The More the Merrier? Diversity and Private Equity Performance
Bibliographic record
Abstract
Abstract This paper explores how diversity among lead partner teams (LPTs) of private equity (PE) funds affects buyout performance. We argue that there is a trade‐off between the ‘bright side’ of diversity (i.e. improved decision‐making due to a broader set of perspectives) and the ‘dark side’ (i.e. deteriorated decision‐making due to a potential for clashes and a lack of cooperation). Our theoretical framework suggests that the net effect on performance depends on whether LPTs are diverse in socio‐demographic or occupational aspects. To test this hypothesis, we develop a comprehensive index that measures LPT diversity along six dimensions. Using a sample of 241 buyouts and 547 involved PE partners, we find that higher scores in the socio‐demographic component (gender, age, nationality) are associated with higher deal returns and multiple expansions. The opposite is true for higher scores in the occupational component (professional experience, educational background, university affiliation). Further results suggest that the ‘bright side’ of diversity gets relatively more important in case of complex buyouts and uncertain deal environments.
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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 | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".