The Impact of Audit Quality and Female Audit Committee Characteristics on Earnings Management: Evidence from the UK
Bibliographic record
Abstract
This study explores the impact of audit quality and the proportion of women on an audit committee on earnings management. Moreover, we examined how age diversity and the presence of non-foreign women on audit committees influence earnings management. Our study utilizes data from 165 UK-based listed companies between 2011 and 2021. A combination of static and dynamic analysis was used to empirically reveal our results. The results show a negative and significant relationship between audit quality and earnings management, as per the Kothari model. The presence of a female audit committee does not affect earnings management. However, when we control for demographic variables like age and nationality, we found that non-foreign female members of the audit committee reduced earnings management, while age diversity among female members had no effect. Additional analysis using the Dechow model revealed that both the presence of a female audit committee and their nationality affected earnings management. Our findings contribute to ongoing discussions on corporate governance by providing evidence that female audit committees and audit quality influence earnings management in UK-listed companies. This study is one of the few that examines demographic attributes (e.g., nationality or age).
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".