Efficiency and Redistribution: An Evaluative Review of Louis Kaplow's <i>The Theory of Taxation and Public Economics</i>
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Bibliographic record
Abstract
Louis Kaplow proposes a two-step methodology for normative policy analysis and illustrates it using various policy reforms. The first step is to identify efficiency gains when hypothetical lump-sum taxes can undo redistributive consequences. The second step evaluates the redistributive effects using a strictly welfaristic social welfare function. I critically review the foundations for Kaplow's procedure and its reliance on strict welfarism. I argue that basing efficiency gains on hypothetical lump-sum tax adjustments can lead to social welfare reducing policies if such tax adjustments are not carried out. I also indicate some conceptual problems with translating welfarism into policy evaluation when individuals have different utility function, and review one promising alternative approach.(JEL H20, H41, H50)
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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 | 0.003 | 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 | 0.001 |
| 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 it