Bibliographic record
Abstract
Executive stock options are a dominant component of managers pay in the United States. This common compensation feature entails two perverse side effects: driving managers to engage in manipulative practices, and generating excessive risk-taking. Tellingly, some scholars blame the first side effect for the wave of Enron-style fraud in 2001-2002 and the second for the 2007-2010 financial crisis. To date, however, no one has investigated the interaction between these two types of adverse incentives, for manipulation and risk-taking. In this paper, we study the effects of manipulation practices on risk-taking decisions of managers holding large amounts of stock options. We first show that sufficient manipulation restrains excessive risk-taking but it does not impede managers from taking beneficially risky projects. We then show that mild levels of manipulation have complex effects on managers’ preference for risk taking, but they too tend to decrease risk taking. Our analysis suggests that when regulation improves disclosure and impedes manipulative practices, excessive risk taking may erupt. Policy-wise, we recommend that anti-manipulative regulatory policies be accompanied by measures designed to prevent excessive risk taking.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".