Bibliographic record
Abstract
This dissertation includes three essays examining the interactions between tax avoidance, corporate governance, and corporate policies. The first essay exploits corporate governance shocks induced by cross-listing in the U.S., and find that firms tend to engage in less tax avoidance after cross-listing. This effect is more pronounced for firms that experience significant improvements in corporate governance, and for firms from countries with weaker shareholder protection and disclosure requirements. Taken together, the results indicate that cross-listing in the U.S. helps align the interests of managers and shareholders and reduces managerial diversion. The second essay examines the importance of tax avoidance to equity pricing, and the role that institutional infrastructure plays in shaping this link. It shows that equity financing costs rise when firms take more aggressive tax positions. Additional analysis implies that stricter investor protection institutions and sound disclosure regulation alleviate investors’ concerns about insider diversion, moderating the positive impact of tax avoidance on equity pricing. Collectively, the results suggest that investors recognize the complementarity between insider diversion and tax avoidance in less protective environments. The third essay investigates the effects of bank debt on corporate tax avoidance. It shows that bank debt is positively associated with corporate tax avoidance. This positive effect is more pronounced for firms with high level of bank debt, with high probability of default, with weak corporate governance, and for firms in countries with weak institutions. These empirical findings are consistent with the view that banks are opportunistic lenders. They perceive the benefits of increased expected cash flows from tax savings to outweigh the risks associated with tax avoidance activities.
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 | 0.001 | 0.003 |
| 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 | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".