Statutory Corporate Tax Rates and Income Distribution — Panel Data From 95 Countries Using Driscoll and Kraay Standard Errors and Quantile via Moments
Bibliographic record
Abstract
The study uses the statutory corporate tax rate to explain before and after tax and transfer income distribution. The unbalanced panel has 95 countries from 1988 to 2018. The study uses Driscoll & Kraay standard errors and Quantile Via Moments. The study finds higher corporate tax rates appear to lessen income inequality in most cases, small coefficients suggest it is minor and insignificant for after-tax and transfer income distribution in developed countries. Furthermore, in an augmented model with fewer observations spanning 1988 to 2005, the average rate of personal income tax progressivity significantly reduces net income inequality while the statutory corporate tax rate is insignificant. Therefore, findings may indicate increases in personal income tax rate progressivity may be more effective policy tools than changes in statutory corporate tax rates to moderate growing income inequality.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".