Gender Power, the Top Management Team, and Firm Credit Default Risk
Bibliographic record
Abstract
This paper considers the impact of the composition of the top management team on the credit default risk of the firm. Finance theory suggests that shareholders prefer higher levels of risk than the risk-averse executives managing the firm. Increasing the influence of female executives may reduce credit default risk, as female executives have been shown to be associated with lower firm risk. Alternatively, as diversity has been shown to improve the quality of group decision-making, a higher but optimal credit default risk may result. This paper uses a matched sample of 6,652 firm-year observations of publicly traded American firms over the period 2010–2020 to investigate the relationship between gender power within the top management team and credit default risk as measured by the Altman Z-score. This paper finds a convex relationship between the Altman Z-score and the influence of female executives. In other words, top management teams where power is shared between female and male executives accept higher levels of credit default risk than teams dominated by just female (or just male) executives. However, this paper also finds that an excessively high credit risk is negatively associated with the influence of female executives.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".