Bibliographic record
Abstract
Purpose This study aims to explore whether an auditee’s audit quality influences its payout policies (i.e. each form of dividend payouts and stock repurchase payouts). Design/methodology/approach Based on a panel data of US public firms, from 2004 to 2018, and Tobit estimators, this study aims to examine whether auditees’ audit quality is related to their payouts and under which circumstances (from the standpoints of auditees’ information asymmetry, refinancing risk, corporate governance and financial constraints) the aforesaid associations are more pronounced. Findings The findings of this study imply that auditees’ audit quality is positively related to auditees’ payouts. Further examination suggests that this positive relationship is stronger for auditees with higher information asymmetry, lower financial constraints and refinancing risk and for those with weaker governance. Finally, this study documents that dividend payouts are more stable for auditees with high-quality audits than those with low-quality audits. The results support the view that auditees’ transparency (reflected in high-quality audits) could be a crucial driver and rationale for their payout policies and, ultimately, overall policies. Originality/value By combining two different research lines of audit quality and corporate payout policies, this paper adds to both literature, as it is a novel one to document the contributing function and impact of audit quality on auditee’s payout policies (tangible financial decisions and policies). The findings are significant considering that it documents high-quality audits affecting the auditees besides their financial reporting quality. This study also shows the moderating roles of the auditee’s information asymmetry, rollover risk, financial constraints and corporate governance in the relation between audit quality and an auditee’s payout decisions. Furthermore, the findings can help shareholders (aiding them in determining companies with high payout policies), regulators and policymakers who emphasize audit quality. The results indicate that policymakers’ and standard setters’ efforts fostering high-quality audits should be in conjunction with firm payout standards.
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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 | 0.009 | 0.057 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 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".