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Record W1605431600 · doi:10.3386/w14667

Retirement Income Security and Well-Being in Canada

2009· report· en· W1605431600 on OpenAlexaffabout
Michael Baker, Jonathan Gruber, Kevin Milligan

Bibliographic record

VenueNational Bureau of Economic Research · 2009
Typereport
Languageen
FieldSocial Sciences
TopicRetirement, Disability, and Employment
Canadian institutionsUniversity of Toronto
FundersNational Institute on Aging
KeywordsSocial securityWell-beingDemographic economicsEconomicsBusinessPolitical science

Abstract

fetched live from OpenAlex

A large international literature has documented the labor market distortions associated with social security benefits for near-retirees. In this paper, we investigate the 'other side' of social security programs, seeking to document improvements in wellbeing arising from the provision of public pensions. To the extent households adjust their savings and employment behavior to account for enhanced retirement benefits, the positive impact of the benefits may be crowded out. We proceed by using the large variation across birth cohorts in income security entitlements in Canada that arise from reforms to the programs over the past 35 years. This variation allows us to explore the effects of benefits on elderly well-being while controlling for other factors that affect well-being over time and by age. We examine measures of income, consumption, poverty, and happiness. For income, we find large increases in income corresponding to retirement benefit increases, suggesting little crowd out. Consumption also shows increases, although smaller in magnitude than for income. We find larger retirement benefits diminish income poverty rates, but have no discernable impact on consumption poverty measures. This could indicate smoothing of consumption through savings or other mechanisms. Finally, our limited happiness measures show no definitive effect.

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)0.011
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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.855
Threshold uncertainty score0.984

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0110.001
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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.423
GPT teacher head0.553
Teacher spread0.130 · 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 designTheoretical or conceptual
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

Citations14
Published2009
Admission routes2
Has abstractyes

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