Exploring Gender and Corporate Governance in an Emerging Market: Bridging Female Leadership, Earnings Management and Tax Avoidance
Bibliographic record
Abstract
This study highlights the pivotal role of women in corporate governance and their potential influence on achieving sustainable goals, particularly in the context of emerging countries. Using the two-step System-Generalized Method of Moments (GMM) with the dynamic short panel data of 351 nonfinancial listed companies in Vietnam from 2010 to 2022, this research examines the dynamics between earnings management and tax avoidance, focusing on the moderating role of women on the board of directors. The results confirm that both accrual-based and real earnings management are positively associated with corporate tax avoidance. However, there is a significant negative relationship between female representation on the board and tax avoidance, as well as a significant moderation of the relationship between earnings management and tax avoidance. This study reinforces that female leadership contributes to reducing earnings management and tax avoidance through improved monitoring and governance of corporate ethical activities, emphasizing the importance of strategically empowering women in leadership roles. The implications of this study are given to minimize harmful financial practices and align corporate strategies with ethical practices.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 | 0.001 |
| 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".