The Interaction Effect of Female Leadership in Audit Committees on the Relationship Between Audit Quality and Corporate Tax Avoidance
Bibliographic record
Abstract
This study examines the moderating role of female audit committee chairs on the relationship between audit quality (measured by audit fees) and corporate tax avoidance. The analysis is based on 165 UK firms between 2011 and 2021 using static panel data regression models and Lewbel’s heteroscedastic identification method to check robustness. The findings highlight the significant role of audit quality in reducing corporate tax avoidance. In addition, the female audit committee chair strengthens the negative relationship between audit quality and tax avoidance. This study has many implications. For corporate governance, it shows the value of female leadership in audit committees, especially in curbing aggressive tax strategies. Firms should increase female representation in key roles, like audit committee chairs, to improve oversight and ethical financial practices. For regulators and policymakers, it supports the case for strengthening gender diversity mandates to improve corporate transparency and accountability. Tax authorities can use the fact that firms with strong audit quality and female-led audit committees are less likely to engage in tax avoidance to focus their audits on companies with weaker governance structures.
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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 | 0.003 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".