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This chapter delves into the profound impact of COVID-19 on Vietnamese households, with a particular focus on the textile, garment, and hospitality sectors. It explores the dual shock of the pandemic—public health crises and economic downturns—and how these shocks disproportionately affected women, who bore the brunt of job losses and increased unpaid care responsibilities. The study employs a gender-sensitive analytical framework to assess the pandemic's effects on household welfare, including income and consumption. It also evaluates the adequacy of social policy interventions in supporting recovery. Key findings include the severe employment disruptions experienced by these sectors, the persistent gender gap in unpaid care work, and the uneven recovery of household incomes. The chapter concludes with policy recommendations aimed at promoting a more inclusive and gender-responsive recovery, emphasizing the need for expanded vocational training, improved access to employment, and investments in childcare services and flexible work arrangements. By examining the pandemic's multifaceted impacts and offering actionable policy suggestions, this chapter provides valuable insights for professionals seeking to understand and address the long-term effects of COVID-19 on vulnerable households.
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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.
9.1 Introduction
Prior to the COVID-19 pandemic, Vietnam’s textile and garment industry and hospitality sector had experienced two decades of rapid growth, emerging as key drivers of the country’s economic development. By 2019, textile and garment exports had surged to $32.80 billion—a remarkable increase from $1.96 billion recorded in 2001, when Vietnam signed the Vietnam–U.S. Bilateral Trade Agreement. Meanwhile, the hospitality sector achieved a milestone in January 2020, welcoming over 2 million international tourists in a single month for the first time. These two sectors formed the backbone of Vietnam’s manufacturing and service industries, playing a vital role in boosting job creation, export performance, and overall economic growth (Figs. 9.1 and 9.2).
Before the onset of COVID-19, tourism accounted for 55–67% of Vietnam’s total service exports, while the textile and garment sector represented 13–15% of manufacturing exports. However, both sectors were particularly vulnerable to external shocks due to their dependence on global trade and tourism flows. When COVID-19 struck, these vulnerabilities were exposed, triggering a severe contraction. By 2021, tourism’s share of service exports had collapsed to just 1.9%—its lowest level on record. Similarly, the textile and garment sector’s share in manufacturing exports fell to 10.1%—illustrating the depth of disruption (see Fig. 9.3).
Fig. 9.3
Export of tourism service and textile and garment, 2014–2023.
The pandemic imposed a dual shock on both sectors. The first was a public health crisis, involving strict containment measures, such as social distancing, quarantines, lockdowns, and transportation bottlenecks that paralyzed tourist hubs and industrial zones. The second was an economic shock, characterized by a sharp decline in household income due to widespread job losses and reduced working hours. This decline in purchasing power significantly dampened domestic demand for tourism and reduced international orders for garments exacerbating supply chain disruptions and rising input costs.
The textile and garment industry, in particular, faced severe challenges including supply chain disruptions, rising input costs, and declining domestic consumption. Many factories were forced to shut down temporarily due to social distancing mandates. Simultaneously, Vietnam’s tourism sector was effectively frozen for nearly 19 months, with the suspension of international travel, occupancy rates fell to as low as 5%, and up to 95% of travel firms ceasing operations. Even after major outbreaks subsided, the recovery of domestic tourism remained sluggish, as the entire service supply chain for tourism activities had been stagnant for an extended period.
9.1.2 Sector Recovery and Labor Market Impacts
Vietnam began reopening in late 2021 through a phased pilot program implemented in key tourist destinations—including Phu Quoc (Kien Giang), Khanh Hoa, Da Nang, and Quang Nam. By 2022, the tourism sector showed clear signs of rebound, with international arrivals reaching 3.66 million and domestic tourist numbers surging to 101.3 million (TIC, 2022). Similarly, the textile and garment industry experienced a strong recovery. Export revenues rose significantly, reaching USD37.6 billion in 2022—surpassing pre-pandemic levels (see Fig. 9.3).
Nevertheless, the labor market impact was significant. The disruptions caused by the COVID-19 pandemic in 2020 led to a substantial reduction in employment across both the tourism and textile/garment sectors in Vietnam. According to data from the General Department of Tourism, the dual shock of the pandemic affected approximately 2.5 million workers in the tourism sector (800,000 directly) and 3 million in textile, garment and footwear industries were affected (Ha, 2022; Tran, 2021). The most severely impacted were tour guides, hotel and motel staff, workers at tourist attractions, and employees in travel, transportation, cruise, and other tourism-related businesses. As a result, the households of these affected workers faced significant economic and social challenges during the pandemic.
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9.1.3 Gender-Sensitive Policy Considerations
Women in both industries bore a disproportionate burden during the crisis. Not only did they experience higher rates of job loss, but they also assumed increased unpaid care responsibilities as families adapted to financial pressures. While the government introduced social support measures aimed at assisting impacted workers and their families, critical questions remain regarding their gender sensitivity, accessibility, and effectiveness.
