Aggregate spillovers magnify the welfare benefits of tax reform
Bibliographic record
Abstract
Relatively small degrees of aggregate increasing returns to scale ubstantially magnify both welfare benefits and income effects associated with tax reform. External returns to scale of 10 per cent increase the welfare benefits of tax reform by roughly one‐third and increase changes in income by significantly more than a model characterized by constant returns to scale. Aggregate spillovers of 20 per cent increase welfare benefits by roughly three‐fourths. Aggregate spillovers significantly reduce tax revenue‐maximizing capital tax rates. This research convincingly demonstrates the importance of precisely identifying the degree of aggregate returns to scale before the benefits of tax reform can be accurately assessed. JEL Classification: E62, O40 Des degrés relativement faibles de rendements croissants à l'échelle augmentent substantiellement les effets positifs de bien‐être et les effets de revenus associés à une réforme fiscale. Des rendements externes à l'échelle de 10 pour‐cent accroissent les effets positifs de bien‐être d'une réforme fiscale d'un bon tiers, et les effets de revenus d'une manière significative par rapport à ce qui'ils seraient dans le cas de rendements constants à l'échelle. Des effets agrégés de retombée de 20 pour‐cent augmentent les effets positifs de bien‐être des trois quarts. Ces effets agrégés de retombée tendent à réduire les taux d'imposition du capital qui maximisent les revenus. Ces travaux montrent l'importance d'une identification précise du degré des rendements agrégés à l'échelle si l'on veut jauger avec justesse les effets positifs de bien‐être d'une réforme fiscale.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 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".