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Abstract

The coronavirus disease 2019 pandemic upended work, family, and social life. These massive changes may have created shifts in exposure to work-life conflict. Using a national survey that followed Canadian workers from September 2019 into April and June 2020, the authors find that work-life conflict decreased among those with no children at home. In contrast, for those with children at home, the patterns depended on age of youngest child. Among individuals with children younger than 6 or between 6 and 12, no decreases in work-life conflict were observed. In contrast, those with teenagers did not differ from the child-free. Although these patterns did not significantly differ by gender, they were amplified among individuals with high work-home integration. These findings suggest an overall pattern of reduced work-life conflict during the pandemic—but also that these shifts were circumscribed by age of youngest child at home and the degree of work-home integration.

Keywords

work-life conflict COVID-19 parenthood role integration work-family conflict

In normal times, the boundaries between work and nonwork roles represent sites of potential tension that many people encounter in their everyday lives (Allen and Martin 2017; Kossek 2016). But 2020 has been anything but normal. In mid-March 2020, as many employees began working at home because of government dictates for social distancing, the abrupt shift in the nature of work-nonwork boundaries generated potential shocks to role conflict. Although research on role conflict typically identifies the family as the primary domain of the “nonwork sphere” (Bellavia and Frone 2005), some have encouraged broadening the scope to make it more inclusive for those without children (Burkett 2000; Kelliher, Richardson, and Boiarintseva 2019; Kossek and Lambert 2004; Young 1999). According to Kossek and Lee (2017), the concept of work-life conflict “is an extension of work-family conflict reflecting the reality that the work role may interfere with individuals’ other personal life roles and interests” (p. 2). People who experience high levels of work-life conflict report that their work role prevents them from concentrating on important things in their family or personal lives, they have insufficient time or energy for the important people in their lives because of their job, and they feel like their work roles undercut their capacity to perform home-related roles.

Population-based studies consistently demonstrate the links between work-life conflict and a range of unfavorable outcomes (Allen et al. 2000; Bellavia and Frone 2005; Schieman and Narisada 2021; Yucel and Fan 2019). Given its importance for well-being, researchers have sought to understand the factors that contribute to work-life conflict (Kelly et al. 2014; Schieman, Milkie, and Glavin 2009). In the context of significant social and economic disruptions, we build upon prior research with a novel question: how have levels of work-life conflict changed during the coronavirus disease 2019 (COVID-19) pandemic? We first articulate the “restricted life spheres” hypothesis, which predicts that overall levels of work-life conflict decreased in the population. We then propose that the presence of children at home generated a countervailing force, which predicts that any observed decrease in work-life conflict is weaker among those with children. In addition, we test two potential modifications of this countervailing force. First, given evidence that working mothers have experienced greater challenges during the pandemic (Landivar et al. 2020; Lewis 2020; McCarthy et al. 2020; Qian and Fuller 2020; Rudolph et al. 2020), we expect children, especially younger ones, to have the strongest countervailing force for employed mothers compared with others. Second, given theory and prior evidence about the importance of “extreme work-home integration” (e.g., Allen, Cho, and Meier 2014), we expect an amplified countervailing force among those with greater work-home integration. To test these hypotheses, we analyze data from the Canadian Quality of Work and Economic Life Study (C-QWELS). In September 2019, we collected data from a nationally representative sample of workers to profile the quality of work and economic life—not anticipating a worldwide pandemic. Then, during a pivotal period of shocks to the economy, work, school and daycare arrangements, and restrictions on social life, we repeated the survey in April and June 2020.

Theoretical Framework and Hypotheses

Restricted Life Spheres

The restricted life spheres hypothesis is based on the idea that because of social distancing and shelter-in-place orders, the “life” side of the work-life equation became severely restricted by April 2020, and this might have reduced overall levels of work-life conflict in the population. As governments implemented virus mitigation strategies, public gatherings were prohibited, stay-at-home orders emerged, and many workers were required to shift to remote work (Government of Canada 2020). For example, central Canada closed nonessential work on March 24, the prairie provinces started closing between March 23 and April 1, the west coast ordered some types of businesses to close on March 26, Atlantic Canada started closing between March 18 and March 26, and northern Canada began closing on March 18. With the closing or limited access to restaurants, gyms, movie theatres, and other sites of social engagement, and the restrictions on in-person gatherings with friends and family outside the household, the parameters of “personal life” abruptly narrowed, and most social interactions migrated online or via phone. No other societal shock, at least in recent memory, has produced such sweeping restrictions on the scope of individuals’ personal and social lives (Stainback, Hearne, and Trieu 2020). It is plausible that this translated into a simultaneous reduction of the risk that one’s work role could compete with the time, energy, and attention demands of one’s personal life. During the early months of the pandemic, there was simply less “life” for work to interfere with, at least outside the home, and this likely generalized broadly in the population. Taken together, these ideas provide the basis for the following hypothesis:

The restricted life spheres hypothesis: overall levels of work-life conflict decreased during the early months of the COVID-19 pandemic.

The Countervailing Force of Children Living at Home

Widespread “stay-at-home” requirements restricted the elements of nonwork to a narrower range of the home sphere. The restricted life spheres hypothesis refers to a limit on life outside the home during the pandemic; inside the home, however, children represent a potent countervailing force. As Williams (2020) contended, the pandemic has amplified a social cleavage: “Today, a key divide is between parents and non-parents.” From this, we would expect the restricted life spheres view to have manifested differently among workers with children at home compared with the child-free. For example, schools across Canada closed in mid-March 2020, and most remained closed for the school year, with a few exceptions across provinces. Quebec reopened most elementary schools in mid-May, and British Columbia, Manitoba, and Prince Edward Island all reopened schools at the beginning of June in some capacity, either part-time, combining in-person and virtual learning, or scheduling online meetings with teachers (Ho 2020; Juric 2020; Reimer and Blunt 2020). When schools and daycare centers closed, the family-related needs on the life side of the work-life ledger altered significantly for many workers. It is therefore plausible that despite the broader restrictions on personal life outside the home, countervailing demands associated with children inside the home became more pronounced because of childcare and related domestic needs. Moreover, the veracity of this prediction likely depends on the ages of children, with younger children requiring greater care and supervision. Given that daycare centers and schools that normally provided care and education to children during work times shut down, additional duties were abruptly thrust upon parents.

The seminal works of Kahn et al. (1964) and Greenhaus and Beutell (1985) provide conceptual guideposts for the countervailing force hypothesis. For example, Kahn et al. (1964) defined interrole conflict as the “simultaneous occurrence of two (or more) sets of pressures such that compliance with one would make more difficult compliance with the other” (p. 19). This definition underscores the specific requirement for pressures stemming from both work and nonwork sources to generate work-life conflict. Likewise, Greenhaus and Beutell’s (1985) classic piece on interrole conflict urged researchers to examine the simultaneous pressures that arise from different roles to produce work-life conflict. Their compelling illustration supports the divergent predictions for those with children compared with the child-free:

Imagine an employee who puts in long and stressful hours in his or her job. In an objective sense, the person’s work activities may interfere with his or her participation in family activities. However, if there is no strong pressure to participate in family activities [italics added], the person is not likely to experience conflict between work and family roles. (p. 82)

The restricted life spheres hypothesis focuses on the “personal life” side of the work-life conflict formulation, but the existence of family-related expectations and obligations is central to the kinds of demands that predict work-life conflict (Greenhaus and Beutell 1985). Roughly 35 years after these ideas were first introduced, the COVID-19 pandemic presents an extraordinary opportunity to test these underlying conceptual distinctions. Although one’s personal life might have been restricted during the pandemic, the family side of the work-life equation became more burdensome, particularly for parents who had to attend to new demands of organizing or overseeing educational content for children and managing daily care that schools or daycares had covered prior to the pandemic (Qian and Fuller 2020). These ideas inspire the following hypothesis:

The children–as–countervailing force hypothesis: the presence of children at home, especially younger ones, will weaken (or reverse) the decrease in work-life conflict that is predicted by the restricted life spheres hypothesis.

Countervailing Force Contingencies: Gender and Work-Home Integration

We propose two potential modifications of the countervailing force hypothesis on the basis of (1) variations between women and men and (2) the level of work-home integration. Both posit differential degrees of decreased work-life conflict associated with having children at home.

