Commuting Distance and Work-to-Family Conflict: The Moderating Role of Residential Attributes

Abstract

Commuting is a boundary-spanning demand that can limit employees’ availability to fulfill family-related roles and routines, leading to work-to-family conflict (WFC). We argue that in cities with challenging housing markets, implications of commuting for WFC may vary by residential attributes, and that the moderating effects of residential attributes may vary by gender due to differences in the work-family interface. Analyses of survey data from individuals in the Greater Toronto Area suggest that (1) the positive association between commuting and WFC is stronger among those who are dissatisfied with their place of residence and live in more disordered neighborhoods, irrespective of gender; (2) homeownership protects against the negative impact of commuting distance on WFC—but only among men; and (3) alternatively, the positive association between commuting and WFC becomes stronger as neighborhood quality increases—but only among women. Our study underscores the importance of “compensation” by positive residential qualities in the relationship between commuting and WFC.

Keywords

sex and gender work-family conflict commuting distance housing neighborhood

Introduction

Commuting is an increasingly important part of the day for the employed, and the prevalence of those doing so has grown in North America at an unprecedented rate over the past decade. In Canada, these rates have risen by 30.3 percent since 1996 (Statistics Canada 2017). This is particularly the case in Toronto, Canada—the context of the current study: a report published by the Board of Trade suggests that in 2011 Toronto had the worst commute times of 21 major cities in North America (Aw 2012) and has seen a 9 percent increase in commuting distance since 1996 (Statistics Canada 2017). This increase in commuting distance parallels population shifts from the Toronto city proper to the 25 surrounding municipalities that make up the Greater Toronto Area (GTA). In 2011, Toronto accounted for 43 percent of the GTA population, down from 45 percent in 2006 (City of Toronto 2012). Furthermore, while Toronto’s population grew by 4 percent between 2006 and 2011, and again between 2011 and 2016, the population in the GTA grew by over 8 percent between 2006 and 2011 and by 6 percent between 2011 and 2016 (https://canadapopulation.org/toronto-population/).

Commuting to and from work is characterized as a boundary-spanning demand that can limit employees’ availability to fulfill family-related roles and routines, leading to work-to-family conflict (WFC; Voydanoff 2007): a form of inter-role conflict through which events in one’s work life interfere with family obligations (Greenhaus and Beutell 1985). WFC is an important antecedent of several individual outcomes, including family absences, poor family role performance, family stress, work satisfaction, burnout/exhaustion, and mental and physical health (Amstad et al. 2011; Goh, Pfeffer, and Zenios 2015; Voydanoff 2005b; Young 2015; Young and Montazer 2018). Thus, as the proportion of those commuting and commuting times/distance increase, it becomes increasingly important to understand the effect of this important part of the day on the conflict between work and family life.

Drawing on data from the Neighbourhood Effects on Health and Well-Being (NEHW) study from Toronto, we add to limited work-family research on the effect of commuting by examining whether the effect of commuting distance on WFC is mitigated by housing or neighborhood attributes: homeownership, neighborhood quality, residential dissatisfaction, and neighborhood disorder. Henceforth, we refer to these factors as residential attributes. Given gender differences in the experience of the work-family interface (Bianchi, Robinson, and Milkie 2006; Glavin, Schieman, and Reid 2011), we also examine whether the impact of residential attributes on the relationship between commuting and WFC is different for men and women.

Background

The Consequences of Commute Time and Distance

Work-family conflict is rooted in role conflict theory, where the demands of one role impinge upon obligations in another role. As Greenhaus and Beutell (1985) outlined, there are three forms of conflict often experienced between work and family: time-, strain-, and behavior-based. Work-family conflict is considered bidirectional, where work interferes with family expectations (WFC) versus when family demands impinge upon work obligations (family-to-work conflict, FWC) (Frone 2003). We focus on the former type of conflict (WFC) with the expectation that commuting is considered a work-related stressor that might disrupt family life.

Common antecedents of WFC documented in the literature include demands and resources across work and family spheres (see Michel et al. 2011). Examples of work-related factors associated with WFC are having a full-time job, work hours, and job demands, control, and creativity. Family or non-work-related factors might include housework, childcare, spouse’s circumstances, and other caregiving roles. Children’s ages might also matter. Parents with young children, for example, tend to report the highest levels of WFC given the associated time commitments (Michel et al. 2011; Nomaguchi and Fettro 2019). As children age, the quality of time spent with children increases. These more rewarding relationships might reduce WFC among parents with older compared to younger children (Nomaguchi 2012).

Despite the myriad of stressors across the two spheres that might impact WFC, our article focuses specifically on the effect of commuting distance. Some scholars suggest that commuting may be beneficial for the experience of WFC as it may offer an opportunity for workers to wind down and recover from work (Nie and Sousa-Poza 2018). However, the limited scholarship that directly (Haines et al. 2018; Jansen et al. 2003; Lee and Son 2013; Lipińska-Grobelny 2016; Reimann, Pausch, and Diewald 2017; Voydanoff 2005a, 2005b) or indirectly (e.g., use of commuting as a covariate, for example, van der Lippe and Lippenyi 2018) examines the association between commuting distance and WFC (Reimann et al. 2017), and commuting time and WFC suggests the opposite in the United States (Voydanoff 2005b), Canada (Haines et al. 2018; Voydanoff 2005a), as well as other countries (i.e., Germany, Reimann et al. 2017; Poland, Lipińska-Grobelny 2016; Nigeria, Adisa, Osabutey, and Gbadamosi 2016; Europe, van der Lippe and Lippenyi 2018; and South Korea, Lee and Son 2013)). Some studies find this positive association among female commuters only (e.g., Hofmeister 2003; Jansen et al. 2003), and only one study (to the best of our knowledge) does not find a significant association between commuting and WFC (Pleck, Staines, and Lang 1980).

According to Voydanoff (2005a, 2005b, 2007), commuting time/distance is a key contributor to WFC given that it takes time away from both the work and home domains, leading to greater conflict between the two. Commuting impacts WFC because it is considered a “boundary-spanning demand”—a time- and psychological-based demand that literally spans both the work and family domains, potentially causing conflict (Voydanoff 2007). From this perspective, commuting can be viewed as a time constraint that impacts the time available for one’s family, home life, and self, causing subsequent strain on family relations and WFC. Thus, increase in commuting time or distance to and from work increases WFC because it limits participation in the family domain (Voydanoff 2007). Given the above findings, we also expect to observe a positive association between commuting and WFC. Given data restrictions, we use geographical information on both the respondent’s place of residence and work to calculate the commute distance between these two domains (kilometers) in our analyses.¹

The Buffering Effects of Residential Attributes

Some cities are particularly challenging with respect to housing market and commuting. In such cities, implications of commuting for WFC may vary by residential attributes. Toronto, Canada, is one such city, with one of the longest commute times (Aw 2012) and most expensive housing markets (Statistics Canada 2014) in Canada.

