Suppliers’ pre-existing information environments and spillover effects of customer ESG disclosure mandates: evidence from China’s global supply chain networks
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
This study investigates whether and, if so, how the adoption of ESG reporting standards in foreign customer countries generates spillover effects along the global supply chain, and the extent to which these effects depends on suppliers’ pre-existing ESG information environments. Our analysis exploits the trade linkages between China and 83 countries that underwent a staggered adoption of ESG disclosure mandates. We find that ESG-reporting firms in China improve their ESG performance after their customer countries mandate ESG reporting standards. This improvement is more pronounced when customer countries impose stronger ESG pressure on suppliers and when suppliers operate in more transparent peer environments, consistent with customers’ enhanced monitoring of suppliers. In addition, the effects are stronger when there is a common set of ESG reporting standards between China and her customer countries. We further find that ESG-reporting firms demonstrate improvements in key sustainability metrics, including increased green projects, fewer workplace accidents, and enhanced gender diversity within their boardrooms. Overall, our study provides novel evidence on the interplay between downstream suppliers’ information environments and upstream customers’ reporting mandates in determining supplier firms’ ESG performance.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 | 0.002 |
| 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