Do creditors punish increased insider ownership? Evidence from India
Bibliographic record
Abstract
Existing literature presents competing arguments regarding influence of insider ownership on borrowing costs for firms. A distinct strand of literature suggests that creditors may find firms with low insider control less attractive and thus may demand higher compensation while lending. However, other studies offer alternative arguments suggesting that creditors might view firms with weak insider control in more favourable way. Moreover, cost of overall borrowings, including short term borrowings, has received much less attention in the literature despite its importance to firms. Against this backdrop, we examine the impact of change in insider ownership on the cost of borrowings in India by analysing data of publicly listed firms in India for year 2001 to 2015. Using within-firm variation over time and exogenous shock to conduct quasi-experiment, we find, consistent with our hypothesis, an inverted-U-shaped relationship between insider ownership and borrowing costs. We present compelling evidence that creditors may not just punish but may also reward increased insider ownership, depending upon pre-existing level of ownership. We find that increase in insider ownership by one percentage point initially leads to increase in borrowing costs by as much as 13 basis points. However, once insiders control approximately 37% of a firm and start approaching majority control, creditors demand lower compensation. However, we do not find influence of change in insider ownership on borrowing costs for firms where insiders maintain majority control over time.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".