Comolli Et Al. 2021

European Journal of Population (2021) 37:643–696
https://doi.org/10.1007/s10680-021-09583-3
Joint Family and Work Trajectories and Multidimensional

Wellbeing
C. L. Comolli1 · L. Bernardi1 · M. Voorpostel2
Received: 20 May 2020 / Accepted: 15 March 2021 / Published online: 14 April 2021
© The Author(s) 2021, corrected publication 2022
Abstract
Informed by the life course perspective, this paper investigates whether and how
employment and family trajectories are jointly associated with subjective, relational
and financial wellbeing later in life. We draw on data from the Swiss Household
Panel which combines biographical retrospective information on work, partnership
and childbearing trajectories with 19 annual waves containing a number of well-
being indicators as well as detailed socio-demographic and social origin informa-
tion. We use sequence analysis to identify the main family and work trajectories for
men and women aged 20–50 years old. We use OLS regression models to assess
the association between those trajectories and their interdependency with wellbe-
ing. Results reveal a joint association between work and family trajectories and
wellbeing at older age, even net of social origin and pre-trajectory resources. For
women, but not for men, the association is also not fully explained by proximate
(current family and work status) determinants of wellbeing. Women’s stable full-
time employment combined with traditional family trajectories yields a subjective
wellbeing premium, whereas childlessness and absence of a stable partnership over
the life course is associated with lower levels of financial and subjective wellbeing
after 50 especially in combination with a trajectory of weak labour market involve-
ment. Relational wellbeing is not associated with employment trajectories, and only
weakly linked to family trajectories among men.
Keywords Subjective wellbeing · Relational wellbeing · Financial wellbeing ·

Family trajectories · Professional trajectories · Sequence analysis
* C. L. Comolli
chiara.comolli@unil.ch
1
University of Lausanne, Lausanne, Switzerland
2
FORS (Swiss Centre of Expertise in the Social Sciences), Lausanne, Switzerland
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644 C. L. Comolli et al.
1 Introduction
In the last decades in contemporary societies, both employment and family trajec-
tories have become more diverse and uncertain (Diewald et al., 2006). Many stud-
ies show that both the rise in non-standard employment histories and the increasing
complexity and multiplicity of family arrangements generate important implications
for individuals’ wellbeing and contribute to growing inequality (Barbieri, 2009;
Kovalenko & Mortelmans, 2014). Overall, family trajectories characterized by early
family formation and unstable partnership histories (Demey et al., 2014; Peters &
Liefbroer, 1997; Zimmermann & Hameister, 2019) and work trajectories character-
ized by non-employment (Falkingham et al., 2020; Ponomarenko, 2016) tend to be
associated with lower wellbeing later in life, compared with delayed family forma-
tion and a strong attachment to the labour market.
As the rich literature on the spillover between the work and family domains dem-
onstrates (Charles & Stephens, 2004), the two have also become more intertwined,
given the simultaneous increase in the share of dual earner couples and in the
demands of both the parent’s and worker’s roles (Drobnič & Guillén, 2011; Van der
Lippe & Peters, 2007). However, despite the advantages of studying the combined
patterns of employment and family arrays have been highlighted earlier (Aassve,
Billari, et al., 2007; McDonough et al., 2015), and their joint impact on wellbeing,
and their interplay, is still rarely addressed in the literature, especially adopting a
holistic and multidimensional perspective (Abbot, 2005). In a holistic view, the life
course is a process in which events and transitions occur in a continuum, shifting
the focus from single events or transitions to long-term work and family trajectories
(Elder, 2001; Piccarreta & Studer, 2019: pp. 1). The multidimensionality of the life
course refers both to the simultaneous look at multiple domains and to the assess-
ment of the influence of such life course trajectories on various wellbeing dimen-
sions (Bernardi et al., 2019).
We build on recent studies showing that some types of work and family trajecto-
ries generate greater vulnerability in later life (McDonough et al., 2015) in terms of
health (Arpino et al., 2018; Lacey et al., 2017; Lacey, Sacker et al., 2016), lower life
satisfaction (Lacey, Stafford et al., 2016; Schmalzle et al., 2019) and financial well-
being (Halpern-Manners et al., 2015; Madero-Cabib & Fasang, 2016). Our study is
unique in measuring the extent to which early to mid-adulthood employment–family
trajectories are jointly related to subjective, relational and financial wellbeing later
on (Bernardi et al., 2019) net of pre-trajectory conditions and more proximal deter-
minants of wellbeing. In particular, our investigation is guided by three research
questions. First, we ask whether work and family trajectories interplay in influencing
multiple dimensions of wellbeing later on. Second, we ask whether the association
between joint work–family trajectories and wellbeing later in life is explained by
early disadvantages and proximate determinants of wellbeing. Early socio-economic
conditions (such as family of origin characteristics) have been shown to shape both
the likelihood of individuals experiencing a certain work–family trajectory and to
affect how critical transitions are related to wellbeing (Arpino et al., 2018; Schafer
et al., 2013). Moreover, family and work trajectories and later wellbeing are directly
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Joint Family and Work Trajectories and Multidimensional… 645
associated, beyond the joint work–family trajectories, through the family and
employment status respondents hold when wellbeing is measured. Finally, we com-
pare the association between trajectories and wellbeing between men and women,
given that the process linking family and work histories to wellbeing is likely to be
gendered: trajectories differ by gender, and the work and life domains generally are
less reconcilable for women (Keizer et al., 2010).
We draw on data from the large-scale, nationally representative longitudinal
Swiss Household Panel (SHP), using a subsample that completed a biographical ret-
rospective calendar covering complete work and family trajectories prior to entering
the panel. This allows us to identify the critical family transitions of parenthood,
partnering and re-partnering after a union dissolution, and on the critical employ-
ment transitions from school to work and in and out of joblessness. The SHP also
contains a variety of indicators of wellbeing recorded yearly. We use sequence anal-
ysis to identify and describe the main trajectories of work and family of the respond-
ents based on the biographical data and estimate linear regression models to assess
the association between these combined trajectories and the wellbeing outcomes.
2 Background
2.1 Family and Work Trajectories and Wellbeing
Family and employment trajectories are both independently related to wellbeing out-
comes. Long-term stable partnerships bring emotional support and social integra-
tion as well as financial and material benefits (Gerstel et al., 1985). Stable unions
tend to be associated with greater life satisfaction (Thomson et al., 2001) and less
loneliness in later life (Peters & Liefbroer, 1997). In contrast, trajectories character-
ized by (multiple) union dissolutions and absence of a partner tend to be linked to
lower affective, subjective and social wellbeing (Demey et al., 2014; Zimmermann
& Hameister, 2019) but also lower economic wellbeing (Aassve, Betti et al., 2007;
Halper-Manners et al., 2015). On the one hand, childlessness means less access to
social resources and support (Nordenmark, 2004) and might still represent a non-
normative family type especially for women (Lacey, Stafford et al., 2016). In the
long term, childlessness is linked to lower life satisfaction (Hansen et al., 2009). Par-
enthood tends to induce positive emotions, a sense of meaning and psychological
growth and to increase social integration (Roeters et al., 2016). On the other hand,
early family formation tends to be linked to lower educational attainment, lower
likelihood of full-time employment and lower subjective wellbeing compared with a
delayed family formation (Schoon et al., 2012).
Strong labour market attachment provides social networks that are beneficial
for relational wellbeing, financial resources as well as opportunities for personal
reward and learning (Clark et al., 2001). Career interruptions not only directly
reduce life satisfaction (Oesch & Lipps, 2013) but also indirectly affect wellbe-
ing later on, by reducing the accumulation of financial assets and tenure and thus
lowering future job prospects (Gangl, 2006), health (Young, 2012) and partnering
chances (Amato & Beattie, 2011). While part-time employment might produce
13
scarring effects and a lower probability of re-entering the labour market with full-
time employment (Fouarge & Muffels, 2008), evidence on the wellbeing conse-
quences of part-time and late return to part-time work trajectories is mixed and
tends to depend on the willingness to work part-time and on the length of the
spell (Falkingham et al., 2020). The longer the part-time spell, the more negative
the consequences for subjective wellbeing, unless the part-time option is chosen
voluntary to reconcile family and work obligations (Ponomarenko, 2016). Finally,
compared with full-time employment, also early retirement, self-employment,
family caring and atypical work have been linked to lower subjective wellbeing
(Falkingham et al., 2020). Subjective wellbeing trajectories after retirement are
more positive when the long-term employment pathway to retirement is charac-
terized by full-time work, compared to transitioning into retirement from inactiv-
ity or after a trajectory of unemployment (Schmalzle et al., 2019).
2.2 Joint Family and Work Trajectories and Wellbeing
The life course framework stresses the multidimensionality of biographies (Elder,

2001) treating the life course as a set of events and transitions occurring in mul-
tiple domains simultaneously (Diewald & Mayer, 2009). In fact, not only work
and family trajectories have become more uncertain, the two are also more inter-
twined than in the past (Aassve, Billari et al., 2007; Drobnič & Guillén, 2011).
The increase in female labour force participation has led to an increase in the
number of dual earner couples in which the negotiation between partners to bal-
ance family and work has become a pressing issue. At the same time, balancing
between multiple roles has become harder given the increasing demands from the
work place (Van der Lippe & Peters, 2007) and the rising standards of parent-
ing (Jacobs & Gerson, 2004). The result is increased conflict between these two
life domains (Matthews et al., 2014) as the rich literature on spillover between
the work and family domains shows (Charles & Stephens, 2004). Therefore, in
order to fully understand the implications of the increasing complexity of lives in
contemporary society, it is paramount to investigate the professional and family
spheres together.
Most studies show that individuals with life course trajectories characterized
by a strong attachment to the labour market in combination with stable partner-
ship and parenthood tend to display the greatest wellbeing. Lacey et al. (2016b)
report that British women who combine marriage and parenthood with little or
no long-term ties to the labour market displayed lower subjective wellbeing dur-
ing retirement age, even when accounting for prior wellbeing. Besides the lack of
access to the benefits provided by labour market work, also children leaving the
parental home has been previously shown to be more stressful for mothers who do
not work (Adelmann et al., 1989). In a recent study, Xue and colleagues (2020)
show that trajectories characterized by late transition to both family formation
and full-time work lead women to higher subjective wellbeing later on. Madero-
Cabib and Fasang (2016) show that when women combine early motherhood
13
with a weak attachment to the labour market their observed financial wellbeing
at retirement age is lower than when women have more continued employment
trajectories. McDonough et al., (2015) find compensatory mechanisms between
the two spheres of work and family life: as much as a history of stable marriage
might compensate for a weak labour market attachment among mothers, absence
of a partner can be compensated by a trajectory of stable full-time employment.
Similarly, Xue et al., (2020) find that childlessness combined with a strong work
orientation also leads to sustained wellbeing among women.
2.3 Early (dis)advantages and Proximate Determinants of Wellbeing
Critical events, trajectories and wellbeing are not equally distributed across individ-
uals in society. Embedded in the life course paradigm, the Cumulative Advantage/
Disadvantage (CAD) theory posits that individuals experience unique trajectories
and outcomes that become increasingly different as individuals age. The benefits
associated with a person’s structural position early in the life course—such as social
origin or childhood experiences—tend to cumulate over time, through path-depend-
ent processes that generate trajectories that lead to certain outcomes later in life,
widening the social difference with other groups as they age (Dannefer, 2018).
Social origin affects life course trajectories and wellbeing both directly and indi-
rectly. Individuals with greater resources, for instance, growing up in higher socio-
economic status families or in better health, not only display better wellbeing out-
comes (Diener et al., 2010) but they are also less likely to experience more stressful
trajectories in both family and work domains (McLanahan, 2004). Multiple studies
demonstrate that an advantaged childhood and adolescence socio-economic status
in the form of family structure, higher parental education, better housing and health
conditions set individuals into own education, work and family trajectories that are
more beneficial for later wellbeing and health outcomes (Arpino et al., 2018; Falk-
ingham et al., 2020; Schafer et al., 2013).
Not only do those pathways influence later-life outcomes directly, but early expe-
riences also influence later outcomes indirectly through more proximal determinants
(Bongaarts, 1978), namely the mid-to-late life opportunities they generate. While
most previous studies tended to assume that personal biographies become irrelevant
for wellbeing once more proximal indicators of work and family circumstances are
taken into consideration (Gustman et al. 1996), Halpern-Manners and colleagues
(2015) demonstrate that work and family trajectories have both a direct effect on
later-life economic wellbeing and an indirect effect through more proximate meas-
ures of work and family circumstances.
2.4 Gender Differences
The process linking family–work trajectories to wellbeing is gendered. While part-

