Effect of Audit Committee on Tax Aggressiveness: French Evidence
Bibliographic record
Abstract
This study investigates the effect of audit committee characteristics on the level of tax aggressiveness. Drawing on a sample of 72 French listed firms from the SBF120 index for the period from 2015 to 2022, this study measures the level of tax aggressiveness through the effective tax rate (cash ETR). The descriptive statistics, correlation matrix, variance inflation factor (VIF), and feasible generalized least squares (FGLS) regression were used for analysis of panel data. The findings reveal that measures of the independence of the audit committee, expertise of the audit committee, and audit committee size are significantly linked to tax aggressiveness. The findings also highlighted that the frequency of audit committee meetings is weakly linked to tax aggressiveness. The effectiveness of audit committee members can send a strong signal to the tax authorities, the shareholders, regulators, and the investors who are concerned about the risk of tax aggressiveness. This study contributes to the existing literature aimed at exploring the effect of audit committee characteristics on tax aggressiveness in a French context. This study has several implications for regulators, policymakers, and academia. It helps the policymakers and regulators in policy reforms who aim at combating aggressive tax practices in France, which is one of the primary objectives within the European Union (EU) framework.
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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 | 0.006 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".