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Record W2127816127

Do university enrollment constraints affect education and earnings

2002· preprint· en· W2127816127 on OpenAlexaboutno aff
Björn Öckert

Bibliographic record

VenueEconstor (Econstor) · 2002
Typepreprint
Languageen
FieldEconomics, Econometrics and Finance
TopicLabor market dynamics and wage inequality
Canadian institutionsnot available
FundersRiksbankens Jubileumsfond
KeywordsEarningsAffect (linguistics)Instrumental variableDemographic economicsEducational attainmentQuarter (Canadian coin)Higher educationEconomicsPanel dataLabour economicsPsychologyEconometricsAccountingEconomic growthGeography
DOInot available

Abstract

fetched live from OpenAlex

In most countries the number of places at the universities is restricted. This paper estimates the effect of university enrollment constraints in 1982 on years of education and earnings in Sweden 1981-96. The effect on educational attainment is related to labor market performance, to estimate the effect of education on earnings. The variation used is driven by discrete jumps in the admission selection to university. The results show that university enrollment constraints affect educational attainment over the entire period studied. In 1996, admitted applicants in 1982 have about one quarter of a year longer education than screened out applicants. The effect of enrollment constraints changes with time. In the end of the panel, admitted applicants in 1982 are no better off than screened out applicants. The estimated return to education in Sweden is very low, both with least square and instrumental variable techniques.

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)0.001
metaresearch head score (Gemma)0.006
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 score0.029
Threshold uncertainty score0.058

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.018
GPT teacher head0.219
Teacher spread0.201 · 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

Citations2
Published2002
Admission routes1
Has abstractyes

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