Corporate fraud, political connections, and media bias: Evidence from China
Bibliographic record
Abstract
Abstract This article empirically examines how political connections ( PCs ) affect a firm's media reaction after corporate fraud. Using data for Chinese listed companies from 2008 to 2021, we find that the media reports more positively for firms with PC s than for others that do not possess such advantages after the enforcement against fraud. The results are robust to a series of robustness checks and endogeneity corrections. When decomposing media reports, we find that PC s only facilitate positive media coverage but do not impede negative media coverage, which is more pronounced in state‐controlled media. This suggests that PC s protect firms’ branding by facilitating positive media reports rather than withholding bad news. Moreover, we find this protective effect is more pronounced in firms with stronger PC s, weaker anti‐corruption regulation, lighter punishment for fraud, private ownership, and more donations. Further, the consequences analysis shows that this kind of protective effect significantly increases the probability of future fraud and stock price crashes. Our findings present a new perspective on the role of PC s and provide evidence for political bias in media coverage.
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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 | 0.004 | 0.004 |
| 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".