nership trajectories have become more complex for both men and women, wom-
en’s work trajectories have become more similar to men’s trajectories (Keizer et al.,
2010; Melchior et al., 2007), making the reconciliation of the two domains more
13
complicated for women (Moen & Sweet, 2004). Women’s increasing participation
on the labour market in the last decades has been in many countries largely concen-
trated on part-time jobs, especially among mothers (Ernst Stähli et al., 2009) and
career breaks remain more common among women (Ponomarenko, 2016). While
the latter expose women more to financial insecurity than men, through more uncer-
tainty and job instability, lower wages and fewer career opportunities and benefits,
evidence on subjective wellbeing is mixed, with some studies showing that unem-
ployment and inactivity have larger negative consequences for life satisfaction
among men (Ponomarenko, 2016). Moreover, if part-time work is stable and seen
as a voluntary strategy to reconcile motherhood and labour market participation, it
might lead to greater wellbeing in the long term (Ponomarenko, 2016).
Family formation tends to take place earlier in the life course for women than
men (Bruckner and Mayer 2004) which often leads to poor education and a weaker
attachment to the labour market and lower subjective wellbeing (Schoon et al.,
2012). In case of divorce or separation, women re-marry less frequently than men
(de Graaf & Kalmijn, 2003). Men have been shown to benefit more than women
from stable unions in terms of life style and wellbeing, and to suffer more from
extended periods as single, in terms of overall and relational wellbeing. Women
tend to suffer being unpartnered less than men because they value more their inde-
pendence and cultivate larger networks of family and friends that compensate the
lack of partner (Baumbusch, 2004). Unstable union histories instead have worse
consequences for women than men in terms of subjective wellbeing and loneliness
(Demey et al., 2014; Peters & Liefbroer, 1997; Zimmermann & Hameister, 2019).
Despite the rapid increase in women’s labour force participation, work practices
are still largely designed based on a predominantly male workforce, without child-
care or domestic work (Moen & Sweet, 2004). Dual-earner couples’ strategy to rec-
oncile work and family is to give priority to men’s career, making women’s career
secondary. While men’s work tends to remain more isolated from family responsi-
bilities, women accommodate working time to family needs when needed (Moen,
2018; Moen & Sweet, 2004), which tend to produce overall more negative conse-
quences for women than men.
2.5 Multidimensional Wellbeing
Wellbeing is a multi-faceted concept, including multiple dimensions that are strongly

related (Chavez et al., 2005). Some see the relationship between such dimensions as
reflecting a unique underlying overall wellbeing evaluation mostly determined by
temperamental predispositions (Diener, 1984; Diener & Lucas, 1999). Others think
of each dimension as reflecting the objective circumstances individuals experience
in the specific domain they refer to (Blanchflowers and Oswald, 2011). The life
domain approach (Campbell et al., 1976) sees subjective wellbeing as the net out-
come of satisfaction with various life domains. Life satisfaction, the cognitive aspect
of subjective well-being (Diener, 1984), is an aggregate measure of satisfaction in
various life domains such as work, finances, relationships or leisure activities (Ber-
nardi et al., 2017; Diener et al., 2003). Domain-specific wellbeing indicators reflect
13
the distance between goals, needs and aspirations—subjective factors—and the

objective circumstances in each domain (Stone et al., 2010). In a bottom-up process,
individuals evaluate separately each domain and each specific evaluation influences
overall life satisfaction (McAdams et al., 2012; Schimmack, 2008). It might be that,
in relation to specific life-course events, satisfaction in some life domains change in
positive direction, while satisfaction in other domains decreases—in a compensa-
tory way—or that particular events trigger positive or negative changes in different
domains at the same time—in a cumulative way (Diewald, 2003). Additionally, per-
sonal characteristics such as age, health conditions or past experiences also influ-
ence the evaluation of the satisfaction in different life domains so that differences
between individuals with similar family, work or financial status can still emerge.
In line with a life domain approach, we understand wellbeing as a multidi-
mensional concept and investigate the extent to which long-term joint employ-
ment–family trajectories are related not only to overall subjective wellbeing, but
to two domain-specific wellbeing indicators: relational and financial wellbeing. A
given family–work history might be associated with a lower (or higher) life satisfac-
tion because that trajectory lowers (increases) the satisfaction with personal relation-
ships and/or because it lowers (increases) financial satisfaction. Investigating these
multiple dimensions together allows us to identify whether specific work–family tra-
jectories bear long-term consequences in some but not other domains and whether
wellbeing in any particular domain respond similarly to overall subjective wellbeing.
Previous studies identify relational satisfaction as an independent but related
component of subjective wellbeing (De Leersnyder et al., 2014; Götz et al., 2018).
Baumeister and Leary (1995) maintain that quality of life is enhanced by lasting,
positive interpersonal relationships and that the lack of satisfaction with personal
relationships puts individuals at risk of loneliness and lower subjective wellbeing
(Shin & Jung, 2019). However, some circumstances such as living alone have been
shown to be predictors of lower relational but not subjective wellbeing (Mellor et al.,
2008). The overall finding is thus that satisfaction with social relationships is related
to life satisfaction, but its determinants are not identical to those of life satisfaction.
Hence, the relevance of investigating how relational wellbeing relates to work and
family trajectories.
Financial wellbeing at older ages has received considerable attention, but studies
mostly measure it through objective outcomes (pension or personal or household
income around retirement) rather than through the subjective evaluation of the finan-
cial situation. The study of the latter allows to investigate how much the perception
of one’s own financial situation depends on material circumstances or their subjec-
tive evaluation (Easterlin, 2006). More importantly, financial wellbeing has rarely
been studied in relation to both employment and family trajectories despite the rec-
ognized importance in life course studies of both domains especially for women’s
later wellbeing (Madero Cabib and Fasang, 2016) and, due to the lack of appropriate
biographical data, researchers mostly use cross-sectional work and family events, or
summary indicators of much more complex life course histories (Halpern-Manners
et al., 2015).
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2.6 The Swiss Context
Switzerland is classified as a conservative–liberal welfare state with strong tradition-

