The Empirical Effects of Islam on Economic Development in Malaysia
Bibliographic record
Abstract
The studies on the effects of religion on economic development and growth had intrigued the economists to further develop quantitative plausible hypotheses for empirical testing. In Malaysia, albeit implementation of Islamic based policy in economic development had been started since 1969 with the inception of the Pilgrimage Management and Fund Board (Tabung Haji), and recently with the development of Islamic banking and takaful sector; there was very little evidence on the effectiveness of such policy that had been reported by the researchers over the last 40 years. This study was to fill the gap and to review the previous research findings on relationship between Islam and economic development. The objective was to investigate the empirical effects of Islam on economic development in Malaysia. The subject of Islam and economic development had raised a hypothesis whether there was a level relationship in short and long-run equilibria. The Islamization index was employed as a proxy of Islam. Real per capita GDP is used as the indicator of economic development. An autoregressive distributed lag (ARDL) approach was utilized for empirical analyses based on 42-year dataset from 1969 to 2011. Basically, the results from the cointegration analyses using ARDL approach suggested that Islam, as a religion of the population, had a significant effect on economic development in Malaysia.
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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 | 0.004 |
| 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 | 0.001 |
| 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".