Connecting Urban Mobility and Gender Roles: Ho Chi Minh City,Vietnam

Abstract

Good transport infrastructure and services improve the well-being of all groups of the population, including women, men, children, the elderly and people with disabilities. However, they are often incorrectly considered ‘gender-neutral’. Mobility is socially determined by gender roles related to reproduction, production and community. This research investigates the cohesive relationship of urban transportation, including mobility, gender and care, by studying the case of Ho Chi Minh City (HCMC). The differences between women’s and men’s participation in transportation are identified and connected to gender-role activities. The findings show a clear relationship between urban mobility and gender roles. There is clear evidence of women’s trade-off between travelling less than men and carrying more unpaid work in the family, which is more pronounced for married women who live with their families. Differences in women’s traffic experiences compared to men’s reveal the disadvantages that women face in traffic. The findings of this research will assist the HCMC transport sector and project executive agencies in designing gender-inclusive projects. The dissemination of this research will draw attention to the gender dimension of transport and encourage gender mainstreaming across the transport sector to promote and support equality and women’s empowerment.

Keywords

Urban mobility gender equality women equal access transportation Ho Chi Minh City Vietnam

Introduction

Transport infrastructure and services play a crucial role in enhancing the well-being of individuals by providing access to economic and social opportunities. As such, these services must be designed to cater to the needs of all segments of the population in an equitable, affordable and responsive manner. This includes women, men, children, the elderly and people with disabilities. However, there is often a misconception that transport infrastructure and services are ‘gender-neutral’. This erroneous assumption implies that public transportation provides equal benefits to both men and women, without any significant differences in travel patterns, modes of transport access, and utilization of transport infrastructure and services (ADB, 2013).

Mobility is socially determined by gender roles related to reproduction, production and community. The relative economic and social status, as well as livelihoods of women and men, also influence the different demands and usages of transport services. People use various modes of transport for different purposes and in different manners. Scholars have discussed these differences in the literature on the subject, but there are still many gaps that need to be filled.

This study, conducted in 2022, investigates the cohesive relationship between urban mobility, gender and care (as in caring for the house or family). The intersection and interaction of these terms are examined through the case study of Ho Chi Minh City (HCMC). Specifically, urban mobility will be studied with a gender focus. The objectives are (a) to identify the difference in transportation participation between men and women and (b) to connect these differences in terms of gender function in mobility and unpaid activities. To achieve the stated objectives, this research examines gender disparities in traffic time and its connection to unpaid work.

The findings show a clear relationship between urban mobility and gender roles. Women tend to travel less than men and have to take on more unpaid family work, especially among married women who live with their families. Differences between women’s traffic experiences with those of men reveal the disadvantages women face in accessing and using transport services.

Literature Review and Research Gaps

Review of Studies on Mobility and Gender, Mobility and Care

Research on the relationship between daily mobility and gender was initiated by feminist transportation geographers and urban planners in the 1970s (Law, 1999). They raised objections to conventional planning assumptions where gender is an irrelevant variable in calculating future infrastructure needs and planning practices are adjusted to suit men’s commuting needs with relatively simple mobility patterns (from home to work and back) that do not account for the reproductive work that women associate with their activities as part of the workforce.

American literature in the 1980s divided the worldview into two spheres, the public world and the home, and expected women to belong to the latter (Lopata, 1980). Evidence from the Uppsala Household Travel Survey, collected in 1971, shows that a woman’s full-time employment reduces her participation in some non-work activities (such as social activities, personal business, shopping and recreation) but has little impact on that of her husband (Hanson & Hanson, 1981). Along with related factors, job tenure, work hours and wages, sex differences in household ‘roles’ are an important factor influencing women to work ‘closer to home’ (Fanning Madden, 1981). Other researchers, who are interested in the relationship between housing, urban design and work, have raised the question. ‘What would a non-sexist city be like?’ (Hayden, 1985). In general, men have longer commuting distances than women. Couples tend to choose a place of residence close to the wife’s place of work to facilitate the wife’s homecare activities (Tkocz & Kristensen, 1994). In a review of how urban spatial structure and the jobs–housing relationship affect commuting patterns (Lin et al., 2015), it was highlighted that gender plays a considerable role in shaping commuting behaviours. These studies indicated that women’s commuting patterns are more influenced by the city’s structure than those of men and are more vulnerable to changes in the industrial structure of the city.

Later, scholars from some related fields also considered gender issues, such as urban planning, transportation, sociology, geography and architecture (De Madariaga & Roberts, 2016; Loukaitou-Sideris et al., 2009). They analysed differences in the travel behaviour of men and women. Such differences are associated with the daily activities of men and women; as well as being related to their social-gender roles, typical life courses and the social organization of production and reproduction in general. Mandel (2004) discussed the importance of urban mobility in the creation of profitable livelihood strategies for women in Porto Novo, Benin. In Delhi, India, poor women often choose slower modes of transport for a cheap price, along with less timings. They have to accept jobs near their homes with low pay. Their limited mobility and constrained accessibility to the transport system are factors that create barriers to accessing the labour market for women (Anand & Tiwari, 2006). In another study also in India, on the Delhi metro, the authors argue that the separation of compartments for women creates confusion regarding women’s equal rights in traffic and limits their ability to negotiate for their rights. The authors suggest that these are emerging concerns that need to be addressed more proactively (Agrawal & Sharma, 2015).

Addressing complex social issues, such as sustainability, will require an improved understanding of the relationship between gender and mobility. Reviewing a lot of literature on gender and mobility, Hanson (2010) shows two disparate strands of thinking that have remained disconnected from each other. One strand has investigated how mobility shapes gender, while the other has examined how gender shapes mobility. In this study, urban mobility and gender roles have been connected to investigate the correlation between the two.

