Organizational Work–Life Policies and the Gender Wage Gap in European Workplaces

Abstract

Many organizations in Europe offer work–life policies to enable men and women to combine work with family life. The authors argue that the availability of organizational work–life policies can also reduce gender inequality in wages. The authors test their expectations using the European Sustainable Workforce Survey, with data from 259 organizations and their employees in 9 European countries. Multilevel analyses show that organizations that offer work–life policies have a smaller gender wage gap. Their findings also suggest that both the type and number of policies matter. Contrary to their expectations, dependent care policies, such as parental leave and childcare support, are less important for the gender wage gap than flexibility policies. Controlling for organizational culture regarding family supportiveness does not alter the results.

Keywords

gender wage gap work–life policies gender culture organizations multilevel

Changes in the relationship between the work and family domains over the past 30 years have led many more organizations to adopt work–life integration policies (Hammer, Van Dyck, & Ellis, 2013; Kelly et al., 2008). Organizations offer employees work–life policies to help them combine work and family life, resulting in happier (Clark, Rudolph, Zhdanova, Michel, & Balter, 2017; Moen, 2015) and more satisfied and committed employees (Butts, Casper, & Yang, 2013). Advocates of these policies also claim that they lead to less gender inequality in organizations because they support the integration of women into the labor force as part of equal employment measures (Kossek & Ollier-Malaterre, 2013). Whether they actually achieve this objective remains unclear, however (Glass, 2004; Huffman, King, & Reichelt, 2017). This article addresses this void by studying the relationship between the availability of organizational work–life policies and gender inequality within these organizations. We argue that women benefit more than men from the availability of family-friendly policies. We focus specifically on wage inequality, one of the most visible forms of gender inequality.

While there have been a few studies examining the relationship between organizational work–family policies and gender inequality in organizations, many of them related family policies at the national level to national gender inequality, for example, by looking at female labor participation, occupational attainment, the motherhood penalty, or the wage gap at country level (Budig, Misra, & Boeckmann, 2016; Korpi, Ferrarini, & Englund, 2013; Mandel & Semyonov, 2005, 2006). It has been shown that women's employment is higher in countries with more developed family policies (Van der Lippe & Van Dijk, 2002), but even there, a gender wage gap persists. One important reason for this persistence is that women are more often employed in the public sector than men (Mandel & Semyonov, 2005, 2006), and because wages in the public sector tend to be lower, an increase in female labor participation in this sector did not reduce the wage gap. So, although motherfriendly policies enable more women to become economically active, they do not diminish gender inequality in earnings (Mandel & Semyonov, 2005; Yaish & Stier, 2009). Gupta, Smith, and Verner (2008) even showed that national family policies giving mothers the right to take long leaves have adverse effects on their wages and, consequently, on gender equality because women's lengthy absences hinder their career progress.

Considering the amount of research focusing on the relationship between national family policies and gender inequality, it is surprising that almost no studies have investigated the implications of organizational work–life policies, as these probably have a more direct influence on employee outcomes than national policies (see Huffman et al., 2017 as a notable exception). This paucity is mainly due to the lack of suitable data. Because organizations are the arenas in which important decisions on recruitment, promotions, and wage rates are made, there is a need for more research on the significance of organizations and employers in creating and maintaining gender inequality in job rewards (Hultin & Szulkin, 2003; Reskin, McBrier, & Kmec, 1999), in relation to the national context. Moreover, gender pay-gap heterogeneity across organizations within the same country further justifies making a distinction between organization and country context.

Studies focusing on organizations mainly attempt to explain gender wage inequality by considering the different returns attached to human capital and other relevant characteristics of men and women, or women's limited access to managerial and supervisory positions (Hultin & Szulkin, 2003; Leicht, 2008). Indeed, a large body of research shows that organizations are sites where inequalities are created and sustained for groups of employees, such as female or migrant employees (Cohen, Huffman, & Knauer, 2009; Tomaskovic-Devey et al., 2006). Given recent findings that work–life policies influence organizational outcomes through different mechanisms for men and women (Abendroth, Melzer, Kalev, & Tomaskovic-Devey, 2017; Casper & Harris, 2008), it is clear that more research is needed to further examine the role of gender in the relationship between work–life policies and wages (Clark et al., 2017; Den Dulk, 2001).

We are aware that the underlying—sometimes implicit—organizational culture might lead both to decisions being made in the organization (such as the adoption of policies) and to gender-related outcomes (Benschop & Doorewaard, 1998; Pocock & Charlesworth, 2017). The organizational culture might produce certain work–life policies (or the absence of such policies), as well as a gender wage gap. We therefore allow as much as possible for the organizational culture associated with gender issues in our study.

We focus in this article both on work–life policies related to dependent care, such as leave and childcare, and on flexibility initiatives that offer alternative work arrangements, such as working from home, part-time work, and flextime. This is an important distinction because flexibility policies can be used to manage various nonwork and personal concerns, whereas family support policies are useful only to those with current or future dependents (Butts et al., 2013). We examine whether dependent care and flexibility policies have different effects on the gender wage gap.

Menezes and Kelliher (2011) called for a multilevel study design to investigate the influence of organizations on employees' outcomes. Most existing studies, however, used data collected from individual employees. Our study is based on a recent large-scale survey of 259 organizations, 869 departments, and 11,011 employees in multiple economic sectors in Bulgaria, Finland, Germany, Hungary, the Netherlands, Portugal, Spain, Sweden, and the United Kingdom (Van der Lippe et al., 2016). This multilevel data set enables us to study the availability of policies in relation to the gender wage gap in organizations while also taking the country level and characteristics of the organization into account.

