The determinants of unemployment in Malaysia: VECM model
Bibliographic record
Abstract
The objective of this research is to identify the determinants which influence the unemployment in Malaysia. Besides that, this research also investigates the relationships between the determinants and the unemployment during short-run and long run. In this study, the dependent variable is the unemployment while the selected determinants which including the GDP growth, foreign direct investment, inflation, interest rate and exchange rate. The quarterly time series data which from first quarter (Q1) 2010 to first quarter (Q1) 2020 are employed in this study. The data are obtained from Bank Negara Malaysia and Ministry of Finance. Regarding the estimation, this study applied the Vector Error Correction Model (VECM) and the Fully Modified Ordinary Least Square (FMOLS). The short run results reveal that all the variables except foreign direct investment have a positive relationship with unemployment. Meanwhile, foreign direct investment will reduce the unemployment during short run. However, only the positive relationship from the GDP growth and the negative relationship from the foreign direct investment are significant. In term of long run, the VECM result portrays that there is a significant negative relationship from the GDP growth to unemployment. Furthermore, foreign direct investment and interest rate have a significant positive long-run relationship with unemployment. Regarding the inflation, the positive relationship from inflation to unemployment is not significant. Subsequently, FMOLS results demonstrate that exchange rate has a significant positive long-run relationship with unemployment. Apart from that, the Granger causality test claims that there is a unidirectional causality from selected determinants to unemployment except exchange rate. Hence, government should implement some effective policies including the expansionary fiscal policy and monetary policy, protective policy and exchange rate control in purpose to reduce the unemployment in Malaysia, especially during this post-Covid-19 pandemic period
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".