Shareholder Activism and Voluntary Disclosure Initiation: The Case of Political Spending
Bibliographic record
Abstract
ABSTRACT Demand for disclosures on environmental, social, and governance (ESG) issues has increased dramatically. Using corporate political spending disclosures as our setting, we conduct a detailed inquiry of 541 political spending‐related shareholder proposals from 2004 to 2012 to highlight the role of shareholder activism as a mechanism to motivate ESG disclosure. Unlike earlier studies, we examine both proposals that went to a vote and proposals that were withdrawn by the activist, allowing us to assess more comprehensively the success of shareholder activism. We find that 20 percent of firms targeted by disclosure proposals begin disclosing in the subsequent year, although implementation rates vary by proposal type—8 percent for proposals subject to a vote versus 56 percent for proposals withdrawn. The sponsor is also important: unions and public pension funds are less likely than other activists to target firms with agency problems and are less successful in having proposals withdrawn, and the implementations they obtain are viewed more negatively by the broader investor base. Our findings highlight shareholder proposals as one mechanism through which investors can successfully express their preferences for corporate disclosure policies. Given activists' long‐standing interest in environmental and social disclosure policies, we believe our findings generalize to a broader set of ESG disclosures.
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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 | 0.019 | 0.087 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| 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".