Question:
You are given the full text of a scientific research paper.

    Your task is to generate a structured **policy brief** that includes the following components:

    - A concise, descriptive **title** that captures the central theme or policy issue addressed in the paper (do not label it with “Title:”).
    - **Policy Problem** - What societal or strategic challenge is the paper addressing?
    - **Scientific Research Findings** - What are the key results relevant to this issue?
    - **Scientific Research Study Methods** - What methodology or data supports these findings?
    - **Policy Implications** - How might the findings guide or influence policy?

    Write each section in a clear, accessible style suitable for policymakers. Avoid overly technical language, but use essential terms when necessary. Structure the output as a labeled five-part policy brief document.

    Scientific Research Paper:
    https://doi.org/10.1177/00221465241265435
Journal of Health and Social Behavior
2024, Vol. 65(4) 468–488
© American Sociological Association 2024
DOI: 10.1177/00221465241265435
journals.sagepub.com/home/hsb
Original Article
Understanding variations in mental well-being has
become a globally salient health challenge (Lesthaeghe
1995; Van de Kaa 1987). The “second demographic
transition” has led to significant shifts in work and
family dynamics, creating complex life trajectories
that warrant an examination of their impacts on
mental health, especially among women who face
unique stressors due to societal gender roles and
expectations (Caballero et al. 2022; Zagel and Van
Winkle 2020).
In industrial societies, demographic, economic,
and cultural transformations have made employment
trajectories unpredictable, with critical factors such
as occupational stress (Chandola et al. 2007),
resource accumulation (Lantz et al. 2005; Read,
Grundy, and Foverskov 2016), and social network
development (Grundy and Sloggett 2003) impacting
mental health. Concurrently, family formation has
evolved with trends such as increasing cohabitation
(Heuveline and Timberlake 2004), higher divorce
rates (Schoen and Canudas-Romo 2006), remarriage
(Coleman, Ganong, and Fine 2000), and the rise of
single-parent families (Heuveline, Timberlake, and
Furstenberg 2003) affecting mental well-being.
The relationship between work, family, and
health necessitates understanding how life course
trajectories, particularly for women, influence later
life mental health (Chen et al. 2018; Dannefer
2003). Women, especially those with social
1265435 HSBXXX10.1177/00221465241265435Journal of Health and Social BehaviorAzar
research-article2024
1Purdue University, West Lafayette, IN, USA
Corresponding Author:
Ariel Azar, Department of Sociology, Purdue University,
100 University Street, Suite 1114, West Lafayette, IN
47907, USA.
Email: arazar@purdue.edu
Work–Family Life Course
Trajectories and Women’s
Mental Health: The Moderating
Role of Defamilization Policies
in 15 European Territories
Ariel Azar1
Abstract
This study employs multichannel sequence analysis of data from the Survey of Health, Ageing, and Retirement
in Europe to explore variations in the association between work–family life trajectories and women’s
mental health across European cohorts born between 1924 and 1965 within different policy contexts.
It finds that trajectories characterized by prolonged employment and delayed familial commitments are
generally associated with increased depressive symptoms. Notably, the strength of this association varies
significantly across cohorts and is notably moderated by defamilization policies. These policies, which aim
to reduce dependency on family for managing social risks, buffer mental health challenges in traditional
family roles but are less effective for women in trajectories with delayed family formation. This investigation
highlights the nuanced ways in which historical and cultural contexts alongside policy environments shape
mental health outcomes at various life stages, offering valuable insights into our understanding of health
disparities across the life course, with an emphasis on exposure to changing institutions.
Keywords
defamilization, life course, mental health, sequence analysis, work–family conflict

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469
disadvantages, often experience greater health
risks, including hypertension, depression, disabil-
ity, and heart disease (Anand, Esposito, and
Villaseñor 2018; Diaz-Toro et al. 2018; Engels et al.
2019; Landös et al. 2018; Leupp 2017; Lucumi
et al. 2017).
This study investigates how gendered welfare
state policies, particularly defamilization policies,
affect these dynamics. Defamilization policies,
which aim to reduce individuals’ dependency on
family for welfare and economic security, may
influence the mental health of women by facilitat-
ing or hindering their ability to balance work and
caregiving roles (Lohmann and Zagel 2016). I
examine the impact of such policies using
SHARELIFE and country-year data covering 14
European countries to understand their role in the
link between midlife work–family formation pat-
terns and mental health later in life.
Given Europe’s varied welfare state models and
diverse cultural dynamics, this research provides
insights into how policy environments interact with
life trajectories to affect women’s mental health. By
analyzing these relationships, the study seeks to
understand how policy interventions might mitigate
the health impacts of work–family conflicts. The
subsequent sections detail the theoretical frame-
works, working hypotheses, methodologies, and
findings, culminating in a discussion that contextu-
alizes the results within the broader literature on
health, life events, and welfare policy interaction.
Background
Work, Family, and Mental Health
Research on disparities in mental health has increas-
ingly focused on the interrelation of work and fam-
ily arrangements, highlighting three main areas of
impact: the effects of work status and labor force
attachment, changes in family structures, and the
interaction between work and family dynamics.
Several studies underline a robust link between
employment status and mental health outcomes.
Job security, workplace environments, and employ-
ment status significantly influence disparities in
mental health (Coduti et al. 2015; Leupp 2017;
LaMontagne et al. 2021; Llena-Nozal, Lindeboom,
and Portrait 2004; Sverke, Hellgren, and Näswall
2002; Witte 1999). Unemployment or precarious
employment often leads to increased stress, depres-
sion, and anxiety, exacerbated by financial instabil-
ity and impaired self-esteem, where employment
serves as a core part of personal identity (Brydsten,
Hammarström, and San Sebastian 2018; Jahoda
1982; Paul and Moser 2009; van der Noordt et al.
2014). Consequently, stable employment provides
financial security and a sense of purpose and
belonging, thereby bolstering mental health.
Shifts in family structures also significantly
impact mental health. Changes in marital status,
cohabitation dynamics, parenting roles, and house-
hold compositions have all been linked to mental
health outcomes (Brown, Bulanda, and Lee 2005;
Dinh et al. 2017; Lawton, Moss, and Kleban 1984;
McDonald et al. 2022; Taylor and Ford 1983;
Williams and Umberson 2004). Life transitions,
such as divorce, widowhood, or transitioning to
single parenthood, can heighten stress and mental
health risks, with stable family environments pro-
viding crucial emotional and practical support
(Cohen and Wills 1985). Conversely, family disrup-
tions can result in loss of support and increased
stress due to financial and emotional challenges,
adversely affecting mental health (Gaydosh and
Harris 2018).
The intersection of work and family life, partic-
ularly work–family conflict, has been a growing
focus due to its profound impact on mental health.
Balancing these duties often leads to stress, burn-
out, and mental health issues, with theories like role
strain and role conflict suggesting that stress arises
when simultaneous social roles conflict (Allen et al.
2000; Greenhaus and Beutell 1985; Schieman and
Glavin 2011). Inadequate workplace policies or
family structures that do not support role integra-
tion can exacerbate this stress, leading to psycho-
logical exhaustion and subsequent mental health
declines over time.
In conclusion, the intricate interplay between
work and family dynamics significantly shapes
mental health. This body of research has adopted a
life course perspective, examining how changes in
work and family influence health throughout an
individual’s life, utilizing frameworks such as criti-
cal periods and cumulative disadvantages to deepen
understanding of these dynamics.
Life Course Trajectories and Mental Health
Work and family life significantly affect health and
well-being throughout different life stages. Long-
term labor market attachment is associated with bet-
ter mental health, whereas weaker attachments, such
as part-time work or homemaking, can have nega-
tive impacts (Madero-Cabib, Azar, and Pérez-Cruz
2019; Montez et al. 2015; Ross and Mirowsky 2002;
Wahrendorf et al. 2013). This is attributed to the

