State and local taxes and economic performance : an empirical study of how much taxes matter to economies
Bibliographic record
Abstract
Per capita income levels and growth vary considerably among the contiguous 48 states of America. This can be attributed to several factors, such as tax policies, government services, economic structures, education, and/or demographic structures. Analysis offered in this study focuses on the influence of taxes, controlling for other factors, on the levels and growth of per capita income among the U.S. states. Evidence does not show that taxes are an important factor in either income levels or growth when other factors are controlled. Nevertheless, estimates on the disaggregate tax variables show taxes on personal incomes have some negative effect on income levels and growth, and the change in overall tax burdens is negatively related to growth. There is strong evidence for conditional convergence among state economies. Development in education and metropolitan areas is important for both income levels and growth. A state's higher energy cost is compensated by higher wages. In addition, the constitution of a state's population affects its economy: the larger the non-labor force, the lower the income level.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".