An Investigation of Tax-Related Corporate Political Activity in China: Evidence From Consumption Bribery
Bibliographic record
Abstract
This article investigates the occurrence and outcomes of corporate tax-related political activity through bribery. Specifically, it examines the extent to which firms bribe government officials through gift-giving, banqueting, and entertaining activities and the extent of payoffs that firms gain from these bribery practices. Using a large hand-collected dataset of Chinese listed firms for the period from 2009 to 2014, I find that firms that spend more on consumption bribery exhibit a significantly lower tax burden. This negative association is mainly driven by small firms, non-state-owned firms, state-owned firms with weak political connections, and firms in competitive industries. Further evidence shows that the tax benefits from bribery are more apparent in more corrupt, less economically developed, and less liberalized regions. Furthermore, the payoffs of tax bribery are mitigated by 45.4% after the implementation of China’s anti-corruption campaign in 2012. These findings have policy implications for governments to optimize investment environments and increase tax revenue by curbing power-for-money deals.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".