Does COVID-19 affect GDP? A relationship between GDP and unemployment rate
Bibliographic record
Abstract
This study aims to examine the long-term relationship between the unemployment rate and the growth of domestic product (GDP) in Malaysia, thereby revealing unemployment's impact on GDP. In this COVID-19 pandemic situation, numerous people have lost their jobs. That indirectly increases the unemployment rate which later has a variety of negative consequences on the government, society, and individuals. The Malaysian government has taken a big step in announcing the Movement Control Order (MCO) to slow down the spread of infections. Such decisions have affected the unemployment rate, as some businesses have to reduce their employees and some high-risk companies temporarily closed to stop the spreading of COVID cases. The cointegration test is employed to identify the relationship between the unemployment rate and GDP and then validate it by analyzing the error. Quarterly unemployment rate and GDP data were obtained from the Department of Statistics Malaysia (DOSM) website from the first quarter of 2010 to the fourth quarter of 2020. The study found that the variables were stationary at first differencing and long-run relationships existed among them. According to the empirical findings in this study, long-run and short-run unemployment rates have a high influence on the GDP rate. However, the result contradicted one work in literature that claimed a negative association between GDP and unemployment for the past fifty years. This could have occurred as a result of the worldwide COVID-19 pandemic.
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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 | 0.002 | 0.001 |
| 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) | 0.000 | 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".