The Role of Economic Integration Policies in Increasing Economic Growth in Selected Southeast Asian Countries
Bibliographic record
Abstract
Southeast Asian countries have come together to form the Association of Southeast Asian Nations (ASEAN), especially the formation of the ASEAN Economic Community (AEC) in 2015, which has united countries into an AEC economic bloc. The aims of the study are to assess the impact of integration policies and the role of the AEC on economic growth during the period 1970–2022. Using quantitative analysis methods through OLS, FEM, REM and long-term impact analysis through the ARDL panel, the research results show that a higher level in economic integration is consistent with a higher level of economic growth. Specifically, FDI has a positive impact on economic growth in the short term and the positive impact is stronger in the long term. At the same time, trade openness has a negative impact on growth in the short term, but this effect is no longer in the long term. The result affirms the very positive nature of the AEC for international integration and contribution to economic growth in the Southeast Asian region. Finally, this study has some policy implications for Southeast Asian countries in the context of implementing economic integration policies and setting growth targets.
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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 | 0.002 | 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".