Can Staggered Boards Improve Value? Causal Evidence from Massachusetts*
Bibliographic record
Abstract
ABSTRACT Staggered boards (SBs) are one of the most potent common entrenchment devices, and their value effects are considerably debated. We study SBs' effects on firm value, managerial behavior, and investor composition using a quasi‐experimental setting: a 1990 law that imposed SBs on all Massachusetts‐incorporated firms. We find that relative to a matched control group of companies, for treated companies the law led to an increase in Tobin's Q, investment in capital expenditures and R&D, patents, and higher‐quality patented innovations, resulting in higher profitability. These effects are concentrated in innovating firms, especially those facing greater Wall Street scrutiny. An increase in institutional and dedicated investors also accompanied the imposition of SBs, facilitating a longer‐term orientation. The evidence suggests that SBs can benefit early‐life‐cycle firms facing high information asymmetries by allowing their managers to focus on long‐term investments and innovations.
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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 | 0.005 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".