Bibliographic record
Abstract
Purpose The purpose of this paper is to investigate the effect of ownership structure arising from China’s unique privatization process on listed firms’ tunneling activities and their interaction with tax avoidance. Design/methodology/approach Using hand-collected data on the incompletely restructured state-owned listed firms and their applicable tax rate, this paper conducts a multivariate regression to test research questions. It also employs a triple differences method to examine whether the observed interaction between tax avoidance and tunneling is mitigated for well-governed firms. Findings It documents that controlling shareholders’ tunneling increases as the percentage of shares owned by state-owned enterprises (SOEs) increases. Evidence also shows that the magnitude of tunneling increases when SOEs controlled by the central government engage in more tax avoidance, suggesting that these firms use tax avoidance to facilitate wealth expropriation. Social implications These findings advance the understanding of the tunneling incentive behind the tax avoidance behavior for a subset of Chinese SOEs and have implications for emerging capital markets that are characterized by concentrated government ownership and weak corporate governance. Originality/value This paper is the first paper to investigate the effect of the incomplete privatization process on tunneling and the interaction between tunneling and tax avoidance activities. It extends prior studies by investigating the incentives behind SOEs’ tax avoidance from the perspective of an agency problem and documenting that good corporate governance plays an important role in deterring the diversionary tax avoidance.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| 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".