A Gender-Sensitive Assessment of COVID-19’s Impact on Vietnamese Households: Evidence from the Textile, Garment, and Hospitality Sectors
Bibliographic record
Abstract
Abstract The COVID-19 pandemic has had profound impacts on the Vietnamese economy, with particularly severe consequences for the textile and garment industry and the hospitality sectors. This paper draws on data from a nationally representative phone survey conducted in 2021, encompassing 998 households, as well as a follow-up survey in 2022 that re-interviewed 777 of those households. The study focused on households with members employed in the aforementioned sectors between May and July 2021. The findings reveal notable gender disparities in the socioeconomic impact, particularly in relation to employment, income loss, and unpaid care responsibilities. One year after the onset of the crisis, many households, especially female-headed ones, continued to experience economic hardship, with income recovery lagging behind employment recovery. Women continue to bear a disproportionate burden of unpaid care work, underscoring persistent gender inequalities. This study calls for the urgent need for gender-responsive policy interventions that recognize and address these disparities, including enhanced support for unpaid care work, improved fiscal sustainability of social transfer programs, and the integration of citizen data through digital platforms to facilitate inclusive and equitable recovery.
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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 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".