Corporate Governance, Managerial Compensation, and Disruptive Innovations
Bibliographic record
Abstract
Whether a CEO manages the innovation efforts of the firm in line with shareholder preferences has a substantial impact on market value and firm growth, which in turn influence aggregate productivity growth and welfare. Using data on U.S. public firms, we find that (i) firms with better corporate governance tend to adopt highly incentivized contracts rich in stock options; and (ii) such contracts are more likely to lead to disruptive innovations -- patented inventions that are in the upper tail of the distribution in terms of quality and originality. We develop and estimate a new dynamic general equilibrium model of firm-level innovation with agency frictions and endogenous determination of executive contracts. The model is used to study the joint dynamics of corporate governance, managerial compensation, and disruptive innovations. Better corporate governance can reduce the influence of the CEO in the determination of the compensation structure. This leads to more incentivized contracts and boosts innovation, with substantial benefits for the shareholders, as well as the broader economy through knowledge spillovers. Shutting down the agency frictions leads to an increase in long-run output growth, which translates into a significant welfare gain in consumption equivalent terms.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| 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".