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
This paper examines how political connections shape media bias and contribute to regulatory noncompliance in China's capital markets. Using a large sample of news articles on publicly listed non-state-owned enterprises (non-SOEs), we find that politically connected firms receive significantly more favorable media coverage than their unconnected peers. A difference-in-differences analysis exploiting a regulatory shock—China's Rule 18 anti-corruption regulation—that forced politically connected directors to resign confirms the link between political ties and biased reporting. Around corporate scandals, politically connected firms face softer media scrutiny, weakening reputational penalties. Critically, we show that this media shielding effect increases the likelihood of repeated regulatory violations. These findings highlight the social costs of the “scandal-covering” role of political connections, which not only distort the information environment but also undermine regulatory deterrence and market discipline.
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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 | 0.004 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it