Internationalization and Private Equity Partnerships: Legal Origin Heterogeneity and Fund Performance
Bibliographic record
Abstract
This study examines the impact of heterogeneity in legal origin between partners on the performance of Private Equity (PE) funds. Using a dataset of 3658 buyouts from 2000 to 2016, we show that internationalized PE partnerships, where Limited Partners (LPs) and General Partners (GPs) are from different legal systems, underperform compared to those within a single legal regime. We attribute this effect to challenges in contract enforcement and monitoring. Interestingly, while GP experience does not mitigate this negative effect, LP experience seems to intensify it, possibly due to overconfidence. We further find that funds with civil law GPs and common law LPs perform worse in terms of IRRs and MOIC, whereas the opposite combination only shows a decrease in MOIC. We explore potential explanations for these patterns, including the role of institutional differences, and discuss their implications for theories of PE internationalization and avenues for future research.
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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 | 0.000 | 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 | 0.002 |
| Open science | 0.000 | 0.001 |
| 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".