Determinants efficiency of Vietnam’s footwear export: A stochastic gravity analysis
Bibliographic record
Abstract
This study was conducted to estimate the determinants as well as the efficiency of Vietnam’s footwear export to 50 trading partners by applying stochastic fronter gravity approach for the period 2001-2018. We found that Vietnam’s footwear export is positively affected by income measured by gross domestic product (GDP), border and landlock situation. The income elasticity of footwear export of Vietnam was about 1.2%. We also showed that the export efficiency of Vietnam’s footwear was not very high with the average ranges from 50.8% to 63.1%. The 10 most efficient countries were Cambodia, Panama, Slovakia, Belgium, Myanmar, Hongkong, Korea, Chile, the US and the Netherlands. We also found that 10 countries with the largest export potential were the US, China, Germany, Japan, Belgium, the UK, Netherlands, Korea, France, Canada. Regarding the determinants of export efficiency, the study provides evidence that trade freedom, financial freedom and importers’ population density positively contributed to efficiency. Our findings also support further integration of Vietnam since membership to many FTA enhances Vietnam’s footwear export efficiency. These FTAs include AFTA, Vietnam-Chile FTA, ASEAN-India FTA, ASEAN-Korean FTA, ASEAN-Japan FTA, ASEAN-China FTA, ASEAN-Australia-New Zealand FTA. Finally, the study recommends a relevant market policy for Vietnam’s footwear export in the coming years. We have provided 4 types of markets with different levels of priorities that Vietnam’s footwear exporters should focus on. The top footwear market priority should be countries with high potential yet low efficiency such as China, Russia, Brazil, Thailand, Sweden, Singapore and Australia.
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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 | 0.002 |
| Bibliometrics | 0.002 | 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".