Financial Reporting Quality and Auditor Dismissal Decisions at Companies with Common Directors and Auditors*
Bibliographic record
Abstract
ABSTRACT We examine the effects of corporate networks involving common directors and auditors (i.e., connections creating single or double ties between companies) on two important monitoring roles: financial reporting quality and auditor dismissal decisions. We also investigate how shocks to the networks, in the form of the audit failing to detect misstatements, affect these networks' structure. The investigations are important because these networks can have significant effects on firm governance and may have different effects when they overlap. We have three primary findings about double‐tie networks: (i) there is no evidence that they improve overall financial reporting quality beyond the effect of single‐tie networks; (ii) they lower directors' willingness to dismiss the auditor, even when there is a signal of an audit failure within the network; and (iii) they allow audit‐quality problems to spread between companies. Our results demonstrate the importance of investigating multiple types of networks and how shocks travel through them. Our findings also lend credence to concerns that “cozy” relationships between directors and auditors diminish the link between poor audit quality and market‐imposed reputation penalties—specifically, auditor dismissals.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".