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Record W2913169569 · doi:10.1108/ijm-04-2017-0059

Unemployment invariance hypothesis, added and discouraged worker effects in Canada

2018· preprint· en· W2913169569 on OpenAlexaboutno aff
Aysıt Tansel, Zeynel Abidin Özdemir

Bibliographic record

VenueInternational Journal of Manpower · 2018
Typepreprint
Languageen
FieldEconomics, Econometrics and Finance
TopicLabor market dynamics and wage inequality
Canadian institutionsnot available
Fundersnot available
KeywordsUnemploymentEconomicsUnemployment rateDiscouraged workerLabour economicsEmpirical researchDemographic economicsMacroeconomics

Abstract

fetched live from OpenAlex

Purpose The purpose of this paper is to explore the long-run relationship between unemployment rate (UR) and labor force participation rate (LFPR) for men and women in Canada. Given that there are differences in the URs and participation rates of men and women, the authors perform separate analysis for them also. Design/methodology/approach The authors use co-integration analysis to investigate the existence of a long-run relationship between UR and LFPR in Canada using time series monthly data for the past 40 years. Findings The finding that there is long-run relationship between UR and LFPR leads the authors to doubt the pertinence of the unemployment invariance hypothesis for Canada. The authors further find evidence for added-worker effect for men and discouraged-worker effect for women in Canada and the authors elaborate on the possible explanations for this seemingly contradictory finding. Practical implications The lack of support for the unemployment invariance hypotheses implies that changes in the participation rate which may be due to aging population, policies of early retirement or constraints on working time will affect the UR in the long run. Originality/value This paper investigates the unemployment invariance hypothesis in Canada to come up with policy implications about long-run UR. The authors further elaborate on the possible explanations for the added-worker effect for men and the discouraged-worker effect for women that the authors find in this study.

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 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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.281
Threshold uncertainty score0.875

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.020
GPT teacher head0.229
Teacher spread0.208 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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
Published2018
Admission routes1
Has abstractyes

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