Covid-19 and The Effectiveness of Monetary Policy Quantitative Easing Indonesia
Bibliographic record
Abstract
The basis for this research is to examine the impact of the Covid-19 pandemic on Indonesian financial markets and the dynamics of monetary policy. Focus on monetary policy conducted by Bank Indonesia regarding Quantitative Easing . The study was conducted using balance sheet information from a sample of 38 private foreign exchange banks in Indonesia to then find out whether policies have an effect on " loan growth ", and how the response to bank loan growth is in responding to the crisis episode caused by the COVID-19 pandemic . The data is taken from the first quarter of 2019 to the second quarter of 2021. Using panel data, a cross section of thirty-eight banks and time series data . This research found. First, the securities of Bank Indonesia conducted Quantitative Easing at the beginning of the year, until September 2020 amounting to Rp. 666 trillion resulted in an increase in total banking assets. Second, the Covid-19 factor has become a significant negative factor for credit growth amid the QE policies that have been carried out by Bank Indonesia. Third, the total bank deposit has a negative correlation with the rate of credit growth but is not significant. Finally, the overall analysis of this research shows that QE (Quantitative Easing) has a small impact on loan growth ( loan growth ) even though it has included the bank health factor ( Bank Health ) which represents the trust factor in the banking system.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".