9.1.4 Scope of This Paper
This paper investigates the impact of the COVID-19 shock on household welfare in Vietnam, with a particular focus on households with members employed in the hospitality and textile sectors. It also examines the role and adequacy of social policy interventions in supporting recovery. The remainder of the paper is structured as follows: Sect. 9.2 outlines the analytical framework used to examine the gender-sensitive impacts of shocks. Section 9.3 analyzes household-level effects and coping strategies. Section 9.4 evaluates the policy response. Section 9.5 concludes with recommendations aimed at promoting a more inclusive and gender-responsive recovery in Vietnam.
9.2 Analytical Framework for a Gender-Sensitive Study
9.2.1 COVID-19 Shock and Household Welfare
The study assesses the gender-differentiated impacts of the COVID-19 pandemic on household welfare in Vietnam, with a particular focus on income and consumption (see Fig. 9.4). The analytical framework employed in this study builds upon a model previously developed and piloted by the author and her research team at the Centre for Analysis and Forecasting (CAF) in earlier research on household-level experiences during the early phase of the COVID-19 pandemic in Vietnam (United Nations Development Programme [UNDP], 2020, 2021). Unlike earlier studies, the present research applies the framework to two sectors—textile and garment, and hospitality—which were among those most severely impacted by COVID-19. Leveraging nationally representative survey data collected in 2021 and 2022, this paper offers a gender-sensitive analysis of household-level recovery from the pandemic.
Fig. 9.4
Analytical framework on COVID-19 impact on household welfare. Note Author’s figure for the survey implementation in 2021–2022
The COVID-19 crisis affected household welfare through two primary channels: economic and non-economic. The economic shock primarily stemmed from a sharp decline in demand for tourism services and manufacturing orders from export markets. This led to widespread disruptions in employment, which are categorized into three main types: (1) job loss, (2) temporary leave from work, and (3) reduced working hours—all of which contributed to a reduced household income. The nature and extent of these job-related impacts were not gender neutral, as men and women occupy different types of jobs and skill levels. Higher-skilled individuals—particularly those with digital literacy and access to technology—were more likely to adapt by shifting to remote work, skewing resilience outcomes along gender lines.
The non-economic shock was driven by public health measures such as social distancing, quarantine, and lockdowns, which disrupted daily life and significantly increased unpaid care work (UCW) within households. These disruptions affected men and women differently, with women typically bearing a disproportionate share of the increased care burden. The impact on UCW can be observed across several dimensions, including: (1) facilitating children’s online education; (2) accessing healthcare and medical services; (3) procuring daily necessities such as food and household supplies; (4) providing care for young children, the elderly, or ill family members; (5) engaging in agricultural activities for household self-consumption; and (6) undertaking household-level manufacturing tasks such as furniture repair. The inability to access market-based services further compounded the care burden. As with paid employment, those with sufficient resources and digital access were better positioned to shift some UCW responsibilities to online alternatives.
Given that men and women tend to occupy different roles in both paid employment and unpaid domestic labor, the overall impact of the shock on household welfare—including income and consumption—is likely to be gender-differentiated. To explore intra-household gender dynamics, the analysis compares male-headed and female-headed households. It assumes the household head plays a central role in managing resources and decision-making. Employment and UCW responsibilities are analyzed with respect to this headship to understand how gendered leadership affects household resilience.
Job mobility during the pandemic was constrained by both structural and psychological barriers. Individuals with higher skill levels, stronger professional networks, better access to labor market information, and fewer household responsibilities are generally more capable of maintaining stable income or seeking job advancement, even under challenging conditions. These determinants of job mobility are, however, inherently gender-sensitive. Women, for example, often face greater constraints due to their disproportionate share of UCW and reduced access to job-related resources. In the context of the COVID-19 shock, additional factors further limited job mobility across both genders. Factors such as limited savings, inadequate qualifications, pessimism about job availability, and reluctance to change employment all contributed to labor immobility—again with gendered implications. These constraints, combined with existing gender disparities, reinforce the importance of analyzing the impact of the COVID-19 shock through a gender-sensitive lens—particularly in terms of household income dynamics and labor market behavior.
Households that experienced income reductions and fell into poverty during the pandemic often relied on coping strategies such as depleting savings, taking on debt, selling assets, or cutting consumption. Access to cash transfers and other social supports was uneven and shaped by gendered vulnerabilities. Men and women often relied on different types of social networks to cope with income shocks, and faced varying levels of exclusion from support schemes. Consequently, both the demand for and perceived effectiveness of social policy support are likely to be gender-differentiated. An observed increase in household income following an initial decline serves as an indicator of recovery, but the pace and extent of recovery may differ significantly across gendered household types.
This study acknowledges several limitations. First, it does not employ econometric modeling to formally test or strengthen the descriptive results. Second, the absence of a qualitative component limits the exploration of underlying mechanisms that drive observed trends, such as why certain households adopt specific coping strategies or perceive social support as more or less effective. Additionally, impacts are measured using binary indicators (e.g., whether a shock occurred) rather than capturing severity, due to constraints of phone-based survey methods during lockdowns.