470
Journal of Health and Social Behavior 65(4)
accumulation of material resources, a sense of pur-
pose, and social connections. In the family domain,
stable relationships, such as marriage and parent-
hood, enhance health through stronger social support
(Grundy and Tomassini 2010; Kravdal et al. 2012;
Lacey et al. 2016; Read, Grundy, and Wolf 2011).
Conversely, marital disruptions, such as divorce or
widowhood, often lead to psychological stress and
well-being decline (Liu 2012; Umberson, Thomeer,
and Williams 2013), with significant impacts
observed particularly in women due to the loss of
marital benefits and the stress of marital dissolution
(Amato 2000; Williams and Umberson 2004).
The timing and sequence of work and family
experiences have historically varied. In sociological
life course research, concepts such as differentia-
tion, (de)standardization, and institutionalization
explain these changes. Differentiation (Brückner
and Mayer 2005; Mayer 1991) refers to the increas-
ing variety of life statuses individuals encounter,
leading to greater life course variability. This is
exemplified by more frequent job changes (Mertens
1998) and higher rates of divorce, remarriage, and
childlessness, indicating diverse life trajectories
(Brückner and Mayer 2005). Standardization refers
to the process where specific life transitions or tim-
ings become normative within a population. For
example, once highly standardized, retirement
shows varied patterns with multiple transitions,
indicating destandardization (Calvo, Madero-
Cabib, and Staudinger 2018). Institutionalization
relates to the regulation of life stages through
norms, laws, or organizational rules (Brückner and
Mayer 2005). Whereas traditional marriage was
once a strictly institutionalized norm, the recogni-
tion of diverse unions over time illustrates its partial
deinstitutionalization.
I use two theoretical models to analyze the link
between diversified work–family trajectories and
mental health: the cumulative (dis)advantage model
and role enhancement/conflict theory. As defined
by Merton (1968), cumulative advantages are the
process in which early benefits increase inequalities
over time. In the context of health research, the
accumulation of (dis)advantages in areas such as
education, employment, and family ties leads to
divergent health trajectories over time (Cunningham
et al. 2018; Dannefer 2003; DiPrete and Eirich
2006; Madero-Cabib, Undurraga, and Valenzuela
2019; Singh et al. 2017). Consequently, individuals,
particularly women, with life paths marked by
long-term harmful exposure in these domains may
experience worsening mental health due to cumula-
tive stress.
Yet concurrent roles within work and family
dimensions are pivotal, with role enhancement the-
ory (Ahrens and Ryff 2006; Barnett and Hyde
2001) arguing that multiple roles improve psycho-
logical well-being through empowerment, resource
availability, and emotional satisfaction (Muller and
Litwin 2011). Conversely, role conflict theory sug-
gests that stress arises from the incompatible
demands of these roles, leading to adverse psycho-
logical effects (Gove 1984; Lahelma et al. 2002).
This is exemplified by findings that employment
benefits women’s mental health unless offset by the
demanding care of young children, with such role
conflict diminishing as children age (Leupp 2017;
Tosi and Grundy 2019).
Work–family trajectories impact mental health
differently, affected by the stress of role conflict or
the rewards from work and family life. These expe-
riences are seen as cumulative advantages or disad-
vantages through a life course lens. Contextual
factors can modify these effects, making certain
disadvantages less impactful. Therefore, I argue
that a nuanced understanding of these dynamics
requires a comprehensive contextual framework.
Bringing the Welfare State into the Picture
In this study, I aim to contribute to the existing
literature by assessing how accumulated social
advantages or disadvantages from work–family tra-
jectories influence the mental health of European
women over 50. I also examine the role of
defamilization policies in moderating this effect, a
perspective that has not been extensively explored
in previous research. Welfare states, as crucial pro-
viders of welfare services and influencers of well-
being (Bambra and Eikemo 2008; Esping-Andersen
1990), shape individual opportunities and life tra-
jectories in both work and family spheres, thereby
impacting health outcomes.
Esping-Andersen’s (1990) influential work, The
Three Worlds of Welfare Capitalism, provides a
widely recognized framework for categorizing wel-
fare states, including examples from Europe, the
United States, and Japan. This typology, which
focuses on reducing market dependency and social
stratification and delineating the welfare responsibili-
ties between state, market, and family, informs much
comparative welfare state research and is founda-
tional in understanding varying work and family
experiences (Corna 2013; Esping-Andersen 1997).
Research leveraging these typologies indicates
that welfare state configurations influence social
stratification and its impact on health (Bambra,

