Covid-19 in indonesia: Socio-economic impact and policy response
Bibliographic record
Abstract
The 2019 Covid-19 Corona virus has had an extremely strong impact in 2020, including Indonesia, on the dynamics of the global economy. In this study the focus is on examining the impact of Covid-19 in Indonesia on GDP growth, Micro Small Medium Entrepreneurs (MSMEs), the tourism sector, employment, and the poverty rate. With the limitation of international and national mobility, it will have a major impact on the level of GDP growth, and the tourism sector which has a large enough contribution and is linked to unemployment and poverty. Economic growth slowed to 2.97% in the first quarter of 2020 and contracted by 5.32% in the second quarter of 2020 (Bank Indonesia, 2020). The Indonesian economy in the fourth quarter of 2020 against the previous quarter experienced a growth contraction of 0.42 percent. Based on a survey conducted by Statistics Indonesia (BPS) of MSMEs in various regions in Indonesia, there were as many as 84 percent of micro and small businesses and 82 percent of medium and large businesses experiencing a decline in income since the Covid-19 pandemic occurred. During 2020, the number of foreign tourist visits to Indonesia reached 4.02 million visits or decreased by 75.03 percent when compared to the number of foreign tourists visiting in the same period in 2019 which totaled 16.11 million visits. The Covid-19 pandemic caused the open unemployment rate which was suppressed at 5.23 percent to increase by 7.07 percent. The percentage of poor people in September 2020 was 10.19 percent, an increase of 0.41 percentage points against March 2020 and an increase of 0.97 percentage points compared to September 2019. The Indonesian government issued various policies in response to Covid-19, including policies for the business world, policies for MSMEs, and policies for the poor in addition to several other policies. © 2021 Karadeniz Technical University. All rights reserved.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".