FDI and Economic Growth: Does WTO Accession and Law Matter Play Important Role in Attracting FDI? The Case of Viet Nam
Bibliographic record
Abstract
This study focused on the impact of FDI on economic growth in the entire of Vietnam and in the provinces which are ranked differently on socio - economic conditions. Based on a panel dataset of 64 provinces and cities in Vietnam and used the fixed - effects estimation method for econometric models, the empirical results show that FDI has a positive impact on economic growth of Vietnam in the period 2000 - 2010. This effect in the provinces with better socio - economic conditions was stronger than in the provinces with worse socio- economic conditions. Promulgating Unified Enterprises and amending Investment Law in 2005 as well as accessing to WTO in 2007 have affected positively in attracting FDI in the period 2006 - 2010. However Law factor has a more positive and stronger impact on FDI attraction of Vietnam than WTO accession. In addition, the study examines the impact of FDI on economic growth by different regions in Vietnam. The results show that FDI has a positive impact on economic growth only exists in 4 of 6 regions of Vietnam in the period 2000 - 2010.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".