Gender inequities can be partly traced back to gender differences in working time arrangements. In fact, it is established knowledge that women as compared to men are more (less) likely to work part-time (overtime). Based on social role theory, however, we also expect gender differences among part-time and overtime workers, such that women and men differ in why they work part-time or overtime. In a preregistered and highly powered study conducted in Germany (N = 3,844–17,361, depending on the analysis), we observed that, on average, women were more likely than men to work part-time (i.e., fewer than 35 hours per week) because of personal or family obligations. Moreover, in comparison to men, women were less likely to work overtime (i.e., at least two hours per week) to attain additional income, but more likely to work overtime to step in for colleagues. Altogether, people had “gendered” reasons to work certain hours. Furthermore, as people’s paid working time arrangements are intertwined with their lives outside of the workplace, we examined women’s and men’s work–life interface and observed that women (as compared to men) deemed it less acceptable to be available for work-related issues during leisure time. We discuss implications for future theorizing and for practitioners who aim to design work schedules that consider the different lived experiences of women and men.

Much in life depends on how much time of one’s life is devoted to paid work (Ng & Feldman, 2008). The more paid hours people work, the more they typically earn and the better their career prospects (e.g., Bourdeau et al., 2019; J. C. Williams et al., 2016). Why, then, do people work part-time, and do women work part-time for different reasons than men (e.g., Allan et al., 2020; Bureau of Labor Statistics, 2018; Pech et al., 2021)? Conversely, why do people work overtime, impeding family involvement and recovery (e.g., Arlinghaus et al., 2019; Ng & Feldman, 2008; Vieten et al., 2022), and do men work overtime for different reasons than women (see also Eagly & Carli, 2007; Van Echtelt et al., 2009)? Answering these questions is key for science and practice, as gender differences in working time arrangements give rise to gaps in pay, career advancement, and pensions (e.g., Antonie et al., 2020; Brereton, 1990; Cha & Weeden, 2014; Frericks et al., 2009).

Yet, the literature on gender differences in working time arrangements appears fragmented. Extant research typically focuses either on part-time work or overtime work (e.g., Rosenfeld & Birkelund, 1995; Van Echtelt et al., 2009; for more integrative work, see Jacobs & Gerson, 2004; Weeden et al., 2016). If we studied the groups of part-time and overtime workers together, however, it would be easier to see how well a parsimonious set of theoretical propositions could explain gender differences within both groups. Moreover, extant research typically asks why women are more likely to work part-time, but less likely to work overtime, than men (e.g., Brereton, 1990; Lips, 2013). Yet, little is known about whether and why there are also gender differences among part-time workers and overtime workers that compose a notable portion of the workforce (e.g., Bureau of Labor Statistics, 2022b; Rotchford & Roberts, 1982): Do men, similar to women, work part-time to engage in caregiving for family members (e.g., Eagly & Carli, 2007)? And do women, similar to men, work overtime to advance their careers (e.g., Berdahl et al., 2018)? Raising these questions again reveals the limits of our understanding of gender differences in working time arrangements (for an elaboration, see below). And if we only incompletely understand these gender differences, possible interventions can only be incomplete as well, allowing inequities to “stall” (e.g., England, 2010; England et al., 2020).

Thus, our work advances theory and research, first, by explaining gender differences in why part-time and overtime is worked (see also, e.g., Burchell et al., 2008; Bureau of Labor Statistics, 2018). Hereby, we illuminate whether there are gender differences among Germans who work part-time (i.e., those who work fewer than 35 hours per week) and overtime (i.e., those who work at least two hours overtime per week), a question not captured by research focusing on gender differences in the number of paid hours. In doing so, we also follow the more recent tradition to study “social processes”—in our case, those that are gendered—that give rise to working time arrangements (Gerstel & Clawson, 2018). Moreover, by considering the groups of part-time and overtime workers (cf. Jacobs & Gerson, 2004; Weeden et al., 2016), we explore how well a parsimonious set of theoretical propositions (Eagly, 1987; Eagly & Wood, 2012) can explain gender differences within both groups, thereby potentially further advancing an integrated understanding of gender differences in working time arrangements.

Second, we examine moderators, including people’s marital, cohabitation, and parental status (e.g., Kjeldstad & Nymoen, 2012; Ng & Feldman, 2008). Hereby, we provide knowledge on the conditions under which gender differences in working time arrangements occur. Our moderators are theoretically relevant because they may shape the influence of gender roles (Eagly, 1987; Eagly & Wood, 2012), which are important drivers of gender differences in many domains of life (e.g., Badura et al., 2018; Livingston, 2014).

Third, as enabled by our theoretical lens, Social Role Theory (SRT; Eagly, 1987), we consider women’s and men’s work–life interface: People’s work life and their private life are mutually related in a “web of time” (Gerstel & Clawson, 2018, p. 77), and gender roles affect not only people’s own actions, but also how others, both from work and from home, may treat women and men (i.e., actor–perceiver dynamics; Rudman & Fairchild, 2004). Hence, we examine how often women and men report being contacted by family members, for instance, while being at work or by coworkers, for instance, while not at work. Thus, we shed light on the gendered experience of the work–life interface (see also, e.g., Shockley et al., 2017).

Finally, our research has practical implications. Organizations may need to pay attention to potentially different needs of women and men when planning and managing work schedules. Still, since our study was conducted in a single country—Germany—it would be interesting to see to what extent our findings generalize to other cultures and contexts.

Women’s paychecks are typically smaller than men’s (Blau & Kahn, 2017; England et al., 2020), whereas men have steeper career trajectories (Badura et al., 2018; Lyness & Grotto, 2018). Such inequities are traced back, in part, to people’s working time arrangements (e.g., Corrigall & Konrad, 2007; Lips, 2013). In many countries, like the United States (Bureau of Labor Statistics, 2018), the United Kingdom (Office for National Statistics, 2020), as well as in our context, Germany (Federal Statistical Office of Germany, 2022), women work part-time more often than men. Conversely, men work overtime more often than women (e.g., Brereton, 1990; Van Echtelt et al., 2009; see also Blagoev & Schreyögg, 2019). Working fewer paid hours results in less income—in absolute terms and for each hour (Goldin, 2014; Ng & Feldman, 2008). For instance, Cha and Weeden (2014) as well as Weeden et al. (2016) showed that women (mothers, in particular) earn less than men, per hour, as men more often work overtime (an enduring difference). In turn, long hours are compensated particularly—even increasingly—well (Cha & Weeden, 2014; Weeden et al., 2016). Working fewer hours for, and at, one’s organization can also slow down people’s career advancement (Bourdeau et al., 2019; Perrigino et al., 2018), as people can be seen as deviating from an “ideal worker norm” (J. C. Williams et al., 2013, 2016). Altogether, women typically work fewer paid hours than men (e.g., Sayer et al., 2009; Shockley et al., 2017), leading to inequities (e.g., Eagly & Carli, 2007). But why?

We utilize SRT (Eagly, 1987) to shed light on the gendered “social processes” (Gerstel & Clawson, 2018) that may give rise to people’s working time arrangements. SRT may help to account for gender differences regarding both part-time work and overtime, based on a parsimonious set of propositions. A first main construct in SRT is the division of labor, which “is evident in the specific activities performed by men and women in a society” (Wood & Eagly, 2012, p. 57). In many individualistic or Western cultures, women as compared to men are often more in charge of caregiving, but less in “breadwinning” (e.g., Eagly et al., 2020; Eagly & Wood, 2012).1 These two components of the division of labor are interrelated: Caregiving typically leaves fewer hours available for paid work (e.g., Feldman, 1990), and vice versa (Ng & Feldman, 2008). Another component is the “segregation” into different occupations, both “vertically” and “horizontally” (e.g., Eagly et al., 2020; England et al., 2020; see also Cha & Weeden, 2014). Men as compared to women are still overrepresented in top leadership roles (for a current review, see Son Hing et al., 2023) as well as in some STEM fields (e.g., Cheryan et al., 2017; Schmader, 2023), but underrepresented in the fields of health care or elementary education (e.g., Block et al., 2019; Croft et al., 2015). Altogether, as per many societies’ division of labor, women typically are more involved in caregiving than men (e.g., Croft et al., 2015; Fisher & Ryan, 2021), and they also have different occupations. In turn, especially women’s greater involvement in caregiving helps to explain their fewer paid hours—a prominent notion in the literature (e.g., Cha & Weeden, 2014; Collins et al., 2021; Eagly & Carli, 2007; Jacobs & Gerson, 2004).

When depicting this prominent notion as a conceptual model, gender represents a predictor of the number of paid hours (or the likelihood of working part-time), and caregiving represents a mediator in this relationship. Yet, if we were to fully understand gender differences, we need to know whether women and men with a similar working time arrangement also differ, for example, as to why they work a particular number of hours (e.g., Burchell et al., 2008; Bureau of Labor Statistics, 2018). This is a different question not captured in the conventional model: For instance, whereas working part-time conventionally represents a (dependent) variable, examining gender differences among part-time workers means that part-time status is used as a selection criterion and held constant. As such, we are now dealing with a different population, as it is meaningfully restricted, and it may or may not be adequate to generalize the available knowledge to it.

Of course, since we know that women work part-time more often than men because of their greater caregiving responsibilities (see above; e.g., Gerstel & Clawson, 2018), women are indeed likely to report these responsibilities as their impetus for working part-time. However, if caregiving really is a strong cause of part-time work, one could reasonably expect those relatively few men who do work part-time to also report caregiving as their underlying reason. Then, we would not expect female and male part-time workers to differ, unless we invoke another theoretical argument (which we can derive from SRT; see below). In summary, knowing why women work part-time more often than men does not provide an explanation as to why (or why not) men work part-time. In the same vein, if we focus solely on a population of overtime workers and assume that aspirations to make more money or advance one’s career are underlying reasons (e.g., Berdahl et al., 2018), we may again assume that women working overtime match their male counterparts in why they work overtime. Again, in summary, knowing why men work overtime more often than women does not explain why women sometimes also work overtime.

Yet, in fact, the gendered division of labor has another consequence relevant for the prediction of gender differences among part-time and overtime workers: the development of gender roles (e.g., Eagly & Steffen, 1984), or “those shared expectations (about appropriate qualities and behaviors) that apply to individuals on the basis of their socially identified gender” (Eagly, 1987, p. 12). Women are often casted as communal (e.g., nurturing) and men as agentic (e.g., ambitious; Eagly et al., 2020). Gender roles trigger several social processes that ultimately produce a vicious cycle (Eagly & Koenig, 2021): As gender roles describe what women and men purportedly “are like,” people are seen as adept at activities that suit their gender role but less so for activities that do not (Eagly & Karau, 2002; Eagly & Koenig, 2021). As gender roles are injunctive (i.e., normative; Eagly & Karau, 2002), they also carry the risk of backlash for “role–deviators” (i.e., a negative evaluation by other people; see Rudman & Fairchild, 2004; M. J. Williams & Tiedens, 2016). Finally, gender roles can affect attitudes and personalities (Hsu et al., 2021) to the extent to which “people adopt them as gender identities” (Wood & Eagly, 2012, p. 81).

Hence, people often show certain behaviors to avoid backlash (e.g., Amanatullah & Morris, 2010; Rudman, Moss-Racusin, Glick, et al., 2012) and act in line with their gender identity (e.g., Badura et al., 2018; Corrigall & Konrad, 2007; Judge & Livingston, 2008). For instance, a woman may work reduced paid hours to pursue more communal activities following the gender roles she was socialized with (e.g., Croft et al., 2014; Weeden et al., 2016). Altogether, gender roles may help to explain not only gender differences in the number of paid hours (e.g., Antonie et al., 2020; Cha & Weeden, 2014; Eagly & Carli, 2007), but also, crucially, gender differences in why certain hours are worked. This dual ability is another key reason for us to utilize SRT, from which we derive our specific predictions in what follows.

Matching the social expectation that especially women be communal (e.g., Eagly, 1987; Eagly et al., 2020), women should be likely to work part-time to engage in caregiving (e.g., Antonie et al., 2020; Feldman, 1990), as we already noted. Working part-time to engage in caregiving is a decision in line with a communal gender identity, and women should avert the risk to incur backlash for role deviations when doing so. By contrast, being communal is not essential to men’s gender role (Eagly et al., 2020). In fact, men who aspire to be a primary caregiver and to work “non-traditional” hours (e.g., part-time or even just with a flexible schedule) can be subject to negative social evaluations (e.g., Rudman & Mescher, 2013; Vandello et al., 2013; see also Gerstel & Clawson, 2018). Therefore, meeting caregiving responsibilities may be a more potent driver for women (vs. men) to work part-time. Conversely, following the expectation to be agentic, especially men may aim to be career-oriented (e.g., Eagly et al., 2020; Rudman, Moss-Racusin, Phelan, et al., 2012). As such, men might typically not self-select into part-time work (see above), as doing so would be at odds with just that expectation (Kjeldstad & Nymoen, 2012; Ng & Feldman, 2008; Weeden et al., 2016). Rather, to the extent that people follow their gender roles, men (more than women) may work part-time when they cannot find a full-time job (e.g., Feldman, 1990; Moulin, 2004; Rosenfeld & Birkelund, 1995; see also Rudman & Mescher, 2013; Vandello et al., 2013).2 Therefore:

Hypothesis 1a: Among people who work part time, women are more likely than men to do so because of personal or family obligations (e.g., caretaking activities).

