Female directorship and real earnings management in Bangladesh: Towards an analytical assessment
Bibliographic record
Abstract
We analyze the association between female directorship on the board and real earnings management in context of an emerging economy, in Bangladesh. To accomplish the task, we utilize a sample of 2193 firm-year observations listed on the Dhaka Stock Exchange throughout the period 2000-2017. Our exploration indicates that the presence of female directors, the proportion of female directors on the board, as well as the presence of independent female director; all of these forms are positively associated with real earnings management. Therefore firms, with female director(s), tend to be involved in higher levels of earnings management through lower price discount, unfavorable credit facility and lower scales of production. This study also underscores that firms with female director(s) are more likely to abide by defensive financial reporting policies and they lean towards employing more income-decreasing earnings. On the other hand, their counterparts in firms with a less representation from female on the board are much less likely to engage in similar practices. So, the persistence of female directors may resolve the problem of income-increasing real earnings management in a significant manner. Additionally, we provide evidence that corporate governance plays a beneficial role in limiting real earnings management especially when the board appoints female director(s).
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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 | 0.000 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".