Recent efforts to shift the focus of transport planners from commuting trips related to ‘mobility of care’ (de Madariaga, 2013). This approach is likely to provide the practical and operational foundations needed to sustain a new approach to data collection and thus policy definition and implementation to better mainstream gender considerations into planning and transportation management. The European Union legislated the first wave of gender mainstreaming in 1999, leading to significant global interventions. However, women’s empowerment still requires attention. De Madariaga and Roberts (2016) explore gender mainstreaming in urban planning, architecture and public spaces, highlighting persistent gender inequalities in urban accessibility. They underscore the role of time, space, social structures and networks. This study continues this research approach, focusing on mobility and gender and mobility and care.

A new mobility-transport system that includes gender-sensitive transport investment planning can play a role as a means of unlocking women’s economic potential.

Investment in transport should focus on facilitating the mobility of people for different purposes and needs and in different modes in which women and men, children and elders experience differently, to make the gender dimensions of transport more evident. Well-designed transport infrastructure can empower women economically and serve as the first step for them to access job opportunities (Jobes et al., 2017). In the northeast of England, where private transport is a key factor in participating in the labour market, there are links between women, transport and labour participation, and the development of sustainable transport systems would support women’s economic inclusion (Dobbs, 2005). There is also a potential to increase social mobility when combined with skills training, capacity building and changes in social norms (Brejnholt et al., 2016). Urban mobility also has a strong impact on women’s access to education, jobs or simply their enjoyment of city life (The World Bank, 2022). While Vella-Brodrick and Stanley (2013) studied a case from Melbourne, Australia, the authors argued that the benefits of transport mobility could extend to psycho-social factors leading to well-being.

However, restrictions on daily mobility would separate women from socio-economic development, exacerbate inequality and reinforce negative social norms. Evidence shows that ignoring gender differences in transport planning and operations is a missed opportunity for women’s economic growth and empowerment (Jobes et al., 2017). In western and southern Africa, Porter (2011) found three key impacts of relative immobility and poor service access on women and girls, including access to education, access to markets and access to health services. Girls living in less accessible villages have a higher likelihood of school drop-out due to hazardous journeys and transport costs, and also the pressure of helping with work at home. Furthermore, for women, a long walk to a health care centre is a key factor preventing them from accessing necessary health care services. Similarly, unavailability or high cost of transport creates a barrier for women to access markets.

A study from two Indian cities found that the lack of access to key services like water and sanitation, along with limited mobility and insecure tenure, can increase the burden of unpaid care work. This exacerbates gender-based disadvantages and leads to ‘time poverty’ (Mahadevia et al., 2019). In another context, in Saudi Arabian countries, before 2018, women were not allowed to drive and were dependent on male drivers or private transportation to go to work. Williams et al. (2019) show that when the cost of transport increases, the female labour force participation decreases. While Indian female labour force participation is very low, especially among married women (Klasen et al., 2020), and women are much less likely than men to work in non-farm sectors, Lei et al. (2019) found that improved transportation infrastructure had a stronger positive effect on women’s non-farm employment. Geographers all over the world have examined and confirmed the assumption that poor local services prevent women from availing of economic opportunities. Investigation into the role of the broader economic, physical and social context in women’s mobility and accessibility constraints is a concern even in North America and Western European countries (Akyelken, 2017).

Asian Development Bank recommended that a well-designed transport infrastructure can engage women in more quality jobs in the transport sector, such as taking on roles as bus drivers, ticket collectors and taxi drivers (ADB, 2011). Transport is one sector that has traditionally been regarded as ‘no place for women’. This may still be the case today in many countries (Turnbull, 2013), though even in some developed countries in Europe, the presence of women in the public transportation sector is relatively low (Semerci & Ergenali, 2016). The public transportation sector is one of the important sectors which is often controlled by the local government; therefore, as employers, they need to take this issue seriously to create a gender-inclusive working environment.

Transport investments that incorporate gender considerations in their design can yield significant benefits for women, as well as children, the elderly and people with disabilities. These benefits include increased access to employment opportunities, markets, education and health services and also increased time to work. Recognizing the opportunity costs associated with a gender-neutral approach is crucial in order to avoid past mistakes and foster commitment and action towards improving transport planning and management.

Ho Chi Minh City: Urban Mobility

Vietnam has been among the world’s fastest-growing economies. As the economy grows, more families can afford cars, potentially leading to congestion and pollution, similar to large Asian cities like Bangkok, Beijing, Manila and Jakarta (ADB, 2006). HCMC, established in 1698, was historically a water-based city. But rapid urbanization has led to the deterioration of its cultural heritage (Vu, 2006) and increased urban flooding. An HCMC flood study explores how women face flood risks. It highlights not only their vulnerability but also their resilience in coping with this challenge (Tran & Downes, 2023).

In these difficult conditions, travelling by personal means of transport and ensuring safety for women becomes unsure, especially when they often travel with children or grocery items after working hours. In HCMC, women face psychological as well as physical pressure much higher than men in using other transportation means due to many reasons, and consequently, this can cause road accidents. While road accidents by men might be many, caused by drunk driving or careless riding, women’s accidents are more often due to being hit by other vehicles or their own carelessness. Robbery and sexual abuse are also other issues for women when participating in urban transportation. They are often the target of road predators who can take advantage of their physical weakness to steal their bags or attack them, causing injuries or accidents.

The above facts, together with the inadequate design of public transport infrastructure, will constrain women’s access to urban public transport and limit its positive distributional impacts. The local government is enhancing public transport and infrastructure, aiming for a 25%‒30%share of daily motorized trips by 2030. However, current transport services may not meet women’s needs, as they often have to make multiple trips to various destinations.

