Women’s Economic Empowerment in Vietnam: Performance and Constraints of Female-Led Manufacturing SMEs
Bibliographic record
Abstract
This paper identifies whether there was a performance difference among micro, small and medium enterprises (MSMEs) led by men and by women in Vietnam during the period 2005–2013 and aims to provide explanations for the differences, if any, in various performance indicators. The paper adopts a quantitative approach using a firm-level panel dataset in the manufacturing sector in 10 provinces/cities in Vietnam in five waves from 2005 to 2013. Fixed effect models are estimated to examine the influence of firm variables and demographic, human capital characteristics of owners/managers on firms’ value added, labor productivity and employment creation. We found that men led MSMEs did not outperform those led by women on average. Although the average value added was lower for female-led firms in the informal sector, the opposite was true in the formal sector where women tend to lead medium-size firms with higher value added and labor productivity. The performance disparity was more envisaged across levels of formality and less clear from a gender perspective. Moreover, while firms owned by businessmen seemed to create more jobs, firms owned by women had a higher share of female employees. No significant difference in business constraints faced by women and by men was found.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".