Home Advantage or Hidden Strain? The Mental Health Effects of Working from Home across Gender,Childcare Status,and Occupational Class before and since the Pandemic

Abstract

Although working from home (WFH) is promoted as a policy supporting work–life balance, whether it benefits mental health remains unclear. Few studies have examined how these effects vary across social groups or addressed selection issues complicating causal inference. We use two-way fixed-effects models to analyze changes in mental health scores, measured with the 12-item General Health Questionnaire, among 39,863 participants in the UK Household Longitudinal Study (2009–2023). We reduce selection bias by using an occupation-level WFH measure derived from the UK Labour Force Survey. Before March 2020, increased WFH in men’s occupations improved their mental health. For women, it benefited those in routine jobs but worsened outcomes for professionals. The pattern reversed from March 2020, with WFH positively impacting the mental health of professional women but not that of men or women in routine jobs. These findings highlight the importance of social positions and institutional contexts in shaping the mental health effects of WFH.

Keywords

gender mental health occupational class pandemic working from home

Sociology of stress scholars have conceptualized mental health inequalities as the result of unequal exposure and vulnerability to chronic stressors across axes of social inequalities such as age, gender, class, and race (Fenwick and Tausig 1994; McLeod 2015; Pearlin 1999). Work plays a central role in this process because it concentrates multiple sources of strain, including job insecurity, high job demands, and blurred work–life boundaries (Clougherty, Souza, and Cullen 2010). However, the nature of work has undergone profound transformations in recent decades, driven by work intensification, the rise of digital technologies, and increasing job insecurity (Kelly and Moen 2020). Therefore, examining how new work practices emerging from these shifts affect different groups of workers is essential for understanding the dynamics of mental health inequalities (Fan and Moen 2023; Kelly, Moen, and Tranby 2011).

One major shift is the rise of working from home (WFH), which accelerated during the COVID-19 pandemic and has now become a central feature of many jobs (Barrero, Bloom, and Davis 2023). By reducing commuting time (Barrero et al. 2020) and enhancing job autonomy and schedule control (Gajendran and Harrison 2007), WFH expands individuals’ temporal and psychosocial resources. As such, it may foster workers’ mental health by alleviating work-related stress and conflicts between work and family demands (Voydanoff 2005). The benefits may be particularly pronounced for women, who are disproportionately affected by such conflicts. Not only does WFH help them remain employed during periods of increased caregiving demands (Chung and van der Horst 2018), but it may also reduce the strain that they face when employed. Similarly, WFH may enhance fathers’ mental health by allowing them to take on more childcare (Carlson, Petts, and Pepin 2021), which is associated with higher emotional rewards for them (McDonnell, Luke, and Short 2019).

However, it remains unclear whether WFH uniformly supports or undermines workers’ mental health. Studies show that WFH can increase mothers’ involvement in childcare and housework, reinforcing the gendered division of unpaid labor (Chung and Booker 2023; Leshchenko and Chung, 2025) and, in turn, worsening their work–family conflicts (Yucel and Chung 2023). Consequently, women with caregiving responsibilities may remain employed yet experience chronic strain and higher mental distress when WFH (Beaufils, Barbuscia, and Cambois 2023; Cullati 2014). Similarly, WFH can lead men to work longer hours as work–family boundaries blur, especially in contexts where biases against flexible workers persist (Lott and Chung 2016), thereby potentially harming their mental health (Berniell and Bietendeck 2020). Furthermore, how WFH affects mental health likely varies with workers’ caregiving responsibilities and occupational position. Indeed, parenthood increases unpaid labor demands (Craig and Mullan 2010), making them more exposed to work–family conflicts that WFH may either exacerbate or alleviate. Workers’ position in the occupational hierarchy influences the degree of autonomy that WFH provides (Gerstel and Clawson 2014) and access to alternative resources for managing competing work and family demands, such as flexible schedules, paid leave, and outsourcing (Kim et al. 2020).

In this article, we examine the impact of WFH on mental health across subgroups defined by gender, childcare responsibilities, and occupational class. We use data from the UK Household Longitudinal Survey and the UK Labour Force Survey. The United Kingdom provides a relevant context because it stands out among European countries for the scale and institutionalization of WFH following the pandemic: As of 2024, over a quarter of workers reported mainly WFH, and nearly half reported doing so at least occasionally (Chung and Yuan 2025).

Building on the existing literature, we ask the following questions:

Research Question 1: What is the causal impact of WFH on the mental health of men and women in the United Kingdom?

Research Question 2: Do the mental health effects of WFH vary by gender, childcare status, and occupational class?

Research Question 3: How have these effects changed before and since the pandemic, which triggered a sharp increase in WFH?

We make several contributions. First, we employ a novel empirical strategy, inspired by Belloni, Carrino, and Meschi (2022), to estimate the causal impact of WFH on mental health. This strategy addresses selection into WFH, where mental health influences the decision to work from home or the ability to do so. By linking the UK Labour Force Survey to the UK Household Longitudinal Study, we construct a detailed occupational-level measure of WFH prevalence. This measure strongly predicts individual WFH behavior yet is unlikely to be influenced by individuals’ mental health, thereby helping to address reverse causality. Additionally, our models include individual fixed effects, allowing us to examine within-person changes in mental health associated with WFH while controlling for all time-invariant individual characteristics that could confound this relationship. Unlike group-randomized workplace interventions (Moen et al. 2011), our approach enables us to generalize causal claims across diverse social groups, providing a broad picture of the effects of WFH in the United Kingdom.

