The Value of Political Ties Versus Market Credibility: Evidence from Corporate Scandals in China
Bibliographic record
Abstract
Abstract This paper compares the value of political ties and market credibility in China by examining the consequence of corporate scandals. We categorize Chinese corporate scandals by whether the scandal is primarily associated with the destruction of (i) the firm's political networks (political scandals), (ii) the firm's market credibility (market scandals), or (iii) both (mixed scandals). Consistent with our hypothesis that scandals signaling the destruction of political ties are associated with greater losses in firm value than scandals signaling the destruction of market credibility, we find that the stock market reacts more negatively to political and mixed scandals than to market scandals. In addition, the greater negative market reactions associated with political and mixed scandals are primarily driven by firms that rely more on political networks. We also find that, compared to market scandals, political and mixed scandals lead to larger decreases in operating performance, greater reduction in loans from state‐owned banks, and higher departure of political directors.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".