Azar
471
Smith, and Pearce 2019; Espelt et al. 2008; Sacker,
Worts, and McDonough 2011). Notably, social-
democratic welfare states mitigate the adverse
health effects of specific old-age labor trajectories
more effectively than corporatist states (Madero-
Cabib, Corna, and Baumann 2019). However, the
broad scope of these typologies can obscure the
specific institutional configurations or policy areas
that moderate the relationship between life course
conditions and health.
Recognizing the gendered dimensions of social
stratification, scholars have developed gender-
focused typologies based on “defamilization”
(Bambra 2007; Saxonberg 2013). Despite these
developments, the gender perspective remains
insufficiently explored in comparative health
research (Uccheddu et al. 2019). This study aims to
fill this gap by incorporating a gender perspective
in the analysis of welfare states, particularly in the
context of health disparities. Bambra et al. (2009)
stress this importance, noting that although social
democratic states have reduced income-related
health disparities, these benefits are not consistently
extended to women (Mackenbach 2012).
Furthermore, viewing welfare states as evolving
entities influenced by political and cultural dynam-
ics is essential (Abbott and DeViney 1992). A mere
snapshot of welfare state policies at a single point
does not adequately capture the interplay between
individual life courses and welfare state dynamics
affecting health. Understanding the impact of insti-
tutional configurations on health requires an appre-
ciation of varied institutional setups encountered by
individuals across different life stages, shaped by
the timing of their birth and historical policy
reforms.
This research aims to bridge political sociology
and social epidemiology by evolving beyond static
definitions of welfare states and shifting from
broad, ungendered interpretations of welfare policy
arrangements. Although general typologies help
identify overarching welfare state trends, they often
fail to capture intracountry variations across policy
sectors that could significantly influence health
outcomes.
By focusing on Europe, this study provides a
foundation for exploring a dynamic, nuanced, and
gendered perspective of the welfare state in life tra-
jectory and health research. The cross-national life
course approach illuminates how diverse institu-
tional configurations shape individual life paths and
health disparities, enhancing our understanding of
how welfare states can promote health equity
through equitable life trajectories.
Defamilization and Health
Defamilization is a crucial concept in understanding
the interplay between gender, work, and family.
Historically burdened with caregiving and house-
hold duties, women’s engagement in the labor market
has been significantly influenced by defamilization
policies. These policies aim to reduce the caregiving
responsibilities traditionally assigned to women,
enhancing their autonomy and ability to participate
fully in the labor force. Such measures help balance
work and family life and lessen financial depen-
dence within families (Bambra 2007; Chau et al.
2017; Lohmann and Zagel 2016).
The effectiveness of defamilization policies is
evident in their capacity to redistribute paid and
unpaid labor, which is vital for understanding how
life course trajectories in work and family contexts
impact health. For example, in environments with
minimal defamilization support, women juggling
intensive domestic and professional roles may face
different mental health challenges compared to
those in contexts with substantial state support for
unpaid labor. Similarly, the mental health outcomes
in traditional family settings with long-term labor
force attachment can vary significantly depending
on the level of defamilization.
Despite the apparent links, few studies have
thoroughly investigated the relationship between
defamilization and mental health, particularly through
the lens of gender disparities. Most research utilizes
traditional welfare state typologies, focusing on
institutional frameworks without considering indi-
viduals’ dynamic exposure to these policies
throughout their life courses (Bambra et al. 2009;
Chung et al. 2013).
This study proposes that defamilization policies
mitigate the stress associated with specific work–
family trajectories, influencing mental health out-
comes later in life. By examining the role that these
policies play in the association between life trajec-
tories and mental health later in life, we can better
understand how welfare states might alleviate the
stress induced by particular life paths, providing
crucial insights into the role of policy in shaping
health across different life stages.
Working Hypotheses
Based on the comprehensive literature review that
explores the interplay between work–family dynam-
ics and mental health over the life course and the
influence of welfare states in shaping this relation-
ship, this study posits several hypotheses to clarify
the connections between life course trajectories,

472
Journal of Health and Social Behavior 65(4)
defamilization policies, and women’s mental health
in various European settings and across different
cohorts:
Hypothesis 1: Life course trajectories and men-
tal health: Women’s mental health in later life is
significantly influenced by their work and fam-
ily trajectories. Stable employment and family
structures throughout life are hypothesized to
lead to better mental health outcomes than frag-
mented or unstable work and family histories.
Hypothesis 2: Cohort differences in the impact
of life trajectories on mental health: The impact
of life course trajectories on later life mental
health outcomes varies across birth cohorts.
Changes in societal norms, employment pat-
terns, family structures, and welfare policies
over time may cause later cohorts to experience
different mental health impacts compared to ear-
lier ones.
Hypothesis 3: Moderating role of defamilization
policies: The level of defamilization policies
within a country is expected to moderate the
impact of work–family life trajectories on men-
tal health outcomes. Higher levels of defamiliza-
tion are predicted to mitigate the adverse effects
of challenging work and family trajectories,
reflecting the policies’ supportive role in enhanc-
ing autonomy and reducing dependency on fam-
ily roles for welfare.
These hypotheses aim to dissect the complex
dynamics affecting women’s mental health by con-
sidering the roles of individual life trajectories, soci-
etal changes, and state policies. The study seeks to
provide an integrated understanding of how various
factors interact to influence health and well-being
among women in diverse European environments.
Data And Methods
Data Source and Sample
I utilized data from the Survey of Health, Ageing,
and Retirement in Europe (SHARE), a panel survey
representative of individuals ages 50 and older
across various European countries. SHARE covers
many topics, including economic, demographic,
health, and family matters (Schröder 2011).
Specifically, I focused on the SHARELIFE data col-
lected during the third and seventh waves of
SHARE, which provides detailed life histories,
including employment and family formation experi-
ences from childhood to advanced age, for women
born between 1924 and 1965. More details on the
data source are provided in Appendix A in the online
version of the article.
I analyzed the relationship between life trajecto-
ries and the risk of depression by merging
SHARELIFE data with information from Waves 1
through 7 of SHARE. This integration resulted in a
final sample of 22,250 women who had compre-
hensive life histories from ages 15 to 50 and data on
depressive symptoms. This sample was initially
identified from SHARELIFE and then supple-
mented with the latest observations from other
SHARE waves to include mental health data and
other covariates. The structure of the analytical
dataset is explained in detail in Appendix A in the
online version of the article.
To address potential selection biases due to
health issues influencing work–family trajectories
and subsequent health (Case and Paxson 2011;
Haas 2008; Haas, Glymour, and Berkman 2011), I
conducted robustness checks, excluding those with
significant early health issues, shown in Figure A.2
in the online version of the article.
Measurement
Research on life courses and health often overlooks
mental health (Angelini, Howdon, and Mierau
2019) despite evidence of it being affected in the
long term by life circumstances (Luo and Waite
2005; Tani et al. 2016). The EURO-D scale, from
SHARE, measures depressive symptoms for cross-
national comparisons in European populations,
assessing 12 symptoms, such as depression, pessi-
mism, and fatigue (for a detailed explanation of the
EURO-D scale and its items, see Appendix B in the
online version of the article). Based on EURO-D’s
association with clinical depression, I used a thresh-
old of four or more symptoms to indicate a high risk
of depression.
To build work–family life histories, participants
were prompted to recall significant milestones,
such as job-related changes and key family events,
annually. These included moving out of their par-
ent’s home, cohabiting, marriage, childbirth, wid-
owhood, and divorce. Leveraging these data, I
crafted a life history data set in which each individ-
ual is observed annually from age 15 up to their age
at the time of the survey. Their recorded state
remained unchanged until they indicated a change
in status. This information was used as an input for
sequence analysis to construct work–family life
histories, as detailed in the empirical strategy sec-
tion. For more information on the measurement of
work–family statuses throughout the life course,
see Appendix B in the online version of the article.

