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Record W2200617051 · doi:10.34989/sdp-2015-2

Changing Labour Market Participation Since the Great Recession: A Regional Perspective

2021· preprint· en· W2200617051 on OpenAlexaff
Calista Cheung, Dmitry Granovsky, Gabriella Velasco

Bibliographic record

VenueEconstor (Econstor) · 2021
Typepreprint
Languageen
FieldSocial Sciences
TopicMigration, Aging, and Tourism Studies
Canadian institutionsBank of Canada
Fundersnot available
KeywordsGreat recessionRecessionPolitical scienceEconomicsPerspective (graphical)HumanitiesLabour economicsKeynesian economicsArt

Abstract

fetched live from OpenAlex

This paper discusses broad trends in labour force participation and part-time employment across different age groups since the Great Recession and uses provincial data to identify changes related to population aging, cyclical effects and other factors. The main population age groups examined are youth (aged 15-24), prime age (25-54) and older (55 and above). Six main findings are reported. First, aging has been the most important driver of reduced participation. On their own, aging effects would have depressed participation rates by more than they fell between 2007 and 2014, and have been partly offset by rising participation rates of older workers. Second, shifting age composition has had the largest impact on the Atlantic provinces, owing primarily to their shrinking prime-age populations as some workers have migrated west. Third, a considerable part of the overall participation rate decline since 2007 reflects a greater share of prime-age and youth populations that are out of the labour force for various reasons including school, illness, and family responsibilities. These changes appear to be driven by both structural and cyclical forces, although the relative importance of each is unclear. Fourth, effects associated with “discouraged workers” have been negligible. Fifth, youth participation rates have fallen the most, by 2.8 percentage points since 2007, with 9 per cent of the decline reflecting purely higher school enrolment rates. Sixth, weak business conditions appear to be the main driver behind the shift toward part-time employment since the Great Recession, with involuntary part-time work explaining almost the entire increase since 2007.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.510
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0020.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.029
GPT teacher head0.304
Teacher spread0.275 · 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.

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

Citations2
Published2021
Admission routes1
Has abstractyes

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