East Java Economic Model: Monetary Policy Implications in the Middle of the Covid-19 Crisis
Bibliographic record
Abstract
East Java is the epicenter of Covid-19 with the highest number of positive cases in Indonesia (11,508 cases as of June 28th, 2020) surpassing DKI Jakarta. The massive spread of the virus had economic consequences. Data showed that East Java's regional income in the third quarter of this year grew negatively by 5.90% (yoy). The contraction of East Java's economic growth will affect the national economy, known that East Java was the second largest contributors to national income, with a contribution of 14.61% in 2017 (Bank_Indonesia, 2018). East Java's economic problems as a result of the pandemic need to involve a comprehensive analysis in order to produce suitable policy implications. The purpose of this study is to predict the development of the Covid-19 case in East Java, to analyze the impact of Covid-19 on the East Java economy including economic growth, inflation, exchange rates, household and government consumption as well as effective macro policy simulations as a novelty of research. This research used descriptive analytical method by involving various simulations. Based onthe results of the study, it is known that (1) the increase in Covid-19 (2.5 times) without any government policy has an impact on the decrease in East Java's regional income by 1.31% or equivalent to 4655 billion Rupiah/quarter, (2) Increasing the number The spread of covid-19 by 2.5 times accompanied by the provision of consumption credit and agricultural credit of 5% will have an impact on increasing East Java's regional income by 0.57% or equivalent to Rp. 2,021 billion/quarter, (3) An increase in consumption credit by 15% means an increase in household consumption. 2.72% or equivalent to Rp. 5,985 billion/quarter, and (4) The policy to increase money supply by 15% has an impact on increasing public consumption by 0.69% or equivalent to Rp. 1,521 billion/quarter. Based on the results, it can be concluded that the most effective policy in order to reduce the economic impact of Covid-19 is the policy to increase the provision of credit to the public in the form of consumption credit or agricultural credit by 15%.
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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 | 0.000 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".