Gender equality and work–family conflict from a cross-national perspective

Abstract

This study examines the relationship between gender ideology at the individual level, gender equality at the country level, and women and men’s experiences of work interference with family (WIF) and family interference with work (FIW). We use data from the 2012 International Social Survey Programme as well as the 2011 to 2015 Human Development Reports. Our sample consists of 24,547 respondents from 37 countries. Based on multilevel mixed-effects logistic models, we find that women are more likely than men to experience WIF and FIW. At the individual level, traditional gender ideology positively predicts WIF and FIW. Women and men who reside in more gender-unequal countries have a higher likelihood of FIW while men in these contexts also are more likely to experience WIF. Societal gender inequality is more consequential for those who hold less traditional gender ideology. In conclusion, gender egalitarianism at the individual level and gender equality at the country level are both associated with less WIF and FIW. Policies that seek to address work–family balance should incorporate measures to promote gender equality.

Keywords

Country-level gender equality cross-national comparison family interference with work gender gender ideology work–family conflict work interference with family

Work–family conflict is a common occurrence all over the world, with both men and women facing high levels of work–family conflict (Allen et al., 2015; Fahlén, 2014; Schieman et al., 2009; Stier et al., 2012). While women tend to report higher levels of work–family conflict than men (Berheide and Anderson-Hanley, 2012; Crompton and Lyonette, 2006; Edlund, 2007), the increase in work–family conflict is particularly steep among men (Aumann et al., 2011; Nomaguchi, 2009) with some recent studies finding higher levels of work–family conflict among men than women (Fahlén, 2014; Martinengo et al., 2010). This is likely to be due to changing gender roles, both women’s increased participation in the labor force and increased expectations for men’s involvement in household work and childcare (Nomaguchi, 2009).

Greenhaus and Beutell (1985) define work–family conflict as, “a form of interrole conflict in which the role pressures from the work and family domains are mutually incompatible in some respect” (p. 77). Yet we know that work–family conflict consists of two distinct components. In particular, work interference with family (WIF) occurs when an individual feels that their work obligations prevent or limit their ability to meet their family obligations while family interference with work (FIW) occurs when an individual feels that their family obligations prevent or limit their ability to meet their work obligations. In general, individuals are more likely to say that work negatively impacts family than vice versa (Hoser, 2012; Mennino et al., 2005). In addition to these two components, we can differentiate individual-level and country-level characteristics associated with work–family conflict. In this study, we focus on gender and gender ideology at the individual level and gender inequality at the country level. While previous research examines gender differences in work–family conflict (e.g. Berheide and Anderson-Hanley, 2012; Fahlén, 2014) and country-level differences in work–family conflict (e.g. Crompton and Lyonette, 2006; McGinnity and Calvert, 2009), there is limited research on gendered effects at both the individual and country level. One exception is Ruppanner and Huffman’s (2014) study, which uses the 2005 International Social Survey to examine the effects of gender and gender empowerment on work–family conflict.

Our study provides a multilevel cross-national examination of gender, gender ideology, and gender inequality on work–family conflict. We address the following research questions: How do gender, gender ideology, and gender inequality influence work–family conflict? Do the effects of gender and gender ideology vary by country context? Are these effects different on WIF versus FIW? This study contributes to a better understanding of work–family conflict in three ways. First, we consider work–family conflict as bidirectional from both work to family and family to work. This distinction is important as gender influences may work differently when looking at work’s interference with family versus family’s interference with work. Second, we examine both individual and country-level variables, focusing on gender-related constructs at both levels, using multilevel models. In particular, we introduce gender ideology as a key explanatory variable at the individual level. Third, we include a broad sample from 37 countries rather than limiting our analysis to one or a handful of case studies.

It is important to understand the different contexts of work–family conflict as work–family conflict has numerous potential consequences for work satisfaction, family satisfaction, life satisfaction, and overall health (Sirgy and Lee, 2018). Work–family conflict has deleterious effects on career outcomes (Liao et al., 2019) and family relationships (Kuo et al., 2018; Vahedi et al., 2019). It is also generally associated with lower levels of mental and physical well-being (Hagqvist et al., 2017; Minnotte and Yucel, 2018). Cross-country research is particularly important in understanding the role of cultural values (Perry-Jenkins and Wadsworth, 2017) and specifically how changing gender ideologies and gender inequality facilitate the combination of work and family roles.

To address our research questions, we analyze data from 24,547 respondents across 37 countries using the 2012 Family and Changing Gender Roles IV module from the International Social Survey Programme (ISSP), supplemented with data on gender inequality from the United Nations (UN). These data allow us to distinguish between WIF and FIW as well as introduce a gender ideology measure, which is one of the strengths of our study. The wide range of countries in our sample with different levels of gender inequality also allows for a broader contribution to comparative sociology.

Prior research on work–family conflict

Theory

We use gender theory to consider the relations between gender, gender ideology, gender inequality, and work–family conflict. As Mennino et al. (2005) note, “Gender theory treats gender as a social structure that is concurrently part of our individual identity and part of the organizational schema of institutions” (p. 110). Here the gendered division of labor within society is important as work is often divided into gendered spheres of (market) work, typically associated with men, and home (work), typically associated with women. The ideology surrounding separate spheres maintains gender inequality by emphasizing different and unequal roles for women and men at home and work and placing greater value on masculinized market work over feminized domestic work (Acker, 1990; Mennino et al., 2005). While there is a great deal of attention to efforts to increase women’s participation in paid labor, it is equally important to promote men’s participation in domestic labor (Auer and Welte, 2009; Goldscheider et al., 2015).

Gender norms are important in shaping work–family experiences (Hobson and Fahlén, 2009; Pfau-Effinger, 2012). This is true at the individual level where one’s ideas about the gendered division of labor may affect how one perceives their work and family roles. Egalitarian women and men expect to share both work and family roles and therefore should experience less conflict between these roles than their more traditional counterparts. For heterosexual couples, egalitarian gender beliefs may allow women to count on their partners more to pick up the slack at home and relieve men from facing all the pressures of being the sole provider for their families (Oppenheimer, 1997).

