How Does Immigration Affect Wages and the Unemployment Rate in Malaysia? A Computable General Equilibrium (CGE) Approach
Bibliographic record
Abstract
Malaysia had approximately 2 million migrants in 2018, and this number was increasing dramatically by 25 percent in 2019. Parallels with the aims of country policy to reduce migrant workers' dependency in 2020, managing the workers needs to be clarified. At the same time, the country still needs to keep them for specific sectors. These issues motivate us to analyze the migrant worker's requirements at different levels of skills and wages. Using Computable General Equilibrium (CGE) modeling, at four-level nested CES production function, this study found high skilled migrants will harm wages for the high skilled and skilled groups while the opposite effect was observed for the semiskilled and low-skilled groups. However, when the migrant stock increases slightly below 1 percent, it will reduce the wages for semiskilled workers due to substitution effects. This study also found that the influx of low-skilled migrant workers will reduce salaries for semiskilled and low-skilled workers. The analysis also indicates that a small rise in high skilled immigrant labour will reduce the unemployment rate; likewise, increasing more than 4 percent will increase the unemployment rate. The results provide the policymaker guidelines to employ foreign workers' best skills to control the inequality of wages among skilled and low-skilled workers.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".