Does the Covid-19 Outbreak Impacts On Economic Growth? An Evidence from Indonesia
Bibliographic record
Abstract
This study aims to analyze the effectS of the COVID-19 pandemic, labor, domestic direct investment (DDI), AND foreign direct investment (FDI) on economic growth in Indonesia. The type of data used in this study is panel data, which is a combination of cross-section and THE time series data (Silvia, 2020). The cross-section data involves 34 provinces and time-series data covers the period from the first quarter of 2018 to the second quarter of 2021. The result found out that the regression coefficient of labor has a positive and significant effect at the 5 percent level, which means that if the number of workers increases by 1 percent, economic growth will increase by 0.03 percent. Furthermore, the FDI variable also has a significant and positive effect on economic growth in Indonesia. We can see in table 3.2 that the FDI variable is significant at the 5 percent level with a regression coefficient of 0.012, this means that an increase in FDI by 1 percent will accelerate economic growth by 0.012 percent. From the results of data processing obtained by the author, it can be seen that the DDI variable has a positive but not significant effect on economic growth in Indonesia, this can be seen from the p-value which is greater than 5 percent. The regression coefficient of -0.001 proves that the COVID-19 pandemic has a negative impact on economic growth in Indonesia. When the COVID-19 pandemic reached the territory of Indonesia, economic growth slowed by 0.001 percent.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".