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Record W2190647588 · doi:10.1353/jef.2015.a596196

The Economic Analysis of University Participation Rates

2015· article· en· W2190647588 on OpenAlexaboutno aff
George Fallis

Bibliographic record

VenueJournal of education finance · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicHigher Education Research Studies
Canadian institutionsnot available
Fundersnot available
KeywordsEconomics

Abstract

fetched live from OpenAlex

Over the postwar period in most developed countries, the university participation rate has risen steadily to well over 30 percent, although there remain differences between countries. Students from lower income families have lower participation rates than those from higher income families. The article provides an economic analysis of these patterns of participation using a model of the market for undergraduate education. The demand for undergraduate education is analyzed using a human capital model in which households choose whether to apply to university in seeking to maximize the present value of lifetime income. The supply of undergraduate education is analyzed using a model of universities as non-profit, multi-product organizations that seek to maximize the quality of undergraduate education produced and the quality of research published by faculty members. The production of undergraduate education is a customer-input technology in which the quality of the students, through peer effects, influences the quality of education produced. The long-run supply function depends upon government policies to finance universities. The market equilibrium depends upon government policy on tuition and the availability of qualified applicants to universities. For illustrative purposes, data and public policy from the province of Ontario, Canada, are presented and analyzed. The article argues that the economic analysis of participation rates has overemphasized demand side variables; and that more attention should be paid to the supply decisions of universities and to government policies regarding the financing of universities and the setting of tuition levels.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.361
Threshold uncertainty score0.512

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.060
GPT teacher head0.437
Teacher spread0.377 · 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.

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
Published2015
Admission routes1
Has abstractyes

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