The Impact of Sukuk Issuance on Economic Growth: Evidence from Malaysia
Bibliographic record
Abstract
During the past two decades, the Islamic finance industry has fulfilled significant development, particularly in the field of Islamic financial instruments. Sukuk represents one of the most prominent tools in Islamic finance in various markets worldwide, particularly in Malaysia. The study aims to examine the impact of Sukuk issuance on economic growth spanning the period 2006–2019. Malaysia is a model where it is the largest issuer of Sukuk. By using the Cointegration Test and Vector Error-Correction Model (VECM), the results show that there is a high level of the absorptive capacity of the Sukuk market in Malaysia. Furthermore, the Johansen Cointegration Test indicates that there is a cointegration relationship between GDP and Sukuk issuance. The long-term equilibrium relationship indicates that the increase of Sukuk issuance by 1% leads to an increase in GDP by 0.04% due to the increase of investments in the sectors issuing Sukuk.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.002 | 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".