Ownership structure and R&D spending: evidence from China's listed firms
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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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 | 0.002 | 0.003 |
| 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".