From Toxic to Transparent: The Effect of Greenpeace’s Detox Campaign on Market Volatility
Bibliographic record
Abstract
In the contemporary structure of political economy, one of the leading actors is Non-Governmental Organisations (NGOs). Some of these organisations, to promote their goals, often engage in public disputes with enterprises that have publicly traded shares on the stock market. Consequently, they serve as channels for negative information relevant to these enterprises that falls within their discourse. In this paper, we examine the impact on the share price volatility of these enterprises due to the public debate initiated by an NGO aiming to change the enterprise’s behaviour on a particular matter (e.g., using more eco-friendly materials). Data from Greenpeace’s Detox Campaign are used to examine its influence on several enterprises. Using GARCH, OLS, and Difference-in-Differences models, we find that volatility increased significantly during the campaign for firms like Burberry (13.71%), Adidas (5.40%), and VFC Group (3.96%). After companies complied, volatility declined, notably in Burberry (−16.84%), Marks & Spencer (−3.24%), and VFC Group (−4.88%). These results highlight how NGO activism can heighten investor uncertainty in the short term but stabilise markets once companies respond, offering key insights for policymakers on the financial impact of civil Society’s engagement.
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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 | 0.002 | 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 | 0.000 |
| 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".