Private Benefits of Control, Ownership, and the Cross-Listing Decision
Bibliographic record
Abstract
This paper investigates how a foreign firm's decision to cross-list its shares in the U.S. is related to the concentration of the ownership of its cash flow rights and of its control rights.Theory has proposed that when private benefits are high, controlling shareholders are less likely to choose to list their firm's shares in the U.S. because the higher standards for transparency and disclosure, as well as the increased monitoring associated with such listings, limit their ability to extract private benefits.We offer evidence that confirms this hypothesis using data on more than 4,000 firms from 31 countries.Using logistic regression analysis, we show that the control rights held by controlling shareholders, as well as the difference between their control rights and their cash flow rights are significantly and negatively related to the existence of a U.S. listing.In addition, we employ duration analysis using a Cox proportional-hazard model to show that the probability of listing in a given year from 1995 to 2001, conditional on not yet having listed, is significantly lower for firms whose managers have high levels of control and for firms whose controlling shareholder owns more control rights than cash flow rights.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".