Second, we adopt an intersectional approach (Collins and Bilge 2020) to investigate whether the effects of WFH on mental health vary according to individuals’ social structural locations—specifically, gender, childcare status, and occupational class. Although previous causal studies have primarily focused on gender differences, these effects are likely to be shaped by multiple, intersecting axes of structural inequalities. Building on the job demands–resources model (Bakker and Demerouti 2007), role strain theory (Greenhaus and Beutell 1985), and the stress process model (Pearlin 1999), we argue that WFH affects workers’ mental health through its interaction with job-related stressors and their interference with family demands. We hypothesize that gender, childcare responsibilities, and occupational class shape how exposed and vulnerable individuals are to these stressors and that the mental health impact of WFH is likely to vary accordingly.

Third, we compare the impact of WFH on mental health before and after onset of the COVID-19 pandemic. Although numerous studies have examined this relationship either before or during the pandemic (Lunde et al. 2022; Vacchiano, Fernandez, and Schmutz 2024), to our knowledge, none have used the same data and analytical approach for both periods. This comparison enables us to question whether broader institutional and normative contexts influence the mental health consequences of WFH. As WFH has become normalized and organizations have adapted during and after the pandemic, it may have become less likely to exacerbate job-related stressors and work–family conflicts, potentially supporting workers’ mental health (Fan and Moen 2023, 2025).

Background

A Review of the Mechanisms Linking Working from Home and Mental Health

WFH may benefit the mental health of both men and women by limiting their exposure and vulnerability to stressors stemming from job demands and their interference with family demands. The job demands–resources model (Bakker and Demerouti 2007) conceptualizes work as involving demands (e.g., workload, physical strain, role conflict) that can lead to job strain if not offset by job resources (e.g., autonomy, social support). These resources help buffer the chronic strain produced by such demands, thereby protecting mental health. By increasing job autonomy, WFH provides psychosocial resources that help workers cope with high job demands and reduce job strain (Gajendran and Harrison 2007). It also enhances schedule control and eliminates commuting time (Barrero et al. 2020), making it a “boundary-spanning resource” that helps workers manage competing demands across different life domains (Voydanoff 2005:829–30). In doing so, it may reduce work–family conflicts (Baum and Rau 2024; Laß and Wooden 2023), defined as situations where work interferes with family responsibilities, which are known to cause chronic strain that negatively affects mental health in both the short and long terms (Greenhaus, Allen, and Spector 2006; Tsukerman, Leger, and Charles 2020). Additionally, WFH availability may strengthen workers’ perceptions of support from managers and organizations, which plays a role in reducing work–family conflicts (Allen 2001).

Conversely, WFH may negatively affect workers’ mental health by increasing rather than decreasing job strain and work–family conflicts. Technologies enabling WFH foster a “norm of responsiveness” that raises job demands through a faster work pace and a culture of “always-on” (Chesley 2014:591; Mazmanian, Orlikowski, and Yates 2013). At the same time, employees who work from home often report lacking supervisor and colleague support, which is a critical resource for coping with high demands (Ha 2021). This imbalance between rising demands and diminishing resources may heighten job strain and offset the autonomy benefits of WFH (Bakker and Demerouti 2007). Moreover, because WFH blurs the physical and psychosocial boundaries between work and family (Clark 2000), it may increase rather than decrease conflicts between the two spheres, acting as “boundary-spanning demands” (Voydanoff 2005:829–30). Evidence indeed suggests that workers who work from home frequently multitask between professional and caregiving duties (Andrew et al. 2020) and struggle to disconnect from work (Kelly and Moen 2020; Mazmanian et al. 2013). They tend to extend their working hours, especially in the case of men (Chung 2022; Glass and Noonan 2016), or their childcare and housework hours, especially in the case of women (Chung and Booker 2023; Leshchenko and Chung 2025). These factors together create conditions for increased job strain and heightened work-to-family and family-to-work conflicts (Yucel and Chung 2023). This may be exacerbated in contexts where the “ideal worker” norm prevails, whereby employees are expected to work long hours and prioritize work over family responsibilities (Acker 1990; van der Lippe and Lippényi 2020).

Existing Evidence on the Mental Health Impact of Working from Home

A large body of research has examined the mental health impact of WFH, with an almost even split between studies reporting negative and positive outcomes (Lunde et al. 2022; Vacchiano et al. 2024). However, few of these studies have used designs that address reverse causality. For example, recent research using UK Household Longitudinal Study (UKHLS) data shows that when individuals gained access to and started using WFH, their mental health improved, for both men and women (Li and Wang 2022). However, this may partly reflect that healthier individuals are more likely to request or receive WFH due to stronger networks or better relations with employers (Hoque and Bacon 2022). Unobserved factors associated with both WFH access and mental health (e.g., holding higher prestige jobs with greater autonomy) may also explain why WFH workers report better mental health (Wheatley 2017). Conversely, studies finding a negative impact of WFH on mental health may be because individuals in poorer health are more likely to request WFH to manage the challenges they face in traditional work environments (Schur, Ameri, and Kruse 2020).

Among the studies that have used empirical designs to address these issues, a few randomized controlled trials conducted before the pandemic show that employees’ control over their workplace improves their mental well-being. For instance, Moen et al. (2016) demonstrated that in a large tech company, an intervention that increased employees’ control over their work location significantly reduced burnout, perceived stress, and psychological distress. Similarly, Bloom et al. (2015) reported higher productivity and job satisfaction among employees who were WFH for nine months in a large Chinese call center. Experiments in Italy and Belgium have also found that partial remote working, one to two days per week, improves well-being (Angelici and Profeta 2024; Delanoeije and Verbruggen 2020). A recent instrumental variable study based on access to mobile work equipment showed positive mental health effects of WFH, especially for men and middle-aged workers (Denzer and Grunau 2024). However, because these studies use nonrepresentative samples, their findings may reflect firm-specific factors, such as organizational culture and how WFH policies are implemented.