To conclude, our contribution is threefold. First, our study investigates whether the gender wage gap can be understood by analyzing the influence of organizational work–life policies. Second, using a unique, large-scale multilevel data set of employees nested in organizations, we are able to test the influence of the organizational context on wage. Third, given that we have nine countries at our disposal, we are able to study whether the association between organizational work–life policies and the gender wage gap differs between national contexts.

Theory

Signaling Theory

Signaling theory is helpful for understanding the influence of work–life policies on the gender wage gap in organizations (Spence, 1973). Signaling theory asserts that people interpret an organization's observable actions as signals of less observable firm characteristics, thereby forming impressions about a firm's motives (Goldberg & Allen, 2008). Consistent with this theory, Grover and Crooker (1995) argued that the availability of work–life support policies might be interpreted as symbolic of corporate concern. The top of the organization is responsible for deciding on organizational goals and policies, including the adoption of work–life policies (Hultin & Szulkin, 2003), which signals how the top feels about work–life issues. For example, companies that aim to be accommodating to fathers' parenting obligations have policies that facilitate their active participation in early childcare. Organizations might also allow their employees to work flextime to help them combine work with family life. Having these policies signals that the organization has accepted that norms associated with the family sphere are important enough to introduce into the work setting and that they take for granted that fathers as well as mothers are capable of and interested in providing early childcare (Haas & Hwang, 2007). By treating its employees well, including those with family needs, the organization promotes an image of itself as being concerned about employees' needs, including work–life-related issues. Important to note is that flexible working conditions are related to type of job and social class (Golden, 2001, 2009; Swanberg, Pitt-Catsouphes, & Drescher-Burke, 2005; Williams, Blair-Loy, & Berdahl, 2013); it is more difficult to work from home as a truck driver than as an IT specialist. Those in managerial and professional jobs have greater access to scheduling flexibility (Golden, 2009), whereas the degree of flexibility for working class employees is lower (Swanberg et al., 2005), implying that they are less able to make use of flexibility arrangements. This flexibility stigma is also rooted in different sociocultural values of work devotion in the lower and higher social classes (Williams et al., 2013). This might have consequences for the way these policies influence employees' earnings, and we therefore control for the employee's type of job.

Applying signaling theory to how work–life policies influence the gender wage gap leads to the following two mechanisms. First of all, if work–life policies are available, they will influence those responsible for wage negotiations, often the Human Resource (HR) managers and the employees' direct supervisors. They signal to HR managers that the top of the organization attaches importance to work–family reconciliation. Having work–life policies breaks with the traditional idea of the ideal worker a highly committed, full-time worker, with limited caregiving responsibilities who gets support from an ideal parent (Haas & Hwang, 2007; Mun & Brinton, 2015; Munn & Greer, 2015)—and thus signals that the organization values work–family reconciliation. Furthermore, offering work–life policies not only to women but also to men breaks with the traditional division of roles, which also signals commitment at the top to more gender equality, not only in relation to work and family tasks but also in other areas, such as wages. Thus, when HR managers perceive that their organization cares about work–life issues, they are likely to interpret this as a commitment to gender equality as well, and they are likely to strive to create more gender equality in the organization, for example, by hiring more women at senior levels and by being conscious of fair and even-handed wage negotiations for male and female employees. Women thus receive higher and men receive lower wages in these organizations.

Second, female employees may feel more able to negotiate about their wage in organizations with more work–life policies. It is well known that female employees are less likely to negotiate a higher wage for themselves (Babcock & Laschever, 2003; Babcock, Laschever, Gelfand, & Small, 2003). Employment relationships are increasingly personalized, with more employment conditions open to negotiation. Unfortunately, personalization may disadvantage members of some demographic groups. The gender pay gap is frequently attributed to differences in how men and women negotiate. Gender differences in negotiations are, however, contextually bound and can be subject to change (Mazei et al., 2015). Work–life policies could function as a situation-based mechanism. Because employees who are mothers value family identity more than employees who are fathers (Van der Horst, 2014), it is likely that female employees will react more strongly than male employees to the presence of work–life policies. If these policies are available, women will perceive more room to negotiate about their wage than when these policies are not available (Clark et al., 2017). The presence of work–life policies signals that an organization takes the tasks of men and women seriously, both at work and at home. When these policies are in place, women do not have the feeling that they are performing poorly if they stay home to take care of a sick child, and this improves their negotiating position. Moreover, because these women can make more use of work–life policies in their organization, combining work with family life will not hamper their career and wages. All in all, this leads to Hypothesis 1: The availability of work–life policies in organizations is associated with a smaller gender wage gap.

We also test whether the type of policy available in an organization matters. In general, we expect that the availability of policies related to dependent care will have a bigger impact on the gender wage gap than flexibility policies because the former type of policy signals that the organization cares more about gender equality than the latter type does. Dependent care policies are designed to help employees combine work and family life, while flexibility policies can be used to manage various nonwork and personal concerns and not necessarily gender-related issues (Butts et al., 2013). In addition, flexibility policies can also be more organization-serving and give employees the impression that the work is never finished (Leslie, Manchester, Park, & Mehng, 2012; Wheatley, 2017). Such policies are therefore less likely to encourage women to negotiate harder about their earnings because they do not signal to women as clearly as policies related to dependent care that their employer is concerned about work–life balance. The same may be true for the HR manager. We therefore expect policies related to dependent care to signal greater organizational concern for gender equality, leading to Hypothesis 2: Dependent care policies are associated with a smaller gender wage gap than flexibility policies.

