Bibliographic record
Abstract
Corporate governance deals with the ways in which suppliers of capital to firms improve their chances of getting a return on their investment (Shleifer and Vishny (1997)). In this dissertation, I analyze three aspects of governance that impact the incentives, policies, and decisions of the firm. In the first essay, Blockholder Attention, I recast the study of blockholder monitoring from a question of whether firms will listen to one that additionally asks are investors willing to talk. Given resource constraints, investors cannot talk to all of the firms they own and allocate their attention to those positions in which they have the most capital invested. Once I isolate blocks that receive the requisite attention, I find robust evidence of effective monitoring: firms with one of these High Attention Blocks experience significant improvements in compensation policies, turnover decisions, and acquisitions. Firms with blocks of relatively less importance do not enjoy any of these gains. In the second essay, CEO Skill in Corporate Acquisitions, I, along with my co-authors, Jeff Jaffe and Torben Voetmann, examine the incidence of differential skill in acquisition decisions. While we find significant evidence of persistent bidder returns when a firm retains the same CEO for consecutive acquisitions, it appears negative skill, or the repeated act of conducting value-destroying acquisitions, is the dominant trait. In the third essay, The Price Effects of Event-Risk Protection: The Results from a Natural Experiment, my co-authors, Karl Okamoto and Natalie Pedersen, and I use court rulings related to the $48.5B LBO of Bell Canada to isolate the pricing effects of change-in-control covenants. Consistent with the existing literature, we find significant evidence that these covenants are priced and this judicial intervention upset the existing bargain between bondholders and issuers.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.025 | 0.007 |
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".