Heterogeneity by Gender, Childcare Status, and Occupational Class

The mental health impact of WFH likely differs by gender and childcare status. Because women continue to bear a disproportionate share of unpaid labor, they experience greater strain in balancing work and family demands (Sullivan 2021). This strain is more pronounced among mothers with dependent children, whose circumstances increase both childcare and housework (Office for National Statistics 2019). For these reasons, in this article, we define childcare status as the presence of children under 15 in the household because this entails daily caregiving demands. Hence, where WFH serves as a boundary-spanning resource, facilitating work–family integration, women—especially mothers—may benefit more than other groups. Conversely, where WFH acts as boundary-spanning demands, intensifying work–family conflict, their mental health would be more negatively affected (Voydanoff 2005:829–30). The latter scenario is likely in organizations where the ideal worker norm and the flexibility stigma dominate (van der Lippe and Lippényi 2020). Indeed, mothers are particularly vulnerable to this stigma, which compounds existing stereotypes about their professional commitment (Blair-Loy 2009; Ridgeway and Correll 2004). In response, they may adopt compensatory behaviors, such as increasing their working hours or staying constantly available online (Howcroft et al. 2025), which can heighten work–family conflicts.

By contrast, fathers who work from home tend to increase their working hours, which may negatively affect their mental health (Glass and Noonan 2016; Kim et al. 2020). Meanwhile, their unpaid care time increases only moderately (Carlson et al. 2021) to a level unlikely to generate work–family conflicts and potentially be beneficial to their mental health given that evidence shows that fathers’ involvement in childcare relate to emotional rewards (McDonnell et al. 2019). This combination—longer working hours but moderate domestic involvement—reflects the influence of prior household bargaining dynamics that continue to shape the division of labor under new professional arrangements, enabling men to maintain clear work–family boundaries and expand professional duties rather than take on additional unpaid tasks (Carlson et al. 2021). As a result, we expect fathers’ mental health to remain stable or even improve under WFH because the potential negative effects of longer working hours may be offset by the benefits of increased job autonomy and engagement in childcare.

The mental health impact of WFH also likely varies by individuals’ occupational class. In this article, we measure occupational class using the National Statistics Socio-economic Classification (NS-SEC), which captures key differences in income and working conditions (Williams 2017). We assume these factors to shape both the experience of WFH and its impact on mental health.

Among higher status occupations, the autonomy and schedule control afforded by WFH may be crucial resources for coping with the “stress of higher status,” which involves greater job demands, longer hours, and higher work–family conflicts (McGinnity and Calvert 2009; Schieman, Whitestone, and Van Gundy 2006:243). In this context, WFH may be particularly beneficial to their mental health. However, this may be less true for mothers in these roles, who are more likely to engage in the aforementioned compensatory behaviors due to the significant stigma surrounding flexibility in high-status professional settings (Stone and Hernandez 2013).

By contrast, workers in lower status occupations generally have less control over their work, with low job and schedule autonomy (Damaske, Zawadzki, and Smyth 2016; Tausig and Fenwick 2011). For them, the autonomy brought by WFH may then be a key resource to mitigate stress levels (Kim et al. 2020). Although traditional gender norms might suggest greater unpaid care burdens and stronger work–family conflicts for women in these roles when WFH, recent evidence reveals a more complex picture: WFH fosters more egalitarian arrangements in low-income families, with men in lower status jobs taking on more routine domestic tasks when WFH (Chung and Booker 2023). Given that these groups have fewer resources to outsource caregiving (Gerstel and Clawson 2014), WFH may serve as a critical buffer, enabling households to meet caregiving demands and supporting mental health for both partners.

Comparing Prepandemic and Postpandemic Periods

The COVID-19 pandemic has fueled some research using population-based cohort data and causal identification strategies to estimate the impact of WFH on mental health. Most studies report that WFH negatively affected mental health during the pandemic, especially among women and parents of dependent children (Bertoni et al. 2025; Gueguen and Senik 2023; Senik et al. 2024). However, these findings may not generalize to contexts outside of the pandemic, which introduced specific stressors that partly confounded the mental health risks of WFH (e.g., fear of infection, social isolation, increased caregiving responsibilities; Perelman et al. 2021). WFH may also have generated specific forms of work–family conflicts and job strain during the pandemic due to the conditions under which it was implemented. In many cases, it was mandatory and full-time (Henke et al. 2016), and workers lacked adequate support, such as sufficient technical infrastructure, IT assistance, or access to a dedicated workspace (Wang et al. 2021). Caregiving demands likely exacerbated these challenges, particularly following the closure of schools and childcare facilities (Bernhardt, Recksiedler, and Linberg 2023).

However, we expect changes as lockdown restrictions in the United Kingdom eased from mid-2021. Childcare facilities and schools reopened, and organizations invested in infrastructure and introduced managerial practices supporting WFH (Wheatley, Hardill, and Buglass 2021). These changes may have helped workers adapt to WFH and benefit from the resources it offers for integrating work and family demands. In parallel, the rise in WFH during the pandemic may have contributed to changing workplace norms, reducing the career penalties and stigma previously associated with flexible working (Fan and Moen 2023; Schieman, Badawy, and Hill 2022). This may have also reduced homeworkers’ tendencies to work longer hours to reciprocate for the “gift” of WFH, as observed prepandemic (Kelliher and Anderson 2010). As a result, the potential benefits of WFH for mental health may have increased in the context of the pandemic and its aftermath. This idea is corroborated by recent research showing that continuing to WFH is linked to greater reductions in psychological distress compared to returning to the office between October 2020 and April 2022 in the United States (Fan and Moen 2023, 2025).