Finally, previous studies suggested that multiple work–life support policies relate more strongly to outcomes than a single policy (Butts et al., 2013). Thus, the more work–life policies are available in an organization, the stronger the signal of corporate concern about gender equality. Organizations offering a variety of family-friendly policies may differ fundamentally from companies that offer only a few family-friendly policies and are thus less friendly to family needs (Grover & Crooker, 1995). In addition, strategic Human Resource Management and system theory suggests that increased value occurs when multiple HR policies are available (Butts et al., 2013). This leads to Hypothesis 3: The larger the number of work–life policies in an organization, the smaller the gender wage gap.

Family-Supportive Organizational Culture

Organizations tend to have a distinct organizational culture, which often entails invisible processes in which gendered assumptions about women and men as well as femininity and masculinity are embedded and reproduced, and gender inequalities are perpetuated (Acker, 2012). Some call these organizational logics, or values that underpin rules (Baron, Hannan, Hsu, & Kocxak, 2007). This gendered substructure probably influences both the presence of work–life policies as well as the level of gender inequality. In this vein, our study focuses on how family-supportive the organizational culture is. By this we mean the shared assumptions, beliefs, and values regarding the extent to which an organization supports the integration of employees' work and family lives. This culture reproduces gender divisions and inequalities and is manifested through practices, policies, and procedures (Acker, 1998). Thus, if there is a family-supportive culture in the organization, more work–life policies are likely to result. Haas and Hwang (2007) indeed showed that the adoption of organizational work–family policies aimed at fathers depends on the culture of the organization. It is unlikely that this relationship works in reverse, as there are too many barriers in the organization to the presence of work–life policies to affect the organizational culture. One such barrier is that employees often have a low sense of entitlement to considerations of family needs, and so even when work–life policies are present, employees often do not use them (Pasamar, 2015). Another barrier may be that time spent working is often seen as representing productivity, commitment, and value, obscuring the positive impact of family-friendly policies on the organizational culture (Lewis, 1997). Moreover, in organizations with a family-supportive culture, women are likely to feel more confident and able to negotiate about their wage than in organizations with a less supportive culture, resulting in a smaller gender wage gap. All in all, this leads to Hypothesis 4: The association between work–life policies and the gender wage gap is explained in part by the effects of organizational culture on both the presence of work–life policies and the gender wage gap. That is, the relationship is partly spurious.

The National Context

The relationship between organizational work–life policies and the gender wage gap may be shaped by the national context in which the organizations operate; this can happen in several ways, as the national context is important in the wage-setting process (Bosch & Weinkopf, 2017). First of all, some countries place more emphasis on gender equality in the sense that both men and women are assumed to spend time working and caring for dependents (Kossek & Ollier-Malaterre, 2013; Ruppanner & Huffman, 2014), and both men and women are allowed to pursue a full-time career (Lewis, Brannen, & Nilsen, 2009). This might be accompanied by extensive policies at the national level to stimulate gender equality on the labor market. National work–life policies and organizational work–life policies may interact in two ways; they could either substitute or reinforce each other (Abendroth, Van der Lippe, & Maas, 2012). In a reinforcing relationship, national work–life policies are needed for organizational work–life policies to be effective, as they reflect a cultural context in which people find work–family issues and gender equality important and are thus likely to also interpret the availability of organizational work–life policies as a signal of organizational concern for these issues. On the other hand, if the relationship is substitutive, only a certain amount of support for work–life issues would be needed, meaning that organizational work–life policies have an effect when national support is lacking but have less effect when there are extended national leave policies in place that already provide this function (Abendroth, Van der Lippe, & Maas, 2012; Den Dulk, Groeneveld, Ollier-Malaterre, & Valcour, 2013).

Second, the economic situation in a country may influence how much importance is attached to the gender wage gap. If the economic situation in a country is poor, physical need satisfaction is most important (Hagerty, 1999). Employees need to have enough income to be able to cope with poor economic circumstances. This suggests that there is less opportunity to be concerned about gender equality issues (Inglehart & Norris, 2003), such as the gender wage gap. In these circumstances, the signaling function of work–life policies will be weaker for HR managers and employees, and the effect of organizational work–life policies on the gender wage gap will therefore be smaller in economically less prosperous countries.

Based on these two arguments, we can make a distinction between the nine countries we analyze in this article. We group them into the Northern European cluster with Sweden and Finland; the Western European cluster with the United Kingdom, the Netherlands, and Germany; the Southern European cluster with Spain and Portugal; and the Eastern European cluster with Bulgaria and Hungary. In the two Nordic countries, Finland and Sweden, egalitarian gender role beliefs are accompanied by generous work–life policies at the national level (Thévenon & Neyer, 2014). In Sweden, for example, all parents are entitled to reduce their working hours to 75% when their children are under the age of 8. In addition, both Finland and Sweden have a stable economy (Organisation for Economic Co-operation and Development, 2017). Note that the gender wage gap remains (Van der Lippe & Van Dijk, 2002), although gender segregation has a radically different impact in the Nordic countries (Grönlund, Halldén, & Magnusson, 2017). The Western European countries, that is, the Netherlands, Germany, and the United Kingdom, are comparable with the Nordic countries in terms of their economic conditions, but the concern for gender equality is weaker, and there are fewer work–life policies available at the national level (Den Dulk, Peters, & Poutsman, 2012). The two Southern European countries, Spain and Portugal, are characterized by a poor economic situation, relatively little concern for gender equality, and, in connection with the latter, limited support for working families and weak public policies (Glass, Anderson, & Simon, 2016; Thévenon & Neyer, 2014). It is, for example, almost impossible for parents with young children to reduce their working hours in Spain. Finally, in the two Eastern European countries, Bulgaria and Hungary, the economic situation is poorer than in the other countries included in our study. Even though gender inequality is lower on the agenda in Bulgaria and Hungary, there are generous maternity and parental leave arrangements after the birth of a child (Thévenon & Neyer, 2014), which are partly seen as encouraging women to leave the labor market to raise children (Saxonberg & Sirovatka, 2006).