9.2.2 Data Collection
This study relies on data from a telephone-based panel survey, referred to as the survey implementation 2021–2022, which the author managed and implemented by her research team at CAF. The survey was designed to investigate the gendered household impacts of COVID-19 on workers in two vulnerable sectors: the textile and garment industry and the hospitality sector. By examining employment disruptions, income variations, and household dynamics, the study provides insights into gender-specific economic vulnerabilities.
The first round of data collection was conducted in mid-2021 and included 998 households across Vietnam. The second round, conducted in mid-2022, successfully re-interviewed 777 of those households, resulting in an attrition rate of approximately 22%. While attrition is common in panel studies, the sampling design was based on the large-scale, systematically randomized 2018 Vietnam Household Living Standards Survey conducted by the National Statistics Office (NSO). This rigorous approach ensured the retained sample remained representative of households affected by sector-specific employment shocks.
9.2.2.1 Sampling Approach and Representativeness
The study sample was drawn from a pool of 8,453 households, with at least one member working in either (1) the textile and garment sector or (2) the hospitality sector during the period from May to July 2021. These households were originally identified from the 2018 Vietnam Household Living Standards Survey (VHLSS 2018, covering 45,838 households), conducted by NSO. The sample is nationally representative of workers in these two sectors (see Fig. 9.5).
Fig. 9.5
Survey site map. Note Author’s figure based on the survey implementation in 2021–2022, using online Google Map to plot the data
The sampling method ensured proportional selection probabilities based on household characteristics such as female composition and dependent members, which are key indicators of vulnerability. To minimize selection bias, a multi-stage replacement sampling strategy was implemented.1 Households that could not be successfully contacted or refused participation were systematically replaced based on pre-established demographic criteria.
9.2.2.2 Weighting and Demographic Adjustments
The final sample design incorporated weighting adjustments to reflect the relative importance of targeted households within the broader population. Two primary factors were used to refine sample selection: (1) Household gender composition—prioritizing households with female-headed structures or high unpaid care burdens; and (2) Dependents in the household—including children under five and older adults aged 65 or more, ensuring vulnerable groups were adequately captured. These refinements ensure alignment between the sampling strategy and the study’s core research objectives—particularly its focus on gender-sensitive labor impacts and household economic resilience during the COVID-19 pandemic.
Table 9.1 presents the distribution of surveyed households across sectors in 2021.
Table 9.1
Distribution of survey sample by sector in 2021 (% of households)
Household sector composition
% of households
Members working in textile and garment only
45.7
Members working in hospitality only
53.3
Members working in both sectors
1.0
Total
100
9.3 COVID-19 Impacts on Household Welfare in Vietnam
9.3.1 Economic Impact Channel: Job Disruptions
Findings from the Survey 2021–2022 indicate widespread labor market disruptions in 2021, with most households reporting that members experienced temporary leave from work, reduced working hours, or layoffs (see Fig. 9.6). By 2022, however, the majority of households had shown signs of employment recovery, reflecting an improving labor market outlook.
Fig. 9.6
Employment impact by gender in 2021 (% of households).
The outbreak of COVID-19 and the implementation of social distancing measures significantly disrupted employment, often affecting households through multiple types of job-related shocks. The largest group (35.9%) reported members simultaneously experiencing temporary leave from work and reduced work hours. Another 15.7% of households had at least one member placed on temporary leave only, while 13.4% were affected solely by reduced hours. A smaller but notable group (14.2%) reported experiencing all three types of disruptions: layoffs, temporary leave, and underemployment. Only 2.8% were impacted by layoffs alone, and 12.3% reported no employment disruption.
In 2021, 36.9% of female-headed households experienced the two most common job-related impacts: temporary leave and reduced hours. This was slightly higher than that of male-headed households, at 35.3%. Female-headed households also appeared to be more vulnerable overall, exhibiting higher rates of layoffs and exposure to multiple simultaneous shocks. The proportion of female-headed households with members who were laid off was 3.2%, compared to 2.6% for male-headed households. Additionally, 17.7% of female-headed households experienced all three types of employment shocks, compared to 12.4% of male-headed households.
By 2022, the follow-up survey showed signs of labor market recovery. Approximately, 93.9% of households reported no longer experiencing employment impacts from COVID-19 (see Fig. 9.7). Still, some disruptions persisted: 2.8% of households continued to report all three types of labor shocks, while 2.3% experienced both temporary leave and underemployment. However, recovery remained gendered. In 2022, 14% of female-headed households reported continued severe employment disruptions, compared to less than 5% of male-headed households. This persistent disparity underscores the need for gender-responsive labor policies during recovery.
Fig. 9.7
Employment impact by gender in 2022 (% of households).
