CSR and Firm Risk: Is Shareholder Activism a Double-Edged Sword?
Bibliographic record
Abstract
Few can argue with the notion that corporations should at least consider corporate social responsibility (CSR) to better understand the impact of their operations on society. However, recent empirical tests suggest CSR has an ambiguous impact on firm performance. To shed new light on this debate, we examine the extent to which voting support for nonbinding shareholder-initiated CSR proposals is empirically linked to changes in firms’ underlying systematic risks. Using a rich dataset of proposals in the US from 1998 to 2011, we contribute several novel findings. First, we show that shareholder voting support is nonlinearly linked to changes in systematic risk. Specifically, proposals with low voting support increase risk while those with high support decrease risk. This nonlinearity is particularly pronounced for consumer-sensitive firms that cater primarily to individual consumers rather than for firms in non-consumer-sensitive industries that produce goods or services meant for industrial or governmental use. Second, the 2007–2009 financial crisis exacerbated increases in firms’ systematic risks for proposals with low voting support. Our results, which highlight asymmetry regarding firms’ CSR initiatives, remain robust when controlling for firm-specific factors as well as shifts in investor sentiment. From a risk management perspective, our findings suggest that CSR initiatives need strong shareholder support to realize benefits from the so-called ‘risk-reduction hypothesis’.
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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 | 0.006 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".