WFH may have also reduced its mental health benefits for some. As WFH spread to a broader range of jobs, including those with lower levels of control, it became more closely supervised (Fana, Massimo, and Moro 2022), reducing the autonomy that supports mental health. For men, this may have been compounded by increased role conflicts during the pandemic as WFH fathers were able to be more involved in childcare and housework (Chung, Birkett, Forbes and Seo, 2021): Greater involvement in childcare and housework may have led some to experience family-to-work conflicts as they were less accustomed to daily domestic demands (Reimann, Peters, and Diewald 2022).

Data and Method

Data

We used data from Waves 1 to 14 (2009–2023) of the UK Household Longitudinal Study (UKHLS), also known as Understanding Society. This is a nationally representative panel survey comprising a stratified, clustered sample of around 60,000 individuals from 40,000 UK households. All adults ages 16 and over in these households are interviewed annually. Response rates have remained high, with 63.6% of eligible adults completing a full interview in Wave 14 and an overall attrition rate of approximately 21%.

For our analysis, we restricted our sample to respondents ages 18 to 65 who were interviewed at least twice, excluding those who were unemployed, economically inactive, or self-employed. After removing observations with missing data (approximately 10%), the final sample comprised 31,761 respondents, amounting to 195,901 person-wave observations. The health and sociodemographic characteristics of the sample are detailed in Appendix A in the online version of the article.

Additionally, we used data from the UK Labour Force Survey (UK LFS), a continuous, nationally representative survey that samples approximately 40,000 households (100,000 individuals) every quarter. The UK LFS provides harmonized, high-frequency data on labor market status, hours worked, job characteristics (including WFH), and occupation and industry codes based on classifications from the Standard Occupational Classification (SOC) and the Standard Industrial Classification.

Measures

Mental Health Outcome: General Health Questionnaire (UKHLS)

We measured mental health using the 12-item General Health Questionnaire (GHQ-12), a widely used and validated screening tool designed to detect psychological distress and evaluate overall mental functioning (Goldberg et al. 1997). The GHQ-12 collects self-reported information on various symptoms, such as loss of concentration, sleep disturbances, difficulty making decisions, unhappiness, and loss of confidence. Respondents rate each item on a 4-point Likert scale ranging from 0 (better than usual) to 3 (much less than usual). We computed a summary index ranging from 0 to 36, with higher scores indicating lower psychological distress and better mental health.

In sensitivity analyses, we also assessed the robustness of our findings across mental health indicators using the Mental Component Summary from the 12-item Short Form Health Survey (MCS-12), a validated measure of psychological well-being (Huo et al. 2018). Scores were standardized from 0 to 100, with again higher values indicating better mental health.

Working from Home (UK LFS)

In the UKHLS, respondents were asked every two waves whether their employer offered the option to work from home and whether they used it. This self-reported WFH measure has been used to study the health effects of flexible working (Chandola et al. 2019; Li and Wang 2022) but, as discussed previously, introduces selection issues.

To address this issue, we used an external, occupational-level measure of WFH prevalence that we built from the UK LFS. In the UK LFS, respondents are asked: “In your main job, do you work mainly . . . (1) in your own home, (2) in the same grounds or buildings as your home, (3) in different places using home as a base, or (4) somewhere quite separate from home?” We classified responses 1 to 3 as indicating as “mainly working from home” and computed, for each quarter and each four-digit SOC2010 occupation, the proportion of individuals mainly WFH.

This occupation- and quarter-specific prevalence was then assigned to UKHLS respondents based on their four-digit SOC2010 occupation and the quarter in which they were interviewed. As a result, each UKHLS respondent was matched with a measure of the prevalence of WFH in their occupation at each wave as independently observed in the LFS.

This variable, which we referred to as average occupational-level WFH, reflected typical WFH use in the respondent’s occupation at the time of interview. Because individuals’ mental health is unlikely to affect WFH prevalence at the occupational level, this measure helped reduce selection bias and allowed us to approximate a quasi-experimental design by leveraging exogenous variation in WFH. It also offered more granular, wave-by-wave tracking of WFH compared to the biennial UKHLS self-reports. Furthermore, our occupation-level measure was based on the proportion of individuals who report “mainly” WFH, capturing regular and intensive WFH use. The UKHLS question asked only whether respondents used WFH at all and may therefore have included occasional or limited use.

Childcare Status and Occupational Class (UKHLS)

To describe childcare responsibilities, we created a variable indicating whether individuals live with at least one child under the age of 15. This cutoff reflected children primarily in the presecondary and early secondary school period, when parental time demands were higher due to children’s limited autonomy (Office for National Statistics 2019). It therefore represented substantial childcare responsibilities while maintaining a sufficient sample size. Alternative specifications were tested (e.g., presence of children age under 3 or 11 in the household; number of coresident children), yielding similar results but with less precise estimates.

Occupational class was measured using the three-category NS-SEC version, which groups occupations by the difficulty of supervising work and the specificity of skills required (Williams 2017). The first category, managerial and professional occupations, includes managers, senior professionals, and higher level administrative roles (e.g., company directors, lawyers, doctors, professors), characterized by high skill specificity and control over work. The second, intermediate occupations, covers clerical, sales, and associate professional roles (e.g., office administrators, nurses, teaching assistants), with moderate autonomy, responsibility, and semiroutine tasks. The third, routine and manual occupations, comprises elementary service and manual roles (e.g., cleaners, security guards, factory operatives, delivery drivers), involving lower autonomy, routine tasks, and higher supervision.¹

Modeling Strategy

First, we verified that our occupational-level WFH measure is a valid proxy for individual WFH behavior by comparing its trend from 2010 to 2023 with self-reported data. We also tested this association using two-way fixed-effects models, controlling for the same variables as in the mental health analyses described in the following.

