A Rising Tide Lifts All Boats: The Effects of Common Ownership on Corporate Social Responsibility
Bibliographic record
Abstract
Common owners face an incredible investment challenge: managing systematic risk. Because common owners hold shares in multiple firms across an industry, an action (or inaction) by one firm that affects industry peers is felt more severely by common owners than by non-common owners. Research has largely focused on common owners’ role in orchestrating competitive dynamics among their portfolio firms, with almost no empirical investigation of how common owners manage systematic risk. Drawing on research showing that one firm’s corporate social responsibility (CSR) can produce positive spillovers for peer firms and that its irresponsibility can harm its peers, we argue that common owners increase firms’ CSR to produce spillovers that reduce systematic risk and multiply their investment returns. Consistent with our theory, we find that common ownership is positively associated with firm CSR. Unpacking that relationship, we find that increases in CSR are driven by common owners with long-term orientations and are concentrated in stakeholder sensitive industries, in which CSR spillovers are most economically impactful. We also find that common owners focus their efforts on financially material CSR over financially immaterial CSR. We use a natural experiment with a quasi-exogenous shock to rule out alternative explanations. Our study contributes to literatures on the antecedents of CSR and outcomes of common ownership, providing a new perspective on how common owners shape corporate strategic behavior. Supplemental Material: The online appendix is available at https://doi.org/10.1287/orsc.2022.1620 .
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.035 | 0.001 |
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".