At the societal level, we draw on the concept of women-friendly welfare states “which would not force harder choices on women than on men, or permit unjust treatment on the basis of sex” (Hernes, 1987 in Borchorst and Siim, 2002: 2). Societal factors, and particularly gender norms, shape the context in which individuals attempt to achieve work–family balance (Hobson et al., 2011). Gender equality is likely to ensure a more equal division of labor and larger supports for accomplishing this (Fahlén, 2014), which would in turn reduce work–family conflict. The extent of gender (in)equality in society may moderate the way individuals’ characteristics (attitudinal or demographic) affect their perception of work–family conflict. The broader cultural context of gender equality is thus likely to influence work–family conflict (Powell et al., 2009).

Goldscheider et al.’s (2015) conceptualization of the two-part gender revolution is useful in understanding potential country-level effects. During the first half of this revolution, women enter the male-dominated work realm. We see this in the large increase in women’s labor force participation, including mothers of small children. At the same time, there is little change in men’s roles, and the result is what Hochschild and Machung (1989) call the “second shift.” During the second half of the gender revolution, men enter the female-dominated domestic realm. Most notably, there is an increase in men’s time in housework and childcare, and this is occurring across industrialized countries (Hook, 2006). Based on these patterns, we would expect that work–family conflict would be greater for those living in countries that are in the first half of the gender revolution because roles are shifting but unequal. Women in countries in the first half of the revolution, with higher levels of gender inequality, are engaging in paid employment while still doing the majority of household labor, which should result in higher FIW. At the same time, men continue to be the primary breadwinners with some expectation that they spend time with children, which should result in higher WIF.

Gender and work–family conflict

Since women still take on the bulk of family work and caregiving and men spend more time in paid employment, it is reasonable to expect men and women will differ in their experiences of work–family conflict. Several studies find that women face more WIF (Berheide and Anderson-Hanley, 2012; Dilworth, 2004; Grzywacz et al., 2002; Voydanoff, 2005) and FIW (Dilworth, 2004; Voydanoff, 2005) than men. These findings are similar across studies using the 2002 ISSP, the last version before 2012 to include the family and changing gender roles module. Based on analysis of five countries from the 2002 ISSP, Crompton and Lyonette (2006) find that women report more work–family conflict than men. Stier et al. (2012), using the 2002 ISSP data from 27 countries, find a similar effect while Edlund’s (2007) analysis of 29 countries using the 2002 ISSP finds particularly large gender differences in Latin America. Even in the Nordic countries, women have more work–family conflict than men (Öun, 2012). Nevertheless, some recent research finds that women and men’s work–family conflict experiences are more similar than different (Cerrato and Cifre, 2018; Shockley et al., 2017).

Gender differences may vary based on WIF or FIW (Allen and Finkelstein, 2014). Fahlén (2014) finds that while women experience more FIW in Denmark, Spain, the Czech Republic, Hungary, and Poland, men experience more WIF in Germany, the United Kingdom, the Czech Republic, and Hungary. Martinengo et al. (2010) find similar patterns in their study of IBM employees in 79 countries. While men experience greater WIF due to work interruptions at home and missing family obligations, women tend to experience more FIW as they care for children, which may result in more absences at work or reduced work hours. This may be because people often respond more to role demands in the domain with higher priority which is shaped by social as well as personal expectations (Greenhaus and Beutell, 1985). Therefore, men are more likely to take on extra work because of breadwinning responsibilities while women, who are more likely to provide urgent childcare even in dual-earner couples (Maume, 2008), may find themselves cutting back at work to deal with family tasks. Beyond time spent at work and home, there are still lingering expectations for women and men regarding work and family. Intensive mothering ideology persists even in highly egalitarian countries (Gíslason and Símonardóttir, 2018), and these ideas that equate “good” motherhood with high involvement often create moral dilemmas for women making decisions regarding work and family (Damaske, 2013). Likewise, breadwinning is not simply about engaging in paid work. Since breadwinning is still largely seen as fathers’ responsibility (Schmidt, 2018), men may also consider these responsibilities in making work and family decisions. Therefore, we hypothesize that women experience more FIW while men experience more WIF (Hypothesis 1).

Gender ideology and work–family conflict

While there is not much research examining the relationship between gender ideology and work–family conflict, there is some evidence that gender ideology may affect work–family conflict. Those with more non-traditional attitudes emphasize equal sharing of family and work responsibilities, whether that means both partners work full-time or part-time (Edlund and Öun, 2016), which suggests each partner would benefit from a greater balance between the two domains. Using the 2012 ISSP, Roeters and Craig (2014) find that more progressive gender ideology is associated with less work–family conflict in Sweden though they also find no effect in Australia, the Netherlands, Germany, and the United Kingdom. In their cross-national comparison, Crompton and Lyonette (2006) find that congruent liberals experience much lower levels of work–family conflict than congruent traditionals, suggesting that egalitarian attitudes as well as a more equal division of labor promote work–family balance.

In addition, women who hold more egalitarian attitudes and witness a more equal division of labor at home are more satisfied with family life and report less stress at home (Crompton and Lyonette, 2005), while men with egalitarian attitudes are more likely to emphasize the importance of family-friendly job characteristics (Kaufman and White, 2015), work fewer hours when they have children (Kaufman and Uhlenberg, 2000), make adjustments to their work schedules (Kaufman and Bernhardt, 2015), and provide urgent childcare when needed (Maume, 2008). On the other hand, those with more traditional attitudes are more likely to refuse a job promotion, which may suggest greater work–family conflict (Mennino and Brayfield, 2002). Indeed, Davis’ (2011) study of older American workers finds that those with more traditional attitudes experience more work–family conflict than those with more egalitarian attitudes, and this holds for men and women. These patterns suggest lower work–family conflict among those with egalitarian attitudes and higher work–family conflict among those with more traditional views. Therefore, we hypothesize that traditional gender ideology is positively associated with WIF and FIW (Hypothesis 2).

