Corporate Governance and Tax Aggressiveness: The Moderating Role of Audit Quality
Bibliographic record
Abstract
Tax-aggressive behavior by firms can undermine tax revenues, corporate transparency, and overall economic governance. Corporate governance mechanisms are increasingly recognized as critical tools for mitigating such behavior, particularly in emerging markets such as Morocco. This study investigates how corporate governance structures influence the reduction in tax aggressiveness in a developing-country context, while also assessing the moderating role of audit quality. Using financial data from firms listed on the Casablanca Stock Exchange, the hypotheses are tested through OLS regression with firm and year fixed effects to examine the impact of board characteristics and audit quality on tax aggressiveness. The results show that the separation of the CEO and chairman roles and larger board size significantly reduce tax-aggressive behavior. Moreover, audit quality strengthens the negative relationship between board size and tax aggressiveness, with higher-quality audits further constraining aggressive tax practices. Additionally, ownership concentration is associated with higher tax aggressiveness, reflected in lower effective tax rates, whereas board independence exhibits no significant association with tax aggressiveness (p-value = 0.500879). Overall, the findings suggest that robust corporate governance and high-quality audits effectively mitigate tax-aggressive practices among Moroccan listed firms. This study contributes novel evidence from the Moroccan context, highlighting governance structures and audit mechanisms most effective at curbing such behavior. Policymakers and regulators are encouraged to promote stronger governance frameworks and enhance audit quality standards, while firms should reinforce these mechanisms to improve tax compliance and transparency
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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 | 0.006 |
| 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) | 0.002 | 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".