From East to West: Gendered Patterns of Work-Family Life Courses in Formerly Divided Europe

From East to West: Gendered Patterns of Work-Family Life Courses in Formerly Divided Europe | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article From East to West: Gendered Patterns of Work-Family Life Courses in Formerly Divided Europe Laura Altweck, Stefanie Hahm, Lilli Plocksties, Lotte Rödenbeck, and 7 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8052975/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Objective. This study explored how employment and family life courses in young adulthood and their relationship with subjective well-being (SWB) in older age. We focus on life trajectories between 1945 and 1990 under contrasting capitalist and former socialist countries, examining differences by sex and country. Methods. We used data from the Survey of Health, Ageing and Retirement in Europe (SHARE) for participants 50+ in East/West Germany, Poland, Czech Republic, and France. Participants were surveyed on health outcomes and retrospectively reconstructed their life courses across employment and family domains. We used sequence and cluster analysis for states between 18 and 30 years, for the domains employment, marital status, and children. Descriptive comparisons across clusters were made using t -tests, ANOVA and Games-Howell post-hoc-tests, as well as chi-square tests and Bonferroni post-hoc-tests. Results. We identified distinct gender-specific trajectories, with fewer differences between Eastern and Western European men compared to women. Eastern European women reported more continuous workforce participation, while Western European women were more likely to exit the workforce or work part-time after family formation. Differences in SWB in later life varied across countries and sex as well as life courses – e.g., early marriage, single parenthood, and long-term unemployment were consistently related to lower SWB across countries. Conclusions. This study underscores how socio-political contexts shape work and family life courses. Despite differing policies, some groups are at risk of consistently experiencing lower SWB. This suggests that additional factors beyond life courses warrant attention in respect to health disparities in older age. Psychology Health Economics & Outcomes Research Sociology Employment family gender norms well-being life course trajectories SHARE Figures Figure 1 Figure 2 Introduction Decades after the socio-political change in the early 1990s, well-being in post-socialist, Eastern Europe remains lower than in Western Europe [ 1 , 2 ]. However, this gap is smaller among men, especially in older cohorts [ 1 ]. Social roles, which change throughout life, can influence health in old age [ 3 ]. Despite evolving attitudes towards gender roles [ 4 , 5 ], life courses remain highly gendered and closely linked to well-being [ 6 ]. This raises an important question: How does the socio-cultural and political context shape life courses differently for men and women? Although it is known that different socio-political environments and societal changes shape life courses in different ways [ 7 , 8 ], their gender-specific impacts are less explored. This study addresses this gap by examining how contrasting socio-political systems with diverging gender norms – capitalist and socialist – produce different work and family life courses and considers their long-term consequences for well-being. This study focuses on the Federal Republic of Germany (FRG; West Germany), the former German Democratic Republic (GDR; East Germany), France, the Czech Republic, and Poland – countries with distinct institutional and political systems in post-war Europe. France and the FRG represent conservative welfare states, characterized by traditional family models and relatively low female labour force participation during the period [ 9 , 10 ]. In contrast, the Czech Republic, Poland, and the GDR exemplify socialist regimes that officially promoted gender equality and women’s employment, based not only on ideology but also practical reasons such as labour demands and social control [ 11 , 12 ]. While these countries do not represent all of Europe, they offer clear contrasts for examining how socio-political contexts shaped gendered life trajectories. Life Course and Gender The life course framework emphasizes the importance of the timing and sequencing of life events from birth to death in shaping long-term outcomes [ 7 , 13 ]. Life courses involve transitions between states (e.g., from single to married) that bring changes in roles, status, and identity [ 14 ]. Occupying multiple roles may be associated with higher well-being through role-enhancement [ 15 ]. However, if roles are not compatible, role conflicts, such as the work-family-conflict, may arise, which can negatively impact subjective well-being and health [ 16 – 18 ]. Work and family life courses are strongly gender-specific [ 6 , 19 , 20 ]: Men are traditionally expected to provide financial security, while women are often expected to prioritize family, often leading to labour exits when work-family conflict is too strong [ 21 ]. Men usually derive most of their social status from work, while women draw from multiple sources, resulting in more diverse work and family trajectories for women [ 22 , 23 ]. Female labour force participation has increased in most European countries [ 4 ], however, traditional gender role expectations still cause women to experience more career disruptions and less linear paths than men [ 19 ]. Following family formation, women are more likely to work part-time and take on caregiving responsibilities, while men often maintain full-time employment [ 24 , 25 ]. Full-time employment is generally linked to better health, especially for men [ 25 , 26 ]. Mothers often experience greater stress and lower satisfaction following childbirth, despite a brief initial boost in well-being [ 27 , 28 ]. Certain work-family typologies, such as being a single working mother, can be particularly detrimental to women’s health [ 29 – 31 ]. Historical and cultural influences on life courses Life-courses are shaped by the social structures and norms of a given time and place [ 7 , 8 , 13 ]. The historical and cultural context of Eastern and Western Europe in the second half of the 20th century was crucial in defining gender roles and life courses. The division of Europe from 1945 to 1990 into capitalist Western and socialist Eastern Europe provides a valuable framework for understanding these differences. In capitalist Western Europe, traditional gender roles dominated after World War II, with women primarily responsible for household duties. In France, gender boundaries were re-established during this period [ 32 , 33 ], and religious institutions reinforced the image of women as caregivers [ 34 ]. In West Germany, women were often legally restricted to domestic roles under the 1958 Equal Rights Act ("housewife marriage"), which allowed women to work only if it did not interfere with their household duties [ 35 ]. Consequently, women in capitalistic France and West Germany were less likely to work [ 10 , 36 ]. Although female labour force participation in France increased after 1970, and career interruptions after motherhood became less frequent, the gender pay gap persisted due to reduced working hours [ 10 ]. Career interruptions and part-time work not only leads to a reduction in women’s income during working life but also negatively affect pension entitlements, contributing to financial insecurity in old age [ 20 ]. In contrast, the socialist regimes in Eastern Europe officially promoted gender equality and encouraged women’s participation in the workforce, motivated not only by ideology but also by practical needs such as labour shortages and political socialization [ 11 , 37 ]. As a result, the population of Eastern Europe, especially East Germany, developed more liberal and egalitarian gender role beliefs, rejecting the ‘male breadwinner’ model (Heller et al., 2024; Matysiak & Steinmetz, 2008). Among post-socialist countries, East Germany was the most egalitarian, while Bulgaria and Hungary were the most traditional, with Poland, Slovenia, Russia, and the Czech Republic in between [ 38 ]. The socialist constitution mandated that all citizens contribute to society through gainful employment [ 39 ]. Policies were also implemented to help women reconcile work and household duties[ 35 , 36 ] – For example, in East Germany, maternity protection was legally established and nearly universal kindergarten access was provided Poland experienced greater workforce continuity than those in West Germany, where part-time work or withdrawal from the workforce was more common [ 12 ]. While socialism increased female employment rates, it also placed a double burden on women, who had to balance caregiving and breadwinning roles [ 11 , 37 ]. Interestingly, work-family-conflict tends to have a stronger negative impact on well-being in countries with higher gender equality in working life than in those with less equality [ 16 ]. Research Questions This study aims to examine whether there are gender-specific differences in work and family trajectories during young adulthood in Eastern and Western Europe during the historical division. We are the first to analyse and compare life course clusters between East and West Germany as well as selected countries in Eastern and Western Europe separately for men and women to uncover gender differences and similarities across socio-political and regional contexts. Using sequence and cluster analysis, we simultaneously examine work, marriage, and family formation to identify complex life-course patterns. We hypothesise that gender differences in trajectories, with greater differences between Eastern and Western European women than between men. Methodology Data and sample Our analysis is based on data from the Survey of Health, Ageing and Retirement in Europe (SHARE) [ 40 ] version 9.0.0. SHARE is the largest pan-European, social science panel study, which focuses on the health and socio-economic living conditions of Europeans aged 50 years and older. Since 2004 eight waves of data have been collected in 28 European countries and Israel with 140,000 participants [ 40 ]. The data from the transformed dataset ‘Job Episodes Panel’ (JEP) was used, which is based on SHARE waves 3 and 7 [ 41 , 42 ]. The JEP contains a retrospective survey of life histories, where important areas of life were surveyed. Wave 3 was collected between autumn 2008 and summer 2009 in 13 European countries and included about 27,000 respondents [ 43 ]. With wave 7, the life history interviews were repeated and also included a refreshment sample, about 80,000 interviews took place between spring and autumn 2017 [ 44 ]. In the current analyses, data from the most recent wave (up to wave 7) was used for each participant. To answer the current research questions, we focused on persons who spent their early adulthood (18–30 years) in Poland, the Czech Republic, East Germany, West Germany, or France during the historical division (1945–1990). Poland, the Czech Republic, and East Germany were chosen as examples of former socialist countries while West Germany and France were chosen as capitalist comparisons. Variables Employment and family sequence variables Regarding life-courses, states from the domains of employment, marital status, and children between the ages 18 and 30 years were examined at annual intervals in the period of 1945 and 1990. Employment . The variable ‘situation’ provides data on every job that a person held for at least six months in the course of their life as well as the period of non-employment for at least six months and, if applicable, what the person did instead (e.g., retirement, unemployment, parental leave, training or military service). For the statistical analysis, the variable was recoded as ‘employed’, ‘non-employed’, and ‘other’. Marital