Too Big to Fool: Moral Hazard, Bailouts, and Corporate Responsibility
Bibliographic record
Abstract
Domestic and international regulatory efforts to prevent another financial crisis have been converging on the idea of trying to end the problem of “too big to fail”—that systemically important financial firms take excessive risks because they profit from success and are (or at least, expect to be) bailed out by government money to avoid failure. The legal solutions being advanced to control this morally hazardous behavior tend, however, to be inefficient, ineffective, or even dangerous—such as breaking up firms and limiting their size, which can reduce economies of scale and scope; or restricting central bank authority to bail out failing firms, which (ironically) exacerbates the risk that an uncontrolled banking failure will trigger another crisis. This article contends that the too-big-to-fail problem is exaggerated. It shows that the evidence for this problem is weak, conflating correlation and causation. It also shows that managerial incentives should mitigate the problem because managers who cause their firms to engage in excessive risk-taking, in the expectation of a government bailout, are taking serious personal risks. That begs the question why systemically important firms sometimes do take excessive risks. The article argues that such risk-taking is more likely to be caused by other factors, including a legally embedded conflict between corporate governance and the public interest that allows managers of those firms to ignore the costs of systemic externalities. To address this, the law should—and the article shows that it realistically could—control excessive risk-taking more directly by requiring managers to account for systemic externalities in their governance decisions. It also argues for the creation of a privatized fund to minimize the public cost of bailing out systemically important firms that might fail (because of exogenous shocks, for example) notwithstanding reduced risk-taking.
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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.015 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.010 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 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".