Toronto’s housing market

According to Statistics Canada (2014), in 2011 (the year the data for this study were collected), Toronto had the most expensive housing market in Ontario and the third most costly housing market in Canada (after Vancouver and Victoria in British Colombia). In 2012, the average price for a detached home in Toronto exceeded CAN$800,000, while the average price for a condominium was over CAN$350,000 (Pigg 2012). The average price of a home in the GTA was approximately CAN$500,000. Apart from its prohibitively high house costs, Toronto also has a high land transfer tax for those who are not first-time homebuyers, which may restrict home sales and mobility, despite the desire to move (Pigg 2012). These phenomena lead to two important trends: nonhomeowners cannot buy into the housing market, and those who own a home but wish to move may not (Pigg 2012).

Due to a growing proportion of young people, immigration, and the “hot” housing market influencing where people choose to live and work, there is also a high demand in the Toronto rental market (Burda 2013). This high demand has led to a 37 percent increase in rent prices in Toronto since 2000, and in 2011, the average rent price was CAN$1,072 for private apartments and CAN$1,518 for condo apartments (Myles 2017). In the City of Toronto, the projected demand for rental homes is expected to outpace supply by 2035 (Burda 2013). Despite the increase in the average price of rent, however, average wages have only increased 10 percent between 1998 and 2011 (Myles 2017). Thus, those who rent may not be able to move, even if they want to because of two existing trends: increased rent prices coupled with limited growth in wages.

Unpacking the association between commuting distance and WFC: Urban location theory

We do not expect the positive association between commuting distance and WFC to be universal in cities with challenging housing markets, such as Toronto. A key focus of our study addresses whether commute distances, referred to as commuting henceforth, impacts WFC differently depending on respondents’ residential attributes. We borrow from urban location theory (e.g., Alonso 1964) research on the relationship between commuting and psychological health (e.g., Stutzer and Frey 2008) to help situate the predicted conditional relationship between commuting and WFC in our study.

Urban location theory, developed in the 1960s, was originally based on the principle that businesses choose locations to maximize their profits. So if businesses are far from the city center, rent should be less to offset transportation costs (Jordaan, Drost, and Makgata 2004). Using the same principle, this theory would predict that individuals choose their place of residence or employment to maximize their utility, measured as subjective well-being (Nie and Sousa-Poza 2018; Stutzer and Frey 2008) or psychological health (Roberts, Hodgson, and Dolan 2011), for example. Studies that have used this theory to examine the relationship between commuting and health maintain that commuting is just another decision that rational individuals make (Stutzer and Frey 2008); if commuters are fully compensated for their commute by the labor market (e.g., higher wages) or the housing market (e.g., better housing or a pleasant living environment), their utility is equalized and, thus, there should be no relationship between commuting distance and health outcomes (Stutzer and Frey 2008). Thus, commuting should be detrimental for those who are not compensated by either of these markets. By the same token, commuting should have minimal to no impact if individuals are compensated by these markets.

We argue that this should also be the case for the positive association between commuting distance and WFC. While both the housing and labor markets may have important consequences for the relationship between commuting and WFC, in this article, we focus on the moderating role of the housing market—in terms of homeownership, residential dissatisfaction, neighborhood quality, and neighborhood disorder—as we suspect it will be an especially important factor in a city with an overpriced housing market and limited access to desirable neighborhoods.² More specifically, we expect commuting in Toronto to be particularly detrimental for the WFC of respondents who have less desirable residential situations—that is, among those who do not own a home, are dissatisfied with their place of residence, or live in less desirable neighborhoods (high in disorder, low in quality).

Gender as an Added Complexity

Gender adds another layer of complexity to the contingent relationship between commuting and WFC by residential attributes. Despite changes in industrialized countries—like Canada—that have seen men increase their contribution to a wide range of domestic tasks and labor and women increase their presence and importance in the workforce, work continues to be defined as a man’s sphere, while domestic life is still defined as a woman’s sphere (Haines et al. 2018). These ideologies will likely have consequences for how the relationship between commuting and WFC is impacted by specific residential attributes among men and women. For example, neighborhood attributes may be more important in the relationship between commuting and WFC for women, since women are more involved in their community and neighborhoods. Alternatively, housing attributes may be more important for the relationship between commuting and WFC for men, as these factors may be closely tied to the traditional role of men as breadwinners.

Are housing attributes more salient for men?

Despite the empirical veracity that family structures and parenting practices change with social and economic contexts, research continues to suggest that gender differences remain regarding work-family role expectations (Glavin et al. 2011; Hochschild 1997). There is still a persistent belief that men are primarily responsible for the breadwinner role and women the homemaker/caretaker role (Roehling and Bultman 2002). Housing attributes, that is, homeownership and ability to live in a desirable home, may be two characteristics that support men’s breadwinning and “successful male” identities (Aumann, Galinsky, and Matos 2011; Christiansen and Palkovitz 2001). Thus, housing attributes may be more important in moderating the relationship between commuting and WFC among men than women. For men who cannot provide for their family, or are unable to show their success, by owning a home or living in a desirable residence, commuting cannot be justified. For these men, commuting may be perceived more as a time strain that detracts from the “good provider” role, for example (Christiansen and Palkovitz 2001). Thus, longer commute distances should result in greater WFC among men who are not compensated for their commute through the housing market compared to women.

Are neighborhood attributes more salient for women?

Aside from the unequal domestic division of labor (Bianchi et al. 2006), research suggests that men and women differentially experience their neighborhood of residence and local community. Whether voluntarily or by necessity, women are more likely than men to integrate into the social fabric of the community, invest in relationships with others in the neighborhood, volunteer at local organizations, and participate in public gatherings and activities (Campbell and Lee 1990; Doucet 2000; Kaminer 1984; Young 2019). Indeed, according to Doucet (2000), women are primarily responsible for community-based responsibilities; they initiate, plan, organize, and manage the majority of planning between households and between households and other social institutions. Women are also more likely to use publicly available resources, such as childcare, social services, and recreational facilities (Bianchi et al. 2006; Hochschild 1997). This literature therefore suggests that, compared to men, women may be more acutely aware of their structural and social surrounding. Thus, given the importance of neighborhoods and community in women’s lives, it is likely that commuting will be particularly detrimental for the WFC of women who live in less desirable neighborhoods (i.e., high in disorder, low in quality) as they will lack the “compensation” that may justify their commutes.