Gender

The focus on care and supervision of children in the household motivates the consideration of potentially divergent dynamics among women and men. There are several reasons to expect that the countervailing force hypothesis might be more applicable to women. According to Blair-Loy (2003), “As an ideal type, the family devotion schema assigns primary responsibility for home and family to women” (p. 6). The family devotion schema aligns with Hays’ (1996) concept of “intensive mothering,” which also describes women’s primary dedication to the family sphere and, more specifically, caregiving responsibilities associated with raising children. In contrast, paid work has traditionally been the central domain for men, with their dedication to the “good provider” role enacted in that sphere (Christiansen and Palkovitz 2001; Coltrane 1996; Moen and Roehling 2005). Despite some societal shifts, traditional gendered orientations regarding the care of children seem to endure (Collins 2020; Dernberger and Pepin 2020), particularly in the face of workplace constraints (Pedulla and Thébaud 2015). From this more traditional gendered lens, we would therefore expect employed mothers to prioritize family-related roles over the work role, especially during a period of elevated childcare needs at home (Qian and Fuller 2020).

The family devotion schema, however, does not exist in isolation. Blair-Loy (2003) also observed that many women experience the pull of the work devotion schema, which “demands that one give an immense time commitment and strong emotional allegiance to one’s firm or career” (p. 7). Working mothers navigate both family and work devotion schemas, recognizing the potential penalties for reducing workload or signaling deficits in work commitment in order to attend to family-related obligations (Cech and O’Connor 2017; O’Connor and Cech 2018; Padavic and Reskin 2002; Williams, Blair-Loy, and Berdahl 2013). To avoid or minimize such penalties, working mothers may integrate roles in ways that allow greater role permeability to complete unfinished work tasks or to get ahead in one’s job (Blair-Loy 2003; Sayer 2007). Offer and Schneider (2011) underscored the gendered complications: “Normative expectations require mothers in contemporary society to fulfill multiple roles as both wage earners and primary caregivers for their family, which presents them with contradictory ideological pressures” (p. 814). Competing work and family devotions therefore create the conditions that generate more interrole conflict among women (Collins 2020). In the context of restrictions on schooling and daycare during the pandemic, care-related needs for children increased (Qian and Fuller 2020), amplifying the salience of the family devotion schema. Collectively, these ideas predict that elevated work-life conflict during the pandemic might have intensified more among working mothers because the care of children, especially younger children, dramatically increased.

The gendered competing devotions hypothesis: the presence of children at home, especially younger ones, will weaken (or reverse) the decrease in work-life conflict more strongly among women compared with men.

An alternative view derives from recent scholarship that emphasizes cultural shifts toward a more egalitarian division of family work (Bianchi, Robinson, and Milkie 2006; Milkie et al. 2002). Fathers in Canada have increased their contributions to parenting-related activities over recent decades (Guppy, Sakumoto, and Wilkes 2019; Houle, Turcotte, and Wendt 2017; also see Craig and Churchill 2020). Although the degree of the shift remains unclear, Canadian mothers still engage in more childcare activities than fathers (Ball and Daly 2012; Houle et al. 2017; Marshall 2011; Moyser and Burlock 2018). However, a study of 1,234 Canadian parents during the pandemic revealed patterns that are relevant to our countervailing forces hypothesis:

As life became more home-centered in spring 2020 compared to any time in the past several generations, the amount of domestic labor increased. In general, the gendered division of household labor appears to have inched toward greater equality during this early period of quarantine, remote work, remote schooling, and social distancing. Our analyses suggest that, at least initially, a regression toward less domestic equality among parents was not the case in Canada. In fact, it appears that many Canadian fathers increased their involvement in domestic labor and comparatively few decreased their share [italics added]. (Shafer, Scheibling, and Milkie 2020:21)

Although both mothers and fathers reported that fathers were doing more compared with prepandemic times, the authors also cautioned about “significant and sizable” disparities in perceptions about men’s contributions (Carlson, Petts, and Pepin 2020; Dyer, Day, and Harper 2014). Collectively, claims of a more egalitarian division of household labor in Canada, especially during the early months of the pandemic, inspire the following hypothesis:

The egalitarian competing devotions hypothesis: the presence of children at home, especially younger ones, will weaken (or reverse) the decrease in work-life conflict similarly among both women and men.

Work-Home Integration

Societal shifts during the COVID-19 pandemic reconfigured the boundaries between work and nonwork for many workers (Rudolph et al. 2020). This reconfigured work-home boundary motivates our consideration of work-home integration as another potential moderator of the countervailing force hypothesis. We predict that high work-home integration will amplify the countervailing force of children at home during the pandemic. The rationale for this view derives from border and boundary theories’ characterization of the work-family boundary as a continuum of complete segmentation to complete integration (Ashforth, Kreiner, and Fugate 2000; Clark 2000; Nippert-Eng 1996). With high segmentation, work is performed away from home at a fixed location; spatial and temporal boundaries reflect the distinctiveness of the domains, with arrangements more akin to the “separate spheres” depiction of work and home roles as having different (often competing) expectations and responsibilities (Coontz 1992). Conversely, with high work-home integration, there is little distinction between roles, especially in the location and timing of work- and family-related role enactments.

Work-home integration increases role permeability, which entails “the degree to which a role allows one to be physically located in the role’s domain but psychologically and/or behaviorally involved in another role” (Ashforth et al. 2000:474). Although integration might ease transitions between roles, thereby relaxing boundaries and facilitating border crossing (Clark 2000; Olson-Buchanan and Boswell 2006), the role permeability associated with it does not translate into less interrole conflict. In fact, role theory posits that individuals should keep roles separate as “the optimal means for successfully enacting multiple roles” (Dumas and Sanchez-Burks 2015:809). As the theory suggests, having dedicated temporal and spatial perimeters around work is crucial to minimizing interrole conflict (Ashforth et al. 2000; Kahn et al. 1964).¹

But this boundary management strategy of segmentation is complicated for individuals whose work-home arrangements exemplify extreme integration and permeability, raising the risk for interrole distractions (Hill et al. 1998; Rau and Hyland 2002). From a “role responsibility management” perspective, the individual who regularly performs work-related activities at home must manage the demands of different roles and enact strategies to allocate time and attention to adequately perform divergent role tasks (Dumas and Sanchez-Burks 2015). Although some scholarship on boundary management strategies underlines individuals’ personal decisions or preferences to integrate or segment (Kossek, Lautsch, and Eaton 2006; Kossek, Noe, and DeMarr 1999), individual choice about the extent and timing of work-home role integration might have been structurally constrained during the early months of the pandemic (Gallacher and Hossain 2020; Messacar, Morrissette, and Deng 2020; Schieman and Badawy 2020). Among workers with high work-home integration, the needs of work and children overlapped completely in time and space. As schools and daycare centers closed, and the care and education of children shifted to the home sphere (Johnstone 2020), enacting work and family roles during the pandemic may have exacerbated competing work-family role pressures among those with children at home (Dizik 2020; Thomason and Williams 2020). In other words, high work-home integration might have intensified the experience of workers with children at home, especially younger children, who are more dependent and require greater supervision than more autonomous teenagers (Allen and Finkelstein 2014; Bedeian, Burke, and Moffett 1988; Erickson, Martinengo, and Hill 2010; Qian and Fuller 2020). Collectively, the implications of high work-home integration for role permeability motivate the following modification to the countervailing force hypothesis:

The role integration-amplification hypothesis: the presence of children at home, especially younger children, will weaken (or reverse) the decrease in work-life conflict more strongly among those with high work-home role integration.

Methods

Samples

To test our hypotheses, we analyze data from a nationally representative sample of Canadian workers from the C-QWELS. The first online survey was fielded from September 19 to September 24, 2019 (n = 2,524). We then followed up with these same study participants from April 17 to April 23, 2020, and then once again from June 17 to June 23, 2020. All study participants are members of the Angus Reid Forum (ARF), a built and managed panel of Canadians who have agreed to participate in research. Panel participants are recruited through a variety of online and offline channels, extensively profiled, and measured to ensure accurate representation of the diversity across Canada’s adult population (http://angusreid.org).²

Sample selection for the present study started with creating a balanced sample matrix of the Canadian population. A randomized sample of ARF members was selected in September 2019 to match this matrix to ensure a broadly representative sample, for which the response rate was 42 percent, with a sample size of 2,524.³ Of these study participants, 1,869 (74 percent) were successfully retained for the April recontact, and 1,843 (73 percent) were successfully retained for the June recontact. Analytic methods described later address sample attrition and selection on the basis of employment in subsequent waves and also adjust analyses for repeated observations of the same individuals. We weighted all findings by gender, age, education, and region according to the most recent census data for September 2019 to ensure broad representation of working Canadians. Appendix Table A1 reports the descriptive statistics.

