Economic Growth in ASEAN-4 Countries: A Panel Data Analysis
Bibliographic record
Abstract
This paper examines the impact of economic variables which are foreign direct investment (FDI), openness and gross fixed capital formation to economic growth which indicates using gross domestic product (GDP) over the period 1981- 2008. The impact of variables to GDP is estimated using three panel estimation models which are called pooled model (pooled), fixed effects model (FEM) and random effects model (REM). The findings show that all variables are correlated with each other and also have the positive relationship to GDP. Hence, all variables may lead economic growth boost when they are increase whereas FDI becomes the most efficient variable in order to assist economic growth and followed by openness and gross fixed capital formation. Otherwise, the result in Ordinary Least Squares (OLS) which implies in this study as well test all variables stationary at 5 percent level of significant. These shows only gross fixed capital formation is significant to growth and contributes the positive effect to GDP in each ASEAN-4 countries. However, OLS estimation result for Indonesia shows the other variable has significant to growth which is openness; while it gives the negative affect the GDP. Instead of Indonesia, openness is not significant at other ASEAN-4 countries such as Malaysia, Thailand and Philippines. Besides, other variable is FDI also not significant in the case of all ASEAN-4 countries. It means that, openness does not correlated to growth for Malaysia, Thailand and Philippines countries; while FDI is not correlated to growth for all ASEAN-4 countries in this study.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".