The above elaborations give rise to the following hypotheses:

Commuting by housing attributes

Hypothesis 1 (H1): Commuting is positively associated with WFC, only among those who are dissatisfied with their place of residence, as compared to those who are satisfied (H1a), more so for men than women (H1b).

Hypothesis 2 (H2): Commuting is positively associated with WFC only among those who do not own a home, as compared to homeowners (H2a), more so for men than women (H2b).

Commuting by neighborhood attributes

Hypothesis 3 (H3): The positive association between commuting and WFC will become stronger with increases in neighborhood disorder (H3a), more so for women than men (H3b).

Hypothesis 4 (H4): The positive association between commuting and WFC will become weaker with increases in neighborhood quality (H4a), more so for women than men (H4b).

Data and Method

Data

We use data from the NEHW study to test our hypotheses. The NEHW study is an individual-level data set gathered using a cross-sectional, multilevel design across 47 neighborhoods in the metropolitan city of the GTA. Face-to-face interviews were conducted between 2009 and 2011 with approximately 20 to 30 respondents in 87 census tracts across the city-defined neighborhoods in Toronto (see O’Campo et al. 2015 for more detail). The data set comprises interviews with 2,412 individuals. To be eligible, participants had to be between 25 and 64 years old, be comfortable speaking and understanding English, and, at the date of interview, reside at their current address for at least six months. The final sample included an overall response rate of over 80 percent. Some of the inclusion criteria may have led to the underrepresentation of some groups. To ensure representation, we include sampling weights based on nativity, gender, age, household members, and income (O’Campo et al. 2015).

The NEHW data are ideal for our purposes because they include information on housing and neighborhood attributes, as well as geographical information on both the respondent’s place of residence and work, which allows us to proximate a measure for commuting distance between these two domains. These data also include several important measures on socioeconomic, demographic, employment, and family characteristics that may have important consequences for our focal associations.

While this data set includes numerous measures that are important for our study, it does not include measures on the number of days the respondent may commute into work. This may be important as commuting may especially impact work-life conflict for individuals who experience this demand more regularly. We, thus, limit our sample to those working full-time based on whether the respondent works more than 30 hours per week (the Canadian standard for full-time work; Statistics Canada 2009) (N = 896). We also limit our analytic sample to those full-time workers who have a calculated distance in kilometers between their place of residence and work that is greater than zero. Consequently, we cannot generalize to those who may commute more sporadically among those employed part-time or those who work mainly from home. These two restrictions resulted in a final analytic sample size of 634 of which 51.7 percent were women.³

Measures

Dependent variable: WFC

We generate an index of WFC by averaging responses to four commonly referenced items borrowed from the National Study of the Changing Workforce (Bond et al. 2003). Respondents were asked to indicate how often “have [they] not had enough time for [their] family or other important people in [their] life because of [their] job,” “have [they] not had the energy to do things with [their] family or other important people in [their] life because of [their] job,” “has [their] job kept [them] from doing as good a job at home as [they] could,” and “has [their] job kept [them] from concentrating on important things in [their] family and personal life?” Response choices include “very often” (5) to “never” (1). Higher scores represent greater conflict (α = .91).

Focal independent variable: Commuting distance

We employ the geodist function in SAS 9.4, which uses the latitude and longitude associated with two geographical points to calculate the exact kilometers between one’s residence and workplace, based on respondents’ reported postal codes for each respective location.⁴ Median commuting time is 7.06 km with an interquartile range of 3.96 to 12.19 km.

Moderators: Residential attributes

Homeowner is a dummy variable that equals 1 if the respondent indicates owning their home versus those who do not own their place of residence (0).

The answer to the question “would you move to a different home if you were able to?” was used to create a dummy variable to capture residential dissatisfaction if they answered “yes” (residential dissatisfaction = 1). Those who answered “no” comprise the reference category (0).

Neighborhood quality index takes the average of 12 items that ask the respondent to indicate how strongly they agrees/disagree with the following: my neighborhood “has clean parks, gardens, green or open spaces,” “has safe places for children to play,” “has food stores where I can easily buy healthy foods such as fruits and vegetables,” and “has good recreational and cultural facilities,” for example. Higher values indicate higher quality (α = .82).

The scale for neighborhood disorder includes 20 commonly used items about the physical and social problems in a given neighborhood, including, for example, litter or trash on the sidewalks and street, graffiti on building and walls, rundown sidewalks, or drug dealers hanging out. Responses range from “a serious problem,” “quite a problem,” to “not at all a problem” (Hill et al. 2008; Young 2015). Responses were averaged so that higher scores reflect greater problems (α = .87).

Covariates

We adjust for a variety of covariates and WFC antecedents that may impact our focal associations (Feng and Boyle 2014; Koslowsky 1997; Michel et al. 2011; Montazer and Young 2017; Novaco, Kliewer, and Broquet 1991; Voydanoff 2007; Young and Montazer 2018), including respondent’s age (in years); foreign-born (=1); presence of children under the age of 3, between 4 and 6, between 7 and 12, and between 13 and 18, with no children in the household as the reference group; marital status (married or common law versus other marital statuses); household income (a continuous measure presented in thousands of Canadian dollars); total number of years of education; work hours; percent of hours worked at home per week; domestic chores; job demands; creative work; job control; and ethnicity/culture.

Work hours are measured as the total number of hours the respondent indicated working in a typical week. We measure the percent of hours worked at home per week by dividing the number of hours the respondent indicated working at home by the total number of hours the respondent indicated working in a typical week. To measure domestic chores, respondents were asked to record the average number of hours spent on 19 domestic tasks per week, such as “preparing family meals,” “washing dishes and cleaning up after meals,” “cutting the lawn,” “taking care of the kids when spouse is home,” and “taking care of kids when spouse is gone” (Sweet, Bumpass, and Call 1988). Responses were summed to generate total domestic hours per week, including household tasks and childcare.