Hypothesis 1b: Among people who work part time, men are more likely than women to do so because they were unable to find a full-time activity.

When it comes to overtime (e.g., Brereton, 1990; van der Lippe et al., 2006; Van Echtelt et al., 2009), following the expectation that especially men be agentic breadwinners (e.g., career-oriented and ambitious; Rudman, Moss-Racusin, Phelan, et al., 2012), men (more than women) should work overtime to advance their careers and to attain additional income (e.g., Berdahl et al., 2018; Lott & Chung, 2016). By climbing up the career ladder and bolstering their paychecks, men can signal that they “put work first” (Berdahl et al., 2018, p. 433)—actions they should value themselves when they conceive of themselves as agentic (e.g., Hsu et al., 2021).

Conversely, women, in particular, are expected to be helpful and cooperative (i.e., communal; e.g., Eagly et al., 2020; Rudman, Moss-Racusin, Phelan, et al., 2012). Therefore, women (more than men) likely work overtime because they “step in” for colleagues (Heilman & Chen, 2005). To the best of our knowledge, this represents a novel prediction, as enabled by SRT, that also highlights how people’s working time arrangements are embedded in a “web of time”: As Gerstel and Clawson (2018, p. 78) noted, “a change in one person’s schedule [often] cascades to others.” In turn, as women are expected to be supportive (Rudman, Moss-Racusin, Phelan, et al., 2012), people may judge women (more than men) harshly if they do not step in and support their coworkers (i.e., backlash represents a gendered social process; M. J. Williams & Tiedens, 2016). As such, coworkers can meaningfully influence people’s working times (Gerstel & Clawson, 2018).

Moreover, women (more than men) may work overtime simply because doing so is mandated by their employer. As women are expected to be cooperative, they may incur backlash and be perceived as “pushy” or “demanding” (Amanatullah & Morris, 2010; Amanatullah & Tinsley, 2013) if they question their employers’ overtime mandates. Moreover, as the genders are often segregated into different occupations (e.g., Eagly et al., 2020), women (more than men) may work in jobs that simply do not come with a say in the determination of work schedules (e.g., Pech et al., 2021). Indeed, an important notion in the extant literature is that women, as a group, may have less control over their working time arrangements then men do (Gerstel & Clawson, 2018). Therefore:

Hypothesis 2a: Among people who work overtime, men are more likely than women to do so because they want to advance professionally or because they want to attain additional income.

Hypothesis 2b: Among people who work overtime, women are more likely than men to do so because it is mandated or because they have to step in for colleagues.

In a “web of time” (Gerstel & Clawson, 2018, p. 77), people’s time spent at the workplace and their time spend at home can mutually influence each other. Hence, to further capture such web of time–effects, we also consider people’s work–life interface. Following our theoretical account, if women and men differ in how many paid hours they work, and why, they may also have different attitudes and experiences reflecting these arrangements that may once again follow their gender roles. The internalization of gender roles results in “gender identities” (e.g., Wood & Eagly, 2012). Hence, men often see themselves as more agentic (e.g., career-oriented or hard-working; Rudman, Moss-Racusin, Phelan, et al., 2012) than women (Hsu et al., 2021). As a result, men (as compared to women) may be more inclined to “put work first” (Berdahl et al., 2018, p. 433). If true, men may deem being available for work-related issues, even during leisure time, as more acceptable than women (see also the “asymmetric permeability perspective” in Boundary Theory; Shockley et al., 2017). We hypothesize:

Hypothesis 3: Men as compared to women are more likely to deem being available for work-related issues during leisure time as acceptable.

Finally, people can also be connected to each other in a “web of time” (Gerstel & Clawson, 2018, p. 77). Therefore, we also consider how women and men experience others to react to them. This notion is particularly important from a gender perspective because gender roles reflect social processes: They influence not only how women and men themselves behave (actor–perspective), but also how others treat them and expect them to behave (perceiver–perspective; Rudman & Fairchild, 2004). As men are casted as agentic (e.g., Rudman, Moss-Racusin, Phelan, et al., 2012), people should expect men to be interested in, and available for, workplace matters, even when they are presently not at work. Conversely, as women are casted as communal (e.g., Eagly et al., 2020), people should expect them to be interested in, and available for, family matters even while being at work. Notably, Shockley et al. (2017) observed that men’s greater number of paid hours, and also their weaker “family boundaries,” produced greater work–interference–with–family (WIF; still, the overall gender difference in WIF was tiny, if not absent in this meta-analysis). Similarly, women’s greater number of “family hours” produced greater family–interference–with–work (FIW; Shockley et al., 2017). Altogether, men should be more likely to report being contacted by others from work in their private life, whereas women should be more likely to report being contacted by family or friends while being at work (as another, divergent process, Shockley et al.’s 2017, observed a stronger “work boundary” among women to also mediate gender differences in FIW).

Hypothesis 4a: Men are more likely than women to be contacted by others from work in their private life.

Hypothesis 4b: Women are more likely than men to be contacted by family/friends while being at work.

In many societies, men are typically more likely to be the breadwinner and women are more likely to be the caregiver in the division of labor (e.g., Eagly et al., 2020; Eagly & Carli, 2007). Yet, women and men should be most likely to pursue these differing activities when they have a partner and live together: Under such living conditions, people can decide how any labor could be divided among them. By contrast, when people are single and live alone, they typically have to earn income by themselves and are, by definition, the sole breadwinner in a household. Moreover, “marriage often precipitates adherence to—or at least recognition of—gendered norms in relationships” (Livingston, 2014, p. 950; see also Tinsley et al., 2015). In turn, if gender roles have a stronger impact among married and cohabiting people, the effects described in our hypotheses should also become stronger (see also Corrigall & Konrad, 2007; Kjeldstad & Nymoen, 2012). Altogether, we hypothesize:

Hypothesis 5: The effects described in the above hypotheses are moderated by (a) people’s marital status and (b) their living situation in terms of cohabiting with their partner, such that the effects are stronger for (a) married people and (b) people living together with their partner in a household.

Open Science Practices

For our research, we used the BAuA–Working Time Survey (BAuA–WTS), which has been conducted by the Federal Institute for Occupational Safety and Health in Germany. The BAuA–WTS has been used in different publications: https://www.baua.de/DE/Angebote/Forschungsdaten/pdf/Publikationsliste-AZB.pdf?__blob=publicationFile&v=2. Information on our survey waves, including the questionnaires, can be retrieved here: https​://​doi.​org​/10.​219​34/b​aua:be​richt​20​16​0812; https​://​doi.​org​/10.​219​34/b​​aua:​be​richt​20​20​0728. The datasets are available here: https​://​doi.​org​/10.​219​34/b​aua.az​b15.s​uf​.2​; https​://​doi.​org​/10.​486​97/b​aua.az​b19.s​uf​.2​. Our analysis code can be retrieved here: https​://​osf.​io/​35k8​m/?​​view​_only=​a7​b​3​d​8​c​a​f​2e2​4​3​2​a​b​7​d​621​14b41​fa​56a. Considering the templates from “AsPredicted” as well as Mertens and Krypotos (2019), we preregistered our study here: https​://​osf.​io/​gtby​m/?​view​_only=​56b17​cb​03​e9f46cfbb​c28864​6c355cf8. Some of the current authors conducted the BAuA–WTS and examined descriptive, but no inferential, statistics concerning the questions examined in the current paper. Thus, some of the authors were intimately familiar with the dataset and had knowledge of parts of the data patterns. We report all exclusions and measures as used for our research (Simmons et al., 2012).

Survey Design and Sample

The BAuA–WTS is a multi-wave survey on people’s working time arrangements and related experiences, including their working time, work–life interface, work design, and well-being (Wöhrmann et al., 2021). Ethical approval was obtained from the BAuA ethics committee (12/19/2019), and all participants provided informed consent.

The survey is conducted biannually, starting in 2015. Data were collected using computer-assisted telephone interviews, which lasted about 35–40 minutes and were conducted by professional interviewers of a social science research institute (Wöhrmann et al., 2021). Participants were recruited within a dual frame approach using a random sample of landline and mobile phone numbers, thereby including employees from a range of professions and branches. Employees aged 15 years and older who worked at least ten hours per week were eligible to participate in the survey. For the current research, we generally excluded participants who were self-employed, family workers, and those who had already reached the statutory retirement age of 66 years (in line with our preregistration).

Not all items relevant for our research were administered in all waves of the BAuA–WTS. Thus, we used the waves conducted in 2015 and 2019. For analyses based on full data from 2015, a total of n = 8,875 women and n = 8,995 men (N = 17,870) were included. For the sample sizes in individual analyses or for selected descriptive statistics, see also our tables as well as our notes in the main text. Participants from 2015 were M = 45.93 years old (SD = 10.74; range 16–65). On average, they had an actual working time of 38.99 hours per week (SD = 10.89; N = 17,711), and 74.20% (of N = 17,785) worked full-time. Of the participants, 48.30% (of N = 17,490) wished to work fewer hours than they currently did, 39.83% wished to work the number of hours they currently worked, and 11.86% wished to work more hours. Furthermore, only few participants (14.11% of N = 17,814) reported that their working hours regularly change, and only a small number of them (6.08% of N = 17,769) reported to work “on call” at least once per month. Participants’ occupational branches were agriculture, forestry, and fisheries (0.8%); manufacturing industry, excluding construction (25.6%); construction (4.4%); trade, transport, and hospitality (19.2%); finance, leasing, and entrepreneurial services (13.3%); public and private services (36.7%). A minority of 32.58% of the participants (of N = 17,194) reported that their organization had a workers’ council. Finally, a total of 65.35% (of N = 17,843) reported to have a child younger than 18 years in their household.

For our analyses based on full data from 2019, a total of n = 3,996 women and n = 4,362 men (N = 8,358) were included. Participants from 2019 were M = 49.72 years old (SD = 9.84; range 16–65). On average, they had an actual working time of 38.93 hours per week (SD = 9.83; N = 8,238), and 73.12% (of N = 8,340) worked full-time. Of the participants, 57.57% (of N = 8,168) wished to work fewer hours than they currently did, 34.51% wished to work the number of hours they currently worked, and 7.92% wished to work more hours. Furthermore, again only few participants (12.35% of N = 8,347) reported that their working hours regularly change, and a small number of participants (3.51% of N = 8,342) reported to work “on call” at least once per month. Participants’ occupational branches were agriculture, forestry, and fisheries (0.8%); manufacturing industry, excluding construction (23.5%); construction (4.1%); trade, transport, and hospitality (17.7%); finance, leasing, and entrepreneurial services (14.7%); public and private services (39.3%). A minority of 29.96% of the participants (of N = 8,103) reported that their organization had a workers’ council. Finally, a total of 68.21% (of N = 8,354) reported to have a child younger than 18 years in their household.

A sensitivity power analysis, run with G*Power (Faul et al., 2007), showed that, for analyses based on full data from 2015 (N = 17,870), gender differences with effect sizes of d ≥ 0.05 could be observed with 95% power (α = .05; two-tailed). Similarly, for analyses based on full data from 2019 (N = 8,358), effect sizes of d ≥ 0.08 could be observed with 95% power—altogether suggesting high power.

National Context: Germany

As the BAuA–WTS has been conducted in Germany, it is important to elaborate on the specifics of this country as they relate to basic gender dynamics, childcare services, and people’s labor rights. To start, gender inequities (e.g., in pay) are stubbornly present in Germany, as they are in many other countries (Federal Statistical Office of Germany, 2023a; OECD, 2023a). Moreover, again similar to gender roles observed in other Western, industrialized countries, such as the United States (for a meta-analysis, see Eagly et al., 2020), women in Germany are typically expected to be less agentic but more communal (or “warm”) than men in Germany (Bosson et al., 2022; Bruckmüller et al., 2012; Ebert et al., 2014; for a recent divergent finding regarding communion, see Obioma et al., 2022). Interestingly, recent research on the extent to which Germans describe themselves, explicitly, as agentic or communal provides only mixed evidence for gender gaps (Kosakowska-Berezecka et al., 2022; Obioma et al., 2022; Troche & Rammsayer, 2011). Yet, as is observed for most countries and underscoring the expectation that women be communal, German women aim to be longer on parental leave than German men, although this gender gap is smaller in comparison to several other countries (e.g., the United Kingdom), though still slightly greater in comparison to the United States (Olsson et al., 2023). Similarly, Germany’s Federal Statistical Office reports that German mothers, much more than men with or without children as well as childless women, work part-time (Federal Statistical Office of Germany, 2022)—a notable tendency that we also observed in our data (see our Results). Furthermore, this gender gap not only has remained remarkably stable over the last decade, the Federal Statistical Office of Germany (2022) even reports, “The percentage of mothers of younger children working part-time in Germany is double the relevant average in the EU” (for an older comparison of European countries, see Lewis et al., 2008).

