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Record W2097274617 · doi:10.1002/jae.2296

TIME VARIATION IN THE DYNAMICS OF WORKER FLOWS: EVIDENCE FROM NORTH AMERICA AND EUROPE

2012· article· en· W2097274617 on OpenAlex
Michele Campolieti, Deborah Gefang, Gary Koop

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.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueJournal of Applied Econometrics · 2012
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicLabor market dynamics and wage inequality
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsAutoregressive modelEconomicsEconometricsImpulse responseBusiness cycleVector autoregressionVariation (astronomy)Demographic economicsMacroeconomicsMathematics

Abstract

fetched live from OpenAlex

SUMMARY Vector autoregressive methods have been used to model the interrelationships between job vacancy rates, job separation rates and job‐finding rates using tools such as impulse response analysis. We investigate whether such impulse responses change across the business cycle or over time, by estimating time‐varying parameter–vector autoregressions for data from North America (the USA and Canada) and Europe (France, Spain and the UK). While the adjustment process of the labour market to shocks in Canada and the USA is similar, we find the adjustment process differs much more across the European countries, with greater persistence in shocks relative to the USA and Canada. Copyright © 2012 John Wiley & Sons, Ltd.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.065
Threshold uncertainty score0.477

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.027
GPT teacher head0.210
Teacher spread0.183 · 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