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Record W7162130847 · doi:10.82308/18762

Essays on the labour market decisions of older workers

2023· dissertation· en· W7162130847 on OpenAlexaboutno aff
Peta-Gay Fairclough Campbell

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldSocial Sciences
TopicRetirement, Disability, and Employment
Canadian institutionsnot available
Fundersnot available
KeywordsPensionSocial securityPreferenceCurrent Population SurveyWork (physics)PopulationPublic policyPopulation ageingJob security

Abstract

fetched live from OpenAlex

This dissertation comprises three essays examining the labour market decision-making of workers aged 50 and older in Canada and the United States. Older workers make up a large share of the labour force, so understanding their labour market decisions can inform public policy on public pension plans and public health care expenditure.In the first essay, I compare the labour market attachment of workers aged 50 to 69 in the United States and Canada from 1997 to 2019. I utilise the panel structure of the Canadian Labour Force Survey and the US Current Population Survey to create six-month and four-month panels for Canada and the United States, respectively. I use the survey questions on reasons for (1) working part-time and (2) being non-employed (unemployed or not-in-the-labour force) to determine how the reasons provided differ based on age and gender. I also examine the different types of labour force transitions within the panel and how it changes with age and gender. The descriptive results show that workers become less attached to the labour market as they age and have a preference for more short-term, flexible work arrangements. The increase in part-time employment is driven primarily by supply-side factors. Personal preference is the main reason in Canada, while retirement and/or social security earning limits is the main reason in the United States. For the workers marginally attached or not attached to the labour market, the dominant factor seems to be retirement or the ending of short-term jobs.The second essay studies the short-term impact of an involuntary job separation on the probability of re-employment and labour market exit for workers in Canada aged 50 to 69 years. Like the first essay, I use the Canadian Labour Force survey from 1997 to 2019 to create six-month panels. I use the panels to estimate linear probability models for returning to work and exiting the labour force. My results show that a prior involuntary separation increases the likelihood of labour market exit by approximately 4.7 percentage point and decreases the chance of re-employment by 5.3 percentage points. The findings suggest that an involuntary job separation is a pathway through which older workers exit the labour market.The third essay uses the US Health and Retirement Survey from 1998 to 2014 to determine whether physical activity protects the cognitive function of retired older workers aged 51 to 75 in the United States. In this essay, I first estimate causal relationships between (1) retirement and cognitive function; (2) retirement and physical activity using the eligibility ages for early and normal social security benefit claiming as instruments for retirement. I also establish the association between physical activity and cognitive function. I then analyse whether engaging in physical activity is a mechanism through which retirement impacts the cognitive outcomes of older workers. My results show that the combined effect of retiring and meeting the physical activity guidelines increases cognitive scores by approximately 0.5 points. This corresponds to a 4.5% increase in the cognitive score compared to the sample average

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)0.002
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.023
Threshold uncertainty score0.047

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.008
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0030.002
Scholarly communication0.0030.001
Open science0.0010.001
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0060.001

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 head0.169
GPT teacher head0.436
Teacher spread0.267 · 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 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
Published2023
Admission routes1
Has abstractyes

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