Institutional influence on syndicate structure and cross‐border leveraged buyouts
Bibliographic record
Abstract
Abstract We explore the extent to which differences in countries’ formal and informal institutions reduce cross‐border leveraged buyout transactions and the potential influence these same institutions have on how private equity (PE) investors choose to enter these transactions. Although institutional differences have frequently been viewed as barriers to cross‐border investment, we find evidence that these same differences may motivate a PE firm's decision to enter the transaction with a syndicate of firms rather than undertaking the transaction on their own. Cultural differences between a PE firm and the target nation are significantly related to the choice to enter the deal via a multinational syndicate. The varying nationalities within the syndicate contribute to enhanced familiarity, with average institutional distances between the syndicate and target firms being significantly lower than for single‐PE‐led deals. Overall, deals undertaken by syndicates are more likely to be successfully completed and require less time in negotiation. These results persist even after accounting for selection bias with regard to target country choice. We explore whether other features of the syndicate are responsible for improved deal outcomes, such as repeated transactions with the same partners, but find no evidence that this is the case.
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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 | 0.009 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 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 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".