Ultimate ownership structure and corporate disclosure quality: evidence from China
Bibliographic record
Abstract
Purpose The purpose of this paper is to examine whether the type of ultimate controllers (i.e. private vs state) affects corporate disclosure quality and whether the relationship between the type of ultimate controllers and corporate disclosure quality is moderated by the separation of ownership and control. Design/methodology/approach This study employs the data of 405 Chinese listed firms in 2005. Annual reports were reviewed to collect the data including the type of ultimate owners, cash‐flow rights, and control rights; and the ratings of corporate disclosure quality were obtained from the Shenzhen Stock Exchange website. Ordered logistic regression tested the hypotheses. Findings It was found that corporate disclosure quality is lower for firms ultimately controlled by individuals than for firms ultimately controlled by the state. Also, the negative effect of private ultimate ownership on corporate disclosure quality is stronger for firms with high deviation of cash‐flow rights and control rights. Practical implications These findings suggest that privatizing state‐owned companies may increase the expropriation of minority shareholders by controlling shareholders if the privatization does not reduce the separation of cash‐flow rights from control rights. Thus, it may be necessary to strengthen the governance role of minority shareholders and constrain the divergence between cash‐flow rights and control rights of the ultimate owners when state‐owned companies are privatized. Originality/value This study contributes to the literature on the expropriation of minority shareholders by examining the main effect of the type of ultimate controllers and the interactive effect of ultimate ownership type and the divergence of ownership and control on corporate disclosure quality.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".