The Design of Financial Policies in Corporate Spin-offs
Bibliographic record
Abstract
We examine differences in financial leverage between parent and spun-off firms that emerge from corporate spin-offs. Our tests control for past financing choices and the costs of adjusting capital structure, factors that can obscure cross-sectional patterns among firms' target leverage ratios. We find that firms that emerge from spin-offs with more financial leverage have a higher cash flow return on assets, lower variability of industry operating income, and a greater proportion of fixed assets. The positive relation between profitability and the use of financial leverage, in a setting that is free of pecking order effects, is particularly important because it contrasts with existing evidence. Our results indicate that the ability to cover debt payments and default-related costs are important determinants of the use of financial leverage, as implied by the trade-off theory of capital structure. We find no evidence that managerial incentives or governance characteristics affect the difference in leverage ratios in firms that emerge from spin-offs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".