Because we have only nine countries in total, we do not formulate hypotheses on how country characteristics interact with effects of organizational work–life policies on the gender wage gap. We do, however, explore whether the organizational work–life policies relate differently to the gender wage gap in the different country clusters and reflect on whether this indicates that the relationship between national and organizational work–life policies is reinforcing or substitutive. If we find that organizational policies influence the gender wage gap only in countries with extensive work–life policies (i.e., the Nordic and Eastern European cluster) but not in countries with more limited national work–life policies (i.e., the Western European and Southern European counties), this would suggest that the relationship is reinforcing, as national policies are needed for organizational policies to be effective. However, if we find that organizational work–life policies mainly influence gender equality in countries with limited national policies, and to a lesser extent in countries with extensive national policies, this would suggest that the relationship is substitutive: The organizational policies replace the function of country-level policies.

Data and Methods

Sample

To test our hypotheses, we used the European Sustainable Workforce Survey (ESWS), which is a multiactor organizational survey conducted within organizations in Bulgaria, Finland, Germany, Hungary, the Netherlands, Portugal, Spain, Sweden, and the United Kingdom (Van der Lippe et al., 2016). We used national business lists of organizations in the chosen industries in the country as our sampling frame. We chose establishments that belong to the six occupational industries under study and made a distinction between those with 20 to 49, 100 to 250, and 250 + employees. We randomly selected an organization to approach from each sampling cell. The six industries are manufacturing, health care, higher education, transport, financial services, and telecommunications. If an establishment within a particular industry and size group refused to participate in the study, we used a matching strategy to include a new organization within the same industry and within the same size category. In this article, we use organization to refer to the establishment. The organizations are in both the private and the public sector. After the organization (often the HR director) agreed to participate, we contacted employees and their department managers at work and asked them to participate in an online or paper-and-pencil questionnaire. A total of 11,011 employees nested in 259 organizations participated in the survey. The participation rate at the organization level varied from 5% to 20% across countries. Given the difficulties involved in gaining access to organizations, nonresponse is an important problem for the majority of studies that sample organizations.¹ Once an organization joined our research, the response rate was good: The within-organization response rate was 61% for employees and almost 98% for HR managers.

For the present analyses, we excluded 1,138 employees who did not disclose their wage. The remaining sample consisted of 9,873 employees. We subsequently excluded cases with incomplete data from the analyses, leaving 8,340 employees from 239 organizations in our analytical sample.

Measures

Dependent variable

To arrive at the monthly earnings of employees, we posed the following question: ‘What are your net monthly earnings from your main job at this organization? Please refer to your average earnings in recent months.’ To explain what net monthly earnings means, we added, ‘what you have left every month after deducting national and local taxes and compulsory national insurance contributions.’ If respondents did not fill in this question, we asked them to give an approximation and provided 21 categories. Depending on the country, the amounts increased by around 90 euros per category. For the Netherlands, for example, there is a 100-euro difference between the categories, with the lowest category being ‘less than 820 euros.’ For Portugal, the difference is 60 euros, and the first category is ‘less than 190 euros.’ All currencies were converted to euros. We then took the mean of each category and recoded every answer correspondingly. Finally, we converted the monthly earnings to the natural log. Of the respondents willing to give information on their monthly earnings, 80% provided their exact wage and 20% filled in one of the categories. We use monthly earnings rather than hourly wages because most employment contracts in these countries are based on fixed monthly salary rather than hourly earnings. We also performed the analyses with hourly wage (constructed by dividing the monthly earnings by contract hours per month), and the results are highly similar.

Independent variables

Employee gender is measured by the variable female (0–1). The establishment's HR manager reported on the availability of dependent care policies and flexibility policies. We looked at two types of work–life policies: dependent care policies and flexibility arrangements. Dependent care policies consist of parental leave and childcare facilities. To construct the availability of parental leave facilities, we asked the HR manager whether the organization offered longer parental leave to women than available in the country (0 = no, 1 = yes), and to men (0 = no, 1 = yes), and whether it offered better paid parental leave to women than available in the country (0 = no, 1 = yes), and to men (0 = no, 1 = yes). To make this policy comparable with the other policies, we created one dummy from these four items, which reflects whether the organization offers additional parental leave policies to its employees (0 = no, 1 = yes). This variable has a score of 0 if no policies are offered and 1 if one, two, three or four, or five policies are offered.² The availability of childcare support reflects whether the organization offers financial assistance for childcare (0 = no, 1 = yes), provides childcare at the workplace (0 = no, 1 = yes), and/or provides assistance in finding or arranging childcare (0 = no, 1 = yes). Again, one variable is constructed out of these three variables that reflects whether the organization provides some form of childcare support (0 = no, 1 = yes), where a score of 0 means that no policies are offered and 1 that one or more policies are offered. Flexibility arrangements consist of the possibility of changing to part-time work, of working from home, and of working flexible hours. The possibility of changing to part-time work reflects whether employees have the option to switch to working fewer hours a week (0 = no, 1 = yes). Working from home reflects whether employees are allowed to work from home during normal working hours (0 = no, 1 = yes). Flexible hours reflects whether employees are allowed to have flexible starting and finishing times (0 = no, 1 = yes). For the number of policy arrangements, we counted the availability of the five dependent care and flexibility arrangements, resulting in a score between 0 and 5.

The family-supportive culture of the organization is measured by asking the employees about the extent to which their organization supports and values the integration of employees' work and family lives as indicated by Thompson, Beauvais, and Lyness (1999). The measure consists of nine items; examples are ‘higher management encourages supervisors to be sensitive to employees' family concerns,’ and ‘my supervisor is very accommodating of family-friendly needs’ with a 5-point Likert scale from 1 = strongly disagree to 5 = strongly agree. The reliability of this scale is .74. The items indicating work–family culture as reported by the employees are aggregated, and their mean is taken to arrive at a measure for family-supportive culture at the organizational level.

