Corporate Social Responsibility and Shareholder Reaction: The Environmental Awareness of Investors
Bibliographic record
Abstract
This study examines whether shareholders are sensitive to corporations' environmental footprint. Specifically, I conduct an event study around the announcement of corporate news related to environment for all US publicly traded companies from 1980 to 2009. In keeping with the view that environmental corporate social responsibility (CSR) generates new and competitive resources for firms, I find that companies reported to behave responsibly toward the environment experience a significant stock price increase, whereas firms that behave irresponsibly face a significant decrease. Extending this view of “environment-as-a-resource,” I posit that the value of environmental CSR depends on external and internal moderators. First, I argue that external pressure to behave responsibly towards the environment―which has increased dramatically over recent decades―exacerbates the punishment for eco-harmful behavior and reduces the reward for eco-friendly initiatives. This argument is supported by the data: over time, the negative stock market reaction to eco-harmful behavior has increased, while the positive reaction to eco-friendly initiatives has decreased. Second, I argue that environmental CSR is a resource with decreasing marginal returns and insurance-like features. In keeping with this view, I find that the positive (negative) stock market reaction to eco-friendly (-harmful) events is smaller for companies with higher levels of environmental CSR.
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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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".