Impacts of Political Connections on Private Enterprise Performance in China and the Analysis of Mediating Effects
Bibliographic record
Abstract
Based on data of Chinese Private Enterprises Survey (CPES) from 2006-2014, this paper uses OLS model and other empirical methods to estimate the impacts of political connections on private enterprise performance in China, as well as heterogeneous effects and mediating effects of different types of political connections on tax burden and non-productive activities expense. The results show that, political connections contribute significantly to private enterprises. Compared with previous political connections, like working experiences in state-owned enterprises and government-affiliated institutions, current political connections, like deputies to the NPC or members of CPPCC, have played better and further roles in enterprise performance. Tax burden and non-productive activities expense have a mediation effect in the relationship between political connections and enterprise performance. This study replenishes new evidence to describe how political connections affect private enterprise performance in China, and partly explains why private enterprises are keen on setting up political connections, which may provide valuable tips to foster a new type of cordial and clean relationship between government and business in China.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 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".