alist elements, historically modest universal transfers and a high degree of depend-
ence on labour income (Esping-Andersen, 1990). From the seventies onwards, there
has been a large increase in part-time work, with women strongly over-represented
in it (Widmer & Ritschard, 2009; Widmer et al., 2003). In 2019, the female part-
time employment rate was 61.7% compared to a European average of 29.9% (Euro-
stat), while 17.1% of Swiss employed men work part-time (8.4% in the EU). Com-
pared with other Western countries, the Swiss unemployment rate has always been
extremely low, below 2% until the early nineties and below 5% afterwards (OECD),
although long-term unemployment exceeds the OECD mean (Lalive & Lehmann,
2017). Women are more likely to be unemployed (5.1% compared to 4.3% for men in
2018) and more likely than men to be out of the labour force at least for some part of
the life course (19.8% of Swiss women were inactive in 2019 versus 11.7% of Swiss
men), although both gaps have narrowed in the last decades (Lalive & Lehmann,
2017) and overall female labour force participation is high in international compari-
son (82.8% in Switzerland versus 71.1 in the EU).
Switzerland’s incentives for a traditional male breadwinner–female caretaker
division include gender-segregated labour markets, high gender employment as well
as wage gaps; generous-dependent tax allowances, household instead of individual
taxation and high marginal tax rates that penalize second earners (Cooke & Baxter,
2010). Furthermore, limited and expensive public childcare and the high costs of
existing services equally set strong trade-offs between employment and care time for
mothers (Wall & Escobedo, 2013). Swiss women on average undertake 64% (66%
among mothers) of housework tasks (Nollert & Gasser, 2017), which is more in line
with Southern (Italy 70.1%; Spain 66.5%) than Continental (France 62%; Germany
61.6%) European countries (OECD 2020). Overall, Switzerland displays great gen-
der divides in family responsibilities that relapse almost entirely on women, who
end up with a weaker and irregular labour market attachment over the life course.
Women still significantly reduce their participation on the labour market during the
transition to parenthood and often do not return to full-time work afterwards (Wid-
mer et al., 2003). As a consequence, while men maintained fairly stable and linear
occupational trajectories throughout the birth cohorts of the first half of the twenti-
eth century, women’s occupational trajectories display much greater diversification
(Widmer & Ritschard, 2009).
2.7 Research Questions and Hypotheses
The aim of the current study is to investigate whether early to mid-adulthood profes-
sional and family trajectories (age 20–50) jointly affect well being later on. First,
we hypothesize not only that both domains influence wellbeing, but also that fam-
ily and work histories interact in shaping wellbeing later in life (H1). Second, in
line with what the life course cumulative disadvantage literature predicts, we expect
13
that accounting for social origin weakens (but not entirely explains) the association
between work and family trajectories and wellbeing (H2) by influencing the likeli-
hood of experiencing a certain work–family trajectory in the first place. Third, we
argue that family and work trajectories, and later wellbeing are directly associated,
beyond the indirect association they have through the family and employment status
respondents hold when wellbeing is measured (H3).
In relation to gender differences in the association between family and work
trajectories and wellbeing, given the more difficult reconciliation between the two
spheres and the greater complexity of women’s life courses in Switzerland illus-
trated above, we expect a stronger association and a stronger interaction between
trajectories and wellbeing for women than for men (H4).
3 Data and Method
3.1 Data and Sample Selection
We draw on data from the first 19 waves of the large-scale, nationally representative
longitudinal Swiss Household Panel (SHP, 1999–2017). The study annually surveys
all members (14 and older) of a random sample of private households in Switzer-
land. Two subsamples of the SHP completed biographical retrospective calendars
providing entire work and family histories, in 2002 (N = 5560) and 2013 (N = 6090).
We focus on life course trajectories during prime working and childbearing age. We
select respondents who provided complete family and work trajectories covering
every year for the ages of 20–50 either in 2002 or 2013 (N = 3087, T = 31). To obtain
wellbeing measures, we select respondents who participated in at least one wave
following the collection of the biographical data (2003–06 and 2014–17, respec-
tively).1 As the age of respondents filling in the biographical calendar in 2002 and
2013 varies, the age at which wellbeing is measured potentially lies between 51 and
93 years old. To increase the homogeneity of the sample, we restrict it to respond-
ents whose wellbeing is measured between 51 and 70 years old (N = 2302). After
excluding missing data2 on control variables, our final analytical sample consists of
1885 individuals (N = 1005 from women and N = 880 for men), with retrospective
information covering 31 years.
3.2 Variables
Based on the biographical information, we construct the prime working and child-
bearing age partnership, childbearing and employment trajectories. We based the
1
The vast majority of respondents participated in the first wave after the biographical calendar was col-
lected. Respectively, 14 (0.6%) and 54 (2.3%) respondents did not participate in the 2003 and 2014 waves
but in the subsequent ones.
2
Missing data on control variables (N = 414) are mostly due to missing information on parental educa-
tion (N = 362) and marital status (N = 39).
13
construction of sequences on the following states in the family sphere: being unpart-
nered, partnered or re-partnered after union termination (dissolution or widowhood)
in combination with being childless or a parent; and the following employment
states: being in education, in full-time, large (50–89%) or small (< 50%) part-time
employment and non-employment. Being unemployed is a rare event in our sam-
ple, and hence, we could not distinguish it from inactivity. For the same reason of a
small number of observations, we did not distinguish divorce, separation and wid-
owhood. Appendix Table 4 illustrates the distribution of family and employment
states by gender.
The SHP provides an extensive list of indicators of wellbeing recorded in the
yearly waves. We focus on general life satisfaction, satisfaction with personal rela-
tionships and satisfaction with the financial situation.3 All satisfaction indicators are
measured on a scale from 0 (not at all satisfied) to 10 (completely satisfied). In all
models, we control for age4 (51–70) and the period in which wellbeing is measured
(2014–17 vs 2003–06). Appendix Table 5 reports summary statistics of the depend-
ent and independent variables included in the analysis.
To test whether the association between specific family and work trajectories and
wellbeing exists beyond the selection process into certain types of trajectories, we
control for a number of background characteristics, all measured prior to the start-
ing age range of the trajectories (before age 20). The survey includes socio-demo-
graphic and social origin information such as country of birth and nationality, living
arrangement at age 15 and fathers’ educational level.
Reverse causality between wellbeing and life course trajectories represents a
potential bias of our estimates. Happier individuals might experience more positive
family and work histories. The association between certain trajectories and wellbe-
ing might be explained by innate conditions that make some individuals happier
than others and also more likely to experience a given trajectory. Unfortunately, we
do not dispose of information on pre-trajectory wellbeing, but we do have informa-
tion on physical and mental health problems before age 20 from the health calendar
collected in 2013. We use this information as an (imperfect) proxy for subjective
wellbeing. Since this would greatly reduce our sample size, we did not include it in
the main analyses, but we conducted robustness checks on the 2013 sample, con-
trolling for early life health indicators. Results for life satisfaction are presented in
Appendix.
Finally, to investigate how much of the association between family and work
trajectories and wellbeing is mediated by the conditions at the time of the survey,
we add current marital status (unpartnered; married or registered partnership; and
divorced, separated or widow), whether men have had children since5 employment
3
The question formulations are as follows. Life satisfaction: “In general, how satisfied are you with your
life if 0 means ‘not at all satisfied’ and 10 means ‘completely satisfied’?”; Satisfaction with personal rela-
tionships: “How satisfied are you with your personal, social and family relationships, if 0 means ‘not at
all satisfied’ and 10 ‘completely satisfied’?” and satisfaction with financial situation: “Overall how satis-
fied are you with your financial situation, if 0 means ‘not at all satisfied’ and 10 ‘completely satisfied’?”.
4
Here, age also measures the time between the end of the trajectory and when wellbeing is recorded.
5
No women in our sample had children after the age of 50.
13
status (full-time work; part-time work; inactive; and unemployed), presence of cur-
rent health problems and net personal income (only in financial wellbeing models).
All these variables are measured at the same time as wellbeing.
3.3 Method
Among the studies that investigate both domains together and their joint influence
on wellbeing, the majority does not model explicitly the domain interaction (Lippert
& Damaske, 2019). In particular, less is known regarding how the two life domains’
long-term trajectories interplay in affecting wellbeing (Aisenbrey & Fasang, 2017).
Halpern-Manners et al., (2015) show that trajectory measures predict outcomes bet-
ter than using point and summary measures (such as the number of events) because
they better capture the full temporal dimension of life course pathways. Here, we
utilize sequence analysis to identify and describe the different trajectories defined by
labour market and family transitions. Sequence analysis offers advantages in terms
of investigating the life course in a dynamic longitudinal perspective, distinguishing
the unfolding of trajectories from earlier experiences and stable factors like social
background and preferences. At the same time, results from sequence analysis make
these complex and heterogeneous life courses much easier to interpret (Aassve, Bil-
lari et al., 2007).
For the sequence analysis, to compare the trajectories and form the typical clus-
ters, we use dynamic Hamming distance, hierarchical clustering and Wards linkage
to identify the family and work clusters separately.6 Clustering allows us to iden-
tify groups of individuals displaying similar family and work histories. We allow
different clustering for men and women as, first, the trajectories likely differ and,
second, the complexity might differ across gender. (For instance, Swiss women’s
work histories might be more complex than Swiss men’s.) The choice of the num-
ber of clusters was based on theoretical grounds and multiple quality criteria (see
Appendix Table 6 for size, R-squared, average Silhouette width and Calinski–Hara-
basz index). The quality criteria do not uniquely indicate a solution, as expected, and
we additionally need to balance a sufficient sample size of groups to be interacted
across domains with a variety of trajectories as rich as possible. The three groups
clustering seems to be the most homogeneously supported solution across quality
measures, sample size and maximum variation. A more detailed description of the
clusters is presented in the next section.
6
Robustness checks have been conducted using simple Hamming distance and longest common subse-
quence (LCS). Very small differences emerge in the family clusters, because, with respect to both Ham-
ming measures, LCS tends to stress distance between sequences linked to state duration more and timing
less. We preferred to highlight timing distances between trajectories as we believe the plan of temporal
structure of family formation is crucial in contemporary societies (Aassve et al., 2007b); Oechsle and
Geissler, 2003). Partitioning Around Medoids (PAM) as clustering method produces qualitatively identi-
cal clusters.
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Fig. 1 State distribution plots of the family clusters. Source: Elaboration of the authors based on SHP
Biographical files 2002, 2013
Once the clusters of typical trajectories are identified, they are treated as cat-
egorical explanatory variables. Linear OLS regression models assess the associa-
tion between typologies of family and work trajectories, their interaction and the
wellbeing outcomes.7 We opt for constructing the trajectories separately for the two
domains and interact the derived clusters instead of using multichannel sequence
analyses because “the joint typologies cannot be regarded as proof of a relationship”
(Piccarretta and Studer 2019: pp. 6). Conclusions based on multichannel analysis
can be drawn only on the mutual association between the domains and not on the
possible dependence of the trajectory in one domain on the trajectory in the other
domain. In other words, this approach is more complete and flexible since all pos-
sible combinations between trajectories in the two domains are considered, not only
those produced by the multichannel analysis. Finally, the results of the clustering
based on one domain only are easier to interpret.
We test our first two hypotheses (H1–H2) of an association between trajectories
and wellbeing and its persistence net of pre-trajectory resources by comparing two
models: Gross and Net, where in the former we only control for age and period,
while in the second we add the pre-trajectories determinants. We test our third
hypothesis (H3) of the existence of both a direct and an indirect association between
trajectories and wellbeing by further adding current family, employment, health and
income status. Given the highly gendered family and employment regimes in Swit-
zerland, we not only allow for different clustering, but we test the extent to which the
associations between trajectories and wellbeing are gendered (H4), running separate
models for men and women.
To favour an easier interpretation, in the next sections, results are presented
graphically. Tables with complete models are available in Appendix of the paper.
7
We follow previous studies (Ferrer-i-Carbonell and Frijters, 2004) showing that the assumptions of
cardinality or ordinality have substantially no impact on empirical results and assume the cardinality of
satisfaction measures.
13
Fig. 2 State distribution plots of the employment clusters. Source: Elaboration of the authors based on
SHP Biographical files 2002, 2013
4 Family and Employment Trajectories
Figures 1, 2 display the state distribution plots of family and work states by the clus-
ters of typical trajectories identified for men and women. State distribution plots
(Billari & Piccarreta, 2005) aggregate the frequency of each state at each time point;
therefore, they give a good overview of the time point–specific distribution of states,
yet do not display individual sequences. We identified three clusters for men’s typ-
ical family trajectories (Fig. 1). Half of Swiss men cluster in a traditional family
trajectory group with a relatively early transition into a partnership and fatherhood
around their early to mid-twenties (“Traditional”). One-third of men group into a
late traditional cluster in which these transitions take place a little later, around the
age of 30 (“Late Traditional”). The state plot shows that in these two clusters after
the age of 30, the majority of men remain partnered with children. In the late tra-
ditional cluster, between the age of 20 and the early 30 s there is still a predomi-
nance of childlessness among men. The last cluster (“Childless”, 20%) groups men
who mostly remain childless for the entire age interval observed. Panel (a), Table 1
reports the distribution of states within typical trajectories showing that the most
prevalent family states in the traditional trajectories for men are partnered with chil-
dren, while the most prevalent in the childless trajectories are unpartnered and part-
nered childless.
Women’s family clusters differ with respect to, first, the age at family formation,
which is lower compared to men. In the biggest cluster, including 47% of women
(“Early Traditional”), already at the age of 20, many of them are partnered and some
of them have children. In their mid-twenties, more than half of women in this clus-
ter have children. The second cluster with around 43% of women (“Traditional”)
still displays a traditional transition to partnership and motherhood, but a bit later
compared with the early transition group. Here, women tend to have children around
their late twenties. Notably, both clusters, as shown in Fig. 1 and Table 1, include
some separation and re-partnering for Swiss women during the last ten years of the
life course trajectory considered, which we did not observe for men with the same
intensity. Yet, those states are not frequent enough to constitute a separate cluster.
The third cluster includes 10% of women (“Childless”) and, as Fig. 1 illustrates,
13
Table 1 States distribution within typical family and work trajectories. Men and women. Source: Elabo-
ration of the authors based on SHP Biographical files 2002, 2013
(a)
Family states Men’s family trajectories
Traditional Late traditional Childless Total
Unpartnered, childless N 1908 2591 2569 7068

% 14.94 29.74 44.32 25.91
Unpartnered separated/div/widow, N 66 74 96 236
childless % 0.52 0.85 1.66 0.87
Partnered, childless N 966 1452 2800 5218
% 7.56 16.67 48.30 19.13
Re-partnered, separated/div/ N 12 16 60 88
widow, childless % 0.09 0.18 1.04 0.32
Unpartnered, with children N 361 39 3 403
% 2.83 0.45 0.05 1.48
with children % 0.61 0.07 0.93 0.51
Partnered, with children N 9306 4523 142 13,971
% 72.86 51.92 2.45 51.21
Re-partnered, separated/div/widow, N 75 10 73 158
with children % 0.59 0.11 1.26 0.58
Total N 12,772 8711 5797 27,280
Family states Women’s family trajectories
Traditional Early traditional Childless Total
Unpartnered, childless N 2955 761 2323 6039

% 23.36 4.92 76.46 19.38
childless % 0.89 0.76 2.53 0.98
Partnered, childless N 2068 3583 499 6150
% 16.35 23.16 16.43 19.74
Re-partnered, separated/div/ N 46 58 8 112
widow, childless % 0.36 0.37 0.26 0.36
Unpartnered, with children N 446 721 54 1221
% 3.53 4.66 1.78 3.92
with children % 0.59 1.60 0.13 1.04
Partnered, with children N 6922 9899 73 16,894
% 54.73 63.99 2.40 54.23
Re-partnered, separated/div/widow, N 25 83 0 108
with children % 0.20 0.54 0.00 0.35
Total N 12,648 15,469 3038 31,155
13
Table 1 (continued)
(b)
Work states Men’s work trajectories
Early full-time Full-time + High Part-time work Total
edu
In education N 2 304 4 310

% 0.01 10.01 0.12 1.14
Full-time N 19,491 2249 648 22,388
% 93.29 74.03 19.35 82.07
Part-time 50–89% N 325 246 2466 3037
% 1.56 8.10 73.66 11.13
Small part-time < 50% N 18 133 40 191
% 0.09 4.38 1.19 0.70
Not employed N 1058 106 190 1354
% 5.06 3.49 5.68 4.96
Total N 20,894 3038 3348 27,280
Work states Women’s work trajectories
Full-time work Return to part-time Not in employ- Total
ment
In education N 57 72 15 144
% 0.86 0.43 0.19 0.46
Full-time N 5455 3116 1252 9823
% 82.23 18.55 16.22 31.53
Part-time 50–89% N 686 5807 604 7097
% 10.34 34.56 7.82 22.78
Small part-time < 50% N 142 3773 170 4085
% 2.14 22.46 2.20 13.11
Not employed N 294 4034 5678 10,006
% 4.43 24.01 73.56 32.12
Total N 6634 16,802 7719 31,155
across all ages the most prevalent state is the one of being unpartnered and childless.
Therefore, the second difference between Swiss men and women regarding typical
family trajectories is that while the cluster of childless men equally include part-
nered (48.3% of states, Table 1) and unpartnered (44.3% of states, Table 1) men,
women in the childless cluster are predominantly unpartnered (76.5% of states,
Table 1). Based on the previous studies illustrated earlier, we can hypothesize that
this cluster of women would be more disadvantaged in terms of wellbeing compared
to men since besides kids, they also tend to lack a stable relationship.
13
Table 2 Educational-level distribution by family and employment clusters. Men and women. Source:
Elaboration of the authors based on SHP Biographical files 2002, 2013. Row percentages
Women’s family clusters Primary (%) Upper secondary Tertiary (%) Total (N)
(%)
Traditional 7.35 66.67 25.98 408