Azar
473
Regarding the moderating variable, defamiliza-
tion, I used a country-year indicator elaborated by
Zagel and Van Winkle (2020), which relies on prin-
cipal components analysis, applied to indicators for
six policy areas between 1924 and 2006 that seek to
remove the burden of family work exclusively from
women, as detailed in Appendix B in the online ver-
sion of the article. Using the country-year index, I
estimated an average for when individuals were 15
to 40 as an indicator of the level of exposure to
defamilization policies. For example, a woman
born in 1940 in Italy had a defamilization exposure
level calculated as the average for all values in Italy
between 1955 and 1990.
Finally, all models included controls for demo-
graphic and health-risk-related variables. For
sociodemographic indicators, all models included
age at the time of the interview, educational level,
income, and occupation of the primary breadwinner
at age 10 as a proxy for childhood socioeconomic
status (SES). For health-related variables, current
age and smoking status were considered as lifestyle
indicators, childhood self-rated health was included
to control for possible health selection effects into
employment and family trajectories, and self-rated
health at the time of interview for physical health
was also included. Also, economic development,
measured as the gross domestic product (GDP) per
capita averaged at the same years as the defamiliza-
tion indices, was included at the country level.
Empirical Strategy
My analysis comprised two stages: starting with
applying multichannel sequence analysis (MCSA)
and cluster analysis to build simultaneous career
and family formation trajectories and followed by
logistic regression models to examine the likelihood
of being at high risk of depression across these tra-
jectories. MCSA, an advanced form of sequence
analysis (MacIndoe and Abbott 2011), facilitates the
comparison of life trajectories in multiple domains
by using operations such as insertion/deletion and
state substitution for optimal matching. This led to
various work–family trajectories, with costs based
on the inverse of state probabilities and a constant
insertion/deletion cost.
Hierarchical agglomerative clustering via the
Ward algorithm was utilized to categorize these tra-
jectories, using Hubert’s gamma, point biserial cor-
relation, average silhouette width, and Hubert’s C
for selecting the best cluster solution, resulting in
six distinct types (Studer 2013).
In the second stage, I linked these trajectories
with the risk of depression variable, employing
fixed effects logistic regression models with adjust-
ments for country and cohort. This approach facili-
tated investigating variations in the relationship
across different defamilization index levels and
cohorts, providing insights into the interaction
effects and average marginal effects.
For more details on the empirical strategy, see
Appendix C in the online version of the article.
Results
Figures 1 and 2 show each trajectory type’s chrono-
gram and sequence index plots. The chronogram
depicts the work and family status percentages for
ages 15 to 50, with employment on the left and fam-
ily trajectories on the right. The x-axis tracks ages
from 15 to 50, and the legend lists 7 employment
and 12 family statuses. The sequence index plot in
Figure 2 illustrates individual trajectories with lines
for each person, where color changes indicate status
transitions, not group proportions.
Here, I use the “careerist, late union” trajectory
type, which comprises 8.1% of the sample, as an
example to understand Figures 1 and 2. The chro-
nogram shows several statuses at age 15, the start-
ing point (education, employment, family work),
with a shift in the proportion from education to
employment as individuals age. The sequence
index plot in Figure 2 displays individual paths,
highlighting prolonged employment with intermit-
tent family work for most women. This illustrates
that the chronogram’s proportions reflect transi-
tions over time, not constant roles, clarifying that
the observed employment or family work percent-
ages result from these individual trajectories.
The careerist, late union type involves women
mainly employed, leaving parental homes unpart-
nered, and then cohabiting and marrying without
children. The “homemaker archetype” (29.6%) fea-
tures women primarily in home/family work, tran-
sitioning from parental home to marriage and
motherhood.
“Entrepreneur
familist”
(7.4%)
includes women consistently in self-employment,
with family paths similar to homemakers.
“Stable job, traditional family” (40.3%) repre-
sents the largest group with stable employment and
traditional family life. Stable job, family diverse
shows similar employment but varied family paths,
with women approaching 50 in different family sta-
tuses. “Job flux, traditional family” describes
women with fluctuating employment but traditional
family trajectories.
Figure 3 depicts the distribution of the trajectory
types by country on the left and by cohort on the
right. Variability between countries is significant. In

474
Journal of Health and Social Behavior 65(4)
all countries, the modal trajectory is either the
homemaker archetype or the stable job, traditional
family. Southern European nations and the
Netherlands predominantly display the former type.
By contrast, countries such as Sweden,
Denmark, Eastern Germany, and the Czech Republic
show a higher prevalence of the latter. There is also
noteworthy variation across cohorts. Later cohorts
lean toward a higher prevalence of stable job, tradi-
tional family, whereas the homemaker archetype
remains dominant in earlier cohorts. Notably, a sig-
nificantly heightened prevalence of nontraditional
Figure 1.  Chronogram Plots of Multichannel Trajectory Types from Ages 15 to 50 in the Work and
Family Dimensions, the Survey of Health, Ageing and Retirement in Europe (SHARE).
Note: N = 26,274.

Azar
475
family formation types also exists in nations and
cohorts where stable job, traditional family emerges
as the predominant trajectory.
Table 1 details the variables used in the study,
with 30% reporting over three symptoms. The aver-
age age of respondents is 62 (SD ≈ 9 years).
Socioeconomically, half have upper secondary/
vocational education, and the average household
income is around EUR 33,000. Predominantly,
women’s childhood households had blue-collar
breadwinners. Health-wise, 17% smoke; 40% drink
weekly; current and childhood self-rated health
Figure 2.  Index Plots of Multichannel Trajectory Types from Ages 15 to 50 in the Work and Family
Dimensions, the Survey of Health, Ageing and Retirement in Europe (SHARE).
Note: N = 26,274.

