Property Rights, Tax Avoidance and Capital Structure: Data from China Stock Markets
Bibliographic record
Abstract
This paper investigates the influence of tax avoidance on capital structure based on share ownership under China’s economic system. Previous research has indicated that tax avoidance exits and has a potential effect on firms’ capital structure, but there is little literature focusing on this influence based on China’s economic system. In light of that, this paper uses A-share data of the Shanghai and Shenzhen stock exchange from 2007 to 2016 as samples to study the impact of tax avoidance on the capital structure based on China’s economic system. The results suggest that, firstly, there is a significant negative correlation between tax avoidance and the debt ratio of the listed companies; secondly, there is a significant difference in the effect of corporate tax avoidance on the debt ratio of different industries and different equity ownership. Besides, by regrouping the samples according to the share ownership and the degree of tax avoidance, it is revealed that China’s unique economic system would lead to an impact of tax avoidance on the capital structure that differs from other countries. Finally, it is found that there is a negative correlation between the degree of tax avoidance of the listed companies and the dynamic adjustment of assets-liability ratio through the extended study, further verifying that there is a substitution relationship between tax avoidance of the listed companies and their debt financing.
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 | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| 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".