Panel data analysis of foreign direct investment, control of corruption, and economic growth: Evidence from ASEAN-6 countrie
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This research is to examine the role of management of foreign direct investment and control of corruption in economic growth. The research data were collected from the ASEAN-6 countries including Indonesia, Malaysia, Thailand, Singapore, Philippines, and Vietnam during the period of 2002-2021. The research utilizes the panel vector autoregressive (PVAR) method developed by Abrrigo and Love (2015) [Abrigo, M. R. M., & Love, I. (2016). Estimation of panel vector autoregression in Stata.] to estimate the research model. The estimation results show that foreign direct investment and corruption control play an important role in promoting economic growth in the ASEAN-6 countries. Furthermore, foreign direct investment and corruption control are closely related to each other, indicating that economic growth is not only directly affected by foreign direct investment and corruption control but also indirectly influenced by each of these factors. This is a new finding of this research compared to previous studies. These findings provide significant empirical evidence for the ASEAN-6 countries, particularly in managing foreign direct investment and controlling corruption to promote economic growth. The implication of these results is that these countries identify appropriate policies to manage FDI and corruption control in order to maximize the level of economic growth.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it