476
7
57
6
21
7
2
7
51
10
20
5
6
13
47
8
21
7
5
10
46
12
20
4
8
8
40
8
29
5
11
9
37
6
34
11
4
14
35
8
28
11
4
8
35
9
31
15
2
10
29
7
37
13
4
7
25
7
41
15
5
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24
18
42
9
3
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54
17
1
8
12
5
51
24
1
7
8
4
64
16
1
4
3
2
72
18
0
0
20
40
60
80
100
Ireland
Spain
Netherlands
Greece
Italy
West Germany
Switzerland
Austria
Belgium
France
Poland
Denmark
Sweden
East Germany
Czech Republic
8
18
7
49
10
7
9
20
7
42
19
4
11
22
7
42
14
5
9
23
8
39
16
5
9
26
7
43
13
2
8
29
8
41
11
4
5
34
7
38
11
5
7
36
7
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9
5
6
42
8
32
6
6
6
44
8
28
7
7
7
47
8
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47
11
23
5
6
8
52
9
22
3
6
8
48
10
22
5
7
0
20
40
60
80
100
1963–1966
1960–1962
1957–1959
1954–1956
1951–1953
1948–1950
1945–1947
1942–1944
1939–1941
1936–1938
1933–1935
1930–1932
1927–1929
1924–1926
1
2
3
4
5
6
Figure 3.  Distribution of Trajectory Types across Countries and Cohorts, the Survey of Health, Ageing and Retirement in Europe (SHARE).
Note: Percentages are shown over the bars. N = 26,274. Trajectory types correspond as follows: 1 = careerist, late union; 2 = homemaker archetype; 3 = entrepreneur familist; 4 =
stable job, traditional family; 5 = stable job, family diverse; 6 = job flux, traditional family.

Azar
477
averages are 2.97 and 3.26, respectively; and 10%
had an illness before age 50. The standardized aver-
ages for defamilization and economic development
exposure are .17 and 10.35, respectively.
Table 2 examines the link between trajectory
types, covariates, and risk of depression. Women
with stable job, traditional family or entrepreneur
familist trajectories have a lower risk of depression.
By contrast, careerist, late union individuals have a
relatively higher prevalence in the high-risk depres-
sion group. The risk of depression correlates with
lower defamilization, with women at higher risk
having experienced significantly lower levels of
defamilization. Typically, women at higher risk of
depression are older, have lower education and
income, come from lower childhood SES, and rate
their current and childhood health as poor, with a
higher prevalence of pre-50 illness. Notably, they
drink less, but this finding reverses and loses sig-
nificance in the multivariate analysis.
To better understand these associations, I con-
ducted a logistic regression to predict the post-50
likelihood of reporting a high risk of depression
considering work/family trajectories, birth cohort,
defamilization, and other covariates. The analysis
includes interactions between trajectories, cohorts,
and defamilization. Models feature country and
year-of-interview fixed effects, clustering standard
errors by country-cohort.
Table 3 presents four logistic regression models
analyzing the likelihood of reporting a high risk of
depression based on work and family trajectories.
Model 1 indicates that women in the entrepreneur
familist trajectory have significantly lower odds of
being at high risk of depression compared to those
in the homemaker archetype. By contrast, stable
job, family diverse women exhibit approximately
16% higher odds. In Model 2, which adjusts for
average GDP per capita from ages 15 to 50, a 1 SD
increase in defamilization exposure is associated
with a 5.8 percentage point rise in the likelihood of
depression. Notably, this association is consider-
able, especially when compared to the effect size of
educational attainment; the difference in the pre-
dicted probability of reporting more than three
depressive symptoms between women with less
than upper secondary education and those with ter-
tiary education or more is 3.0 percentage points, as
estimated from Model 2. Furthermore, the signifi-
cant magnitude of this association appears even
Table 1.  Descriptive Statistics, the Survey of Health, Ageing and Retirement in Europe.
Mean (SD) or %
Depressive symptoms
  ≤3
69.03
  >3
30.97
Age
62.15 (8.89)
Educational level
  Less than upper secondary
46.34
  Upper secondary and vocational training
34.00
  Tertiary
19.66
Total income (in thousands EUR)
32.92 (47.63)
Breadwinner occupation at age 10
  White collar
17.67
  Blue collar
81.44
  Military
.89
Smokes (reference = doesn’t smoke)
17.35
Drinks weekly (reference = doesn’t drink weekly)
39.18
Self-rated health (1–5)
3.02 (1.06)
Illness before age 50 (reference = didn’t experience illness)
10.59 (1.06)
Self-rated childhood health (1–5)
3.26 (.99)
Defamilization index exposure level (standardized)
.17 (1.20)
GDP per capita exposure level (standardized)
10.35 (4.04)
N
21,743
Note: GDP = gross domestic product.

478
Journal of Health and Social Behavior 65(4)
when controlling for country-specific unobserved
heterogeneity by including country fixed effects,
indicating that the observed relationship is robust
and sizable within countries.
Model 3 reveals significant interactions between
trajectory types and cohorts, presented as average
marginal effects in Figure 4a. Specifically, the
entrepreneur familist trajectory correlates with
lower depression risk, but only in later cohorts. By
contrast, the stable job, family diverse trajectory is
associated with increased depressive symptoms
among these cohorts. Intriguingly, the relationship
between the careerist, late union trajectory and
depression risk reverses across cohorts: It decreases
the risk in earlier cohorts but increases it in later
ones. Adding this interaction to the model slightly
enhances the model performance, increasing the
pseudo R2 by about 1%. Despite being small, fur-
ther analyses using a bootstrap likelihood ratio test
with 1,000 replications showed that this increase
was significant.
Model 4 examines the interaction between
defamilization exposure and trajectory types,
assessing their impact on mental health, with results
shown in Figure 4b. This interaction is significant
for the careerist, late union and entrepreneur
familist trajectories and significantly increases the
fit of the model relative to Model 2 by around 9%.
Further analyses estimating a bootstrap likelihood
ratio test with 1,000 replications showed that the
inclusion of the defamilization-trajectory interac-
tion significantly enhanced the model performance.
Women in the careerist, late union trajectory expe-
rience no differences in their risk of depression
Table 2.  Bivariate Descriptive Statistics, the Survey of Health, Ageing and Retirement in Europe.
Low Risk of
Depression
(≤3 Symptoms)
High Risk of
Depression
(>3 Symptoms)
p Value
Trajectory type
  Homemaker archetype
68.17
31.83
.000
  Careerist, late union
66.08
33.92

