Financial system stability in Indonesia and its relationship with economic growth before and during the Covid-19 pandemic
Bibliographic record
Abstract
The purpose of this study is to assess the condition of financial system stability in Indonesia both before and during the Covid-19 pandemic and to look at its relationship with economic growth. This study develops six sub-sector groups described in 19 indicators in order to assess the condition of the financial system. Quarterly data for 6.5 years from Quarter I 2016 to Quarter II 2022 was evaluated. To assess the condition of the financial system, this study uses a composite index approach with the normalized max-min method. The correlation analysis method is used to assess the relationship between the index of financial system conditions and economic growth. The results showed that during the pandemic, there was a more significant increase in pressure on financial conditions than before the pandemic. The financial system instability index during the pandemic in the second quarter of 2020 was 3 times higher than the average and more than 5 times higher than the same quarter in 2019. In addition, the relationship between the financial condition index and economic growth is at 0.77 (strong category). The implication is this research can provide insight to the government, financial institutions, and the public regarding the condition of financial system stability before and during the Covid-19 pandemic. This research suggests that the government should control credit restructuring policies during the pandemic and strengthen financial institutions. This research has limitations in terms of objects that only include conventional financial institutions. Further studies can use other objects such as Islamic financial institutions.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 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".