The Interrelation between Audit Quality and Managerial Reporting Choices and Its Effects on Financial Reporting Quality
Bibliographic record
Abstract
ABSTRACT Two distinct lines of research have been dedicated to empirically testing how financial reporting quality (measured as the earnings response coefficient or ERC) is associated with management's choice of reporting bias and with audit quality. However, researchers have yet to consider how ERCs are affected by either the auditor's reaction to changes in the manager's reporting bias or the manager's reaction to changes in audit quality. Our study provides theoretical guidance on these interrelations and how changes in the manager's or the auditor's incentives affect both reporting bias and audit quality. Specifically, when the manager's cost (benefit) of reporting bias increases (decreases), we find that expected bias decreases, inducing the auditor to react by reducing audit quality. Because we also find that the association between expected audit quality and ERCs is always positive, changes in managerial incentives for biased reporting lead to a positive association between ERCs and expected reporting bias. When the cost of auditing decreases or the cost of auditor liability increases, we find that expected audit quality increases, inducing the manager to react by decreasing reporting bias. In this case, changes in the costs of audit quality lead to a negative association between ERCs and expected reporting bias. Finally, we demonstrate the impact of our theoretical findings by focusing on the empirical observations documented in the extant literature on managerial ownership and accounting expertise on the audit committee. In light of our framework, we provide new interpretations of these empirical observations and new predictions for future research.
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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 | 0.017 | 0.116 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".