Redistribution and Employment Policies with Endogenous Unemployment
Bibliographic record
Abstract
We study the features of optimal transfers to the non-employed which include those unable to work, the voluntarily unemployed, and the involuntarily unemployed. Both voluntary and involuntary unemployment are endogenous. We analyze optimal government policies in the presence of two types of involuntary unemployment, frictional and that induced by efficiency wages. We consider how the quality of the government's information affects policies and also study time-consistent policies. The models are simple, yet rich enough to reflect real-world policies, including transfers to the disabled, welfare for non-working employables, unemployment insurance, employment subsidies, and taxes on workers and firms. Nous étudions les transferts aux sans-emploi, lesquels peuvent être incapables de travailler, chômeurs volontaires ou chômeurs involontaires. Le nombre de chômeurs volontaires et involontaires est endogène. Nous caractérisons les politiques gouvernementales optimales pour le cas où le chômage involontaire est frictionnel et pour celui où il est causé par des salaires efficaces. Nous étudions la cohérence temporelle des politiques et l'impact qu'a sur elles la qualité de l'information à la disposition du gouvernement. Comportant des transferts aux non-employables, de l'aide sociale aux sans-emplois capables de travailler, de l'assurance-chômage, des subventions à l'emploi et des taxes payées par les firmes et les travailleurs, les modèles étudiés sont simples mais demeurent néanmoins de bonnes représentations de la réalité.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".