Does Debt Structure Explain the Relationship between Agency Cost of Free Cash Flow and Dividend Payment? Evidence from Saudi Arabia
Bibliographic record
Abstract
This paper investigates the impact of debt financing on dividend payments when they face the agency costs of free cash flow. It focuses on a sample of 120 firms listed on the Saudi Stock Exchange during the period of 2011–2021. The findings from the Generalized Least Squares regression model revealed that the presence of agency costs of free cash flows may limit the funds available for dividend payments. Regarding the moderating effect of debt structure, the research highlights the significant role of long-term debt in making more prudent use of free cash flow. The use of long-term debt becomes more effective and can enhance shareholder wealth when a firm is facing agency costs of free cash flow. More specifically, bondholders primarily focus on affirmative covenants which require the firm to undertake specified actions such as maintaining assets and financial ratios, or paying taxes, but they do not restrict financing activities such as dividend payments. Since interest and debt repayments are fixed obligations, using free cash flow for dividend disbursement is considered a more profitable and beneficial approach for shareholders in the context of Saudi Arabia. This study contributes to our understanding of financial management under different debt structures and improves our scientific knowledge of the culture of Saudi firms regarding the dividend distribution policy.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".