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Record W3124438543

Replacing Family Income During the Retirement Years: How Are Canadians Doing?

2010· preprint· en· W3124438543 on OpenAlexaboutno aff
Sebastien Larochelle-Côté, John Myles, Garnett Picot

Bibliographic record

VenueRePEc: Research Papers in Economics · 2010
Typepreprint
Languageen
FieldSocial Sciences
TopicRetirement, Disability, and Employment
Canadian institutionsnot available
Fundersnot available
KeywordsEarningsDemographic economicsPopulationEconomicsPensionLabour economicsDemographyHealth and Retirement StudySociology
DOInot available

Abstract

fetched live from OpenAlex

This paper examines the extent to which family income during working years is replaced during the retirement years. It does so by tracking cohorts as they age from their mid-50s to their late 70s, using a taxation-based longitudinal data source that covers 26 years from 1982 to 2007. Earlier work by the same authors examined this question with respect to the 50% of the population with strong labour force attachment during their mid-50s. This paper extends that work to include almost all Canadians (80% to 85% of the population). The adult-equivalent-adjusted family income available to the median Canadian during his or her late 70s is about 80% of that observed when the same person was in his or her mid-50s (a replacement rate of 0.8). Replacement rates in retirement are negatively correlated with income earned around age 55. Median replacement rates are 1.1 among individuals in the bottom income quintile, 0.75 in the middle quintile, and 0.7 in the top quintile. In retirement, public pensions and other transfers more than replace earnings and other income of bottom quintile individuals. However, some individuals have very low replacement rates. For example, 20% of individuals in the middle income quintile had replacement rates below 0.6. More recent cohorts had higher family incomes in retirement than did earlier cohorts as a result of higher earnings and private-pension income.

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)0.006
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.361
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0020.001
Research integrity0.0010.003
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 head0.108
GPT teacher head0.388
Teacher spread0.280 · 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

Citations1
Published2010
Admission routes1
Has abstractyes

Explore more

Same venueRePEc: Research Papers in EconomicsSame topicRetirement, Disability, and EmploymentFrench-language works237,207