Measures

Work-life conflict was measured at each wave using three questions that have appeared in the National Study of the Changing Workforce to measure work-life conflict in the past month (O’Connor and Cech 2018; Voydanoff 2004; Yucel 2019): “How often did your job keep you from concentrating on important things in your family or personal life?” “How often did you not have enough time or energy for the important people in your life because of your job?” and “How often did your work keep you from doing as good a job at home as you could?” Response choices are coded as follows: 1 = “never,” 2 = “rarely,” 3 = “sometimes,” 4 = “often,” and 5 = “very often.” We averaged the responses to create the index (SEPα = .90, APRα = .91, JUNα = .91).⁴

Age of youngest child living at home is measured at each wave and compares those without any children living at home with individuals whose youngest children are younger than 6, between 6 and 12, or between 13 and 18.

We use responses to the following questionnaire item to measure work-home integration: “How often do you do any paid or unpaid work at home that is part of your job?” We compare those who report “every day/I work mainly from home” to those who report working at home less frequently. When describing the results, we refer to this as “high work-home integration.”

Time-stable controls include social statuses that are typically stable over a short time period. We use dummy-codes for gender (men = 0, women = 1) and visible minority status (nonminority = 0, minority = 1).⁵ We also adjust for age (in years) and region of residence of study participants. For education, we compare those with a university undergraduate degree or higher with those with less than a university degree. For household income, we compare individuals in the $50,000 to $99,999 income bracket (the modal category) with individuals in each of the following: less than $25,000, $25,000 to $49,999, $100,000 to $149,999, $150,000 to $199,999, and $200,000 or more.⁶ We adjust for occupation, comparing professionals (reference) with those in higher administration, clerical, sales, service, or production occupations, and whether participants are salaried versus paid hourly or paid some other way.

Time-varying controls take into account statuses with the potential to fluctuate. We measure marital status at each wave by contrasting married with nonmarried (single or never married, previously married, cohabiting). We measure financial strain with three items. The first two items ask, “How often did you have trouble paying the bills” and “How often did you not have enough money to buy food, clothes, or other things your household needed?” Response categories are coded as follows: 1 = “never” 2 = “rarely,” 3 = “sometimes,” 4 = “often,” and 5 = “very often.” The third item asks, “How do your finances usually work out by the end of the month?” Response choices are coded as follows: 1 = “a lot of money left over,” 2 = “a little money left over,” 3 = “just enough to make ends meet,” 4 = “barely enough to get by,” and 5 = “not enough to make ends meet.” We averaged the items to create a financial strain index (α = .85). We measure schedule control using the following item: “How much control do you have in scheduling your work hours?” Response choices are coded as follows: 0 = “none/very little,” 1 = “some,” and 2 = “a lot/complete.” Work hours contrasts those who work 40 to 49 hours per week (reference) with those who work fewer than 30, 30 to 39, and 50 or more hours per week.

Analytic Plan

We use mixed models in our focal analyses. The mixed models apportion variance in work-life conflict into time-varying variation within the individual and time-stable variation between individuals, thereby taking repeated observations of the same individuals over time into account. The basic form of the mixed model is as follows:

Y_ti = γ₀₀ + γ₁₀April_ti + γ₂₀June_ti + γ₃₀Under 6_ti + γ₄₀6 to 12_ti + γ₅₀13 to 18_i + U_0i + R_ti.

The mixed model treats observations of work-life conflict at each wave (t) as nested within individuals (i). Consequently, in this equation, Y_ti is the work-life conflict measure for study participant i at time t, for i = 1 . . ., n and t = September, April, or June survey wave. The mixed model allows the intercept γ₀₀ to vary across individuals because of time-stable characteristics, and the random error term U_0i then takes residual variation due to unobserved time-stable characteristics into account. The random error term R_{_ti} accounts for unobserved influences on work-life conflict due to time-varying factors. The variables April and June indicate whether the time-varying measures correspond to the April or June survey (with September as the reference), with the result that γ₁₀ indicates the average change in work-life conflict between September and April, while γ₂₀ indicates the average change in work-life conflict between September and June. Similarly, the variables under 6, 6 to 12, and 13 to 18 indicate whether, at each wave, a respondent’s youngest child is younger than 6, 6 to 12, or 13 to 18 (with individuals not living with children as the reference). Consequently, γ₃₀ indicates the difference in work-life conflict across waves between individuals with children younger than 6 and those without children, γ₄₀ indicates the difference in work-life conflict across waves between individuals with children ages 6 to 12 and those without children, and γ₅₀ indicates the difference in work-life conflict across waves between individuals with teenagers and those without children. An interaction between the indicators of time and the youngest child indicators then shows whether changes in work-life conflict between waves differs by parenting statuses.⁷

Mixed models incorporate all information available from each observation into model estimates, regardless of the number of waves in which a respondent provides information (Yang and Land 2013), thereby providing model estimates that are robust to sample attrition. Model estimation does assume that attrition is “missing at random,” meaning that missingness is not attributable to values on the missing data and can be explained by additional factors included as covariates (Enders 2010). Our inclusion of a broad set of covariates should aid the model in meeting this assumption, and even when data are not missing at random, estimates with this assumption often provide superior estimates as opposed to models relying on listwise deletion (Allison 2001). We estimate all mixed models in Stata 16.1 using full-information maximum likelihood estimation with robust standard errors.

Results

Did Overall Levels of Work-Life Conflict Decrease during the COVID-19 Pandemic?

In model 1 of Table 1, we test the bivariate association between the survey wave indicator (April or June) and work-life conflict, thereby indicating the unadjusted change in work-life conflict between September and the subsequent waves. Both of these coefficients are statistically significant and negative, and the coefficients are also of similar magnitude. This indicates that average levels of work-life conflict decreased to a similar degree from September 2019 to April 2020 (b = –.333, p < .001) and June 2020 (b = –.317, p < .001). Taken together, these patterns align with the predictions of the restricted life spheres hypothesis: overall average levels of work-life conflict decreased significantly in the working population during the early months of the COVID-19 pandemic.

Table 1.

Work-Life Conflict Regressed on Survey Wave, Child at Home, and Controls.

	Model 1	Model 2	Model 3
Survey wave (reference: September)
April	−.333***	−.196***	−.258***
June	−.317***	−.193***	−.234***
Age of Youngest Child (reference: no children)
Under 6		.099*	.004
6 to 12		.244***	.134*
13 to 18		.084	.036
Survey Wave × Age of Youngest Child
April × Under 6			.241**
April × 6 to 12			.231*
April × 13 to 18			.051
June × Under 6			.146*
June × 6 to 12			.195*
June × 13 to 18			.075
Women		.056	.056
Work-home integration		.127***	.127***
Visible minority		.062	.061
Age		−.009***	−.009***
Married		.064	.065
Bachelor’s degree or higher		.098*	.099*
Income (reference: $50,000–$99,999)
<$25,000		−.110	−.111
$25,000–$49,999		−.100	−.100
$100,000–$149,999		−.014	−.013
$150,000–$200,000		.008	.007
>$200,000		−.025	−.024
Financial strain		.283***	.283***
Professional		.242***	.242***
Salaried		.121**	.122**
Schedule control (reference: none)
Some		−.056	−.059
A lot/complete		−.146***	−.148***
Work hours (reference: 40–49 hours)
<30 hours		−.477***	−.471***
30–39 hours		−.224***	−.220***
≥50 hours		.361***	.364***
Region (reference: Ontario)
Atlantic		−.081	−.081
Quebec		−.140**	−.137**
Manitoba		.084	.084
Saskatchewan		−.102	−.100
Alberta		−.048	−.048
British Columbia		−.059	−.060

Note: Regression coefficients are shown in the table.

p < .05. **p < .01. ***p < .001.

In model 2, we add the age of youngest child living at home and all other study variables. With the introduction of the additional covariates, the decrease in levels of work-life conflict holds for April (b = –.196, p < .001) and June (b = –.193, p < .001). Moreover, for the effect of youngest child at home, those with children between 6 and 12 have the highest level of work-life conflict (b = .244, p < .001), followed by those with children younger than age 6 (b = .099, p < .05). Although individuals with teenagers have higher work-life conflict compared with the child-free, that difference is not statistically significant.