Furthermore, we expect a gender difference in the effect of gender ideology because of gendered assumptions about the importance of work and family domains. Traditional views are likely to create more conflict for women than men. While there are increased expectations for men to be involved at home, men who focus more on breadwinning are still largely accepted (Schmidt, 2018). On the other hand, there is more pressure for women to engage in paid labor without a corresponding reduction in expectations regarding family and motherhood (Gíslason and Símonardóttir, 2018). We expect that traditional women in the labor force are in the most difficult spot when it comes to managing work and family because they will feel the pull and responsibility of home and family while also carrying on at work. We thus hypothesize that the positive association between traditional ideology and FIW will be stronger for women (Hypothesis 2a).

Country-level gender inequality and work–family conflict

Work–family conflict varies across countries in part because gender norms and equality vary across countries (Korpi, 2000). Blumberg (1984) and Blumberg and Coleman (1989) argue that women’s economic power varies at the micro and macro levels. Even when individual women possess economic resources, broader societal gender ideologies may facilitate or constrain their work–family choices. Researchers commonly define gender empowerment as women’s economic and political participation (Syed, 2010). In a study of five countries using the 2002 ISSP, Crompton and Lyonette (2006) find that Finland and Norway have lower work–life conflict than Britain, France, or Portugal and suggest that the Nordic countries benefit from “woman-friendly” policies. In a recent study of 19 European countries, Masuda et al. (2019) find that country-level gender egalitarianism is negatively associated with work–family conflict. They suggest that this pattern is due to more supportive policies and more family-friendly work cultures in such contexts. On the other hand, a recent meta-analysis finds that individuals in countries with a higher economic gender gap experience more FIW than those in countries with smaller gender gaps (Allen et al., 2015). Therefore, we hypothesize that country-level gender inequality increases WIF and FIW (Hypothesis 3).

Societal levels of gender equality may have a differential impact on women and men’s work–family conflict. Powell et al. (2009) assert that there will be greater gender differences in work–family experiences in countries with lower gender equality than more egalitarian countries. Direct tests of this proposition are limited but greater gender inequality in work and family roles in more traditional countries suggest support for this theory (Ollier-Malaterre and Foucreault, 2017). In a study of 10 European countries, Fahlén (2014) finds that countries with higher levels of gender equality experience a smaller gender gap in work–family conflict. In other words, in countries with more support for gender equality, like the Nordic countries, men and women experience similar levels of work–family conflict, whereas in countries with less support for gender equality, like the Central and Eastern European countries, there is a greater gap between men and women in work–family conflict. Using the 2005 ISSP data for 31 countries, Ruppanner and Huffman (2014) find that gender empowerment increases WIF, and this pattern holds for women and men as well as parents and non-parents. However, they also find that greater gender empowerment reduces FIW for childless women and to a lesser extent mothers. This pattern may be due, at least in part, to a reduction in women’s housework in more gender-equal countries (Ruppanner and Maume, 2016). Therefore, we hypothesize that the positive association between country-level gender inequality and FIW is greater for women (Hypothesis 3a).

A somewhat surprising finding, Strandh and Nordenmark (2006) find that Swedish women in their study have the highest levels of work–family conflict and the difference between Swedish and Czech and Hungarian women remains even after controlling for work characteristics. In trying to explain this difference, Strandh and Nordenmark note that Swedes have much more egalitarian attitudes than those living in the Czech Republic and Hungary. They therefore suspect that a more unequal division of labor is less problematic for the latter than the former. If this is the case, those with more traditional gender attitudes in countries with more gender inequality would be expected to experience lower levels of work–family conflict than their more egalitarian counterparts. Therefore, we hypothesize that the positive association between traditional gender ideology and WIF and FIW will be weaker in countries with more gender inequality (Hypothesis 3b).

Work hours, family hours, family status, and work–family conflict

Other work and family characteristics may be important in explaining work–family conflict. It makes sense that more time in either domain would create potential tension or conflict. Indeed, work hours are positively associated with work–family conflict for both men and women (Gallie and Russell, 2009; Hofacker and Konig, 2013; Ollo-López and Goñi-Legaz, 2017; Roeters and Craig, 2014; Stier et al., 2012; Strandh and Nordenmark, 2006). Likewise, time on domestic work increases work–family conflict (Crompton and Lyonette, 2006). While household tasks increase FIW for women, these tasks increase WIF for men (Cerrato and Cifre, 2018).

The presence of children in the household increases work–family conflict (Crompton and Lyonette, 2006; Fahlén, 2014; Ollo-López and Goñi-Legaz, 2017; Stier et al., 2012). Nevertheless, country context may enhance or diminish the effect of children on work–family conflict. For example, children have a greater negative effect on women’s work–family reconciliation in Germany, while the effect of children on women’s work–family reconciliation is rather limited in Denmark (Hennig et al., 2012). Also, motherhood seems to have less of an impact on employment for women in countries such as Finland, Sweden, and Denmark, where work and family are considered more balanced, compared with Germany, Switzerland, and the United Kingdom (Hennig et al., 2012). Meanwhile, fathers in more gender egalitarian countries perform a higher share of childcare than fathers in more traditional contexts (Altintas and Sullivan, 2017). Thus, it may be no surprise that fathers in countries with greater gender empowerment are more likely than others (e.g. childless women, childless men, mothers) to experience WIF and FIW (Edlund, 2007; Ruppanner and Huffman, 2014). We therefore hypothesize that the effect of children on WIF and FIW will be greater for women but weaker for men living in countries with more gender inequality (Hypothesis 3c).

Methods

Data

We use individual-level data from the 2012 ISSP Family and Changing Gender Roles IV (http://www.issp.org/). This dataset best suits our study as it has indicators of WIF and FIW as well as gender ideology and other predictors of interest. While 40 countries participated in the 2012 ISSP, we use data from the 37 countries for which data are available for our variables, excluding South Africa, Turkey, and Taiwan. For South African respondents, data on occupation are not coded due to the lack of funding, and data on health are unavailable. For Turkish respondents, data on the presence of young children are unavailable. Taiwanese respondents are excluded from our study because the country-level information is unavailable. These 37 countries are in Europe, North and South Americas, Asia and Oceania (see Appendix 1). After deleting cases with missing values from the original sample, our analysis samples include 24,547 respondents. We supplement the ISSP data with country-level data from the UN for our gender inequality measure (see below).

