Essays in empirical corporate finance
Bibliographic record
Abstract
This dissertation is a collection of three essays that explore topics in empirical corporate finance. The first essay studies the quality of proxy advisors' recommendations. Using a comprehensive sample of say-on-pay recommendations from the two largest proxy advisors over the period 2012 – 2019, I document that proxy advisors fail to filter out industry-level returns that are presumably beyond the control of management in evaluating a CEO’s pay package. This finding contradicts the predictions of standard agency theory that CEOs should be evaluated on their relative performance in the presence of common shocks. I use adjustment for industry performance as the quality measure of recommendations. I find a decrease in quality (1) when proxy advisors are busy, and (2) when proxy statements and pay contracts are complex. My analysis suggests that proxy advisors’ capacity constraints are likely explanations for the limited applications of relative performance evaluations in their recommendations. The second essay, coauthored with Jan Bena, develops a novel measure of disagreement in voice between active and passive mutual funds using their proxy votes that captures shareholder conflicts in public firms. We show that the disagreement in voice between passive and active funds is associated with a decrease in firm value and suggest that the firm value loss is due to conflicting incentives between the two groups. The third essay, coauthored with Jan Bena and Guangli Lu, studies the impact of national culture on within-firm pay inequality using a unique administrative dataset covering closely-held immigrant-owned firms in Canada from 2001-2017. We find that within-firm pay inequality varies significantly with a firm owner’s country of origin. Firms owned by immigrants from more individualistic countries have higher pay inequality. Using a difference-in-differences analysis, we find a significant increase in within-firm pay inequality after the firm is taken over by immigrant owners from countries with higher within-firm-pay-inequality or more individualistic cultures.
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 | 0.007 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.015 | 0.004 |
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".