Corporate fraud, political connections, and media bias: Evidence from China
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.006 |
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 it