CEO inside debt holdings and trade credit
Bibliographic record
Abstract
Abstract This study investigates the relationship between chief executive officer (CEO) inside debt holdings (pension benefits and deferred compensation) and use of supplier‐provided trade credit. We provide evidence that CEO inside debt holdings are negatively related to the use of trade credit. Our results are robust to the use of alternative regression estimation and alternative measures of key variables. We exploit the final enforcement of Section 409A of the Internal Revenue Code (IRC) as an exogenous shock to inside debt holdings. Our difference‐in‐difference regression analysis establishes a causal relationship. In addition, we provide evidence that our documented results are not driven by omitted variable bias. We also employ instrumental variable regression estimation using heteroskedasticity‐based instruments to mitigate the endogeneity concern. Our cross‐sectional analyses reveal that the relationship between CEO inside debt holdings and trade credit is more pronounced in firms with poor information environments and greater financing constraints. Overall, findings from our study suggest that CEO inside debt has important implications for the financing policy of the firm.
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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 | 0.001 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".