Corporate Governance and Tax Avoidance: Evidence from Greek Service-Sector Firms
Bibliographic record
Abstract
This study investigates the relationship between corporate governance mechanisms and tax avoidance in Greek service-sector firms over the period 2014–2023. Using panel data, the analysis evaluates the influence of board characteristics, audit committees, auditor quality, and ownership structures on firms’ tax behavior. The results reveal that traditional governance mechanisms—such as board size, independence, audit committee composition, and gender diversity—do not significantly constrain tax avoidance, reflecting the formalistic rather than substantive adoption of governance practices in Greece. In contrast, external audit quality and ownership structure emerge as critical determinants. Engagement with high-quality auditors, particularly Big 4 firms, is associated with reduced tax aggressiveness, while state ownership similarly curbs avoidance, consistent with reputational and political accountability incentives. Conversely, managerial and foreign ownership are positively related to aggressive tax planning. The findings underscore the contextual nature of governance effectiveness: in weak enforcement environments, formal mechanisms serve largely symbolic roles, whereas external oversight and ownership incentives carry greater weight. This study contributes to agency and institutional theory by highlighting the limits of formal governance reforms absent substantive independence and enforcement.
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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 | 0.003 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".