Bibliographic record
Abstract
This thesis consists of chapters on the governance role of institutional investors. Chapter 1 provides an introduction of the three essays as well as provides a brief summary of their main findings. Chapter 2 examines whether certain types of institutional investors use their ownership rights as a mechanism of exerting influence on portfolio firms to amplify their political voice. We show that ownership may be an important mechanism through which institutional investors amplify their political influence. In particular, we find that the probability that a firm’s Political Action Committee (PAC) donates to a politician supported by an investor’s PAC nearly doubles after the investor acquires a large stake, and that it increases five-fold when the investor obtains a board seat. The relationship is stronger for private funds, and those with high partisanship, suggesting the relationship is driven by investor preferences rather than strategic concerns. Chapter 3 examines whether institutional investors may partially offset the social trade-offs due to outsourcing government services to private enterprises. Consistent with prior theoretical work, I find evidence that institutional investors, in particular investors with a long holding horizon, have a positive impact on social outcomes of privatized prisons. Overall this chapter provides evidence that social trade-offs due to privatization may be partially mitigated by institutional investors due to litigation and reputation risks. Chapter 3 examines whether institutional investors have an impact on product market outcomes. The empirical literature on the potential anti-competitive effects of common-ownership relies heavily on financial institution mergers to make causal inferences. I find that more than 85% of newly-formed common-ownership relationships due to such financial institution mergers are no longer commonly-held by the acquiring institution during the post-merger period (with most being liquidated in the first quarter following the merger). Firms that are no longer commonly-held by the merged institution drive the anti-competitive results found in previous studies. The fact that portfolio firms are so quickly rebalanced casts doubt on the utility of financial institution mergers as a natural experiment. Taken together, my thesis finds that institutional investors have a crucial governance role that benefits society.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".