status . All long-term partnerships of the participants are stored in the variable ‘married’ with the categories ‘married’ and ‘not married’. Children . The variable ‘nchildren’ lists the number of biological and adopted children. An adopted child was registered as a child in the household of its adoptive parents from the date of adoption whereas a biological child from the year of birth. The variable was recoded as ‘no children’, ‘one child’, and ‘2 + children’. Predictor and descriptive variables The following predictor and descriptive variables were used: Country (place of residence before 1990, i.e., Germany, Poland, the Czech Republic, or France; the variable ‘dn009_’ was used to further specify whether participants lived in East or West Germany), sex (‘male’ or ‘female’), age (in years at survey time), education (in years),, and location (‘city’ [a big city, suburbs or outskirts of a big city], ‘town’ [large or small], and ‘rural area or village’). Subjective household income was measured with the question “Thinking of your household's total monthly income, would you say that your household is able to make ends meet...” the with responses 1 (with great difficulty), 2 (with some difficulty), 3 (fairly easily), and 4 (easily). Higher responses reflect better subjective household income. Subjective health was operationalised using the single item ‘Would you say your state of health is...?’ with the response options ‘Excellent’ (= 1), ‘Very good’ (= 2), ‘Good’ (= 3), ‘Fair’ (= 4) and ‘Poor’ (= 5) [ 45 ]. The items were inverted so higher scores indicated better subjective health. Quality of life in old age (QoL) was measured with the CASP-12 scale [ 46 ]. Twelve items capture the four dimensions of control, autonomy, self-realisation and pleasure, using a four-point Likert scale with the response options ‘Often’ (= 1), ‘Sometimes’ (= 2), ‘Rarely’ (= 3) and ‘Never’ (= 4) ranging between 12 and 48. Higher scores represent greater QoL, with the following cut-off criteria: low QoL, < 35; moderate, 35–37; high, 37–39; and very high, ≥ 39 [ 47 ]. The reliability for some of the subscales were poor [ 46 ], but for the global measure of QoL reliability was high (α = 0.83) [ 48 ]; therefore the overall measure was used. Statistical Analysis For data processing and data analysis R [ 49 ] and Jamovi version 2.3.21.0 [ 50 ] were used. The packages haven [ 51 ], tidyverse [ 52 ], psych [ 53 ], Hmisc [ 54 ] dplyr [ 51 ], panelr [ 55 ] and car [ 56 ] were used for recoding and filtering the data. The packages TraMineR [ 57 ] WeightedCluster [ 58 ], and fastcluster [ 59 ] were used for the sequence and cluster analyses, including the creation of the corresponding graphs. The packages chisq.posthoc.test [ 60 ] emmeans [ 61 ], and nnet [ 62 ] were used for the descriptive analyses. Sequence and Cluster Analysis Sequence analysis[ 63 , 64 ] was employed to identify typical patterns in employment, marriage, and reproductive life courses (refer to supplementary file 1 for an example). This method enables the quantification of life histories by analysing 'states' (e.g., employment or unemployment) and transitions between these states (e.g., from unemployment in year 1 to employment in year 2). As we examined employment (states: employment, no employment, other employment), marriage (states: married, not married), and reproduction (states: 0 children, 1 child, 2 + children) simultaneously, we combined all 18 possible combinations across the three domains into one sequence. So for example, the condition ‘no children and married’ each have the three employment variations ‘employed’, ‘not employed’, and ‘other employment’. As sequence analysis cannot handle missing data, participants were only included that did not have any missing data on the sequence variables. Using SA, all sequences were compared pairwise to assess their similarity [ 65 ]. Optimal matching analysis was used, which is a widely used SA method and measures sequence similarity based on the number of changes required to transform one sequence into another (i.e., altering a state in each wave). Sequences are considered similar if they share similar states at similar times. The optimal matching method also allows flexibility in defining substitution costs. We set all substitutions at a cost of 1, except transitions from no children to two or more children (see supplementary file 2 for the substitution cost matrix). Optimal matching generates a pairwise distance matrix, displaying the 'distance' between all pairs of individual sequences. Using the distance matrix, we performed cluster analysis to identify patterns in the sequences. We utilized Ward's hierarchical clustering method [ 66 ], which iteratively merges the two closest groups. This method is preferred for its ability to minimize within-cluster discrepancies [ 63 ]. We explored options ranging from two to 50 clusters. To determine the most appropriate number of clusters we examined the cluster cut-off criteria Average Silhouette Width (ASW), Point Biserial Correlation (PBC), and Hubert’s C (HC) [ 58 , 67 ]. Higher ASW and PBC values but lower HC values indicate better cluster quality [ 58 , 67 ]. Following [ 68 ], the sequence clusters were validated using parametric bootstraps, generating null models for randomized sequences and distances. The quality of the obtained clustering is compared with that of clustering similar but non-clustered data; if the clustering quality is comparable to the non-clustered data, the structure is weak. Descriptive Statistics For the sample as well as cluster descriptions, we calculated mean values and standard deviations for continuous variables, and relative frequencies for categorical variables. Descriptive comparisons across groups (sex-country groups & sequence clusters) were made using t -tests, ANOVA and Games-Howell post-hoc-tests, as well as chi-square tests and Bonferroni post-hoc-tests. Results Sample description The final sample consisted on 13,957 participants ( n Czech men = 1,683, n Czech women = 2,314, n West German women = 1,253, n West German men = 1,230, n East German women = 411, n East German men = 350, n French women = 1,856, n French men = 1,455, n Polish men = 1,590, and n Polish women = 1,815). See table 1 for a sample description and supplementary file 3 for sample description by country and sex. On average at interview, participants were 68.7 years old ( SD = 8.1), reported 11.6 years ( SD = 3.6) of education, were mostly satisfied with their household income ( M = 3.0, SD = 0.9), and mostly lived in a town (40.6%) or rural area (39.4%). The average subjective health ranged between good and fair while the average QoL was moderate. When considering the period between 1945 and 1990: At age 18, 5.7% were married, 2.8% had children, and 57.1% were employed. At age 30, 86.3% were married, 80.6% had children, and 85.5% were employed. Sequence and cluster analysis A total of 5,658 distinct sequences were identified and, with 4.1%, the most frequent sequence was ‘employed, no children, and not married’. The 26 cluster structure consistently showed the highest values and was therefore chosen. For details regarding cluster selection see supplementary file 4. Cluster descriptions See Fig. 1 for the graphical representation of the sequence clusters. Employment clusters . One group of clusters included sequence clusters where individuals were working throughout but transitioned from neither having any children nor being married at 18 years to having a family at 30 years. This included clusters 1, 2, 3, 5, 6, 8, 9, 10, 12, 16, and 17 (59.9% of the total sample). Individuals in clusters 1, 2, 6, and 10 got married and had children relatively young, in their early 20s. Notably, in cluster 1 the second child was delayed by a few years while in cluster 6 individuals only had one child. Instead, individuals in clusters 3, 9, 12, 16, and 17 got married in their mid-20s. Similar to cluster 6, individuals in cluster 9 only had one child while those in cluster 16 also only had one child, which arrived a few years after marriage. Individuals in clusters 5 and 8 only got married towards the end of their 20s. No employment clusters. Another group of sequence clusters was denoted by individuals generally not working, which included clusters 11, 15, 20, 22, 23, and 24 (14.8%). Individuals in clusters 11, 22, 23, and 24 transitioned from not being married and being in various types of (non-)employment into being married and non-employment before their mid-20s. Notably individuals in cluster 23 only have one child. A similar pattern was clear in cluster 20, where individuals were not married and working until their mid-20s, them they got married and worked for a few more years, before leaving employment altogether. Instead in cluster 15, marriage was accompanied by not working for a few years, and then (re-)joining employment in the end-20s. Other employment clusters. The last group of sequence clusters was denoted by large amounts of other types of employment, especially in the early 20s – clusters 7, 13, 18, 19, 21, 25 (14.7%). The sequences in cluster 25 denoted states of other types of employment and no family nearly until the end-20s, where individuals either married or began to work, but not both. Individuals in cluster 7, 13, 18, and 21 began their 20s without a family and were in other types of employment and in the mid-20s began working. In the latter three clusters, individuals started a family approximately a year after their employment began. Lastly, individuals in cluster 19 were in other types of employment throughout, but got married and started a family in their early to mid-20s. No family clusters. Another sequence cluster group – clusters 4, 14, and 26 – was denoted by individuals working but not being married (10.6%). While in cluster 4 individuals did not have any children, in cluster 14 some individuals got married and became single again while others had children without getting married. In cluster 26, it appears that individuals got divorced, as they transitioned from not being married or having children, to being married with one or more children and then transitioned to not being married. Associations with the employment-family clusters See supplementary file 5 for details of the descriptive statistics by employment-family cluster. Age differed significantly across the sequence clusters ( F (25, 3197) = 7.3, p < .001). Individuals belonging to the clusters 11 (around 24 years transitioned to married and no employment), 19 (other employment throughout), 23 (around 23 years transitioned to married and no employment with one child), and 24 (not married and no employment throughout) were significantly older ( M = [70.3-71.93], SD = [8.6–8.9]) compared to most other clusters ( M = [66.3–69.8], SD = [7.6–8.6]) ( p < .05). Education differed significantly across the sequence clusters ( F (25, 3197) = 118.0, p < .001). Individuals in nearly all the clusters denoted by other types of employment (nr. 7, 13, 18, 21, & 25) reported the highest education ( M = [14.2–16.3], SD = [3.4–5.2]) compared to most other clusters ( M = [9.0-11.8], SD = [2.6–4.2]) ( p < .05). Only cluster 19 (getting married and in other type of employment throughout) showed one of the lowest education levels ( M = 10.0, SD = 4.2), along with most non-employment clusters (nr. 11, 15, 22, 23, 24) ( M = [9.0-10.5], SD = [2.7–3.4]), and clusters 10 and 16 (getting married and in employment throughout) ( M = [10.3–12.0], SD = [2.7–3.4]) ( p < .05). Subjective household income differed significantly across the sequence clusters ( F (25, 2887) = 21.7, p < .001). Most notably, clusters 15, 22, and 24 (no employment) showed the lowest subjective household income ( M = [2.5–2.7], SD = [1.0]) compared to the other clusters ( M = [2.6–3.4], SD = [0.8-1.0]) ( p < .05). Instead, cluster 3 (employment) as well as clusters 7, 13, 18, 21, and 25 (other types of employment) showed higher subjective household income ( M = [3.1–3.4], SD = [0.8–0.9]) compared to the other clusters ( M = [2.5–3.2], SD = [0.9-1.0]) ( p < .05). Location differed significantly across the sequence clusters ( X 2 (18) = 339, p < .001). Individuals in cluster 2 (employment) were less likely to live in a city (12.7) while those in clusters 7 and 25 (other types of employment) were more likely (32.3–36.6%), compared to others (10.0-32.6%). Instead, individuals in cluster 6 (employment) (30.0%) were less while those in cluster 19 (other type of employment) (60.4%) were more likely to live in a rural area, compared to the others (25.9–49.1%). Subjective health differed significantly across the sequence clusters ( F (25, 3197) = 14.0, p < .001). Clusters 7, 13, 18, 21, and 25 (which were denoted by other types of employment) showed the highest subjective health ( M = [2.7–2.9], SD = [0.9-1.0]) compared to most other clusters ( M = [2.3–2.6], SD = [9.3–1.1]) ( p < .05). Instead clusters 22, 23, and 24 – denoted by near simultaneous transitions into marriage and non-employment – reported the lowest subjective health ( M = [2.0-2.3], SD = [0.9-1.0]) compared to the other clusters ( M = [2.3–2.9], SD = [0.9–1.1]) ( p < .05). QoL differed significantly across the sequence clusters ( F (25, 2714) = 10.1, p < .001). Individuals in cluster 10 (family in early 20s) and cluster 24 (no employment) reported the lowest QoL ( M = [33.5–35.3], SD = [6.5–7.1]) compared to the other clusters ( M = [35.6–38.7], SD = [5.7-7.0]) ( p < .05). Clusters 13, 18, 21, and 25 – denoted by other types of employment - instead reported the highest QoL ( M = [38.5–38.7], SD = [5.7-6.0]) compared to the other clusters ( M = [33.5–38.3], SD = [5.6–7.1]) ( p < .05). Compared to some clusters ( M = [33.5–38.7], SD = [5.6–7.1]), individuals in cluster 7 (other type of employment) ( M = 38.3, SD = 5.7) reported higher, while individuals in cluster 12 (employment) and 15 (no employment) reported lower QoL ( M = [35.6–36.7], SD = [5.9–6.6]) ( p < .05). See Fig. 2 for distributions across sex and country. Sex . The distribution of men and women significantly differed across the sequence clusters ( X 2 (25) = 3119, p < .001). Men predominantly made up the employment clusters ( p < .001), merely clusters 1, 2, and 6 were filled with women ( p .05). Instead, the no employment clusters were nearly exclusively made up of women ( p < .001). The other types of employment clusters were also mainly made up of men ( p < .001), apart from cluster 19 (marrying but continuing other types of employment) which was made up of more women ( p < .001). The largest no family but working cluster (nr. 4) was made up of men ( p < .001). The cluster working with children but not married (nr. 14) as well as the divorce cluster (nr. 26) were mainly made up of women ( p < .001). Sex and country . When considering sex and country together, the distribution across the sequence clusters also differed significantly ( X 2 (225) = 5791, p < .001). The non-employment clusters (nr. 11, 20, 22, & 23) were least likely to be made up of men irrespective of country and were more likely to be made up of West German and French women ( p < .05). Also, Czech women were less likely but Polish women more likely to belong to clusters 11 and 22 (no employment) ( p < .05). Instead, both Czech and Polish women were more likely to belong to cluster 15 (leaving employment around the time of marriage and returning to employment in the late 20s) ( p < .05). The clusters denoted by early family formation and working (nr. 1, 2, 6, & 10) were more likely to contain Czech women but less likely to contain French and West German women ( p < .05). Polish women were also more likely to belong to clusters 1 and 10 and East German women more likely to belong to clusters 2, 6, and 10 ( p < .05). The clusters denoted by later family formation and working (nr. 5 & 8) were more likely to contain West German, French, and Polish men but less likely to contain Czech and Polish women ( p < .05). The clusters denoted by other types of employment (nr. 7, 13, & 25) were more likely to contain West German and French women but less likely to contain Czech and Polish women ( p < .05). Cluster 4 (not married and working) was more likely to contain West German, French, and Polish men but less likely to contain East German, Czech, and Polish women ( p < .05). Discussion This study aimed to investigate whether work and family trajectories differ between Eastern and Western Europe, focusing on gender differences shaped by the socio-political context of the European division between 1945 and 1990. Using sequence and cluster analysis, we compared life course typologies in selected countries across Eastern and Western Europe as well as East and West Germany. Our findings revealed distinct gender-specific trajectories, with fewer differences between Eastern and Western European men compared to women. Eastern European women experienced more continuous workforce participation, taking on the dual role of worker and caregiver. In contrast, Western European women were more likely to exit the workforce or work part-time after marriage or childbirth. The results also highlighted country-specific differences, with Poland showing a mixture of liberal and traditional influences. Gendered life courses As expected, the results showed significant gender differences in life course trajectories, particularly in employment patterns, which is in line with previous studies [ 6 , 19 , 20 , 23 ]. Men predominantly occupied the employment clusters, reflecting continuous full-time work, which is consistent with traditional expectations of men as primary ‘breadwinners’. Only a few employment clusters were dominated by women, mainly those also characterised by family foundation in their early 20s, aligning with the socialist image of the ‘working mother’ [ 9 , 12 ]. The ‘no employment’ clusters were mainly made up of women, highlighting their association with family roles [ 20 , 21 ]. Notably, individuals who transitioned into non-employment alongside marriage in their early 20s had the lowest levels of education, subjective household income, health, and QoL. This reflects previous findings that such life-course trajectories can limit one’s educational attainment and financial wellbeing [ 69 , 70 ], which is important for long-term health outcomes [ 26 ]. Given that these clusters are predominantly composed of women, they face particular social, economic and health disadvantages. In contrast, clusters denoted by ‘other’ types of employment were largely male-dominated and were generally linked to higher education and income as well as better health and QoL. Additionally, they were more likely to live in a city as compared to rural areas or towns. This suggests that individuals in these clusters were likely pursuing higher degrees of education, which is associated with greater job flexibility, better career prospects, and improved health outcomes [ 71 ]. An exception was cluster 19, mainly comprised of women reporting other types of employment and early family foundation: They reported lower levels of education, income, and health (but not QoL) and were more likely to live in rural areas. Men were more likely to be in clusters without any family responsibilities, while women dominated clusters involving work and caregiving after divorce or as single parents, with these clusters showing slightly below average levels of education and health. This aligns with existing literature, which shows that women’s life courses tend to be more diverse [ 22 , 23 ], but that certain work-family trajectories, such as being a single working mother, can negatively impact women’s health [ 29 , 30 ]. However, these clusters did not differ much from the average regarding health and QoL in this sample, which might be due to other factors such as the socio-political context. Gender differences depending on socio-political context The results demonstrate a clear interaction of gender and sociopolitical context regarding life course trajectories. Women in Eastern European countries, particularly the Czech Republic, exhibited more continuous workforce participation compared to their Western European counterparts. This reflects the influence of socialist regimes that promoted gender equality in the labour market [ 9 , 12 ]. These women often experienced early marriage and childbirth alongside steady employment, supported by state policies such as childcare provisions. However, this also contributed to the ‘double burden’ of balancing work and caregiving responsibilities [ 11 , 37 ]. The results indicate that this burden primarily posed health risks for single or divorced women. A seemingly modern cluster of women re-entering the labour market after leaving due to family formation emerged in both Poland and the Czech Republic, a pattern also identified in recent studies (e.g., 6). However, East German women were also overrepresented in some clusters characterised by continuous employment – 2, 6, and 10 – highlighting the strong norm of female workforce participation in the former East Germany. Compared to the more male-dominated employment clusters, especially cluster 10 showed lower levels of education, income, health, and QoL, suggesting the continued impact of a ‘double burden’ as well. In contrast, East German men barely differed from men in other countries. Despite both Poland and the Czech Republic falling in the middle of the traditional-liberal spectrum regarding gender roles [ 38 ], some differences emerged. Czech women followed a stereotypically socialist trajectory, while Polish women demonstrated a mixture of liberal and traditional influences. Some Polish women reflected the socialist image of the ‘working mother’, maintaining continuous employment or only temporarily leaving the labour market, while others permanently exited the workforce during early family formation, which was linked to lower education, subjective household income, and health. It is noteworthy, that Polish men and women generally showed the lowest levels of education, income, and health in this study. These results are possibly due to Poland’s unique cultural context, where socialist policies coexisted with traditional religious norms, influencing both gender roles and life course patterns [ 12 ] France and West Germany exhibited more traditional patterns, where women’s employment was shaped more strongly by family responsibilities, often exiting the workforce or transitioning to part-time employment after marriage or childbirth (Meurs & Pora, 2020; Spellerberg, 1996). These patterns align with earlier research showing that traditional gender roles in capitalist societies emphasized women’s domestic responsibilities [ 10 , 20 , 36 ], which were reinforced by state and religious institutions [ 32 – 35 ]. In general, men’s life course patterns were relatively consistent across countries and socio-political systems, primarily reflecting continuous full-time work, which is consistent with previous studies [ 6 , 19 , 20 , 23 ]. Strengths and Limitations This study offers important insights into gendered life courses by providing a comparative analysis of Eastern and Western Europe during a historically significant period, including the unique case of East and West Germany. Using sequence and cluster analysis, it captures the complexity of work, family, and partnership patterns over time. Analysing men and women separately highlights gender-specific differences that are often hidden in broader studies. Another strength is the relatively large sample sizes for most countries, which allow for meaningful cross-national comparisons. However, there are several limitations. First, the sample size for East Germany was smaller than for other countries, which may have limited the ability to detect more differences between the former East Germany and the other countries. Future research should include larger samples from East Germany to provide more robust comparisons. Additionally, the use of