The following questions were prefaced with “[i]n your current job, how often . . . ” We use three questions to tap job demands. Respondents were asked (1) “is your job physically demanding and/or tiring?” (2) “have you felt overwhelmed by how much you had to do at work?” and (3) “do the demands of your job exceed those doable in an eight-hour workday?” We use two items to tap creative work: (1) “does your job require you to be creative” and (2) “does your job allow you to develop your skills and abilities?” We use one item to tap control over one’s job: “does someone else decide how you do your work?” (R). Responses to all questions include “never” (1), “rarely” (2), “sometimes” (3), or “frequently” (4). The average from each set of items was constructed to create a scale of demands (α = .65), a scale of creative work (α = .75), and a one-item measure for job control. These items are similar to those used in other work-family research (Bond et al. 2003; Young 2015).

To measure ethnicity/culture, respondents were asked, “To which ethnic or cultural group do you feel you belong to?” We used the responses to this question to assign them to the following ethnic/cultural groupings: Arab or West Asian, African, Caribbean, East Asian or Pacific Rim, European, Latin, and South Asian, with Canadian/North American as the reference category. These categories were created from Statistics Canada (2008) ethnicity coding.

According to previous research, the effect of commuting on outcomes may depend on the mode of transportation and impedance—anything blocking or thwarting movement and goal attainment, such as traffic and slow speed (Novaco et al. 1991)—throughout the duration of the trip (Stutzer and Frey 2008). Furthermore, decision to commute is likely dependent on any known impedance factors and mode of transportation available to the individual. Although extant studies that examine the relationship between commuting time/distance and WFC do not control for mode of transportation or impedance factors (e.g., Haines et al. 2018; Voydanoff 2005a, 2005b, 2007), it is necessary to adjust for these measures, if possible. While the NEHW study does not specifically include such measures, we best approximate commuting mode and impedance in the following manner.⁵ We use the answer to three questions that ask the respondent to estimate how many days/week (0–7) they travel by (1) a motor vehicle such as train, bus, car, or streetcar, (2) by bicycle, and (3) by walking to get to places such as work, stores, and movies to approximate three continuous measures for commuting mode: number of days in the last week that the respondent traveled in a motor vehicle, number of days in the last week that the respondent traveled by bicycle, and number of days in the last week that the respondent traveled by walking. We also control for the availability of public transportation (poor to excellent) in the respondent’s neighborhood, with higher values indicating better availability.

We next use two measures to tap at commuting impedance. First, we use median commute time by census tract from the 2011 Statistics Canada National Household Survey to proximate the median commute time in Toronto per census tract. This is a continuous variable that varies by the respondent’s census tract and gives an approximation of how long it takes individuals to reach work or home depending on their place of residence (in minutes). This variable, indirectly, taps at traffic. Second, we capture the seriousness of traffic (neighborhood traffic) in the respondents’ residential region with a question that asks them to indicate whether heavy road traffic is a “serious problem” (5), “quite a problem” (4), “more or less a problem “(3), “a minor problem” (2), or “not a problem for them” (1) in their neighborhood. Higher values suggest more serious traffic. We believe this measure somewhat captures the experience of traffic delays in one’s day-to-day commute. While more efficient measures might exist, we are limited here by our data.

Analytic Strategy

We use hierarchical linear modeling (HLM) for all analyses (Raudenbush and Bryk 2002). This approach is appropriate for these data because the NEHW study clusters respondents by neighborhood so that error terms across respondents within the same neighborhood are likely correlated. HLM addresses clustering concerns and separates the variance in outcome across neighborhoods (Level 2) as a proportion of the total variance in each outcome (Level 1). All variables were grand-mean centered, making the intercept interpretable at the mean value of the predictor variables and to minimize collinearity among predictors (Raudenbush and Bryk 2002). Sixteen percent of the sample was missing on at least one of the variables used in the analyses (mainly household income). Thus, multiple imputation methods, with five data sets imputed, were used to impute cases missing values on any of these variables (Little and Rubin 1987). The results produced from each imputed data set were then combined to produce one overall analysis. Results of the analysis with the nonimputed data (N_men = 263, N_women = 269) generated comparable results to those presented here.

Correlations among all study variables are reported in the appendix. Descriptive statistics are reported in Table 1. We present analyses with WFC as outcome in Table 2. In this table, we begin by reporting the unadjusted linear effect of commuting distance on our outcome.⁶ In the next five models, we adjust for demographic (Model 2), socioeconomic (Model 3), work/family (Model 4), traffic and commuting (Model 5), and residential attributes (Model 6). In Table 3, we report significant two-way interactions between our residential attributes and commuting distance that were not moderated by gender (i.e., we did not find a three-way interaction between residential attributes, distance, and gender to be significant). In Table 4, we report significant two-way interactions between residential attributes and commuting in predicting WFC separately for men and women if tests indicated significant three-way interactions between the specific residential attribute, commuting, and gender. The results presented in Tables 3 and 4 provide a test for Hypotheses 1 to 4. We control for previous noted covariates (Table 2) across all analyses presented in Tables 3 and 4.

Table 1.

Descriptive Statistics for All Variables in the Study (Weighted).

Variable Name	Men (N = 309)	Women (N = 331)	Total (N = 640)
	Mean (standard deviation)	Mean (standard deviation)	Mean (standard deviation)
Commuting distance to/from work	8.85 (6.30)	7.83 (4.88)*	8.39 (5.63)
Work-to-family conflict	8.90 (3.36)	9.05 (3.27)	8.97 (3.31)
Homeowner (vs. not a homeowner)	0.67	0.68	0.68
Residential dissatisfaction (vs. residential satisfaction)	0.54	0.59	0.56
Neighborhood quality	3.05 (0.57)	3.05 (0.51)	3.05 (0.54)
Neighborhood disorder	1.90 (0.76)	1.91 (0.72)	1.90 (0.74)
Covariates
Age	43.25 (10.02)	45.37 (8.92)*	44.21 (9.52)
Foreign-born (vs. native-born)	0.61	0.68*	0.64
Ethnic/cultural identification (vs. North American/Canadian)
Arab or West Asian	0.07	0.07	0.07
African	0.04	0.06	0.05
Caribbean	0.04	0.13*	0.08
East Asian Pacific Rim	0.07	0.05	0.07
European	0.12	0.13	0.12
Latin	0.05	0.06	0.06
South Asian	0.12	0.12	0.12
Total years of education	16.69 (3.78)	16.58 (3.19)	16.64 (3.48)
Household income (in thousands of dollars)	97.18 (75.22)	98.98 (73.16)	97.99 (74.11)
Marital status
Married or common-law (vs. other)	0.77	0.50*	0.65
Presence of children in Household (vs. no children)
Age of 3 or under	0.23	0.12*	0.18
Between 4 and 6 years	0.14	0.11	0.13
Between 7 and 12 years	0.22	0.16	0.20
Between 13 and 18 years	0.19	0.20	0.20
Household chores	13.54 (10.03)	21.25 (12.46)*	17.02 (11.96)
Job demands	2.88 (0.71)	2.94 (0.65)	2.90 (0.68)
Creative work	3.12 (0.77)	3.24 (0.63)*	3.17 (0.70)
Job control	2.46 (0.89)	2.47 (0.79)	2.47 (0.84)
Hours worked per week	42.97 (6.93)	40.40 (5.52)*	41.81 (6.36)
Percent of hours worked at home per week	6.80 (14.31)	7.77 (14.05)	7.23 (14.18)
Availability of public transportation	4.19 (0.98)	4.21 (0.92)	4.20 (0.95)
Number of days traveled by motor vehicle	6.30 (1.65)	5.86 (1.81)*	6.10 (1.74)
Number of days traveled by bicycle	0.24 (1.04)	0.22 (0.85)	0.23 (0.95)
Number of days traveled by walking	4.39 (2.89)	4.69 (2.31)	4.53 (2.61)
Neighborhood traffic	2.67 (1.22)	2.51 (1.20)	2.56 (1.21)
Median commute duration in Toronto	29.99 (2.87)	29.75 (2.60)	29.88 (2.73)