We then estimated linear two-way fixed-effects regression models to examine the impact of occupational-level WFH on GHQ-12. Individual fixed effects controlled for time-invariant characteristics of individuals, whether observed (e.g., race, education, family background) or unobserved (e.g., attitudes), and time fixed effects accounted for period-specific factors affecting all individuals at a given time (e.g., economic recessions). This modeling strategy increased confidence in our estimates showing causal effects (Allison 2009). Nonetheless, our fixed-effects models could not fully account for changes over time in who engages in WFH or in the conditions under which WFH is implemented. As a result, estimated effects may partly reflect shifts in the composition of homeworkers or the characteristics of WFH. We therefore interpreted our estimates as capturing the average effects of WFH under the prevailing conditions at each period rather than a stable, exogenous treatment effect.

Building on this framework, our models were specified as:

\begin{array}{l} Y_i t = β_{1} . W F H_j t + β_{2} . X_i t + α_i + \\ λ_t + ε_i t, \end{array}

where Y_it was the mental health score for individual i at time t, WFH_jt was the average WFH prevalence in occupation j at time t, X_it was a vector of time-varying controls, α_i were individual fixed effects, λ_t were wave fixed effects, and ε_it was the error term.

We estimated three sets of models to examine the impact of average occupational-level WFH on mental health and its heterogeneity by gender, childcare responsibilities, and occupational class. First, we included an interaction term between average occupational-level WFH and gender to assess gender differences in the effect. Second, we estimated models stratified by gender that included an interaction between average occupational-level WFH and childcare status. Third, we similarly estimated gender-stratified models interacting average occupational-level WFH with occupational class to explore how this effect varied along the occupational gradient. All analyses were conducted separately for the prepandemic (before March 2020) and postpandemic (from March 2020 onward) periods.

In all models, we controlled for time-varying sociodemographic and work-related factors likely to influence both occupational changes and mental health outcomes. These include age (mean-centered), age squared, marital status (never married, married/cohabiting, divorced/separated/widowed), household composition (presence of children and age of the youngest child), logged real net monthly income, working hours, and job changes between waves.

For all models, we report both parameter estimates and average marginal effects to facilitate interpretation and comparison across subgroups.

Results

Average Occupational-Level WFH in UK LFS and Self-Reported Access to and Use of WFH in UKHLS

Figure 1 shows the evolution of average WFH between 2010 and 2023, comparing the measures from the UK LFS and the UKHLS. The proportion of individuals reporting access to and use of WFH in the UKHLS follows closely the same pattern as the proportion of those reporting mainly WFH in the UK LFS for both men and women. Both sources show a consistent upward trend, with a marked increase from 2020 onwards. As shown in Appendix B in the online version of the article, this increase is concentrated in intermediate and professional occupations, and routine occupations show no such trend. Nevertheless, some routine roles (e.g., call and contact center roles, telephonists, communication operators, market research interviewers, customer service representatives, fitness instructors) have experienced an increase in WFH over time. This variation likely contributes to the findings presented later on the impact of average occupational-level WFH on the mental health of individuals in routine jobs.

Figure 1.

Proportion of Individuals Reporting Access to and Use of WFH in Their Main Occupation in UKHLS and Proportion of Individuals Reporting Being Mainly WFH in UK LFS by Year.

It is important to note that although WFH became widespread in 2020 due to lockdowns, it was only in the summer of 2021, once the official lockdown restrictions were eased, that workers began reporting home as their main workplace. In other words, despite the widespread adoption of WFH during the pandemic, employees initially regarded it as a temporary measure rather than a permanent shift. It was not until 2022 that workers widely recognized WFH as a lasting work arrangement, explaining the increase in its reported prevalence during that period.

Appendix C in the online version of the article presents results from two-way fixed-effects models that examine the relationship between average occupational-level WFH and individuals’ self-reported access to and use of WFH. They confirm the occupational-level measure as a robust proxy for individual WFH practices: Increases in WFH prevalence within occupations over time correlate with higher self-reports of WFH use in the UKHLS. Notably, these associations are consistent for both men and women and remain significant when analyses are restricted to the prepandemic period, suggesting that this relationship is not solely driven by the pandemic context.

Gender Differences in the Effect of WFH on Mental Health

Table 1 presents results from fixed-effects models estimating the effects of average occupational-level WFH on GHQ-12 scores. It examines how changes in the average proportion of WFH within individuals’ occupations impact their GHQ scores and whether this differs between men and women. As mentioned above, higher scores indicate better mental health.

Table 1.

Effects of Average Occupational-Level WFH on GHQ-12, with Gender Interaction (Two-Way Fixed-Effects Models).

Period	Term	Estimate	95% Confidence Interval	p Value
January 2009–February 2020	Mean WFH	.682	[.183, 1.182]	.008
	Mean WFH × women	−.861	[−1.627, −.094]	.028
	N respondent-wave observations = 158,777N unique respondents = 29,331Adjusted R² = .419Within R² = .004
March 2020–October 2023	Mean WFH	−.316	[−1.106, .475]	.433
	Mean WFH × women	1.415	[.267, 2.563]	.016
	N respondent-wave observations = 18,590N unique respondents = 8,193Adjusted R² = .486Within R² = .006

Sources: Data are from the UK Household Longitudinal Study and the UK Labour Force Survey.

