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Record W4414113890 · doi:10.1111/iere.70080

College Loans and Human Capital Investment

2025· article· en· W4414113890 on OpenAlexfundno aff
Chao Fu, Hsuan-Chih Lin, Atsuko Tanaka

Bibliographic record

VenueInternational Economic Review · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicHigher Education Research Studies
Canadian institutionsnot available
FundersSocial Sciences and Humanities Research Council of CanadaNational Science Foundation
KeywordsHuman capitalLoanInvestment (military)Government (linguistics)Student loanWelfareRevenue

Abstract

fetched live from OpenAlex

ABSTRACT College loans facilitate access to education, but the repayment burden may distort posteducation human capital investment. We examine the role of college loans and loan repayment policies through a structural model of individuals' dynamic decisions on borrowing/saving, labor supply, and costly human capital investment. We estimate two versions of the model using data from NLSY79: one with natural borrowing limits and another with parameterized limits. Counterfactual simulations suggest that, relative to the standard fixed repayment plan, income‐driven repayment (IDR) plans modestly increase educational attainment, lifetime earnings, and individual welfare; accounting for lifetime income taxes, they also increase government revenue.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.887
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.035
GPT teacher head0.423
Teacher spread0.388 · 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 teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreOther

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

Citations0
Published2025
Admission routes1
Has abstractyes

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