Significantly different from men (p < .05, two-tailed t-test and chi-square tests for continuous and binary variables, respectively).

Table 2.

Work-to-Family Conflict Regressed of Commuting Distance, Covariates, and Home/Neighborhood Moderators (N = 634).

Variable Name	Distance	Plus demographic covariates	Plus socioeconomic covariates	Plus work/family antecedents	Plus traffic and commuting-related covariates	Plus residential attributes
	(1)	(2)	(3)	(4)	(5)	(6)
	b (standard error)	b (standard error)	b (standard error)	b (standard error)	b (standard error)	b (standard error)
Commuting distance to/from work (km; one way)	0.05 (0.02)*	0.06 (0.02)**	0.06 (0.02)**	0.06 (0.02)**	0.05 (0.02)*	0.04 (0.02)*
Covariates
Female		0.38 (0.26)	0.37 (0.26)	0.57 (0.26)*	0.66 (0.26)**	0.64 (0.25)*
Age		−0.01 (0.01)	−0.01 (0.01)	0.01 (0.01)	0.01 (0.01)	0.01 (0.01)
Foreign-born (vs. native-born)		−0.70 (0.46)	−0.72 (0.46)	−0.55 (0.42)	−0.55 (0.40)	−0.43 (0.40)
Ethnic/cultural identification (vs. North American/Canadian)
Arab or West Asian		0.88 (0.62)	0.95 (0.62)	0.05 (0.55)	0.08 (0.55)	−0.05 (0.55)
African		−1.10 (0.74)	−0.74 (0.74)	−1.03 (0.66)	−0.89 (0.66)	−1.01 (0.67)
Caribbean		0.18 (0.62)	0.34 (0.62)	0.27 (0.55)	0.25 (0.54)	0.10 (0.54)
East Asian Pacific Rim		−0.31 (0.64)	−0.23 (0.64)	−0.71 (0.59)	−0.61 (0.58)	−0.74 (0.58)
European		1.61 (0.67)*	1.76 (0.67)**	1.65 (0.61)*	1.40 (0.59)*	1.28 (0.61)*
Latin		1.94 (0.67)**	2.05 (0.67)**	1.16 (0.62)	0.84 (0.61)	0.67 (0.62)
South Asian		−0.36 (0.53)	−0.32 (0.53)	−0.67 (0.48)	−0.73 (0.47)	−0.83 (0.47)
Total years of education			0.08 (0.04)*	0.05 (0.03)	0.06 (0.03)	0.06 (0.03)
Household income (in thousands of dollars)			0.002 (0.002)	0.002 (0.002)	0.001 (0.002)	0.003 (0.002)
Marital status
Married or common-law (vs. other)				0.43 (0.28)	0.36 (0.28)	0.43 (0.28)
Presence of children in household (vs. no children)
Age of 3 or under				0.26 (0.36)	0.15 (0.36)	0.23 (0.36)
Between 4 and 6 years				−0.00 (0.39)	0.02 (0.40)	−0.03 (0.39)
Between 7 and 12 years				0.04 (0.32)	0.09 (0.32)	0.002 (0.32)
Between 13 and 18 years				0.93 (0.32)**	1.00 (0.32)**	0.97 (0.32)**
Household chores				0.02 (0.01)*	0.02 (0.01)*	0.02 (0.01)*
Job demands				1.39 (0.18)***	1.45 (0.18)***	1.31 (0.18)***
Creative work				−0.60 (0.18)***	−0.67 (0.18)***	−0.55 (0.18)***
Job control				−0.61 (0.14)***	−0.58 (0.14)***	−0.59 (0.14)***
Hours worked per week				0.12 (0.02)***	0.12 (0.02)***	0.12 (0.02)***
Percent of hours worked at home per week				0.02 (0.01)*	0.02 (0.01)*	0.02 (0.01)*
Availability of public transportation in neighborhood					−0.37 (0.12)**	−0.41 (0.13)**
Number of days traveled by motor vehicle					0.02 (0.07)	0.03 (0.07)
Number of days traveled by bicycle					0.06 (0.12)	0.08 (0.12)
Number of days traveled by walking					−0.08 (0.04)	−0.08 (0.04)
Neighborhood traffic					0.35 (0.10)***	0.20 (0.12)
Median commute duration in Toronto					0.12 (0.05)*	0.13 (0.05)*
Residential attributes
Residential dissatisfaction (vs. residential satisfaction)						0.64 (0.24)**
Homeowner (vs. not a homeowner)						0.23 (0.30)
Neighborhood disorder						0.59 (0.20)**
Neighborhood quality						0.42 (0.25)
Intercept	9.02 (0.18)***	9.07 (0.19)***	9.12 (0.19)***	9.15 (0.17)***	9.14 (0.17)***	9.17 (0.16)***

p < .05. **p < .01. ***p < .001 (two-tailed test).

Table 3.

Adjusted Regression of Work-to-Family Conflict on Commuting Distance, Residential Dissatisfaction, and Neighborhood Disorder (N = 634).

