Board-Gender Diversity, Family Ownership, and Dividend Announcement: Evidence from Asian Emerging Economies
Bibliographic record
Abstract
In eras of intense debates on the appointment of women on corporate boards, this research sheds light on the structure of board in Asian emerging economies by examining how women on board of family businesses separately and collectively affect the dividend announcement of business organizations. On the basis of the panel data of four Asian emerging economies—China, Malaysia, Pakistan, and India—for the period 2010–2018, the results from our Tobit regression showed the adverse (negative) and significant impact of women on boards and in family businesses upon dividend announcement. It is important that policymakers should not view firms with one eye. There should be a spillover on board gender diversity from international to domestic levels, and international firms should be set as an example for domestic firms for the inclusion of women on boards. It might be the best time for Asian emerging economies to take productive action for balancing the gender in boardroom settings, and to set a minimum mass of women on boards for better and more effective decision making.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".