Does Profitability Moderate the Relationship Between the Leverage and Dividend Policy of Manufacturing Firms in Nigeria and South Africa?
Bibliographic record
Abstract
This study examines the moderating role of profitability in the relationship between leverage and dividend policy in listed manufacturing firms in Nigeria and South Africa. Using a sample of 915 firm-year observations from 2013 to 2022, the analysis employs panel Tobit regression to manage the censored nature of dividend data, with logistic regression applied as a robustness check. The findings reveal a negative association between leverage and dividend payout ratio for Nigerian firms, while this association is less pronounced and statistically insignificant in South Africa, reflecting a more flexible financial environment. Profitability strengthens the leverage–dividend policy relationship in Nigeria, enabling firms to maintain dividends despite high leverage; however, this moderating effect is weaker in South Africa. These results underscore the importance of context-specific financial strategies, recommending that Nigerian policymakers improve access to affordable credit, while South African policymakers focus on sustaining market stability. This study advances the understanding of dividend policy in emerging markets by clarifying how leverage and profitability interact to shape dividend practices.
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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 | 0.000 | 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".