Note: WFH = working from home; GHQ-12 = 12-item General Health Questionnaire.

Before March 2020, increases in average occupational-level WFH led to significant improvements in men’s mental health, as shown by the positive main effect on the GHQ-12. However, the significant negative interaction term indicates that this benefit was smaller for women. As illustrated in Figure 2, the marginal effect of average occupational-level WFH on the GHQ-12 was positive and statistically significant for men but nonsignificant for women.

Figure 2.

Marginal Effects of Average Occupational-Level WFH on GHQ-12 by Gender, before and from March 2020.

From March 2020 onward, this gender pattern reversed. Significant positive interaction terms suggest that women gained greater mental health benefits than men as WFH became more common in their occupations. Marginal effects show that increases in average occupational-level WFH led to significant improvements in women’s GHQ scores, and effects for men were nonsignificant. Appendix D in the online version of the article shows that MCS-12 results follow the same pattern as the GHQ-12, confirming consistency across mental health measures.

Variations in the Effect of WFH on Mental Health with Childcare Status

Table 2 presents estimates from gender-stratified fixed-effects models assessing whether the effect of average occupational-level WFH on GHQ-12 scores varies by childcare status (living with children under 15 years old).

Table 2.

Effects of Average Occupational-Level WFH on GHQ-12, with Childcare Status Interaction (Two-Way Fixed-Effects Models).

Period	Term	Estimate	95% Confidence Interval	p Value
Women
January 2009–February 2020	Children < 15 years old (vs. no children)	.076	[−.084, .235]	.353
	Mean WFH	.309	[−.391, 1.01]	.386
	Mean WFH × children < 15 years old (vs. no children)	−.962	[−1.905, −.019]	.046
	N respondent-wave observations = 87,361N respondents = 16,053Adjusted R² = .414Within R² = .002
March 2020–October 2023	Children < 15 years old (vs. no children)	−.254	[−1.011, .502]	.509
	Mean WFH	.848	[−.461, 2.157]	.203
	Mean WFH × children < 15 years old (vs. no children)	.037	[−1.68, 1.754]	.966
	N respondent-wave observations = 15,903N respondents = 5,902Adjusted R² = .482Within R² = .004
Men
January 2009–February 2020	Children < 15 years old (vs. no children)	−.156	[−.351, .038]	.115
	Mean WFH	.728	[.091, 1.366]	.025
	Mean WFH × children < 15 years old (vs. no children)	−.137	[−1.023, .748]	.760
	N respondent-wave observations = 74,297N respondents = 13,687Adjusted R² = .419Within R² = .005
March 2020–October 2023	Children < 15 years old (vs. no children)	−.068	[−.773, .637]	.849
	Mean WFH	.07	[−.863, 1.004]	.882
	Mean WFH × children < 15 years old (vs. no children)	−.399	[−1.564, .767]	.501
	N respondent-wave observations = 13,823N respondents = 5,107Adjusted R² = .532Within R² = .003

Sources: Data are from the UK Household Longitudinal Study and the UK Labour Force Survey.

Note: WFH = working from home; GHQ-12 = 12-item General Health Questionnaire.

For men, no significant interaction between average occupational-level WFH and childcare status was found in either period.

For women, before March 2020, the interaction was significant, indicating that women with children under 15 experienced smaller mental health benefits from WFH compared to those without children. However, the marginal effects of average occupational-level WFH presented in Figure 3 were not significant for women regardless of childcare responsibilities, nor did they differ significantly between groups, indicating no clear evidence of an effect within or between these subgroups.

Figure 3.

Marginal Effects of Average Occupational-Level WFH on GHQ-12 by Gender and Childcare Status, before and from March 2020.

Variations in the Effect of WFH on Mental Health with Occupational Class

Table 3 presents estimates from gender-stratified fixed-effects models assessing whether the effects of average occupational-level WFH on GHQ-12 scores vary by occupational class.

Table 3.

Effects of Average Occupational-Level WFH on GHQ-12, with Occupational Class Interaction (Two-Way Fixed-Effects Models).

Period	Term	Estimate	95% Confidence Interval	p Value
Women
January 2009–February 2020	Mean WFH	−.735	[−1.459, −.011]	.047
	Mean WFH × intermediate (vs. professional)	.627	[−.482, 1.736]	.267
	Mean WFH × routine (vs. professional)	2.507	[1.398, 3.617]	.000
	N respondent-wave observations = 86,355N = 15,953Adjusted R² = .414Within R² =.003
March 2020–October 2023	Mean WFH	1.071	[.016, 2.127]	.047
	Mean WFH × intermediate (vs. professional)	−.577	[−1.949, .795]	.409
	Mean WFH × routine (vs. professional)	−2.955	[−5.927, .016]	.051
	N respondent-wave observations = 15,427N respondents = 5,774Adjusted R² =.476Within R² = .005
Men
January 2009–February 2020	Mean WFH	.485	[−.200, 1.17]	.165
	Mean WFH × intermediate (vs. professional)	−.047	[−1.108, 1.013]	.930
	Mean WFH × routine (vs. professional)	.497	[−.373, 1.366]	.262
	N respondent-wave observations = 73,415N respondents = 13,599Adjusted R² = .419Within R² = .005
March 2020–October 2023	Mean WFH	.019	[−.879, .916]	.968
	Mean WFH × intermediate (vs. professional)	−.401	[−1.961, 1.16]	.614
	Mean WFH × routine (vs. professional)	2.111	[−.386, 4.608]	.097
	N respondent-wave observations = 13,228N respondents = 4,953Adjusted R² = .536Within R² = .006

Sources: Data are from the UK Household Longitudinal Study (Understanding Society) and the UK Labour Force Survey.

