Does Public Enforcement Work in Weak Investor Protection Countries? Evidence from China*
Bibliographic record
Abstract
ABSTRACT We examine the efficacy of public enforcement in weak investor protection countries by examining the outcomes of a comprehensive public enforcement campaign in China. The campaign, launched in 2007, was designed to help enforce China's first mandatory Corporate Governance Code, issued in 2002. The 2007 campaign was characterized by several important features: (i) the campaign required firms to identify their problems before the securities regulators conducted on‐site inspections; (ii) the campaign provided a detailed checklist of the status of a firm's compliance with the Code; (iii) the campaign was transparent with regard to the disclosure and correction of identified problems; and (iv) the campaign threatened penalties for firms that failed to correct the identified governance problems in a timely manner. Our empirical analyses suggest that the 2007 campaign was effective in improving publicly listed firms' corporate governance. The corporate governance improvement was associated with a reduction in earnings management, higher earnings response coefficients, and higher operating accounting performance. In addition, we find preliminary evidence that the detailed checklist is partially responsible for the efficacy of the campaign. Our results suggest that public enforcement, if properly implemented, works in increasing shareholder value in weak investor protection countries.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.007 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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 teacher head, 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".