The effect of leverage on the tax‐cut versus investment‐subsidy argument
Why this work is in the frame
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Bibliographic record
Abstract
Abstract Two common methods of attracting corporate investment are investment incentives and tax incentives. It is important to use the two incentives in the correct proportions, otherwise the government will give up too much value in the process of attracting investment. This paper examines the effect of tax cut and investment subsidy on the government's net benefit from a project. Earlier studies concluded that it was optimal to use only investment subsidy and no tax cuts. We show that this is not true when debt financing is possible, and it is generally optimal (from the government's perspective) to use a combination of tax reduction and investment subsidy. The optimal tax rate and optimal investment subsidy are identified and analyzed in the paper. It is shown that using a sub‐optimal combination of incentives can result in substantial reduction of benefits for the government.
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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 | 0.002 | 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 | 0.001 | 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