Early traditional 14.03 70.54 15.43 499
Childless 14.29 52.04 33.67 98
Total 11.34 67.16 21.49 1005
Men’s family clusters Primary (%) Upper secondary Tertiary (%) Total (N)
(%)
Traditional 3.88 53.64 42.48 412

Late traditional 3.56 43.42 53.02 281
Childless 2.67 55.61 41.71 187
Total 3.52 50.8 45.68 880
Women’s work clusters Primary (%) Upper secondary Tertiary (%) Total (N)
(%)
Full-time work 12.15 57.94 29.91 214

Return to part-time work 9.41 68.08 22.51 542
Not in employment 14.86 73.09 12.05 249
Total 11.34 67.16 21.49 1′005
Men’s work clusters Primary (%) Upper secondary Tertiary (%) Total (N)
(%)
Early full-time work 3.41 58.16 38.43 674

Full-time work after higher 1.01 4.08 94.9 98
education
Part-time work 6.48 47.22 46.3 108
Total 3.52 50.8 45.68 880
Table 2 reports the educational-level distribution in each cluster8., 9 While around

70% and 67% of women in the early traditional and traditional clusters, respectively,
have an upper secondary education at most, this proportion is 52% in the cluster of
childless women, who are more likely to have a tertiary degree than women with a
family. For men, educational differences across family clusters are much smaller,
although the highest proportion of tertiary educated men is found in the traditional
late group (53%) and not among the childless men who actually display the lowest
proportion of tertiary educated among the three clusters.
Figure 2 shows the state distribution plots for the work domain by the identi-
fied typical clusters. Swiss men disproportionately work full-time during their
8
The Chi-squared test allows us to reject the hypothesis of independence of the two variables (𝜒,
2
= 26.35, p = 0.000).
9
Weighted distributions are essentially identical (available upon request).
13
Table 3 Joint distribution of family and employment clusters. Men and women. Source: Elaboration of
the authors based on SHP Biographical files 2002, 2013. Cell percentages
Men Traditional Late traditional Childless Total (N)
Early full-time work 37.95 23.18 15.45 674

Full-time work after higher 4.2 5.11 1.82 98
education
Part-time work 4.66 3.64 3.98 108
Total (N) 412 281 187 880
Women Traditional Early traditional Childless Total (N)
Full-time work 6.47 7.96 6.87 214

Return to part-time 20.8 30.65 2.49 542
Not in employment 13.33 11.04 0.4 249
Total (N) 408 499 98 1′005
employment trajectories. The vast majority of them enter the labour market quite
early, as in the case of the first cluster comprising around 77% of men in the sam-
ple, and stay in full-time employment for their prime-working age (“Early full-time
work”). The state plot and Panel (b) in Table 1 show that men in this group rarely
experience joblessness and especially at the beginning of their career and work very
little part-time. The second largest cluster (“Part-time work”, 12%) include men who
mostly work in a 50–89% part-time job. Some of these men work full-time when
they enter the labour market, but part-time work heavily prevails in most of their
career. Finally, the last cluster of men of similar size (“Full-time work after higher
education”, 11%) resembles the first regarding the predominance of full-time work;
however, in this cluster men stay longer in education and enter the labour market
a bit later. As Fig. 2 shows by the age of 23–24, still 40% of them are in education
and, in fact, 95% of them are tertiary educated compared to the 38% of those in the
early labour market entry cluster and 46% of the part-time work cluster (Table 2).
Women’s employment trajectories in Switzerland are very different from men.
Almost one-third of them cluster into the group of the not employed for most of
their prime working age (“Not in employment”). Almost 80% of them work full- or
part-time early in the career, but by the age of 30, this share is below 20% (Fig. 2
and Table 1). The largest cluster of women (“Return to part-time work”, 50%) is
mostly characterized by a similar labour supply decline during childbearing years,
between the mid-twenties and the mid-thirties, but also by a return to the labour
market working part-time. Figure 2 shows that in this cluster by the age of 40, more
than 80% are employed again. Finally, 21% of women in the sample cluster in the
full-time work group (“Full-time work”). In this cluster, non-employment is rare and
concentrated very early or late in the career, and although by the age of 50 almost
a quarter of them works part-time, most of their prime working age is spent in full-
time work. There is quite a large difference in the educational level of women in the
three clusters. Unsurprisingly, the largest share of women with tertiary education is
found in the full-time cluster where 30% of women have university education. In the
13
Fig. 3 OLS estimates of family and work trajectories’ association with life satisfaction. Interaction
model. Men and women. Source: Elaboration of the authors based on SHP Biographical files 2002, 2013
and SHP panel (2003–2017). Note Gross model controls only for age and period; Net model controls for
age, period and pre-trajectory controls; Direct model controls for age, period, pre-trajectory controls and
current controls
Fig. 4 Predicted life satisfaction, interaction model. Men and women. Source: Elaboration of the authors
based on SHP Biographical files 2002, 2013 and SHP panel (2003–2017)
return to part-time cluster, 22.5% of women have tertiary education, while only 12%
do in the not-employed cluster. These descriptive statistics suggest that disadvan-
tages tend to accumulate and less skilled workers, especially women, tend to have a
weaker attachment to the labour market than highly skilled ones.
Table 3 presents the joint distribution of the clusters in the sample. The largest
group of men in the sample (38%) belongs to the early full-time job trajectory in
combination with the traditional family trajectory, while the second and third largest
groups of men (23.2% and 15.5%, respectively) belong to the same early full-time
job trajectory but in combination with a later family formation or childless trajec-
tory. The rarest combination comprises the childless trajectory with full-time job
13
Fig. 5 OLS estimates of family and work trajectories’ association with satisfaction with personal rela-
tionships. Interaction model. Men and women. Source: Elaboration of the authors based on SHP Bio-
graphical files 2002, 2013 and SHP panel (2003–2017). Note Gross model controls only for age and
period; Net model controls for age, period and pre-trajectory controls; Direct model controls for age,
period, pre-trajectory controls and current controls
Fig. 6 Predicted satisfaction with personal relationships, interaction model. Men and women. Source:
Elaboration of the authors based on SHP Biographical files 2002, 2013 and SHP panel (2003–2017)
with higher education (below 2% in the sample). Among Swiss women, the most
common combinations are an early (31%), or a slightly postponed family forma-
tion (21%) together with a return to part-time work after childrearing age. The sub-
sequent most common clusters combine joblessness with family formation, while
childlessness is in general quite infrequent (3–7%), and extremely rare in combina-
tion with the employment trajectory of non-employment (0.4%).
13
Fig. 7 OLS estimates of family and work trajectories’ association with satisfaction with financial situa-
tion. Interaction model. Men and women. Source: Elaboration of the authors based on SHP Biographical
files 2002, 2013 and SHP panel (2003–2017). Note Gross model controls only for age and period; Net
model controls for age, period and pre-trajectory controls; Direct model controls for age, period, pre-
trajectory controls and current controls
Fig. 8 Predicted satisfaction with financial situation, interaction model. Men and women. Source: Elabo-
ration of the authors based on SHP Biographical files 2002, 2013 and SHP panel (2003–2017)
5 Multivariate Analysis Results
Figures 3, 4, 5, 6, 7, 8 present the results from the OLS linear regression models for
three wellbeing outcomes: life satisfaction, satisfaction with personal relationships
and with the financial situation. All figures present on the left panel results for men
and on the right panel results for women. Complete tables are included in Appendix
(Tables 7,8,9,10,11,12).
Figure 3 plots the coefficients of the association between family and work tra-
jectories, and their interaction, and subjective wellbeing from the three different
13
models (gross, net of pre-trajectory determinants, and direct, controlling for current
status). For men, family trajectories are not significantly associated with subjective
wellbeing in themselves nor in interaction with any type of employment trajectory.
Table 13 in Appendix shows that controlling for having experienced health issues
before the age of 20, the combination of a late family formation with early full-time
work is associated with higher life satisfaction for men. This might also be a result
of the younger cohorts included in the sample for these robustness checks analyses,
but it seems that securing a stable career before starting a family is for Swiss men
associated with higher subjective wellbeing later on.
For working women without a family (the cluster Childless), life satisfaction is
substantially lower compared with working women who have children during their
life course. The association is not explained by pre-trajectory resources (not even by
health conditions before the age of 20, Appendix Table 13), and part of the associa-
tion is direct: Once controlling for the proximate determinants (Appendix Table 8),
the negative direct relationship between childlessness and women’s subjective well-
being declines but remains negative and significant. This is not so surprising, as
women who have been childless for most of their lives are likely to remain in this
status after the age of 50, when wellbeing is measured. Current partnership status
is strongly associated with men and women subjective wellbeing but differently, as
previous studies suggest. While being single instead of partnered reduces life satis-
faction among both men and women, being separated, divorces or widow is associ-
ated with a lower life satisfaction only among women.
Women’s weaker attachment to the labour market—both as non-employment or
later return to part-time—in combination with a late traditional family formation,
is also significantly associated with lower life satisfaction net of resources and in
a direct as well as indirect manner. While early family formation and the return to
work after childrearing are each negatively associated with wellbeing relative to late
family and full-time work, their combination appears to attenuate the lower life sat-
isfaction. The earlier women have children, the earlier they re-enter the labour mar-
ket (provided that they re-enter) and the longer they profit from the beneficial effects
of being employed.
To give a more exhaustive picture of the wellbeing profiles associated with given
family and work trajectories, Fig. 4 presents predicted life satisfaction10 for relevant
clusters of family and work trajectories.11 Among women with a traditional family
trajectory, those with a full-time work trajectory score significantly higher on life
satisfaction (around 0.5 points in the 0–10 scale) compared with those who experi-
ence a break in their career (not statistically significant for the not employed trajec-
tory). Within the group of women with a full-time working trajectory, those with
a traditional family trajectory score about 1 point higher on life satisfaction than
10
Based on the net model. Results do not differ substantially if we plot predicted life satisfaction from
the direct model.
11
We find no difference in the association between family trajectories with early (for women) versus late
(for men) family formation and wellbeing measures so, for simplicity, the figures report only presents the
associations for the traditional transitions. Traditional transitions for both men and women mean family
formation takes place round their mid-20 s.
13
childless women. The latter display a pretty stable subjective wellbeing irrespec-
tively of their working histories; even a trajectory of stable full-time work does not
compensate for the lower subjective wellbeing of women without children. It is full-
time working women with a traditional family history that have an advantage with
respect to all other family–work constellations. Career interruptions during chil-
drearing do not pay off for women later in life. For Swiss men, instead, looking at
predicted life satisfaction (Fig. 4) across family and work trajectories we find no sta-
tistically significant differences (identical results are obtained looking at traditional
late family formation—not shown).
Figures 5, 6 report results on relational wellbeing. For men with a weak attach-
ment to the labour market, we do not observe any difference across family trajecto-
ries, but for men in the early full-time work trajectory, we find that having a family
with children is associated with higher relational wellbeing compared with remain-
ing childless (Fig. 6). The association is not explained by early resources that drive
men into long-term childlessness; however, as soon as they have a child later on, the
association disappears (Fig. 5). It is interesting to note that among men, being cur-
rently unpartnered rather than married or in a registered partnership is associated
with a 1-point drop in satisfaction with relationships, relative to a baseline satisfac-
tion of 7.7 (Appendix Table 9). This is not observed among women whose relational
wellbeing seems to be unrelated to current marital status and their family history.
This is in line with previous literature showing that women more successfully sub-
stitute the missing support of a partner, fostering larger networks of friends and fam-
ily (Baumbusch, 2004; Zimmermann & Hameister, 2019).
Early, compared to later family formation appears to be associated with higher
satisfaction with personal relationship, but the difference is not statistically signifi-
cant (Fig. 5). No association is visible between employment trajectories and satis-
faction with personal relations among women. The strongest determinant of wom-
en’s relational wellbeing at older age is their living arrangement when adolescent:
growing up with a lone parent reduces women’s satisfaction with personal relation-
ships by 0.3 points (Appendix Table 10). Overall, it seems that relational wellbeing
is linked more to family ties than labour market ties for both Swiss men and women.
However, the association with long-term family and employment trajectories is
very weak and social origin (for women) and current family status (for men) are the
strongest determinants of relational wellbeing.
We do not find that family and work trajectories interact in affecting men’s finan-
cial wellbeing in Switzerland (Figs. 7, 8). Given the low variation in Swiss men’s
trajectories, the general stability of male’s professional lives over the life course and
the implied financial security, this result is not surprising. Swiss women’s trajecto-
ries of family and professional life are more heterogeneous than men’s, and these
complexities are likely to be problematic for women’s financial wellbeing. Figures 7,
8 show that long-term childlessness (mostly coupled with singlehood) is associated
with a significantly lower satisfaction with the financial situation compared with
women who do have a family. This is a disadvantage that is independent of women’s
labour market history. Moreover, the negative association between having no partner
or children and financial wellbeing is not explained by women of low social origin
or pre-trajectory conditions, but it disappears once the current situation is taken into
13
account (Fig. 7). It is the lower income of single women that explains the negative
effect of not having had a partner (and/or children) throughout most of their lives on
financial wellbeing (Fig. 7). The risk of lower financial wellbeing for women with no
families is common to all types of work trajectories, but the difference with women
with a family is smallest and not statistically different from zero among women with
a part-time work career. The difference among full-time working women is larger
because the financial security of dual-earner couples is higher. (The point estimates
gap with childless women is above 1 point in the 0–10 scale, Fig. 8.) Predictably,
we find the largest difference among women with non-working trajectories. Women
who never worked but do have a partner and a family report a financial wellbeing
almost identical to full-time working women in dual-earner couples. Instead, women
who never worked and remained childless and often unpartnered most their lives
report a significantly lower financial wellbeing. The gap is larger than among work-
ing women (although confidence intervals are much larger too) as the point esti-
mates indicate a predicted satisfaction with financial situation of around 5 for the
latter group and above 8 for women with a traditional family biography. Finally, the
combination of a history of non-employment with no family is persistently associ-
ated with lower financial wellbeing of women, even if they re-partner, find a job
or their income increases (direct model, Fig. 7). There is a clear long-term risk of
much lower financial wellbeing for women who combine a very weak labour market
attachment with remaining unpartnered and childless.
6 Discussion
The findings of this study are multiple. First, work and family trajectories in prime
working age do interplay in determining wellbeing outcomes at later ages (H1).
Men’s subjective wellbeing benefits from a delayed entry into the labour market
combined with a traditional family formation and from securing a stable career
before starting a family (in the youngest 2013 sample). Women enjoy a long-term
financial and overall wellbeing advantage when full-time work is combined with a
traditional (but not too early) family formation. This confirms earlier studies indi-
cating that a stable attachment to both work and family comes with an economic
and mental health premium for both men and women. Satisfaction with personal
relationships, instead, showed surprisingly little association with work and family
trajectories. Relational wellbeing in Switzerland is strongly linked to current part-
nership for men, and bonds from family of origin for women.
Second, we find an association between trajectories and wellbeing net of early
life resources, such as social origin and socio-economic background (H2). Third,
women who during most of their life remain childless or unpartnered, compared to
women who form a family, display a lower life satisfaction that remains such even
when they partner. Similar results apply to their satisfaction with the financial situ-
ation. This suggests a long-lasting effect of women’s weak family trajectories on
their financial and overall wellbeing. Our hypothesis that there exists a direct link
between trajectories and wellbeing is thus supported (H3).
13
All in all, Swiss women’s wellbeing at later ages is more than men’s dependent on
family and work trajectories, and their interaction (H4). While family ties are para-
mount for women’s overall and financial long-term wellbeing, the beneficial effect
of family history is moderated by professional ties. Women who are unpartnered
and childless for most their lives report a particularly lower financial wellbeing if
they never worked. However, women who never worked but have had a family report
a financial wellbeing almost identical to full-time working women in dual-earner
couples. Our findings are in line with previous studies showing that working part-
nered mothers in Switzerland display the highest and single women the lowest well-
being (Perrig-Chiello et al., 2008). However, our findings further suggest a cumula-
tive long-term risk of low financial wellbeing for women who combine a very weak
labour market attachment with no family formation. Moreover, despite employment
attenuating this vulnerability, even full-time work does not compensate entirely for
the financial dependence on a more normative family form.
The study has a few limitations. First, physical and mental health problems before
age 20 provide only an imperfect proxy of pre-trajectory wellbeing. Reverse causal-
ity between wellbeing and life course trajectories hence still represents a potential
bias of our estimates. The association we observe between certain trajectories and
wellbeing might be explained by innate conditions that make some individuals hap-
pier and more likely to experience a given trajectory. However, while physical and
mental health alone might not provide an exact measure of innate wellbeing, we
are confident that that coupled with the rich array of other pre-trajectory indicators,
we include (social origin, parental social status and living conditions during adoles-
cence) very closely picture the wellbeing conditions that might lead to more or less
privileged trajectories.
Second, the rarity of some of the most vulnerable trajectories in Switzerland
hinders a sharp distinction between the representative trajectories. For instance,
the most insecure work trajectory among men is that of part-time work, which
may reflect underemployment but may also result from men choosing to dedicate
their time to other wellbeing enhancing activities (e.g. leisure, social relationships).
Another example is that of family trajectories characterized by multiple marriages
or lone parenthood that do not emerge as typical trajectories. Although cases exist,
they are not enough to constitute a trajectory on their own. Much of the difference
across family trajectories, instead, emerges regarding the age at family formation,
which does not seem to make a remarkable difference for long-term wellbeing in the
Swiss context. However, even in this relatively protected environment we do spot
alarming differences between more and less vulnerable groups.
Third, given the limited number of observations it was not possible to increase
the number of critical events used to generate the sequences. Therefore, unemploy-
ment could not be distinguished from inactivity which, especially for women, rep-
resent very different sources of vulnerability. For the same reason, different types
of unions such as marriages and cohabitations, and union dissolutions, such as
divorces, separations and widowhood could not be distinguished. Relatedly, due to
the reduced sample size, the study could not further address the moderating role of
resources in the link between trajectories and wellbeing. Socio-economic and health
background characteristics influence not only which family or work trajectories
13
individuals experience, but also how they manage the double commitments to work
and family, how they react to critical transitions in life and, therefore, also how well-
being is affected by those events and trajectories.
Finally, our sequence analysis suffers from limitations that are common to all
studies using this method. Being an exploratory data-driven approach, it poses prob-
lems with respect to the possibility of handling trajectories only partially observed.
The handling of missing data and censored sequences remains an unresolved issue at
the moment (Piccarretta & Studer 2019); therefore, as in other studies, we limit the
analysis to complete sequences. Creating a missing state for each domain and then
interacting them would have created too many categories and uninterpretable esti-
mates. More importantly, the life course holistic interpretation typical of sequence
analysis necessarily loses the focus on the theoretical mechanisms behind events and
transitions that generate a particular long-term trajectory and of studying the impact
of time-varying covariates on life courses. For these reasons, the holistic approach is
rather complementary to other model-based analyses of the life course (Piccarreta &
Studer, 2019).
Notwithstanding such limitations, this study robustly shows a stronger interaction
of family and work trajectories in shaping overall and financial wellbeing in older
age for women compared to men. We confirm previous studies (Halpern-Manners
et al., 2015; Madero-Cabib & Fasang, 2016) by showing that the spillover between
work and family has consequences for women’s wellbeing also beyond childbear-
ing ages, but we also introduce novel perspectives. For instance, Madero-Cabib
and Fasang (2016) show that Swiss women who combine early motherhood with a
weak attachment to the labour market suffer lower financial wellbeing at retirement
age. However, our study shows that this is not always the case. Swiss women actu-
ally benefit from an early family formation if they return to work after childrearing,
because they return at a relatively younger age compared to women who partner and
have children later on (who either never return to work or return to work at an older
age). We further show that the same moderating positive consequences of returning
to work after an early family formation among women influence not only financial
but also, and even more, women’s subjective wellbeing. Finally, we add that while
the consequences in terms of financial wellbeing of a combination of early family
formation and an intermittent career can be resolved if women’s income recovers
later on in the life course, the effects on life satisfaction are much more persistent
beyond later family and employment events.
The unique contribution of our study on life course development of wellbeing lies
in its comprehensive character. The main conclusion we draw is that a biography
characterized by a prolonged lack of partnership and children—representing a non-
normative family trajectory in the Swiss context—endangers Swiss women’s finan-
cial security more than a history of weak attachment to the labour market. The lat-
ter, however, generates significant and persistent negative effects on women’s overall
happiness. Interestingly, neither men’s nor women’s work trajectories in Switzerland
seem to generate long-term positive social network externalities influencing rela-
tional wellbeing in older age. This shows the importance of understanding wellbeing
in a multidimensional way as different aspects of it are differently determined by
early life resources, family and work trajectories and current events.
13
Appendix
See Tables 4, 5, 6, 7, 8, 9, 10, 11, 12 and 13.
Table 4 Family and work states distribution by gender. Source: Elaboration of the authors based on SHP
Biographical files 2002, 2013 and SHP panel (2003–2017)
Family states Gender
Man Woman Total
Unpartnered, childless N 7068 6039 13,107

