Gender diversity and bank risk-taking: female directors and executives
Bibliographic record
Abstract
Purpose The authors investigate how a gender-diverse board, a gender-diverse executive team, or a female chief executive officer (CEO) impact bank balance sheet and equity risk. Design/methodology/approach Using panel data of U.S. bank holding companies over the period of 1992–2019, the authors conduct panel regressions with bank and year-fixed effects to analyze how female directors, female executives, and female CEOs impact a wide range of bank risk measures, controlling for the bank, board and executive characteristics. Findings The authors find female directors significantly reduce all types of risk. Female executives reduce some balance sheet risk but have an insignificant effect on bank equity risk. However, the presence of female CEOs does not significantly reduce bank risk-taking. During financial crises, female CEOs even increase equity risk. Social implications The findings are important to shed light on the ongoing debate on how gender quota policy could be efficiently used to balance the need for gender diversity while ensuring corporate performance. It could also improve social welfare by guiding proper public policy to ensure the efficient use of social labor capital and curb banks' excessive risk-taking incentives. Originality/value The authors provide the first empirical evidence demonstrating that female directors and female executives in the banking industry have different impacts on bank risk-taking. The authors also provide the first empirical evidence that female leaders have a different impact on two different types of risks: balance sheet and equity risk. The study is also the first to analyze the impact of female executives over multiple financial crises.
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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 | 0.004 |
| 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) | 0.006 | 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".