Sectoral differences were also significant. In 2021, 93.0% of households in the hospitality sector experienced employment impacts, compared to 80.7% in the textile and garment sector. Moreover, the severity of employment impacts was greater in hospitality. A total of 17.8% of those households experienced all three types of disruptions, which was nearly double the proportion observed in the textile and garment sector (9.3%). By 2022, although conditions improved, disparities between sectors persisted. Approximately, 9.4% of hospitality-sector households continued to report employment disruptions, compared to only 2.1% in the textile and garment sector. These findings suggest that the hospitality sector experienced both more severe and more prolonged labor market impacts from the COVID-19 pandemic (Fig. 9.8).
9.3.2 Non-economic Impact Channel: Unpaid Care Work (UCW)
More than half of the surveyed households reported an increase in unpaid care work (UCW) during the pandemic—particularly regarding children’s education, family healthcare, and daily shopping. These UCW demands rose significantly during the COVID-19 waves of 2020 and 2021 due to limited access to schools, healthcare, and essential services under lockdown measures. The most common difficult was supporting children’s education, reported by 41% of households in both April 2020 and May 2021 (see Fig. 9.9). In contrast, households that were able to adapt effectively to digital technologies—transitioning to online platforms for work and study—were less likely to experience such difficulties. These findings underscore how digital access and adaptability influenced the extent of UCW burdens during the pandemic.
Fig. 9.9
UCW impact due to COVID-19 by gender (% of households).
Note Education: Caring for children’s education; Health: Accessing health services; Shopping: Daily food shopping; Home care: care for children/elderly; Agriculture: UCW in farming; Manufacture: UCW in home production.
In April 2020, 59.8% of households reported increased UCW, rising to 63.7% in May 2021, reflecting the continued impact of the pandemic during periods of social distancing. However, by September 2022, when socioeconomic activities had largely resumed and normalization was underway, the proportion of households reporting increased UCW dropped to 24.3%. Of those, only around 6% reported serious ongoing difficulties.
Interestingly, male-headed households consistently reported higher rates of UCW-related difficulties throughout the pandemic. In April 2020, 60.8% of male-headed households compared to 54.9% of female-headed households; 64.6% versus 59.6% in May 2021 reported increased burdens. This gap widened slightly in May 2021 (64.6% vs. 59.6%, respectively) but narrowed considerably by September 2022, reaching 24.6% for male-headed and 22.3% for female-headed households.
In 2021, 59.9% of households reported that UCW was shared equally. However, 28.2% said that women held the primary responsibility, compared to only 11.9% attributing that responsibility to men. By 2022, this imbalance persisted: 31.2% identified women as the main providers of UCW, versus 14.7% for men (see Fig. 9.10). Among female-headed households, women were significantly more likely to report being the sole UCW providers—a reflection of both household structure and persistent gender norms.
The gender gap in UCW was most pronounced in health-related responsibilities, with a 26.7% point difference in 2021 and 20.8 in 2022. This was followed by children’s education (22.9 and 21.9% points, respectively). Even the smallest gaps—caring for young children and the elderly—remained significant (17.4 and 19.4% points). These findings underscore how pandemic conditions intensified structural imbalances in domestic labor. Overall, the gender gap in UCW remained nearly unchanged: 16.3% points in 2021 and 16.5% points in 2022.
9.3.3 Impact on Job Mobility
It is important to note that the Survey 2021–2022 was unable to re-interview 22.4% of the original sample in the second round conducted in 2022. However, the survey successfully captured labor mobility trends among the remaining respondents. The greater employment disruption experienced in the hospitality sector in 2021 corresponded with a higher rate of labor mobility among affected households in 2022 (see Fig. 9.11). Approximately, one-third of workers in the hospitality sector and 21% of those in the textile and garment sector transitioned to jobs in other sectors. In total, 26.1% of surveyed households in 2022 had previously worked in either the garment or hospitality sector but had since moved into other areas of employment. Conversely, 41% of households reported no job mobility, citing various reasons for remaining in the same sector, including a lack of opportunities, risk aversion, or anticipation of sector recovery.
A shortage of skills emerged as the most common reason cited for the lack of job mobility, followed closely by the intention to wait until the pandemic subsided. In 2021, the largest proportion of respondents (33%) reported that temporary jobs were unavailable, discouraging them from seeking new opportunities. Meanwhile, 31.6% stated that they lacked the skills necessary to transition to another job (see Fig. 9.12). Additionally, 29.2% opted to wait for the pandemic to pass, relying on household savings to cope with economic difficulties. A smaller proportion—7.4%—reported that the burden of unpaid care work (UCW) prevented them from searching for new employment.
Fig. 9.12
Reasons for no job mobility in response to the COVID-19 pandemic (% of respondents).
By 2022, survey data revealed a shift in these dynamics. The share of respondents citing reasons such as waiting for the pandemic to pass, living temporarily off savings, or fear of change declined sharply. However, the skills gap persisted as the most commonly reported barrier to job mobility. In 2022, 45.6% of respondents identified a lack of skills as the primary obstacle. These findings highlight the need for targeted policy interventions to promote vocational training and facilitate employment transitions—particularly for individuals who were laid off or experienced income loss during the pandemic. This can be achieved through accessible training channels, such as online learning platforms, mobile applications, and television-based educational programs, to ensure broad reach and inclusivity.
