Profitability, state ownership, tax reporting and corporate social responsibility: evidence from Chinese listed firms
Bibliographic record
Abstract
Purpose – This paper aims to examine corporate social responsibility (CSR) in the context of listed Chinese firms. In particular, it examines the relationships between CSR and profitability, state ownership and tax reporting behavior. Design/methodology/approach – The paper is an empirical study using CSR reports published by the Chinese Academy of Social Sciences and financial data collected from the China Stock Market Financial Statement Database (CSMAR). Findings – The paper finds that state ownership is positively associated with CSR and its three components including the governance, social and environmental scores; firm profitability is positively associated with CSR and its market score; and tax reporting behavior is negatively associated with the environmental score. But the result is weak. Research limitations/implications – The results in this study should be treated with some caution as the sample size of 85 observations represents only a small fraction of China’s listed firms. A larger sample size is desirable and may affect our results. Social implications – This paper is of interest to policy-makers, corporate management and academics who wish to explore the relationship between CSR and other firm characteristics. Originality/value – This paper is the first study which provides a comprehensive examination of CSR and its four components in connection with Chinese firms. In particular, it examines the relationship between CSR and profitability and state ownership.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| 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".