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Record W1515975132 · doi:10.59962/9780774852111-010

Cohort, Year, and Age Effects in Canadian Wage Data

2007· article· en· W1515975132 on OpenAlexafffundabout
John Burbidge, Lonnie Magee, A. Leslie Robb

Bibliographic record

VenueUniversity of British Columbia Press eBooks · 2007
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicLabor market dynamics and wage inequality
Canadian institutionsMcMaster University
FundersSocial Sciences and Humanities Research Council of CanadaHealth CanadaMcMaster UniversityQueen's UniversityUniversity of TorontoYork University
KeywordsCohortWageReal wagesCohort effectDemographyEconomicsDemographic economicsHourly wageMedicineLabour economics

Abstract

fetched live from OpenAlex

We use Canadian SCFs 1971-1993 to study the wages of full-time, full-year male and female workers. Median real wages of 24-year-old males without a university degree fell by 25% between 1978 and 1993. For 24-year-old females the decline was more modest and reversed in 1987, but real wages in 1993 were still significantly lower than they were in 1978. We investigate whether these changes are permanent “cohort” effects or more temporary “year” effects. Graphs of median wages against year and age indicate some periods where year effects are more prominent than cohort effects and other periods where the reverse is true. We then compare the results from two models, one assigning the trends to year effects, the other assigning them to cohort effects, and use these models to produce real wage projections.

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)0.005
metaresearch head score (Gemma)0.014
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 score0.021
Threshold uncertainty score0.154

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.014
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.010
Science and technology studies0.0020.001
Scholarly communication0.0020.001
Open science0.0020.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.017
GPT teacher head0.184
Teacher spread0.166 · 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

Citations5
Published2007
Admission routes3
Has abstractyes

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