Home Bias and Corporate Environmental Social Responsibility
Bibliographic record
Abstract
This paper analyzes the impact of executives’ hometown identification on corporate environmental social responsibility (CESR) using a sample of Chinese A-share-listed companies from 2007 to 2018. It finds that: the CESR scores of companies are higher when executives work in their hometowns, indicating that executives’ hometown identification significantly improves the fulfillment of CESR; mechanism tests show that the above relationship is more significant in regions with superior environmental quality, indicating that executives take CESR more seriously in their hometowns more due to social pressure; further tests found that executive characteristics, such as executive type and age, have a regulating effect on this relationship. In addition, the nature of property rights of listed companies also affects executives’ hometown identification. Executives of state-owned enterprises have a stronger hometown identification, which enhances the fulfillment of CESR to a higher extent. In the context of the micro level of the enterprise, this paper provides positive evidence that an informal system, named as “hometown identity”, can enhance the performance of CESR and the pressure effect implicitly behind the social network, which enriches and expands the research related to CESR fulfillment.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".