Multinational Enterprises and Vietnam’s Exports: Comparing Economy-wide and Firm-level Evidence
Bibliographic record
Abstract
This paper examines the role of foreign multinational enterprises (MNEs) have played in Vietnam’s exports in 1995-2014. Economy-wide estimates suggest MNE share of Vietnam’s export grew from about one quarter to about two-thirds during this period. MNE shares of GDP were much smaller (6 to 18 percent); correspondingly export-production ratios were much (4.7 to 9.6 times) higher in MNEs than in the non-MNEs sector. If comparisons are limited to formal enterprises, wholly-foreign MNEs (WFs), which account for the vast majority of MNEs in Vietnam, tend to have relatively high export propensities and account for the vast majority of MNE exports. These data thus suggest that MNEs, and particularly WFs, make unusually large direct contributions to exports in Vietnam compared to other economic activities. On the other hand, these compilations cannot establish if export propensities differ significantly among ownership groups after accounting for other, related firm-level and industry-level characteristics. Most importantly, this paper highlights several substantial problems revealed by compilations of the firm-data which much be addressed before more reliable, rigorous analysis of the firm-level data will be possible.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 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) | 0.006 | 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".