Data and Methodology

This research is designed to directly address gender differences in traffic and its relation to gender roles. It aims to answer three main research questions:

Is there a difference in total traffic time undergone between women and men?

Are there differences in traffic times between women and men in specific activities?

Is there a correlation between traffic time and gender roles in the family?

The following specific hypotheses will be conducted to answer the above questions:

H₁: Women spend less total traffic time than men do.

H₂: Women spend less time in traffic for commuting activities than men do.

H₃: Women spend more time in traffic for shopping and escorting activities than men do.

H₄: Women spend more time doing unpaid work than men do.

H₅: Women trade travelling time outside the home for doing unpaid work inside.

This study also considers other approaches to identify subjective experiences in daily transportation, preferable modes of transport, and experiences related to physical and mental health because of traffic pressures. Research steps include the questionnaire design, data collection, data analysis and discussing different perspectives to address the differences in women’s and men’s experiences, and their concerns about the access, usage and benefits of transport infrastructure and services. The following sections include the data sources used and the approach in more detail.

Survey

In this study, the concept of mobility of care (Madariaga & Zucchini, 2019) is referenced to design the data collection questionnaire, along with the real context in HCMC. The transportation activities are divided into numerous small categories such as work-related, learning and training, escorting and shopping, entertaining, networking, health-related and community-related issues. Subdividing activities helps to visualize trips into categories and record data into definitions of work and care. This approach will help users recognize the importance of caring work, and support transportation planners in designing systems that would fit all groups of the population.

The survey collects information on transport behaviour and activities of people between the ages of 15 and 65 living in HCMC from all occupations and jobs. This study employed non-probability sampling techniques, specifically referral sampling and quota sampling, to ensure representatives from diverse socio-economic backgrounds in the interview phase. A pilot study was conducted with 30 individuals from various occupations to identify and refine the sampling and questionnaire methodologies. Based on the pilot test results, the questionnaire was adjusted to improve response rates while maintaining data integrity.

The sampling approach was then implemented as follows: a list of 50 individuals from HCMC with diverse employment statuses, age groups, genders and residential areas was compiled through the social network. Direct interviews were conducted with these individuals. Following the interviews, participants were requested to assist in this research by referring others in their networks to participate in interviews. After five months, approximately 1,000 responses were collected. Preliminary data analysis was conducted to identify any under-represented socio-economic groups compared to the 2019 HCMC gender and age distribution statistics (CityFacts, 2019).

To address identified gaps in representation by occupation, purposive sampling continued by verifying interviewee residency across all 22 HCMC districts. This resulted in the recruitment of an additional 150 participants. In total, 1,150 individuals were interviewed for this study. After removing invalid responses, there is a sample of 1,047. People working in the transportation sectors such as taxis, buses, Grab, Gojek, Be and rental cars were excluded from the sample selection for analysis.

The questionnaire is designed for the specific research objectives described above, so it includes necessary information, such as demographics, time used for daily and weekly transport activities, transportation costs, vehicle ownership and individual experience of public transport. The survey also asked participants about their employment and income status. In addition, the time spent on unpaid work inside the house was collected, that is, time spent on doing housework, child care, and taking care of elders and ill people (see more details of the definition of variables in Table A1 in Appendix A).

When designing the questionnaire and conducting a pilot test on 30 people, it was found that categorical questions received higher and more precise responses than questions that required participants to fill in their own answers. Therefore, to increase the response rate and approximate accuracy, questions were designed related to time by providing categories for participants to answer (see example in Table A1 in Appendix A). Before the analysis, a random number selection algorithm within each category was assigned to each response. By doing so, approximate accuracy for questions related to time was ensured, as well as the fluctuation of the observed variable, and the strengths of quantitative analysis could thus be used to advantage.

Empirical Strategy

The empirical analysis is performed following the random-effect models. The interested indicators include travel times for different activities and time spent on unpaid work. The individual and household characteristics are controlled to determine differences in characteristics between groups that could explain the differences in mobility. The random-effect models approach is used in this study to investigate the differences between women and men in time spent travelling and also in unpaid work.

T i = α 0 + α 1 F e m a l e + β X i + ε i,

(1)

where T_i stands for the interested indicators measured by the hours that an individual i spends for a specific activity, female is a female dummy indicator. The vector X of control variables is controlled for observable characteristics, including age group, education, income, marital status, household size and number of children or elderly in the family. The parameter estimate α1 will give the mean of the difference between women and men. The error term εi standard at the individual level captures all unobservable factors.

Equation (1) addresses hypotheses H₁– H₄, which investigates the differences between women and men in time spent in traffic and unpaid work. If differences exist, there is a hypothesis that women have traded time between unpaid work at home and being involved in traffic outside. To test this hypothesis (H₅), the following regression specification is used:

T i = α 0 + α 1 U W i + α 2 F e m a l e + α 3 F e m a l e * U W i + β X i + ε i,

(2)

where T_i stands for the time spent in daily travelling activities, UWi is the total time used for daily unpaid work in-house. The interaction Female * UWi assesses the correlation between the time used for unpaid work with time for travelling between men and women. The interested coefficient is α3, which will give the average marginal difference between women and men when sacrificing travelling time for unpaid work.

Descriptive Statistics of the Sample

Table 1 briefly describes the information on the main variables used in the analysis. Respondents’ ages included in the analysis from 15 to 64 years old, with an average age of the respondent group being 35.48, of which 50% are under 35 years old and 25% are between 44 and 64 years old. The ratio of women and men in this study sample is 54% female and 46% male. The number of married people accounts for 55%. Most families have four members, with the typical family type in cities being a nuclear family with a husband, wife and two children.

