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Record W2513121343

Intergenerational Income Transmission: New Evidence from Canada

2016· preprint· en· W2513121343 on OpenAlexaboutno aff
Wen‐Hao Chen, Yuri Ostrovsky, Patrizio Piraino

Bibliographic record

VenueRePEc: Research Papers in Economics · 2016
Typepreprint
Languageen
FieldSocial Sciences
TopicIntergenerational and Educational Inequality Studies
Canadian institutionsnot available
Fundersnot available
KeywordsEarningsSocial mobilityEconomicsNet national incomeIncome elasticity of demandDemographic economicsIncome distributionPermanent income hypothesisAssertionLabour economicsSociologyGross incomePublic economicsInequalityMonetary economics
DOInot available

Abstract

fetched live from OpenAlex

Comparative studies of intergenerational earnings and income mobility largely rank Canada as one of the most mobile countries among advanced economies, such as Denmark, Finland and Norway. The assertion that Canada is a highly mobile society is drawn from intergenerational income elasticity estimates reported in Corak and Heisz (1999). Corak and Heisz used data from the earlier version of the Intergenerational Income Database (IID), which tracked income of Canadian youth only into their early thirties. Recent theoretical literature, however, suggests that the relationship between childrens? and parents? lifetime income may not be accurately estimated when children?s income are not observed from their mid-careers? known as lifecycle bias. The present study addresses this concern by re-examining the extent of intergenerational earnings and income mobility in Canada using the updated version of the IID, which tracks children well into their mid-forties, when mid-career income are observed.

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)0.009
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.041
Threshold uncertainty score0.296

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.015
Science and technology studies0.0060.002
Scholarly communication0.0030.001
Open science0.0020.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.107
GPT teacher head0.393
Teacher spread0.286 · 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

Citations4
Published2016
Admission routes1
Has abstractyes

Explore more

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