Control variables

We control for a number of variables indicated in the literature as being of influence on the gender wage gap. At the employee level, these include contracted working hours and number of overtime hours per week, level of education, tenure, type of job, family circumstances, and age and age squared. Contract hours capture the number of hours per week that the employee is contracted to work for the organization, and overtime hours are calculated by subtracting the contract hours from the hours actually worked. Level of education was measured with the following question: ‘What is the highest level of education that you have completed?’ There were eight main categories: not completed primary education (0), primary education or first stage of basic education (1), lower level secondary education or second stage of basic education (2), upper secondary education (3), postsecondary, nontertiary education (4), first stage of tertiary education—bachelor (5), second stage of tertiary education—master (6), and doctoral degree (7). We include this variable linearly in the analyses. Tenure is indicated by number of years the employee has been working for the organization. Type of job is based on the following question: ‘What is your occupation? Please give a full description of your occupation, for example nurse in intensive care, cashier at a bakery counter.’ Scores are recoded into International Standard Classification of Occupations (ISCO) codes. We include ISCO codes on the one-digit level (Ganzeboom, De Graaf, & Treiman, 1992).³ To control for family circumstances, we added cohabitation with partner to the model, as well as the presence of young children in the household. Last, we control for employees' age and age squared to account for the possible nonlinear effect of age on earnings. To control for organizational characteristics, we add the log of the number of employees in the organization as an interval variable, whether it is a public or private organization, the percentage of women in the organization, and the percentage of women in the top management of the entire organization.⁴ Last, we include sector (manufacturing, health care, higher education, transport, financial services, or telecommunications) and country. Descriptive statistics of all variables can be found in Table 1.

Table 1.

Descriptives of All Variables.

	Total		Men		Women
	M	SD	M	SD	M	SD
Monthly earnings	1666.67	1342.69	1928.87	1473.76	1460.77**	1190.73
Log monthly earnings	7.13	0.84	7.29	0.85	7.01**	0.82
Parental leave policy	0.21		0.22		0.20
Childcare policy	0.28		0.25		0.31**
Change to part-time policy	0.89		0.90		0.88**
Working at home policy	0.52		0.53		0.52
Flextime policy	0.74		0.78		0.71**
Number of work–family policies	2.64	1.19	2.68	1.18	2.61*	1.20
Individual level
Female	0.56
Contracted working hours	36.98	7.40	38.59	6.06	35.71**	8.07
Overtime hours per week	2.52	4.19	2.87	4.27	2.25**	4.11
ISCO1: managers	0.06		0.07		0.05
ISCO2: professionals	0.33		0.33		0.33
ISCO3: technicians and associate professionals	0.29		0.26		0.31**
ISCO4: clerical support workers	0.16		0.12		0.20**
ISCO5: services and sales workers	0.05		0.03		0.06**
ISCO6: skilled agricultural, forestry, and fishery workers	0		0		0
ISCO7: craft and related trades workers	0.04		0.08		0.01**
ISCO8: plant and machine operators and assemblers	0.05		0.09		0.02**
ISCO9: elementary occupations	0.02		0.03		0.02**
Level of education	4.42	1.43	4.30	1.52	4.52**	1.34
Tenure at the organization	10.48	9.75	10.46	9.89	10.50	9.65
Age	41.92	10.96	42.16	11.06	41.73	10.86
Age squared	1877.00	941.72	1899.71	959.07	1859.10	927.23
Living with minor child	0.42		0.41		0.42
Living with a partner	0.74		0.76		0.72**
Organizational level
Family-supportive culture	3.49	0.23	3.49	0.23	3.50	0.23
Log number of workers in organization	5.57	1.43	5.45	1.35	5.66**	1.49
% women in top management	0.24	0.23	0.19	0.19	0.28**	0.25
% women in the organization (×10)	4.98	1.46	4.39	1.36	5.46**	1.35
Public sector	0.35		0.26		0.41**
Industry
Manufacturing	0.24		0.34		0.16**
Health care	0.24		0.11		0.34**
Higher education	0.16		0.13		0.18**
Transport	0.13		0.18		0.09**
Financial services	0.13		0.10		0.16**
Telecommunication	0.10		0.14		0.07**
Country
United Kingdom	0.07		0.07		0.07
Germany	0.08		0.08		0.08
Finland	0.08		0.07		0.09**
Sweden	0.10		0.12		0.08**
Netherlands	0.24		0.26		0.22**
Portugal	0.11		0.10		0.12
Spain	0.07		0.08		0.07**
Hungary	0.12		0.12		0.12
Bulgaria	0.13		0.10		0.15**
N	8,340		3,677		4,663

Note. Significant difference between men and women based on F-test.

*p < .05. **p < .01. ISCO = International Standard Classification of Occupations.

Analytical Strategy

The relationship between the availability of work–life policies and the gender wage gap is analyzed using a three-level regression model. Because it is our aim to explain cross-organizational variation in the gender wage gap, we allow the effect of gender to vary across all levels (Schmidt-Catran & Fairbrother, 2016). The dependent variable in this analysis is the employee's log monthly earnings. We first estimate a null model from which we can determine to what extent employees' wages depend on the department and organization in which they work. In Model 1, we include only gender to see how big the gender gap in monthly earnings is. This modeling strategy is commonly used in analyzing the gender wage gap (Cohen et al., 2009; Hensvik, 2014; Mandel & Semyonov, 2005).⁵ Model 2 also includes the control variables, the work–life policies, and the interactions of gender with every work–life policy separately to see whether individual work–life policies influence women's and men's earnings and, consequently, the gender wage gap. In this model, we estimate country-fixed effects by including country dummies in the analyses. Model 2 tests Hypotheses 1 and 2. Model 3 includes the number of work–life policies as well as the interaction of that number with female. This model tests Hypothesis 3, which presumes that the gender wage gap is smaller when there are more work–life policies present in the organization. In Model 4, we add the work–family culture in the organization and the interaction of the work–family culture with gender to test Hypothesis 4; here, we see whether the relationship between work–life policies and the gender wage gap is confounded by the family-supportive organizational culture.

