Bibliographic record
Abstract
This paper empirically investigates the decisions of publicly traded firms where to incorporate.We study the features of states that make them attractive to incorporating firms and the characteristics of firms that determine whether they incorporate in or out of their state of location.We find that states that offer stronger antitakeover protections are substantially more successful both in retaining in-state firms and in attracting out-of-state incorporations.We estimate that, compared with adopting no antitakeover statutes, adopting all standard antitakeover statutes enabled the states that adopted them to more than double the percentage of local firms that incorporated in-state (from 23% to 49%).Indeed, the incorporation market has not even penalized the three states that passed two extreme antitakeover statutes that have been widely viewed as detrimental to shareholders.We also find that there is commonly a big difference between a state's ability to attract incorporations from firms located in and out of the state, and we investigate several possible explanations for this home-state advantage.Finally, we find that Delaware's dominance is greater than has been recognized and can be expected to increase further in the future.Our findings have significant implications for corporate governance, regulatory competition, and takeover law.
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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 | 0.005 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 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".