Bibliographic record
Abstract
This paper provides analysis of the trends in dividend policy and differentials in firm and country specific factors for payers and non-payers of dividends and examines the predictions concerning the amount of dividends paid by listed non-financial firms in African countries. Using a panel dataset over the period 1994-2011 from 13 African countries, the study found that dividend payers are more profitable, have larger firm size, greater investment, higher retention of earnings and less financial leverage than non-paying firms. The results show that in countries where the GDP per capita is low, firms are more likely to pay dividends. The level of corruption is high for non-payers of dividends. The study also found a positive significant relationship between dividend payout, profitability, investment opportunities and firm size. However, a significant negative relationship was reported between dividend payout, financial leverage, corruption and gross domestic product per capita. The study further found that the dividend trends were very low and stable. The conclusion, therefore, indicates that although firm specific factors are important in Africa in determining dividend policy regarding payout, country specific factors play very significant roles in determining the dividend payout of African firms.
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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 | 0.001 |
| 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) | 0.002 | 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".