% 25.91 19.38 22.43
Unpartnered separated/divorced/widow, childless N 236 306 542
% 0.87 0.98 0.93
Partnered, childless N 5218 6150 11,368
% 19.13 19.74 19.45
Re-partnered, separated/divorced/widow, childless N 88 112 200
% 0.32 0.36 0.34
Unpartnered, with children N 403 1221 1624
% 1.48 3.92 2.78
Unpartnered separated/divorced/widow, with children N 138 325 463
% 0.51 1.04 0.79
Partnered, with children N 13,971 16,894 30,865
% 51.21 54.23 52.82
Re-partnered, separated/divorced/widow, with children N 158 108 266
% 0.58 0.35 0.46
Total N 27,280 31,155 58,435
% 100.00 100.00 100.00
Work states Man Woman Total
In education N 310 144 454

% 1.14 0.46 0.78
Full-time N 22,388 9823 32,211
% 82.07 31.53 55.12
Part-time 50–89% N 3037 7097 10,134
% 11.13 22.78 17.34
Small part-time < 50% N 191 4085 4276
% 0.70 13.11 7.32
Not in employment nor education N 1354 10,006 11,360
% 4.96 32.12 19.44
Total N 27,280 31,155 58,435
% 100.00 100.00 100.00
13
Table 5 Summary statistics and distribution of categorical variables. Source: Elaboration of the authors
based on SHP Biographical files 2002, 2013 and SHP panel (2003–2017)
(a)
Continuous variables N Mean Std. Dev. Min Max
Life satisfaction 1884 8.38 1.36 0 10

Satisfaction with personal relationships 1885 8.61 1.31 0 10
Satisfaction with financial situation 1883 7.88 1.89 0 10
Age 1885 60.07 5.66 51 70
Net income 1684 67,479.07 80,984.67 100 2,560,900
Net income (Thousands) 1684 67.48 80.98 0.1 2560.9
(b)
Categorical variables Categories N %
Women work clusters Full-time work 214 21.29

Return to part-time 542 53.93
Not in employment 249 24.78
Men work clusters Early full-time 674 76.59
Full-time + High education 98 11.14
Part-time work 108 12.27
Women family clusters Early Traditional 499 49.65
Traditional 408 40.6
Childless 98 9.75
Men family clusters Traditional 412 46.82
Late Traditional 281 31.93
Childless 187 21.25
Sex Men 880 46.7
Women 1005 53.3
Period 2003 870 46.15
2014 1015 53.85
Swiss Born in Switzerland or Swiss national 1337 70.93
Born abroad 548 29.07
Education Primary 145 7.69
Upper Secondary 1222 59.52
Tertiary 618 32.79
Living arrangement at 14 Lived with both parents 1′571 83.34
Lived with lone parent 183 9.71
Lived alone or other living arrangement 131 6.95
Current employment status full-time paid work 478 25.36
part-time paid work 364 19.31
inactive 1023 54.27
unemployed 17 0.9
other 3 0.16
13
Table 5 (continued)
(b)
Categorical variables Categories N %
Current marital status Unpartnered 130 6.9

