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Record W3125123069

Cohort Size and Youth Earnings: Evidence from a Quasi-Experiment

2011· preprint· en· W3125123069 on OpenAlexaffabout
Louis‐Philippe Morin

Bibliographic record

VenueRePEc: Research Papers in Economics · 2011
Typepreprint
Languageen
FieldHealth Professions
TopicEmployment and Welfare Studies
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsEarningsGraduation (instrument)CensusShock (circulatory)CohortDemographic economicsEconomicsCohort effectSupply shockLabour economicsDemographyBusinessMedicineMonetary economicsPopulationAccountingEngineeringSociology
DOInot available

Abstract

fetched live from OpenAlex

In this paper, I use data from the Canadian Labour Force Surveys (LFS), and the 2001 and 2006 Canadian Censuses to estimate the impact of an important labour supply shock on the earnings of young high-school graduates. The abolition of Ontario's Grade 13 generated a very large cohort of high-school graduates that simultaneously entered the Ontario labour market, generating a sudden increase in the labour supply. This provides a rare occasion to measure the impact of cohort size on earnings without the supply shock being possibly confounded with unobserved trends—a recurring problem in the literature. The Census findings suggest that the effect of the supply shock is statistically and economically important, depressing weekly earnings by 5 to 9%. The findings from the Census are supported by the LFS results that suggest that the immediate impact of the supply shock—measured about six months after high-school graduation—is also important.

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)0.046
metaresearch head score (Gemma)0.053
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 score0.046
Threshold uncertainty score0.244

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0460.053
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0010.002
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0140.002

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 head0.129
GPT teacher head0.429
Teacher spread0.299 · 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

Citations1
Published2011
Admission routes2
Has abstractyes

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