Table 1, section A, describes the travel time for daily activities. In general, transportation in HCMC takes up quite a lot of time, mainly due to traffic congestion caused by overloaded traffic, resulting in slow movement. Another factor is that HCMC is very large, so commuting from one district to another is common here. In this study sample, on average, one spends about 3.09 hours per day travelling on the road, with 50% of the people spending more than 2.65 hours travelling and 25% spending more than 4.06 hours. Of these, the maximum time is spent commuting to work with an average of about 1.41 hours (including all travel for income-generating purposes such as going to the office or meeting business partners or delivering goods to customers). People spend 0.6 hours travelling for educational and training activities, and for escorting and shopping and other activities, they spend an average of about 0.34 hours, 0.42 hours and 0.47 hours, respectively. Note that in HCMC, motorbikes are the main means of transportation, and for convenience and flexibility, most people often combine taking their children to school in the morning on their way to work and stopping at the markets to buy food on their way home in the afternoon.

Table 1.

Descriptive Statistics of the Studied Sample.

	Mean	SD	p25	p50	p75
Age	35.48	12.04	25	35	44
Female	0.54	0.50	.	.	.
Married (1 = yes)	0.55	0.50	.	.	.
Household size	3.75	1.84	3	4	5
A. Time Travelling for Daily Activities
Work	1.41	1.06	0.69	1.21	1.85
Education	0.60	0.87	0.00	0.14	0.93
Escorting	0.34	0.57	0.00	0.06	0.49
Shopping	0.42	0.49	0.09	0.30	0.51
Others	0.47	0.65	0.00	0.24	0.72
Total	3.09	1.80	1.73	2.65	4.06
B. Time Travelling for Weekly Activities
Entertainment	0.85	0.89	0.23	0.60	1.10
Visiting	0.61	0.70	0.11	0.39	0.86
Health	0.32	0.45	0.00	0.19	0.43
Social	0.35	0.60	0.00	0.02	0.46
Total	2.13	1.94	0.81	1.56	2.89
Monthly expense for transportation (in million VND)	842	1,480	250	500	1,000
C. Time Spent for In-house Daily Activities
Housework	0.90	0.79	0.37	0.74	1.19
Childcare	0.53	0.84	0.00	0.00	0.84
Care of elders and the sick	0.16	0.45	0.00	0.00	0.00
Total unpaid work	1.59	1.46	0.62	1.15	2.11
Number of respondents	1,047

Note: The sample selection includes people from 15 to 64 years old living in HCMC.

Table 1, section B, describes the travel time for activities that do not occur regularly but usually occur weekly, such as outdoor entertainment, visiting relatives and friends, health check-ups or buying medicine and social community activities. In this study sample, the average travel time on the roads that people spend per week is about 2.13 hours, of which entertainment and visiting occupy 0.85 hours and 0.61 hours, respectively, while travel time for health care and social activities accounts for about 0.32 and 0.35 hours per week, respectively.

This study estimates the travel cost as a total, including the cost of buying gasoline, the cost of bus tickets, and the cost of transportation services such as taxis and motorbike taxis. With consumer prices at the time of data collection in April 2022, the average cost was 842,000 VND (equivalent to about 37 USD with the exchange rate at the time of this study), of which 50% of the people spent less than 500,000 VND (equivalent to 22 USD) per month.

Table 1, section C, describes the time that people spend on in-house daily activities, including housework (such as cooking, cleaning and gardening), childcare and taking care of elderly or ill people. On average, they spend about 1.59 hours per day on unpaid work, including the three types of work mentioned above, of which about 0.9 hours is used on housework, about 0.53 hours on childcare, and about 0.16 hours on taking care of the elderly or the sick in the family. However, childcare and eldercare activities depend on family circumstances, so most respondents spend very little time on these activities, while a few people spend many hours a day on them.

Gender Differences in Transportation Modes and Traffic Experiences

This section discusses the different characteristics of transportation modes and traffic experiences of different groups, focusing on gender differences.

Use of Transportation Modes (Persons over 18 Years Old)

Motorbikes are the primary mode of transport in Vietnam, accounting for 82.3% of all vehicles (Statista, 2022). Their popularity is due to the narrow streets, making motorbikes most convenient for navigating traffic and finding parking. Additionally, they are more affordable than cars, with new motorbikes costing around $1,000 or less, while a new car would cost at least ten times that amount.

Figure 1a shows that 88% of respondents reported that motorcycles are their most frequently used mode of transportation, while 17% reported that taxis are also a frequently used mode. The number of people using taxis in HCMC has increased significantly in recent years, with taxi services rapidly developing, with more than 20 traditional and about 10 technology-based taxi companies (SEC, 2023). Meanwhile, only 15% of respondents reported using buses frequently, a high figure compared to the national average of less than 5% (Statista, 2022). Frequent bus users are typically young students or retired seniors. In HCMC, the infrastructure is not conducive to walking and cycling. Although there are sidewalks for pedestrians, they are often occupied by businesses using them as parking spaces for customers and employees, or restaurants placing tables and chairs on the sidewalks for customers, making walking difficult (Bui, 2023) (Figure 2). There are no dedicated bike lanes throughout HCMC, so cyclists must share the road with larger vehicles, which is not safe (Figure 3). As a result, choosing to ride a bike is mainly for short distances by older people (Figure B2 in Appendix B).

Figure 1.

Source: Authors’ calculations based on survey.

Figure 2.

Source: Photo in Bui (2023).

Figure 3.

