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Record W4315641457 · doi:10.1111/ecin.13135

Optimal unemployment policy

2023· article· en· W4315641457 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueEconomic Inquiry · 2023
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicLabor market dynamics and wage inequality
Canadian institutionsUniversité du Québec à Montréal
FundersSimon Fraser UniversityRoyal Economic SocietyUniversité du Québec à Montréal
KeywordsUnemploymentEconomicsVariety (cybernetics)Consumption smoothingIndirect InferenceConsumption (sociology)Fiscal policyInferenceExternalityPopulationPublic economicsLabour economicsEconometricsMacroeconomicsMicroeconomicsComputer science

Abstract

fetched live from OpenAlex

Abstract This paper shows that the optimal policy to deal with unemployment features important roles for monitoring of search and job search assistance, with the optimal combined policy also incorporating more generous unemployment insurance (UI). These results are significantly different from the previous literature, which has overwhelmingly focused on UI on its own. I incorporate two empirically relevant phenomena that have often been ignored: private consumption smoothing and fiscal externalities from income taxes. I estimate a job search model using indirect inference on data from the March Current Population Survey, and simulate the model to evaluate a variety of policy reforms.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.564
Threshold uncertainty score0.988

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.013

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.

Opus teacher head0.059
GPT teacher head0.288
Teacher spread0.229 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it