Do Political Connection Disruptions Increase Labor Costs in a Government-Dominated Market? Evidence from Publicly Listed Companies in China
Bibliographic record
Abstract
This paper investigates whether the disruption of political connections increases labor costs among Chinese listed firms. Using the Communist Party of China's Rule No. 18 as an exogenous shock that forces firms to lose their politically connected independent directors, we find that the disruption of political connections is associated with an increase in labor costs (both in terms of aggregate labor costs per firm and average labor costs per employee) and an increase in employee turnover. Such increases do not lead to labor productivity improvements, and cannot be attributed to changes in corporate policies or the composition of labor forces after Rule No. 18. We also find that firms with higher unemployment risk and skilled labor risk increase their labor costs to a larger extent. Our results are robust to alternative labor cost measures, controlling for potential confounding events, and alternative political connection channels. Our study shows an unintended labor market consequence—increases in labor costs—of political connection disruptions for firms that are adversely affected by such disruptions.
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How this classification was reachedexpand
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".