Variable Name	Residential dissatisfaction	Neighborhood disorder
	(1)	(2)
	b (standard error)	b (standard error)
Commuting distance to/from work (km; one way)	0.04 (0.02)	0.05 (0.02)*
Home/Neighborhood measures
Residential dissatisfaction (vs. residential satisfaction)	0.58 (0.24)*	0.60 (0.24)*
Homeowner (vs. not a homeowner)	0.20 (0.30)	0.18 (0.30)
Neighborhood disorder	0.57 (0.20)**	0.55 (0.20)**
Neighborhood quality	0.38 (0.25)	0.41 (0.25)
Interaction terms
Distance × Residential Dissatisfaction	0.11 (0.04)**
Distance × Neighborhood Disorder		0.06 (0.03)*
Intercept	9.15 (0.16)***	9.17 (0.16)***

Note. Results include all control variables presented in Table 1.

p < .05. **p < .01. ***p < .001 (two-tailed test).

Table 4.

Conditional Effect of Commuting Distance by Homeownership, and Neighborhood Quality on Work-to-Family Conflict for Men (Models 1 and 2) and Women (Models 3 and 4).

Variable Name	Men (N = 304)		Women (N = 330)
	Homeownership	Neighborhood quality	Homeownership	Neighborhood quality
	(1)	(2)	(3)	(4)
	b (standard error)	b (standard error)	b (standard error)	b (standard error)
Commuting distance to/from work (km; one way)	0.06 (0.03)*	0.08 (0.03)**	−0.02 (0.03)	−0.01 (0.03)
Home/Neighborhood measures
Residential dissatisfaction (vs. residential satisfaction)	0.25 (0.32)	0.57 (0.32)	0.90 (0.37)*	0.97 (0.37)**
Homeowner (vs. not a homeowner)	0.29 (0.42)	0.26 (0.40)	0.06 (0.45)	0.28 (0.45)
Neighborhood disorder	0.49 (0.29)	0.41 (0.31)	0.51 (0.28)	0.51 (0.28)
Neighborhood quality	−0.09 (0.37)	0.13 (0.38)	0.68 (0.35)	0.81 (0.35)*
Interaction terms
Distance × Homeowner	−0.31 (0.06)***		−0.03 (0.08)
Distance × Neighborhood Quality		−0.07 (0.06)		0.15 (0.06)**
Intercept	8.65 (0.24)***	8.58 (0.23)***	9.33 (0.23)***	9.35 (0.22)***

Note. Results include all control variables presented in Table 1.

p < .05. **p < .01. ***p < .001 (two-tailed test).

Results

Table 1 provides summary statistics for all variables used in the analyses by gender and for the entire sample. We note significant gender differences by means and proportions based on t-tests and chi-square tests, respectively, with asterisks. Results of this table indicate that women, on average, report lower commuting distances to work than men. There are no significant differences in average WFC, neighborhood quality, or neighborhood disorder between men and women in our sample. Furthermore, there are no significant gender differences in the proportion of homeowners or those who are dissatisfied with their place of residence.

Commuting Distance and WFC

Table 2 presents results for the relationship between commuting and WFC for the entire sample. Unadjusted results for the relationship between commuting and WFC (Model 1) indicate a positive association between the two. This relationship persists with the addition of demographic (Model 2) covariates, socioeconomic (Model 3) covariates, work/family (Model 4) antecedents, commuting-related (Model 5) covariates, and residential attributes (Model 6).

Contingent Effect of Commuting Distance on WFC

The next two tables provide a test for our hypotheses. Adjusted results presented in Model 1 of Table 3 indicate that the positive association between commuting distance and WFC is moderated by residential dissatisfaction (b_{Distance×Dissatisfaction} = 0.11, p < .01). The results of this model provide support for H1a: commuting is positively associated with WFC only among respondents who are dissatisfied with their place of residence. These results are presented graphically in Figure 1a. Contrary to the predictions of H1b, this relationship is not stronger among men, as compared to women (b_{Distance×Dissatisfaction×Gender} = −0.07, p = .43; not shown in Table 3).

Figure 1.

Graphical representation of the effect of commuting distance by (a) residential dissatisfaction and (b) neighborhood disorder on work-to-family conflict.

Results of Model 2 provide support for H3a: the relationship between commuting and WFC becomes more positive with increases in neighborhood disorder (see Figure 1b). However, as with results for residential dissatisfaction, this association does not vary by gender and thus we do not find support for H3b (b_{Distance×Disorder×Gender} = −0.03, p = .55; not shown in Table 3).

Table 4 presents contingent effects of commuting on WFC by homeownership and neighborhood quality, separately for men (Models 1 and 2) and women (Models 3 and 4). Model 1 indicates that the relationship between commuting and WFC is positive only among men who do not own a home (b_{Distance×Homeowner×Gender} = 0.24, p < .01; F-statistic = 8.86, p < .01; not shown in Table 4). Among men who are homeowners, the relationship between commuting and WFC is significant and negative (b_{Distance among Men} = −0.25, p < .001). Among women, homeownership does not moderate the relationship between distance and WFC. Results of these two models provide support for H2. The results of model 1 are presented graphically in Figure 2.

Figure 2.

Graphical representation of the effect of commuting distance by homeownership on work-to-family conflict among men.

In Models 2 and 4, we include an interaction term between residential quality and distance for men and women, respectively (b_{Distance×Quality×Gender} = 0.22, p < .01; F-statistic = 8.03, p < .01; not shown in Table 4). While an increase in neighborhood quality does not moderate the positive association between distance and WFC among men (b_{Distance×Quality} = −0.07, p > .05), among women commuting is positively associated with WFC with increases in neighborhood quality. When neighborhood quality is rated as low (=0), we do not observe a significant association between commuting and WFC among women in our sample (b_{Distance among Women} = −0.01, p > .05). Figure 3 presents the conditional association between commuting and WFC by neighborhood quality among women. While the results for neighborhood quality indicate gender-contingent effects of commuting by neighborhood quality, this effect is not as we predicted (H5).

Figure 3.

Graphical representation of the effect of commuting distance by neighborhood quality on work-to-family conflict among women.

Discussion

In 2011, Toronto, Canada, had one of the worst commutes in North America (Aw 2012). Toronto also had, and continues to have, prohibitively high housing and rent prices. Indeed, in 2011 Toronto had the most expensive housing market in Ontario and the third most expensive housing market in Canada (Statistics Canada 2014). Given these statistics and borrowing from the tenets of urban location theory (Alonso 1964), we set out to test whether the positive association between commuting and WFC would be altered by individuals’ residential attributes.

