Ethnic minority CEO turnover: Resource‐based and leadership categorization perspectives
Bibliographic record
Abstract
Summary We use the resource‐based theory and leadership categorization theory to develop hypotheses about ethnic minority CEO turnover. Using survival analysis, we test the hypotheses and find that, as a group, ethnic minority CEOs at US firms experience only about half of the risk of turnover at any time as do nonethnic minority CEOs. However, the risk is not spread evenly across ethnic minority subgroups. Asian and Hispanic CEOs experience lower risk of turnover than nonethnic minority CEOs. Black CEOs of US firms do not share this reduced risk of turnover. We find that the resource‐based theory is consistent with the turnover experience of Asian and Hispanic ethnic minority CEOs, but that it is not useful for explaining Black CEO turnover. Some implications of our findings are the following: (1) In research, all minorities should not be treated as a single homogenous group, and (2) in practice, it may be useful to increase CEO social capital to lengthen tenure.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".