When money talks, ESG falls silent: Evidence from US lobbying and disclosure
Bibliographic record
Abstract
This study examines the relationship between lobbying contributions and environmental, social, and governance (ESG) disclosure among S&P 500 firms in the United States (US) from 2014 to 2022. Using firm-level data from Bloomberg and the LSEG Workspace databases, we implement the GMM-SYS Arellano-Bond estimator to assess how the amount of lobbying contributions influences sustainability transparency. Our findings reveal a significant inverse relationship between the two variables. This suggests that firms engaging more intensively in lobbying may strategically limit their transparency in sustainability reporting, potentially using political influence as a substitute for public accountability. These results underscore the importance for regulators to reassess existing disclosure frameworks and consider targeted interventions—such as enhanced oversight or incentive schemes—to discourage greenhushing practices and ensure that lobbying does not undermine the credibility of ESG communication.
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How this classification was reachedexpand
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".