  Entrepreneur familist
70.14
29.86

  Stable job, traditional family
72.08
27.92

  Stable job, family diverse
68.23
31.77

  Job flux, traditional family
62.77
37.23

Age
61.92 (8.68)
62.66 (9.32)
.000
Educational level
  Less than upper secondary
63.18
36.82
.000
  Upper secondary and vocational training
72.22
27.78

  Tertiary
77.29
22.71

Total income (in thousands EUR)
35.43 (49.99)
27.31 (41.36)
.000
Breadwinner occupation at age 10
  White collar
74.40
25.60
.000
  Blue collar
67.83
32.17

  Military
72.16
27.84

Smokes (reference = doesn’t smoke)
16.63
18.70
.000
Drinks weekly (reference = doesn’t drink weekly)
41.40
33.81
.000
Self-rated health (1–5)
2.73 (.99)
3.52 (1.02)
.000
Illness before age 50 (reference = didn’t experience illness)
8.53
15.17
.000
Self-rated childhood health (1–5)
3.26 (.99)
2.48 (1.01)
.000
Defamilization index exposure level (standardized)
.20 (1.23)
0.09 (1.13)
.000
GDP per capita exposure level (standardized)
10.58 (3.94)
9.85 (4.21)
.000
N
15,009
6,734

%
69.03
30.97

Note: The p values correspond to χ2 tests for categorical variables and t tests for continuous variables. Standard
deviations are in parentheses. For categorical variables, the conditional distribution of depressive symptomatology
is shown. For continuous variables, the mean of the variable for each category of depressive symptomatology is
displayed. GDP = gross domestic product.

479
Table 3.  Logistic Regression Models over the Probability of Reporting Depressive Symptomatology for Work and Family Trajectory Types, the Survey of
Health, Ageing and Retirement in Europe.
Model 1
Model 2
Model 3
Model 4

OR
SE
OR
SE
OR
SE
OR
SE
Trajectory type (reference = homemaker archetype)
  Careerist, late union
1.030
(.102)
1.023
(.100)
.493***
(.116)
.995
(.096)
  Entrepreneur familist
.780**
(.081)
.771**
(.080)
1.111
(.280)
.784**
(.079)
  Stable job, traditional family
.966
(.064)
.961
(.063)
.886
(.163)
.958
(.064)
  Stable job, family diverse
1.162*
(.106)
1.146
(.103)
.802
(.190)
1.146
(.107)
  Job flux, traditional family
1.054
(.114)
1.051
(.113)
.933
(.257)
1.059
(.118)
Cohort
1.101
(.090)
1.079
(.086)
1.079
(.087)
1.081
(.086)
Age
1.022
(.027)
1.041
(.030)
1.033
(.029)
1.041
(.030)
Educational level (reference = less than upper secondary)
  Upper secondary and vocational training
.899
(.059)
.904
(.059)
.898*
(.058)
.902
(.059)
  Tertiary
.870
(.066)
.862*
(.066)
.858**
(.064)
.856**
(.065)
Total income (logged)
.953***
(.013)
.951***
(.014)
.954***
(.013)
.951***
(.014)
Breadwinner occupation at age 10 (reference = white collar)
  Blue collar
.972
(.062)
.971
(.062)
.971
(.062)
.968
(.062)
  Military
.750
(.174)
.761
(.174)
.751
(.173)
.759
(.174)
Smokes (reference = doesn’t smoke)
1.088
(.079)
1.087
(.080)
1.096
(.082)
1.093
(.081)
Drinks weekly (reference = doesn’t drink weekly)
1.049
(.063)
1.046
(.062)
1.047
(.061)
1.045
(.061)
Self-rated health (1–5)
.470***
(.018)
.472***
(.018)
.470***
(.018)
.471***
(.018)
Illness before age 50 (reference = didn’t experience illness)
1.284***
(.114)
1.276***
(.113)
1.295***
(.115)
1.279***
(.114)
Self-rated childhood health (1–5)
1.077***
(.024)
1.083***
(.024)
1.082***
(.025)
1.085***
(.024)
Defamilization index average exposure
1.350***
(.116)
1.250*
(.147)
GDP per capita average exposure
1.033
(.034)
1.05
(.035)
1.035
(.034)
Trajectory type × Cohort
  Careerist, late union
1.095***
(.031)

  Entrepreneur familist
.953
(.030)

  Stable job, traditional family
1.012
(.024)

(continued)

480
Model 1
Model 2
Model 3
Model 4

OR
SE
OR
SE
OR
SE
OR
SE
  Stable job, family diverse
1.044
(.029)

  Job flux, traditional family
1.018
(.034)

Trajectory type × Defamilization
  Careerist, late union
1.281**
(.141)
  Entrepreneur familist
.826
(.112)
  Stable job, traditional family
1.089
(.102)
  Stable job, family diverse
1.075
(.115)
  Job flux, traditional family
1.048
(.182)
Constant
.667
(1.445)
.171
(.408)
.242
(.573)
.168
(.399)
Pseudo R2
.1200
.1214
.1219
.1225

Observations
22,100
Note: GDP = gross domestic product.
*p < 0.1. **p < 0.05. ***p < 0.01.
Table 3.  (continued)

Azar
481
relative to the homemaker archetype at low
defamilization levels. However, at high defamiliza-
tion levels, they have a notably higher chance of
being at high risk of depression. Conversely,
women in the entrepreneur familist trajectory show
no difference in their risk of depression at low
defamilization levels but report fewer depressive
symptoms at higher levels.
Addressing the potential selection bias of women
into life trajectories due to early or midlife health
issues, all models adjust for self-reported illnesses
before observing the trajectories. To ensure the
robustness of Model 4’s results, I recalculated the
model excluding women who reported illnesses
before age 50, mitigating the bias from health-
driven self-selection into trajectories affecting later
mental health. The Appendix A in the online version
of the article shows these findings, confirming the
results remain consistent with a more conservative
assessment, excluding these women.
-.2
-.1
0
.1
.2
1924–1926
1927–1929
1930–1932
1933–1935
1936–1938
1939–1941
1942–1944
1945–1947
1948–1950
1951–1953
1954–1956
1957–1959
1960–1962
1963–1966
Careerist, Late Union
-.2
-.1
0
.1
.2
1924–1926
1927–1929
1930–1932
1933–1935
1936–1938
1939–1941
1942–1944
1945–1947
1948–1950
1951–1953
1954–1956
1957–1959
1960–1962
1963–1966
Enterpreneur Familist
-.2
-.1
0
.1
.2
1924–1926
1927–1929
1930–1932
1933–1935
1936–1938
1939–1941
1942–1944
1945–1947
1948–1950
1951–1953
1954–1956
1957–1959
1960–1962
1963–1966
Stable Job, Family Diverse
-.4
-.2
0
.2
.4
-1
-.5
0
.5
1
1.5
2
2.5
3
Defamilization (std)
Careerist, Late Union
-.4
-.2
0
.2
.4
Effects on Pr(Eurod3)
-1
-.5
0
.5
1
1.5
2
2.5
3
Defamilization (std)
Enterpreneur Familist
A
B
Figure 4.  Average Marginal Effect of Selected Work–Family Trajectory Types on the Probability
of Women Reporting More than Three Depressive Symptoms by Birth Cohort and Defamilization
Level Exposure, the Survey of Health, Ageing and Retirement in Europe (SHARE). (a) Cohort. (b)
Defamilization Level Exposure.
Note: The 95% confidence intervals are shown in gray. Average marginal effects are relative to following a homemaker
archetype trajectory type. Defamilization levels exposure values are standardized. N = 22,100.

