<scp>The Effect of Tax Convexity on Corporate Investment Decisions and Tax Burdens</scp>
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Bibliographic record
Abstract
Abstract This paper examines the effect of convexity in the corporate tax schedule on corporate investment decisions and tax burdens. Using a contingent‐claims model, we show that greater tax convexity results in (i) earlier exit, (ii) delayed investment (except for small entry cost), and (iii) reduced corporate risk taking (except for small entry cost and unfavorable operating conditions). Also, the effective tax burden is an increasing function of tax convexity. The convexity of the tax schedule has a nontrivial impact on corporate investment decisions and investment levels. These results are relevant for economic growth, which depends (at least partly) on investment levels, and tax policy makers should be aware of these effects when making adjustments that might impact the convexity of the corporate tax schedule.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it