The Effects of Bilateral and Multilateral Official Development Assistance on Vietnam’s Economic Growth
Bibliographic record
Abstract
This study investigates the effects of bilateral and multilateral official development assistance on Vietnam’s economic growth from 1986 to 2022. Utilizing the autoregressive distributed lag (ARDL) bounds testing approach, our results show that in the shortrun, bilateral official development assistance has a significant positive influence on economic growth, whereas multilateral official development assistance has a significant negative influence on economic growth. However, the empirical findings reveal that both bilateral and multilateral official development assistance have no influence on economic growth in the longterm. Given that bilateral official development assistance has a significantly positive impact on economic growth in the shortrun, Vietnam should strengthen partnerships with donor countries. Tailoring projects to align with bilateral donors’ interests can lead to more effective interventions. In addition, multilateral official development assistance has been found to have a negative impact on economic growth in the shortrun, possibly due to complex approval and implementation processes. Therefore, the government should advocate for more flexible project requirements and reduce bureaucratic hurdles. Simplifying the approval process can help accelerate project implementation and enhance immediate economic benefits. Moreover, because official development assistance does not impact on economic growth in the longterm, Vietnam should focus on sustainable development strategies that reduce dependency on external aid. This includes investing in human capital, innovation, and technology to foster endogenous 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 | 0.004 |
| 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) | 0.003 | 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".