Source: Photo in Nguyen and Nguyen (2022).

In a dynamic city like HCMC, both women and men actively participate in socio-economic activities. The socio-cultural environment fundamentally poses no barriers for women to choose their means of transportation. The differences might be due to gender-related psychological factors; for example, women tend to choose safer modes of transportation when possible. Figure 1b reflects this reality, with self-driven vehicles such as motorcycles and cars. The percentage of men who frequently use them is 91% and 19%, while for women it is 86% and 7%, respectively. These percentages also correspond to vehicle ownership and having a driving licence as seen in Figures 1c and 1d, where the percentage of men owning vehicles and having a driving licence is higher than that of women for both motorcycles and cars. Also, Figure 1b shows that the percentage of women choosing public buses, bikes and walking is higher than that of men. However, the transportation infrastructure in HCMC is not yet well developed for these three modes. Busy city life makes people prioritize on saving time, and reducing fares and ensuring a smooth journey. For commuters, using public transport is less about the exact route and more about getting the best value of time, fare, and convenience (Ceder, 2021). Therefore, it is seen that investing in transportation infrastructure and public transportation, bike lanes and well-managed pedestrian paths that excel in delivering fast, affordable and convenient travel will support the mobility of all citizens, including women who frequently use these modes.

Physical and mental Health

Traffic congestion and air pollution are causes of physical and mental health problems for road users, especially in large cities like HCMC. Most roads are congested during peak hours (Chi, 2023) (Figure 4). During the rainy season in the city, flooding occurs due to tidal water rising from the Saigon River as the drainage system cannot handle it, causing roads to be flooded (Figure 5). Heavy rain also causes trees to fall and obstruct traffic (VOV News, 2020). In such difficult traffic conditions, daily commuting in the city creates stress. Figure 6a shows that about half of the respondents frequently experience physical and mental stress. Women seem to experience more physical stress than men, possibly because of their smaller bodies but they also have to control motorcycles the same size as men do. Women also often travel with young children and groceries. The weather in HCMC is hot all year round, and women have a habit of covering themselves to protect their bodies. This also makes it more difficult to control motorcycles which is stressful (Figure 7). In addition, women are also victims of traffic jams and snatch thefts (Figure 8). Good infrastructure and convenient public transportation will support women who have to commute, giving them confidence in traffic and helping them participate in socio-economic activities.

Figure 4.

Source: Photo in Chi (2023).

Figure 5.

Source: Photo in VOV News (2020).

Figure 6.

Source: Authors’ calculations based on survey.

Figure 7.

Source: Photo in Hoang (2021).

Figure 8.

Source: Photo in Tu Quy (2016).

Figure 6b shows that the percentage of people living alone who feel stressed is higher than those living with their families. This may be due to the subjective psychology of facing loneliness after returning home with no one with whom to share one’s frustrations on the road. Further research is necessary on the impact on health and the stress caused by traffic.

Travelling by Bus

Thirty-two transport companies provide buses in HCMC on different routes under the common management of the Ho Chi Minh City Public Transport Management Centre (Info, 2023). To encourage people to take buses and reduce personal vehicles on the road, most bus routes are subsidized and have low fares, and the city also has a policy of free tickets for the elderly, people with disabilities and low prices for students. However, the number of bus passengers is still quite low compared to expectations and has decreased in recent years (Huu, 2022). In this study, as seen in the results illustrated in Figure 9, most participants rated buses in HCMC as cheap (5,000‒15,000 VND equivalent to 25‒60 US cents), convenient and safe, but they did not rate highly the modernity and civility of buses, which could be the reason why buses are not chosen as a regular means of transportation.

Figure 9.

Source: Authors’ calculations based on survey.

Up to one-fifth of bus users experience negative incidents, such as theft or harassment. Notably, women are disproportionately affected by harassment, with approximately 20.5% reporting experiences of being the subject of unpleasant gestures or offensive language. Conversely, men report a lower rate of harassment at around 15%. Men are more likely to experience theft, with approximately 13% reporting loss of money or belongings on buses, compared to only 9.5% of women. These findings highlight the need for a multifaceted approach by the city government. While policies to expand bus routes and invest in new vehicles are important, addressing safety concerns through measures that promote civilized behaviour among all public transport users is equally crucial.

Findings and Discussions

Gender Differences in Travelling Activities

This section examines gender differences in travel time for daily activities, including commuting, education, escorting, shopping and others. It also examines gender differences in travel time for weekly activities, including entertainment, visiting, health care and social services. The section does not discuss whether more traffic participation is good or bad; it only tests traffic time differences through a gender lens. Although there is evidence of the impact of traffic participation on physical and mental health (Harrison et al., 2022; Lu et al., 2013; Wanner et al., 2012), there is also evidence that there is a correlation between urban mobility and job opportunities and job quality (Blumenberg & Manville, 2004; Grengs, 2010). The correlation between mobility constraints and social exclusion is apparent (Akyelken, 2017).

Table 2 shows that women spend less time on daily traffic than men do. In total, for all daily activities, a woman spends 0.426 hours less than men. This difference is evident in most activities, with travel for work being the most pronounced. Figure 10a shows that on average, men spend about 1.65 hours on travel for work, while women spend an average of about 1.2 hours on this activity.

Table 2.

Traffic Time by Gender for Daily Activities (Hours).