The reasons for limited job mobility differed more significantly by gender than by sector (see Fig. 9.13). Notably, men appeared more vulnerable than women, with a higher proportion of male respondents reporting a lack of skills as the primary barrier to job mobility—a gap that widened over time. In contrast, differences by sector were more pronounced in 2021 but diminished by 2022. That year, households in the hospitality sector were particularly vulnerable compared to those in the textile and garment sector. These households reported not only limited access to temporary employment and insufficient skills for alternative jobs, but also reduced ability to rely on savings as a coping mechanism during the pandemic. However, by 2022, following substantial job mobility, sector-based differences had largely disappeared, suggesting that the immediate vulnerabilities in the hospitality sector had diminished as workers transitioned into other forms of employment.
Fig. 9.13
Reasons for no job mobility in response to the COVID-19 pandemic by gender and sector (% of respondents).
Average household income showed clear signs of recovery by 2022 (see Fig. 9.14). In May 2021, a year after the initial COVID-19 outbreak, households remained in a financially challenging position. Between the peak of the crisis in April 2020 and May 2021, household income declined modestly to an average of VND 1.84 million. However, income recovery became evident by September 2022. The average income rose to VND 2.535 million in March 2022, and continued to increase, reaching VND 2.782 million by September 2022. This upward trend was observed consistently across population groups, indicating a broad-based recovery.
Fig. 9.14
Real income per capita (million VND).
Note Based on the formality of the household head’s main job, defined as having a formal contract with compulsory social insurance contribution.
Throughout all survey rounds, the impact of the pandemic on income did not differ significantly by the gender of the household head, although female-headed households consistently reported lower income levels compared to male-headed counterparts. Notably, the income decline among female-headed households in May 2021 was marginal, suggesting that while disparities persisted, the pandemic did not significantly worsen them.
Despite improvements in employment, the recovery in income remained uneven. This was reflected in a sharp decline in the proportion of households reporting only minor income decreases (within 10%) in 2022. According to the survey, approximately one-third of households experienced an increase in income during the same period. However, another one-third reported significant losses, with income decreasing by more than 50% within a six-month period (see Fig. 9.15). These findings underscore the continued vulnerability among a substantial portion of the population, even as the broader labor market shows signs of stabilization. The persistence of these disparities highlights the need for ongoing monitoring of income inequality and targeted support for the most affected groups.
A comparison of household income in May 2021 and April 2020 shows that 12.7% of households experienced a severe income decline of more than 50%, identifying them as the most heavily affected. At the same time, approximately one-third of households experienced only a slight decrease of around 10%, while 27.9% reported an increase in income during 2021. However, by 2022, this pattern shifted considerably. Within six months, the share of households experiencing an income decrease of more than 50% rose to 23.9% by September 2022, up from March 2022. In contrast, the proportion of households reporting only a slight income decrease of 10% fell sharply from 34.2% to just 6.5%. These results suggest that despite aggregate recovery, a significant portion of the population remained vulnerable to economic shocks, emphasizing the importance of continued targeted support for the most affected groups.
Based on the poverty line established by the Ministry of Labour, Invalids, and Social Affairs (MOLISA) for the 2021–2025 period, the national poverty rate increased from 48.4% in April 2020 to 49.6% in May 2021, reflecting the protracted impact of the COVID-19 pandemic (see Fig. 9.16). This modest increase was concentrated among rural households, those engaged in the informal sector, male-headed households, and households with high dependency ratios. However, robust income recovery in 2022 enabled most vulnerable groups to surpass the poverty threshold. The poverty rate declined to 27.2% by March 2022, and further to 21% by September 2022. One notable exception was among formal workers, who experienced a slight increase in the poverty rate—from 13.5 to 15.1%—though this change was not statistically significant.
Fig. 9.16
Poverty rate based on the MOLISA poverty line 2021–2025 (% of households).
The most substantial poverty reductions were observed among informal sector workers and rural households. Despite these improvements, gender disparities in poverty widened in 2022. Female-headed households reported a poverty rate of 27.4%, significantly higher than the 20.2% recorded for male-headed households. These findings suggest that the negative income impacts of the pandemic were more persistent for female-headed households, reinforcing their greater vulnerability during the recovery process.
9.3.5 Household Coping Measures and Impact on Household Consumption
Cutting expenses emerged as the most widely used coping strategy during the COVID-19 pandemic, with approximately half of all households reporting this approach. In addition, around one-fifth of households indicated that they used savings or acquired new loans to manage the economic impacts of the crisis. Only a small share of households resorted to more severe or less accessible coping strategies, such as selling valuable assets, postponing the payment of living expenses, or rescheduling existing debt. These findings indicate that while most households relied on immediate adjustments, such as reducing expenditures or accessing financial reserves, fewer engaged in strategies that might compromise long-term financial stability (Fig. 9.17).
Fig. 9.17
Measures to respond to the COVID-19 pandemic by gender (% of households).