According to urban location theory (Alonso 1964), individuals choose their location (place of residence/employment) to maximize their utility—better individual outcomes, including higher subjective well-being and psychological health (Nie and Sousa-Poza 2018; Roberts et al. 2011; Stutzer and Frey 2008), and lower WFC, as argued here. Commuting, then, is just another decision that rational individuals make (Stutzer and Frey 2008); if commuters are fully compensated for their commute by the housing market (e.g., better housing or a pleasant living environment), for example, their utility is equalized and there should be no consequence of commuting distance for WFC. Commuting should only be detrimental for those who are not compensated by this market. This theory, then, suggests a conditional model for the relationship between commuting and WFC. We proposed that this would be particularly relevant in a city where both commuting and the housing market are taxing on individuals’ lives. Given gender differences in roles and ideologies that see men more attached to the work sphere and women more attached to the home and community spheres (Doucet 2000; Haines et al. 2018; Hochschild 1997), we predicted that these conditional relationships would vary by the gender of the respondent.

Contingent Commuting Effects on WFC: Role of Residential Attributes

Our findings for the additive relationship between commuting and WFC corroborate previous research that commuting is detrimental for individuals’ experiences of WFC (e.g., Haines et al. 2018; Hofmeister 2003; Jansen et al. 2003; Lee and Son 2013; Lipińska-Grobelny 2016; Reimann et al. 2017; Voydanoff 2005a, 2005b). However, as predicted, we find that this effect is not universal.

Unlike previous studies on the relationship between commuting and subjective well-being (e.g., Nie and Sousa-Poza 2018; Roberts et al. 2011; Stutzer and Frey 2008), we find that favorable residential attributes are protective against the experience of WFC. More specifically, we find that irrespective of gender, the relationship between commuting and WFC is not significant among those who report being satisfied with their place of residence and weaker for those who live in neighborhoods low in disorder. Alternatively, commuting is more detrimental to respondents’ WFC when they are dissatisfied with their place of residence and perceive greater disorder in their community. Contrary to expectations, these results hold regardless of gender. For these individuals, commuting distance may be considered a stressor that impinges upon their family time—a boundary-spanning demand (Voydanoff 2007)—that generates more WFC, rather than a time-based endeavor that is balanced by the rewards of living further from one’s place of work, but in a desirable residence or an orderly neighborhood.

The Added Dimension of Gender

While the positive association between commuting and WFC is augmented by negative residential attributes, as discussed above, we note important gender differences when the moderating effects of “desirable” residential attributes—homeownership and neighborhood quality—are examined in the relationship between commuting and WFC. More specifically, we find that among men, homeownership is protective: increase in commuting is associated with a significant decline in WFC. Conversely, commuting is detrimental for the experience of WFC among men who are not homeowners. Among women, we only report one contingency: an increase in neighborhood quality intensifies the positive association between distance and WFC.

Our finding that favorable housing situations are protective against the negative association between commuting distance and WFC, only among men, may be due to men’s breadwinning and “successful male” identity (Aumann et al. 2011; Christiansen and Palkovitz 2001). For men who cannot provide for their family, or who are not able to show their success, by owning a home, commuting distance cannot be justified. These individuals experience higher WFC with increases in commuting distance in a city with one of the worst commute times in North America (Aw 2012). Thus, longer commuting distances among these men may become more of a time strain that detracts from the “good provider”—or a “good partner, friend, or offspring”—role, leading to more WFC (Christiansen and Palkovitz 2001).

We assumed that neighborhood quality would help protect the detrimental impact of commuting distance on WFC. However, this is not the case. In fact, we find—among women only—that WFC is worse among those with longer commutes who live in higher quality neighborhoods. While this result may be counterintuitive, gender differences in experiences of and involvement in residence and local community provide a story for this finding. If women are primarily responsible for community-based responsibilities (Doucet 2000), more likely than men to integrate into the social fabric of the community (Campbell and Lee 1990; Kaminer 1984; Young and Montazer 2018), and use publicly available resources more often (Bianchi et al. 2006; Young 2019), then commuting means less time for these activities for women. This may be especially true when they live in higher quality neighborhoods where community involvement and integration are more salient and expected. Our ideas here are speculative. We encourage future research to elaborate on our findings using dyadic (couple-level) data that allow for the measurement of gender ideologies, and community and neighborhood involvement.

Limitations and Conclusion

Despite the important contributions of our results to existing literature, there are limitations of our study worth noting. First, the analyses are based on cross-sectional data and cannot address issues of causal order for the relationships between our predictors, some controls, and outcomes—only longitudinal data can establish causality. Second, while we made every attempt to include proxy controls for commute impedance—including traffic—and mode of transportation, the data do not include these specific measures and we cannot assess whether the relationship between commuting and WFC is impacted by these factors. For example, our inability to decipher the mode of transportation does not allow us to examine whether the WFC of those who take public transportation, as opposed to drive, to work is impacted less by commuting since they may be able to compensate for the time demands of commuting by doing work or family-related tasks, for example.⁷

Third, our study does not include job resources measures, such as schedule control, that may impact when individuals may commute to work (i.e., nonrush hours), for example. Future research should replicate our findings by including variable job resources, and traffic and commute impedance measures in the analyses. Fourth, our study is generalizable only to those working full-time. Finally, we employed an objective measure of commuting distance, which is not without measurement error. For example, two people who live and work in the same two postal codes may travel different distances if the area associated with a specific postal code is large. Furthermore, our measure of commute distance does not capture the time it takes respondents to commute to and from work.⁸

Despite these limitations, our study documents unexplored and unique ways in which commuting distance impacts WFC. Using data from Toronto, Canada, our results suggest that the impact of some boundary-spanning job demands, such as commuting, on WFC may not be universal, and the context within which such demands impact the lives of the employed matters.

Footnotes

Appendix

Correlations among Study Variables (N = 634).

