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Record W4210633885 · doi:10.3386/w29704

Idiosyncratic Income Risk and Aggregate Fluctuations

2022· preprint· en· W4210633885 on OpenAlexaff
Davide Debortoli, Jordi Galı́

Bibliographic record

VenueNational Bureau of Economic Research · 2022
Typepreprint
Languageen
FieldEconomics, Econometrics and Finance
TopicMonetary Policy and Economic Impact
Canadian institutionsBank of Canada
FundersMinisterio de Economía y CompetitividadGeneralitat de CatalunyaEuropean CommissionCentres de Recerca de Catalunya
KeywordsSystematic riskAggregate (composite)Aggregate incomeEconometricsEconomicsMathematicsIncome distributionMaterials scienceNanotechnologyInequality

Abstract

fetched live from OpenAlex

We study the role of idiosyncratic income shocks for aggregate fluctuations within a simple heterogeneous household framework with no binding borrowing constraints.We show that the presence of idiosyncratic income shocks affects the economy's response to an aggregate shock in a way that can be captured by a consumption weighted average of the changes in uncertainty generated by the shock.We apply this framework to two example economies -an endowment economy and a New Keynesian economy-and show that under plausible calibrations the impact of idiosyncratic income shocks on aggregate fluctuations is quantitatively small, since most of the changes in uncertainty are concentrated among poorer (low consumption) households.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0020.002
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.343
GPT teacher head0.435
Teacher spread0.092 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations8
Published2022
Admission routes1
Has abstractyes

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