retrospective survey data introduces potential recall bias, as participants may have difficulty accurately recalling life events from several decades ago. Lastly, the study also focused on a limited number of countries in Eastern and Western Europe, which may limit the generalizability of the findings, as we saw differing results even within the socialist and capitalist country groups. Future studies should include a broader range of countries to capture a more comprehensive view of life course diversity across Europe. Conclusion This study contributes to research on gendered life course trajectories by considering the socio-political context of the socialist Eastern and capitalist Western parts of Europe during the division from 1945 to 1990. The findings underscore the significant role of socio-political context in shaping these trajectories, with Eastern European women experiencing more continuous work patterns, while Western European women were more likely to face career interruptions after family formation. Still existing health disparities in older age, particularly lower outcomes in Poland, suggest additional factors beyond life courses that warrant further research. Future studies should explore these influences to better understand the long-term effects of different life course patterns. Declarations Data availability The data analysed in this study are from the SHARE‑ERIC (Survey of Health, Ageing and Retirement in Europe). Access to the micro-data is granted free of charge for scientific use, subject to registration and the Conditions of Use stipulated by SHARE-ERIC. Researchers wishing to access the data can register as a user directly with the SHARE Research Data Center: https://share-eric.eu/ References Guriev S, Melnikov N (2018) Happiness convergence in transition countries. J Comp Econ 46(3):683–707. 10.1016/j.jce.2018.07.003 Nikolova M (2016) Minding the happiness gap: Political institutions and perceived quality of life in transition. Eur J Political Econ 45:129–148. 10.1016/j.ejpoleco.2016.07.008 Henretta JC (2007) Early childbearing, marital status, and women's health and mortality after age 50. J Health Soc Behav 48(3):254–266. 10.1177/002214650704800304 Stavrova O, Fetchenhauer D, Schlösser T (2012) Cohabitation, Gender, and Happiness. J Cross-Cult Psychol 43(7):1063–1081. 10.1177/0022022111419030 Heller A, Altweck L, Hahm S, Michalski N (2024) The (fe-)male breadwinner? Beliefs about gender roles in East Germany, in Thirty years after the Berlin Wall: German Unification and Transformation Research. Routledge. , A. Heller and P. Schmidt, Eds Altweck L et al (2023) ., Even Now Women Focus on Family, Men on Work: An Analysis of Employment, Marital, and Reproductive Life-Course Typologies in Relation to Change. Appl Res Qual Life 18(3):1205–1223 (in En;en). 10.1007/s11482-022-10130-3 . in Health-Related Quality of Life Mayer KU (2004) Whose Lives? How History, Societies, and Institutions Define and Shape Life Courses. Res Hum Dev 1:161–187. 10.1207/s15427617rhd0103_3 Mills M (2007) Individualization and the Life Course: Toward a Theoretical Model and Empirical Evidence. In: Howard C (ed) in Contested Individualization. Palgrave Macmillan US, New York, pp 61–79 Braun M, Scott J, Alwin DF (1994) Economic necessity or self-actualization? Attitudes toward women's labour-force participation in East and West Germany. Eur Sociol Rev 10(1):29–47. 10.1093/oxfordjournals.esr.a036314 Meurs D, Pora P (2020) Gender Equality on the Labour Market in France: A Slow Convergence Hampered by Motherhood. Ecostat 510:109–130. 510-511-51210.24187/ecostat.2019.510t.1990 Plomien A (2008) From socialism to capitalism: women and their changed relationship with the labor market in Poland. In: Heather (ed) in Globalization, uncertainty and women's careers: An international comparison. Elgar, Cheltenham, UK, pp 247–274 Matysiak A, Steinmetz S (2008) Finding Their Way? Female Employment Patterns in West Germany, East Germany, and Poland. Eur Sociol Rev 24(3):331–345. 10.1093/esr/jcn007 Elder GH, Johnson MK, Crosnoe R (2003) The Emergence and Development of Life Course Theory, in Handbooks of Sociology and Social Research, Handbook of the life course , J. T. Mortimer and M. J. Shanahan, Eds., New York: Springer, pp. 3–19. [Online]. Available: https://​link.springer.com​/​chapter/​10.1007/​978-​0-​306-​48247-​2_​1 Mitchell BA (2003) Life course theory. In: Ponzetti JJ (ed) in The International Encyclopedia of marriage and Family Relationships. Macmillan Reference, New York Moen P, Robison J, Dempster-McClain D (1995) Caregiving and Women's Well-being: A Life Course Approach. J Health Soc Behav 36(3):259. 10.2307/2137342 Hagqvist E, Gådin KG, Nordenmark M (2017) Work–Family Conflict and Well-Being Across Europe: The Role of Gender Context, (in En;en), Soc Indic Res , vol. 132, no. 2, pp. 785–797. 10.1007/s11205-016-1301-x Glavin P, Schieman S (2012) Work–Family Role Blurring and Work–Family Conflict. Work Occup 39(1):71–98. 10.1177/0730888411406295 Creary SJ, Gordon JR, Conflict R, Overload R, Strain R (2016) In: Shehan CL, Duncan M (eds) in Wiley Blackwell encyclopedias in social science, The Wiley Blackwell encyclopedia of family studies. West Sussex, Malden, MA, Oxford: Wiley Blackwell,, Chichester, pp 1–6 Wahrendorf M, Zaninotto P, Hoven H, Head J, Carr E (2018) Late Life Employment Histories and Their Association With Work and Family Formation During Adulthood: A Sequence Analysis Based on ELSA. journals Gerontol Ser B 73(7):1263–1277. Psychological sciences and social sciences10.1093/geronb/gbx066 Madero-Cabib I, Fasang AE (2016) Gendered work–family life courses and financial well-being in retirement. Adv life course Res 27:43–60. 10.1016/j.alcr.2015.11.003 Lyu X, Fan Y (2020) Research on the relationship of work family conflict, work engagement and job crafting: A gender perspective. Curr Psychol. 10.1007/s12144-020-00705-4 van der Meer PH (2014) Gender, Unemployment and Subjective Well-Being: Why Being Unemployed Is Worse for Men than for Women. Soc Indic Res 115(1):23–44. 10.1007/s11205-012-0207-5 Engels M et al (2019) ., Gendered work-family trajectories and depression at older age. Aging Ment Health 23(11):1478–1486. 10.1080/13607863.2018.1501665 Rubiano Matulevich EC, Viollaz M Gender Differences in Time Use: Allocating Time between the Market and the Household: World Bank Policy Research Working Paper No. 8981, 2019. [Online]. Available: https://​ssrn.com​/​abstract=3437824 Tomczyk S, Altweck L, Schmidt S (2021) How is the way we spend our time related to psychological wellbeing? A cross-sectional analysis of time-use patterns in the general population and its associations with wellbeing and life satisfaction. BMC Public Health Paul KI, Moser K (2009) Unemployment impairs mental health: Meta-analyses. J Vocat Behav 74(3):264–282. 10.1016/j.jvb.2009.01.001 Musick K, Meier A, Flood S (2016) How Parents Fare. Am Sociol Rev 81(5):1069–1095. 10.1177/0003122416663917 Krämer MD, Rodgers JL (2020) The impact of having children on domain-specific life satisfaction: A quasi-experimental longitudinal investigation using the Socio-Economic Panel (SOEP) data. J Pers Soc Psychol. 10.1037/pspp0000279 van Hedel K et al (2016) ., Work-Family Trajectories and the Higher Cardiovascular Risk of American Women Relative to Women in 13 European Countries. Am J Public Health 106(8):1449–1456. 10.2105/AJPH.2016.303264 McKetta S, Prins SJ, Platt J, Bates LM, Keyes K (2018) Social sequencing to determine patterns in health and work-family trajectories for U.S. women, 1968–2013, SSM - population health , vol. 6, pp. 301–308. 10.1016/j.ssmph.2018.10.003 Fritzell S et al (2012) ., Does non-employment contribute to the health disadvantage among lone mothers in Britain, Italy and Sweden? Synergy effects and the meaning of family policy. Health Place 18(2):199–208. 10.1016/j.healthplace.2011.09.007 Dörfler S, Wernhart, and, Georg (2016) Die Arbeit von Männern und Frauen: eine Entwicklungsgeschichte der geschlechtsspezifischen Rollenverteilung in Frankreich, Schweden und Österreich, [Online]. Available: https://​nbn-resolving.org​/​urn:​nbn:​de:​0168-​ssoar-​57291-​9 Colvin KR (2012) Solidarity or Suspicion: Gender, Enfranchisement, and Popular Culture in Liberation France, jowh , vol. 24, no. 2, pp. 89–114. 10.1353/jowh.2012.0016 Veil M (2005) Geschlechterbeziehungen im deutsch-französischen Vergleich–ein Blick auf Familien-und Arbeitsmarktpolitik. In: Achcar G, Simon D, Veil M (eds) in Geschlechterbeziehungen im deutsch-französischen Vergleich–ein Blick auf Familien-und Arbeitsmarktpolitik. Wissenschaftszentrum Berlin für Sozialforschung (WZB), Berlin Nebe K (2020) #FamilienLeben – 50 Jahre wissenschaftliche Beratung für eine nachhaltige Familienpolitik. Sozialer Fortschritt 69:8–9. 10.3790/sfo.69.8-9.529 Spellerberg A (1996) Frauen zwischen Familie und Beruf, in Wohlfahrtsentwicklung im vereinten Deutschland: Sozialstruktur, sozialer Wandel und Lebensqualität , W. Zapf and R. Habich, Eds., Berlin: Edition Sigma, pp. 99–120. [Online]. Available: http://​hdl.handle.net​/​10419/​122772 Wanat E (2023) Frauen in Polen zwischen Rechtskonservatismus und Feminismus, Polen-Analysen , no. 307, pp. 2–7. 10.31205/PA.307.01 Robila M, Krishnakumar A (2004) The Role of Children in Eastern European Families. Child Soc 18(1):30–41. 10.1002/chi.773 Roth K (2004) Arbeit im Sozialismus - Arbeit im Postsozialismus: Erkundungen zum Arbeitsleben im östlichen Europa . Münster: LIT, [Online]. Available: http://​www.h-net.org​/​reviews/​showrev.php​?​id=​19957 Börsch-Supan A et al (2013) ., Data Resource Profile: the Survey of Health, Ageing and Retirement in Europe (SHARE). Int J Epidemiol 42(4):992–1001. 10.1093/ije/dyt088 Antonova L, Aranda L, Pasini G, Trevisan E (2014) Migration, family history and pension: The second release of the SHARE Job Episodes Panel Brugiavini A, Orso CE, Genie MG, Naci R, Pasini G (2020) SHARE Job Episodes Panel Börsch-Supan A (2022) Survey of Health, Ageing and Retirement in Europe (SHARE) Wave 3 - SHARELIFE Bergmann M, Scherpenzeel A, Börsch-Supan A (2019) SHARE Wave 7 Methodology: Panel innovations and life histories. MEA, Max Planck Institute for Social Law and Social Policy, Munich Börsch-Supan A (2022) Survey of Health, Ageing and Retirement in Europe (SHARE) Wave 2 Borrat-Besson C, Ryser V-A, Gonçalves J (2015) An evaluation of the CASP-12 scale used in the Survey of Health, Ageing and Retirement in Europe (SHARE) to measure Quality of Life among people aged 50+ Conde-Sala JL, Portellano-Ortiz C, Calvó-Perxas L, Garre-Olmo J (2017) Quality of life in people aged 65 + in Europe: associated factors and models of social welfare-analysis of data from the SHARE project (Wave 5). Qual Life Res 26(4):1059–1070. 10.1007/s11136-016-1436-x Oliver A, Sentandreu-Mañó T, Tomás JM, Fernández I, Sancho P (2021) Quality of Life in European Older Adults of SHARE Wave 7: Comparing the Old and the Oldest-Old. J Clin Med 10(13). 10.3390/jcm10132850 Core Team R R: A language and environment for statistical computing. [Online]. Available: https://​www.r-project.org​/​ The jamovi (2022) project , [Online]. Available: https://​www.jamovi.org​/​ Wickham H, François R, Henry L, Müller K (2023) dplyr: A Grammar of Data Manipulation , [Online]. Available: https://​dplyr.tidyverse.org​/​ Wickham H et al (2019) ., Welcome to the Tidyverse. JOSS 4(43):1686. 10.21105/joss.01686 Revelle W (2023) psych: Procedures for Psychological, Psychometric, and Personality Research , [Online]. Available: https://​personality-project.org​/​r/​psych/​ Harrell FE Jr, Dupont C (2023) Hmisc: Harrell Miscellaneous Long JA (2020) CRAN: Contributed Packages Fox J, Weisberg S (2011) In: Angeles L (ed) An R companion to applied regression, 2 edn. Sage, London, New Delhi, Singapore, Washington DC Gabadinho A, Ritschard G, Müller NS, Studer M (2011) Analyzing and Visualizing State Sequences in R with TraMineR. J Stat Soft 40(4). 