Variable Name	1	2	3	4	5	6	7	8	9	10	11	12	13	14	15	16	17	18	19	20	21	22	23	24	25	26	27	28
1. Commuting distance
2. Work-to-family conflict	.10*
3. Residential dissatisfaction (vs. residential satisfaction)	.04	.11*
4. Homeowner (vs. not a homeowner)	.10	.04	−.29*
5. Neighborhood disorder	.00	.19*	.17*	−.19*
6. Neighborhood quality	.04	−.06	−.18*	.11*	−.42*
7. Female (vs. male)	−.04	.08	.00	−.13*	.06	−.01
8. Age	.02	−.01	−.25*	.13*	−.05	−.05	.11*
9. Foreign-born (vs. Canadian-born)	.14*	−.05	.03	−.07	−.05	−.07	.14*	.12*
10. Total years of education	.05	.08*	−.13*	.17*	−.08*	.17*	−.02	.00	−.06
11. Household income (in thousands of dollars)	−.04	.07	−.25*	.57*	−.15*	.08*	−.04	.12*	−.24*	.25*
12. Married or common-law (vs. other)	.08	.08	−.12*	.44*	−.22*	.10*	−.38*	−.03	.12	.05	.44*
Presence of children in household (vs. no children)
13. Age of 3 and under	−.04	.10	.10	.01	−.03	.11	−.20*	−.60*	.02	.15*	.03	.52*
14. Between 4 and 6 years	−.03	.14*	.09	.18	−.02	−.05	−.10	−.43*	.11	.09	.10	.46*	.65*
15. Between 7 and 12 years	.09	.10	.13	.12	−.07	−.02	−.12	−.19*	.23*	.06	.13*	.53*	.21*	.38*
16. Between 13 and 18 years	.12*	.10	−.01	.07	−.15*	.11*	.02	.09	.35*	.02	.03	.45*	−.38*	−.32*	.28*
17. Household chores	−.03	.09*	.13*	−.02	−.02	.06	.42*	.08*	.10*	−.03	−.07	−.05	.05	.01	.03	−.01
18. Job demands	−.02	.36*	.10*	.04	.05	−.03	.11*	−.02	−.01	.08	.03	.05	.10	.07	.03	.04	.16*
19. Creative work	.03	.02	−.12*	.13*	−.11*	.20*	.06	.05	−.07	.29*	.21*	.15*	.04	.09	.05	.03	.09	.24*
20. Job control	.03	−.13*	−.08	.01	−.07	.04	.02	.11*	.07	.04	.09*	.06	−.14*	−.12	.06	.06	.09*	−.07	.19*
21. Hours worked per week	−.02	.29*	−.06	.06	−.02	−.02	−.21*	−.04	−.17*	.21*	.19*	.10*	.06	.08	.05	−.01	−.06	.27*	.19*	.14*
22. Percent of hours worked at home/week	−.05	.16*	−.08	.13*	−.00	.02	.03	.03	−.11*	.17*	.26*	.08	−.00	.03	.03	−.03	.03	.17*	.23*	.09*	.28*
23. Availability of public transportation	−.06	−.14	−.01	.08	−.12*	.36*	.08	.00	−.19*	.13*	.16*	.10	.11	−.02	−.04	.08	−.02	−.03	.13*	.05	−.05	.01
24. Number of days traveled by car	.17*	.04	−.07	.14*	−.09*	.03	−.12*	.04	.09	−.03	−.00	.10*	.08	−.01	.01	.15*	−.08*	.06	−.08	−.05	.00	−.08*	−.01
25. Number of days traveled by bicycle	−.08*	−.00	−.04	.01	.04	.00	−.05	.03	−.11*	.01	.01	−.02	−.04	.01	−.01	−.05	.02	−.07	.08	−.014	−.01	.05	.02	−.31*
26. Number of days traveled by walking	−.11*	−.01	−.03	−.09	.09*	.01	−.05	−.02	−.12*	.12*	.08	−.11*	.03	−.07	−.12*	−.19*	.01	.00	.12*	−.06	.07	.01	.06	−.12*	.06
27. Neighborhood traffic	−.00	.18*	.11*	−.28*	.49*	−.19*	−.03	−.11*	−.06	.03	−.05	−.13*	−.05	−.16*	−.09	.06	−.02	.06	−.01	.03	.07	.02	−.05	−.09*	.10*	.06
28. Median commute duration in Toronto	.11*	.07	.06	.05	.01	−.22*	−.06	−.06	.09	−.10*	−.16*	−.01	.10	.02	.01	.03	.07	−.027	−.09*	−.03	−.04	−.10*	−.10*	.16*	−.06	−.05	−.03
Ethnic/cultural identification (vs. North American/Canadian) ^a
29. Arab or West Asian	.04	−.00	.08	.13	.08	.07	.11	.27*	.20*	−.02	−.31*	−.15	−.26	−.07	.14	.05	−.05	.21*	.03	−.04	−.04	.04	−.03	.07	.00	.13	.14	−.01
30. African	.16	−.05	.33*	−.42*	−.06	.01	.32*	.05	.17*	−.22*	−.33*	−.18	.07	.12	.20	.03	.17*	−.03	−.18*	.04	−.16	.05	−.24*	.39*	−.05	−.28*	−.30*	.14
31. Caribbean	.10	−.08	.13	−.17	−.16	.05	.38*	.16	.22*	−.17*	−.27	−.10	−.28	−.24	−.15	.02	.21*	−.06	−.07	.15	−.26*	−.46*	−.04	−.02	−.37*	−.13	−.14	.17*
32. East Asian Pacific Rim	.23	−.05	−.01	−.10	.04	−.08	−.07	−.04	.20*	−.01	−.26*	.27*	.15	.00	.36*	.28*	.09	.08	−.04	.07	−.07	−.10	.05	.10	−.02	−.07	−.06	.11
33. European	−.03	−.08	−.03	−.08	.09	−.16*	−.05	−.04	.27*	−.03	−.07	.08	−.06	.18	.10	.23	−.08	−.16*	−.06	.04	−.10	−.17	−.11	.01	.01	.04	.14	.00
34. Latin	−.12	.18*	−.04	−.21	.05	−.06	.06	−.14	.18*	.07	−.10	.04	.39*	.15	−.10	.16	.19*	.14	.09	.06	.02	−.19	−.34*	.11	−.26	−.09	.04	.07
35. South Asian	.14*	−.10	.10	.03	−.09	.03	.04	−.07	.27*	.03	−.13	.35*	−.02	−.10	.18	.39*	.03	.04	−.19*	.02	−.12	−.04	.04	.06	−.49*	−.09	−.05	.10

We do not report correlations among the various ethnic/cultural groups as they are created from the same variable.

p < .05.

Declaration of Conflicting Interests

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

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: Grants awarded by Canadian Institute for Health Research grant MOP-84439 and the Social Science and Health Research Council grant 410-2007-1499 (Blair Wheaton and Patricia O’Campo, principal investigators).

Notes

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