These numbers certainly have to do with the availability of childcare services. The Federal Statistical Office of Germany (2023b) reports that “only” about a third of all children younger than three are enrolled in day care centers, to which they would have a right (Make it in Germany, n.d.-a; this web site is provided by Germany’s Federal Government). Older preschool children likewise have a right to be, and most typically are, at a “Kindergarten,” yet only “sometimes also in the afternoon,” and for most families not without costs (Make it in Germany, n.d.-a). This may explain why at least one parent—typically the mother—works part-time (Beham et al., 2019). Still, childcare costs are comparatively low in Germany (OECD, 2023b).

German workers also enjoy a number of notable labor rights: For instance, parents can go on parental leave for up to three years after a child is born (Make it in Germany, n.d.-b). Parents’ employment typically cannot be terminated while they are on leave, and they can resume their previous employment, at the same terms, upon their return (Make it in Germany, n.d.-b). Still, parents are not paid by their employer while on leave, but they “can apply for parental allowance, a form of financial support from the government” (Make it in Germany, n.d.-b). Moreover, parents can work part-time while they are on leave, which may again explain the popularity of this working time arrangement (among women). As such, these labor rights are again relevant to the interpretation of any findings obtained from our German participants, and future research is needed to examine their generalizability.

Main Study Variables

We focused on the following questions as used in the BAuA–WTS: To examine Hypotheses 1a and 1b, we used the question: “For what reason do you work part time? If there are multiple reasons, please tell me what the main reason is.” Participants were provided with multiple response options, yet we only examined the following options: (1) “because of other personal or family obligations, such as taking care of children or other people in need of care, a longer working time is not possible” (for Hypothesis 1a; selected by 55.12% of the N = 4,586 available participants) and (2) “a full time activity was not available” (for Hypothesis 1b; selected by 10.23%). The answers were coded as 1 = “yes,” if a participant indicated that a reason applied to them, and as 0 = “no,” if not. This question regarding part-time work was asked only in the first wave of the BAuA–WTS. Thus, to examine Hypotheses 1a and 1b, we analyzed data from 2015. Participants were considered part-time workers when they actually worked fewer than 35 hours per week. This number of hours is a common cutoff point for differences between part-time and full-time employees (e.g., Farber, 2017; Messenger, 2018).

To examine Hypotheses 2a and 2b, we used the question: “Why are you working longer than contractually agreed?” Participants were again provided with multiple response options, yet we examined the following options: (1) “to attain additional income” and (2) “to advance professionally” (both for Hypothesis 2a; 11.12% of the N = 4,614 available participants selected “income”; 8.09% of the N = 4,539 selected “advancement”), as well as (3) “because it was mandated” and (4) “because you often have to step in for colleagues” (both for Hypothesis 2b; 20.60% of the N = 4,596 selected “mandated”; 35.25% of the N = 4,610 selected “step in”). Participants could indicate one or more reasons for working overtime (if participants indicated multiple reasons, they were asked to indicate their main reason). The answers were again coded as 1 = “yes,” if a participant indicated that a reason applied to them, and as 0 = “no,” if not. Unlike the question dealing with part-time work, participants were asked about their reason(s) for working overtime in multiple waves of the BAuA–WTS. Yet, only the third wave from 2019 included all of the theoretically relevant response options. Thus, to examine Hypotheses 2a and 2b, we analyzed data from 2019. People who worked at least two additional hours per week were considered overtime workers. The limit of 2 hours makes it possible to exclude employees with occasional and minor overtime (e.g. in flextime arrangements).

We examined Hypothesis 3 using this question: “How often do you personally deem it acceptable to be available for work-related issues during leisure time?” Participants could respond with 4 = “often,” 3 = “sometimes,” 2 = “rarely,” or 1 = “never” (M = 2.31; SD = 0.88; N = 17,810). For the sake of consistency, we used data from 2015.

We examined Hypotheses 4a and 4b using the questions (emphases added): “How often are you being contacted by employees, colleagues, supervisors, or customers in your private life” (M = 2.28; SD = 0.91; N = 17,850) and “How often are you being contacted by family, friends, or other people due to reasons unrelated to work while being at work?” (M = 2.19; SD = 0.79; N = 17,858). Participants could respond with 4 = “often,” 3 = “sometimes,” 2 = “rarely,” or 1 = “never.” For the sake of consistency, we used data from 2015.

Finally, to examine Hypothesis 5, we considered participants’ marital status (e.g., Kjeldstad & Nymoen, 2012; Ng & Feldman, 2008), coded as 0 = “all others” and 1 = “married” (59.14% of the N = 17,841 available participants from 2015 were married; for 2019, 61.67% of N = 8,347). Likewise, we considered a dummy-coded variable indicating whether or not people were living together with a partner in a household, coded as 1 = “yes” and 2 = “no” (28.66% of the N = 17,839 available participants from 2015 did not live together with a partner; for 2019, 27.97% of N = 8,351). Our main predictor is participants’ gender, coded as 1 = “men” and 2 = “women” (for descriptives by gender, see Table 1).

Control Variables

We controlled for participants’ (1) age (see above); (2) occupational position, using two dummy-coded variables: 0 = “blue-collar worker” as the reference category (14.43% of all N = 17,870 participants from 2015; 11.57% of all N = 8,358 participants from 2019), 1 = “white-collar worker” (76.68% for 2015; 78.02% for 2019), and 2 = “public official” (8.90% for 2015; 10.41% for 2019); (3) company size, using two dummy-coded variables: 1 = “fewer than 50 employees” as the reference category (37.12% of the available N = 17,428 participants from 2015; 32.37% of the available N = 8,235 participants from 2019), 2 = “50–249 employees” (26.86% for 2015; 26.44% for 2019), and 3 = “250 or more employees” (36.02% for 2015; 41.19% for 2019); and (4) highest level of education, based on the International Standard Classification of Education (ISCED), using one variable: 0 = “middle or low”(52.52% of the N = 17,829 available participants from 2015; 43.52% of the N = 8,318 available participants from 2019), and 1 = “high” (47.48% for 2015; 56.48% for 2019).

In our preregistration, we planned to use the General Linear Model for our analyses. Specifically, we estimated binary logistic regressions for Hypotheses 1a, 1b, 2a, and 2b, and linear metric models (i.e., OLS-regressions) for Hypotheses 3, 4a, and 4b.

Preliminary Analyses

We examined gender differences in the number of paid hours as well as the likelihoods of working part-time and overtime for the 2015 BAuA–WTS. Consistent with prior findings, women (M = 34.63; SD = 11.02) worked fewer hours than men (M = 43.26; SD = 8.88), t(17,709) = 57.52, p < .001, d = 0.86, 95% CI [0.83, 0.90]. Women were also more likely to work part-time, χ²(1) = 3,084.85, p < .001, r = .41, but less likely to work overtime (more than two hours per week), χ²(1) = 280.00, p < .001, r = -.13, than men (see also our section on the national context above).

Table 1.
Descriptive Statistics for Dependent and Control Variables, Separately for Women and Men
 MenWomen
  % or M (SD% or M (SD
2015     
Part-time: Personal or family obligations (H1a) 23.54 59.11 
Part-time: Full-time not available (H1b) 11.48 10.07 
Acceptable to be available during leisure time (H3) 2.38 (0.89) 2.24 (0.85) 
Being contacted in private life (H4a) 2.26 (0.89) 2.29 (0.93) 
Being contacted while at work (H4b) 2.27 (0.78) 2.11(0.79) 
      
Working full-time (≥ 35 hours per week) 94.25 53.83 
Actual working time (hours) 43.27 (8.88) 34.63 (11.02) 
      
Age (years) 45.42 (10.98) 46.44 (10.47) 
Blue-collar 20.84 7.92 
White-collar 70.61 82.83 
Public official 8.55 9.25 
Company size < 50 employees 30.20 44.30 
Company size 50–249 employees 27.65 26.04 
Company size ≥ 250 employees 42.16 29.65 
Education (high) 50.56 44.36 
2019     
Overtime: Advance professionally (H2a) 7.71 8.56 
Overtime: Income (H2a) 14.11 7.33 
Overtime: Mandated (H2b) 19.50 22.01 
Overtime: Step in (H2b) 26.75 46.04 
      
Working full-time (≥ 35 hours per week) 93.57 50.75 
Actual working time (hours) 42.64 (7.87) 34.86 (10.15) 
      
Age (years) 49.04 (10.17) 50.46 (9.42) 
Blue-collar 16.83 5.83 
White-collar 73.75 82.68 
Public official 9.42 11.49 
Company size < 50 employees 26.57 38.79 
Company size 50–249 employees 26.85 25.98 
Company size ≥ 250 employees 46.58 35.23 
Education (high) 59.64 53.03 
 MenWomen
  % or M (SD% or M (SD
2015     
Part-time: Personal or family obligations (H1a) 23.54 59.11 
Part-time: Full-time not available (H1b) 11.48 10.07 
Acceptable to be available during leisure time (H3) 2.38 (0.89) 2.24 (0.85) 
Being contacted in private life (H4a) 2.26 (0.89) 2.29 (0.93) 
Being contacted while at work (H4b) 2.27 (0.78) 2.11(0.79) 
      
Working full-time (≥ 35 hours per week) 94.25 53.83 
Actual working time (hours) 43.27 (8.88) 34.63 (11.02) 
      
Age (years) 45.42 (10.98) 46.44 (10.47) 
Blue-collar 20.84 7.92 
White-collar 70.61 82.83 
Public official 8.55 9.25 
Company size < 50 employees 30.20 44.30 
Company size 50–249 employees 27.65 26.04 
Company size ≥ 250 employees 42.16 29.65 
Education (high) 50.56 44.36 
2019     
Overtime: Advance professionally (H2a) 7.71 8.56 
Overtime: Income (H2a) 14.11 7.33 
Overtime: Mandated (H2b) 19.50 22.01 
Overtime: Step in (H2b) 26.75 46.04 
      
Working full-time (≥ 35 hours per week) 93.57 50.75 
Actual working time (hours) 42.64 (7.87) 34.86 (10.15) 
      
Age (years) 49.04 (10.17) 50.46 (9.42) 
Blue-collar 16.83 5.83 
White-collar 73.75 82.68 
Public official 9.42 11.49 
Company size < 50 employees 26.57 38.79 
Company size 50–249 employees 26.85 25.98 
Company size ≥ 250 employees 46.58 35.23 
Education (high) 59.64 53.03 

Note. For the four variables regarding overtime, in particular, only participants who worked at least two additional hours per week were considered.

Part-Time

As preregistered, to examine Hypotheses 1a and 1b, we included only participants who worked part-time (i.e., fewer than 35 hours per week; n = 4,074 women; n = 515 men; N = 4,589; see also our section on the national context). The results are presented in Table 2. Women were more likely than men to work part-time due to personal or family obligations, in line with Hypothesis 1a. No overall gender difference was observed for the reason that a full-time activity was not available, which is not in line with Hypothesis 1b. The models including moderators (Table 2) indicated interaction effects for gender and marriage as well as gender and living together with a partner, for both “personal or family obligations” and “a full-time activity was not available.” Men were more likely to work part-time for personal or family obligations when they were married or lived with a partner (vs. not), whereas women were generally quite likely to work part-time for this reason (e.g., even when unmarried). Therefore, the gender difference was slightly smaller among participants who were married or lived together with a partner (inconsistent with Hypothesis 5).

Moreover, women who were married or those who lived together with a partner were less likely to work part-time because no full-time position was available as compared to other women, whereas marriage or living together with a partner did not make a difference here for men. As a result, among participants who were unmarried or did not live together with a partner, women were more likely than men to work part-time because no full-time position was available (see Allan et al., 2020; Pech et al., 2021), but the gender difference reversed among participants who were married or lived together with a partner, such that men were slightly more likely than women to work part-time because no full-time position was available.