10.18637/jss.v040.i04 Studer M (2013) WeightedCluster Library Manual: A practical guide to creating typologies of trajectories in the social sciences with R. 24. 10.12682/LIVES.2296-1658.2013.24 Müllner D (2013) fastcluster: Fast Hierarchical, Agglomerative Clustering Routines for R and Python. J Stat Soft 53(9). 10.18637/jss.v053.i09 Ebbert D (2019) _chisq.posthoc.test: A Post Hoc Analysis for Pearson's Chi-Squared Test for Count Data_ , [Online]. Available: https://​cran.r-project.org​/​package=chisq.posthoc.test Lenth RV et al (2023) emmeans: Estimated marginal means, aka least-squares means, [Online]. Available: https://​cran.r-project.org​/​web/​packages/​emmeans/​index.html Ripley B, Venables W (2016) Package 'nnet' , [Online]. Available: https://​staff.fmi.uvt.ro​/~​daniela.zaharie/​dm2019/​RO/​lab/​lab3/​biblio/​nnet.pdf Gauthier J-A, Widmer ED, Bucher P, Notredame C (2010) Sociol Methodol 40(1):1–38. 10.1111/j.1467-9531.2010.01227.x . 1. Multichannel Sequence Analysis Applied to Social Science Data Pollock G (2007) Holistic trajectories: a study of combined employment, housing and family careers by using multiple-sequence analysis. J Royal Stat Society: Ser (Statistics Society) 170(1):167–183. 10.1111/j.1467-985X.2006.00450.x Abbott A (1995) Sequence Analysis: New Methods for Old Ideas. Annu Rev Sociol 21(1):93–113. 10.1146/annurev.so.21.080195.000521 Ward JH (1963) Hierarchical Grouping to Optimize an Objective Function. J Am Stat Assoc 58(301):236–244. 10.1080/01621459.1963.10500845 Hennig C, Liao TF (2010) Comparing latent class and dissimilarity based clustering for mixed type variables with application to social stratification Studer M (2021) Validating Sequence Analysis Typologies Using Parametric Bootstrap. Sociol Methodol 51(2):290–318. 10.1177/00811750211014232 McMunn A et al (2015) ., De-standardization and gender convergence in work–family life courses in Great Britain: A multi-channel sequence analysis. Adv life course Res 26:60–75. 10.1016/j.alcr.2015.06.002 Firat M, Visser M, Kraaykamp G (2023) Work-family trajectories across Europe: differences between social groups and welfare regimes. Front Sociol 8:1100700. 10.3389/fsoc.2023.1100700 Zajacova A, Lawrence EM (2024) The Relationship Between Education and Health: Reducing Disparities Through a Contextual Approach. Annu Rev Public Health 39(1):273–289. 10.1146/annurev-publhealth-031816-044628 Tables Table 1. Sample description M/n SD / % Country, n (%) Czech West Germany East Germany France Poland 3997 2483 761 3311 3405 28.6 17.8 5.5 23.7 24.4 Sex, n (%) Man Woman 6308 7649 45.2 54.8 Age, M ( SD ) 68.7 8.14 Education (years), M ( SD ) 11.6 3.56 Subjective household income 3.0 0.94 Location city 2461 20.0 town 4995 40.6 rural 4849 39.4 Self-rated health, M ( SD ) 2.5 0.97 Quality of life, M ( SD ) 36.9 6.20 Marital status, n (%) 18 years Not married Married 13155 802 94.3 5.7 30 years Not married Married 1911 12046 13.7 86.3 Children, n (%) 18 years None One Two plus 13566 355 36 97.2 2.5 .3 30 years None One Two plus 2713 3732 7512 19.4 26.7 53.8 Work, n (%) 18 years Work None Other 7964 770 5223 57.1 5.5 37.4 30 years Work None Other 11933 1744 280 85.5 12.5 2.0 Note . Missing values for education years: 1182(8.5 %), self-related health: 15(0.1%), quality of life: 2008(16.8%) and subjective household income: 1381(10.9%) Additional Declarations The authors declare no competing interests. Supplementary Files Appendix1.EXAMPLESTATESSEQUENCESANDDISTANCES.docx Appendix2.SUBSTITUTIONCOSTMATRIX.docx Appendix3.DESCRIPTIONOFTHECOUNTRYSEXSAMPLES.docx Appendix4.MOSTFREQUENTSEQUENCES.docx Appendix5.xlsx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8052975","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":541316563,"identity":"39ee4151-6a0e-4d8d-af92-80855ff31522","order_by":0,"name":"Laura Altweck","email":"data:image/png;base64,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","orcid":"https://orcid.org/0000-0002-6623-1958","institution":"University of Greifswald","correspondingAuthor":true,"prefix":"","firstName":"Laura","middleName":"","lastName":"Altweck","suffix":""},{"id":541319912,"identity":"cb029f7d-743a-42d6-ba10-bf48bfaba37e","order_by":1,"name":"Stefanie Hahm","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Stefanie","middleName":"","lastName":"Hahm","suffix":""},{"id":541319913,"identity":"34a636e7-4247-46ee-aea2-3c93435f26d4","order_by":2,"name":"Lilli Plocksties","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Lilli","middleName":"","lastName":"Plocksties","suffix":""},{"id":541319914,"identity":"c9ceb1db-d382-4066-8472-eb0ef67020ca","order_by":3,"name":"Lotte Rödenbeck","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Lotte","middleName":"","lastName":"Rödenbeck","suffix":""},{"id":541319915,"identity":"af66f186-5416-4968-a338-ba2a67ffe7c3","order_by":4,"name":"Thomas McLaren","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Thomas","middleName":"","lastName":"McLaren","suffix":""},{"id":541319916,"identity":"5fa25e32-16a7-46ef-8440-3549020d8197","order_by":5,"name":"Sven Speerforck","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Sven","middleName":"","lastName":"Speerforck","suffix":""},{"id":541319917,"identity":"b13045e5-641c-4c98-9efc-f1a639b0e280","order_by":6,"name":"Georg Schomerus","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Georg","middleName":"","lastName":"Schomerus","suffix":""},{"id":541319918,"identity":"38f2d968-1cc6-42cf-9cc9-2f881d012b83","order_by":7,"name":"Manfred E. Beutel","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Manfred","middleName":"E.","lastName":"Beutel","suffix":""},{"id":541319919,"identity":"000ec80b-1874-408b-a24c-508ff8f185d9","order_by":8,"name":"Elmar Brähler","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Elmar","middleName":"","lastName":"Brähler","suffix":""},{"id":541319920,"identity":"312c7858-f99a-4d01-9986-f1fd2e4c8352","order_by":9,"name":"Silke Schmidt","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Silke","middleName":"","lastName":"Schmidt","suffix":""},{"id":541319921,"identity":"0f354895-d878-4ba8-bc44-6ea496f4d38a","order_by":10,"name":"Holger Muehlan","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Holger","middleName":"","lastName":"Muehlan","suffix":""}],"badges":[],"createdAt":"2025-11-07 04:50:08","currentVersionCode":1,"declarations":{"humanSubjects":true,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":true,"humanSubjectConsent":true,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-8052975/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8052975/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":95798503,"identity":"3b2d730f-6b00-4161-a61c-adcb776043d1","added_by":"auto","created_at":"2025-11-13 08:16:56","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":296638,"visible":true,"origin":"","legend":"","description":"","filename":"Manuskriptfinalv2.docx","url":"https://assets-eu.researchsquare.com/files/rs-8052975/v1/cb86f6d5986c50577cd5fb4f.docx"},{"id":95669390,"identity":"921e68ce-ecbc-402b-8d77-8322cba64c60","added_by":"auto","created_at":"2025-11-11 17:10:50","extension":"json","order_by":1,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":342,"visible":true,"origin":"","legend":"","description":"","filename":"rs8052975.json","url":"https://assets-eu.researchsquare.com/files/rs-8052975/v1/83593f32a79d99c873fe3768.json"},{"id":95669395,"identity":"e551527c-fdeb-411d-84c8-98aef717b725","added_by":"auto","created_at":"2025-11-11 17:10:50","extension":"xml","order_by":2,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":136377,"visible":true,"origin":"","legend":"","description":"","filename":"rs80529750enriched.xml","url":"https://assets-eu.researchsquare.com/files/rs-8052975/v1/2ed8bb4d0cc9db948a48dd52.xml"},{"id":95799252,"identity":"aa1dbf97-37cd-4a0e-8d32-8e7023b4a5aa","added_by":"auto","created_at":"2025-11-13 08:19:18","extension":"xml","order_by":3,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":135284,"visible":true,"origin":"","legend":"","description":"","filename":"rs80529750structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-8052975/v1/03dd985c9da93c52eaf86083.xml"},{"id":95669406,"identity":"728ca041-c3d9-464f-afb5-b73196c81f28","added_by":"auto","created_at":"2025-11-11 17:10:50","extension":"html","order_by":4,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":147312,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8052975/v1/123eeff9aacc282f40e56441.html"},{"id":95669391,"identity":"39d4909c-767a-4b7e-8c78-6a91e6a18bec","added_by":"auto","created_at":"2025-11-11 17:10:50","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":2085065,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8052975/v1/7a6359a1b425f93e5005d63f.png"},{"id":95669393,"identity":"cb31ed01-0d07-4dc3-ae04-bf1a6d7695da","added_by":"auto","created_at":"2025-11-11 17:10:50","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1361899,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend\u003c/p\u003e","description":"","filename":"Figure2.Countrysexsamplebytheclustersolution.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8052975/v1/7b63ff85c5b6c40ac82e5d33.jpg"},{"id":95804584,"identity":"d7f3e29b-5aff-4e06-87c0-54bad35174ba","added_by":"auto","created_at":"2025-11-13 08:38:35","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4889656,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8052975/v1/37656a98-6a8b-46dd-9ad1-5c3609e7f056.pdf"},{"id":95669389,"identity":"0a3fc99a-75d8-41a0-84ad-8b6dbc86c430","added_by":"auto","created_at":"2025-11-11 17:10:50","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":29979,"visible":true,"origin":"","legend":"","description":"","filename":"Appendix1.EXAMPLESTATESSEQUENCESANDDISTANCES.docx","url":"https://assets-eu.researchsquare.com/files/rs-8052975/v1/feaf5fd1a1a19df869f87221.docx"},{"id":95797203,"identity":"32dfa3e7-0288-42fc-84fc-8f91f50914d3","added_by":"auto","created_at":"2025-11-13 08:01:52","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":32364,"visible":true,"origin":"","legend":"","description":"","filename":"Appendix2.SUBSTITUTIONCOSTMATRIX.docx","url":"https://assets-eu.researchsquare.com/files/rs-8052975/v1/deb5d469a67cfebec51a26e5.docx"},{"id":95669402,"identity":"d9ffbcdd-c951-4e6f-ac07-fe912d977d56","added_by":"auto","created_at":"2025-11-11 17:10:50","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":37703,"visible":true,"origin":"","legend":"","description":"","filename":"Appendix3.DESCRIPTIONOFTHECOUNTRYSEXSAMPLES.docx","url":"https://assets-eu.researchsquare.com/files/rs-8052975/v1/47fd1cd61a717b1c52478813.docx"},{"id":95798997,"identity":"ae7a327b-19f5-4962-b87d-0e21f63bb6c6","added_by":"auto","created_at":"2025-11-13 08:18:19","extension":"docx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":30225,"visible":true,"origin":"","legend":"","description":"","filename":"Appendix4.MOSTFREQUENTSEQUENCES.docx","url":"https://assets-eu.researchsquare.com/files/rs-8052975/v1/7c36ea13738156690a4f4fcb.docx"},{"id":95669404,"identity":"3bd44647-c438-46b3-89f8-b038f080ab59","added_by":"auto","created_at":"2025-11-11 17:10:50","extension":"xlsx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":82011,"visible":true,"origin":"","legend":"","description":"","filename":"Appendix5.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-8052975/v1/80e839192fe71b6c45df595a.xlsx"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eFrom East to West: Gendered Patterns of Work-Family Life Courses in Formerly Divided Europe\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eDecades after the socio-political change in the early 1990s, well-being in post-socialist, Eastern Europe remains lower than in Western Europe [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. However, this gap is smaller among men, especially in older cohorts [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Social roles, which change throughout life, can influence health in old age [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Despite evolving attitudes towards gender roles [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], life courses remain highly gendered and closely linked to well-being [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. This raises an important question: How does the socio-cultural and political context shape life courses differently for men and women?