Measures

WIF and FIW

Existing research (e.g. Ruppanner and Huffman, 2014) demonstrates the importance of addressing bidirectionality of interference between work and family. We thus separately measure WIF and FIW. WIF is based on two statements: “I have come home from work too tired to do the chores which need to be done” and “it has been difficult for me to fulfill my family responsibilities because of the amount of time I spent on my job.” FIW is measured similarly with two statements: “I have arrived at work too tired to function well because of the household work I had done” and “I have found it difficult to concentrate at work because of my family responsibilities.” All statements are based on occurrence during the past 3 months, on a scale from 1 (several times a week) to 4 (never). In a preliminary analysis, for each type of work–family conflict, responses are reverse-coded and added to form a scale ranging from 2 to 8, where higher scores indicate more interference. The distribution of WIF data is approximately normal, whereas data on FIW are severely right skewed, suggesting that FIW conflict is relatively rare. Thus, we measure FIW dichotomously (1 = one or both responses indicated that the respondent experiences the conflict at least several times a month, 0 = otherwise). For comparability, we also construct a binary measure of WIF conflict in a similar fashion. (In a preliminary analysis, we ran models with both scale and binary measures of WIF, and there was little difference in the directions and significance of coefficients.) Our measures of work–family conflict are limited, compared with the ones based on numerous items developed in studies with specific samples—for example, “male alumni of an eastern technical college” (Kopelman et al., 1983: 203), “elementary and high school teachers and administrators” (Netemeyer et al., 1996: 402), and “graduates from an Executive MBA program at a large western university” (Carlson et al., 2000: 258). Earlier studies using the ISSP data also had to do with a single item (e.g. Ruppanner and Huffman, 2014) or two items (e.g. Edlund, 2007). These studies also dichotomize their work–family conflict measures for reasons similar to ours.

The difference in distributions of data on WIF and FIW just noted is in line with Edlund (2007), who showed, in his analysis of the 2002 ISSP data, that the respondents from dual-earner couples where both partners are employed 15 + hours per week experienced one of the three types of work–family interface: balance (no conflict), paid work overload (i.e. WIF), and dual conflict (i.e. both WIF and FIW). Half of the respondents in Edlund’s study experienced a work–family balance. Of those who experienced a conflict, paid-work overload was the more common form, while only 12 percent of the respondents experienced a dual conflict. Of our analysis sample, which is broader in focus and includes anyone who is employed regardless of marital/partner status, 56 percent experienced WIF and 18 percent FIW (16% experienced both; 42% experienced neither).

Individual-level predictors

Our individual-level predictors are gender (1 = female, 0 = male), gender role traditionalism, weekly hours of paid work and family work, marital status (1 = married, 0 = single), and the presence of preschool-aged and school-aged children, respectively, at home (1 = yes, 0 = no). There are seven items related to gender ideology in the 2012 ISSP. In our study, gender role traditionalism is the extent of respondents’ agreement on a 5-point scale (“strongly agree” to “strongly disagree”) with the following statements: “A pre-school child is likely to suffer if his or her mother works” and “All in all, family life suffers when the woman has a full-time job.” We reversed the scale so that higher scores indicate more traditional attitudes. We summed scores across the two items to form a scale from 2 to 10. Alpha is .77. These items were selected based on a country-specific analysis with principal axis factoring to assess the dimensionality of the seven available indicators. Unsurprisingly, although the factor analysis derived a single factor with no exception, there is substantial variation in how individual items load on the factor, suggesting that meanings of traditional gender ideology vary greatly across countries (Gibbons et al., 1997; Walter, 2018). Of the 37 countries included in our study, Bulgaria, India, the Philippines, and Venezuela show different patterns, and thus different items are used to form a scale. For example, in Bulgaria, the statements, “A job is all right, but what most women really want is a home and children” and “Being a housewife is just as fulfilling as working for pay” load most highly on the factor (see Appendix 2 for details).

Hours of paid work are based on a single question, “How many hours, on average, do you usually work for pay in a normal week, including overtime?” Hours of family work are based on the two questions, “On average, how many hours a week do you spend looking after family members (e.g. children, elderly, ill or disabled family members)?” and “On average, how many hours a week do you personally spend on housework, not including childcare and leisure time activities?” Age, education, high-status occupation (1 = managerial/professional, 0 = other occupations), and health are included as controls as they empirically or logically correlate with at least one of the key individual-level variables and at least one of the dependent variables. Health is measured with respondents’ self-ratings ranging from 1 (excellent) to 5 (poor). Original codes are reversed so that higher scores indicate better subjective health. Basic descriptive statistics of the study variables are summarized in Appendix 3.

Country-level predictor

We merge the 2012 ISSP data with the UN’s Gender Inequality Index (GII; http://www.hdr.undp.org/en/composite/GII). Researchers who examined the work/family interface with ISSP data from earlier years incorporated the UN’s Gender Development Index (GDI) and Gender Empowerment Measure (GEM) to measure their country-specific variables. In 2010, GII replaced the GDI and GEM in response to the criticism that those measures did not suit developing countries with low per capita gross domestic product (GDP). The GII is intended to capture “the loss of achievement due to gender inequality in three dimensions: reproductive health, empowerment and labour market participation” (United Nations, 2013: 31) and builds on five indicators: maternal mortality ratio, adolescent fertility rate, seats in national parliaments (percent female), percentage of the population ages 25+ that have reached secondary education (by gender), and labor force participation rate (by gender). It is a measure of consequences of gender inequality, or disempowerment, rather than of gender ideology. The 2012 ISSP database contains the cross-national surveys administered in the period 2011 through 2014. For each country, we use the GII score published in the same year as its survey was administered. If the survey encompassed 2 years, we chose the year with more months covered by the survey (see Appendix 1).

