Optimal redistribution when different workers are indistinguishable
Bibliographic record
Abstract
Abstract. Using the standard non linear income and commodity taxation framework, we examine the optimal policy to be adopted when the same labour disutility can receive two opposite interpretations: taste for leisure and activity limitation. In the absence of complete information about individual characteristics, an income tax does not allow distinguishing lazy from handicapped individuals. One may rely, however, on a combination of commodity and income taxes to redistribute from the former to the latter when they differ in their preferences for commodities. JEL Classification: H21, H41 Redistribution optimale quand les divers travailleurs sont indistincts. A l’aide du cadre d’analyse conventionnel de fiscalité non linéaire sur les revenus et les biens, ce mémoire examine la politique optimale à adopter quand le même niveau de désutilité du travail peut être interprété de deux manières opposées : goût pour le loisir et souffrance attribuable à une infirmité. En l’absence de renseignements complets à propos des caractéristiques des individus, un impôt sur le revenu ne permet pas de distinguer les individus paresseux de ceux qui sont handicapés. On peut cependant compter sur une combinaison de taxes sur les revenus et les biens pour redistribuer du premier groupe vers le second quand leurs préférences pour les biens diffèrent.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
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".