Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it