Bibliographic record
Abstract
To explore the importance of the board of director nomination process (that is, who nominates a given director for a position on the firm’s board) for the voting outcomes, disciplining of management, and overall monitoring quality of the board of directors.,We exploit a recent regulation passed by the US Securities and Exchange Commission (SEC) requiring disclosure of the board nomination process. In particular, we focus on firms’ use of executive search firms versus allowing internal members (often simply the CEO) to nominate new directors to serve on the board of directors.,We show that companies that use search firms to find board members pay their CEOs significantly higher salaries and significantly higher total compensations. Further, companies with search firm-identified independent directors are significantly less likely to fire their CEOs following negative performance. In addition, companies with search firm-identified independent directors are significantly more likely to engage in mergers and acquisitions (M&A) and see abnormally low returns from this M&A activity. We instrument the endogenous choice of using an executive search through the varying geographic distance of companies to executive search firms. Using this instrumental variable framework, we show search firm-identified independent directors’ negative impact on firm performance, consistent with firm behavior and governance consequences we document.,Given the recent law passage, we are the first to directly analyze the nomination process, and show a surprisingly large predictive effect of seemingly arm’s-length nominations. This has clear implications for thinking carefully through how independence is defined in the director nomination process.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.043 | 0.005 |
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".