Corporate Governance and Dividend Policy
Bibliographic record
Abstract
In this paper, we have analyzed the effects of corporate governance mechanisms on dividend policy of 134 listed firms on the Main Board of the Nigerian Exchange, over fifteen (15) years (2008-2022) using the Generalized Method of Moments. Corporate governance mechanisms considered in this study are foreign ownership, institutional ownership, board size, board independence, audit committee gender, risk committee gender, and big4 audit firms. Dividend policy proxy is dividend per share. The research design is both <em>ex post facto</em> and <em>correlational</em>; the population is 156, from which 134 listed firms were selected, meaning that 22 listed firms were excluded. This paper obtained data from the annual reports and accounts of the listed firms, over the period covered. The data set was analysed using descriptive analysis, Pearson correlation analysis and GMM panel regression analysis. These analyses were carried out with STATA 15.1. The results show that foreign ownership has significant effect on dividend policy; institutional ownership has insignificant effect on dividend policy; board size has significant effect on dividend policy; board independence has significant effect on dividend policy; audit committee gender has insignificant effect on dividend policy; risk committee gender has significant effect on dividend policy; the presence of big4 is not significant with dividend policy. These results are useful to regulators, market participants, shareholders, suppliers, lenders (creditors), managers, employees, scholars and researchers. However, the findings of this paper are (I) limited by the 134 listed firms on the Main Board of the NGX (2) variables used in the study (3) period of coverage (2008-2022). Future research may wish to consider the whole 156 listed firms; expand the period of study or consider other variables of corporate governance, such as family ownership, ownership concentration, CEO ownership, board ownership, board tenure, board education, board compensation, board turnover, nomination committee gender, remuneration committee gender, joint auditors, etc.
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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 | 0.001 | 0.001 |
| 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".