Do Locally Based Independent Directors Reduce Corporate Misconduct? Evidence from Chinese Listed Firms
Bibliographic record
Abstract
ABSTRACT We explore the influence of the localness of independent directors on Chinese listed firms' fraudulent and non-compliant practices. We are motivated by the dynamics between monitoring and favoritism—the moving parts driving the association between geographic proximity and monitoring outcomes. In our analysis of A-share listed firms in China between 2007 and 2013, we find that local independent directors at both the provincial and the city-levels reduce the frequency and magnitude of the misconduct by listed firms. Furthermore, the monitoring effect is stronger for independent directors who are in the same province/different city than those in the same province/same city, which suggests that while the monitoring effect of localness remains constant, the favoritism effect is stronger for independent directors who reside in the same city. We also find that political connections negatively moderate the effect of local independent directors' monitoring function, especially with non-state-owned firms. Data Availability: All data are available from public databases and annual reports of listed firms identified in the paper, except for the CSMAR data, which are available from the company upon request.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".