The contribution of life and non-life insurances on ASEAN economic growth
Bibliographic record
Abstract
ASEAN is one of the regions with great potential of the world economic power. Countries included in the association of Southeast Asian countries are predicted to show strong economic growth. There are many factors for the development of the ASEAN region such as insurance industry. The Southeast Asian insurance industry, with a stable and long-term financial asset commitment, could play a bigger role in supporting the region's overall economic growth. This study aims at investigating the contribution of the insurance sector measured by three parameters; namely insurance penetration, insurance density and premium volume. The research was conducted to investigate the factors which are related to the insurance industry and could affect the economic growth of 6 countries; namely Singapore, Malaysia, Philippine, Thailand, Vietnam and Indonesia, in ASEAN area over the period 2005-2015 using a fixed effect model. The result revealed that premium volume of life insurance and non-life insurance, respectively, maintained positive and significant effects on the econom-ic growth. Life insurance penetration and density also give significant effects on economic growth while non-life insurance penetration and density are not statistically significant for the economic growth.
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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 | 0.001 |
| 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".