Finally, we analyze the fourth model separately for the country clusters to show the relationship between organizational policies and the gender wage gap within the different clusters of countries. We present these results separately rather than as a three-way interaction as this better suits our exploratory purpose regarding the different effects of work–life policies on the gender wage gap within these country clusters and because such results are more easily interpretable than three-way interactions of nominal variables such as country clusters.

Results

Table 1 shows that women on average earn less than men. They furthermore work somewhat more often in organizations with childcare support, but less often in organizations where flexibility policies are available. Moreover, Table 1 shows that women are more often found in larger organizations and organizations with a higher percentage of female leaders. Women work less in the manufacturing, transport, and telecommunication sectors.

Table 2 presents the results regarding differences in the gender gap in monthly earnings, with Appendix A providing the results for the control variables in these models. As can be seen in Model 1, overall women earn less than men. The unadjusted gender gap in monthly earnings is 15.3% (calculated using: 100 × (exp(−0.14) − 1); (Huffman et al., 2017)). Note that this gap is unadjusted for working hours and other control variables. The variance at the employee and organizational level furthermore indicates that there is a significant variation of 70% between organizations in wages, confirming the need to perform multilevel analyses. The variance of the effect of being female is .02, which is significant at the .01 level; this indicates that the gender wage gap varies between organizations. Adding the work–life policies separately and their interactions with being female in Model 2 shows that two work–life policies appear to partly explain the variation in monthly earnings between men and women, namely the availability of policies with respect to changing to part-time hours and working from home. This partly supports Hypothesis 1: Some work–life policies diminish the gender wage gap. However, not all policies are negatively related to the gender wage gap. We must reject Hypothesis 2 because the results of Model 2 do not indicate that dependent care policies are more important than flexibility-related policies for decreasing the gender wage gap. Rather, the opposite seems to be true. Model 3 combines the separate work–life policies in one indicator, and, in line with our expectations, more available work–life policies relate to a smaller gender wage gap. This supports Hypothesis 3. Model 4 adds the family-supportive culture of the organization and its interaction with gender. To test whether adding the family-supportive culture to the model weakens the interaction of gender with work–life policies, we conducted a Wald test comparing the regression coefficients of Models 3 and 4 for the interaction between female and number of policies (Paternoster, Brame, Mazerolle, & Piquero, 1998). This showed that the coefficient does not significantly decline when family-supportive culture is included (z = −.141, p = .888), and thus Hypothesis 4 is not supported. Neither the main effect of family-supportive culture nor its interaction with gender is significant.⁶ Figure 1, which is based on Model 4, depicts how women's and men's earnings are affected by the number of work–family policies in their organization. It shows that the gender gap in earnings is smaller when more work–family policies are available not only because women earn more in such organizations but also because men earn somewhat less.

Table 2.

Multilevel Analyses for Gender Wage Gap in Nine European Countries (Dependent Variable is Log Monthly Earnings).

	Model 1	Model 2	Model 3	Model 4
Female	−0.144** (0.015)	−0.211** (0.037)	−0.158** (0.030)	−0.035 (0.179)
Parental leave policy		0.083^† (0.050)
Childcare policy		−0.083^† (0.044)
Changing to part-time policy		−0.116^† (0.064)
Working from home policy		−0.043 (0.043)
Flextime policy		0.093* (0.048)
Female × Parental Leave Policy		−0.023 (0.030)
Female × Childcare Policy		0.008 (0.026)
Female × Changing to Part-Time Policy		0.121** (0.038)
Female × Working From Home Policy		0.051* (0.026)
Female × Flextime Policy		−0.013 (0.031)
Number of policies			−0.007 (0.018)	−0.007 (0.018)
Female × Number of Policies			0.026** (0.010)	0.028** (0.010)
Family-supportive culture				−0.019 (0.079)
Female × Family-Supportive Culture				−0.036 (0.052)
Constant	7.238** (0.047)	6.059** (0.161)	6.049** (0.153)	6.112** (0.310)
Variable (female)	0.020 (0.005)	0.007 (0.003)	0.009 (0.003)	0.009 (0.003)
Variable (organizations)	0.500 (0.047)	0.056 (0.006)	0.057 (0.007)	0.057 (0.007)
Variable (employees)	0.190 (0.003)	0.153 (0.002)	0.153 (0.002)	0.153 (0.002)

Note. Standard errors are in parentheses.

*p<.05. **p<.01. ^†p<.10.

Figure 1.

Total number of policies and monthly earnings of men and women (in log).

Table 3 shows the results of Model 4 for each cluster of countries. In the Eastern European cluster, the presence of multiple organizational work–life policies indicates a smaller gender wage gap, while no such association is found in the Western, Northern, and Southern European countries. However, the Wald test shows that this coefficient in the Eastern European cluster does not differ significantly from any of the other clusters.⁷

Table 3.

Multilevel Analysis for Gender Wage Gap in European Clusters of Countries (Dependent Variables is Log Monthly Earnings).