	(1)	(2)	(3)	(4)	(5)	(6)
	Total	Work	Education	Escort	Shop	Other
Female (Ref: Male)	–0.426***	–0.293***	–0.088	–0.087*	0.029	–0.084*
Female (Ref: Male)	(–3.73)	(–4.74)	(–1.76)	(–2.46)	(0.95)	(–2.07)
Married	–0.293	0.007	–0.250***	–0.002	–0.067	–0.136*
Married	(–1.74)	(0.08)	(–3.43)	(–0.04)	(–1.46)	(–2.27)
N	1,011	1,016	1,019	1,019	1,019	1,012
r2_a	0.0708	0.222	0.241	0.111	0.0416	0.116
Other control variables	Yes	Yes	Yes	Yes	Yes	Yes

Source: Authors’ calculations based on survey.

Note: This table reports the main coefficient of interest (see the full results in Table A2 in Appendix A). The sample selection includes people from 15 to 64 years old living in HCMC. The standard errors are clustered at the individual level. Dependent variables are traffic time for (1) total daily activities, (2) work activities, (3) education and training activities, (4) escorting activities, (5) shopping activities and (6) other daily activities. *p < .10, **p < .05 and ***p < .01.

Figure 10b shows that most people usually go to two or three destinations on their daily trips, so they can combine taking their children to school on their way to work and buying food on the way home in the evening. For the group of married people, row 2 in Table 2 shows that they spend about one-third of an hour less on the road than the group of unmarried people; specifically, they spend more time on travel for work and less time on education and training and other extracurricular activities. Figure 11a illustrates the specific differences between the two groups: married people spend an average of about 1.65 hours on travel for work while unmarried people spend only about 1.1 hours; conversely, unmarried people spend an average of about 0.96 hours on travel for education and training purposes while those who are married spend only about one-third of that time on the same activity.

Figure 10.

Source: Authors’ calculations based on survey.

Table 3 shows that, overall, there is no significant difference between women and men in travel time for weekly activities. The main difference is between the group of those who are married and those not unmarried, with the former spending less time on travel for entertainment and social activities than the latter. In total, married people spend 0.6 hours less per week on the above activities than unmarried people. Figure 11b illustrates the specific differences between the two groups: while unmarried people spend an average of 1.07 hours on travel for entertainment purposes and 0.4 hours on travel for social activities, married people spend only 0.67 hours and 0.3 hours on these activities, respectively.

Figure 11.

Source: Authors’ calculations based on survey.

Table 3.

Traffic Time by Gender for Weekly Activities (Hours).

	(1)	(2)	(3)	(4)	(5)
	Total	Ent.	Visit	Health	Social
Female (Ref: Male)	–0.150	–0.066	–0.024	–0.029	–0.027
Female (Ref: Male)	(–1.26)	(–1.22)	(–0.55)	(–1.00)	(–0.70)
Married	–0.607***	–0.335***	–0.044	–0.064	–0.144**
Married	(–3.46)	(–4.25)	(–0.67)	(–1.46)	(–2.60)
N	1,016	1,018	1,019	1,019	1,017
r2_a	0.127	0.136	0.0659	0.00449	0.0684
Other control variables	Yes	Yes	Yes	Yes	Yes

Source: Authors’ calculations based on survey.

Note: This table reports the main coefficient of interest (see the full results in Table A3 in Appendix A). The sample selection includes people from 15 to 64 years old living in HCMC. The standard errors are clustered at the individual level. Dependent variables are traffic time for (1) total weekly activities, (2) entertainment activities, (3) visiting activities, (4) health care activities and (5) social activities. *p < .10, **p < .05 and ***p < .01.

Gender Differences in House Activities

Worldwide data indicate a rise in women’s labour force participation and a narrowing of the gap with men. However, progress varies across regions (Klasen et al., 2020), and men are still more likely to be in labour markets (Ortiz-Ospina & Tzvetkova, 2017). Women often hold lower-paid, less-protected jobs, and gender biases limit access to better opportunities (Scarpari & Clay, 2020). Women are more than twice as likely to contribute to family work (ILO, 2018), and are more likely to be vulnerably employed (Lo Bue et al., 2022). Despite increased workforce participation, women’s traditional home and family roles remain unchanged, causing stress and health issues. Career advancement is also limited due to multiple role demands. Gender significantly influences unpaid work patterns. Women spend more time on housework, childcare and volunteer activities related to family, regardless of their employment status. Several theories explain these gendered patterns, but none fully capture the gender differences (Shelton, 2006). Jokubauskaitė et al. (2022) proposed a new method to estimate the value of leisure, part of the value of travel time savings. They suggest assigning a wage rate to unpaid homework, which impacts women more due to their higher contribution to domestic tasks. The majority of research on gender and unpaid work concentrates on housework, although other forms of unpaid labour deserve consideration. In this study, we consider not only time spent on housework but also childcare time and taking care of elder or ill persons in the house.

In a modern city like HCMC, the cost of living and education for children is very high compared to other cities in Vietnam (Bang, 2022). Therefore, both men and women have to participate in the labour force to earn and support the family. Women have a sense of responsibility to share financial obligations with men. But sharing in home activities is not equal; the balance of unpaid work still leans more towards women.

Table 4 shows the difference in time spent on unpaid work activities by women and men. There is clear evidence that women spend 0.55 hours more per day than men on unpaid activities, including housework, childcare and care for the elderly and sick, with nearly 0.4 hours more time than men and childcare being the most significant areas. (Leisure time is not an activity counted in unpaid work.) In Table 4, the difference between the group of married and unmarried people is very clear in their roles in the family; married people naturally have to spend more time on childcare and compensate for this by spending less time on caring for the elderly and personal leisure than unmarried people. The difference is illustrated by Figure 12a, where men spend an average of 0.69 hours per day on housework while women spend an average of 1.08 hours. The total time men spend on unpaid work is 1.34 hours per day while that of women is 1.79 hours. Figure 12b shows the exchange of leisure time for unpaid work between a group of unmarried people and a group of those who are married; while the former have an average of 1.52 hours per day for leisure activities, married people have only half that amount (0.8 hours). Conversely, married persons spend an average of 1.84 hours per day on unpaid work, while the unmarried spend only about 1.3 hours per day on the same.

