China's Anti‐Corruption Campaign and Financial Reporting Quality
Bibliographic record
Abstract
ABSTRACT We examine the impact of China's anti‐corruption campaign on firm‐level financial reporting quality (FRQ). As an important component of the anti‐corruption campaign, in October 2013, “Rule 18” was issued to prohibit party and government officials from serving as directors for publicly listed firms. The regulation led to a large number of official directors resigning from their roles as directors involuntarily. As such, Rule 18 has effectively weakened, if not fully discontinued, the political connections of the firms that previously hired officials as directors. Our empirical analyses employ a difference‐in‐differences research design with firm fixed effects and propensity‐score matching to examine the pre‐ and post‐period FRQ around the enactment of Rule 18. We find that, compared to propensity‐score‐matched control firms, FRQ of firms with resigned official directors increases after Rule 18. Further evidence suggests that the impact is stronger when firms are located in regions with more developed financial markets and in regions with higher judiciary efficiency. We also find that the effect is more pronounced when firms are non‐state‐owned, received preferential credits, and face refinancing pressure.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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".