Three essays on going private transactions: management buyouts versus leveraged buyouts
Bibliographic record
Abstract
This dissertation consists of three essays that examine differences between two different forms of going-private transactions: leveraged buyouts (LBOs) and management buyouts (MBOs). My main focus is to look at what separates MBOs and LBOs in terms of motivations and outcomes. I argue that the desire for decision-making control of the firm is what drives both the choice of an MBO over an LBO and many strategic decisions leading up to the transaction itself. The first essay surveys the literature and develops testable hypotheses that distinguish between MBOs and LBOs. Using control to differentiate between these two types of transactions is important because I wish to examine how decision-making control of the firm motivates each participant in the transaction. This leads to hypotheses about pre-buyout ownership structure, which affects the probability of choosing either an MBO or LBO. The second essay examines the determinants of LBOs and MBOs, and the effects of financing conditions and firm characteristics in a sample of U.S. firms that went private through LBOs and MBOs from 2000 to 2011 relative to the firms that were public during this period. I find significant differences between the LBO and MBO samples. Tighter lending conditions decrease the probability of an LBO but increase the probability of an MBO. In addition, while liquidity and growth opportunities are negatively correlated with the probability of going private for both LBOs and MBOs, the latter plays a stronger role for MBOs. The third essay analyzes differences in the determinants of the premiums paid when taking a firm private. Since the choice between an MBO and an LBO is one of self-selection, the empirical methodology must take this into account. I use a two-stage regression model to control for this selection bias. The first-stage probit model shows that ownership is the main determinant of the selection of the transaction type. The second-stage model shows that the determinants of MBO and LBO premiums are substantially different. The level of insider ownership has a large negative effect on MBO premiums, but no effect on LBO premiums. Overall, this dissertation contributes to the existing literature by examining heterogeneity among firms that go private. I test this heterogeneity and find significant differences between MBO and LBO firms, both in terms of the motivations for taking the firm private and the premiums paid in the transaction.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".