Married or registered partnership 1392 73.85
Separated, divorced, widow 363 19.26
Child (men) Have a child at age 50 756 85.9
No child 124 14.1
Father’s education Primary 617 32.73
Upper Secondary 887 47.06
Tertiary 381 20.21
Health problems before age 20 No health issues before age 20 661 35.07
Health issues before age 20 354 18.78
Missing 870 46.15
Table 6 Cluster solutions quality criteria. Source: Elaboration of the authors based on SHP Biographical
files 2002, 2013 and SHP panel (2003–2017)
Family clusters N R2 ASW CH Family clusters N R2 ASW CH
men women
2 clusters 1200 0.285 0.59 598.1 2 clusters 1423 0.299 0.74 674.3
303 0.47 158 0.42
454 0.32 678 0.23
303 0.39 158 0.37
454 0.31 582 0.32
97 0.06 96 0.54
206 0.70 158 0.26
Work clusters men N R2 ASW CH Work clusters N R2 ASW CH
Women
2 clusters 345 0.264 −0.25 539.1 2 clusters 1248 0.189 0.24 367.3
1158 0.94 333 0.62
161 − 0.04 793 0.00
1158 0.92 333 0.59
6 0.7 466 − 0.01
155 0.11 327 0.35
1158 0.90 333 0.54
N is the number of observations per cluster. R2, Pseudo R2, is the share of the discrepancy explained by
the clustering solution. ASW, the average Silhouette width, is the coherence of assignments: high coher-
ence indicates high between-group distances and strong within-group homogeneity. CH, Calinski–Hara-
basz index, is the Pseudo F computed from the distances
13
Table 7 Predicted life satisfaction. Men. Source: Elaboration of the authors based on SHP Biographical files 2002, 2013 and SHP panel (2003–2017)
Model Model Model Model Model Model
(1) (2) (3) (4) (5) (6)
Gross Net Direct Gross Net Direct
Family trajectory: traditional (Ref)

Late traditional 0.117 0.104 0.078 0.139 0.126 0.097
(−0.071–0.306) (−0.083–0.291) (−0.108–0.263) (−0.076–0.354) (−0.087–0.340) (−0.115–0.309)
Childless −0.073 −0.059 −0.076 −0.120 −0.106 −0.107
(−0.286–0.140) (−0.270–0.152) (−0.370–0.217) (−0.364–0.125) (−0.348–0.137) (−0.416–0.203)
Work trajectory: early full-time work (ref)
Full-time work after higher education 0.166 0.153 0.155 0.249 0.238 0.231
(−0.107–0.440) (−0.120–0.427) (−0.116–0.427) (−0.175–0.673) (−0.184–0.660) (−0.188–0.650)
Part-time work −0.073 −0.060 −0.048 −0.179 −0.165 −0.142
(−0.326–0.180) (−0.311–0.191) (−0.298–0.203) (−0.578–0.219) (−0.560–0.231) (−0.535–0.250)
Joint Family and Work Trajectories and Multidimensional…
Late traditional* full-time work after higher −0.266 −0.250 −0.221

education (−0.844–0.311) (−0.823–0.323) (−0.790–0.348)
Late traditional*part-time work 0.149 0.122 0.109
(−0.457–0.756) (−0.483–0.726) (−0.493–0.711)
Childless*full-time work after higher 0.198 0.146 0.131
education (−0.562–0.958) (−0.608–0.900) (−0.623–0.885)
Childless* part-time work 0.211 0.232 0.220
(−0.394–0.816) (−0.368–0.833) (−0.379–0.819)
Age 0.032*** 0.033*** 0.033*** 0.032*** 0.034*** 0.033***
(0.017–0.046) (0.019–0.048) (0.014–0.051) (0.018–0.047) (0.019–0.048) (0.015–0.051)
Year: 2014 (Ref)
Year: 2003 −0.195** −0.151 −0.139 −0.196** −0.150 −0.138
(−0.369–−0.021) (−0.335–0.033) (−0.347–0.069) (−0.370 to −0.021) (−0.334–0.034) (−0.346–0.071)
671
13
Table 7 (continued)
672

(1) (2) (3) (4) (5) (6)
13
Born in Switzerland or Swiss nationality (Ref)

Born abroad −0.248** −0.249** −0.251** −0.253**
(−0.442 to −0.055) (−0.450 to −0.048) (−0.444 to −0.057) (−0.455 to −0.052)
Lived with both parents (Ref)
Lived with lone parent −0.168 −0.145 −0.173 −0.150
(−0.433–0.096) (−0.409–0.118) (−0.439–0.093) (−0.415–0.115)
Lived alone or other living arrangements −0.346* −0.326* −0.343* −0.324*
(−0.695–0.003) (−0.672–0.021) (−0.693–0.008) (−0.672–0.024)
Father upper secondary education (Ref)
Father Primary Education −0.196** −0.174* −0.191** −0.171*
(−0.384 to −0.009) (−0.360–0.013) (−0.379–−0.003) (−0.358–0.016)
Father tertiary education 0.108 0.097 0.104 0.092
(−0.099–0.316) (−0.110–0.303) (−0.104–0.312) (−0.115–0.299)
Current marital status: married or registered partnership (Ref)
Unpartnered −0.587*** −0.594***
(−0.998 to −0.176) (−1.005 to −0.182)
Sep, div, widow −0.108 −0.111
(−0.343–0.126) (−0.346–0.124)
Currently have children: Yes (Ref)
Currently have children: No 0.325* 0.306*
(−0.032–0.683) (−0.055–0.667)
C. L. Comolli et al.
Table 7 (continued)
(1) (2) (3) (4) (5) (6)
Suffers from current illness: No (Ref)

Suffers from current illness: Yes −0.162 −0.163
(−0.359–0.035) (−0.362–0.035)
Current employment status: Inactive (Ref)
Full-time work 0.074 0.077
(−0.156–0.303) (−0.154–0.308)
Part-time paid work −0.142 −0.153
(−0.445–0.161) (−0.458–0.152)
Unemployed −1.161*** −1.118***
(−1.913 to −0.408) (−1.875 to −0.360)

Constant 6.573*** 6.592*** 6.670*** 6.545*** 6.570*** 6.645***
(5.663–7.483) (5.684–7.501) (5.448–7.892) (5.632–7.458) (5.658–7.482) (5.420–7.871)
Observations 879 879 879 879 879 879
R-squared 0.041 0.063 0.091 0.043 0.065 0.093
Confidence intervals in parenthesis. *p < 0.1, **p < 0.05, ***p < 0.01
673
13
Table 8 Predicted life satisfaction. Women. Source: Elaboration of the authors based on SHP Biographical files 2002, 2013 and SHP panel (2003–2017)
674

(1) (2) (3) (4) (5) (6)
13
Family trajectory: Traditional (Ref)

Early traditional −0.072 −0.052 −0.035 −0.363 −0.369 −0.377
(−0.259–0.115) (−0.240–0.136) (−0.220–0.149) (−0.828–0.102) (−0.837–0.099) (−0.838–0.084)
Childless −0.665*** −0.692*** −0.332 −0.884*** −0.923*** −0.540**
(−1.004 to −0.326) (−1.032 to −0.352) (−0.733–0.069) (−1.365 to −0.403) (−1.406 to −0.439) (−1.060 to −0.020)
Work trajectory: Full−time work (Ref)
Return to part-time −0.119 −0.164 −0.220* −0.405** −0.461** −0.516**
(−0.361–0.123) (−0.409–0.081) (−0.466–0.026) (−0.800 to −0.009) (−0.860 to −0.063) (−0.910 to −0.123)
Not in employment −0.146 −0.183 −0.348** −0.246 −0.307 −0.482**
(−0.427–0.136) (−0.467–0.101) (−0.637 to −0.058) (−0.671–0.179) (−0.738–0.125) (−0.910 to −0.053)
Early traditional*return to part−time 0.463* 0.486* 0.505*
(−0.064–0.991) (−0.043–1.015) (−0.014–1.023)
Early traditional*not in employment 0.109 0.157 0.202
(−0.479–0.697) (−0.435–0.748) (−0.378–0.781)
Childless*return to part-time 0.398 0.407 0.358
(−0.362–1.159) (−0.355–1.170) (−0.384–1.100)
Childless*not in employment 0.371 0.380 0.317
(−1.121–1.864) (−1.114–1.874) (−1.135–1.769)
Age 0.022*** 0.022*** 0.023** 0.022*** 0.023*** 0.023**
(0.006–0.038) (0.006–0.038) (0.005–0.041) (0.006–0.038) (0.006–0.039) (0.005–0.042)
Year: 2014 (Ref)
Year: 2003 −0.363*** −0.313*** −0.294*** −0.365*** −0.315*** −0.291***
(−0.546 to −0.181) (−0.506 to −0.120) (−0.499 to −0.089) (−0.549 to −0.182) (−0.509 to −0.121) (−0.497 to −0.086)
Table 8 (continued)
(1) (2) (3) (4) (5) (6)

Born abroad −0.194* −0.221** −0.193* −0.224**
(−0.395–0.006) (−0.419 to −0.022) (−0.395–0.009) (−0.424 to −0.024)
(−0.515–0.101) (−0.493–0.107) (−0.519–0.099) (−0.495–0.106)
Lived alone or other living arrangements −0.059 0.020 −0.040 0.038
(−0.388–0.271) (−0.302–0.342) (−0.371–0.291) (−0.284–0.361)
Father primary education −0.095 −0.107 −0.098 −0.110

(−0.297–0.107) (−0.303–0.089) (−0.300–0.104) (−0.307–0.086)
(−0.242–0.247) (−0.216–0.261) (−0.244–0.246) (−0.218–0.260)
Current marital status: Married or registered partnership (Ref)
Unpartnered −0.773*** −0.798***
(−1.211 to −0.336) (−1.238 to −0.357)
Sep, div, widow −0.670*** −0.668***
(−0.879 to −0.462) (−0.877 to −0.460)
Suffers from current illness: Yes −0.261** −0.252**
(−0.469 to −0.054) (−0.460 to −0.044)
675
13
Table 8 (continued)
676

(1) (2) (3) (4) (5) (6)
13

(−0.169–0.476) (−0.145–0.504)
(−0.390–0.089) (−0.382–0.098)
Unemployed −1.587*** −1.567***
(−2.706 to −0.468) (−2.686 to −0.447)
Other situation −0.544 −0.548
(−2.115–1.026) (−2.118–1.022)
Constant 7.402*** 7.496*** 7.811*** 7.566*** 7.679*** 7.967***
(6.402–8.402) (6.492–8.500) (6.611–9.011) (6.527–8.604) (6.635–8.723) (6.741–9.193)
Observations 1005 1005 1005 1005 1005 1005
R-squared 0.042 0.049 0.109 0.047 0.053 0.113
Confidence intervals in parenthesis.*p < 0.1, **p < 0.05, ***p < 0.01
Table 9 Predicted satisfaction with personal relationships. Men. Source: Elaboration of the authors based on SHP Biographical files 2002, 2013 and SHP panel (2003–
2017)
(1) (2) (3) (4) (5) (6)

Late traditional 0.002 -0.002 −0.031 0.047 0.041 0.014
(−0.191–0.196) (−0.196–0.192) (−0.223–0.162) (−0.174–0.268) (−0.181–0.263) (−0.206–0.234)
Childless −0.288** −0.285** −0.139 −0.296** −0.294** −0.167
(−0.507 to −0.069) (−0.505 to −0.066) (−0.444–0.166) (−0.548 to −0.044) (−0.546 to −0.042) (−0.488–0.154)
Work trajectory: Early full-time work (ref)
Full-time work after higher education −0.269* −0.267* −0.254* −0.086 −0.075 −0.072
(−0.550–0.011) (−0.550–0.016) (−0.535–0.027) (−0.523–0.350) (−0.513–0.364) (−0.507–0.363)
Part-time work −0.104 −0.094 −0.060 −0.150 −0.154 −0.147
(−0.365–0.156) (−0.355–0.167) (−0.320–0.201) (−0.561–0.260) (−0.565–0.257) (−0.554–0.260)

education (−0.904–0.279) (−0.915–0.270) (−0.923–0.253)
Late traditional*part-time work −0.060 −0.033 −0.021
(−0.685–0.564) (−0.661–0.595) (−0.646–0.604)
Childless*full-time work after higher −0.286 −0.308 −0.209
education (−1.068–0.497) (−1.091–0.475) (−0.991–0.573)
(−0.422–0.823) (−0.403–0.845) (−0.314–0.929)
Age 0.019** 0.019** 0.018* 0.019** 0.019** 0.019*
(0.004–0.034) (0.004–0.034) (−0.000–0.037) (0.004–0.034) (0.004–0.034) (−0.000–0.037)
Year: 2014 (Ref)
Year: 2003 −0.183** −0.139 −0.141 −0.183** −0.137 −0.139
677
13
(−0.363 to −0.004) (−0.330–0.052) (−0.357–0.075) (−0.362 to −0.003) (−0.328–0.055) (−0.355–0.078)
Table 9 (continued)
678

