Stockholder Wealth Effects of Corporate Inversions: Is It just Tax or Does Governance Matter too?
Bibliographic record
Abstract
This study investigates the effects of governance on stockholder wealth effects when a firm announces its intention to invert. Using a sample of 59 inversions during the period 1994 to 2014, we find that irrespective of the type of inversion and other firm characteristics, stockholders gain when the new country of incorporation has stronger country-level governance. Gains also vary significantly with the nature of the inversion: firms which invert purely for tax-related reasons without any intention to merge with another firm experience negative stockholder wealth effects. In addition to the cross-sectional tests, this study also uses Mylan¡¯s inversion and subsequent events as an example to show that stockholders are indeed affected by whether or not a firm reincorporates in a jurisdiction with weaker stockholder rights protection. Such weaker protection insulated Mylan¡¯s management from removal and cost shareholders significantly by allowing it to successfully reject a takeover attempt by Teva. Therefore, stockholders would need to evaluate the benefits from inversion in the form of tax savings against the potential costs arising from weaker corporate governance.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".