Table 4.

Time Used for Daily Unpaid Work (by Gender).

	(1)	(2)	(3)	(4)	(5)
	Unpaid Work	Housework	Childcare	Care for Old and Sick	Leisure
Female (ref: male)	0.552***	0.391***	0.169***	–0.008	–0.099
Female (ref: male)	(6.40)	(7.96)	(3.76)	(–0.28)	(–1.87)
Married	0.503***	0.073	0.537***	–0.108**	–0.346***
Married	(3.98)	(1.02)	(8.15)	(–2.70)	(–4.46)
N	1,019	1,019	1,019	1,019	1,019
r2_a	0.192	0.0946	0.331	0.0926	0.278
Other control variables	Yes	Yes	Yes	Yes	Yes

Source: Authors’ calculations based on survey.

Note: This table reports the main coefficient of interest (see full results in Table A4 in Appendix A). The sample selection includes people from 15 to 64 years old living in HCMC. The standard errors are clustered at the individual level. Dependent variables are time used for (1) total daily unpaid work, (2) housework, (3) childcare, (4) care of elders and the sick and (5) leisure time for personal activities. *p < .10, **p < .05 and ***p < .01.

Figure 12.

Source: Authors’ calculations based on survey.

Correlation Between Travelling Activity and Gender Roles in the Family

The results from the two previous sections show that there are gender differences in time spent on travel outside and time spent on activities in-house. These results indicate that women generally spend more time on activities in-house than men and spend less time travelling than men. The question arises whether there is a trade-off between travel time and housework time. This section will provide an answer through a gender lens.

To test hypothesis H₅ that there is a correlation between traffic participation time and gender roles in the family, the correlation between time spent on unpaid work and daily traffic times was examined. In Table 5, the coefficient of interest is the interaction term Female * Unpaid work, which shows that the difference between women and men in the correlation between time spent on unpaid work and daily traffic times is extremely significant. The second row shows that compared to men, if women spend an additional hour on housework, they will reduce their travel time by about 0.366 hours, mainly by reducing time spent on travel for work, education and training and other extracurricular activities. This suggests that women who spend more time on unpaid work will tend to choose jobs that require less travel to save time. This may cause them to do a trade-off with better job opportunities (Scarpari & Clay, 2020). The statistics also reflected the fact that more than half of the women say that a workplace close to home is an important factor when choosing a job (see Figure B1 in Appendix B).

Table 5.

Time Used for Unpaid Work vs. Daily Traffic Time (by Gender).

Travelling Hours	(1)	(2)	(3)	(4)	(5)	(6)
Travelling Hours	Total	Work	Edu.	Escort	Shop	Other
Unpaid work	0.516***	0.101**	0.186***	0.175***	0.129***	0.122***
Unpaid work	(8.91)	(3.12)	(7.29)	(9.73)	(8.04)	(5.80)
Female * Unpaid work	–0.366***	–0.131**	–0.171***	–0.077***	–0.084***	–0.107***
(Ref: Male)	(–4.94)	(–3.15)	(–5.24)	(–3.35)	(–4.08)	(–3.94)
N	1,023	1,028	1,031	1,031	1,031	1,024
r2_a	0.134	0.228	0.271	0.163	0.0972	0.134
Other control variables	Yes	Yes	Yes	Yes	Yes	Yes

Source: Authors’ calculations based on survey.

Note: This table reports the main coefficient of interest (see the full results in Table A5 in Appendix A). The sample selection includes people from 15 to 64 years old living in HCMC. The standard errors are clustered at the individual level. Dependent variables are traffic time for (1) total daily activities, (2) work activities, (3) education and training activities, (4) escorting activities, (5) shopping activities and (6) other daily activities. *p < .10, **p < .05 and ***p < .01.

The correlation between time spent on unpaid work and weekly traffic times was examined. In Table 6, the coefficient of interest is the interaction term Female * Unpaid work, which shows the difference between women and men in the time spent on unpaid work and weekly traffic times. In the second row, compared to men, if a woman increases her housework time by one hour per day, she will reduce her weekly travel time by about 0.446 hours per week, including travel for entertainment, visiting, health care and social activities.

Table 6.

Time Used for Unpaid Work vs. Weekly Traffic Time (by Gender).

	(1)	(2)	(3)	(4)	(5)
	Total	Ent.	Visit	Health	Social
Unpaid work	0.585***	0.161***	0.154***	0.152***	0.120***
Unpaid work	(9.69)	(5.70)	(6.72)	(10.24)	(6.11)
Female * Unpaid work (Ref: Male)	–0.446***	–0.143***	–0.096**	–0.106***	–0.102***
Female * Unpaid work (Ref: Male)	(–5.77)	(–3.95)	(–3.28)	(–5.57)	(–4.06)
N	1,028	1,030	1,031	1,031	1,029
r2_a	0.192	0.142	0.111	0.106	0.0893
Other control variables	Yes	Yes	Yes	Yes	Yes

Source: Authors’ calculations based on survey.

Note: This table reports the main coefficient of interest (see full results in Table A6 in Appendix A). The sample selection includes people from 15 to 64 years old living in HCMC. The standard errors are clustered at the individual level. Dependent variables are traffic time for (1) total weekly activities, (2) entertainment activities, (3) visiting activities, (4) health care activities and (5) social activities. *p < .10, **p < .05 and ***p < .01.

