Social Enterprise Law in Action: Organizational Characteristics of U.S. Benefit Corporations
Bibliographic record
Abstract
The benefit corporation is now the most widely adopted innovation in state corporate law in nearly two decades.Thirty-three states and the District of Columbia have passed legislation enabling the formation of benefit corporations.In these states, mission-driven for-profit firms can adopt the benefit corporation legal form to protect directors from liability as they pursue both a social purpose and private profits.Despite scholarly and political interest, little is known about the firms that incorporate as benefit corporations.This Article draws on an innovative national empirical study of benefit corporations, the first of its kind, to understand how business owners are using social enterprise law.The Article provides the most comprehensive count of benefit corporations available and original, data-driven analysis of benefit corporations' national dynamics and organizational characteristics.The findings reveal that at least 7704 benefit corporations have been formed since 2010, with Oregon, New York, Nevada, Delaware, and Colorado home to most.The field is highly varied, but there is a lot of inactivity and a substantial portion of benefit corporations are not evidently delivering any social or environmental benefits.Of the firms with an online presence, 71% do not describe themselves as benefit corporations, contradicting proponents' assertions that the legal status provides market differentiation.These findings suggest that benefit corporation legislation serves a subset of firms, yet it falls short of its transformative promise to upend the prevailing model of shareholder supremacy.Statutes are not well tailored to new, small, privately held businesses, and lack of oversight enables inappropriate firms to become and remain benefit corporations.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".