Easing the squeeze: How do acquisitions relieve target firms’ financial constraints?
Bibliographic record
Abstract
Using private firm financial data, we investigate how acquisitions alleviate financial constraints in private firms. We find that targets’ internal financing improves after acquisitions because they can retain higher proportions of earnings and borrow interest-free capital from their parent companies. Targets also receive better external financing as they obtain more debt financing with lower interest rates, borrow more trade credit from suppliers, and collect receivables from customers more quickly. Our findings suggest that internal and external financing improvements contribute to reducing targets’ financial constraints. • How acquisitions alleviate financial constraints in private targets post-acquisition? • Targets retain higher proportions of earnings and borrow interest-free capital from their parents. • Targets obtain more debt financing with lower interest. • Targets receive more trade credit from suppliers and collect receivables from customers more quickly. • Improvements in both internal and external financing help alleviate target firms’ financial constraints.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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 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".