	Northern European cluster	Western European cluster	Southern European cluster	Eastern European cluster
Female	0.835 (0.820)	−0.446* (0.179)	0.231 (0.392)	−0.220 (0.523)
Total number of policies	0.097 (0.038)	−0.036* (0.017)	−0.024 (0.042)	0.011 (0.034)
Female × Total Number of Policies	0.111 (0.070)	0.012 (0.012)	0.010 (0.020)	0.054* (0.024)
Constant	5.723** (0.564)	5.977** (0.264)	6.360** (0.684)	4.578** (0.819)
Variable (female)	0.125 (0.039)	0.001 (0.001)	0.000 (0.000)	0.021 (0.009)
Variable (organizations)	0.010 (0.011)	0.012 (0.002)	0.066 (0.016)	0.066 (0.015)
Variable (employees)	0.268 (0.010)	0.066 (0.002)	0.173 (0.006)	0.172 (0.005)
N	1,479	3,245	1,519	2,097

Note. Standard errors are in parentheses. Furthermore, controlling for all variables included in Model 4 of Table 2.

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

Sensitivity Analyses

We performed a number of sensitivity analyses to test the robustness of our analyses. Some of these analyses are presented in the Appendices. First, we added the number of work–life policies as a set of dummy variables instead as a continuous variable to test for a nonlinear relationship. The results suggest a linear relationship between number of work–life policies and gender wage gap, as the beta coefficient increases the more work–family policies there are (see Appendix B). Second, without reporting the outcome in an appendix,⁸ we checked whether the results are different for employees with family responsibilities. It could be that dependent care policies have a stronger effect on employees with family responsibilities (i.e., employees with children and, to a lesser extent, elderly partners who may need care) than on other employees because they signal that the organization is particularly concerned about the needs of employees who are combining work and family. Those with family responsibilities no longer need to negotiate about policies that they need but can instead concentrate on wage negotiations. Because women still bear more responsibility than men for childcare, we expect this to be especially true for mothers. To this end, we tested a three-way interaction between type of policies, family responsibilities, and gender, but its coefficient was not significant. Third, although we controlled for a number of key indicators, endogeneity may be a source of bias (Cohen & Huffman, 2007). We therefore also analyzed the gender wage gap within organizations using fixed effects for organizations. This shows more directly how women and men within one organization are affected by work–family policies in terms of their monthly earnings. The results are highly similar to the analysis between organizations, giving us confidence that the results are robust (see Appendix C).⁹ Finally, in Appendix D, we report descriptive statistics about the distribution of work–life policies in organizations by industry.

Discussion

This study investigated the relevance of organizational work–life policies for the gender wage gap. Capitalizing on rich data on employees in organizations in nine European countries, our findings suggest that the availability of work–life policies in organizations matters for the gender wage gap. Female employees earn less than their male counterparts on average, but the wage gap is smaller in organizations where work–life policies are available. This is not only because women in these organizations earn more but also because men earn less. Furthermore, our findings suggest that both the type and number of policies matter. Contrary to our expectations, dependent care policies, such as parental leave and childcare support, are less important than flexibility policies for the gender wage gap. For parental leave policies, this may have a similar explanation as Gupta et al. (2008) pose for the fact that more extensive work–life policies at the national level correlate with lower earnings for women: Extensive leave policies enable (mainly) women to be absent from work for lengthy periods of time, and such lengthy absences hinder their career progress and thus their wages. Work–life policies that facilitate women's absence from the workplace may therefore have adverse consequences for gender wage equality. However, this does not explain why childcare policies were not found to influence gender equality in wages. After all, organizational childcare policies facilitate the presence of mothers (and fathers) in the workplace rather than their absence. There has been less research on this issue, and we therefore performed an extra analysis to study whether childcare support policies are more important to employees with family responsibilities. This appears not to be the case, however. One possible explanation is that childcare support—unlike flexibility policies—is costly for the organization. Consequently, rather than empowering female employees in their wage negotiations, the presence of childcare support policies might actually reduce women's negotiating position, as they feel indebted to the organization for the childcare that is provided. This might explain why women's wages do not increase in organizations with childcare policies. In addition, our finding that there is a relationship between multiple work–life policies and a smaller gender wage gap indicates that one specific policy can never effectuate as much change as a number of policies. This is in line with previous work (i.e., Butts et al., 2013) and supports the idea that the more work–life policies are available in an organization, the stronger the signal of corporate concern for gender equality (Connelly, Certo, Ireland, & Reutzel, 2011).

Moreover, while a family-supportive organizational culture is expected to influence both the availability of work–life policies and gender equality in wages, our analyses show that when controlling for this, work–life policies continue to send their own signal. Signaling theory therefore proves useful in understanding how work–life policies facilitate not only work–life balance but also gender equality in organizations. Policy adoption by organizations is symbolic and therefore effective in reducing the gender wage gap (Huffman et al., 2017).

Conclusion

Using a large-scale survey of organizations, we were able to demonstrate the importance of the organizational level for understanding the gender wage gap, in addition to the national context. After all, the organizational level is where decisions regarding recruitment, promotions, and wage rates are made, and as such, it is important to look at the organizational context to explain the gender wage gap. Using multilevel organizational data, we were able to assess the influence of organizational policies on employees by analyzing information provided by the organization rather than information provided by the individual employees. This nested data structure makes our results more reliable (Snijders & Bosker, 2012).

