female CEO Tenure
Bibliographic record
Abstract
Abstract We use the glass cliff to study the appointment and employment duration of 193 female CEOs between 1992 and 2014 in a sample of large, small and mid‐size North American firms. Consistent with the glass cliff, we find that women are appointed as CEOs in precarious situations. However, we find female CEOs are 40% less likely to face turnover at any point after appointment than male CEOs. This conflicts with an implication of the glass cliff and differs significantly from existing research which shows that female CEOs have only a slightly lower risk of turnover than male CEOs. Our larger, more recent sample captures changes in the labour market that explain the departure from the results of earlier studies. We find evidence that the lower turnover rate of female CEOs is related to firms’ desire to avoid the negative publicity that would accompany their termination, and we also show that greater education has a positive impact on CEO job security.
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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 | 0.037 | 0.088 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".