Bibliographic record
Abstract
This thesis consists of three chapters on empirical corporate finance.The first chapter is titled "Empirical Evidence on Debt Governance" and is single-authored work.A large theoretical literature suggests that debt can mitigate agency problems, yet empirical evidence is limited due to identification problems.To provide evidence, I examine whether firms implement independent directors as a substitute when debt governance becomes ineffective.Key to my analysis are two central features of debt as a governance device: first, it is only effective with bankruptcy penalties, second, it only matters for firms close to default.Consistent with debt inducing discipline, across countries, board independence is negatively related to bankruptcy penalties, and especially so among risky firms.For them, a change from strictest to softest penalties is associated with a 46% increase in the number of independent directors.Comparing changes in board independence of risky firms to those of safe firms around bankruptcy reforms confirms the cross-country results.My findings underline the role of agency problems in explaining corporate capital structure, and can further account for several unresolved issues in corporate governance (e.g., differences in bankruptcy penalties can explain 37% of the 37 percentage point "board independence gap" between the US and the UK).Moreover, my findings have important implications for optimal bankruptcy design: in the absence of bankruptcy penalties, management may have difficulties to credibly commit to forgo inefficient actions and thus to secure financing.The second chapter is titled "Dual-Class Firms, Agency Problems, and Debt Governance: Evidence from Korea" and is single-authored work.I examine whether debt (or, more generally, a performance threshold) can reduce agency problems between controlling shareholders and minority shareholders in dual-class firms.To do so, I use two key features of debt governance.First, it is only effective with bankruptcy penalties, second, it only matters for firms close to default.I measure agency problems by the voting premium.Using a Korean bankruptcy reform that reduced penalties, I find that risky firms exhibited a 59% increase in the voting premium after the reform relative to safe firms.My findings have important implications for the heated debate on dual-class share structure.The third chapter is titled "Creditor Rights, Corporate Leverage and Investments, and the Firm Type" and is joint work with Axel Kind, Jiri Tresl, and Sabine Wende.Stronger creditor rights reduce credit costs and thus may allow firms to increase leverage and investments.Yet, they also increase distress costs and thus may prompt firms to lower leverage and undertake risk-reducing but unprofitable investments.Using a German bankruptcy reform, on average, Covariates Yes Yes
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.114 | 0.035 |
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".