In model 3, we test the children–as–countervailing force hypothesis, which predicts that the observed decrease in work-life conflict from September to April/June should be weaker among those with children at home. Four statistically significant interaction terms confirm this prediction, but the patterns depend on age of youngest child. First, the interaction for child younger than 6 is significant for April (b = .241, p < .01) and June (b = .146, p < .05). Second, the interaction for children ages 6 to 12 is significant for April (b = .231, p < .05) and June (b = .195, p < .05). These interaction coefficients are positive, indicating that the decrease in work-life conflict is significantly weaker among workers with young children in the household compared with those with no children living at home. In contrast, interactions between survey wave and having a teenager in the household are not significant, suggesting that there is little difference in changes in work-life conflict between those with teenagers and the child-free.

To further explicate and interpret these interaction coefficients, the top row of Table 2 reports predicted marginal effects derived from model 3 of Table 1. Among the child-free, work-life conflict decreased significantly from September to April (b = –.258, p < .001) and June (b = –.234, p < .001). In contrast, the nonsignificant coefficients indicate that we observe little change in work-life conflict among those with children younger than 6 or among those with children ages 6 to 12 over the survey waves. Among those with teenagers, we observe a small but nonsignificant decrease in work-life conflict from September to April; however, by June that negative coefficient increases in absolute value and becomes significant (b = –.182, p < .05).

Table 2.

Predicted Marginal Effects for Age of Youngest Child and Work-Life Conflict by Survey Wave and Work-Home Integration.

	April	June
Total
Age of youngest child
No children	−.258***	−.234***
Under 6 years	−.017	−.088
6 to 12 years	−.027	−.039
13 to 18 years	−.140	−.182*
High work-home integration
Age of youngest child
No children	−.471***	−.401***
Under 6 years	−.132	.137
6 to 12 years	−.076	.055
13 to 18 years	.119	−.148
Low work-home integration
Age of youngest child
No children	−.180***	−.174***
Under 6 years	.004	−.174*
6 to 12 years	−.043	−.114
13 to 18 years	−.143	−.110

Note: All models include the full set of control variables.

p < .05. ***p < .001.

Figure 1 illustrates the predicted values, showing (1) a decrease in work-life conflict among those with no children at home, (2) little change for those with children younger than 13, and (3) a smaller decrease in work-life conflict that strengthens by June among those with teenagers living at home. Collectively, these patterns generally support the children–as–countervailing force hypothesis, but the countervailing force is stronger for those study participants who have children younger than age 13 in the household.

Figure 1.

Age of youngest child at home and work-life conflict by survey wave.

Testing for Differences between Women and Men

Does the countervailing force hypothesis differ between women and men? To answer this question, we test a three-way interaction term among survey wave, children at home, and gender. Table 3 reports these results, indicating no support for statistically significant gender-contingent effects. One of the three-way interaction coefficients, survey wave by child 6 to 12 by gender, is marginally significant (b = .327, p = .08). The coefficient for that same three-way interaction term for the June survey wave is slightly weaker and not marginally significant (b = .264, p = .149). Although these patterns hint at the possibility of greater countervailing effects of having a youngest child between the ages of 6 and 12 among women compared with men (in April), the overall patterns fail to find clear and consistent evidence of significant gender differences.

Table 3.

Three-Way Interaction Coefficients for Survey Wave, Children at Home, and Gender Predicting Work-Life Conflict.

Survey wave (reference: September)
April	−.270***
June	−.269***
Age of youngest child (reference: no children)
Under 6	−.065
6 to 12	.196*
13 to 18	−.004
Survey Wave × Age of Youngest Child
April × Under 6	.221*
April × 6 to 12	.081
April × 13 to 18	.095
June × Under 6	.151
June × 6 to 12	.080
June × 13 to 18	.063
Women	.002
Age of Youngest Child × Women
Under 6 × Women	.140
6 to 12 × Women	−.149
13 to 18 × Women	.087
Survey Wave × Women
April × Women	.025
June × Women	.072
Survey Wave × Age of Youngest Child × Women
April × Under 6 × Women	.048
April × 6 to 12 × Women	.327
April × 13 to 18 × Women	.057
June × Under 6 × Women	.006
June × 6 to 12 × Women	.264
June × 13 to 18 × Women	−.014

Note: Regression coefficients are shown in the table. All models include the full set of control variables shown in Table 1.

p < .05. ***p < .001.

Testing the Role Integration-Amplification Hypothesis

The next set of analyses test the role integration-amplification hypothesis by comparing those with high versus low work-home integration. As the role integration-amplification hypothesis predicts, we observe a stronger set of survey wave–by–age of children interaction coefficients among workers with high work-home integration (see the first column in Table 4). For example, focusing on two of the most striking differences, the interaction for child younger than 6 is significant for June (b = .538, p < .01) among those with high work-home integration but not for those with low work-home integration (b = .000, ns). Likewise, the interaction for child ages 6 to 12 is significant for June (b = .456, p < .01) among those with high work-home integration but not for those with low work-home integration (b = .060, ns).

Table 4.

Work-Life Conflict Regressed on Survey Wave and Child at Home Separately by Work-Home Integration.

	High Work-Home Integration	Low Work-Home Integration
Survey wave (reference: September)
April	−.471***	−.180***
June	−.401***	−.174***
Age of youngest child (reference: no children)
Under 6	−.020	.004
6 to 12	−.040	.184*
13 to 18	−.247	.073
Survey Wave × Age of Youngest Child
April × Under 6	.339*	.185*
April × 6 to 12	.395*	.137
April × 13 to 18	.590**	.038
June × Under 6	.538**	.000
June × 6 to 12	.456**	.060
June × 13 to 18	.253	.064
Women	.114	.024
Visible minority	.129	.063
Age	−.011***	−.009***
Married	.163*	.053
Bachelor’s degree or higher	.191**	.064
Income (reference: $50,000–$99,999)
>$25,000	−.003	−.101
$25,000–$49,999	−.096	−.104
$100,000–$149,999	−.009	−.016
$150,000–$200,000	.022	.011
>$200,000	−.083	.056
Financial strain	.310***	.295***
Professional	.203**	.247***
Salaried	.169*	.101*
Schedule control (reference: none)
Some	−.312***	−.025
A lot/complete	−.457***	−.090*
Work Hours (reference: 40–49 hours)
<30 hours	−.456***	−.472***
30–39 hours	−.382***	−.186***
≥50 hours	.447***	.319***
Region (reference: Ontario)
Atlantic	−.164	−.081
Quebec	−.155	−.132*
Manitoba	−.032	.077
Saskatchewan	−.078	−.103
Alberta	−.009	−.076
British Columbia	−.044	−.070

Note: Regression coefficients are shown in the table.

p < .05. **p < .01. ***p < .001.

To help explicate and interpret the contingencies by level of work-home integration, referring back to the middle and bottom rows of Table 2, we report the predicted marginal effects for those with high versus low work-home integration. Among the child-free with high work-home integration, work-life conflict decreased significantly from September to April (b = –.471, p < .001) and June (b = –.401, p < .001). In contrast, the remaining nonsignificant coefficients among those with high work-home integration indicate little change in work-life conflict among those with children in any of the three age groups over the study period. When comparing those coefficients with the ones observed among workers with low work-home integration, a main takeaway is that high work-home integration widens the differences between individuals with no children at home and the child-free. Figure 2 illustrates this divergence among workers with high work-home integration (Figure 2A) compared with low work-home integration (Figure 2B). High work-home integration amplifies the patterns that we first illustrated in Figure 1.

Figure 2.

Age of youngest child at home and work-life conflict by survey wave and work-home integration.

Supplemental Analyses: Predicting Work Hours and Attrition

We are aware of the ways that the pandemic seems to have exacerbated some social inequalities, especially related to employment patterns across gender and parental status (Landivar et al. 2020; Qian and Fuller 2020). Recognizing these possibilities, we performed extensive supplemental analyses that explicitly focus on how gender and age of youngest child potentially influence changes in work hours and employment status during the study period. Here, we briefly summarize the key patterns that are most relevant to the focal associations established above. First, overall work hours decreased during the pandemic (see model 1 in Appendix Table A2). Second, age of youngest child at home is unrelated to work hours. However, as model 2 shows, we observe a positive interaction between having a teenager at home and both April and June wave indicators. This interaction coefficient suggests that having a teenager at home offsets the overall decrease in work hours during the pandemic; that is, workers with teenagers differed significantly from the child-free in terms of reduced work hours. As for gender, women report fewer overall work hours. However, we did not find statistically significant two-way interactions between gender and survey wave or three-way interactions among gender, survey wave, and age of youngest child (separate analyses not shown but available upon request).