Analytical strategy

For the main analysis, we estimate multilevel mixed-effects models to assess the effects of individual-, country-, and cross-level variables on WIF and FIW. WIF and FIW are binary variables and thus modeled with logistic regression. In the models shown below in Tables 1 and 2, we estimate random intercept models, only allowing the intercept to vary by country. The models shown in Table 3, because of the consideration of cross-level interactions, include the random intercept and the random slopes for gender role traditionalism and the presence of young children. For the models with cross-level effects, we follow the recommendation that a random slope also be entered as a predictor of a random intercept (e.g. Aguinis and Gottfredson, 2013; Kim, 2009). Some of our research hypotheses concern a gender difference in the effect of a variable. Chow tests also indicated that the effects of independent variables differ by gender (WIF model: χ² (11) = 77.87; FIW model: χ² (11) = 74.01). Therefore, we run our models by gender as well as for the whole sample.

Table 1.

Multilevel models of work interference with family (WIF).

	Model 1	Model 2	Model 3
	Pooled	Men	Women
	Coefficient	Coefficient	Coefficient
	(SE)	(SE)	(SE)
Fixed effects
Female	.294***
[0, 1]	(.029)
Gender ideology	.050***	.042***	.059***
[2, 10]	(.007)	(.010)	(.010)
GII	1.045*	1.301*	.492
[.042, .544]	(.525)	(.537)	(.598)
Paid work time	.019***	.018***	.022***
[1, 80]	(.001)	(.001)	(.002)
Family work time	.001	−.001	.001
[0, 95]	(.001)	(.001)	(.001)
Married	−.164***	−.086	−.219***^b
[0, 1]	(.033)	(.049)	(.044)
Pre-schoolers	.104**	.173***	−.005^b
[0, 1]	(.038)	(.052)	(.058)
School-aged children	.072*	.078	.055
[0, 1]	(.030)	(.042)	(.043)
Age	−.016***	−.016***	−.017***
[15, 93]	(.001)	(.002)	(.002)
Education	.064***	.024	.108***^a
[0, 6]	(.011)	(.015)	(.016)
High-status occupation	.096**	.070	.131*
[0, 1]	(.035)	(.049)	(.051)
Health	−.331***	−.253***	−.428***^a
[1, 5]	(.015)	(.020)	(.023)
Intercept	.178*	.083	.539***
	(.073)	(.078)	(.082)
Random effects
Intercept	−.924***	−.923***	−.831***
	(.123)	(.129)	(.128)
LR test (vs 1-level logistic model)	χ²(1) = 669.49	χ²(1) = 301.91	χ²(1) = 375.41
Sample size	24,547	12,450	12,097

GII: Gender Inequality Index; LR: likelihood ratio.

Range of values is in brackets. Values for the continuous variables are centered to the mean.

a,b

Gender difference is significant at the .001 and .05 levels, respectively.

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

Table 2.

Multilevel models of family interference with work (FIW).

	Model 1	Model 2	Model 3
	Pooled	Men	Women
	Coefficient	Coefficient	Coefficient
	(SE)	(SE)	(SE)
Fixed effects
Female	.304***
[0, 1]	(.040)
Gender ideology	.111***	.077***	.138***^b
[2, 10]	(.009)	(.014)	(.013)
GII	4.574***	5.134***	3.803***
[.042, .544]	(.765)	(.850)	(.747)
Paid work time	.006***	.002	.011***^b
[1, 80]	(.001)	(.002)	(.002)
Family work time	.010***	.010***	.009***
[0, 95]	(.001)	(.001)	(.001)
Married	−.195***	−.235***	−.134*
[0, 1]	(.043)	(.068)	(.057)
Pre-schoolers	.288***	.326***	.312***
[0, 1]	(.047)	(.067)	(.068)
School-aged children	.134***	−.020	.278***^a
[0, 1]	(.039)	(.059)	(.054)
Age	−.012***	−.012***	−.011***
[15, 93]	(.002)	(.002)	(.002)
Education	−.008	−.025	.007
[0, 6]	(.014)	(.021)	(.020)
High-status occupation	.039	.086	−.009
[0, 1]	(.049)	(.073)	(.067)
Health	−.245***	−.168***	−.315***^a
[1, 5]	(.020)	(.029)	(.028)
Intercept	−1.885***	−1.803***	−1.678***
	(.107)	(.121)	(.105)
Random effects
Intercept	−.538***	−.450***	−.596***
	(.122)	(.128)	.127
LR test (vs 1-level logistic model)	χ²(1) = 935.03	χ²(1) = 465.99	χ²(1) = 402.72
Sample size	24,547	12,450	12,097

GII: Gender Inequality Index; LR: likelihood ratio.

Range of values is in brackets. Values for the continuous variables are centered to the mean.

a,b

Gender difference is significant at the .001 and .01 levels, respectively.

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

Table 3.

Models of family interference with work (FIW) with cross-level interaction variables.

	Model 1	Model 2	Model 3	Model 4
	Men	Women	Men	Women
	Coefficient	Coefficient	Coefficient	Coefficient
	(SE)	(SE)	(SE)	(SE)
Fixed effects
Gender ideology	.091***	.142***^a	.079***	.138***^a
[2, 10]	(.018)	(.015)	(.014)	(.013)
GII	5.292***	3.802***^a	5.534***	4.504***^c
[.042, .544]	(.879)	(.744)	(.807)	(.776)
Paid work time	.002	.012***^a	.002	.012***^a
[1, 80]	(.002)	(.002)	(.002)	(.002)
Family work time	.010***	.009***	.009***	.008***
[0, 95]	(.001)	(.001)	(.002)	(.001)
Married	−.228***	−.138*	−.249***	−.141*
[0, 1]	(.068)	(.057)	(.069)	(.057)
Pre-schoolers	.320***	.317***	.428***	.362***
[0, 1]	(.067)	(.068)	(.091)	(.079)
School-aged children	−.025	.284***^a	−.025	.287***^a
[0, 1]	(.060)	(.054)	(.060)	(.054)
Age	−.012***	−.011***	−.011***	−.010***
[15, 93]	(.002)	(.002)	(.002)	(.002)
Education	−.024	.006	−.030	.005
[0, 6]	(.021)	(.020)	(.021)	(.020)
High-status occupation	.090	−.002	.079	−.011
[0, 1]	(.073)	(.067)	(.073)	(.067)
Health	−.173***	−.314***^b	−.170***	−.318***^b
[1, 5]	(.029)	(.028)	(.029)	(.028)
Gender ideology × GII	−.462***	−.397**
	(.131)	(.123)
Pre-schoolers × GII			−1.240*	−1.866***
			(.579)	(.531)
Intercept	−1.791***	−1.656***	−1.817***	−1.673***
	(.124)	(.105)	(.115)	(.106)
Random effects
Intercept	−.419**	−.601***	−.525***	−.583***
	(.131)	(.128)	(.136)	(.132)
Gender ideology slope	−2.847***	−3.028***
	(.361)	(.375)
Pre-schoolers slope			−1.247***	−1.766**
			(.352)	(.598)
Slope × intercept	−.768	−.884	.489	−.029
	(.479)	(.547)	(.399)	(.395)
LR test (vs 1-level logistic model)	χ²(3) = 437.78	χ²(3) = 364.95	χ²(3) = 477.76	χ²(3) = 413.41
Sample size	12,450	12,097	12,450	12,097