(1) (2) (3) (4) (5) (6)
13

Born abroad −0.164 −0.182* −0.168 −0.188*
(−0.365–0.037) (−0.391–0.026) (−0.369–0.033) (−0.397–0.021)
(−0.440–0.110) (−0.458–0.089) (−0.444–0.108) (−0.463–0.086)
Lived alone or other living arrangements −0.020 −0.036 −0.026 −0.042
(−0.383–0.343) (−0.396–0.324) (−0.390–0.338) (−0.404–0.319)
(−0.200–0.190) (−0.198–0.189) (−0.201–0.190) (−0.199–0.189)
(−0.169–0.262) (−0.190–0.237) (−0.173–0.258) (−0.196–0.232)
Unpartnered −0.980*** −0.986***
(−1.406 to −0.553) (−1.413 to −0.559)
Sep, div, widow −0.171 −0.178
(−0.414–0.072) (−0.422–0.066)
Currently have children: No 0.193 0.190
(−0.178–0.564) (−0.185–0.564)
Table 9 (continued)
(1) (2) (3) (4) (5) (6)

Suffers from current illness: Yes −0.200* −0.204*
(−0.405–0.005) (−0.409–0.002)
(−0.177–0.298) (−0.179–0.299)
(−0.494–0.136) (−0.502–0.130)
Unemployed 0.008 0.049
(−0.772–0.789) (−0.736–0.834)
Constant 7.613*** 7.612*** 7.747*** 7.599*** 7.600*** 7.736***

(6.677–8.550) (6.668–8.556) (6.480–9.015) (6.659–8.539) (6.653–8.547) (6.465–9.007)
Observations 880 880 880 880 880 880
R-squared 0.026 0.031 0.062 0.028 0.033 0.064
679
13
Table 10 Predicted satisfaction with personal relationships. Women. Source: Elaboration of the authors based on SHP Biographical files 2002, 2013 and SHP panel
680
(2003–2017)
13
(1) (2) (3) (4) (5) (6)

Early Traditional 0.112 0.125 0.125 0.332 0.309 0.285
(−0.064–0.287) (−0.052–0.301) (−0.052–0.303) (−0.104–0.768) (−0.130–0.748) (−0.160–0.729)
Childless −0.098 −0.121 −0.029 0.017 −0.029 0.051
(−0.415–0.220) (−0.439–0.197) (−0.415–0.357) (−0.434–0.468) (−0.482–0.424) (−0.451–0.553)
Work trajectory: Full-time work (Ref)
Return to part-time 0.036 −0.002 −0.019 0.153 0.096 0.065
(−0.191–0.263) (−0.231–0.228) (−0.257–0.218) (−0.217–0.524) (−0.277–0.470) (−0.314–0.445)
Not in employment 0.132 0.094 0.106 0.313 0.244 0.240
(−0.132–0.395) (−0.172–0.359) (−0.173–0.384) (−0.086–0.712) (−0.160–0.648) (−0.173–0.653)
Early traditional*return to part-time −0.224 −0.190 −0.159
(−0.719–0.271) (−0.686–0.306) (−0.660–0.341)
Early traditional*not in employment −0.339 −0.278 −0.245
(−0.891–0.213) (−0.833–0.277) (−0.804–0.314)
Childless*return to part-time −0.026 −0.002 −0.012
(−0.739–0.687) (−0.717–0.713) (−0.728–0.704)
Childless*not in employment −0.630 −0.581 −0.592
(−2.029–0.770) (−1.981–0.819) (−1.993–0.809)
Age 0.019** 0.019** 0.027*** 0.019** 0.020** 0.027***
(0.004–0.034) (0.004–0.034) (0.010–0.045) (0.004–0.034) (0.004–0.035) (0.009–0.045)
Year: 2014 (Ref)
Year: 2003 −0.130 −0.081 −0.013 −0.136 −0.087 −0.020
(−0.301–0.041) (−0.262–0.099) (−0.210–0.184) (−0.307–0.036) (−0.269–0.095) (−0.218–0.179)

Table 10 (continued)
(1) (2) (3) (4) (5) (6)

Born abroad −0.180* −0.220** −0.174* −0.213**
(−0.368–0.008) (−0.411 to −0.029) (−0.363–0.015) (−0.406 to −0.020)
Lived with lone parent −0.289** −0.280* −0.284* −0.276*
(−0.578 to −0.000) (−0.569–0.009) (−0.573–0.005) (−0.565–0.014)
Lived alone or other living arrangements 0.072 0.092 0.059 0.081
(−0.236–0.381) (−0.218–0.402) (−0.251–0.369) (−0.230–0.393)
(−0.242–0.136) (−0.241–0.137) (−0.242–0.137) (−0.241–0.138)

Father tertiary education −0.015 −0.012 −0.015 −0.010
(−0.244–0.214) (−0.242–0.218) (−0.245–0.215) (−0.241–0.220)
Unpartnered −0.190 −0.174
(−0.611–0.231) (−0.599–0.251)
Sep, div, widow −0.145 −0.149
(−0.346–0.056) (−0.350–0.053)
(−0.210–0.190) (−0.210–0.191)
681
13
682

(1) (2) (3) (4) (5) (6)
13

(−0.103–0.519) (−0.119–0.508)
Part-time paid work 0.158 0.153
(−0.073–0.388) (−0.078–0.385)
Unemployed −0.118 −0.111
(−1.196–0.960) (−1.191–0.969)
Other situation 1.186 1.186
(−0.327–2.698) (−0.329–2.701)
Constant 7.474*** 7.556*** 7.047*** 7.345*** 7.450*** 6.975***
(6.537–8.410) (6.616–8.496) (5.890–8.203) (6.372–8.319) (6.472–8.429) (5.792–8.158)
Observations 1005 1005 1005 1005 1005 1005
R-squared 0.014 0.023 0.030 0.016 0.024 0.031
Table 11 Predicted satisfaction with financial situation. Men. Source: Elaboration of the authors based on SHP Biographical files 2002, 2013 and SHP panel (2003–2017)
(1) (2) (3) (4) (5) (6)

Late traditional 0.084 0.067 −0.078 0.070 0.048 −0.088
(−0.175–0.343) (−0.186–0.320) (−0.333–0.176) (−0.226–0.366) (−0.241–0.337) (−0.378–0.202)
Childless −0.068 −0.053 −0.507** −0.091 −0.091 −0.513**
(−0.361–0.225) (−0.339–0.233) (−0.914 to −0.099) (−0.428–0.245) (−0.419–0.238) (−0.941 to −0.085)
Work trajectory: early full-time work (ref)
Full−time work after higher education 0.330* 0.394** 0.188 0.485 0.570* 0.290
(−0.046–0.705) (0.024–0.763) (−0.186–0.563) (−0.099–1.069) (−0.001–1.141) (−0.292–0.871)
Part-time work −0.124 −0.091 0.025 −0.356 −0.383 −0.067
(−0.472–0.224) (−0.431–0.249) (−0.319–0.370) (−0.905–0.193) (−0.918–0.153) (−0.606–0.472)

education (−0.965–0.618) (−0.966–0.578) (−0.782–0.777)
Late traditional*part-time work 0.303 0.362 0.024
(−0.532–1.138) (−0.457–1.181) (−0.802–0.849)
Childless*full-time work after higher education −0.461 −0.528 −0.596
(−1.507–0.586) (−1.549–0.492) (−1.633–0.440)
(−0.384–1.281) (−0.227–1.400) (−0.578–1.078)
Age 0.042*** 0.044*** 0.041*** 0.042*** 0.044*** 0.041***
(0.022–0.062) (0.024–0.063) (0.015–0.066) (0.022–0.062) (0.024–0.063) (0.015–0.066)
Year: 2014 (Ref)
Year: 2003 0.165 0.387*** 0.285* 0.162 0.385*** 0.291**
(−0.075–0.405) (0.137–0.636) (−0.003–0.573) (−0.078–0.402) (0.136–0.634) (0.002–0.579)
683
13
684

(1) (2) (3) (4) (5) (6)
13

Born abroad −0.771*** −0.685*** −0.777*** −0.689***
(−1.033 to −0.509) (−0.963 to −0.407) (−1.039 to −0.515) (−0.968 to −0.410)
Lived with lone parent −0.414** −0.388** −0.431** −0.389**
(−0.773 to −0.056) (−0.750 to −0.027) (−0.792 to −0.071) (−0.753 to −0.025)
Lived alone or other living arrangements −0.209 −0.237 −0.207 −0.237
(−0.683–0.264) (−0.720–0.245) (−0.681–0.268) (−0.722–0.248)
Father primary education −0.274** −0.204 −0.278** −0.211
(−0.528 to −0.020) (−0.461–0.052) (−0.533 to −0.024) (−0.468–0.046)
Father tertiary education −0.254* −0.345** −0.262* −0.346**
(−0.535–0.027) (−0.627 to −0.062) (−0.544–0.019) (−0.629 to −0.062)
Unpartnered 0.031 0.044
(−0.539–0.602) (−0.527–0.615)
Sep, div, widow −0.168 −0.171
(−0.489–0.154) (−0.493–0.151)
Currently have children: No 0.497* 0.526**
(−0.007–1.001) (0.017–1.035)
(1) (2) (3) (4) (5) (6)

(−0.377–0.166) (−0.380–0.166)
Full-time work −0.074 −0.067
(−0.395–0.247) (−0.390–0.256)
Part-time paid work 0.094 0.101
(−0.324–0.513) (−0.320–0.522)
Unemployed −1.592*** −1.606***
(−2.694 to −0.490) (−2.712 to −0.500)

Net personal income 0.003*** 0.003***
(0.002–0.004) (0.002–0.004)
Constant 5.262*** 5.445*** 5.598*** 5.272*** 5.467*** 5.600***
(4.009–6.515) (4.214–6.676) (3.905–7.291) (4.015–6.529) (4.232–6.701) (3.903–7.297)
Observations 880 880 816 880 880 816
R-squared 0.025 0.077 0.115 0.027 0.081 0.118
685
13
Table 12 Predicted satisfaction with financial situation. Women. Source: Elaboration of the authors based on SHP Biographical files 2002, 2013 and SHP panel (2003–
686
2017)
13
(1) (2) (3) (4) (5) (6)

Early Traditional −0.195 −0.146 −0.082 −0.577* −0.632* −0.443
(−0.457–0.068) (−0.407–0.116) (−0.352–0.188) (−1.227–0.074) (−1.279–0.014) (−1.107–0.222)
Childless −0.801*** −0.880*** −0.446 −1.115*** −1.232*** −0.522
(−1.277 to −0.324) (−1.352 to −0.408) (−1.007–0.114) (−1.790 to −0.440) (−1.902 to −0.561) (−1.260–0.215)
Work trajectory: full−time work (Ref)
Return to part−time −0.083 −0.227 −0.157 −0.476* −0.667** −0.428
(−0.423–0.256) (−0.567–0.112) (−0.512–0.198) (−1.028–0.077) (−1.218 to −0.117) (−0.997–0.140)
Not in employment 0.114 −0.010 0.150 −0.014 −0.223 0.091
(−0.281–0.509) (−0.403–0.383) (−0.287–0.587) (−0.609–0.581) (−0.819–0.372) (−0.555–0.738)
Early traditional*return to part-time 0.591 0.689* 0.515
(−0.147–1.328) (−0.042–1.420) (−0.229–1.260)
Early traditional*not in employment 0.202 0.377 0.245
(−0.621–1.024) (−0.441–1.194) (−0.613–1.102)
Childless*return to part-time 0.985* 0.997* 0.427
(−0.080–2.050) (−0.059–2.052) (−0.603–1.457)
Childless*not in employment −1.871* −1.930* −2.500**
(−3.959–0.216) (−3.994–0.133) (−4.483 to −0.518)
Age 0.026** 0.027** 0.033** 0.025** 0.025** 0.031**
(0.004–0.049) (0.004–0.049) (0.006–0.061) (0.002–0.047) (0.003–0.047) (0.004–0.058)
Year: 2014 (Ref)
Year: 2003 0.249* 0.408*** 0.329** 0.253* 0.420*** 0.332**
(−0.007–0.506) (0.140–0.676) (0.025–0.633) (−0.003–0.509) (0.152–0.689) (0.028–0.637)