Our country-specific results suggest that organizational work–life policies reduce the gender wage gap particularly in Hungary and Bulgaria. As these countries have extensive national-level work–life policies, this might suggest a reinforcing relationship, that is, organizational work–life policies affect the gender wage gap particularly when there are also national work–life policies in place. However, although the Northern European countries (Sweden and Finland) also have extensive national-level work–life policies, we do not find that organizational work–life policies influence the gender wage gap there, and we are therefore unable to conclude that work–life policies at the country and organizational levels reinforce each other. As there was no evidence that organizational work–life policies affect the gender wage gap in Western European countries (Germany, the Netherlands, and the United Kingdom) or Southern European countries (Spain and Portugal)—which all have more limited national policies—we also have no evidence that organizational policies in particular function as substitutes for national policies in their effect on the gender wage gap. In addition to their extensive work–life policies, Hungary and Bulgaria are also less affluent countries. This might suggest that the effect of work–life policies on the gender wage gap is particularly important in countries with substantive national policies and a poorer economic situation. This was a surprising finding, as we expected the reverse to be the case. After all, in less affluent countries, we expect a greater focus on providing everyone with a better salary than on narrowing the wage gap. A possible explanation may be that in countries with a poorer economic situation, a smaller number of organizations offer work–life policies because they are costly in the short term. As such, the select group of organizations that do offer work–family policies send out a strong signal of corporate concern regarding gender equality. As we have only nine countries in our data set, we cannot test associations between country characteristics and the importance of organizational work–life policies for the gender wage gap empirically, but this seems a fruitful direction for future cross-national research.

We recognize that there are also limitations to the research presented here. First and foremost, organizations that provide generous work–life policies are likely to attract women and employees with family responsibilities. This selection effect could operate at the job-seeking stage or in the amount of time employees are affiliated with the employer. It could even lead job applicants to infer that an organization with family-friendly policies might pay poorly because such policies compensate women for poor wages. There may even be reverse causation: Organizations in which women have comparatively more power (i.e., in which the gender pay gap is smaller) may be more likely to develop and implement work–family support policies. We have tried to take this into account by controlling for the number of women working in the organization, the sector, the family-friendly culture, and the percentage of women in top management. Moreover, we ran additional within-organization analyses, which showed that the gender wage gap also becomes smaller within organizations when there are more work–life policies available. This gives us confidence with respect to our research strategy, but we nevertheless encourage new research using a longitudinal panel design that tracks organizations and their employees over a longer period. Moreover, future research focusing on the mechanisms behind the association that we have found could deepen our understanding of the relationship between work–family policies and the gender wage gap. Second, reports of work–family policies by HR managers might be affected by social-desirability bias. Haas and Hwang (2016) survey personnel (HR) directors because they are typically used as informants about organizational behavior and are well informed about the entire organization. HR managers have been respondents in other company studies on work–family integration (Den Dulk, Peters, & Poutsman, 2012; Galinsky, Bond, Sakai, Kim, & Giuntoli, 2008). Nevertheless, there are limits to the reliability and validity of responses secured from single organizational representatives, particularly high-ranking ones who might feel pressure to present their companies in a favorable light. Finally, there could be the problem of endogeneity, as is the case for any study that is not longitudinal or experimental. However, we believe that our article contributes to the literature because of its unique organizational data, with individual reports by employees. Moreover, we have included controls for all sorts of factors that are likely to influence an organization's work–life policies and the gender wage gap.

Our results leave several questions unanswered, for example, what happens when work–life policies are actually used by male and female employees. In this article, we focused deliberately on the availability of work–life policies because we aimed to study the signal sent by the organization. Previous studies show that the use of family-friendly policies, such as parental leave, or part-time work, both primarily by women, can increase gender inequalities in organizations (Glass, 2004; Plantenga, Remery, Helming, Meulders, & Kanjuo-Mrčela, 2013) because managers believe that users of work–life support policies are less devoted to the organization, create additional work for supervisors and colleagues, and receive unfair benefits at the expense of others. Perceptions that policy users are less committed may also result in lower rewards and fewer advancement opportunities for employees (Kirby & Krone, 2002). Men may not take advantage of policies as much as women because they fear social stigmatization (Kossek, Lautsch, & Eaton, 2006). Because women are more likely to make use of work–life policies, this is expected to increase the gender wage gap. Thus, although there are instrumental benefits associated with work–life policy use, there may be drawbacks in terms of earnings. Future research should explore the different effects of the availability and use of policies. Finally, because we asked HR managers about policies available in their establishment, we assume that they have reported their formal policies. However, it can be argued that both formal and informal policies signal that the organization is concerned about work–family reconciliation and gender equality, an avenue that could be researched in future studies.

Footnotes

Appendix A. Effects of Control Variable Models 2 to 4 in Table 2

Acknowledgments

We thank Siyka Kovacheva (Bulgaria), Jouko Nätti (Finland), Sonja Drobnič (Germany), Eva Fodor (Hungary), Maria Guerreiro (Portugal), Teresa Castro-Martin (Spain), Margaretha Bäck-Wiklund (Sweden), Stefan Szücs (Sweden), and Suzan Lewis (United Kingdom) for support with the data collection in their own country. Data are archived at the Utrecht University repository and are available from the corresponding author upon request.

Declaration of Conflicting Interests

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

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research received funding from the European Research Council under the European Union's Seventh Framework Programme (FP/2007-2013)/ERC Grant Agreement n. 340045.

Notes

Author Biographies

Tanja Van der Lippe is a professor of sociology at the Department of Sociology, Utrecht University, and chair of the Research School ICS, both in the Netherlands. Her research interests include work and care of men and women, a sustainable workforce, time use and time pressure, and the position of men and women in European countries.

Leonie Van Breeschoten is currently doing her PhD at the Department of Sociology/ICS, Utrecht University, the Netherlands. She does research into questions regarding the combination of work and family life in European countries.

Margriet Van Hek is a postdoctoral researcher at the Department of Sociology/ICS, Utrecht University, the Netherlands. Her postdoctoral research focuses on the impact of female leadership on social inequalities in public and private organizations. Her main research interests comprise gender issues, social stratification, organizational sociology, educational sociology, and cultural tastes and lifestyles.

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