Ownership structure and R&D spending: 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
Purpose This paper seeks to examine the effect of ownership concentration, inside ownership and state ownership on the R&D spending practices for China's listed firms. The paper argues that corporate ownership structures including ownership concentration, inside ownership and state ownership are important for corporate expenditures on R&D in China, whose firms present a high ownership concentration and a high level of state ownership. Design/methodology/approach The paper takes the form of an empirical study using a sample of 780 listed Chinese firms for six years from 2000 to 2005. Findings It is found that firms with concentrated share ownership have lower R&D spending, and firms with inside ownership have lower R&D spending. However, firms with a higher level of state ownership spend more on R&D. Research limitations/implications Given that corporate ownership structure and tax policy have changed dramatically in China in recent years, future studies should be conducted to explore the association between firms' R&D investment activities and those ownership structure and tax policy changes. Social implications This study is of interest to the policy makers, corporate management, and academics who wish to examine corporate R&D and innovation activities and those factors, including ownership structure, which are associated with R&D investment decisions. Originality/value This is the first study that examines the relationship between ownership and R&D spending for Chinese listed firms.
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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 | 0.000 | 0.000 |
| 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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 it