The Shareholder Value Myth: How Putting Shareholders First Harms Investors, Corporations, and the Public
Bibliographic record
Abstract
- Proves that shareholder primacy has no basis in law or economics and does not deliver better bottom-line results - Suggests better ways to think about shareholders and their relationship to corporations - Written by one of America’s most distinguished legal scholars Executives, investors, and the business press routinely chant the mantra that corporations are required to “maximize shareholder value.” The results have been disastrous. “Shareholder primacy” thinking causes corporate managers to focus myopically on short-term earnings reports at the expense of long-term performance; discourages investment and innovation; harms employees, customers, and communities; and causes companies to indulge in reckless, sociopathic, and socially irresponsible behaviors. It’s the kind of thinking that led directly to the recent worldwide economic collapse. Jack Welch, once a shareholder primacy true believer, has famously called it “the dumbest idea in the world.” Lynn Stout proves that there is in fact no legal obligation for corporations to maximize shareholder value—scholars, lawyers, and corporate officers just assumed there was. Nor, she demonstrates, is maximizing shareholder value the optimal economic model—that’s just another unproven assumption, one that is conceptually muddled and, Stout shows, unsupported by the actual evidence on what drives good corporate performance. As if this wasn’t enough, Stout also shows how shareholder primacy actually hurts individual investors by obscuring their real, diverse, human interests in the name of serving a hypothetical, homogeneous, abstract, and conscienceless shareholder. Stout looks at new theories that better serve the needs not only of actual human beings who invest but of corporations and society as well. “Calm, careful, plainspoken, and relentless argumentation that peels away the distracting layers of abstract mumbo jumbo to expose the lunacy of the underlying theory for all to see. Lynn Stout does the world a great favor in exposing shareholder value theory for what it is: flawed and damaging.” —Roger Martin, Dean, Rotman School of Management, University of Toronto, and author of Fixing the Game
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 | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.049 |
| Scholarly communication | 0.018 | 0.024 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.009 | 0.013 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".