Benefit Corporations and Public Markets: First Experiments and Next Steps
Bibliographic record
Abstract
Part I begins by considering the leading benefits and costs for a benefit corporation that chooses to go public. It starts there both to begin gaining an understanding of the challenges public companies will face and also to consider whether going public is likely to actually be an attractive option at all for some set of social enterprises. Some of the benefits and costs of going public are the same for benefit corporations as for ordinary corporations—access to new sources of capital and new accountability mechanisms are benefits, but legal compliance and pressures from shareholders to show quick results are costs. But, there are also special benefits and costs for benefit corporations, or the benefits and costs that other companies face may play out differently for social enterprises. On the benefit side, access to new sources of capital may be even more important for social enterprises, which may find it harder to attract investors given the commitment to doing good. Additionally, founders and early investors may use publicly traded stock as a way of achieving a profitable exit from their investment, though this benefit may be less important to investors in social enterprises whose desire for profit may be tempered by the desire to do good as well. Going public also shifts control from the founders and early investors to the board of directors, which could possibly allow for more commitment to the social mission of the business. But, the shift in control may pose a risk to the social mission. Public shareholders, especially shareholder activists, may create pressures to pursue profit at the expense of the social mission. Part II considers early experiments in publicly traded social enterprises, or at least arrangements that come close to being such. It surveys various public exchanges such as the Social Stock Exchange in the UK, the Social Venture Connection in Canada, and the Impact Exchange in Singapore and Mauritius. It also surveys early experiments in crowdfunding of social enterprises, online sites for impact investing by accredited investors, and a few individual corporations, including Laureate Education, a Delaware public benefit corporation, which (as of the time of writing) has filed an S-1 registration statement in preparation to go public, and Etsy, a B Lab-certified public corporation. Finally, Part II ponders various lessons to be learned from these early experiments. Part III moves to consider various mechanisms by which benefit corporations, investors, stock markets, and other gatekeepers might be able to realize the benefits of going public while minimizing the costs. Disclosure of actions taken to advance a firm’s social mission can give incentives to behave well, to maintain a good reputation, and to provide information to investors that could affect their choice of which firms to invest in. The revised fiduciary duty of benefit corporations may also help ensure that they pursue their dual missions, with a greater chance of suits being brought in public companies with thousands of shareholders (potential plaintiffs) if the corporation fails to pursue their dual mission. Board representation rules and processes could give various stakeholder groups other than shareholders a say in decision making. Relatedly, various stakeholder groups could be given several possible kinds of voting rights. Dual-class share structures may help ensure that control remains with shareholders committed to the social mission. Time-phased or tenure voting may give more power to shareholders with long-term interests in the company. And, investor screening devices may help keep investors that are not committed to a company’s mission from investing in that company. Finally, a variety of gatekeepers could help ensure accountability and fidelity to the corporate mission. Possible gatekeepers include certifiers like B Lab, auditors, insurers, lawyers, and exchanges themselves. Finally, Part IV focuses on the specific role of stock exchanges and similar institutions as ways to help facilitate the various governance and shareholding mechanisms discussed in Part III. Exchanges, through their listing requirements, could require or encourage the mechanisms discussed in Part III. If different exchanges create different requirements, they would allow experimentation as to what works and what doesn’t, reducing the challenges that publicly traded benefit corporations will face. Exchange listing could also work as a branding device for companies. Exchanges could provide a focus for the involvement of various corporate gatekeepers, helping to coordinate expectations and best practices for how social enterprises should govern themselves to best pursue their dual missions.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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".