Ownership Concentration in Privatized Firms: The Role of Disclosure Standards, Auditor Choice, and Auditing Infrastructure
Bibliographic record
Abstract
ABSTRACT We rely on a unique data set to estimate the impact of disclosure standards and auditor‐related characteristics on ownership concentration in 190 privatized firms from 31 countries. Accounting transparency can help alleviate the agency conflict between minority investors and controlling shareholders, which is evident in the extent of ownership concentration, since the expropriation of corporate resources hinges on these private benefits remaining hidden. After controlling for other country‐level and firm‐level determinants, we find weak (no) evidence that extensive disclosure standards (auditor choice) reduce ownership concentration. In contrast, we report strong, robust evidence that ownership concentration is lower in countries with securities laws that specify a lower burden of proof in civil and criminal litigation against auditors, consistent with Ball's [2001] predictions. Collectively, our research implies that minority investors worldwide value legal institutions that discipline auditors in the event of financial reporting failure over both the presence of a Big 5 auditor and better disclosure standards. Re‐estimating our regressions on a broad sample of western European public firms provides similar evidence on all of our predictions.
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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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".