Do women directors impact the cost of bank financing? Evidence from Australia
Bibliographic record
Abstract
We investigate the effect of women directors on the cost of bank loans for a sample of Australian listed firms during the period 2002-2017. More women on both boards and committees are associated with lower loan spreads, reduced default risk, and improved financial reporting quality. The reduction in loan spread is seen from when the first female director joins the board, and the effect of that is stronger than the effects of additional (second and subsequent) women joining the board. Moreover, women’s leadership status reinforces this effect. Our results are consistent with the idea that female directors exhibit greater risk aversion than male directors, that women are not treated as tokens and are influential—even as a minority group on the board—and that they enhance corporate governance and reporting quality. Investigamos el efecto de las mujeres consejeras en el coste de los préstamos bancarios para una muestra de empresas australianas que cotizan en bolsa durante el período 2002-2017. Un mayor número de mujeres tanto en los consejos de administración como en los comités se asocia con menores diferenciales de los préstamos, un menor riesgo de impago y una mejor calidad de los informes financieros. La reducción del diferencial de los préstamos se observa desde la primera mujer consejera, y resulta más intensa que los efectos asociados a las siguientes (segunda y siguientes) mujeres incorporadas al consejo. Además, el estatus de liderazgo de la mujer refuerza este efecto. Nuestros resultados son coherentes con las nociones de que las mujeres consejeras muestran una mayor aversión al riesgo que los hombres consejeros, que las mujeres no son tratadas como simbolo siendo influyentes incluso como grupo minoritario en el consejo y que mejoran el gobierno corporativo y la calidad de la información.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".