Table 2.
Logistic Regression Results for Hypotheses 1a and 1b Concerning Part-Time Work
Personal or family obligations (0 = “no”; 1 = “yes”)A full-time activity was not available (0 = “no”; 1 = “yes”)
 b (SEb (SEb (SEb (SEb (SEb (SE
 OR [95% CI] OR [95% CI] OR [95% CI] OR [95% CI] OR [95% CI] OR [95% CI] 
Intercept -0.79*** (0.22) -1.24*** (0.25) -1.77*** (0.30) -2.16*** (0.31) -2.39*** (0.32) -2.32*** (0.34) 
Female (vs. male) 1.53*** (0.12) 2.23*** (0.20) 2.52*** (0.27) -0.09 (0.16) 0.51* (0.22) 0.64* (0.26) 
4.61*** [3.64, 5.82] 9.32*** [6.25, 13.92] 12.49*** [7.43, 21.00] 0.91 [0.67, 1.25] 1.67* [1.08, 2.57] 1.90* [1.14, 3.18] 
Married (vs. other)  2.24*** (0.25)   0.14 (0.30)  
 9.44*** [5.83, 15.28]   1.15 [0.64, 2.05]  
Female × married  -1.21*** (0.25)   -1.01** (0.32)  
 0.30*** [0.18, 0.49]   0.36** [0.20, 0.67]  
Living with partner (vs. not)   2.08*** (0.28)   0.20 (0.31) 
  7.97*** [4.56, 13.92]   1.22 [0.67, 2.22] 
Female × living with partner   -1.23*** (0.29)   -1.09*** (0.33) 
  0.29*** [0.16, 0.52]   0.34*** [0.18, 0.64] 
Control variables Age (in years); occupational position (blue-collar; white-collar; public official); company size (< 50; 50–249; ≥ 250 employees); education (low/middle; high) 
n 4399 4399 4399 4403 4403 4403 
Pseudo R2 0.116 0.120 0.104 0.048 0.069 0.067 
AIC 5364.04 5350.59 5446.36 2749.69 2692.34 2692.93 
BIC 5427.93 5414.49 5510.26 2800.83 2756.25 2756.83 
Personal or family obligations (0 = “no”; 1 = “yes”)A full-time activity was not available (0 = “no”; 1 = “yes”)
 b (SEb (SEb (SEb (SEb (SEb (SE
 OR [95% CI] OR [95% CI] OR [95% CI] OR [95% CI] OR [95% CI] OR [95% CI] 
Intercept -0.79*** (0.22) -1.24*** (0.25) -1.77*** (0.30) -2.16*** (0.31) -2.39*** (0.32) -2.32*** (0.34) 
Female (vs. male) 1.53*** (0.12) 2.23*** (0.20) 2.52*** (0.27) -0.09 (0.16) 0.51* (0.22) 0.64* (0.26) 
4.61*** [3.64, 5.82] 9.32*** [6.25, 13.92] 12.49*** [7.43, 21.00] 0.91 [0.67, 1.25] 1.67* [1.08, 2.57] 1.90* [1.14, 3.18] 
Married (vs. other)  2.24*** (0.25)   0.14 (0.30)  
 9.44*** [5.83, 15.28]   1.15 [0.64, 2.05]  
Female × married  -1.21*** (0.25)   -1.01** (0.32)  
 0.30*** [0.18, 0.49]   0.36** [0.20, 0.67]  
Living with partner (vs. not)   2.08*** (0.28)   0.20 (0.31) 
  7.97*** [4.56, 13.92]   1.22 [0.67, 2.22] 
Female × living with partner   -1.23*** (0.29)   -1.09*** (0.33) 
  0.29*** [0.16, 0.52]   0.34*** [0.18, 0.64] 
Control variables Age (in years); occupational position (blue-collar; white-collar; public official); company size (< 50; 50–249; ≥ 250 employees); education (low/middle; high) 
n 4399 4399 4399 4403 4403 4403 
Pseudo R2 0.116 0.120 0.104 0.048 0.069 0.067 
AIC 5364.04 5350.59 5446.36 2749.69 2692.34 2692.93 
BIC 5427.93 5414.49 5510.26 2800.83 2756.25 2756.83 

Note. Data from 2015. Only respondents in part-time work (i.e., < 35 hours per week) are included in this analysis. OR = Odds Ratio. * = p < .05; ** = p < .01; *** = p < .001

Overtime

To examine Hypotheses 2a and 2b, we included only participants who worked overtime (n = 2,182 women; n = 2,791 men), excluding participants who had less than two or “negative” overtime hours, as well as participants without contractually agreed-upon working hours. Please note that, in our preregistration, we planned to exclude “persons with zero or negative overtime hours and employees without contractually agreed working hours” (emphasis added). However, in the original survey, only participants with at least two overtime hours were asked the question that we analyzed.

No gender difference emerged for the reason “advancing professionally” (Table 3). Yet, women were significantly less likely than men to work overtime in order to attain additional income. These results partly support Hypothesis 2a. There were no interactions regarding these two reasons for working overtime (not in line with Hypothesis 5).

Women were also significantly more likely than men to work overtime because it was mandated (Table 4), in line with Hypothesis 2b. Yet, there were, again, no interactions regarding this reason, and the main effect for gender was no longer significant when the models included moderators (i.e., marriage and living together with a partner). Finally, women were more likely than men to work overtime to step in for colleagues, in line with Hypothesis 2b. Moreover, interactions emerged with regard to this reason: Marriage or living together with a partner did not play a role for women, whereas men were even less likely to work overtime for this reason when being married or living together with a partner. Therefore, the gender difference was slightly larger among participants who were married or lived together with a partner (consistent with Hypothesis 5).

Table 3.
Logistic Regression Results for Hypothesis 2a Concerning Overtime Work
Advance professionally (0 = “no”; 1 = “yes”)Attain additional income (0 = “no”; 1 = “yes”)
 b (SEb (SEb (SEb (SEb (SEb (SE
 OR [95% CI] OR [95% CI] OR [95% CI] OR [95% CI] OR [95% CI] OR [95% CI] 
Intercept -2.17*** (0.18) -2.03*** (0.20) -2.04*** (0.22) -0.16 (0.13) -0.09 (0.14) -0.14 (0.16) 
Female (vs. male) 0.12 (0.11) -0.04 (0.18) 0.01 (0.21) -0.65*** (0.11) -0.63*** (0.17) -0.54** (0.19) 
1.13 [0.91, 1.42] 0.96 [0.68, 1.36] 1.01 [0.67, 1.52] 0.52*** [0.42, 0.65] 0.53*** [0.39, 0.74] 0.58** [0.40, 0.85] 
Married (vs. other)  -0.24 (0.16)   -0.12 (0.13)  
 0.78 [0.58, 1.07]   0.89 [0.69, 1.13]  
Female × married  0.26 (0.23)   -0.06 (0.22)  
 1.29 [0.83, 2.02]   0.94 [0.61,1.44]  
Living with partner (vs. not)   -0.19 (0.17)   -0.03 (0.14) 
  0.83 [0.59, 1.17]   0.97 [0.74, 1.28] 
Female × living with partner   0.16 (0.25)   -0.19 (0.23) 
  1.17 [0.72, 1.89]   0.82 [0.52, 1.30] 
Control variables Age (in years); occupational position (blue-collar; white-collar; public official); company size (< 50; 50–249; ≥ 250 employees); education (low/middle; high) 
n 4474 4474 4474 4549 4549 4549 
Pseudo R2 0.004 0.005 0.004 0.109 0.110 0.109 
AIC 2514.84 2516.10 2517.34 2814.03 2815.58 2813.39 
BIC 2559.68 2573.76 2574.99 2858.99 2873.39 2871.19 
Advance professionally (0 = “no”; 1 = “yes”)Attain additional income (0 = “no”; 1 = “yes”)
 b (SEb (SEb (SEb (SEb (SEb (SE
 OR [95% CI] OR [95% CI] OR [95% CI] OR [95% CI] OR [95% CI] OR [95% CI] 
Intercept -2.17*** (0.18) -2.03*** (0.20) -2.04*** (0.22) -0.16 (0.13) -0.09 (0.14) -0.14 (0.16) 
Female (vs. male) 0.12 (0.11) -0.04 (0.18) 0.01 (0.21) -0.65*** (0.11) -0.63*** (0.17) -0.54** (0.19) 
1.13 [0.91, 1.42] 0.96 [0.68, 1.36] 1.01 [0.67, 1.52] 0.52*** [0.42, 0.65] 0.53*** [0.39, 0.74] 0.58** [0.40, 0.85] 
Married (vs. other)  -0.24 (0.16)   -0.12 (0.13)  
 0.78 [0.58, 1.07]   0.89 [0.69, 1.13]  
Female × married  0.26 (0.23)   -0.06 (0.22)  
 1.29 [0.83, 2.02]   0.94 [0.61,1.44]  
Living with partner (vs. not)   -0.19 (0.17)   -0.03 (0.14) 
  0.83 [0.59, 1.17]   0.97 [0.74, 1.28] 
Female × living with partner   0.16 (0.25)   -0.19 (0.23) 
  1.17 [0.72, 1.89]   0.82 [0.52, 1.30] 
Control variables Age (in years); occupational position (blue-collar; white-collar; public official); company size (< 50; 50–249; ≥ 250 employees); education (low/middle; high) 
n 4474 4474 4474 4549 4549 4549 
Pseudo R2 0.004 0.005 0.004 0.109 0.110 0.109 
AIC 2514.84 2516.10 2517.34 2814.03 2815.58 2813.39 
BIC 2559.68 2573.76 2574.99 2858.99 2873.39 2871.19 

Note. Data from 2019. Only respondents with at least two hours overtime per week are included in this analysis. OR = Odds Ratio. * = p < .05; ** = p < .01; *** = p < .001

Table 4.
Logistic Regression Results for Hypothesis 2b Concerning Overtime Work
Overtime mandated (0 = “no”; 1 = “yes”)Step in for colleagues (0 = “no”; 1 = “yes”)
 b (SEb (SEb (SEb (SEb (SEb (SE
 OR [95% CI] OR [95% CI] OR [95% CI] OR [95% CI] OR [95% CI] OR [95% CI] 
Intercept -0.03 (0.11) 0.13 (0.13) 0.19 (0.14) -0.87*** (0.12) -0.62*** (0.13) -0.70*** (0.14) 
Female (vs. male) 0.20* (0.08) 0.05 (0.12) 0.00 (0.14) 0.78*** (0.07) 0.53*** (0.10) 0.55*** (0.12) 
1.22* [1.05, 1.43] 1.05 [0.83, 1.34] 1.00 [0.76, 1.33] 2.19*** [1.93, 2.49] 1.70*** [1.39, 2.08] 1.73*** [1.35, 2.20] 
Married (vs. other)  -0.28** (0.11)   -0.43*** (0.09)  
 0.75** [0.61, 0.93]   0.65*** [0.54, 0.78]  
Female × married  0.22 (0.16)   0.38** (0.13)  
 1.25 [.92, 1.70]   1.46** [1.13, 1.89]  
Living with partner (vs. not)   -0.33** (0.12)   -0.25* (0.11) 
  0.72** [0.57, 0.91]   0.78* [0.63, 0.96] 
Female × living with partner   0.25 (0.17)   0.31* (0.14) 
  1.29 [0.93, 1.79]   1.36* [1.03, 1.81] 
Control variables Age (in years); occupational position (blue-collar; white-collar; public official); company size (< 50; 50–249; ≥ 250 employees); education (low/middle; high) 
n 4533 4533 4533 4545 4545 4545 
Pseudo R2 0.004 0.054 0.054 0.038 0.041 0.039 
AIC 2514.84 4360.72 4358.89 5691.40 5671.73 5686.17 
BIC 2559.68 4418.49 4416.67 5736.35 5729.52 5743.97 
Overtime mandated (0 = “no”; 1 = “yes”)Step in for colleagues (0 = “no”; 1 = “yes”)
 b (SEb (SEb (SEb (SEb (SEb (SE
 OR [95% CI] OR [95% CI] OR [95% CI] OR [95% CI] OR [95% CI] OR [95% CI] 
Intercept -0.03 (0.11) 0.13 (0.13) 0.19 (0.14) -0.87*** (0.12) -0.62*** (0.13) -0.70*** (0.14) 
Female (vs. male) 0.20* (0.08) 0.05 (0.12) 0.00 (0.14) 0.78*** (0.07) 0.53*** (0.10) 0.55*** (0.12) 
1.22* [1.05, 1.43] 1.05 [0.83, 1.34] 1.00 [0.76, 1.33] 2.19*** [1.93, 2.49] 1.70*** [1.39, 2.08] 1.73*** [1.35, 2.20] 
Married (vs. other)  -0.28** (0.11)   -0.43*** (0.09)  
 0.75** [0.61, 0.93]   0.65*** [0.54, 0.78]  
Female × married  0.22 (0.16)   0.38** (0.13)  
 1.25 [.92, 1.70]   1.46** [1.13, 1.89]  
Living with partner (vs. not)   -0.33** (0.12)   -0.25* (0.11) 
  0.72** [0.57, 0.91]   0.78* [0.63, 0.96] 
Female × living with partner   0.25 (0.17)   0.31* (0.14) 
  1.29 [0.93, 1.79]   1.36* [1.03, 1.81] 
Control variables Age (in years); occupational position (blue-collar; white-collar; public official); company size (< 50; 50–249; ≥ 250 employees); education (low/middle; high) 
n 4533 4533 4533 4545 4545 4545 
Pseudo R2 0.004 0.054 0.054 0.038 0.041 0.039 
AIC 2514.84 4360.72 4358.89 5691.40 5671.73 5686.17 
BIC 2559.68 4418.49 4416.67 5736.35 5729.52 5743.97 

Note. Data from 2019. Only respondents with at least two hours overtime per week are included in this analysis. OR = Odds Ratio. * = p < .05; ** = p < .01; *** = p < .001

Recall that participants were asked to indicate a main reason for working overtime, if they indicated multiple reasons. Thus, in our preregistration, we planned to examine not only participants’ responses while indicating one or more reasons (i.e., potential multiple choices)—as we did above—but also their main reason (if they had indicated multiple reasons). Yet, analyses of these “single choices” are unlikely to be conclusive, as the response options that were relevant for our research (see Method) were generally selected quite infrequently as a main reason (participants typically indicated their work could not be done within regular hours, which is another response option that was not relevant for our research). In line with these considerations, analyses based on the “single choices” revealed only one robust gender difference, such that women were more likely than men to work overtime to step in for colleagues. The results are reported in the Appendix in Tables A1–A3.