GII: Gender Inequality Index; LR: likelihood ratio.

Range of values is in brackets. Values for the continuous variables are centered to the mean.

a,b,c

Gender difference is significant at the .001, .01, and .05 levels, respectively.

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

Results

Work interference with family and family interference with work

An unconditional model of WIF shows the log-odds of WIF is .27 (SE = .07), or the probability of .57, while a corresponding model of FIW shows the log-odds of FIW is −1.62 (SE = .15), or the probability of .17. Women’s log-odds value is higher than men’s in both WIF and FIW (see Appendix 4). The interclass correlation statistics show that 5 and 20 percent of the variance in WIF and FIW, respectively, are attributed to the country-level. This baseline finding suggests that the country-level variable (i.e. GII) and cross-level variables may better predict FIW than WIF. For both types of conflict, the country-level variability is greater for men (see Appendix 4).

Predicting WIF

Table 1 summarizes results from multilevel logistic models of WIF for both genders (Model 1), men (Model 2), and women (Model 3). Contrary to Hypothesis 1, women are more likely to experience WIF than men (p < .001, Model 1). As an alternative to Model 1 (not tabled), we included the random effect of gender, and found that the fixed effect of gender was similar (b = .32, p < .01). As expected, based on Hypothesis 2, traditional gender ideology positively predicts WIF (Models 1–3), showing that those with more traditional views experience higher levels of WIF. While the effect appears greater for women, this gender difference is not statistically significant. As expected, based on Hypothesis 3, GII positively predicts WIF for all respondents (Model 1) and men (Model 2) but is not significant for women (Model 3). The gender difference in the effect of GII is not significant.

Unsurprisingly, work hours positively predict WIF for both men and women while family hours have no significant effect on WIF for either men or women. Marital status significantly predicts WIF only for women (Model 3). Married women are less likely to experience WIF than single women. The effects of parenthood are positive, regardless of the age of children (Model 1). However, having a young child significantly increases WIF for men but not for women. This gender difference is significant at the .05 level. The effect of older children, which is positive, is similar for both genders, but it is generally weaker than the effect of young children. (In an additional analysis, we estimated our models with interaction variables formed by marital and parental status to assess the effect of single parenthood on WIF. None of the interactions was significant.) Age has a negative effect on WIF, perhaps suggesting improved abilities to juggle work and family life or fewer competing demands as one gets older. Both education and high-status occupation positively predict WIF for women. The better the self-reported health, the lower the log-odds of WIF for both men and women, while the effect is significantly larger for women.

Predicting FIW

Table 2 summarizes results from multilevel logistic models of FIW for both genders (Model 1), men (Model 2), and women (Model 3). Consistent with Hypothesis 1, women are significantly more likely to experience FIW than men. An alternative model with the random effect of gender showed a similar but not significant (p = .053) fixed effect of gender, suggesting that the effect of binary gender on FIW greatly varies cross-nationally. In support of Hypothesis 2, more traditional gender ideology predicts higher probabilities of experiencing FIW. As expected, based on Hypothesis 2a, the effect is significantly greater for women. GII is significantly and positively associated with FIW across the models. In other words, those who live in countries with higher gender inequality are more likely to experience FIW, in support of Hypothesis 3. However, we are unable to support H3a given that there is no gender difference in the effect of GII.

The number of work hours has a significant positive effect on FIW among women, but not men. This gender difference is significant. On the other hand, the number of family hours, which was not significant in the WIF models, has significant positive effects on FIW. Not surprisingly, those who spend more time engaged in family labor are more likely to think family life interferes with work. Similar to the WIF model, married women are less likely to experience FIW than unmarried women. Yet, unlike the WIF model, marriage also significantly reduces the probability of experiencing FIW for men. Having young children increases the likelihood of FIW for both genders and older children increases this likelihood only for women. The gender difference in the effect of older children is significant (p < .001). (We estimated additional models with the interactions of marital and parental status, but none of the interactions was significant.) The effects of education and higher status occupations on FIW are not statistically significant. Meanwhile, age and health have significant negative effects on FIW across models, while the effect of health is significantly stronger for women than men (p < .001).

Cross-level interactions of gender ideology, parenthood, and GII

Table 3 shows results from the effects of interactions of GII with gender ideology (for Hypothesis 3b) and with parenthood (for Hypothesis 3c) for the FIW models. While the interaction effects are similar in the WIF and FIW models, they are stronger in the FIW model and thus we focus on results from these models. First, for both genders, there is a significant negative interaction between traditional gender ideology and GII, supporting Hypothesis 3b. In other words, the positive effect of gender traditionalism is less positive for individuals in countries with more gender inequality. Put differently, the positive effect of GII is diminished for those with more traditional views. It is not straightforward to interpret the effect of an interaction term formed by continuous variables. The contour graph in Figure 1 (based on Model 2) illustrates how the two variables interact, which combined with the direct effects, predicts FIW. Individuals’ traditional gender ideology matters more to predict FIW in countries with lower GII scores, as shown by more shades with lower scores on the y-axis. At GII score of .1, the positive relationship between traditional gender ideology and FIW holds with seven shades (note that the darker the shade, the more likely FIW), at the score of .3, it becomes weaker with five shades, and at the score of .5, it is no longer evident with a single shade. Similarly, GII score matters more for FIW for less gender traditional individuals, as shown by more shades with lower scores on the x-axis.

