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Record W4389683831 · doi:10.1002/pam.22553

Grads on the go: Measuring college‐specific labor markets for graduates

2023· article· en· W4389683831 on OpenAlexaboutno aff
Johnathan G. Conzelmann, Steven W. Hemelt, Brad J. Hershbein, Shawn Martin, Andrew Simon, Kevin Stange

Bibliographic record

VenueJournal of Policy Analysis and Management · 2023
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicRegional Economics and Spatial Analysis
Canadian institutionsnot available
FundersInstitute of Education SciencesRussell Sage Foundation
KeywordsQuarter (Canadian coin)InstitutionInvestment (military)Public institutionHigher educationDemographic economicsLabour economicsSocial mobilityEconomicsPolitical scienceBusinessEconomic growthGeographyLaw

Abstract

fetched live from OpenAlex

Abstract This paper introduces a new measure of the labor markets served by colleges and universities across the United States. About 50% of recent college graduates are living and working in the metro area nearest the institution they attended, with this figure climbing to 67% in‐state. The geographic dispersion of alumni is more than twice as great for highly selective 4‐year institutions as for 2‐year institutions. However, more than one quarter of 2‐year institutions disperse alumni more diversely than the average public 4‐year institution. In one application of these data, we find that the average strength of the labor market to which a college sends its graduates predicts college‐specific intergenerational economic mobility. In a second application, we quantify the extent of “brain drain” across areas and illustrate the importance of considering migration patterns of college graduates when estimating the social return on public investment in higher education.

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 score0.014
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0040.003
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0060.001

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 head0.059
GPT teacher head0.246
Teacher spread0.187 · 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

Citations11
Published2023
Admission routes1
Has abstractyes

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