Ownership Concentration, State Ownership, and Effective Tax Rates: Evidence from China’s Listed Firms*
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract This paper examines the effect of ownership concentration and state ownership on the tax reporting practices of China’s publicly listed firms. I argue that ownership concentration and state ownership are important for tax reporting practices in China because listed firms have high ownership concentrations and high levels of state ownership. Using a sample of 758 listed Chinese firms over the 1998–2008 time period, I find that firms with concentrated share ownership have lower effective tax rates. I also find that firms whose largest shareholders are government‐related have higher effective tax rates compared to firms whose largest shareholders are nongovernment related. In other words, the nature of the largest shareholder (government vs. nongovernment) matters. I also show that ownership‐concentrated firms are able to achieve preferential statutory tax rates compared to firms with low ownership concentration regardless of the identity of the largest shareholder.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it