Figure 1.

Probability of FIW.

Second, there is a significant negative interaction between young children and GII for both women and men, partially consistent with Hypothesis 3c. In other words, regardless of gender, the effect of young children is smaller, or less positive, in countries with more gender inequality. As shown above, living in countries with high gender inequality increases the risk of experiencing FIW, which trumps the effect of young children. As shown in Figure 2 (based on Model 4), on the lowest end of GII, having young children clearly disadvantages workers, while as the score increases, this disadvantage lessens, and eventually is reversed. (For countries with GII score of about .34, there is no difference in FIW between those who have young children and those who do not, controlling for the other variables.) Finally, considering the interactions renders the previously non-significant gender difference in the effect of GII significant at the .001 (Models 1 vs 2) and .05 level (Models 3 vs 4).

Figure 2.

The effect of young children on FIW with 95% CIs.

Discussion and conclusion

This study examined predictors of WIF and FIW across 37 countries using the 2012 ISSP data. We posed three questions. First, how do gender, gender ideology, and gender inequality affect work–family conflict? Second, do the effects of gender and gender ideology vary by country context? Third, do effects differ for WIF and FIW? Based on our first hypothesis, we expected women to experience more FIW and men to experience more WIF. However, we do not find the reversed gender gap hypothesized for WIF but rather we find that women experience a higher risk of both WIF and FIW, which is consistent with earlier analyses of the 2002 ISSP data (Crompton and Lyonette, 2006; Edlund, 2007; Stier et al., 2012). Individuals are generally more responsive to role demands in the domains with higher priority in part shaped by societal expectations (Greenhaus and Beutell, 1985), but women may be more likely than men to perceive equally challenging demands in both the work and family domains. Furthermore, women’s high commitment to both work and family may contribute to their greater sense of work–family conflict (Grönlund and Öun, 2018).

As expected, based on our second hypothesis, more traditional gender ideology is associated with higher risks of WIF and FIW. Similar to previous research in the United States and Sweden (Davis, 2011; Roeters and Craig, 2014), we find that workers with more traditional views are more likely to suffer from WIF and FIW. In other words, gender egalitarian workers have a reduced risk of work–family conflict. This could be due to the greater emphasis on equal sharing of work and family responsibilities among egalitarian individuals (Edlund and Öun, 2016). Also as expected (H2a), while this pattern exists for both men and women, gender ideology has a stronger effect on FIW for women than men. Perhaps egalitarian women have more pragmatic expectations for domestic labor and engage in less intensive forms of childcare and housework. Among partnered individuals, egalitarian or “liberal” women, if they engage in a less traditional division of domestic work, experience less conflict (Crompton and Lyonette, 2006). On the other hand, for individuals in heterosexual relationships, egalitarian men may adjust their work lives to take on greater household and childcare responsibilities, thus facilitating reduced conflict for their female partners (Kaufman and Bernhardt, 2015; Maume, 2008). This may also lead to a happier home life as egalitarian women with lower work–family conflict report greater marital satisfaction (Minnotte et al., 2010).

Based on Goldscheider et al.’s (2015) conceptualization of the two-part gender revolution, we expected higher levels of work–family conflict in countries with higher levels of gender inequality, likely in the first half of the gender revolution. Indeed, we find that country-level gender inequality elevates the risk of WIF and FIW, which supports our third hypothesis. This finding is also consistent with Masuda et al.’s (2019) finding that country-level gender egalitarianism reduces work–family conflict. It is likely that women and men in more gender-unequal countries struggle to engage in paid and unpaid labor with limited support from states or employers. Unexpectedly, and contrary to Hypothesis 3a, the positive effect of gender inequality on both types of conflict appears to be stronger for men than women, although our null tests of the gender difference in the effect of GII are inconclusive. Men in more gender-unequal countries are likely to face greater work pressures as they are expected to take on the primary or sole breadwinning role. Although men in more gender-equal countries may face greater pressures at home, they also benefit from more generous state support (Gallie and Russell, 2009; Sjöberg, 2010). On the other hand, women may experience WIF regardless of country-level gender inequality while gender inequality increases FIW. Recent research shows that mothers in less traditional states spend less time in housework (Ruppanner and Maume, 2016). Therefore, traditional women in more gender-equal countries may find themselves to be the exception. They may enter the labor force more out of financial need or social pressure than from a desire to pursue a career. If they then carry on their traditional roles at home, they are likely to have a higher total workload, which could contribute to work–family conflict. Our research shows that it is important to consider the impact of gender equality at the country level. Country-level equality, attained by dismantling traditional gender norms through work–family policies, may also reduce or eliminate the gender gap in work–family conflict (Fahlén, 2014).

Our study finds ample support that paid work time and family work time increase WIF and FIW, respectively, and these effects are very similar for men and women. Several previous studies show a positive association between work hours and work–family conflict (Crompton and Lyonette, 2006; Gallie and Russell, 2009; Stier et al., 2012; Strandh and Nordenmark, 2006). Our study adds the parallel analysis of hours of family work, suggesting that more time on childcare and housework means potential interference with paid work, just as too much paid work can spill over into family work.