Next, we evaluate whether children at home and gender predict if study participants (1) stopped working or (2) dropped out of the study by April or June. Overall, the second column of Appendix Table A3 shows that having a child between the ages of 6 and 12 is associated with a lower likelihood of stopping work by June. Gender does not predict the likelihood of stopping work or dropping out during the study period. In separate analyses (not shown), we tested interactions between children at home and gender. In only one instance did we find a statistically significant interaction coefficient: the patterns suggest that child-free men were more likely to drop out of the sample compared with men with children ages 6 to 12 or teenagers living at home; we did not observe these patterns among women. Net of all study variables, we did not find statistically significant coefficients that suggested that women with younger children at home were significantly more likely to stop working or drop out of the study. Collectively, these supplemental analyses provide limited support for systematic or consistent differences by children and gender in the likelihood of stopping work or dropping out during the study period.

Discussion

In analyses of a nationally representative sample of Canadian workers, we found that average levels of work-life conflict decreased among workers without children living at home during the early months of the COVID-19 pandemic. This overall pattern is similar in the seven-month study period (September to April) and the nine-month period (September to June) and is consistent with the restricted life spheres hypothesis. However, one key takeaway from our study is that the age of youngest child at home moderates those patterns. Specifically, individuals with young children in the household (<13 years old) did not experience the same decrease in work-life conflict that we observed for their counterparts with no children at home. These findings support the children–as–countervailing force hypothesis. But that countervailing force did not generalize to parents whose youngest children at home are teenagers. By June, those with teenagers experienced an average decrease in work-life conflict that was similar to that of the child-free.

Although all parents presumably had restricted opportunities in the public sphere (like most everyone else), our findings suggest this might have been offset by countervailing forces created by having younger children at home during the lockdown. Among the most important of these forces were the unique demands related to younger children’s education, supervision, and daily care (Qian and Fuller 2020). Ultimately, these factors likely reduced these parents’ opportunities for decreased levels of work-life conflict that were demonstrated among workers with no children at home. In contrast, among parents whose youngest children were teenagers, it is possible that teenagers were able to manage their own schoolwork without much parental oversight or supervision. In many districts, final exams were reduced or canceled, and grades were not able to be lower than prepandemic marks. Thus, the lessening pressures on teens meant that parents may have also had fewer conflicts from their work into parenting, given that educational aspects of parenting adolescents were muted. Moreover, by June, teenagers may have been able to see friends and be away from home at least some of the time because of some loosening of social restrictions. These dynamics might account for why we observed comparable decreases in average work-life conflict among those with teens and those with no children living at home.

Our insights about the contrast between the child-free and those with young children at home shed light on the conceptual definition of interrole conflict offered by Greenhaus and Beutell (1985) decades ago. They claimed that individuals without pressures to participate in the family role would experience less work-life conflict. The present study provides a novel test of this proposition, as our findings establish how pressures from the “life” side of the work-life conflict equation are important for producing interrole conflict. Most aspects of social life became restricted with the lockdown of the pandemic (Stainback et al. 2020), thus reducing work-life conflict at the aggregate level. There was less “life” outside the home for work to interfere with. We interpret some portion of the overall reduction in work-life conflict as reflecting this underlying dynamic. At the same time, the “family” side of work-life conflict ledger became more prominent, especially for workers with young children (Qian and Fuller 2020). We suspect that this new reality and the role challenges associated with it offset the overall decrease in work-life conflict among those with young children.

Although Greenhaus and Beutell (1985) were focused primarily on the “family” side of the work-family conflict concept, the nuances in the present study underscore the importance of expanding the conceptual terrain of how the work role competes with the time, energy, and attention available for life more generally outside of the work sphere. This reinforces the case made by other scholars who have encouraged broadening the work-family concept to include individuals without family-related obligations or responsibilities, especially children who require care and attention (Burkett 2000; Kelliher et al. 2019; Kossek and Lambert 2004; Kossek and Lee 2017; Young 1999). As our findings suggest, the widespread and abrupt societal transformations associated with COVID-19 provide a unique opportunity to reflect on gradations in restricted life spheres and the countervailing forces in people’s major role arrangements. More specifically, our work inspires further differentiation of “personal” versus “family” sphere needs as scholars continue to examine elements of the work-life conflict puzzle; this shift in focus will bring more attention to a significant segment of the working population: individuals with no children at home. Relatedly, there is no doubt that family needs extend beyond the immediate household, and particularly during pandemic times, elderly family members may have created new strains for workers who wanted to provide assistance but were unable to do so because of work demands or other restrictions. When study participants are responding to questions about work-related conflicts into the family sphere, they may well be including elements of “family” beyond their immediate household into the calculus.

Another key takeaway elaborates on the countervailing force hypothesis with inspiration from Allen et al.’s (2014) ideas: “The study of individuals in extremely integrated or segmented work and family situations may help us better understand work-family boundary dynamics” (p. 117). Here, we focused on high work-home integration, that is, the regular performance of work related to one’s job while at home. We found some support for the role integration-amplification hypothesis: among those with high work-home role integration, the gap between the child-free and those with children became even more evident. Having younger children at home (in their workspaces)—and in need of extra care and attention to education that institutions could no longer provide—likely created new role burdens for many workers during the pandemic (Qian and Fuller 2020). Parents attempting to get their jobs done without offices or places outside the home to conduct their work likely presented role-related challenges. Work and family roles were forced into the same space, with a good portion of parents having partners simultaneously trying to meet work demands. Moreover, the interference of work into home life became more consequential, as home became the place for educating children, and ignoring or neglecting children’s needs on these fronts meant potentially impeding their development. Without much guidance to supplement the often informal and minimal educational content available from schools and teachers in the immediate educational chaos of the early days of the pandemic, parents could feel great conflicts in choosing work tasks over their children. Doing the work required of them for pay came with regular reminders that the expanded parent role was being neglected. These new and unprecedented demands occurred regularly and viscerally with few barriers when children were awake, even in homes with ample space and even when partners sometimes took charge. Parents might have felt as if they were being neglectful with every ignored question about schoolwork, every request for a snack, and every time a parent had to put a young child in front of TV or video as a “babysitter.” Taken together, it seems plausible that these dynamics might have helped offset the overall decrease in levels of work-life conflict.

Another key finding in our study is the lack of gender differences in the countervailing force hypotheses. Women and men did not differ in the relationship between children at home and work-life conflict and the way that relationship changed during the pandemic. At first glance, this pattern might seem to run counter to the notion that working mothers experienced a uniquely high level of work-life conflict during the pandemic. Being mindful of research that has demonstrated gender inequalities during the pandemic (e.g., see Landivar et al. 2020; Qian and Fuller 2020), we performed extensive supplemental analyses to search for any trace of gender- and children-based contingencies in changes in work hours or attrition linked to stopping work or dropping out of the study. These supplemental analyses failed to unearth clear or consistent patterns that would indicate bias in our observed focal associations. Nonetheless, we wish to emphasize the evidence others have discovered about divergent experiences between working mothers and fathers during the pandemic. For example, Qian and Fuller (2020) found that Canadian mothers were more likely to become unemployed from February to May 2020, compared with fathers, especially for those whose youngest children were ages 6 to 12. One might surmise from their findings that we should have seen increased work-life conflict among working women with young children. But we did not; nor did we find that (1) women with younger children were more likely to stop working or drop out during the study period or (2) high baseline levels of work-life conflict predicted stopping work or dropping out more strongly for working mothers.

We recognize other possible reasons for the lack of clear gender differences in our focal associations. For example, the kinds of work-life conflict working women with young children experienced during the early months of the pandemic might not be captured by the items we use to measure work-life conflict. Work that does not create much interrole conflict (as currently measured) can still become imperative to drop in a crisis of care. When the institutions that provided education and care for children went glaringly absent, work that is low-paid, part-time, and less rewarded—the kind of jobs working mothers, particularly the less educated, tend to have—is pushed aside first (Fuller 2018). Related to these ideas, Qian and Fuller (2020) observed the ways that low education amplified the gender employment gap early on during the pandemic. In reflecting on the implications for our focal associations, testing such education-based differences would require a four-way interaction among gender, survey wave, age of youngest child, and education level. Although education-based differences might provide greater insights into gender differences, our sample is insufficiently large to credibly test this additional potential contingency. In sum, we urge caution in drawing definitive conclusions about the lack of gender differences in our study and encourage further analyses of gendered experiences across the educational spectrum.