Work–Life Interface

In line with Hypothesis 3, women deemed it less acceptable to be available for work-related issues during leisure time than men (Table 5), but there were no interactions involving marriage or living together with a partner. Not in line with Hypothesis 4a, women and men did not differ in how often they were contacted by others in their private life (Table 6). Not in line with Hypothesis 4b, women (vs. men) were also less often contacted by family, friends, or others due to reasons unrelated to work while being at work (see Shockley et al.’s 2017, finding on women’s stronger “work boundaries”). There were two interactions regarding Hypothesis 4b: Marriage or living together with a partner did not play a role among men, but women were less often contacted when they were married or lived together with a partner (vs. not). Thus, among participants who were not married or lived together with a partner, women were slightly more often contacted than men, but the gender difference reversed among participants who were married or lived together with a partner, such that men were slightly more often contacted than women (not in line with Hypothesis 5).

Table 5.
Linear Regression Results for Hypothesis 3
Acceptable to be available for work-related issues during leisure time
(1 = “never”; 2 = “rarely”; 3 = “sometimes”; 4 = “often”)
 b (SEb (SEb (SE
 [95% CI] [95% CI] [95% CI] 
Intercept 2.27*** (0.02) 2.24*** (0.02) 2.24*** (0.02) 
Female (vs. male) -0.18*** (0.01) -0.15*** (0.02) -0.17*** (0.02) 
[-0.20, -0.15] [-0.19, -0.11] [-0.22, -0.12] 
Married (vs. other)  0.06** (0.02)  
 [0.02, 0.10]  
Female × married  -0.04 (0.03)  
 [-0.09, 0.01]  
Living with partner (vs. not)   0.04* (0.02) 
  [0.00, 0.09] 
Female × living with partner   -0.01 (0.03) 
  [-0.06, 0.05] 
Control variables Age (in years); occupational position (blue-collar; white-collar; public official); company size (< 50; 50–249; ≥ 250 employees); education (low/middle; high) 
n 17314 17314 17314 
R2 0.029 0.030 0.030 
AIC 44150.59 44078.32 44073.47 
BIC 44204.91 44148.15 44143.30 
Acceptable to be available for work-related issues during leisure time
(1 = “never”; 2 = “rarely”; 3 = “sometimes”; 4 = “often”)
 b (SEb (SEb (SE
 [95% CI] [95% CI] [95% CI] 
Intercept 2.27*** (0.02) 2.24*** (0.02) 2.24*** (0.02) 
Female (vs. male) -0.18*** (0.01) -0.15*** (0.02) -0.17*** (0.02) 
[-0.20, -0.15] [-0.19, -0.11] [-0.22, -0.12] 
Married (vs. other)  0.06** (0.02)  
 [0.02, 0.10]  
Female × married  -0.04 (0.03)  
 [-0.09, 0.01]  
Living with partner (vs. not)   0.04* (0.02) 
  [0.00, 0.09] 
Female × living with partner   -0.01 (0.03) 
  [-0.06, 0.05] 
Control variables Age (in years); occupational position (blue-collar; white-collar; public official); company size (< 50; 50–249; ≥ 250 employees); education (low/middle; high) 
n 17314 17314 17314 
R2 0.029 0.030 0.030 
AIC 44150.59 44078.32 44073.47 
BIC 44204.91 44148.15 44143.30 

Note. Data from 2015. * = p < .05; ** = p < .01; *** = p < .001

Table 6.
Linear Regression Results for Hypotheses 4a and 4b
Being contacted in private life
(1 = “never”; 2 = “rarely”; 3 = “sometimes”; 4 = “often”)
Being contacted while at work
(1 = “never”; 2 = “rarely”; 3 = “sometimes”; 4 = “often”)
 b (SEb (SEb (SEb (SEb (SEb (SE
 [95% CI] [95% CI] [95% CI] [95% CI] [95% CI] [95% CI] 
Intercept 2.20*** (0.02) 2.20*** (0.02) 2.19*** (0.03) 2.17*** (0.02) 2.16***
(0.02) 
2.11***
(0.02) 
Female (vs. male) -0.01 (0.01) 0.02 (0.02) 0.01 (0.03) -0.18*** (0.01) -0.13*** (0.02) -0.12*** (0.02) 
[-0.04, 0.02] [-0.03, 0.06] [-0.04, 0.06] [-0.20, -0.15] [-0.17, -0.10] [-0.16, -0.07] 
Married (vs. other)  0.01 (0.02)   0.02 (0.02)  
 [-0.03, 0.05]   [-0.01, 0.06]  
Female × married  -0.05 (0.03)   -0.07** (0.02)  
 [-0.10, 0.01]   [-0.12, -0.02]  
Living with partner (vs. not)   0.02 (0.02)   0.09*** (0.02) 
  [-0.03, 0.06]   [0.06, 0.13] 
Female × living with partner   -0.03 (0.03)   -0.08** (0.03) 
  [-0.09, 0.03]   [-0.13, -0.03] 
Control variables Age (in years); occupational position (blue-collar; white-collar; public official); company size (< 50; 50–249; ≥ 250 employees); education (low/middle; high) 
n 17350 17350 17350 17357 17357 17357 
R2 0.020 0.020 0.020 0.016 0.017 0.016 
AIC 45686.33 45609.68 45605.47 40733.49 40668.36 40641.67 
BIC 45740.67. 45679.53 45675.32 40787.84 40738.21 40711.53 
Being contacted in private life
(1 = “never”; 2 = “rarely”; 3 = “sometimes”; 4 = “often”)
Being contacted while at work
(1 = “never”; 2 = “rarely”; 3 = “sometimes”; 4 = “often”)
 b (SEb (SEb (SEb (SEb (SEb (SE
 [95% CI] [95% CI] [95% CI] [95% CI] [95% CI] [95% CI] 
Intercept 2.20*** (0.02) 2.20*** (0.02) 2.19*** (0.03) 2.17*** (0.02) 2.16***
(0.02) 
2.11***
(0.02) 
Female (vs. male) -0.01 (0.01) 0.02 (0.02) 0.01 (0.03) -0.18*** (0.01) -0.13*** (0.02) -0.12*** (0.02) 
[-0.04, 0.02] [-0.03, 0.06] [-0.04, 0.06] [-0.20, -0.15] [-0.17, -0.10] [-0.16, -0.07] 
Married (vs. other)  0.01 (0.02)   0.02 (0.02)  
 [-0.03, 0.05]   [-0.01, 0.06]  
Female × married  -0.05 (0.03)   -0.07** (0.02)  
 [-0.10, 0.01]   [-0.12, -0.02]  
Living with partner (vs. not)   0.02 (0.02)   0.09*** (0.02) 
  [-0.03, 0.06]   [0.06, 0.13] 
Female × living with partner   -0.03 (0.03)   -0.08** (0.03) 
  [-0.09, 0.03]   [-0.13, -0.03] 
Control variables Age (in years); occupational position (blue-collar; white-collar; public official); company size (< 50; 50–249; ≥ 250 employees); education (low/middle; high) 
n 17350 17350 17350 17357 17357 17357 
R2 0.020 0.020 0.020 0.016 0.017 0.016 
AIC 45686.33 45609.68 45605.47 40733.49 40668.36 40641.67 
BIC 45740.67. 45679.53 45675.32 40787.84 40738.21 40711.53 

Note. Data from 2015. * = p < .05; ** = p < .01; *** = p < .001

Exploratory Analyses

We explored interaction effects related to having a child younger than 18 years in the household (see Tables 7 and 8; see also Corrigall & Konrad, 2007; Kjeldstad & Nymoen, 2012; Weeden et al., 2016). We used the same datasets (or survey waves) as above. In 2015, 6,182 respondents (35%) reported that at least one child was living together with them in their household. Men (36%) were slightly more likely than women (34%) to live with a child (or multiple children), χ²(1) = 6.39, p = .01, r = .02 (a very small effect). Moreover, there was only one interaction concerning part-time work and the reason “a full-time position was not available”: Among participants with children, women were slightly less likely than men to work part-time because a full-time position was not available, whereas women were slightly more likely than men to work part-time for this reason among childless participants.

Finally, in further exploratory analyses on people’s reasons for working part-time or overtime, we added to our set of preregistered control variables (see Method) the following ones: (1) participants’ occupational branches, (2) whether their organizations were unionized (i.e., had a workers’ council), and (3) whether a child younger than 18 years lived in their household (see Tables A4–A6 in the Appendix).3 In these analyses, regarding H1a on part-time, the only main difference was that the interactions between gender and either marriage or cohabitation disappeared. There was no difference for our results regarding H1b. Similarly, regarding H2a on overtime, there was no difference for the reason “advancing professionally,” and, for the reason “attaining additional income,” the observed main effect of gender remained significant in two of the three models. Finally, regarding H2b on overtime and the reason “overtime was mandated,” the main effect of gender in the first model disappeared. However, except for the disappearance of the interaction between gender and cohabitation, no changes were observed for the reason “stepping in for colleagues.”

Table 7.
Exploratory Analyses on the Effects of Having Children for Working Time Arrangements
Part-time workOvertime work
 Personal or family obligations A full-time activity was not available Advance professionally Attain additional income Overtime mandated Step in for colleagues 
 b (SEb (SEb (SEb (SEb (SEb (SE
 OR OR OR OR OR OR 
Intercept -4.08*** (0.31) -1.08*** (0.30) -2.13*** (0.19) -0.21 (0.13) 0.00 (0.12) -0.80*** (0.12) 
Female (vs. male) 1.24*** (0.19) 0.33 (0.18) 0.09 (0.14) -0.71*** (0.14) 0.17 (0.09) 0.72*** (0.08) 
3.45*** 1.39 1.10 0.49*** 1.19 2.06*** 
Child (vs. no child) 2.35*** (0.25) -0.55 (0.36) -0.13 (0.16) 0.16 (0.13) -0.11 (0.11) -0.25** (0.10) 
10.47*** 0.58 0.88 1.17 0.90 0.78** 
Female × child 0.44 (0.26) -0.79* (0.38) 0.09 (0.24) 0.19 (0.22) 0.07 (0.16) 0.17 (0.14) 
1.55 0.45* 1.09 1.21 1.07 1.19 
Control variables Age (in years); occupational position (blue-collar; white-collar; public official); company size (< 50; 50–249; ≥ 250 employees); education (low/middle; high) 
n 4399 4399 4475 4550 4534 4546 
Pseudo R2 0.116 0.120 0.004 0.110 0.053 0.039 
AIC 4434.81 2647.82 2517.95 2810.99 4366.84 5687.37 
BIC 4498.72 2711.72 2575.61 2868.80 4424.61 5745.17 
Part-time workOvertime work
 Personal or family obligations A full-time activity was not available Advance professionally Attain additional income Overtime mandated Step in for colleagues 
 b (SEb (SEb (SEb (SEb (SEb (SE
 OR OR OR OR OR OR 
Intercept -4.08*** (0.31) -1.08*** (0.30) -2.13*** (0.19) -0.21 (0.13) 0.00 (0.12) -0.80*** (0.12) 
Female (vs. male) 1.24*** (0.19) 0.33 (0.18) 0.09 (0.14) -0.71*** (0.14) 0.17 (0.09) 0.72*** (0.08) 
3.45*** 1.39 1.10 0.49*** 1.19 2.06*** 
Child (vs. no child) 2.35*** (0.25) -0.55 (0.36) -0.13 (0.16) 0.16 (0.13) -0.11 (0.11) -0.25** (0.10) 
10.47*** 0.58 0.88 1.17 0.90 0.78** 
Female × child 0.44 (0.26) -0.79* (0.38) 0.09 (0.24) 0.19 (0.22) 0.07 (0.16) 0.17 (0.14) 
1.55 0.45* 1.09 1.21 1.07 1.19 
Control variables Age (in years); occupational position (blue-collar; white-collar; public official); company size (< 50; 50–249; ≥ 250 employees); education (low/middle; high) 
n 4399 4399 4475 4550 4534 4546 
Pseudo R2 0.116 0.120 0.004 0.110 0.053 0.039 
AIC 4434.81 2647.82 2517.95 2810.99 4366.84 5687.37 
BIC 4498.72 2711.72 2575.61 2868.80 4424.61 5745.17 

Note. For the analyses concerning part-time work, data from 2015 were used, and only respondents in part-time work (i.e., < 35 hours per week) were included. For the analyses concerning overtime work, data from 2019 were used, and only respondents with at least two hours overtime per week were included. OR = Odds Ratio. * = p < .05; ** = p < .01; *** = p < .001