Note: WFH = working from home. GHQ-12 = 12-item General Health Questionnaire.

Before March 2020, among women, significant interaction terms indicate that the effects of average occupational-level WFH varied across occupational classes. Specifically, higher average occupational-level WFH led to poorer mental health for women in professional occupations, as indicated by negative main effects on the GHQ-12. The interaction effects for routine occupations are positive and significant, indicating that higher average occupational-level WFH benefited more women in routine jobs than those in professional roles. Figure 4 illustrates these patterns, showing significantly positive marginal effects of average occupational-level WFH on the GHQ-12 for women in routine occupations, contrasted with negative marginal effects for those in professional roles. Analyses with the MCS-12 (Appendix F in the online version of the article) again show similar patterns, confirming the heterogeneity of the effect of average occupational-level WFH on mental health across occupational classes.

Figure 4.

Marginal Effects of Average Occupational-Level WFH on GHQ-12 by Gender and Occupational Class, before and from March 2020.

From March 2020 onward, this trend reversed. Increases in average occupational-level WFH led to significant improvements in the mental health of women in professional occupations, as indicated by positive main effects on the GHQ-12. However, a significant interaction term shows that effects on the GHQ-12 were significantly smaller for women in routine occupations. As shown in Figure 4, the marginal effect of occupational-level WFH on mental health was positive and statistically significant only for women in professional roles. Again, sensitivity analyses using the MCS-12 show similar results.

Among men, no significant interactions between average occupational-level WFH and occupational class were found in either period, suggesting that the mental health effects of WFH were homogeneous across occupational classes.

Discussion

In this article, we provide a holistic picture of the impact of WFH on mental health in different subgroups of the UK population before and after its widespread adoption during the COVID-19 pandemic. By focusing on within-individual changes and using an external occupational-level measure of WFH, we address selection issues and provide robust causal evidence on its mental health impact at the UK population level. Although previous research has primarily focused on gender differences, we demonstrate that the mental health consequences of WFH vary at the intersection of gender, childcare responsibilities, and occupational class and that these patterns have shifted as WFH became more institutionalized following the onset of the pandemic.

Our findings indeed reveal important heterogeneity in the mental health effects of WFH. Before March 2020, increases in the proportion of individuals WFH within men’s occupations had significant positive effects on their mental health, and these effects were homogeneous across occupational classes. For women, variations in the level of WFH within their occupation did not have significant overall effects on mental health; however, analyses revealed heterogeneous effects across occupational classes. Specifically, increased WFH worsened mental health for women in higher status positions—where WFH was more common—and it improved mental health for women in routine occupations—where WFH remained rare.² The significant interaction between childcare status and WFH suggests that stressors at the work–family interface underlie these differences in the mental health effects of WFH.

Several mechanisms may explain the patterns observed in how changes in WFH within occupations affected mental health across gender and occupational class before the pandemic. For men, the positive effect of WFH likely reflects that they benefited from the increased job resources associated with WFH, such as job autonomy or schedule control, that helped them manage job demands while enabling greater and rewarding childcare involvement (Carlson et al. 2021). This aligns with evidence that before the pandemic, WFH was primarily accessible to workers with health limitations or in highly skilled positions, for whom it provided greater job autonomy (Milasi, González-Vázquez, and Fernández-Macías 2021). It was also associated with a modest increase in unpaid labor, insufficient to generate role conflicts (Wheatley 2012).

For women in higher status roles, the observed negative impact of WFH mental health echoes prior research showing that unlike other flexible arrangements, such as reduced hours or flextime, WFH can lead to greater work–life conflicts (Russell, O’Connell, and McGinnity 2009; Yucel and Chung 2023). This suggests that for these higher status women, who constituted most women WFH before the pandemic, WFH acted as boundary-spanning demands: It allowed them to maintain demanding, lucrative positions, often at the cost of increased work–family conflicts offsetting the benefits of job autonomy (Chung and van der Horst 2018). These conflicts may have been further intensified by behaviors aimed at countering the flexibility stigma, such as extending work hours or multitasking (Cech and Blair-Loy 2014; Stone and Hernandez 2013).

By contrast, the positive impact of WFH that we observe for women in lower status occupations suggests that WFH may have functioned as a key boundary-spanning resource for them. Because these women have lower schedule control (Damaske et al. 2016; Tausig and Fenwick 2011) and fewer resources to outsource unpaid care (Gerstel and Clawson 2014), WFH likely helped them better integrate work and family demands (see also, Chung and Booker, 2023). This aligns with research integrating individuals’ occupational class into the job demands–resources framework, which finds that job resources reduce work strain more for lower status than higher status workers (Koltai and Schieman 2015). Taken together, those results indicate that at that time, WFH was not an effective workplace policy for alleviating psychological distress among higher status women, who most frequently used it, although it did benefit those with fewer resources to manage competing work and family demands.

Interestingly, patterns of gender and occupational class differences in the mental health effects of WFH reversed from March 2020. Women, especially those in higher status jobs, experienced significant improvements in their mental health as the level of WFH within their occupation increased, but no effect was observed for men. It is important to note that in the UK LFS, the increase in WFH from March 2020 reflects workers whose home-based work arrangements persisted beyond the initial months of the pandemic rather than those who temporarily shifted to WFH. Then, these results may highlight the negative impact of returning to in-person work after the early pandemic period (Fan and Moen 2023, 2025) rather than the benefits of WFH itself.