Before concluding, we wish to acknowledge several other potential study limitations. Elaborating further on the work-life conflict measure, we expect that if we were able to more carefully measure the experiences of some categories of workers whose jobs became very stressful—for example, health care workers—we might also see work interfered with their lives too, but in ways that differ from the operationalization of work-life conflict here. Essential workers’ jobs became more fraught and dangerous, and it likely took extra energy and concentration to separate themselves from contaminating family members with the virus. Given that ours is a national survey of workers across all different types of sectors or occupations, we do not have sufficient cases within each kind of occupation to credibly evaluate these kinds of occupation-specific nuances. However, we recognize the potentially unique work-life experiences of these workers and the supports they did or did not receive to prevent work-related demands from spilling over into their home lives.

Another limitation relates to our interpretations related to the restricted life spheres hypothesis. We surmised that decreased levels of work-life conflict might be due to restricted life spheres outside of work during the early lockdown months—and that freedom from domestic responsibilities associated with having children at home reinforced that reduction. However, we recognize that other potential explanations are plausible. For example, it is possible that aggregate levels of employer expectations for work capacity, productivity, and intensity decreased during the early months of the pandemic—potentially contributing to a decreased sense of time pressure (see Craig and Churchill 2020). This view implies that managers and organizations acknowledged the unprecedented strain of the pandemic and eased expectations—and the associated job-related demands. This suggests a shared and generalized empathic understanding among employers that life had shifted abruptly and all members of society were trying to adjust to the rapid changes. Thus, lowered work-role expectations and demands, above and beyond the realities of restricted life outside of work, might also explain why work-life conflict decreased.

One of the key takeaways is the divergence between those without children at home (both women and men) and those with younger children at home (both women and men). Among the latter group, we encourage future research to consider if differences between women and men might have been contingent upon two factors that are unmeasured in our study: the division of housework and childcare. Notably, for example, a recent study of Canadian parents during the pandemic suggests slightly more egalitarian work-home and family-care arrangements between women and men (see Shafer et al. 2020), though the study did not examine gender inequalities in supervising schooling. This sharing of the load at home might contribute to the lack of gender differences in our study. Future research might also seek to evaluate whether the degree and impact of supportive work-family culture shifted during the early months of the pandemic—and whether any such shifts differentially shaped levels of work-life conflict among working mothers and fathers. Knowledge about these dynamics would certainly be enhanced with qualitative interviews that probe the perceived or actual trade-offs between work and family during the pandemic and its link to gender, parenthood, and changing levels of work-life conflict. And, finally, while juxtaposing our findings alongside recent studies such as Qian and Fuller’s (2020) focus on the gender employment gap among parents with young children, it does seem plausible that the more salient or consequential direction of interrole conflict during the early months of the pandemic might have been family-to-work conflict. Craig and Churchill (2020) find that women’s satisfaction with work-family balance during the pandemic may have felt off—with too much toward the family side. Unfortunately, we did not assess family-to-work or life-to-work conflict at baseline in September 2019.

In conclusion, our study demonstrates how Canadian workers’ work-life conflict changed during the initial shock of lockdown of the 2020 COVID-19 pandemic. As we observed at the beginning of this article, many scholars have demonstrated how work-life conflict can be detrimental for health and well-being. Therefore, the fact that work-life conflict decreased for many individuals, during such a short period of time, likely had implications for mental and physical health. One future research direction might be to evaluate if the decreased work-life conflict for some workers offset other pandemic-related stressors that harmed health; this idea implies yet another countervailing mechanism related to health. At the same time, our discoveries highlight the potential for a new take on emotional inequality: parents did not experience the same decrease in work-life conflict, possibly because of the new requirements associated with work-family role integration brought on by the pandemic in the face of constrained institutions that normally provide care, supervision, and education to children during the typical workday. Collectively, our observations underscore the importance of recognizing that the status of employed parent during the pandemic carries an enormous weight of responsibilities beyond the job. And our study reminds work-family scholars about the critical importance of considering the potentially divergent experiences of those without children living in the household—and what their experiences might also mean for the generalized discussions about stress in the work-home interface.

Footnotes

Funding

The author(s) disclosed receipt of the following financial support for the research,authorship,and/or publication of this article: Funding from the University of Toronto COVID-19 Action Initiative 2020 and Tri-Council Bridge funding supports this research (Scott Schieman,principal investigator).

ORCID iDs

Scott Schieman

Philip J. Badawy

Alex Bierman

1

observed,however,“Though integration has intuitive appeal as the most ‘balanced’ approach to work and home lives,in actuality there is no one desirable state of integration or segmentation” (p. 755).

2

The ARF recruits via a widespread invitation approach and a double opt-in screening procedure across a variety of channels. This ensures a demographic balance that captures the diversity across subsegments of the population. Panels are maintained through advanced sampling techniques and frequent verifications of personal identity,contact information,and demographic characteristics. Relying on a combination of sampling regions based upon configurations of electoral districts and past voting trends,the ARF reflects the general population by continually verifying and recruiting so that the sociodemographic characteristics of each sampling region match actual subpopulations according to census and electoral data.

3

We removed 29 participants who reported “gender fluid” because of insufficient cell size for gender-based comparisons.

4

Workers who were not working in April or June did not answer work-life conflict items. In ancillary analyses,we adjusted model estimates for loss of data due to becoming unemployed by including a covariate that indicated experiencing unemployment over the course of the study,but results were not different from those presented here,indicating no substantial biases in analyses due to missing values on work-life conflict among the unemployed.

5

The ARF profile data includes panelists’ responses to this yes/no question: “Would you say you are a member of a visible minority here in Canada (in terms of your ethnicity/race)?” Visible minority status is self-categorized. This measurement approach is a common means of assessing minority race and ethnicity in Canada.

6

Analyses also include a category for “don’t know” or “prefer not to say.”

7

Although it is common in mixed modeling to allow random coefficients for measures of time,which create “growth curve models,” the primary motivation for these additional model parameters is in estimating between-individual variation in changes over time,as well as accounting for time-stable variation in changes over time using time-stable covariates. Because the primary focus of the current research is not on variation in rates of change due to between-person factors,the inclusion of these random effects would add model complexity without addressing the focal questions. In the interests of parsimony,then,we do not model the coefficients for time as random effects.

Author Biographies

Scott Schieman is a professor of sociology and Canada Research Chair at the University of Toronto. He is the lead investigator on the C-QWELS and the American Quality of Work and Economic Life Study. Since 2011,he has also led a biannual longitudinal survey of work,family,and well-being in Canada. His research has been supported by the Canadian Institutes of Health Research and the Social Science and Humanities Research Council of Canada.

Philip J. Badawy is currently a PhD candidate in sociology at the University of Toronto. His research is focused on the intersection of work and family life and the implications of these dynamics for workers’ stress and health,with a specific focus on how these processes change over time. One pillar of his research program centers on the dynamics of schedule control and its implications for worker well-being.

Melissa A. Milkie is a professor of sociology at the University of Toronto. Author of Changing Rhythms of American Family Life,her research centers on links among gender,work-family strains,and well-being. With a unique focus on gendered culture,she identifies cultural forces linked to mothering and fathering across time and region. Her research has been supported by the Social Science and Humanities Research Council of Canada and the U.S. National Institutes of Health.

Alex Bierman is an associate professor of sociology at the University of Calgary. His research broadly focuses on the degree to which social inequality and social change condition stress exposure and the consequences of stress exposure for well-being,particularly in the context of aging.

References

Allen

Tammy D.

Cho

Eunae

Meier

Laurenz L.

2014. “Work-Family Boundary Dynamics.” Annual Review of Organizational Psychology and Organizational Behavior 1:99–121.

Allen

Tammy D.

Finkelstein

Lisa M.

2014. “Work-Family Conflict among Members of Full-Time Couples: An Examination of Family Life Stage, Gender, and Age.” Journal of Occupational Health Psychology 19(3):376–84.

Allen

Tammy D.

Herst

David E. L.

Bruck

Carly S.