Table 8.
Exploratory Analyses on the Effects of Having Children for Experiences Related to Working Time Arrangements
Acceptable to be available during leisure timeBeing contacted in private lifeBeing contacted while at work
 b (SEb (SEb (SE
 [95% CI] [95% CI] [95% CI] 
Intercept 2.26*** (0.02) 2.19*** (0.02) 2.12***
(0.02) 
Female (vs. male) -0.18*** (0.02) -0.01 (0.02) -0.16*** (0.01) 
[-0.21, -0.14] [-0.04, 0.02] [-0.19, -0.13] 
Child (vs. no child) 0.04* (0.02) 0.03 (0.02) 0.16*** (0.02) 
[0.00, 0.08] [-0.01, 0.07] [0.12, 0.19] 
Female × child -0.01 (0.03) -0.00 (0.03) -0.03 (0.02) 
[-0.06, 0.05] [-0.06, 0.05] [-0.08, 0.02] 
Control variables Age (in years); occupational position (blue-collar; white-collar; public official); company size (< 50; 50–249; ≥ 250 employees); education (low/middle; high) 
n 17318 17354 17361 
R2 0.030 0.021 0.023 
AIC 44084.42 45614.87 40544.74 
BIC 44154.26 45684.73 40614.59 
Acceptable to be available during leisure timeBeing contacted in private lifeBeing contacted while at work
 b (SEb (SEb (SE
 [95% CI] [95% CI] [95% CI] 
Intercept 2.26*** (0.02) 2.19*** (0.02) 2.12***
(0.02) 
Female (vs. male) -0.18*** (0.02) -0.01 (0.02) -0.16*** (0.01) 
[-0.21, -0.14] [-0.04, 0.02] [-0.19, -0.13] 
Child (vs. no child) 0.04* (0.02) 0.03 (0.02) 0.16*** (0.02) 
[0.00, 0.08] [-0.01, 0.07] [0.12, 0.19] 
Female × child -0.01 (0.03) -0.00 (0.03) -0.03 (0.02) 
[-0.06, 0.05] [-0.06, 0.05] [-0.08, 0.02] 
Control variables Age (in years); occupational position (blue-collar; white-collar; public official); company size (< 50; 50–249; ≥ 250 employees); education (low/middle; high) 
n 17318 17354 17361 
R2 0.030 0.021 0.023 
AIC 44084.42 45614.87 40544.74 
BIC 44154.26 45684.73 40614.59 

Note. Data from 2015. * = p < .05; ** = p < .01; *** = p < .001

In a preregistered and highly powered study, we examined gender differences in why part-time and overtime is worked. On average, women were more likely than men to work part-time because of personal or family obligations. Moreover, women as compared to men were, on average, less likely to work overtime to attain additional income, but more likely to work overtime to step in for colleagues (cf. “web of time”; Gerstel & Clawson, 2018, p. 77). Women also deemed it less acceptable to be available for work-related issues during leisure time, revealing gender differences in the work–life interface. Not as predicted, however, women reported being contacted by family members, friends, or other people while being at work less often than men. Finally, there were only inconsistent moderating effects of marriage and people living together with a partner. Altogether, even when women and men had “similar” schedules (i.e., part-time or overtime), they were still “different,” as they worked a certain number of hours for different reasons (e.g., Allan et al., 2020; Bureau of Labor Statistics, 2018).

Theoretical Implications

We reasoned that SRT (e.g., Eagly, 1987; Eagly & Wood, 2012) would allow us to make predictions on the gendered “social processes” (Gerstel & Clawson, 2018) that give rise to gender differences among both part-time and overtime workers, using a parsimonious set of basic propositions. Specifically, we theorized that the gendered division of labor (e.g., Croft et al., 2015; Eagly & Wood, 2012) and resulting gender roles lead women and men to have gendered reasons for working either part-time or overtime. From extant research, we know that gender roles not only can become internalized (Wood & Eagly, 2012), they also cause backlash reactions toward people who deviate from gender roles (e.g., Rudman, Moss-Racusin, Phelan, et al., 2012; M. J. Williams & Tiedens, 2016). These social processes can trigger a vicious cycle (Eagly & Koenig, 2021) by which people’s behaviors and experiences follow their gender role.

In fact, for instance, men had an agentic reason (i.e., attaining additional income), whereas women had rather communal reasons (e.g., stepping in for colleagues), for working overtime. These findings illustrate the importance of people’s “web of time”—why people work with a given schedule can be influenced by others (e.g., colleagues; Gerstel & Clawson, 2018). Our observation of gender differences among the restricted groups of part-time or overtime workers is worth highlighting because past research typically examined gender differences in the number of paid hours worked (see our preliminary analyses and, e.g., Antonie et al., 2020; Feldman, 1990). Thus, a more novel theoretical implication of our research is that gender roles and the division of labor can produce gender differences within the groups of part-time and overtime workers (see also, e.g., Bureau of Labor Statistics, 2018; Feldman, 1990).

Still, not all results were in line with our predictions, which suggests that extensions to our theorizing are needed. To start, we did not observe, among part-time workers, an overall gender difference in the likelihood of reporting that a full-time activity was not available. One reason for this unexpected finding might be that two diverging processes were at work: On the one hand, men may aim to meet the expectation to be devoted to a career (Berdahl et al., 2018; Rudman, Moss-Racusin, Phelan, et al., 2012). As such, they may only work part-time when they cannot find a full-time activity—the predicted gender role–process (see also Bureau of Labor Statistics, 2022c; cf. Office for National Statistics, 2022). On the other hand, reflecting occupational segregation (e.g., Eagly et al., 2020; England et al., 2020), women might work relatively more in jobs that simply do not offer sufficient full-time schedules (henceforth, an “occupations–process”; see Kjeldstad & Nymoen, 2012; Pech et al., 2021). Altogether, a relevant extension to our theoretical reasoning would be that the division of labor and gender roles can both shape people’s working time arrangements, such that these two processes sometimes have the net effect of cancelling out gender differences in why people work with a certain schedule. This extension is noteworthy because SRT itself does not make a specific prediction as to how the division of labor and gender roles may work in concert to influence people’s schedules (of course, it is also important to keep in mind that SRT was not developed as a “grand theory” of people’s working time arrangements, in particular).

The aforementioned extension to our theorizing may also help explain our moderator effects regarding part-time work: Women as compared to men were more likely to report that no full-time activity was available among participants who were unmarried or not cohabiting, but men reported this reason somewhat more often than women among married or cohabiting participants. If marriage, in fact, “precipitates” gender role conformity (Livingston, 2014), the gender role–process could become stronger among married participants, resulting in a gender difference in the direction that we initially predicted. Thus, integrating multiple, potentially divergent processes, beyond the effects of gender roles as derived from SRT, certainly represents a fruitful endeavor (Kjeldstad & Nymoen, 2012; Pech et al., 2021).

Relatedly, recall that we observed an overall gender difference in the likelihood with which overtime was worked because it was mandated (women > men). This gender difference could actually result from a gender role–process and an occupations–process: Women are socially expected to be cooperative and accommodating (e.g., Kulik & Olekalns, 2012; Rudman, Moss-Racusin, Phelan, et al., 2012). Thus, if women followed this expectation, they likely agree to an employer’s mandate to work overtime. At the same time, women may work in occupations in which employees have less of a say in the decision to work overtime (Gerstel & Clawson, 2018). Here, the two basic processes may work in converging ways to produce a gender difference. Again, this is an important point because SRT itself does not delineate how exactly the two basic processes jointly give rise to gender differences in working time arrangements. Thus, further research that pinpoints these two processes would be interesting.

All told, both a gender role–process and an occupations–process appear relevant to explaining gender differences in working time arrangements. In this respect, please also consider our additional exploratory analyses that included further controls, which also captured people’s occupations. In these analyses, many—but not all—findings remained virtually unchanged relative to the models not considering people’s occupations. Therefore, people’s occupations, just like their gender roles, seem to be only one piece of the puzzle.

Finally another unexpected result regarding people’s work–life interface was that, although women deemed it less acceptable to be available for work-related issues during leisure time than men (as predicted), highlighting how people’s paid work relates to their private life in a “web of time” (Gerstel & Clawson, 2018, p. 77), there was no gender difference in how frequently people reported being contacted by others in their private life (see also Shockley et al., 2017). Yet, given that this finding was observed in an analysis based on a large sample size (N = 17,350), it is informative nevertheless. Also, women (vs. men) reported being less often contacted by family, friends, or other people due to reasons unrelated to work while being at work. This finding again suggests that extensions to our theoretical reasoning would be helpful. Perhaps men were contacted more than women while being at work because they typically work more paid hours (e.g., BLS, 2018; Brereton, 1990), thereby simply prolonging the time period in which they could be contacted (see also Shockley et al., 2017). Further examining this process would be particularly interesting because it neither reflects a gender role–process, nor a conventional occupations–process. It might simply be an epiphenomenon, reflecting men’s longer working hours.

Practical Implications

Part-time workers and overtime workers are relevant groups in the workforce (e.g., Bureau of Labor Statistics, 2022b; Rotchford & Roberts, 1982). Our research highlights the gendered reasons as to why people work part-time or overtime in the first place. Hereby, our insights help guide individual workers and organizations to plan and manage work schedules—specifically, when people aim to increase or decrease their paid hours. For instance, if people with caregiving responsibilities aim to work full-time, work–life policies (e.g., flexible hours) could be helpful (but see, e.g., Bourdeau et al., 2018), especially for women, as they were more likely than men to work part-time due to personal or family obligations (e.g., Bureau of Labor Statistics, 2018; Federal Statistical Office of Germany, 2022). Conversely, if the goal is to cut back on overtime—when overtime hours are costly for organizations (literally) or for individuals (metaphorically) who aim to spend more time with family—interventions could consider that women are more likely than men to work overtime as they step in for colleagues, which calls for an improved work design and coordination among coworkers. Similarly, as gender equality depends on men engaging more in caregiving at home (e.g., Croft et al., 2015; Meeussen et al., 2020), knowledge on why men, in particular, work overtime (to attain additional income) can aid in promoting equality.

Limitations and Directions for Future Research

Although we already worked toward a more integrated understanding of gender differences in working time arrangements (cf. Jacobs & Gerson, 2004; Weeden et al., 2016), future research is needed to address the limitations of our research. Although our study included a particularly large sample and, thus, had high statistical power, this design feature came at the cost of a cross-sectional analysis. Hence, future work is needed to study, longitudinally (e.g., Corrigall & Konrad, 2007), the causes and consequences of the gendered working time arrangements and experiences that we highlighted. In particular, women and men segregate into different jobs, reflecting and sustaining their gender roles (see above; Eagly et al., 2020), and, therefore, giving rise to the currently observed gender differences (see also Kjeldstad & Nymoen, 2012; Pech et al., 2021).

As we already mentioned, another relevant limitation of our work is that it was conducted in a single, Western culture: Germany. It is well-known that gender roles differ across cultures (Bosson et al., 2022), so that gender effects do as well (e.g., Shan et al., 2019; M. J. Williams & Tiedens, 2016). Similarly, childcare costs differ widely across countries, with costs in Germany being relatively low (OECD, 2023b). Thus, when analyzing data from other countries, more interactions involving having children may be observed than we did. Altogether, cross-cultural examinations are clearly an important avenue for future research. A final question for future research is whether people’s responses to survey questions, as we analyzed them, might be subject to self-presentational concerns, such that women and men underreport working time arrangements and experiences that deviate from their gender role. Using indirect survey techniques could go a long way in addressing this issue.

Conclusion

It is well-known that women often work fewer paid hours than men, which is relevant knowledge for organizations and societies that aim to achieve greater gender equality (e.g., Cha & Weeden, 2014). Based on this knowledge, one might assume—as it turns out, incorrectly—that those men who work part-time are similar to their female counterparts, and that those women who work overtime are similar to male overtime workers. Our study highlighted that women and men who work part-time or overtime meaningfully differ from each other, even though they had the same working time arrangement (e.g., Bureau of Labor Statistics, 2018; Feldman, 1990). When it comes to working time arrangements, thus, women and men can be similar, but still different.

Contributed to conception and design: JM, NB, AMW, CBS, JH

Contributed to acquisition of data: NB, AMW, CBS

Contributed to analysis and interpretation of data: NB, JM

Drafted and/or revised the article: JM, NB, AMW, CBS, JH

Approved the submitted version for publication: JM, NB, AMW, CBS, JH

We thank the BAuA for conducting the WTS that provided the basis for our research.

We do not have any competing interests to declare.