(1) (2) (3) (4) (5) (6)

Born abroad −0.638*** −0.520*** −0.659*** −0.536***
(−0.917 to −0.359) (−0.812 to −0.228) (−0.939 to −0.380) (−0.829 to −0.243)
(−0.679–0.175) (−0.720–0.150) (−0.660–0.193) (−0.692–0.176)
Lived alone or other living arrangements 0.135 0.228 0.155 0.243
(−0.322–0.592) (−0.227–0.684) (−0.302–0.612) (−0.213–0.698)
Father primary education −0.258* −0.194 −0.255* −0.195
(−0.538–0.022) (−0.482–0.095) (−0.535–0.024) (−0.483–0.093)

(−0.130–0.548) (−0.176–0.515) (−0.135–0.543) (−0.169–0.521)
Unpartnered −1.059*** −1.101***
(−1.670 to −0.449) (−1.713 to −0.489)
Sep, div, widow −1.200*** −1.204***
(−1.523 to −0.878) (−1.526 to −0.882)
(−0.411–0.188) (−0.400–0.196)
687
13
688

(1) (2) (3) (4) (5) (6)
13

Full-time work −0.076 −0.078
(−0.567–0.415) (−0.570–0.414)
(−0.562–0.154) (−0.559–0.155)
Unemployed −0.935 −0.928
(−2.599–0.729) (−2.588–0.732)
Other situation −0.536 −0.507
(−3.135–2.063) (−3.098–2.083)
Net personal income 0.016*** 0.016***
(0.012–0.021) (0.012–0.021)
Constant 6.378*** 6.629*** 5.853*** 6.699*** 7.026*** 6.162***
(4.975–7.781) (5.236–8.021) (4.020–7.687) (5.246–8.152) (5.582–8.469) (4.300–8.023)
Observations 1003 1003 868 1003 1003 868
R-squared 0.024 0.053 0.134 0.033 0.063 0.144
Table 13 Predicted life satisfaction. Robustness check with pre-trajectories health conditions. Women and men. Source: Elaboration of the authors based on SHP Bio-
graphical files 2013 and SHP panel (2014–2017)
(1) (2) (3) (4) (5) (6)
Net Direct Direct, Net Direct Direct,
Interaction Interaction
Men Women
Late traditional 0.194 0.148 0.280*
(−0.055–0.444) (−0.100–0.397) (−0.012–0.572)
Childless 0.170 0.186 0.138
(−0.125–0.465) (−0.297–0.669) (−0.363–0.639)
Full−time work after Higher education 0.159 0.146 0.346
(−0.127–0.445) (−0.138–0.430) (−0.089–0.782)

Part-time work −0.203 −0.162 −0.192
(−0.622–0.216) (−0.586–0.262) (−0.805–0.422)
Early traditional 0.164 0.183 −0.199
(−0.087–0.415) (−0.063–0.429) (−0.757–0.359)
Childless −0.463** −0.192 −0.758**
(−0.897 to −0.029) (−0.700–0.315) (−1.406 to −0.109)
Return to part-time −0.204 −0.266* −0.683***
(−0.514–0.106) (−0.579–0.047) (−1.159 to −0.208)
Not in employment −0.159 −0.325 −0.653**
(−0.552–0.234) (−0.728–0.079) (−1.244 to −0.063)
689
13
690

(1) (2) (3) (4) (5) (6)
13
Late traditional* full−time work after higher −0.448

education (−1.056–0.160)
Late traditional*part−time work −0.549
(−1.579–0.482)
Childless*full−time work after higher educa- −0.071
tion (−0.874–0.732)
Childless* part−time work 0.589
(−0.395–1.573)
Early traditional*return to part−time 0.516
(−0.117–1.149)
Early traditional*not in employment 0.365
(−0.416–1.146)
Childless*return to part−time 1.173**
(0.266–2.080)
Childless*not in employment 2.432*
(−0.292–5.157)
Age 0.033*** 0.039*** 0.035** 0.028*** 0.026** 0.026**
(0.013–0.053) (0.011–0.067) (0.007–0.064) (0.007–0.049) (0.001–0.052) (0.000–0.051)
Born abroad −0.139 −0.123 −0.124 −0.118 −0.145 −0.167
(−0.466–0.188) (−0.449–0.203) (−0.449–0.202) (−0.425–0.188) (−0.444–0.154) (−0.466–0.132)
(1) (2) (3) (4) (5) (6)

Lived with lone parent −0.223 −0.083 −0.049 −0.196 −0.133 −0.155
(−0.587–0.142) (−0.452–0.286) (−0.419–0.321) (−0.630–0.238) (−0.554–0.287) (−0.574–0.265)
Lived alone or other living arrangements −0.062 −0.003 0.014 0.324 0.425* 0.435*
(−0.545–0.421) (−0.480–0.475) (−0.463–0.491) (−0.146–0.794) (−0.033–0.882) (−0.021–0.892)
Father primary education −0.193 −0.164 −0.167 −0.136 −0.141 −0.134
(−0.441–0.054) (−0.409–0.080) (−0.413–0.078) (−0.401–0.128) (−0.397–0.115) (−0.390–0.123)
Father tertiary education 0.051 0.077 0.061 −0.009 0.020 0.002
(−0.248–0.349) (−0.219–0.373) (−0.235–0.357) (−0.331–0.313) (−0.294–0.334) (−0.311–0.316)

Health issues before age 20: No (Ref)
Health issues before age 20: Yes −0.080 −0.078 −0.078 0.015 0.006 −0.015
(−0.312–0.152) (−0.309–0.153) (−0.310–0.154) (−0.238–0.267) (−0.239–0.252) (−0.260–0.229)
Suffers from current illness: Yes −0.091 −0.091 −0.178 −0.195
(−0.371–0.189) (−0.371–0.189) (−0.457–0.100) (−0.473–0.084)
Unpartnered −0.311 −0.327 −0.686** −0.697**
(−0.829–0.208) (−0.845–0.190) (−1.243 to −0.129) (−1.257 to −0.137)
Sep, div, widow −0.206 −0.210 −0.731*** −0.724***
(−0.518–0.106) (−0.522–0.101) (−1.016 to −0.446) (−1.009 to −0.440)
691
13
692

(1) (2) (3) (4) (5) (6)
13

Currently have children: No 0.171 0.164
(−0.363–0.706) (−0.374–0.701)
Full−time work 0.195 0.166 0.259 0.248
(−0.145–0.534) (−0.177–0.509) (−0.164–0.681) (−0.173–0.670)
Part−time paid work −0.171 −0.202 −0.079 −0.096
(−0.591–0.249) (−0.625–0.221) (−0.382–0.225) (−0.398–0.207)
Unemployed −1.608*** −1.586*** −2.338*** −2.422***
(−2.581 to −0.635) (−2.559 to −0.612) (−3.876 to −0.801) (−3.956 to −0.888)
Other situation 0.415 0.492
(−2.215–3.044) (−2.128–3.112)
Constant 6.547*** 6.174*** 6.374*** 6.993*** 7.402*** 7.787***
(5.286–7.808) (4.290–8.059) (4.474–8.274) (5.686–8.300) (5.726–9.078) (6.086–9.489)
Observations 474 474 474 540 540 540
R-squared 0.050 0.095 0.106 0.037 0.113 0.128
Funding Open Access funding provided by Université de Lausanne. The project was supported by the
Swiss National Science Foundation (grant 100017_182301, project WELLWAYS). This study has been
realized using the data collected by the Swiss Household Panel (SHP), which is based at FORS-the Swiss
Centre of Expertise in the Social Sciences.
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License,
which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as
you give appropriate credit to the original author(s) and the source, provide a link to the Creative Com-
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ses/by/4.0/.
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European Journal of Population (2021) 37:643–696

Joint Family and Work Trajectories and Multidimensional

C. L. Comolli1 · L. Bernardi1 · M. Voorpostel2

Keywords Subjective wellbeing · Relational wellbeing · Financial wellbeing ·

2.1 Family and Work Trajectories and Wellbeing

2.2 Joint Family and Work Trajectories and Wellbeing

The life course framework stresses the multidimensionality of biographies (Elder,

2.3 Early (dis)advantages and Proximate Determinants of Wellbeing

The process linking family–work trajectories to wellbeing is gendered. While part-

Wellbeing is a multi-faceted concept, including multiple dimensions that are strongly

the distance between goals, needs and aspirations—subjective factors—and the

2.6 The Swiss Context

Switzerland is classified as a conservative–liberal welfare state with strong tradition-

2.7 Research Questions and Hypotheses

3 Data and Method

3.1 Data and Sample Selection

4 Family and Employment Trajectories

Unpartnered, childless N 1908 2591 2569 7068

Unpartnered, childless N 2955 761 2323 6039

In education N 2 304 4 310

Traditional 7.35 66.67 25.98 408

Traditional 3.88 53.64 42.48 412

Full-time work 12.15 57.94 29.91 214

Early full-time work 3.41 58.16 38.43 674

Table 2 reports the educational-level distribution in each cluster8., 9 While around

Early full-time work 37.95 23.18 15.45 674

Full-time work 6.47 7.96 6.87 214

5 Multivariate Analysis Results

See Tables 4, 5, 6, 7, 8, 9, 10, 11, 12 and 13.

Unpartnered, childless N 7068 6039 13,107

In education N 310 144 454

Life satisfaction 1884 8.38 1.36 0 10

Women work clusters Full-time work 214 21.29

Current marital status Unpartnered 130 6.9

Family trajectory: traditional (Ref)

Late traditional* full-time work after higher −0.266 −0.250 −0.221

Model Model Model Model Model Model

Born in Switzerland or Swiss nationality (Ref)

Suffers from current illness: No (Ref)

(−1.913 to −0.408) (−1.875 to −0.360)

Model Model Model Model Model Model

Family trajectory: Traditional (Ref)

Born in Switzerland or Swiss nationality (Ref)

Father primary education −0.095 −0.107 −0.098 −0.110

Model Model Model Model Model Model

Current employment status: Inactive (Ref)

Gross Net Direct Gross Net Direct

Family trajectory: Traditional (Ref)

(−0.365–0.156) (−0.355–0.167) (−0.320–0.201) (−0.561–0.260) (−0.565–0.257) (−0.554–0.260)

Model Model Model Model Model Model

Born in Switzerland or Swiss nationality (Ref)

Gross Net Direct Gross Net Direct

Suffers from current illness: No (Ref)

Constant 7.613*** 7.612*** 7.747*** 7.599*** 7.600*** 7.736***

Gross Net Direct Gross Net Direct

Family trajectory: Traditional (Ref)

(−0.301–0.041) (−0.262–0.099) (−0.210–0.184) (−0.307–0.036) (−0.269–0.095) (−0.218–0.179)

Gross Net Direct Gross Net Direct

Born in Switzerland or Swiss nationality (Ref)

2.1 Family and Work Trajectories and Wellbeing

2.2 Joint Family and Work Trajectories and Wellbeing

2.3 Early (dis)advantages and Proximate Determinants of Wellbeing

2.6 The Swiss Context

2.7 Research Questions and Hypotheses

3 Data and Method

3.1 Data and Sample Selection

4 Family and Employment Trajectories

5 Multivariate Analysis Results

Constant 7.613* 7.612* 7.747* 7.599* 7.600* 7.736*