As of April 2020, at the peak of the pandemic, the majority of affected households responded to income reduction by adopting the strategy of cutting expenses. Specifically, 67.6% of households reported reducing their spending. Of these, 42% cut expenses by less than 10%, 30.8% reduced spending by 10–30%, and 12% reported reductions exceeding 30%. By May 2021, the situation had only slightly improved, with the proportion of households reporting reduced spending decreasing marginally to 65.2%—a drop of 2.4% points. However, by September 2022, the trend showed a more significant recovery. The share of households reducing their expenses fell by 20% points compared to April 2020. More specifically, the proportion of households cutting spending by less than 10% decreased from 42% in April 2020 to 17.1% in September 2022. Similarly, the share of households that cut expenses by 10–30% fell from 30.8 to 18.9%. The percentage of households reducing expenses by more than 30% showed only a slight decline, from 12 to 11%. These findings suggest a gradual easing of financial strain by 2022, although a substantial proportion of households continued to rely on cutting expenditure as their primary coping mechanism.
In May 2021, there was a slight decline of 3% points in the proportion of households relying on savings for household consumption compared to April 2020. Simultaneously, the use of new loans increased marginally by 1.5% points, suggesting a shift in coping behavior as the pandemic persisted. As time progressed, reliance on both strategies declined. The proportion of households using savings fell sharply from 24% in April 2020 to just 6.8% in September 2022. Similarly, the proportion of households taking out new loans fell from 20 to 11.9% over the same period. These declines may reflect both an easing of financial distress and the depletion of accessible resources, such as household savings or available credit.
Only a small proportion of affected households—around 2%—reported selling valuable assets as a coping strategy in 2021. This low figure may be due to a lack of disposable assets or a reluctance to liquidate them under duress. By 2022, this coping measure was rarely reported, suggesting it was used only as a last resort.
A gender difference in coping strategies was evident during the 2021 outbreak. A higher proportion of female-headed households relied on savings for household consumption compared to male-headed households. Conversely, a slightly higher rate of male-headed households reported reducing expenses. By 2022, gender differences in expenditure reduction had largely disappeared. However, female-headed households continued to report a higher reliance on savings, indicating greater financial vulnerability and a more limited set of alternative coping mechanisms.
9.4 Support During the COVID-19 Pandemic Outbreak
Since the outbreak of the COVID-19 pandemic, the Vietnamese government implemented several emergency social protection measures aimed at supporting affected populations. In 2020, as the pandemic began to spread, the government introduced its first large-scale intervention through Resolution No. 42/NQ-CP, dated April 9, 2020.2 This initiative introduced a VND 62 trillion support package designed to aid individuals facing economic hardship. The package marked an unprecedented step in Vietnam’s social protection policy. However, its implementation encountered several critical challenges. While social welfare beneficiaries—such as those registered as poor or in need of social assistance—were eligible, workers with disrupted employment, particularly those in the informal economy, encountered significant barriers to accessing aid. One of the most notable issues was that individuals without permanent residence registration struggled to qualify for or receive support.
As documented by UNDP (2020), the first support package suffered from several key limitations:
1.
Inadequate forecasting of the pandemic’s impact on employment;
2.
Burdensome administrative procedures and overly restrictive eligibility criteria;
3.
Benefit levels insufficient to meet household needs;
4.
Ineffective communication and outreach;
5.
Weak local fundraising capacity and uncertainty among authorities regarding beneficiary identification.
These limitations hindered the effectiveness of the support program, especially for informal and migrant workers, highlighting the need for more adaptive and inclusive social protection mechanisms in times of crisis.
In 2021, following widespread criticism of the initial social support package, the Vietnamese government introduced a second emergency relief measure to better address the evolving needs of workers and employers. Enacted through Resolution No. 68/NQ-CP (dated July 1, 20213), which outlined multiple support measures, and Decision No. 23/2021/QD-TTg (dated July 7, 20214), which detailed the regulatory framework for implementation. With a total budget of VND 26 trillion, this second package sought to expand coverage, streamline procedures, and increase inclusivity. Key components included one-time cash assistance to various categories of affected individuals:
1.
VND 3.71 million per formally contracted worker,
2.
VND 3 million per household business owner, and
3.
VND 50,000 per day (or VND 1.5 million per month) for workers without a labor contract.
This second package represented a more targeted attempt to reach informal workers and small-scale business owners, groups largely excluded from earlier relief efforts.
Despite these improvements, the second relief package’s one-time support was broadly viewed as insufficient to meet workers’ basic living needs. According to the National Multidimensional Poverty Standard outlined in Decree No. 07/2021/ND-CP,5 the minimum monthly living standard is VND 2 million per person per month in urban areas and VND 1.5 million in rural areas. Yet under the second package, even urban-based informal workers were eligible for only VND 1.5 million per month, an amount below the minimum wage and urban poverty threshold. Moreover, the policy restricted individuals to a single benefit payment despite the extended nature of the pandemic’s economic impact. Although the policy allowed local authorities discretion to identify informal workers, budget constraints-especially in less-developed provinces-limited their ability to disburse funds as financial resources were simultaneously being diverted to pandemic’s prevention measures. Consequently, many informal workers remained excluded from state support, despite being among the most vulnerable during the crisis.