The datasets are available here: https://doi.org/10.21934/baua.azb15.suf.1; https://doi.org/10.48697/baua.azb19.suf.1

Table A1.
Logistic Regression Results for Hypothesis 2a Concerning Overtime Work: Single Choice—Main Reason for Overtime Only
Advance professionally (0 = “no”; 1 = “yes”)Attain additional income (0 = “no”; 1 = “yes”)
 b (SEb (SEb (SEb (SEb (SEb (SE
 OR [95% CI] OR [95% CI] OR [95% CI] OR [95% CI] OR [95% CI] OR [95% CI] 
Intercept -4.10*** (0.45) -4.00*** (0.49) -3.82*** (0.50) -2.03*** (0.24) -2.11*** (0.28) -2.12*** (0.31) 
Female (vs. male) -0.29 (0.30) -0.70 (0.51) -0.45 (0.50) -0.45 (0.24) -0.45 (0.38) -0.08 (0.40) 
0.75 [0.42, 1.35] 0.50 [0.18, 1.35] 0.64 [0.24, 1.70] 0.64 [0.40, 1.02] 0.64 [0.30, 1.35] 0.92 [0.42, 2.02] 
Married (vs. other)  -0.20 (0.37)   0.14 (0.27)  
 0.82 [0.40, 1.70]   1.15 [0.67, 1.98]  
Female × married  0.64 (0.62)   0.02 (0.48)  
 1.91 [0.57, 6.42]   1.02 [0.40, 2.59]  
Living with partner (vs. not)   -0.45 (0.39)   0.14 (0.30) 
  0.63 [0.30, 1.36]   1.15 [0.64, 2.07] 
Female × living with partner   0.20 (0.62)   -0.55 (0.49) 
  1.22 [0.36, 4.09]   0.58 [0.22, 1.50] 
Control variables Age (in years); occupational position (blue collar; white collar; public official); company size (< 50; 50–249; ≥ 250 employees); education (low/middle; high) 
n 4347 4347 4347 3844 3844 3844 
Pseudo R2 0.007 0.009 0.010 0.072 0.073 0.074 
AIC 564.88 567.71 567.28 818.14 821.59 820.68 
BIC 609.52 625.11 624.67 855.67 871.62 870.71 
Advance professionally (0 = “no”; 1 = “yes”)Attain additional income (0 = “no”; 1 = “yes”)
 b (SEb (SEb (SEb (SEb (SEb (SE
 OR [95% CI] OR [95% CI] OR [95% CI] OR [95% CI] OR [95% CI] OR [95% CI] 
Intercept -4.10*** (0.45) -4.00*** (0.49) -3.82*** (0.50) -2.03*** (0.24) -2.11*** (0.28) -2.12*** (0.31) 
Female (vs. male) -0.29 (0.30) -0.70 (0.51) -0.45 (0.50) -0.45 (0.24) -0.45 (0.38) -0.08 (0.40) 
0.75 [0.42, 1.35] 0.50 [0.18, 1.35] 0.64 [0.24, 1.70] 0.64 [0.40, 1.02] 0.64 [0.30, 1.35] 0.92 [0.42, 2.02] 
Married (vs. other)  -0.20 (0.37)   0.14 (0.27)  
 0.82 [0.40, 1.70]   1.15 [0.67, 1.98]  
Female × married  0.64 (0.62)   0.02 (0.48)  
 1.91 [0.57, 6.42]   1.02 [0.40, 2.59]  
Living with partner (vs. not)   -0.45 (0.39)   0.14 (0.30) 
  0.63 [0.30, 1.36]   1.15 [0.64, 2.07] 
Female × living with partner   0.20 (0.62)   -0.55 (0.49) 
  1.22 [0.36, 4.09]   0.58 [0.22, 1.50] 
Control variables Age (in years); occupational position (blue collar; white collar; public official); company size (< 50; 50–249; ≥ 250 employees); education (low/middle; high) 
n 4347 4347 4347 3844 3844 3844 
Pseudo R2 0.007 0.009 0.010 0.072 0.073 0.074 
AIC 564.88 567.71 567.28 818.14 821.59 820.68 
BIC 609.52 625.11 624.67 855.67 871.62 870.71 

Note. Data from 2019. Only respondents with at least two hours overtime per week are included in this analysis. OR = Odds Ratio. * = p < .05; ** = p < .01; *** = p < .001

Table A2.
Logistic Regression Results for Hypothesis 2b Concerning Overtime Work: Single Choice—Main Reason for Overtime Only
Overtime mandated (0 = “no”; 1 = “yes”)Step in for colleagues (0 = “no”; 1 = “yes”)
 b (SEb (SEb (SEb (SEb (SEb (SE
 OR [95% CI] OR [95% CI] OR [95% CI] OR [95% CI] OR [95% CI] OR [95% CI] 
Intercept -1.56*** (0.18) -1.52*** (0.21) -1.34*** (0.22) -3.00*** (0.24) -2.58*** (0.25) -2.59*** (0.27) 
Female (vs. male) 0.14 (0.15) 0.33 (0.23) 0.13 (0.25) 1.35*** (0.14) 0.79*** (0.20) 0.59* (0.24) 
1.15 [0.86, 1.56] 1.39 [0.89, 2.16] 1.14 [0.69, 1.87] 3.84*** [2.94, 5.02] 2.20*** [1.50, 3.23] 1.80* [1.14, 2.86] 
Married (vs. other)  -0.05 (0.20)   -0.83*** (0.23)  
 0.95 [0.64, 1.40]   0.44*** [0.28, 0.69]  
Female × married  -0.35 (0.30)   0.97*** (0.27)  
 0.71 [0.40, 1.26]   2.63*** [1.55, 4.46]  
Living with partner (vs. not)   -0.33 (0.21)   -0.70** (0.24) 
  0.72 [0.48, 1.08]   0.50** [0.31, 0.79] 
Female × living with partner   -0.01 (0.31)   1.05*** (0.29) 
  0.99 [0.54, 1.80]   2.86*** [1.64, 5.01] 
Control variables Age (in years); occupational position (blue collar; white collar; public official); company size (< 50; 50–249; ≥ 250 employees); education (low/middle; high) 
n 4347 4347 4347 4347 4347 4347 
Pseudo R2 0.065 0.067 0.068 0.071 0.077 0.076 
AIC 1604.12 1604.54 1602.97 2148.00 2137.38 2134.12 
BIC 1648.76 1661.93 1660.36 2192.65 2194.78 2191.52 
Overtime mandated (0 = “no”; 1 = “yes”)Step in for colleagues (0 = “no”; 1 = “yes”)
 b (SEb (SEb (SEb (SEb (SEb (SE
 OR [95% CI] OR [95% CI] OR [95% CI] OR [95% CI] OR [95% CI] OR [95% CI] 
Intercept -1.56*** (0.18) -1.52*** (0.21) -1.34*** (0.22) -3.00*** (0.24) -2.58*** (0.25) -2.59*** (0.27) 
Female (vs. male) 0.14 (0.15) 0.33 (0.23) 0.13 (0.25) 1.35*** (0.14) 0.79*** (0.20) 0.59* (0.24) 
1.15 [0.86, 1.56] 1.39 [0.89, 2.16] 1.14 [0.69, 1.87] 3.84*** [2.94, 5.02] 2.20*** [1.50, 3.23] 1.80* [1.14, 2.86] 
Married (vs. other)  -0.05 (0.20)   -0.83*** (0.23)  
 0.95 [0.64, 1.40]   0.44*** [0.28, 0.69]  
Female × married  -0.35 (0.30)   0.97*** (0.27)  
 0.71 [0.40, 1.26]   2.63*** [1.55, 4.46]  
Living with partner (vs. not)   -0.33 (0.21)   -0.70** (0.24) 
  0.72 [0.48, 1.08]   0.50** [0.31, 0.79] 
Female × living with partner   -0.01 (0.31)   1.05*** (0.29) 
  0.99 [0.54, 1.80]   2.86*** [1.64, 5.01] 
Control variables Age (in years); occupational position (blue collar; white collar; public official); company size (< 50; 50–249; ≥ 250 employees); education (low/middle; high) 
n 4347 4347 4347 4347 4347 4347 
Pseudo R2 0.065 0.067 0.068 0.071 0.077 0.076 
AIC 1604.12 1604.54 1602.97 2148.00 2137.38 2134.12 
BIC 1648.76 1661.93 1660.36 2192.65 2194.78 2191.52 

Note. Data from 2019. Only respondents with at least two hours overtime per week are included in this analysis. OR = Odds Ratio. * = p < .05; ** = p < .01; *** = p < .001

Table A3.
Exploratory Analyses on the Effects of Having Children for Working Time Arrangements: Single Choice—Main Reason for Overtime Only
Overtime work
 Advance professionally Attain additional income Overtime mandated Step in for colleagues 
 b (SEb (SEb (SEb (SE
 OR [95% CI] OR [95% CI] OR [95% CI] OR [95% CI] 
Intercept -4.09*** (0.47) -2.16*** (0.25) -1.57*** (0.19) -2.89*** (0.24) 
Female (vs. male) -0.40 (0.37) -0.44 (0.30) 0.23 (0.18) 1.27*** (0.16) 
0.67 [0.32, 1.38] 0.64 [0.36, 1.16] 1.26 [0.89, 1.79] 3.54*** [2.60, 4.83] 
Child (vs. no child) -0.04 (0.38) 0.43 (0.27) 0.03 (0.20) -0.44 (0.26) 
0.96 [0.46, 2.01] 1.54 [0.92, 2.59] 1.03 [0.69, 1.53] 0.65 [0.39, 1.07] 
Female × child 0.35 (0.61) 0.04 (0.47) -0.32 (0.32) 0.25 (0.30) 
1.42 [0.43, 4.67] 1.04 [0.41, 2.61] 0.73 [0.39, 1.37] 1.28 [0.71, 2.31] 
Control variables Age (in years); occupational position (blue collar; white collar; public official); company size (< 50; 50–249; ≥ 250 employees); education (low/middle; high) 
n 4348 3845 4348 4348 
Pseudo R2 0.008 0.077 0.066 0.072 
AIC 568.44 818.01 1606.35 2147.39 
BIC 625.83 868.05 1663.74 2204.78 
Overtime work
 Advance professionally Attain additional income Overtime mandated Step in for colleagues 
 b (SEb (SEb (SEb (SE
 OR [95% CI] OR [95% CI] OR [95% CI] OR [95% CI] 
Intercept -4.09*** (0.47) -2.16*** (0.25) -1.57*** (0.19) -2.89*** (0.24) 
Female (vs. male) -0.40 (0.37) -0.44 (0.30) 0.23 (0.18) 1.27*** (0.16) 
0.67 [0.32, 1.38] 0.64 [0.36, 1.16] 1.26 [0.89, 1.79] 3.54*** [2.60, 4.83] 
Child (vs. no child) -0.04 (0.38) 0.43 (0.27) 0.03 (0.20) -0.44 (0.26) 
0.96 [0.46, 2.01] 1.54 [0.92, 2.59] 1.03 [0.69, 1.53] 0.65 [0.39, 1.07] 
Female × child 0.35 (0.61) 0.04 (0.47) -0.32 (0.32) 0.25 (0.30) 
1.42 [0.43, 4.67] 1.04 [0.41, 2.61] 0.73 [0.39, 1.37] 1.28 [0.71, 2.31] 
Control variables Age (in years); occupational position (blue collar; white collar; public official); company size (< 50; 50–249; ≥ 250 employees); education (low/middle; high) 
n 4348 3845 4348 4348 
Pseudo R2 0.008 0.077 0.066 0.072 
AIC 568.44 818.01 1606.35 2147.39 
BIC 625.83 868.05 1663.74 2204.78 

Note. Data from 2019 were used, and only respondents with at least two hours overtime per week were included. OR = Odds Ratio. * = p < .05; ** = p < .01; *** = p < .001

Table A4.
Exploratory Logistic Regression Results for Hypotheses 1a and 1b Concerning Part-Time Work, With Additional Controls
Personal or family obligations (0 = “no”; 1 = “yes”)A full-time activity was not available (0 = “no”; 1 = “yes”)
 b (SEb (SEb (SEb (SEb (SEb (SE
 OR [95% CI] OR [95% CI] OR [95% CI] OR [95% CI] OR [95% CI] OR [95% CI] 
Intercept -4.24*** (0.32) -4.08*** (0.35) -4.51*** (0.39) -1.05** (0.35) -1.49** (0.37) -1.37*** (0.38) 
Female (vs. male) 1.46*** (0.14) 1.33*** (0.23) 1.50*** (0.30) 0.16 (0.18) 0.73* (0.24) 0.85** (0.28) 
4.33*** [3.29, 5.69] 3.78*** [2.41, 5.92] 4.49*** [2.50, 8.07] 1.17 [0.38, 1.65] 2.07* [1.30, 3.30] 2.33** [1.36, 4.00] 
Married (vs. other)  0.66* (0.28)   0.43 (0.33)  
 1.94* [1.12, 3.38]   1.54 [0.81, 2.94]  
Female × married  0.11 (0.29)   -1.13** (0.35)  
 1.11 [0.63, 1.98]   0.32** [0.16, 0.64]  
Living with partner (vs. not)   0.72* (0.33)   0.37 (0.33) 
  2.06* [1.09, 3.90]   1.45 [0.76, 2.76] 
Female × living with partner   -0.07 (0.34)   -1.08** (0.35) 
  0.93 [0.48, 1.81]   0.34** [0.17, 0.67] 
Control variables Age (in years); occupational position (blue-collar; white-collar; public official); company size (< 50; 50–249; ≥ 250 employees); education (low/middle; high); branches (agriculture, forestry, and fisheries; manufacturing industry, excluding construction; construction; trade, transport, and hospitality; finance, leasing, and entrepreneurial services; public and private services); unionization of the company (yes; no); child younger than 18 years living in household (yes; no) 
n 4122 4122 4122 4122 4122 4122 
Pseudo R2 0.276 0.289 0.284 0.106 0.119 0.118 
AIC 4128.08 4054.69 4081.02 2389.80 2358.53 2357.45 
BIC 4222.96 4162.20 4188.53 2484.69 2466.05 2464.95 
Personal or family obligations (0 = “no”; 1 = “yes”)A full-time activity was not available (0 = “no”; 1 = “yes”)
 b (SEb (SEb (SEb (SEb (SEb (SE
 OR [95% CI] OR [95% CI] OR [95% CI] OR [95% CI] OR [95% CI] OR [95% CI] 
Intercept -4.24*** (0.32) -4.08*** (0.35) -4.51*** (0.39) -1.05** (0.35) -1.49** (0.37) -1.37*** (0.38) 
Female (vs. male) 1.46*** (0.14) 1.33*** (0.23) 1.50*** (0.30) 0.16 (0.18) 0.73* (0.24) 0.85** (0.28) 
4.33*** [3.29, 5.69] 3.78*** [2.41, 5.92] 4.49*** [2.50, 8.07] 1.17 [0.38, 1.65] 2.07* [1.30, 3.30] 2.33** [1.36, 4.00] 
Married (vs. other)  0.66* (0.28)   0.43 (0.33)  
 1.94* [1.12, 3.38]   1.54 [0.81, 2.94]  
Female × married  0.11 (0.29)   -1.13** (0.35)  
 1.11 [0.63, 1.98]   0.32** [0.16, 0.64]  
Living with partner (vs. not)   0.72* (0.33)   0.37 (0.33) 
  2.06* [1.09, 3.90]   1.45 [0.76, 2.76] 
Female × living with partner   -0.07 (0.34)   -1.08** (0.35) 
  0.93 [0.48, 1.81]   0.34** [0.17, 0.67] 
Control variables Age (in years); occupational position (blue-collar; white-collar; public official); company size (< 50; 50–249; ≥ 250 employees); education (low/middle; high); branches (agriculture, forestry, and fisheries; manufacturing industry, excluding construction; construction; trade, transport, and hospitality; finance, leasing, and entrepreneurial services; public and private services); unionization of the company (yes; no); child younger than 18 years living in household (yes; no) 
n 4122 4122 4122 4122 4122 4122 
Pseudo R2 0.276 0.289 0.284 0.106 0.119 0.118 
AIC 4128.08 4054.69 4081.02 2389.80 2358.53 2357.45 
BIC 4222.96 4162.20 4188.53 2484.69 2466.05 2464.95 