Sutton

Martha

. 2000. “Consequences Associated with Work-to-Family Conflict: A Review and Agenda for Future Research.” Journal of Occupational Health Psychology 5(2):278–308.

Allen

Tammy D.

Martin

Angela

. 2017. “The Work-Family Interface: A Retrospective Look at 20 Years of Research in JOHP.” Journal of Occupational Health Psychology 22(3):259–72.

Allison

Paul

. 2001. Missing Data. Thousand Oaks, CA: Sage.

Ashforth

Blake, E.

Kreiner

Glen E.

Fugate

Mel

. 2000. “All in a Day’s Work: Boundaries and Micro Role Transitions.” Academy of Management Review 25(3):472–91.

Ball

Jessica

Daly

Kerry

, eds. (2012). Father Involvement in Canada: Diversity, Renewal, and Transformation. Vancouver, Canada: University of British Columbia Press.

Bedeian

Arthur G.

Burke

Beverly G.

Moffett

Richard G.

1988. “Outcomes of Work-Family Conflict among Married Male and Female Professionals.” Journal of Management 14(3):475–491.

Bellavia

Gina M.

Frone

Michael R.

2005. “Work-Family Conflict.” Pp. 113–48 in Handbook of Work Stress, edited by Barling

Kelloway

E. K.

Frone

M. R.

Thousand Oaks, CA: Sage.

10.

Bianchi

Suzanne M.

Robinson

John P.

Milkie

Melissa A.

2006. The Changing Rhythms of American Family Life. New York: Russell Sage.

11.

Blair-Loy

Mary

. 2003. Competing Devotions: Career and Family among Women Financial Executives. Cambridge, MA: Harvard University Press.

12.

Burkett

Elinor

. 2000. The Baby Boom: How Family-Friendly American Cheats the Childless. New York: Free Press.

13.

Carlson

Daniel L.

Petts

Richard

Pepin

Joanna R.

2020. “Changes in Parents’ Domestic Labor during the COVID-19 Pandemic.” SocArXiv. Retrieved December 13, 2020. https://osf.io/preprints/socarxiv/jy8fn/.

14.

Cech

Erin A.

O’Connor

Lindsey T.

2017. “‘Like Second-Hand Smoke’: The Toxic Effect of Workplace Flexibility Bias for Workers’ Health.” Community, Work & Family 20(5):543–72

15.

Christiansen

Shawn, L.

Palkovitz

Rob

. 2001. “Why the ‘Good Provider’ Role Still Matters: Providing as a Form of Paternal Involvement.” Journal of Family Issues 22(1):84–106.

16.

Clark

Sue C.

2000. “Work/Family Border Theory: A New Theory of Work/Family Balance.” Human Relations 53(6):747–70.

17.

Collins

Caitlyn

. 2020. Making Motherhood Work: How Women Manage Careers and Caregiving. Princeton, NJ: Princeton University Press.

18.

Coltrane

Scott

. 1996. Family Man: Fatherhood, Housework, and Gender Equity. New York: Oxford University Press.

19.

Coontz

Stephanie

. 1992. The Way We Never Were: American Families and the Nostalgia Trap. New York: Basic Books.

20.

Craig

Lyn

Churchill

Brendan

. 2020. “Working and Caring at Home: Gender Differences in the Effects of Covid-19 on Paid and Unpaid Labor in Australia.” Feminist Economics. doi:10.1080/13545701.2020.1831039

21.

Dernberger

Brittany N.

Pepin

Joanna R.

2020. “Gender Flexibility, but Not Equality: Young Adults’ Division of Labor Preferences.” Sociological Science 7(2):36–56.

22.

Dizik

Alina

. 2020. “How to Work from Home with Your Kids during Coronavirus.” BBC, April 4. Retrieved December 13, 2020. https://www.bbc.com/worklife/article/20200401-how-to-work-from-home-with-your-kids-during-coronavirus.

23.

Dumas

Tracy L.

Sanchez-Burks

Jeffrey

. 2015. “The Professional, the Personal, and the Ideal Worker: Pressures and Objectives Shaping the Boundary between Life Domains.” Academy of Management Annals 9(1):803–43.

24.

Dyer

W. J.

Day

Randal D.

Harper

James M.

2014. “Father Involvement: Identifying and Predicting Family Members’ Shared and Unique Perceptions.” Journal of Family Psychology 28(4):516–28.

25.

Enders

Craig K.

2010. Applied Missing Data Analysis. New York: Guildford.

26.

Erickson

Jenet J.

Martinengo

Giuseppe

Hill

E. Jeffrey

. 2010. “Putting Work and Family Experiences in Context: Differences by Family Life Stage.” Human Relations 63(7):955–79.

27.

Fuller

Sylvia

. 2018. “Segregation across Workplaces and the Motherhood Wage Gap: Why Do Mothers Work in Low-Wage Establishments?” Social Forces 96(4):1443–76.

28.

Gallacher

Guillermo

Hossain

Iqbal

. 2020. “Remote Work and Employment Dynamics under COVID-19: Evidence from Canada.” Canadian Public Policy/Analyse de Politiques 46(S1):S44–54.

29.

Government of Canada. 2020. “Coronavirus Disease (COVID-19): Outbreak Update.” Retrieved December 13, 2020. https://www.canada.ca/en/public-health/services/diseases/2019-novel-coronavirus-infection.html?topic=tilelink.

30.

Greenhaus

Jeffrey, H.

Beutell

Nicholas J.

1985. “Sources of Conflict between Work and Family Roles.” Academy of Management Review 10(1):76–88.

31.

Guppy

Neil

Sakumoto

Larissa

Wilkes

Rima

. 2019. “Social Change and the Gendered Division of Household Labor in Canada.” Canadian Review of Sociology 56(2):178–203.

32.

Hays

Sharon

. 1996. The Cultural Contradictions of Motherhood. New Haven, CT: Yale University Press.

33.

Hill

E. Jeffrey

Miller

Brent C.

Weiner

Sara P.

Colihan

Joe

. 1998. “Influences of the Virtual Office on Aspects of Work and Work/Life Balance.” Personnel Psychology 51(3):667–83.

34.

Salarina

. 2020. “When Will School Resume? What We Know, Province by Province.” CTV News, May 4. Retrieved December 13, 2020. https://www.ctvnews.ca/health/coronavirus/when-will-school-resume-what-we-know-province-by-province-1.4923667.

35.

Houle

Patricia

Turcotte

Martin

Wendt

Micheal

. 2017. “Changes in Parents’ Participation in Domestic Tasks and Care for Children from 1986–2015.” Spotlight on Canadians: Results from the General Social Survey. Catalogue No. 89-652-X2017001. Ottawa: Statistics Canada.

36.

Johnstone

Hillary

. 2020. “COVID-19 School Closures Throw Child Care, Work Placements into Doubt.” CBC News, March 13. Retrieved December 13, 2020. https://www.cbc.ca/news/canada/ottawa/ottawa-parents-students-react-school-closures-1.5496245.

37.

Juric

Sam

. 2020. “Some Schools Reopening to Help P.E.I Students Who Need Individualized Support, Give Respite to Families.” CBC, April 30. Retrieved December 13, 2020. https://www.cbc.ca/news/canada/prince-edward-island/pei-respite-care-covid-19-1.5551198.

38.

Kahn

Robert L.

Wolfe

Donald M.

Quinn

Robert P.

Snoek

J. Diedrick

Rosenthal

Robert A.

1964. Organizational Stress: Studies in Role Conflict and Ambiguity. New York: John Wiley.

39.

Kelliher

Clare

Richardson

Julia

Boiarintseva

Galina

. 2019. “All of Work? All of Life? Reconceptualizing Work-Life Balance for the 21st Century.” Human Resource Management Journal 29(2):97–112.

40.

Kelly

Erin L.

Moen

Phyllis

Oakes

Michael

Fan

Wen

Okechukwu

Cassandra

Davis

Kelly D.

Hammer

Leslie B.

, et al. 2014. “Changing Work and Work-Family Conflict: Evidence from the Work, Family, and Health Network.” American Sociological Review 79(3):485–516.

41.

Kossek

Ellen E.

2016. “Managing Work-Life Boundaries in the Digital Age.” Organizational Dynamics 45(3):258–70.

42.

Kossek

Ellen, E.

Lambert

Susan J.

2004. Work and Life Integration: Organizational, Cultural, and Individual Perspectives. London: Taylor & Francis.

43.

