The Role of Pension Business Benefits in Institutional Block Ownership and Corporate Governance*
Bibliographic record
Abstract
ABSTRACT We investigate whether potential pension contracting benefits lead institutions that provide pension services to acquire ownership blocks in firms and the implications of such blockholdings on the firms' corporate governance. We use the 2006 Pension Protection Act, which expanded pension participation in certain states, as a quasi‐exogenous shock and find an increase in block ownership by pension‐providing institutions in firms with substantial operations in affected states. Further, we find that the acquisition of a large block increases the likelihood that the institution will provide future pension services to the firm. With regard to corporate governance, we find that the acquisition of large pension blockholdings is associated with higher CEO pay and lower CEO turnover following poor financial performance. However, contrary to the prediction of the private benefits hypothesis, we do not find consistent evidence that large pension blockholdings are associated with declining firm profitability, suggesting that pension institutions are incentivized to exert monitoring to preserve the investment value of their blockholdings. Overall, our evidence is consistent with pension service institutions acquiring ownership blocks to obtain pension contracts, but our evidence does not support the prediction that they use their influence to compromise shareholder value.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".