Redistributive Politics with Distortionary Taxation
Bibliographic record
Abstract
This paper proposes a first step towards a positive theory of tax instruments. We present a model of redistributive politics that extends those of Myerson [R. Myerson, Incentives to cultivate minorities under alternative electoral systems, Amer. Polit. Sci. Rev. 87 (1993) 856-869] and Lizzeri and Persico [A. Lizzeri, N. Persico, The provision of public goods under alternative electoral incentives, Amer. Econ. Rev. 91 (2001) 225-239]. Two politicians compete in terms of targeted redistributive promises financed through distortionary taxes. We solve for the case of both targetable and non-targetable taxes. We prove that there is an imperfect efficiency-targetability trade off on the tax side. Politicians prefer targetable taxes over non-targetable ones, especially when the latter are less efficient. Yet, targetable taxation is always used even when it is very inefficient compared to non-targetable taxes.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".