Development of the Sukuk Market in Indonesia during the Era of President Jokowi’s Administration: A Study of the Role of the Financial Market and Macroeconomics in Indonesia
Bibliographic record
Abstract
As in the case of the sukuk market in other parts of the developing world, the sukuk market in Indonesia has developed relatively well since the beginning of sukuk issuance in Indonesia. This research aims to find the determinants of sukuk market development in Indonesia, specifically during the period of President Jokowi’s leadership. How does the banking sector influence the sukuk market? And how do the bond market and stock market in the conventional financial industry influence sukuk development? The quantitative analysis used an Error Correction Model (ECM). This model explains the conditions of short-term and long-term influence of a time series model. Time series data were collected monthly from January 2018 to December 2022. The findings show that only the banking variable had a significant effect, in both the long term and short term, on the development of the sukuk market. In the long term, the banking variable was proven to have a positive effect while in the short term, the banking variable had a significant negative effect. This research also found that the stock market variable was able to produce a positive effect on the sukuk market in the long term and did not have a significant effect on short term. The research results indicate a positive long-term contribution to sukuk development in Indonesia. Nevertheless, in the short term, per capita GDP in Indonesia was not seen to have a significant influence on the development of sukuk in Indonesia. Received: 15 January 2025 / Accepted: 28 February 2025 / Published: 02 March 2025
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".