Regional Economic Growth in Malaysia: Does Aggregate Overqualification Matter?
Bibliographic record
Abstract
This paper explores the impact of aggregate overqualification on regional economic growth in Malaysia from 2005 to 2017 using Dynamic Panel Data (DPD) approach. The aggregate overqualification was gauged as the percentage of workers with at least a bachelor’s degree qualification who employed in an occupation below than the professional job level. Following the method, while the incidence stood at 1 percent, it was however higher in Kuala Lumpur (4.4 percent) and Selangor (3.9 percent) and was much lower in Perak (-0.26 percent) and Perlis (-0.12 percent). Moreover, the incidence was higher after 2010. Empirical findings revealed strong evidence of negative impact of the aggregate overqualification on regional economic growth. Yet, the magnitudes of the effect were smaller, between 0.02 and 0.03. Further analysis revealed the negative impact was greater in most developed states and for the period after 2010. The findings depict that there is a growth penalty for not being fully utilised the knowledge and skills of highly educated workers at the regional labour market.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.033 |
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; both teacher heads agree on what is shown here.
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".