Households accessed assistance from multiple sources during the COVID-19 pandemic, with the majority receiving aid from government programs. In both 2021 and 2022, over 60% of households reported receiving support from the official government packages (see Fig. 9.18). Community based organizations such as the Women’s Union, Youth Union, and Farmer’s Union also played an increasingly important role. In 2021, 14.8% of households received cash or in-kind support from these groups, and this figure rose to 27.9% in 2022, indicating a growing role for grassroots organizations in pandemic response. Additional support came from businesses, private organizations, relatives, and individual benefactors. However, the share of households receiving assistance from these non-governmental actors declined from 21.2% in 2021 to 10% in 2022—likely due to resource exhaustion and decreased demand as the pandemic subsided.
Fig. 9.18
Support sources during the COVID-19 pandemic (% of households receiving assistance).
Although the overall proportion of support was similar across household groups, disparities emerged by employment formality, household head gender, and sector of employment. In 2021, households in the informal sector were significantly more likely to receive government support than those in the formal sector—a difference of 33% points (70% vs. 37%, respectively). By 2022, this gap narrowed to 14% points (64% vs. 50%), indicating a slight improvement in equity. Sectoral differences were also evident. Households in the textile and garment industry received less government support compared to those in the hospitality sector (58% vs. 68% in 2021, and 54% vs. 75% in 2022). A similar pattern was observed with respect to gender. In 2021, 66% of male-headed households received government aid, compared to only 54% of female-headed households. This 12-point gender gap remained largely unchanged in 2022, pointing to ongoing inequities in access to state support based on household leadership. These findings raise concerns about the targeting effectiveness and inclusiveness of government social assistance, particularly for vulnerable populations in the informal sector, female-headed households, and lower-access industries like textile and garment.
A large majority of households expressed demand for government support. In 2021, 79.2% of surveyed households indicated a need for assistance (see Fig. 9.19). This need was consistently high across all demographic groups, but notably higher among female-headed households—85.7% compared to 77.9% for male-headed households. However, the actual coverage of aid was much lower. In 2021, only 26.6% of households reported receiving any form of assistance. By 2022, this figure rose modestly to nearly one-third of households, yet still fell well below the level of expressed need. Disparities in access were also evident by gender, employment formality, and geographic location. In 2021, female-headed households were nearly 10% points less likely to receive support than male-headed ones. Gaps of approximately 14% points were also observed between formal and informal sector workers, as well as between urban and rural households. These findings highlight critical shortcomings in the reach and targeting efficiency of support programs.
Fig. 9.19
Demand for support and support coverage (% of households).
Within households, men were more likely than women to be designated as recipients of aid. In 2022, 43.9% of households reported that husbands were responsible for receiving support from the government programs, compared to only 24.5% reporting wives as recipients (Fig. 9.20). This gender gap persisted across all forms of aid. For non-governmental support (i.e., assistance from businesses, relatives, or benefactors), 21% of husbands compared to 12.9% of wives were reported as recipients. The most pronounced difference was observed in support received from local mass organizations, where 42.5% of households reported men as recipients, compared to only 9.2% for women, a 33.3% point difference. These disparities raise concerns about intra-household power dynamics and the gender inclusiveness of aid delivery systems.
Fig. 9.20
People receiving support during the COVID-19 pandemic (% of households).
Among households that received government cash support, the majority expressed high satisfaction with the program. In 2021, 79% of households rated the support as highly effective. However, this positive perception declined slightly in the following year to 70.5%. At the same time, dissatisfaction remained relatively low with 13% of households rating the support as ineffective in 2021, compared to 9% in 2022. These findings suggest that while general satisfaction remained strong, there was a gradual decline in perceived effectiveness, likely due to the limited coverage, insufficient benefit levels, or delays in implementation (Fig. 9.21).
Fig. 9.21
Evaluation of government support effectiveness during the COVID-19 outbreak (% of households).
Perceptions of support effectiveness also varied notably by gender. In 2021, 16% of female-headed households viewed the assistance as ineffective, compared to only 7.5% of male-headed households. By 2022, this gender gap had narrowed. Sectoral differences were more pronounced. In 2021, 48.6% of textile and garment households rated support as very effective, compared to just 20% in the hospitality sector. Dissatisfaction was particularly high among hospitality households, with 20% describing the support as ineffective. The primary concerns cited by dissatisfied households included the inadequacy of benefit levels and the administrative complexity of accessing support barriers particularly acute for informal workers and vulnerable populations.
9.5 Conclusion
The COVID-19 pandemic deeply impacted Vietnam’s economy, with the textile/garment and hospitality sectors hit the hardest due to their high reliance on labor and external demand. Households with workers in these sectors, especially female-headed households, faced severe income disruptions. Although employment largely recovered by 2022, the extent and pace of recovery varied by industry, with the hospitality sector experiencing greater and more prolonged disruption. This underscores the need for sustained policy support targeting affected and at-risk groups.
