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Record W2615908972 · doi:10.24148/wp2008-30

The Adjustments of Hours and Real Wages to Technology Shocks: Assessing the Role of Nominal Rigidities

2011· article· en· W2615908972 on OpenAlexafffund
Zheng Liu, Louis Phaneuf, Glenn D. Rudebusch

Bibliographic record

VenueFederal Reserve Bank of San Francisco, Working Paper Series · 2011
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicMonetary Policy and Economic Impact
Canadian institutionsUniversité du Québec à Montréal
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsEconomicsTechnology shockReal wagesWageShock (circulatory)Monetary policyMonetary economicsEconometricsLabour economicsDynamic stochastic general equilibrium

Abstract

fetched live from OpenAlex

Nominal rigidities are generally viewed as important for the transmission of monetary policy. We argue that nominal rigidities are important also for the transmission of technology shocks, especially for explaining their effects on hours and real wages. Evidence suggests that a positive technology shock leads to a short-run decline in labor hours and a gradual rise in real wages. We examine the ability of an RBC model augmented with real frictions, a pure sticky-price model, a pure sticky-wage model, and a model combining sticky prices and sticky wages in accounting for this evidence. We find that, according to this metric, the model with nominal wage and price rigidities is more successful than others. This finding is robust and holds true for a relatively small Frisch elasticity of hours and a relatively high frequency of price reoptimization that are consistent with microeconomic evidence.

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)0.014
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 score0.011
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.014
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.052
GPT teacher head0.242
Teacher spread0.189 · 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

Citations0
Published2011
Admission routes2
Has abstractyes

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