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Record W2131943391 · doi:10.1177/0973801014557391

Income and Employment Multiplier Effects of the Malaysian Higher Education Sector

2015· article· en· W2131943391 on OpenAlexaff
Siew Hwa Yen, Wooi Leng Ong, Koon Peng Ooi

Bibliographic record

VenueMargin The Journal of Applied Economic Research · 2015
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicFiscal Policy and Economic Growth
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsHigher educationEconomicsPrivate sectorMultiplier (economics)Labour economicsPublic sectorIncome in kindDemographic economicsEconomic growthGross incomePublic economicsEconomyMacroeconomics

Abstract

fetched live from OpenAlex

The aim of this article is to examine the income and employment multiplier effects of the higher education sector in Malaysia based on conventional input–output methodology. We examined simple, total, Type I and Type II income and employment multiplier effects of private and public higher education institutions (HEIs) in Malaysia. We found that private HEIs have larger direct and indirect income impacts than public HEIs. With the presence of household spending, both public and private HEIs have greater induced income impacts than direct and indirect income generation effects. We also found that Type I multipliers for private and public HEIs lead to additional income of 1.34 and 1.32 for every initial Ringgit of labour income, respectively, while Type II income multipliers for private and public HEIs account for additional income of 3.09 and 3.05, respectively. Higher education creates 1.21 workers per RM 10,000 investment. The overall results show that private higher education has a relatively greater income effect on the economy.compared to public higher education. The higher education sector is also found to be ineffective in creating new employment in the economy. JEL Classification: I23, I25

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.219
Threshold uncertainty score0.371

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
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 science0.0010.000
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 head0.073
GPT teacher head0.298
Teacher spread0.225 · 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 designTheoretical or conceptual
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

Citations17
Published2015
Admission routes1
Has abstractyes

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