Other Side of Voluntary Clawback Provisions in Executive Compensation Contracts: Evidence from the Investment Efficiency
Bibliographic record
Abstract
This study examines how firm-initiated clawback provisions in executive compensation contracts affect firms’ investment efficiency. While existing the literature provides evidence on positive aspects of adopting clawback provisions, the potential impact of clawback adoption on firms’ long-term investment efficiency remains unexplored. Using three investment proxies (i.e., capital expenditure, new investment, and total investment), we find that clawback adopters tend to reduce their long-term investments after the clawback provisions are put in place, compared to nonadopters. In particular, we find evidence that the adoption of clawback policies decreases the investment efficiency in the post-adoption period, especially for the firms whose ex ante probability of underinvestment is high. Our additional analyses reveal that observed reduction in the investment efficiency for the firms that are likely to underinvest is more evident for the firms with financial constraints and the firms that adopt risk-taking and performance-based clawback triggers. In contrast, the clawback adopters that are likely to overinvest do not change their investment behavior in the post-adoption period. Overall, our findings suggest that the impleme ntation of clawback provisions may lead to unintended consequences for firms’ long-term investment practices, resulting in a decrease in the investment efficiency.
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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 | 0.009 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".