The Moderating Effect of Female Directors on the Relationship Between Ownership Structure and Tax Avoidance Practices
Bibliographic record
Abstract
The primary objective of this study is to investigate the intricate relationship between different ownership structures, such as family, institutional, managerial, and foreign ownership, and tax avoidance practices. It also seeks to explore the moderating influence of female board members in shaping these relationships. This study utilizes balanced panel data from 72 industrial and service firms listed on the Amman Stock Exchange during the period of 2018 to 2023. The Generalized Method of Moments (GMM) was employed to estimate the results. The results indicate that family and foreign ownership positively influence tax avoidance practices, suggesting that families may engage in tax avoidance to benefit from rent extraction, while foreign investors may pressure managers to manipulate tax liabilities or shift profits across countries to minimize taxes. In contrast, the presence of female directors as well as institutional and managerial ownership is associated with a reduction in tax avoidance. Female directors play a moderating role in the relationship between ownership structure and tax avoidance. Their presence in interaction with institutional ownership reduces tax avoidance by focusing on tax compliance strategies. However, this effect changes in family and foreign-owned firms, where control over decision-making lies with the families or foreign shareholders, limiting the impact of female directors in promoting compliance and aligning their role with the tax avoidance strategies preferred by the controlling owners.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".