While there was a notable shift in 2022, with more individuals changing their mindset from waiting to actively seeking employment, a persistent barrier to job mobility remained: the lack of skills. Approximately, 46% of households who did not switch jobs cited insufficient skills as the main reason, pointing to an urgent need for expanded vocational training, particularly through online, mobile, and broadcast platforms.
Household incomes showed signs of improvement by 2022, but this recovery was uneven, especially for female-headed households. These households continued to bear a disproportionate burden unpaid care work (UCW), limiting their ability to re-engage economically. In both years, women primarily handled caregiving, reinforcing long-standing gender inequalities in the labor market.
The majority of affected households—particularly female-headed households—expressed a clear demand for support during the pandemic, although actual coverage remained low, with only one-third receiving assistance. A notable finding was the gender imbalance: men were significantly more likely to receive support on behalf of the household—even within female-headed households. Despite these gender disparities, most households expressed high levels of trust in the government’s pandemic response system. However, dissatisfaction was also common, particularly among those who found the benefit levels too low relative to their income losses and the complexity of the application procedures too burdensome. To address these shortcomings, there is a clear need to increase the budget capacity and to digitize the support system—including unified citizen databases—which could improve delivery and targeting in future crises.
Current support packages also struggle to address transient poverty, which affects households facing temporary but acute shocks. In 2022, 24.7% of female-headed households experienced falling into poverty compared to 18.2% of male-headed households. Moreover, many faced limited access to basic services during the height of the pandemic—40% for education, 30.7% for healthcare, and 31.9% for food/basic needs in 2021. These challenges had eased by late 2022 but highlight the needs for more agile and inclusive support mechanisms.
Furthermore, the design and implementation of policy support must be approached through a gender-sensitive lens, particularly given the persistent and significant gender gap in UCW. Across both 2021 and 2022, women were significantly more involved in caregiving, with a 16–17% point overall gap, and even larger differences for other household tasks. These persistent gaps call for gender-sensitive policy interventions that recognize and reduce women’s care burdens, including investments in childcare services, flexible work arrangements, and remote access to education and healthcare.
9.6 Policy Recommendations
Evidence from the survey highlights several policy directions focused on skills development, labor market access, and gender equity, which are critical for supporting vulnerable groups during and after crisis periods such as the COVID-19 pandemic. To support a more inclusive and resilient recovery, several key policy directions should be prioritized:
Expand vocational training opportunities, with a strong focus on digital skills.
Short-term, practical training programs should be designed to meet the need of vulnerable groups—particularly women, low-skilled individuals, and older workers. These programs should be accessible through local organizations and employment service centers to ensure accessibility and local relevance.
Improve access to employment and support labor mobility by strengthening coordination between employers and job placement services. Creating more opportunities for inter-provincial job connections and organizing both online and offline job fairs can help workers in disadvantaged or remote areas find meaningful employment. Additionally, policies should encourage job creation in industries undergoing change or facing long-term disruption. Address persistent gender inequalities, especially in unpaid care work. Public awareness campaigns, developed in collaboration with local groups such as the Women’s Union or Youth Union, can challenge gender stereotypes and promote more balanced caregiving responsibilities. Promoting shared responsibility within households and communities is crucial to reducing the burden on women. Integrate gender sensitivity into all recovery efforts, ensuring that policies acknowledge and actively work to close the gender gap in unpaid labor. Investments in childcare services, flexible work arrangements, and remote education or healthcare services will help women—particularly those from vulnerable households—participate more fully in economic life.
These policy recommendations reflect a multi-dimensional response that addresses immediate labor market needs, long-standing gender inequities, and structural gaps in skills and support systems. Implementing them effectively will be key to ensuring inclusive and resilient recovery for all groups.
Acknowledgments
This paper is based on a survey I managed, and I would like to express my gratitude to the project “The Impact of COVID-19 on Inclusive Development: Rapid and Post-Pandemic Assessment in the Mekong Sub-Region”, which is funded by the International Development Research Centre (IDRC) and coordinated by the Cambodia Development Resource Institute (CDRI) and the Center for Analysis and Forecasting (CAF) in Vietnam.
Open Access This chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.
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The sampling process involved a multi-stage replacement strategy to ensure representativeness and minimize selection bias. First, 1,000 first-priority replacement households were selected using Probability Proportional to Size (PPS) sampling, with “size” based on predefined priority criteria. Each household in this group was assigned a sampling weight of 1/si1/si, where sisi is the selection probability. Second, 3,000 additional households were drawn using the same PPS method and criteria to serve as potential replacements. These were matched exactly to the official sample households they could replace, inheriting the same sampling weights to maintain comparability. Third, 4,000 second-priority replacements were selected using the same method and criteria. The use of two tiers of replacements was intended to preserve the similarity with the official sample, with priority given to replacements from the first group.
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