How Aggressive Tax Planning Facilitates the Diversion of Corporate Resources: Evidence from Path Analysis
Bibliographic record
Abstract
ABSTRACT In measuring tunneling with intercorporate loans disclosed by Chinese listed companies, we analyze the underlying channels through which aggressive tax planning facilitates the diversion of corporate resources by firm insiders. Using path analysis, we document that the path from tax aggressiveness to related loans is mediated by both the additional cash flows from tax savings and the increased financial opacity from tax planning, and that additional cash flows plays a much more important role than opacity in helping controlling shareholders to divert corporate resources under the guise of tax aggressiveness. Beyond the two mediated paths, we also detect a residual, direct path from tax aggressiveness to related loans. After an exogenous shock from the government crackdown on diversionary related loans, we find the direct path is fully mediated by the two indirect paths, suggesting that tunneling via related loans only occurs at firms where insiders can mask tunneling under the cover of opacity or can justify related loans on grounds of abnormal cash flows from tax savings. Our evidence supports the notion that greater outside scrutiny increases the hurdle for, but does not entirely eradicate, diversion facilitated by tax aggressiveness. Collectively, our research lends some support to recent theory on the importance of taxes to corporate governance by demonstrating how the agency costs of tax planning allow certain shareholders to benefit from firm activities at the expense of others.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| 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".