Still, there are several reasons why WFH may have become beneficial for the mental health of professional women in the postpandemic period. First, as WFH became more normalized, the stigma surrounding flexible working, which disproportionately affects them, may have weakened (Blair-Loy 2009; Ridgeway and Corell 2024), along with the compensatory behaviors that it triggers that tend to exacerbate job strain and work–family conflicts (Howcroft et al. 2025). Additionally, WFH-related strain may have been alleviated as organizational and policy support for remote work increased (Wheatley et al. 2021).

Conversely, several factors may explain why the mental health benefits of WFH observed before the pandemic did not persist for men from March 2020 onward. As noted previously, WFH expanded to a broader range of occupations, and studies note that new forms of control emerged, including bureaucratic procedures and technology-mediated managerial oversight (Fana et al. 2022). This likely reduced the autonomy WFH provided and consequently, its associated mental health benefits. In addition, men WFH assumed greater unpaid labor during the pandemic (Chung et al., 2021), exposing some to heightened work–family conflicts (Collins et al. 2021). Similar dynamics may also explain why the prepandemic mental health benefits of WFH weakened for women in routine jobs. Once limited to very specific cases, WFH expanded to a wider range of jobs while childcare demands increased and home-working conditions remained less supportive than in higher status jobs (Ewers and Kangmennaang 2023). Further research using postpandemic data is needed to determine whether these trends have persisted and whether the normalization and institutionalization of WFH have durably reshaped its mental health effects.

Importantly, our results show that for both time periods, the mental health impact of WFH varies across individuals’ social structural location, particularly at the intersection of gender and occupational class. They suggest that the stressors associated with WFH are not simply additive across axes of inequality; for example, WFH can serve as a compensatory resource for women in lower status jobs that mitigates work–family conflicts and supports their mental health. This highlights the need for an intersectional approach to fully understand the mental health effects of WFH. Further research should explore other axes of inequality, such as race-ethnicity, age, and caregiving responsibilities beyond childcare, including eldercare. In additional analyses, we found no significant differences in the mental health effects of WFH by eldercare and race-ethnicity. However, this may reflect limitations in the measures used, indicating a need for more focused investigation in future studies.

This article has several limitations. First, increases in WFH within occupations may coincide with other changes in job quality that also affect mental health, such as greater access to other flexible working arrangements (e.g., flextime) or shifts in workload and managerial practices. Therefore, our results may partly reflect the impact of these broader changes. However, supplementary analyses showed no significant association between our occupational-level WFH measure and self-reported use of other flexible working arrangements, suggesting that these factors are unlikely to confound our findings.

Second, as noted previously, shifts in WFH effects before and after the pandemic likely reflect changes in both its users and its implementation—factors that our design cannot fully capture. Quasi-experimental studies, for instance, exploiting policy reforms expanding WFH access, are needed to better isolate its impact from concurrent shifts in workforce and job characteristics (Xue et al. 2025). Nonetheless, because the effects of WFH seem context-dependent, such causal studies should be complemented by broader analyses like the present one.

Third, although we hypothesized that WFH affects mental health through job strain and work–family conflict, our analyses do not directly test these pathways using mediation analysis or specific measures of these stressors. Nor do we examine potential moderators, such as the frequency of WFH or the quality of the WFH environment. This is due to the scope of our article, which prioritizes addressing reverse causality by relying on an external measure of WFH because individual WFH use and self-reported stressors are influenced by mental health. Although this approach strengthens the causal interpretation of our findings, it restricts our ability to explore mediating mechanisms. Combining it with descriptive approaches is necessary to fully understand how WFH intersects with mental health inequalities.

Despite these limitations, this study provides robust evidence of the potential mental health benefits of WFH while also showing the conditions under which these benefits may be lost. For both men and women, the mental health outcomes of WFH depend on circumstances tied to their social positions and how organizations and policies manage the demands of flexible work. As such, WFH can be a key resource to mitigate the growing mental health challenges faced by today’s workforce. However, realizing this potential requires WFH to be implemented alongside supportive organizational practices and broader social policies that help workers manage the intensifying demands of both paid work and family life.

Supplemental Material

sj-docx-1-hsb-10.1177_00221465261429966 – Supplemental material for Home Advantage or Hidden Strain? The Mental Health Effects of Working from Home across Gender, Childcare Status, and Occupational Class before and since the Pandemic

Supplemental material, sj-docx-1-hsb-10.1177_00221465261429966 for Home Advantage or Hidden Strain? The Mental Health Effects of Working from Home across Gender, Childcare Status, and Occupational Class before and since the Pandemic by Constance Beaufils and Heejung Chung in Journal of Health and Social Behavior

Footnotes

ORCID iD

Constance Beaufils

Notes

Supplemental Material

Appendices A to F are available in the online version of the article.

Author Biographies

Constance Beaufils, PhD, holds a research chair on health inequalities at the French National Institute of Health and Medical Research. Her research examines how paid and unpaid work shape health inequalities, drawing on mixed methods and life-course and occupational health perspectives, with a focus on the French and British contexts. Her work has been published in leading journals, including Social Science & Medicine and Journal of Gerontology: Series B, among others.

Heejung Chung is professor of work and employment at King’s College London and director of the King’s Global Institute for Women’s Leadership. Her research examines flexible and remote working, work–family conflict, and gender inequalities across welfare states, with publications in journals including Work, Employment, & Society; Human Relations; and European Sociological Review. She is author of The Flexibility Paradox: Why Flexible Working Leads to (Self-)Exploitation (Policy Press, 2022).

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