\u003c/p\u003e\u003cp\u003eAlthough it is known that different socio-political environments and societal changes shape life courses in different ways [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], their gender-specific impacts are less explored. This study addresses this gap by examining how contrasting socio-political systems with diverging gender norms \u0026ndash; capitalist and socialist \u0026ndash; produce different work and family life courses and considers their long-term consequences for well-being.\u003c/p\u003e\u003cp\u003eThis study focuses on the Federal Republic of Germany (FRG; West Germany), the former German Democratic Republic (GDR; East Germany), France, the Czech Republic, and Poland \u0026ndash; countries with distinct institutional and political systems in post-war Europe. France and the FRG represent conservative welfare states, characterized by traditional family models and relatively low female labour force participation during the period [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. In contrast, the Czech Republic, Poland, and the GDR exemplify socialist regimes that officially promoted gender equality and women\u0026rsquo;s employment, based not only on ideology but also practical reasons such as labour demands and social control [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. While these countries do not represent all of Europe, they offer clear contrasts for examining how socio-political contexts shaped gendered life trajectories.\u003c/p\u003e\n\u003ch3\u003eLife Course and Gender\u003c/h3\u003e\n\u003cp\u003eThe life course framework emphasizes the importance of the timing and sequencing of life events from birth to death in shaping long-term outcomes [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Life courses involve transitions between states (e.g., from single to married) that bring changes in roles, status, and identity [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Occupying multiple roles may be associated with higher well-being through role-enhancement [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. However, if roles are not compatible, role conflicts, such as the work-family-conflict, may arise, which can negatively impact subjective well-being and health [\u003cspan additionalcitationids=\"CR17\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eWork and family life courses are strongly gender-specific [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]: Men are traditionally expected to provide financial security, while women are often expected to prioritize family, often leading to labour exits when work-family conflict is too strong [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Men usually derive most of their social status from work, while women draw from multiple sources, resulting in more diverse work and family trajectories for women [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Female labour force participation has increased in most European countries [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e], however, traditional gender role expectations still cause women to experience more career disruptions and less linear paths than men [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Following family formation, women are more likely to work part-time and take on caregiving responsibilities, while men often maintain full-time employment [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eFull-time employment is generally linked to better health, especially for men [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Mothers often experience greater stress and lower satisfaction following childbirth, despite a brief initial boost in well-being [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Certain work-family typologies, such as being a single working mother, can be particularly detrimental to women\u0026rsquo;s health [\u003cspan additionalcitationids=\"CR30\" citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e].\u003c/p\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eHistorical and cultural influences on life courses\u003c/h2\u003e\u003cp\u003eLife-courses are shaped by the social structures and norms of a given time and place [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. The historical and cultural context of Eastern and Western Europe in the second half of the 20th century was crucial in defining gender roles and life courses. The division of Europe from 1945 to 1990 into capitalist Western and socialist Eastern Europe provides a valuable framework for understanding these differences.\u003c/p\u003e\u003cp\u003eIn capitalist Western Europe, traditional gender roles dominated after World War II, with women primarily responsible for household duties. In France, gender boundaries were re-established during this period [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e], and religious institutions reinforced the image of women as caregivers [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. In West Germany, women were often legally restricted to domestic roles under the 1958 Equal Rights Act (\"housewife marriage\"), which allowed women to work only if it did not interfere with their household duties [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Consequently, women in capitalistic France and West Germany were less likely to work [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Although female labour force participation in France increased after 1970, and career interruptions after motherhood became less frequent, the gender pay gap persisted due to reduced working hours [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Career interruptions and part-time work not only leads to a reduction in women\u0026rsquo;s income during working life but also negatively affect pension entitlements, contributing to financial insecurity in old age [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eIn contrast, the socialist regimes in Eastern Europe officially promoted gender equality and encouraged women\u0026rsquo;s participation in the workforce, motivated not only by ideology but also by practical needs such as labour shortages and political socialization [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. As a result, the population of Eastern Europe, especially East Germany, developed more liberal and egalitarian gender role beliefs, rejecting the \u0026lsquo;male breadwinner\u0026rsquo; model (Heller et al., 2024; Matysiak \u0026amp; Steinmetz, 2008). Among post-socialist countries, East Germany was the most egalitarian, while Bulgaria and Hungary were the most traditional, with Poland, Slovenia, Russia, and the Czech Republic in between [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. The socialist constitution mandated that all citizens contribute to society through gainful employment [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Policies were also implemented to help women reconcile work and household duties[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e] \u0026ndash; For example, in East Germany, maternity protection was legally established and nearly universal kindergarten access was provided Poland experienced greater workforce continuity than those in West Germany, where part-time work or withdrawal from the workforce was more common [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. While socialism increased female employment rates, it also placed a double burden on women, who had to balance caregiving and breadwinning roles [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Interestingly, work-family-conflict tends to have a stronger negative impact on well-being in countries with higher gender equality in working life than in those with less equality [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eResearch Questions\u003c/h3\u003e\n\u003cp\u003eThis study aims to examine whether there are gender-specific differences in work and family trajectories during young adulthood in Eastern and Western Europe during the historical division. We are the first to analyse and compare life course clusters between East and West Germany as well as selected countries in Eastern and Western Europe separately for men and women to uncover gender differences and similarities across socio-political and regional contexts. Using sequence and cluster analysis, we simultaneously examine work, marriage, and family formation to identify complex life-course patterns. We hypothesise that gender differences in trajectories, with greater differences between Eastern and Western European women than between men.\u003c/p\u003e"},{"header":"Methodology","content":"\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003eData and sample\u003c/h2\u003e\u003cp\u003eOur analysis is based on data from the Survey of Health, Ageing and Retirement in Europe (SHARE) [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e] version 9.0.0. SHARE is the largest pan-European, social science panel study, which focuses on the health and socio-economic living conditions of Europeans aged 50 years and older. Since 2004 eight waves of data have been collected in 28 European countries and Israel with 140,000 participants [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe data from the transformed dataset \u0026lsquo;Job Episodes Panel\u0026rsquo; (JEP) was used, which is based on SHARE waves 3 and 7 [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. The JEP contains a retrospective survey of life histories, where important areas of life were surveyed. Wave 3 was collected between autumn 2008 and summer 2009 in 13 European countries and included about 27,000 respondents [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. With wave 7, the life history interviews were repeated and also included a refreshment sample, about 80,000 interviews took place between spring and autumn 2017 [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. In the current analyses, data from the most recent wave (up to wave 7) was used for each participant.\u003c/p\u003e\u003cp\u003eTo answer the current research questions, we focused on persons who spent their early adulthood (18\u0026ndash;30 years) in Poland, the Czech Republic, East Germany, West Germany, or France during the historical division (1945\u0026ndash;1990). Poland, the Czech Republic, and East Germany were chosen as examples of former socialist countries while West Germany and France were chosen as capitalist comparisons.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eVariables\u003c/h3\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eEmployment and family sequence variables\u003c/h2\u003e\u003cp\u003eRegarding life-courses, states from the domains of employment, marital status, and children between the ages 18 and 30 years were examined at annual intervals in the period of 1945 and 1990.\u003c/p\u003e\u003cp\u003e\u003cb\u003eEmployment\u003c/b\u003e. The variable \u0026lsquo;situation\u0026rsquo; provides data on every job that a person held for at least six months in the course of their life as well as the period of non-employment for at least six months and, if applicable, what the person did instead (e.g., retirement, unemployment, parental leave, training or military service). For the statistical analysis, the variable was recoded as \u0026lsquo;employed\u0026rsquo;, \u0026lsquo;non-employed\u0026rsquo;, and \u0026lsquo;other\u0026rsquo;.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMarital status\u003c/b\u003e. All long-term partnerships of the participants are stored in the variable \u0026lsquo;married\u0026rsquo; with the categories \u0026lsquo;married\u0026rsquo; and \u0026lsquo;not married\u0026rsquo;.