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Record W2043519970 · doi:10.1155/2012/458723

Financial Factors and Labour Market Transitions of Older Workers in Canada

2012· article· en· W2043519970 on OpenAlexafffundabout
Xuyang Chen, Maxime Fougère, Bruno Rainville

Bibliographic record

VenueInternational Journal of Population Research · 2012
Typearticle
Languageen
FieldSocial Sciences
TopicRetirement, Disability, and Employment
Canadian institutionsEmployment and Social Development Canada
FundersHuman Resources and Skills Development Canada
KeywordsPensionAccrualShock (circulatory)Labour economicsWork (physics)Demographic economicsEconomicsBusinessFinanceEarnings

Abstract

fetched live from OpenAlex

This paper looks at the influence of financial factors on the labour market transitions of Canadian older workers. Also, in contrast to previous studies, the analysis focuses on transitions between full-time work, part-time work, and retirement. Sequential annual observations of employment and retirement choices are examined for samples of full-time and part-time workers, drawn from the Survey of Labour and Income Dynamics (SLID), 2001–2006. Measures of potential pension wealth and one-year and peak pension accruals are imputed using data from the Survey of Consumer Finances, 1973–1997, and the SLID, 1997–2006. Regression results indicate that financial factors influence workers to move from full-time to part-time jobs and support the evidence found in previous studies that retirement is usually a process, not a single event. Also, an increase in pension accruals increases the probability of working full-time for lower-income earners only. Among nonfinancial factors, a negative health shock increases the probability of working part-time or retiring for full-time workers but has little effect on the labour market transitions of part-time workers. Finally, these results suggest that policies to encourage phased retirement are unlikely to have a significant labour market effect since bridge employment is already a common transition process among older workers.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.030
Threshold uncertainty score0.540

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.219
GPT teacher head0.484
Teacher spread0.265 · 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

Citations4
Published2012
Admission routes3
Has abstractyes

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