482
Journal of Health and Social Behavior 65(4)
Discussion
This study analyzed the life trajectories of European
women born between 1924 and 1965, uncovering
how work and family histories relate to mental well-
being (Cohen and Manning 2010). Employing life
course and institutional frameworks, it explored the
impact of defamilization policies and generational
shifts on mental health (Cornwell, Laumann, and
Schumm 2008; Liefbroer 1999).
Findings demonstrate a nuanced evolution in
work–family patterns across different generations,
affecting mental health. This aligns with established
literature that connects life course events with
health through social roles, family, and work
engagement (Grundy, Read, and Väisänen 2020;
Repetti, Taylor, and Seeman 2002; Simon and
Barrett 2010; Zella and Harper 2020). This research
sheds light on these dynamics and provides evi-
dence in support of Hypothesis 1, demonstrating
that specific life patterns, such as entrepreneur
familist, may lead to a lower risk of depression,
reflecting how the unfolding of life trajectories in
work–family dimensions can shape mental health
later in life. The accumulation of self-employment
throughout the life course together with the forma-
tion of a traditional family can enhance mental
health through the existence of emotional backing,
a feeling of safety, and hands-on help, all of which
are essential for managing life challenges brought
by self-employment (Cohen and Wills 1985). These
insights emphasize the importance of considering
the overall influence of these trajectories on mental
health and how societal norms and institutions may
promote specific life paths that could adversely
affect individual well-being when not adhered to.
The main objective of this study, however, was
to extend beyond observing work–family trajecto-
ries in isolation and place them within historical
and policy contexts. Results reveal the relationship
between life trajectories and mental health across
different generations. This supports Hypothesis 2,
which states that the association between work–
family life course trajectories and mental health
outcomes in later life differ across birth cohorts.
Previous research indicates that the timing of mar-
riage affects health, with early marriage being detri-
mental (Dupre, Beck, and Meadows 2009; Grundy
and Holt 2000) and late marriage sometimes being
protective (Dupre and Nelson 2016; Hughes and
Waite 2009; McFarland, Hayward, and Brown
2013). This study suggests that such relationships
may depend on the historical context in which life
courses unfold and how family interacts with labor
force participation. For example, earlier cohort
women who marry late, engage in the labor market
long term, and have no children (the careerist, late
union type) are at less risk of depression. By con-
trast, among later cohorts, this trajectory type cor-
relates with a greater risk of depression. It is not the
individual choice to focus on a career and delay
marriage that inherently impacts personal fulfill-
ment and mental wellness but, rather, the cultural
norms and pressures that shape and sometimes
challenge these decisions, indicating that societal
expectations play a significant role in mental health
outcomes when understanding them as the result of
life and family life paths.
For women who have combined long-term self-
employment with traditional family roles (entrepre-
neur familist), those from more recent cohorts
experience less risk of depression in later life, a
reflection of societal changes (Pampel 2011;
Parkinson et al. 2018) toward valuing flexibility
and autonomy in the professional sphere while
maintaining traditional family roles (Scherger,
Nazroo, and May 2016). These shifts suggest a
growing appreciation for balancing self-employ-
ment and family commitments, aligning with mod-
ern ideals of individual choice and work–life
harmony (Inglehart 1997). In this context, women
may achieve greater control and fulfillment in both
professional and personal domains, contributing to
improved mental health outcomes.
Interestingly, this study also highlights that tra-
ditional employment paired with nontraditional
family formation tends to negatively impact mental
health in later cohorts. This finding suggests a con-
flict because the stability expected from traditional
jobs clashes with the fluidity of nontraditional fam-
ily setups despite growing societal acceptance. The
resulting stress and mental health difficulties high-
light the challenges faced by those navigating non-
traditional life courses within rapidly changing
societal norms. This underscores the ongoing need
for supportive structures tailored to unconventional
family structures.
Beyond historical changes, I use an institutional
theory perspective (Beckfield et al. 2015) to explore
how policy context, particularly defamilization poli-
cies aimed at reducing caregiving burdens and pro-
moting work–life balance (Bambra 2007; Chau et al.
2017; Lohmann and Zagel 2016), might shape the
relationship between life trajectories and mental
health, aligning with Hypothesis 3. These policies,
which intend to support women’s workforce involve-
ment and reduce family financial reliance, interact
with individual life trajectories and affect mental

