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Record W1538480921

Intracohort Income Status Maintenance: An Analysis of the Later Life Course

2001· preprint· en· W1538480921 on OpenAlexaboutno aff
Steven G. Prus

Bibliographic record

VenueRePEc: Research Papers in Economics · 2001
Typepreprint
Languageen
FieldSocial Sciences
TopicIntergenerational and Educational Inequality Studies
Canadian institutionsnot available
Fundersnot available
KeywordsGenerosityCohortWelfareLife course approachDemographic economicsPosition (finance)Socioeconomic statusDistribution (mathematics)Convergence (economics)DemographySocial statusEconomicsSocioeconomicsPsychologyEconomic growthSociologyPolitical scienceMedicineSocial psychologyPopulationSocial science
DOInot available

Abstract

fetched live from OpenAlex

This paper examines the extent to which an individual's income status position relative to others in one's own cohort is maintained over the later life course. Changes in the income status of individuals are estimated within a synthetic cohort. Using a series of cross-sectional datafiles from about every fifth Survey of Consumer Finances starting in 1974, the findings show that individuals born between 1924 and 1928 with early life socio-economic status advantages, namely high education, improve their absolute and relative income status position vis-à-vis others in their own cohort with status disadvantages from ages 46 to 64. Over the ages of 65 to 74, the pattern of economic well-being of individuals with status advantages and disadvantages reflects an income status convergence. Because Canada's old-age public welfare state is relatively well-developed in terms of comprehensiveness and generosity, it does a good job at countering the effects of status background characteristics on the distribution of income in old age; that is, it substantially weakens the relationship between education and income as individuals enter old age. In absence of these programs (i.e. up to age 64), the relative position of those with high education and other advantaged groups is strengthened.

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)0.003
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 score0.057
Threshold uncertainty score0.954

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
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 head0.060
GPT teacher head0.405
Teacher spread0.345 · 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

Citations0
Published2001
Admission routes1
Has abstractyes

Explore more

Same venueRePEc: Research Papers in EconomicsSame topicIntergenerational and Educational Inequality StudiesFrench-language works237,207