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Record W1585468769

La maturation du systeme de revenu de retraite du Canada : niveaux de revenu, inegalite des revenus et faibles revenus chez les gens ages

2000· preprint· fr· W1585468769 on OpenAlexaboutno aff
John Myles

Bibliographic record

VenueRePEc: Research Papers in Economics · 2000
Typepreprint
Languagefr
FieldSocial Sciences
TopicRetirement, Disability, and Employment
Canadian institutionsnot available
Fundersnot available
KeywordsPolitical scienceHumanitiesArt
DOInot available

Abstract

fetched live from OpenAlex

Ce document reexamine les tendances sur le plan du niveau et de la repartition des revenus chez les Canadiens ages dans le contexte de ce qui est sans doute la principale source de changement a l'interieur de ces tendances depuis la fin des annees 70, la maturation des regimes publics et prives de pensions proportionnelles aux gains au Canada. L'elargissement du role des pensions proportionnelles aux gains dans les annees 80 et 90 est en grande partie le resultat de changements qui se sont produits dans les annees 50 et 60. Le Regime de pensions du Canada et le Regime de rentes du Quebec (RPC/RRQ) ont ete mis en oeuvre en 1966 et la premiere cohorte a recevoir les prestations integrales du RPC/RRQ a eu 65 ans en 1976. Les cohortes qui ont pris leur retraite apres cette periode ont egalement beneficie de l'elargissement des pensions professionnelles privees qui s'est produit entre les annees 50 et les annees 70. On s'appuie sur une decomposition detaillee des revenus par source pour montrer que la maturation de ces programmes de prestations proportionnelles aux gains a non seulement entraine une augmentation importante des revenus reels moyens, mais egalement une reduction considerable de l'inegalite des revenus chez les gens ages, principalement en raison des prestations du RPC/RRQ. La hausse des revenus reels a profite dans une proportion disproportionnee aux aines a revenu inferieur, ce qui a contribue a la diminution bien connue des taux de faible revenu chez les gens ages.

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)0.016
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.485
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0160.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.002
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.002
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 head0.097
GPT teacher head0.376
Teacher spread0.279 · 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

Citations3
Published2000
Admission routes1
Has abstractyes

Explore more

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