What is different about private equity‐backed acquirers?
Bibliographic record
Abstract
Abstract This paper investigates whether private equity (PE)‐backed acquirers have a “parenting advantage” in the mergers & acquisitions (M&A) market. We employ a sample of 788 PE‐backed firms and a carefully matched control group of 6,652 non‐PE‐backed peers, for which we observe the entire acquisition history over a 19‐year time span. Difference‐in‐differences estimates suggest that PE backing induces a sizeable but short‐lived boost to acquisition activity, while the type and complexity of acquisitions are similar to those of non‐PE‐backed peers. These results are consistent with the idea that PE backing enhances execution and speed in the M&A market. We find that portfolio firms benefit from this boost through improved valuations and margins. The extent to which this is true, however, depends on the institutional setting of the PE owner. Our results indicate that add‐on acquisitions are detrimental if PE owners are late buyers or suffer from limited attention problems.
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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 | 0.008 |
| 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 | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".