\u003c/p\u003e\u003cp\u003e\u003cb\u003eChildren\u003c/b\u003e. The variable \u0026lsquo;nchildren\u0026rsquo; lists the number of biological and adopted children. An adopted child was registered as a child in the household of its adoptive parents from the date of adoption whereas a biological child from the year of birth. The variable was recoded as \u0026lsquo;no children\u0026rsquo;, \u0026lsquo;one child\u0026rsquo;, and \u0026lsquo;2\u0026thinsp;+\u0026thinsp;children\u0026rsquo;.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003ePredictor and descriptive variables\u003c/h3\u003e\n\u003cp\u003eThe following predictor and descriptive variables were used: \u003cb\u003eCountry\u003c/b\u003e (place of residence before 1990, i.e., Germany, Poland, the Czech Republic, or France; the variable \u0026lsquo;dn009_\u0026rsquo; was used to further specify whether participants lived in East or West Germany), \u003cb\u003esex\u003c/b\u003e (\u0026lsquo;male\u0026rsquo; or \u0026lsquo;female\u0026rsquo;),\u003cb\u003eage\u003c/b\u003e (in years at survey time), \u003cb\u003eeducation\u003c/b\u003e (in years),, and \u003cb\u003elocation\u003c/b\u003e (\u0026lsquo;city\u0026rsquo; [a big city, suburbs or outskirts of a big city], \u0026lsquo;town\u0026rsquo; [large or small], and \u0026lsquo;rural area or village\u0026rsquo;). \u003cb\u003eSubjective household income\u003c/b\u003e was measured with the question \u0026ldquo;Thinking of your household's total monthly income, would you say that your household is able to make ends meet...\u0026rdquo; the with responses 1 (with great difficulty), 2 (with some difficulty), 3 (fairly easily), and 4 (easily). Higher responses reflect better subjective household income. \u003cb\u003eSubjective health\u003c/b\u003e was operationalised using the single item \u0026lsquo;Would you say your state of health is...?\u0026rsquo; with the response options \u0026lsquo;Excellent\u0026rsquo; (=\u0026thinsp;1), \u0026lsquo;Very good\u0026rsquo; (=\u0026thinsp;2), \u0026lsquo;Good\u0026rsquo; (=\u0026thinsp;3), \u0026lsquo;Fair\u0026rsquo; (=\u0026thinsp;4) and \u0026lsquo;Poor\u0026rsquo; (=\u0026thinsp;5) [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. The items were inverted so higher scores indicated better subjective health. \u003cb\u003eQuality of life in old age (QoL)\u003c/b\u003e was measured with the CASP-12 scale [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. Twelve items capture the four dimensions of control, autonomy, self-realisation and pleasure, using a four-point Likert scale with the response options \u0026lsquo;Often\u0026rsquo; (=\u0026thinsp;1), \u0026lsquo;Sometimes\u0026rsquo; (=\u0026thinsp;2), \u0026lsquo;Rarely\u0026rsquo; (=\u0026thinsp;3) and \u0026lsquo;Never\u0026rsquo; (=\u0026thinsp;4) ranging between 12 and 48. Higher scores represent greater QoL, with the following cut-off criteria: low QoL, \u0026lt;\u0026thinsp;35; moderate, 35\u0026ndash;37; high, 37\u0026ndash;39; and very high, \u0026ge;\u0026thinsp;39 [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. The reliability for some of the subscales were poor [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e], but for the global measure of QoL reliability was high (α\u0026thinsp;=\u0026thinsp;0.83) [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]; therefore the overall measure was used.\u003c/p\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003eStatistical Analysis\u003c/h2\u003e\u003cp\u003eFor data processing and data analysis R [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e] and Jamovi version 2.3.21.0 [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e] were used. The packages haven [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e], tidyverse [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e], psych [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e], Hmisc [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e] dplyr [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e], panelr [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e] and car [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e] were used for recoding and filtering the data. The packages TraMineR [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e] WeightedCluster [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e], and fastcluster [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e] were used for the sequence and cluster analyses, including the creation of the corresponding graphs. The packages chisq.posthoc.test [\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e] emmeans [\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e], and nnet [\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e] were used for the descriptive analyses.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eSequence and Cluster Analysis\u003c/h2\u003e\u003cp\u003eSequence analysis[\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e, \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e] was employed to identify typical patterns in employment, marriage, and reproductive life courses (refer to supplementary file 1 for an example). This method enables the quantification of life histories by analysing 'states' (e.g., employment or unemployment) and transitions between these states (e.g., from unemployment in year 1 to employment in year 2). As we examined employment (states: employment, no employment, other employment), marriage (states: married, not married), and reproduction (states: 0 children, 1 child, 2\u0026thinsp;+\u0026thinsp;children) simultaneously, we combined all 18 possible combinations across the three domains into one sequence. So for example, the condition \u0026lsquo;no children and married\u0026rsquo; each have the three employment variations \u0026lsquo;employed\u0026rsquo;, \u0026lsquo;not employed\u0026rsquo;, and \u0026lsquo;other employment\u0026rsquo;. As sequence analysis cannot handle missing data, participants were only included that did not have any missing data on the sequence variables.\u003c/p\u003e\u003cp\u003eUsing SA, all sequences were compared pairwise to assess their similarity [\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e]. Optimal matching analysis was used, which is a widely used SA method and measures sequence similarity based on the number of changes required to transform one sequence into another (i.e., altering a state in each wave). Sequences are considered similar if they share similar states at similar times. The optimal matching method also allows flexibility in defining substitution costs. We set all substitutions at a cost of 1, except transitions from no children to two or more children (see supplementary file 2 for the substitution cost matrix).\u003c/p\u003e\u003cp\u003eOptimal matching generates a pairwise distance matrix, displaying the 'distance' between all pairs of individual sequences. Using the distance matrix, we performed cluster analysis to identify patterns in the sequences. We utilized Ward's hierarchical clustering method [\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e], which iteratively merges the two closest groups. This method is preferred for its ability to minimize within-cluster discrepancies [\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e]. We explored options ranging from two to 50 clusters.\u003c/p\u003e\u003cp\u003eTo determine the most appropriate number of clusters we examined the cluster cut-off criteria Average Silhouette Width (ASW), Point Biserial Correlation (PBC), and Hubert\u0026rsquo;s C (HC) [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e, \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e]. Higher ASW and PBC values but lower HC values indicate better cluster quality [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e, \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e]. Following [\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e], the sequence clusters were validated using parametric bootstraps, generating null models for randomized sequences and distances. The quality of the obtained clustering is compared with that of clustering similar but non-clustered data; if the clustering quality is comparable to the non-clustered data, the structure is weak.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eDescriptive Statistics\u003c/h2\u003e\u003cp\u003eFor the sample as well as cluster descriptions, we calculated mean values and standard deviations for continuous variables, and relative frequencies for categorical variables. Descriptive comparisons across groups (sex-country groups \u0026amp; sequence clusters) were made using \u003cem\u003et\u003c/em\u003e-tests, ANOVA and Games-Howell post-hoc-tests, as well as chi-square tests and Bonferroni post-hoc-tests.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003eSample description\u003c/h2\u003e\u003cp\u003eThe final sample consisted on 13,957 participants (\u003cem\u003en\u003c/em\u003e\u003csub\u003eCzech men\u003c/sub\u003e = 1,683, \u003cem\u003en\u003c/em\u003e\u003csub\u003eCzech women\u003c/sub\u003e = 2,314, \u003cem\u003en\u003c/em\u003e\u003csub\u003eWest German women\u003c/sub\u003e = 1,253, \u003cem\u003en\u003c/em\u003e\u003csub\u003eWest German men\u003c/sub\u003e = 1,230, \u003cem\u003en\u003c/em\u003e\u003csub\u003eEast German women\u003c/sub\u003e = 411, \u003cem\u003en\u003c/em\u003e\u003csub\u003eEast German men\u003c/sub\u003e = 350, \u003cem\u003en\u003c/em\u003e\u003csub\u003eFrench women\u003c/sub\u003e = 1,856, \u003cem\u003en\u003c/em\u003e\u003csub\u003eFrench men\u003c/sub\u003e = 1,455, \u003cem\u003en\u003c/em\u003e\u003csub\u003ePolish men\u003c/sub\u003e = 1,590, and \u003cem\u003en\u003c/em\u003e\u003csub\u003ePolish women\u003c/sub\u003e = 1,815). See table 1 for a sample description and supplementary file 3 for sample description by country and sex. On average at interview, participants were 68.7 years old (\u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;8.1), reported 11.6 years (\u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3.6) of education, were mostly satisfied with their household income (\u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3.0, \u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.9), and mostly lived in a town (40.6%) or rural area (39.4%). The average subjective health ranged between good and fair while the average QoL was moderate. When considering the period between 1945 and 1990: At age 18, 5.7% were married, 2.8% had children, and 57.1% were employed. At age 30, 86.3% were married, 80.6% had children, and 85.5% were employed.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003eSequence and cluster analysis\u003c/h2\u003e\u003cp\u003eA total of 5,658 distinct sequences were identified and, with 4.1%, the most frequent sequence was \u0026lsquo;employed, no children, and not married\u0026rsquo;. The 26 cluster structure consistently showed the highest values and was therefore chosen. For details regarding cluster selection see supplementary file 4.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003eCluster descriptions\u003c/h2\u003e\u003cp\u003eSee Fig.\u0026nbsp;1 for the graphical representation of the sequence clusters.\u003c/p\u003e\u003cp\u003e\u003cb\u003eEmployment clusters\u003c/b\u003e. One group of clusters included sequence clusters where individuals were working throughout but transitioned from neither having any children nor being married at 18 years to having a family at 30 years. This included clusters 1, 2, 3, 5, 6, 8, 9, 10, 12, 16, and 17 (59.9% of the total sample). Individuals in clusters 1, 2, 6, and 10 got married and had children relatively young, in their early 20s. Notably, in cluster 1 the second child was delayed by a few years while in cluster 6 individuals only had one child. Instead, individuals in clusters 3, 9, 12, 16, and 17 got married in their mid-20s. Similar to cluster 6, individuals in cluster 9 only had one child while those in cluster 16 also only had one child, which arrived a few years after marriage. Individuals in clusters 5 and 8 only got married towards the end of their 20s.