Income and Employment Multiplier Effects of the Malaysian Higher Education Sector
Bibliographic record
Abstract
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
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".