Bibliographic record
Abstract
With the standard non‐linear income taxation framework with heterogeneity of preferences, in this paper the optimality of workfare as a screening tool is examined. It is assumed that workfare does not serve as a human capital investment, participation is mandatory, and administrative costs are negligible. Imposing alternative cardinalizations on individuals utilities allows for the possibility that the government optimally redistributes income to or from high disutility of labour individuals. Under either case, it is never optimal to impose workfare on these individuals. It is also shown that non‐productive workfare can be an efficient policy tool, in contrast to the results found in Besley and Coate (1995), Brett (1998), and Beaudry and Blackorby (1997). JEL Classification: H21, H23 Optimalité du workfare en présence de préférences hétérogènes. L'auteur examine l'timalité du workfare en tant qu'instrument de tamisage à l'aide du cadre conceptuel traditionnel d'imposition non‐linéaire des revenus en présence de préférences hétérogènes. On postule que le workfare n'est pas un processus d'investissement en capital humain, que la participatioun est obligatoire, et que les coûts d'administration sont négligeables. Quand on postule aussi des utilités cardinales différentes pour les individus, il devient possible pour le gouvernement de redistribuer optimalement les revenus en faveur ou au détriment des individus pour qui le travail a une très grande désutilité. Dans l'un et l'autre cas, on montre que le workfare n'est jamais une politique optimale. On montre aussi que le workfare dans des activités non‐productives peut être un instrument de politique publique efficient, contrairement a ce qu'affirment Besley et Coate (1995), Brett (1998) et Beaudry et Blackorby (1997).
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".