The impact of intrafirm incentive conflicts on the interplay between tax incidence and economic efficiency
Bibliographic record
Abstract
Abstract We study how corporate taxation interacts with intrafirm incentive conflicts between shareholders and managers and how this interaction impacts the firm's economic decisions and outcomes. In our model, investment under asymmetric information facilitates entrenchment and rent extraction by the privately informed manager. We show that when the future investment payoff is exogenous, a corporate tax cut increases managerial rents, reduces pre‐tax investment profitability, increases the firm's optimal investment hurdle rate, and reduces investment. When the manager can exert upfront project development effort to increase the expected investment payoff, a tax rate reduction not only encourages more effort but also leads the firm to increase the investment hurdle rate to curtail rents. In equilibrium, a lower tax rate always benefits the manager, but the sensitivity of the project's return to the manager's effort determines whether the firm will increase or decrease investment in response to a tax cut, and whether the firm's resulting pre‐tax profit will increase or decrease. Overall, our study shows that intrafirm incentive conflicts can be an important factor in the interplay between tax incidence and economic efficiency, two central themes in corporate tax policy debates.
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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 | 0.005 | 0.025 |
| 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 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".