Marriage and children have opposite effects. Our findings of the negative effects of marriage on WIF (for women) and FIW suggest that a spouse may relieve pressures at home and encourage a better balance between family and work tasks, which is consistent with Stier et al.’s (2012) findings that married individuals experience lower work–family conflict. In contrast, the effects of parenthood are largely positive. It appears that the presence of young children imposes more pressure on men’s than women’s paid work, which in turn disrupts home life, whereas it is older children that put more pressure on women’s than men’s home life, which in turn spills into paid work. Our findings are consistent with the idea that mothers’ care for children more often interrupts their paid work while fathers more often miss family obligations due to work interruptions (Martinengo et al., 2010; Maume, 2008). This has potential consequences for both mothers and fathers. Research suggests that mothers are more likely to respond to work–family conflict by scaling back at work (Young and Schieman, 2018). However, while fathers are less likely to provide urgent childcare than mothers (Maume, 2008), fathers who care for sick children are more likely to report negative conflict (Dilworth, 2004).This is important because work–family conflict may limit fathers’ involvement with children (Kuo et al., 2018). Indeed, fathers, on average, prefer to work fewer hours than they actually do (Hobson and Fahlén, 2009).

Perhaps the most interesting of our findings are from the models of FIW involving the interactions of GII with gender ideology (Hypothesis 3b supported) and the presence of young children (Hypothesis 3c partially supported). To reiterate, we find that individuals’ gender ideology matters less if they live in more gender-unequal countries. Conversely, the gender equality of one’s country matters less for individuals with more traditional gender ideology. Of course, it is easier to change individuals’ attitudes or mind-set than the level of gender equality prevailing in their country. Researchers of the work–family interface thus need to realize that gender ideology, albeit an established predictor of well-being, has varying impacts depending on the national contexts. Likewise, we find that the positive effect of young children on FIW is weaker in countries that are more gender unequal. Certainly, this should not be taken to mean that parents of young children are better off juggling paid work and family life in more gender-unequal countries. Rather, in gender-unequal countries, individuals are likely to experience severe work–family conflict under more various family circumstances not limited to the presence of infants/toddlers in their own home.

Our study has some limitations. First, while our measure of WIF and FIW is consistent with previous research using the ISSP (e.g. Edlund, 2007; Ruppanner and Huffman, 2014), this concept is likely more nuanced than the measures included in this large-scale survey. Indeed, work–family conflict is not universal, and emergent research suggests the potential of work–family facilitation in which work and family roles support one another (Stoiko et al., 2017; Wayne et al., 2007). Future research would benefit from considering a more holistic view of work–family life. Second, our measure of gender ideology is based on the Western feminist literature. This is problematic because we analyze data from countries encompassing various gender norms and traditions. Ways in which gender roles are prescribed and valued vary greatly across societies (Emrich et al., 2004; Gibbons et al., 1997; Hofstede, 2001). We measure gender role ideology with only two of the seven items to reduce nonequivalence. The downside of this approach is the elimination of data that are relevant to only some but not other countries. We are faced with a trade-off between maximizing the number of items to construct a scale for gender ideology and keeping as many countries as possible to consider. Because of our interest in the effect of country-level gender inequality on work–family conflict, we prioritize the latter goal of including as many countries as possible. Future research would benefit from continuing efforts to increase cross-country data comparability through the improvement of international survey questionnaires and statistical techniques to minimize the trade-off (see Lomazzi, 2018, for a promising approach). Third, due to data limitations, we are unable to consider detailed indicators of responsibilities either in paid work or family work. For instance, we use having own children within certain age ranges as proxies of family constraints. Ideally, information such as time spent on particular childcare tasks should be considered. Moreover, family tasks are not limited to looking after one’s children. Individuals living in some of the countries (e.g. Japan) included in the ISSP database are experiencing severe consequences of population aging. Data collection and analysis efforts for future research on work/family conflict need to also keep in mind the effects of family eldercare on workers. Fourth, although our study uses a large-N cross-national comparative design, it is far from being inclusive of a large number of countries. Most regrettably, we are unable to include any country from Africa. Using comparable datasets from countries in different parts of the world has the potential of facilitating both generalized and contextualized understandings of work/family conflict. Toward this end, it is important to continue assessing and improving standard measures used in the general social surveys across nations.

Despite its limitations, the current study contributes to the literature on cross-national similarities and differences in work–family conflict. Returning to our research questions, we make three conclusions. First, gender egalitarian ideologies at the individual level and gender equality at the country-level are important in reducing work–family conflict. It seems egalitarian individuals may be putting into practice a more equal division of labor as both egalitarian men and women are less likely to experience WIF or FIW than their traditional counterparts. At the same time, gender equality at the country-level also reduces the odds of WIF and FIW, suggesting that these countries may be well on their way to the second half of the gender revolution (Goldscheider et al., 2015). Second, the effects of gender ideology do vary by country context. Traditional gender ideology has less impact on work–family conflict in gender-unequal countries, suggesting that practical considerations, such as work and family tasks themselves, may be more important than abstract ideology in these contexts. On the other hand, individual ideology has a greater impact on work–family conflict in gender egalitarian countries. Perhaps those with traditional views feel out of place in taking on work and family roles in a context where this is the norm. Third, there are few distinctions between WIF and FIW when it comes to gender, gender ideology, and gender inequality. In conclusion, our comparative research of 37 countries shows that individual egalitarianism and country-level gender equality are important factors in reducing work–family conflict. Policies that seek to address work–family balance should incorporate measures to promote gender equality.

Footnotes

Appendix

Appendix 4.

Log-odds and intra-class correlations (ICC) from two-level unconditional models.

	Pooled	Men	Women
	Estimate	Estimate	Estimate
	(SE)	(SE)	(SE)
WIF [0, 1], log-odds:	.268	.150	.376
WIF [0, 1], log-odds:	(.070)	(.075)	(.072)
ICC	.050	.056	.051
ICC	(.012)	(.013)	(.012)
FIW [0, 1], log-odds:	−1.618	−1.814	−1.472
FIW [0, 1], log-odds:	(.151)	(.168)	(.143)
ICC	.200	.234	.181
ICC	(.038)	(.044)	(.036)
Sample size	24,547	12,450	12,097

WIF: work interference with family; FIW: family interference with work.

Authors’ note

An earlier version of this paper was presented at the 2017 ASA Annual Meeting in Quebec. The authors thank the editor and anonymous reviewers for valuable feedback.

Funding

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

ORCID iD

Gayle Kaufman

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