Conclusion

This article aims to explore gender differences in traffic time and unpaid work in HCMC. A survey of 1,150 people between the ages of 15 and 65, from different occupations and jobs, was conducted to collect data on their transport behaviour and activities. Random effect models were applied to analyse the differences between women and men in time spent travelling and doing unpaid work. The findings reveal the distinct transport patterns and behaviours of women and men, as well as their experiences and affordability of public transportation. The testing models significantly supported four among five initiative hypotheses, including H₁—women spend less in total traffic time than men do; H₂—women spend less time in traffic for commuting activities than men do; H₄—women spend more time doing unpaid work than men do; H₅—women trade travelling time outside for doing unpaid work inside. Hypothesis H₃ is not supported by the empirical estimation, which states that women spend more time in traffic for shopping and escorting activities than men do.

This research is at the forefront of explaining the link between travel time and unpaid work by gender. The connection between urban mobility and gender roles has been demonstrated here. The results imply that gender roles and norms shape the transport choices and preferences of women and men and that they have implications for decent work and economic growth. The findings suggest that public policies should take into account these gender differences and address the barriers and challenges that women face in accessing transport services.

Footnotes

Acknowledgements

This work was supported by the FFJ/Michelin Fellowship. The authors gratefully acknowledges the generous support and assistance of the Fondation France-Japon de l’EHESS (FFJ) and Michelin Foundation. This manuscript is a refined version of the authors’ own research presented in the working article http://ffj.ehess.fr/index/article/426.html.

Declaration of Conflicting Interests

The author declared no potential conflicts of interest with respect to the research, authorship and/or publication of this article.

Funding

The author received no financial support for the research, authorship and/or publication of this article.

Appendix A

Table A6.

Time Used for Unpaid Work Versus Weekly Traffic Time.

	(1)	(2)	(3)	(4)	(5)
	Total	Ent.	Visit	Health	Social
Unpaid work	0.585***	0.161***	0.154***	0.152***	0.120***
	(9.69)	(5.70)	(6.72)	(10.24)	(6.11)
Female (Ref: Male)	0.414*	0.137	0.0805	0.0877*	0.110*
	(2.50)	(1.78)	(1.29)	(2.16)	(2.06)
Female # Unpaid work (Ref: Male)	–0.446***	–0.143***	–0.096**	–0.106***	–0.102***
	(–5.77)	(–3.95)	(–3.28)	(–5.57)	(–4.06)
Age groups (Ref: 15–20 ages)
20s group	–0.372	–0.150	–0.074	–0.092	–0.053
	(–1.57)	(–1.35)	(–0.82)	(–1.58)	(–0.69)
30s group	–0.705**	–0.443***	–0.091	–0.126	–0.051
	(–2.59)	(–3.48)	(–0.88)	(–1.88)	(–0.58)
40s group	–0.702*	–0.421**	–0.136	–0.130	–0.0241
	(–2.49)	(–3.20)	(–1.28)	(–1.88)	(–0.26)
50s group	–0.481	–0.414**	–0.029	–0.035	–0.007
	(–1.55)	(–2.85)	(–0.25)	(–0.46)	(–0.07)
60–65	–0.553	–0.412*	–0.086	–0.089	–0.011
	(–1.24)	(–2.01)	(–0.51)	(–0.83)	(–0.08)
Education (Ref: Primary)
Secondary	0.185	0.086	0.061	0.033	–0.001
	(0.57)	(0.57)	(0.50)	(0.42)	(–0.00)
High school/vocational	0.921**	0.387*	0.229	0.031	0.266*
	(2.86)	(2.57)	(1.88)	(0.39)	(2.55)
College/university	1.129***	0.508***	0.313*	0.031	0.266*
	(3.47)	(3.34)	(2.55)	(0.39)	(2.53)
Postgraduate	1.371***	0.533**	0.446***	0.040	0.340**
	(3.90)	(3.24)	(3.35)	(0.46)	(2.99)
Income (in VND millions) (Ref: no income)
Less than 5	0.09	0.044	–0.127	0.068	0.106
	(0.35)	(0.37)	(–1.32)	(1.09)	(1.29)
5–10	–0.154	–0.0971	–0.133	0.052	0.028
	(–0.62)	(–0.83)	(–1.41)	(0.84)	(0.34)
10–15	–0.0567	0.00246	–0.111	0.0793	–0.0142
	(–0.21)	(0.02)	(–1.08)	(1.19)	(–0.16)
15–20	–0.009	0.073	–0.165	0.044	0.048
	(–0.03)	(0.50)	(–1.38)	(0.57)	(0.47)
More than 20	0.490	0.248	0.037	0.115	0.094
	(1.61)	(1.75)	(0.32)	(1.54)	(0.96)
Household size	–0.019	0.007	–0.014	–0.008	–0.005
	(–0.62)	(0.52)	(–1.16)	(–1.02)	(–0.45)
Constant	1.063**	0.535**	0.372**	0.168	–0.013
	(2.89)	(3.11)	(2.67)	(1.85)	(–0.11)
N	1,028	1,030	1,031	1,031	1,029
r2_a	0.192	0.142	0.111	0.106	0.0893
p	7.63e–40	1.57e–27	1.24e–20	2.16e–19	7.79e–16

Source: Authors’ calculations based on survey.

Note: The sample selection includes people from 15 to 64 years old living in HCMC. The standard errors are clustered at the individual level. Dependent variables: (1) total traffic time for weekly activities, (2) traffic time for entertainment activities, (3) traffic time visiting activities, (4) traffic time for health care activities, and (e) traffic time for social activities; *p < .10, **p < .05 and ***p < .01.

Appendix B

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