Kossek

Ellen E.

Lautsch

Brenda A.

Eaton

Susan C.

2006. “Telecommuting, Control, and Boundary Management: Correlates of Policy Use and Practice, Job Control, and Work Family Effectiveness.” Journal of Vocational Behavior 68(2):347–67.

44.

Kossek

Ellen, E.

Lee

Kyung-Hee

. 2017. “Work-Family Conflict and Work-Life Conflict.” In: Oxford Research Encyclopedia. Retrieved December 13, 2020. https://oxfordre.com/business/view/10.1093/acrefore/9780190224851.001.0001/acrefore-9780190224851-e-52.

45.

Kossek

Ellen E.

Noe

Raymond A.

DeMarr

Beverly J.

1999. “Work-Family Role Synthesis: Individual and Organizational Determinants.” International Journal of Conflict Management 10(2):102–29.

46.

Landivar

Liana C.

Ruppanner

Leah

Scarborough

William J.

Collins

Caitlyn

. 2020. “Early Signs Indicate That COVID-19 Is Exacerbating Gender Inequality in the Labor Force.” Socius 6. Retrieved December 13, 2020. https://journals.sagepub.com/doi/full/10.1177/2378023120947997.

47.

Lewis

Helen

. 2020. “The Coronavirus Is a Disaster for Feminism.” The Atlantic, March 19. Retrieved December 13, 2020. https://www.theatlantic.com/international/archive/2020/03/feminism-womens-rights-coronavirus-covid19/608302/.

48.

Marshall

Katherine

. 2011. “Generational Change in Paid and Unpaid Work.” Catalogue No. 11-008-X. Ottawa: Statistics Canada.

49.

McCarthy

Ellen

Gibson

Caitlin

Andrews-Dyer

Helenda

Joyce

Amy

. 2020. “A Working Mom’s Quarantine Life.” The Washington Post, May 6. Retrieved December 13, 2020. https://www.washingtonpost.com/lifestyle/2020/05/06/coronavirus-pandemic-working-moms-quarantine-life/?arc404=true.

50.

Messacar

Derek

Morrissette

René

Deng

Zechuan

. 2020. “Inequality in the Feasibility of Working from Home during and after COVID-19.” Ottawa: Statistics Canada.

51.

Milkie

Melissa A.

Bianchi

Suzanne M.

Mattingly

Marybeth J.

Robinson

John P.

2002. “Gendered Division of Childrearing: Ideals, Realities, and the Relationship to Parental Well-Being.” Sex Roles 47(1):21–38.

52.

Moen

Phyllis

Roehling

Patricia V.

2005. The Career Mystique: Cracks in the American Dream. Boulder, CO: Rowman & Littlefield.

53.

Moyser

Melissa

Burlock

Amanda

. 2018. “Time Use: Total Work Burden, Unpaid Work, and Leisure.” Women in Canada: A Gender-Based Statistical Report. Catalogue No. 89-503-X. Ottawa: Statistics Canada.

54.

Nippert-Eng

Christina E.

1996. Home and Work: Negotiating Boundaries through Everyday Life. Chicago: University of Chicago Press.

55.

O’Connor

Lindsey T.

Cech

Erin A.

2018. “Not Just a Mother’s Problem: The Consequences of Perceived Workplace Flexibility Bias for All Workers.” Sociological Perspectives 61(5):808–29.

56.

Olson-Buchanan

Julie B.

Boswell

Wendy R.

2006. “Blurring Boundaries: Correlates of Integration and Segmentation between Work and Nonwork.” Journal of Vocational Behavior 68(3):432–45.

57.

Offer

Shira

Schneider

Barbara

. 2011. “Revisiting the Gender Gap in Time-Use Patterns: Multitasking and Well-Being among Mothers and Fathers in Dual-Earner Families.” American Sociological Review 76(6):809–33.

58.

Padavic

Irene

Reskin

Barbara

. 2002. Women and Men at Work. Thousand Oaks, CA: Pine Forge.

59.

Pedulla

David S.

Thébaud

Sarah

. 2015. “Can We Finish the Revolution? Gender, Work-Family Ideals, and Institutional Constraint.” American Sociological Review 80(1):116–39.

60.

Qian

Yue

Fuller

Sylvia

. 2020. “COVID-19 and the Gender Employment Gap among Parents of Young Children.” Canadian Public Policy 46(S2):S89–101.

61.

Rau

Barbara L.

Hyland

Maryanne M.

2002. “Role Conflict and Flexible Work Arrangements: The Effects on Applicant Attraction.” Personnel Psychology 55(1):111–36.

62.

Reimer

Will

Blunt

Marney

. 2020. “Back to School in June: Manitoba Students, Staff Head Back to Class for Small Group Sessions.” Global News, June 1. Retrieved December 13, 2020. https://globalnews.ca/news/7011199/coronavirus-manitoba-back-to-school/.

63.

Rudolph

Cort W.

Allan

Blake

Clark

Malissa

Hertel

Guido

Hirschi

Andreas

Kunze

Florian

Shockley

Kristen

, et al. 2020. “Pandemics: Implications for Research and Practice in Industrial and Organizational Psychology.” Industrial and Organizational Psychology: Perspectives on Science and Practice.

64.

Sayer

Liana

. 2007. “Gender Differences in the Relationship between Long Employee Hours and Multitasking.” Research in the Sociology of Work 17:403–35.

65.

Schieman

Scott

Badawy

Philip J.

2020. “The Status Dynamics of Role Blurring in the Time of COVID-19.” Socius 6. Retrieved December 13, 2020. https://journals.sagepub.com/doi/full/10.1177/2378023120944358.

66.

Schieman

Scott

Milkie

Melissa A.

Glavin

Paul

. 2009. “When Work Interferes with Life: Work-Nonwork Interference and the Influence of Work-Related Demands and Resources.” American Sociological Review 74(6):966–88.

67.

Schieman

Scott

Narisada

Atsushi

. 2021. “A Less Objectionable Greed? Work-Life Conflict and Unjust Pay during a Pandemic.” Research in Social Stratification and Mobility 71:100564.

68.

Shafer

Kevin

Scheibling

Casey

Milkie

Melissa A.

2020. “The Division of Domestic Labour before & during the COVID-19 Pandemic in Canada.” Canadian Review of Sociology 57(4)523–49.

69.

Stainback

Kevin

Hearne

Brittany N.

Trieu

Monica M.

2020. “COVID-19 and the 24/7 News Cycle: Does COVID-19 News Exposure Affect Mental Health?” Socius 6. Retrieved December 13, 2020. https://journals.sagepub.com/doi/full/10.1177/2378023120969339.

70.

Thomason

Bobbi

Williams

Heather

. 2020. “What Will Work-Life Balance Look Like after the Pandemic? Harvard Business Review, April 16. Retrieved December 13, 2020. https://hbr.org/2020/04/what-will-work-life-balance-look-like-after-the-pandemic.

71.

Voydanoff

Patricia

. 2004. “The Effects of Work Demands and Resources on Work-to-Family Conflict and Facilitation.” Journal of Marriage and Family 66(2):398–412.

72.

Williams

Joan C.

2020. “The Pandemic Has Exposed the Fallacy of the ‘Ideal Worker.’” Harvard Business Review, May 11. Retrieved December 13, 2020. https://hbr.org/2020/05/the-pandemic-has-exposed-the-fallacy-of-the-ideal-worker.

73.

Williams

Joan C.

Blair-Loy

Mary

Berdahl

Jennifer L.

2013. “Cultural Schemas, Social Class, and the Flexibility Stigma.” Journal of Social Issues 69(2):209–34.

74.

Yang

Land

Kenneth C.

2013. Age-Period-Cohort Analysis: New Models, Methods, and Empirical Applications. Boca Raton, FL: CRC.

75.

Young

Mary B.

1999. “Work-Family Backlash: Begging the Question, What’s Fair?” Annals of the American Academy of Political and Social Science 562(1):32–46.

76.

Yucel

Deniz

. 2019. “Job Autonomy and Schedule Flexibility as Moderators of the Relationship between Work-Family Conflict and Work-Related Outcomes.” Applied Research in Quality of Life 14(5):1–18.

77.

Yucel

Deniz

Fan

Wen

. 2019. “Work-Family Conflict and Well-Being among German Couples: A Longitudinal and Dyadic Approach.” Journal of Health and Social Behavior 60(3):377–95.