Note. Data from 2015. Only respondents in part-time work (i.e., < 35 hours per week) are included in this analysis. OR = Odds Ratio. * = p < .05; ** = p < .01; *** = p < .001

Table A5.
Exploratory Logistic Regression Results for Hypothesis 2a Concerning Overtime Work, With Additional Controls
Advance professionally (0 = “no”; 1 = “yes”)Attain additional income (0 = “no”; 1 = “yes”)
 b (SEb (SEb (SEb (SEb (SEb (SE
 OR [95% CI] OR [95% CI] OR [95% CI] OR [95% CI] OR [95% CI] OR [95% CI] 
Intercept -2.38*** (0.42) -2.39*** (0.42) -2.32*** (0.42) 0.26 (0.35) 0.23 (0.35) 0.23 (0.35) 
Female (vs. male) 0.09 (0.12) -0.18 (0.19) -0.12 (0.22) -0.46*** (0.12) -0.41* (0.18) -0.35 (0.21) 
1.09 [0.85, 1.39] 0.84 [0.58, 1.22] 0.88 [0.57, 1.37] 0.63*** [0.49, 0.80] 0.66* [0.46, 0.94] 0.70 [0.47, 1.05] 
Married (vs. other)  -0.30 (0.17)   -0.06 (0.15)  
 0.74 [0.53, 1.03]   0.95 [0.71, 1.26]  
Female × married  0.42 (0.24)   -0.09 (0.23)  
 1.52 [0.95, 2.40]   0.91 [0.58, 1.43]  
Living with partner (vs. not)   -0.23 (0.19)   -0.04 (0.16) 
  0.79 [0.55, 1.14]   0.96 [0.71, 1.31] 
Female × living with partner   0.28 (0.26)   -0.18 (0.24) 
  1.33 [0.80, 2.19]   0.84 [0.52, 1.35] 
Control variables Age (in years); occupational position (blue-collar; white-collar; public official); company size (< 50; 50–249; ≥ 250 employees); education (low/middle; high); branches (agriculture, forestry, and fisheries; manufacturing industry, excluding construction; construction; trade, transport, and hospitality; finance, leasing, and entrepreneurial services; public and private services); unionization of the company (yes; no); child younger than 18 years living in household (yes; no) 
n 4321 4321 4321 4394 4394 4394 
Pseudo R2 0.005 0.006 0.005 0.120 0.120 0.120 
AIC 2439.56 2439.75 2441.53 2645.33 2648.46 2644.90 
BIC 2535.13 2548.06 2549.85 2741.16 2757.06 2753.50 
Advance professionally (0 = “no”; 1 = “yes”)Attain additional income (0 = “no”; 1 = “yes”)
 b (SEb (SEb (SEb (SEb (SEb (SE
 OR [95% CI] OR [95% CI] OR [95% CI] OR [95% CI] OR [95% CI] OR [95% CI] 
Intercept -2.38*** (0.42) -2.39*** (0.42) -2.32*** (0.42) 0.26 (0.35) 0.23 (0.35) 0.23 (0.35) 
Female (vs. male) 0.09 (0.12) -0.18 (0.19) -0.12 (0.22) -0.46*** (0.12) -0.41* (0.18) -0.35 (0.21) 
1.09 [0.85, 1.39] 0.84 [0.58, 1.22] 0.88 [0.57, 1.37] 0.63*** [0.49, 0.80] 0.66* [0.46, 0.94] 0.70 [0.47, 1.05] 
Married (vs. other)  -0.30 (0.17)   -0.06 (0.15)  
 0.74 [0.53, 1.03]   0.95 [0.71, 1.26]  
Female × married  0.42 (0.24)   -0.09 (0.23)  
 1.52 [0.95, 2.40]   0.91 [0.58, 1.43]  
Living with partner (vs. not)   -0.23 (0.19)   -0.04 (0.16) 
  0.79 [0.55, 1.14]   0.96 [0.71, 1.31] 
Female × living with partner   0.28 (0.26)   -0.18 (0.24) 
  1.33 [0.80, 2.19]   0.84 [0.52, 1.35] 
Control variables Age (in years); occupational position (blue-collar; white-collar; public official); company size (< 50; 50–249; ≥ 250 employees); education (low/middle; high); branches (agriculture, forestry, and fisheries; manufacturing industry, excluding construction; construction; trade, transport, and hospitality; finance, leasing, and entrepreneurial services; public and private services); unionization of the company (yes; no); child younger than 18 years living in household (yes; no) 
n 4321 4321 4321 4394 4394 4394 
Pseudo R2 0.005 0.006 0.005 0.120 0.120 0.120 
AIC 2439.56 2439.75 2441.53 2645.33 2648.46 2644.90 
BIC 2535.13 2548.06 2549.85 2741.16 2757.06 2753.50 

Note. Data from 2019. Only respondents with at least two hours overtime per week are included in this analysis. OR = Odds Ratio. * = p < .05; ** = p < .01; *** = p < .001

Table A6.
Exploratory Logistic Regression Results for Hypothesis 2b Concerning Overtime Work, With Additional Control Variables
Overtime mandated (0 = “no”; 1 = “yes”)Step in for colleagues (0 = “no”; 1 = “yes”)
 b (SEb (SEb (SEb (SEb (SEb (SE
 OR [95% CI] OR [95% CI] OR [95% CI] OR [95% CI] OR [95% CI] OR [95% CI] 
Intercept 1.03*** (0.28) 1.01*** (0.28) 1.09*** (0.28) 0.23 (0.25) 0.20 (0.25) 0.29 (0.25) 
Female (vs. male) 0.06 (0.09) -0.07 (0.13) -0.12 (0.15) 0.58*** (0.07) 0.34*** (0.11) 0.38*** (0.13) 
1.06 [0.89, 1.26] 0.93 [0.72, 1.21] 0.89 [0.66, 1.19] 1.79*** [1.56, 2.06] 1.40*** [1.13, 1.74] 1.46*** [1.13, 1.89] 
Married (vs. other)  -0.24 (0.12)   -0.42*** (0.10)  
 0.79 [0.62, 1.00]   0.65*** [0.53, 0.80]  
Female × married  0.19 (0.16)   0.37** (0.14)  
 1.21 [.88, 1.67]   1.45** [1.11, 1.90]  
Living with partner (vs. not)   -0.29* (0.13)   -0.18 (0.11) 
  0.75* [0.58, 0.96]   0.83 [0.67, 1.04] 
Female × living with partner   0.23 (0.17)   -0.27 (0.15) 
  1.26 [0.90, 1.77]   1.31 [0.98, 1.76] 
Control variables Age (in years); occupational position (blue-collar; white-collar; public official); company size (< 50; 50–249; ≥ 250 employees); education (low/middle; high); branches (agriculture, forestry, and fisheries; manufacturing industry, excluding construction; construction; trade, transport, and hospitality; finance, leasing, and entrepreneurial services; public and private services); unionization of the company (yes; no); child younger than 18 years living in household (yes; no) 
n 4379 4379 4379 4390 4390 4390 
Pseudo R2 0.064 0.065 0.065 0.052 0.055 0.052 
AIC 4172.49 4172.41 4169.77 5426.03 5411.92 5423.24 
BIC 4268.26 4280.95 4278.31 5521.84 5520.50 5531.82 
Overtime mandated (0 = “no”; 1 = “yes”)Step in for colleagues (0 = “no”; 1 = “yes”)
 b (SEb (SEb (SEb (SEb (SEb (SE
 OR [95% CI] OR [95% CI] OR [95% CI] OR [95% CI] OR [95% CI] OR [95% CI] 
Intercept 1.03*** (0.28) 1.01*** (0.28) 1.09*** (0.28) 0.23 (0.25) 0.20 (0.25) 0.29 (0.25) 
Female (vs. male) 0.06 (0.09) -0.07 (0.13) -0.12 (0.15) 0.58*** (0.07) 0.34*** (0.11) 0.38*** (0.13) 
1.06 [0.89, 1.26] 0.93 [0.72, 1.21] 0.89 [0.66, 1.19] 1.79*** [1.56, 2.06] 1.40*** [1.13, 1.74] 1.46*** [1.13, 1.89] 
Married (vs. other)  -0.24 (0.12)   -0.42*** (0.10)  
 0.79 [0.62, 1.00]   0.65*** [0.53, 0.80]  
Female × married  0.19 (0.16)   0.37** (0.14)  
 1.21 [.88, 1.67]   1.45** [1.11, 1.90]  
Living with partner (vs. not)   -0.29* (0.13)   -0.18 (0.11) 
  0.75* [0.58, 0.96]   0.83 [0.67, 1.04] 
Female × living with partner   0.23 (0.17)   -0.27 (0.15) 
  1.26 [0.90, 1.77]   1.31 [0.98, 1.76] 
Control variables Age (in years); occupational position (blue-collar; white-collar; public official); company size (< 50; 50–249; ≥ 250 employees); education (low/middle; high); branches (agriculture, forestry, and fisheries; manufacturing industry, excluding construction; construction; trade, transport, and hospitality; finance, leasing, and entrepreneurial services; public and private services); unionization of the company (yes; no); child younger than 18 years living in household (yes; no) 
n 4379 4379 4379 4390 4390 4390 
Pseudo R2 0.064 0.065 0.065 0.052 0.055 0.052 
AIC 4172.49 4172.41 4169.77 5426.03 5411.92 5423.24 
BIC 4268.26 4280.95 4278.31 5521.84 5520.50 5531.82 

Note. Data from 2019. Only respondents with at least two hours overtime per week are included in this analysis. OR = Odds Ratio. * = p < .05; ** = p < .01; *** = p < .001

1.

In SRT (Wood & Eagly, 2002, p. 699), “the interaction between the physical specialization of the sexes, especially female reproductive capacity, and the economic and social structural aspects of societies” explains the division of labor. Hence, there are cultural differences in the division of labor and resulting gender roles (e.g., Wood & Eagly, 2002, 2012; see also Eagly et al., 2020).

2.

Relying on data from the U.S., the Bureau of Labor Statistics (2018, 2022a) lists “economic” and “noneconomic” reasons for working part-time (cf. Feldman, 1990; Office for National Statistics, 2022), as well as how many women and men work part-time for economic reasons, which include being unable to find a full-time activity (Bureau of Labor Statistics, 2022c). In line with our hypotheses, in 2021, 17% of men worked part-time, and 19% of this group did so for economic reasons; by contrast, 28% of the women worked part-time, and “only” 12% of this group did so for economic reasons (Bureau of Labor Statistics, 2022c; cf. Office for National Statistics, 2022; but, importantly, see also Allan et al., 2020; Kjeldstad & Nymoen, 2012; Pech et al., 2021). Moreover, in 2016, 34% of the women, but only 8% of the men, worked part-time “voluntarily” due to “childcare problems” or “other family and/or personal obligations” (Bureau of Labor Statistics, 2018; cf. Federal Statistical Office of Germany, 2022).

3.

We would like to thank our reviewers for suggesting these additional analyses.

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