Board Gender Diversity, Auditor Fees, and Auditor Choice
Bibliographic record
Abstract
Abstract We examine whether the presence of female directors and female audit committee members affect audit quality in terms of audit effort and auditor choice by using observations from a sample of U.S. firms, spanning the years 2001–2011. We find, after controlling for endogeneity and other board, firm, and industry characteristics, that firms with gender‐diverse boards (audit committees) pay 6 percent (8 percent) higher audit fees and are 6 percent (7 percent) more likely to choose specialist auditors compared to all‐male boards (audit committees). Our findings suggest that boards (audit committees) with female directors (members) are likely to demand higher audit quality, ceteris paribus.
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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 | 0.004 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.007 | 0.001 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".