Azar
483
health outcomes (Bambra 2007; Chau et al. 2017;
Lohmann and Zagel 2016). On the one hand, women
who pursue nontraditional family trajectories like the
careerist, late union type face increased depression
risks in highly defamilized environments, indicating
a misalignment between policy support and their
needs or experiences. This phenomenon can be
understood through role conflict theory, which sug-
gests that stress from balancing personal and profes-
sional life exacerbates mental health issues. Moen
(2022) and Moen and Roehling’s (2005) concept of
“career mystique plus” further illustrates this, where
women are expected to excel in both career and care-
giving roles, with significant stress and penalties for
failing to meet these dual expectations.
On the other hand, I also find that women adher-
ing to a traditional family path, who marry at norma-
tive ages and have children, and who are long-term
self-employed (entrepreneur familist) derive mental
health benefits in high defamilization contexts. Role
enhancement theory suggests that the positive iden-
tities and support networks from their family roles
and self-employment align well with defamilization
policies, which alleviate caregiving burdens and
facilitate less conflict between family and profes-
sional roles, thereby improving mental well-being
(Ahrens and Ryff 2006; Barnett and Hyde 2001). As
part of these policies, flexible work initiatives sup-
port better work–family balance and enhance
employees’ well-being (Moen et al. 2016).
The contrasting impacts of defamilization poli-
cies on women with varied life trajectories and
employment types underscore the complexity of
understanding how policy contexts interact with
different population segments. This highlights the
necessity of addressing how policy contexts engage
with women’s diverse life courses. By adopting an
approach that recognizes the pluralization of work–
family life courses, we can better understand how
gendered welfare state policies influence mental
health (Bambra 2007).
This study is not without limitations. It provides
historical depth by focusing on 14 European coun-
tries and birth years from 1924 to 1965. Still, it may
not fully capture the diversity of cultural and policy
environments globally or later generational changes.
Additionally, relying on self-reported mental well-
being data introduces the potential for subjective
biases, which may differ from clinical evaluations
(Spitzer and Weber 2019). The cross-sectional
design also limits the ability to draw causal connec-
tions between work–family dynamics and mental
health. Moreover, although it examines the impact
of defamilization policies on women’s mental
health, significant policies or societal factors may be
overlooked. Future research should address these
issues with longitudinal studies that cover a more
comprehensive geographic and temporal range.
Future research should explore how work–family
trajectories influence mental health, focusing on
stressors and protective factors (Carvalho et al. 2018;
Zhou et al. 2018). Broadening the scope to include
diverse populations and generations is vital for
understanding the evolving interplay between work,
family, and mental health. To understand the cultural
contexts and policy environments that affect these
relationships, conducting cross-cultural and cross-
national studies across diverse countries and cohorts
is crucial. Such research is essential for examining
how the welfare state and social policies, including
labor market and health care regulations, influence
women’s mental health outcomes and interact with
work–family dynamics (Bambra 2007).
Research should also consider cultural settings
with varying gender equality and family life views
to dissect the complex interplay of life course, insti-
tutional factors, and cultural influences on health
disparities (Perlman and Gleason 2007).
This study, focusing on European women, pro-
vides systematic insights into how work–family
dynamics and defamilization policies relate to
depressive symptoms through a life course perspec-
tive. It underscores the need for informed actions by
policymakers and researchers to facilitate work–
family integration, enhance mental health, and strive
for genuine gender equity in Europe (Moen 2003).
This endeavor aims to transform gender equity from
an ideal into a tangible reality, motivating future
research to further unravel these complex dynamics.
Acknowledgments
I am grateful for comments and suggestions from Linda
Waite, Jason Beckfield, Jenny Trinitapoli, Ignacio Cabib,
UPenn’s PSC Colloquium assistants, and anonymous
reviewers.
Funding
The author disclosed receipt of the following financial
support for the research, authorship, and/or publication of
this article: This work received support from the Institute
of Humane Studies under Grant Nos. IHS017599 and
IHS017396.
ORCID iD
Ariel Azar
 https://orcid.org/0000-0002-4868-8081

484
Journal of Health and Social Behavior 65(4)
SUPPLEMENTAL MATERIAL
Appendices A through C are available in the online ver-
sion of the article.
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Author Biography
Ariel Azar is an assistant professor of sociology at Purdue
University. His research investigates the interplay between
policy contexts, political systems, and broader social
structures, focusing on their influence on health disparities
and trajectories throughout the life course, with special
attention to migrant and LGBT populations. Azar also
holds a visiting scholar position at the Population Research
Institute at Pennsylvania State University and is a faculty
associate at the Center on Aging and the Life Course at
Purdue University.

    Policy Brief:

Answer:
**Work-family life course trajectories and women’s mental health: The moderating role of defamilization policies in 15 European territories**

**Policy Problem**Despite substantial demographic and labour-market change, European welfare systems have not kept pace with the mental-health needs that arise from women’s increasingly diverse work–family life courses. Longitudinal evidence across 15 territories shows that women who follow “non-standard” trajectories—such as prolonged full-time employment before delayed partnership or child-bearing, or unstable work paired with traditional caregiving—face markedly higher odds of late-life depressive symptoms than those in continuous homemaking or stable-job/traditional-family paths. Crucially, generous defamilization policies (e.g., affordable childcare, earnings-related parental leave, individualised pensions) buffer these risks for women in traditional family roles yet offer little protection—and can even coincide with worse outcomes—for women pursuing career-centred or family-diverse trajectories. The mis-alignment between women’s contemporary life courses and the reach of current defamilization measures is therefore generating avoidable, cohort-specific mental-health inequalities and undermining gender-equity goals across Europe.

**Scientific Research Findings** • Women following traditional life paths (e.g., stable jobs combined with traditional family structures) reported better mental health outcomes compared to those with less conventional trajectories.
• Delayed family formation combined with prolonged employment was associated with higher depressive symptoms, especially in contexts with high defamilization policies.
• Defamilization policies moderated the impact of life trajectories on mental health, with greater policy support benefiting women in traditional roles but proving less effective for those following nontraditional life paths.

**Scientific Research Study Methods**We analyse SHARE-LIFE histories for 22 250 women born 1924-1965 in 15 European territories, merging them with Waves 1–7 SHARE interviews to obtain contemporaneous health and sociodemographic data . Using multichannel sequence analysis, we track each respondent’s annual employment and family statuses from ages 15-50, then apply Ward hierarchical clustering to group the resulting dual-domain sequences into six characteristic work-family trajectory types . We measure late-life mental health with the EURO-D index and classify high risk as ≥4 depressive symptoms. Next, we fit country- and cohort-fixed-effects logistic regressions that link trajectory type to the probability of high depressive symptoms, controlling for education, income, childhood SES, smoking, drinking, self-rated childhood and current health, and pre-50 serious illness . To test policy moderation, we add a standardized defamilization index—averaged across ages 15-40 from country-year welfare-state accounts—and its interaction with each trajectory . Robustness checks exclude respondents with early health problems and replicate models across cohorts to ensure findings are not driven by selection or period effects.

**Policy Implications** These research findings are highly relevant for policymakers focused on improving women’s mental health through social and welfare policies. The results highlight that defamilization policies, which aim to reduce reliance on family for care, can have varied impacts depending on women’s life course trajectories. For women in traditional roles, these policies provide significant mental health benefits. However, for those with delayed family formation or unconventional work patterns, the policies may need to be adapted to address the unique stressors they face. Policymakers should consider expanding these policies to better support diverse life paths, ensuring mental health benefits across all demographic groups.