Non‐Market Strategies and Credit Benefits: Unpacking Heterogeneous Political Connections in Response to Government Anti‐Corruption Initiatives
Bibliographic record
Abstract
Abstract This paper explores how political connections influence firms’ credit benefits, especially when the political environment improves. The authors distinguish two types of political connections – connections to government officials and connections to council deputies – according to whether the political benefits they provide are exclusive and definite. Employing a panel data set comprised of Chinese listed firms’ bank‐loan contracts from 2008 to 2014, they find politically connected firms – and particularly firms with connections to government officials – enjoy significantly lower loan costs than their non‐connected counterparts. Moreover, they find that anti‐corruption efforts, which reflect improvement in the political environment, reduce the credit benefits of political connections, but only for firms that have connections to government officials. Results emphasize the value of unpacking the heterogeneity of political connections and illuminate the importance of more complete assessment of corporate political strategies in changing political environments.
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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 | 0.000 |
| 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 | 0.002 |
| 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 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".