Bibliographic record
Abstract
ABSTRACT This study examines whether common ownership by institutional investors is associated with auditor sharing among their investee companies. Auditor sharing can enhance audit quality through facilitated monitoring and improve financial reporting comparability—two benefits that enable common owners to internalize externalities across their portfolio firms (i.e., to reduce negative spillovers from audit failures and to capture positive spillovers from improved comparability across commonly owned peer investees). Using same‐industry company pairs in the United States, I find that common ownership is positively associated with the likelihood of sharing the same audit firm, and this association is stronger when co‐owners have longer investment horizons or more aligned incentives. A quasi‐experimental test leveraging BlackRock's acquisition of Barclays provides consistent evidence. Additional analyses indicate that shared board members serve as a potential channel through which auditor sharing arises. Finally, commonly owned, auditor‐sharing companies exhibit higher audit quality and are more likely to collectively dismiss auditors following revealed failures, consistent with improved oversight. These findings contribute to the literature on shared auditors, auditor choice, and common ownership by showing how a noncontractual relationship induced by common ownership shapes auditor sharing across companies and influences the shared auditor's incentives.
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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.011 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".