Bibliographic record
Abstract
Empirical studies show that a large portion of the diversification discount can be explained by controlling for firm-specific characteristics. Although these studies leave no doubt that there is a self-selection component in firm’s decision to diversify, the failure to explain the entire discount implies that some conglomerates destroy value and raises a question: why firms choose to diversify and what prevent them from value-increasing divestitures. This paper provides an answer to this question. It argues that the agency cost in a conglomerate is positively related to the number of divisions with good investment opportunities. Therefore, benefits of conglomeration offset agency costs for conglomerates with a number of bad divisions and make diversification profitable for bad firms. However, when investment opportunities of some divisions improve, the agency cost increases and offsets the benefits of diversification. Unfortunately, if investors cannot correctly price all of the conglomerate’s divisions, the conglomerate cannot receive the fair price for its good divisions and, therefore, cannot implement value-increasing divestitures. As a result, the paper predicts a negative relationship between the age of the conglomerate and the diversification discount, while a failure to control for the self-selection bias may lead to an incorrect conclusion that this relationship is positive. By looking at the exogenous shocks to the economy, the paper also predicts more refocusing activities and greater value-distortion of diversification during economic booms.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| 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".