\u003c/p\u003e\u003cp\u003e\u003cb\u003eNo employment clusters.\u003c/b\u003e Another group of sequence clusters was denoted by individuals generally not working, which included clusters 11, 15, 20, 22, 23, and 24 (14.8%). Individuals in clusters 11, 22, 23, and 24 transitioned from not being married and being in various types of (non-)employment into being married and non-employment before their mid-20s. Notably individuals in cluster 23 only have one child. A similar pattern was clear in cluster 20, where individuals were not married and working until their mid-20s, them they got married and worked for a few more years, before leaving employment altogether. Instead in cluster 15, marriage was accompanied by not working for a few years, and then (re-)joining employment in the end-20s.\u003c/p\u003e\u003cp\u003e\u003cb\u003eOther employment clusters.\u003c/b\u003e The last group of sequence clusters was denoted by large amounts of other types of employment, especially in the early 20s \u0026ndash; clusters 7, 13, 18, 19, 21, 25 (14.7%). The sequences in cluster 25 denoted states of other types of employment and no family nearly until the end-20s, where individuals either married or began to work, but not both. Individuals in cluster 7, 13, 18, and 21 began their 20s without a family and were in other types of employment and in the mid-20s began working. In the latter three clusters, individuals started a family approximately a year after their employment began. Lastly, individuals in cluster 19 were in other types of employment throughout, but got married and started a family in their early to mid-20s.\u003c/p\u003e\u003cp\u003e\u003cb\u003eNo family clusters.\u003c/b\u003e Another sequence cluster group \u0026ndash; clusters 4, 14, and 26 \u0026ndash; was denoted by individuals working but not being married (10.6%). While in cluster 4 individuals did not have any children, in cluster 14 some individuals got married and became single again while others had children without getting married. In cluster 26, it appears that individuals got divorced, as they transitioned from not being married or having children, to being married with one or more children and then transitioned to not being married.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003eAssociations with the employment-family clusters\u003c/h2\u003e\u003cp\u003eSee supplementary file 5 for details of the descriptive statistics by employment-family cluster.\u003c/p\u003e\u003cp\u003e\u003cb\u003eAge\u003c/b\u003e differed significantly across the sequence clusters (\u003cem\u003eF\u003c/em\u003e(25, 3197)\u0026thinsp;=\u0026thinsp;7.3, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001). Individuals belonging to the clusters 11 (around 24 years transitioned to married and no employment), 19 (other employment throughout), 23 (around 23 years transitioned to married and no employment with one child), and 24 (not married and no employment throughout) were significantly older (\u003cem\u003eM\u003c/em\u003e = [70.3-71.93], \u003cem\u003eSD\u003c/em\u003e = [8.6\u0026ndash;8.9]) compared to most other clusters (\u003cem\u003eM\u003c/em\u003e = [66.3\u0026ndash;69.8], \u003cem\u003eSD\u003c/em\u003e = [7.6\u0026ndash;8.6]) (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.05).\u003c/p\u003e\u003cp\u003e\u003cb\u003eEducation\u003c/b\u003e differed significantly across the sequence clusters (\u003cem\u003eF\u003c/em\u003e(25, 3197)\u0026thinsp;=\u0026thinsp;118.0, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001). Individuals in nearly all the clusters denoted by other types of employment (nr. 7, 13, 18, 21, \u0026amp; 25) reported the highest education (\u003cem\u003eM\u003c/em\u003e = [14.2\u0026ndash;16.3], \u003cem\u003eSD\u003c/em\u003e = [3.4\u0026ndash;5.2]) compared to most other clusters (\u003cem\u003eM\u003c/em\u003e = [9.0-11.8], \u003cem\u003eSD\u003c/em\u003e = [2.6\u0026ndash;4.2]) (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.05). Only cluster 19 (getting married and in other type of employment throughout) showed one of the lowest education levels (\u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;10.0, \u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;4.2), along with most non-employment clusters (nr. 11, 15, 22, 23, 24) (\u003cem\u003eM\u003c/em\u003e = [9.0-10.5], \u003cem\u003eSD\u003c/em\u003e = [2.7\u0026ndash;3.4]), and clusters 10 and 16 (getting married and in employment throughout) (\u003cem\u003eM\u003c/em\u003e = [10.3\u0026ndash;12.0], \u003cem\u003eSD\u003c/em\u003e = [2.7\u0026ndash;3.4]) (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.05).\u003c/p\u003e\u003cp\u003e\u003cb\u003eSubjective household income\u003c/b\u003e differed significantly across the sequence clusters (\u003cem\u003eF\u003c/em\u003e(25, 2887)\u0026thinsp;=\u0026thinsp;21.7, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001). Most notably, clusters 15, 22, and 24 (no employment) showed the lowest subjective household income (\u003cem\u003eM\u003c/em\u003e = [2.5\u0026ndash;2.7], \u003cem\u003eSD\u003c/em\u003e = [1.0]) compared to the other clusters (\u003cem\u003eM\u003c/em\u003e = [2.6\u0026ndash;3.4], \u003cem\u003eSD\u003c/em\u003e = [0.8-1.0]) (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.05). Instead, cluster 3 (employment) as well as clusters 7, 13, 18, 21, and 25 (other types of employment) showed higher subjective household income (\u003cem\u003eM\u003c/em\u003e = [3.1\u0026ndash;3.4], \u003cem\u003eSD\u003c/em\u003e = [0.8\u0026ndash;0.9]) compared to the other clusters (\u003cem\u003eM\u003c/em\u003e = [2.5\u0026ndash;3.2], \u003cem\u003eSD\u003c/em\u003e = [0.9-1.0]) (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.05).\u003c/p\u003e\u003cp\u003e\u003cb\u003eLocation\u003c/b\u003e differed significantly across the sequence clusters (\u003cem\u003eX\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e (18)\u0026thinsp;=\u0026thinsp;339, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001). Individuals in cluster 2 (employment) were less likely to live in a city (12.7) while those in clusters 7 and 25 (other types of employment) were more likely (32.3\u0026ndash;36.6%), compared to others (10.0-32.6%). Instead, individuals in cluster 6 (employment) (30.0%) were less while those in cluster 19 (other type of employment) (60.4%) were more likely to live in a rural area, compared to the others (25.9\u0026ndash;49.1%).\u003c/p\u003e\u003cp\u003e\u003cb\u003eSubjective health\u003c/b\u003e differed significantly across the sequence clusters (\u003cem\u003eF\u003c/em\u003e(25, 3197)\u0026thinsp;=\u0026thinsp;14.0, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001). Clusters 7, 13, 18, 21, and 25 (which were denoted by other types of employment) showed the highest subjective health (\u003cem\u003eM\u003c/em\u003e = [2.7\u0026ndash;2.9], \u003cem\u003eSD\u003c/em\u003e = [0.9-1.0]) compared to most other clusters (\u003cem\u003eM\u003c/em\u003e = [2.3\u0026ndash;2.6], \u003cem\u003eSD\u003c/em\u003e = [9.3\u0026ndash;1.1]) (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.05). Instead clusters 22, 23, and 24 \u0026ndash; denoted by near simultaneous transitions into marriage and non-employment \u0026ndash; reported the lowest subjective health (\u003cem\u003eM\u003c/em\u003e = [2.0-2.3], \u003cem\u003eSD\u003c/em\u003e = [0.9-1.0]) compared to the other clusters (\u003cem\u003eM\u003c/em\u003e = [2.3\u0026ndash;2.9], \u003cem\u003eSD\u003c/em\u003e = [0.9\u0026ndash;1.1]) (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.05).\u003c/p\u003e\u003cp\u003e\u003cb\u003eQoL\u003c/b\u003e differed significantly across the sequence clusters (\u003cem\u003eF\u003c/em\u003e(25, 2714)\u0026thinsp;=\u0026thinsp;10.1, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001). Individuals in cluster 10 (family in early 20s) and cluster 24 (no employment) reported the lowest QoL (\u003cem\u003eM\u003c/em\u003e = [33.5\u0026ndash;35.3], \u003cem\u003eSD\u003c/em\u003e = [6.5\u0026ndash;7.1]) compared to the other clusters (\u003cem\u003eM\u003c/em\u003e = [35.6\u0026ndash;38.7], \u003cem\u003eSD\u003c/em\u003e = [5.7-7.0]) (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.05). Clusters 13, 18, 21, and 25 \u0026ndash; denoted by other types of employment - instead reported the highest QoL (\u003cem\u003eM\u003c/em\u003e = [38.5\u0026ndash;38.7], \u003cem\u003eSD\u003c/em\u003e = [5.7-6.0]) compared to the other clusters (\u003cem\u003eM\u003c/em\u003e = [33.5\u0026ndash;38.3], \u003cem\u003eSD\u003c/em\u003e = [5.6\u0026ndash;7.1]) (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.05). Compared to some clusters (\u003cem\u003eM\u003c/em\u003e = [33.5\u0026ndash;38.7], \u003cem\u003eSD\u003c/em\u003e = [5.6\u0026ndash;7.1]), individuals in cluster 7 (other type of employment) (\u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;38.3, \u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;5.7) reported higher, while individuals in cluster 12 (employment) and 15 (no employment) reported lower QoL (\u003cem\u003eM\u003c/em\u003e = [35.6\u0026ndash;36.7], \u003cem\u003eSD\u003c/em\u003e = [5.9\u0026ndash;6.6]) (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.05).\u003c/p\u003e\u003cp\u003eSee Fig.\u0026nbsp;2 for distributions across sex and country.\u003c/p\u003e\u003cp\u003e\u003cb\u003eSex\u003c/b\u003e. The distribution of men and women significantly differed across the sequence clusters\u003c/p\u003e\u003cp\u003e(\u003cem\u003eX\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e (25)\u0026thinsp;=\u0026thinsp;3119, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001). Men predominantly made up the employment clusters (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001), merely clusters 1, 2, and 6 were filled with women (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001) and cluster 3 was equally distributed (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;.05). Instead, the no employment clusters were nearly exclusively made up of women (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001). The other types of employment clusters were also mainly made up of men (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001), apart from cluster 19 (marrying but continuing other types of employment) which was made up of more women (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001). The largest no family but working cluster (nr. 4) was made up of men (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001). The cluster working with children but not married (nr. 14) as well as the divorce cluster (nr. 26) were mainly made up of women (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001).\u003c/p\u003e\u003cp\u003e\u003cb\u003eSex and country\u003c/b\u003e. When considering sex and country together, the distribution across the sequence clusters also differed significantly (\u003cem\u003eX\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e (225)\u0026thinsp;=\u0026thinsp;5791, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001). The non-employment clusters (nr. 11, 20, 22, \u0026amp; 23) were least likely to be made up of men irrespective of country and were more likely to be made up of West German and French women (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.05). Also, Czech women were less likely but Polish women more likely to belong to clusters 11 and 22 (no employment) (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.05).\u003c/p\u003e\u003cp\u003eInstead, both Czech and Polish women were more likely to belong to cluster 15 (leaving employment around the time of marriage and returning to employment in the late 20s)\u003c/p\u003e\u003cp\u003e(\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.05).\u003c/p\u003e\u003cp\u003eThe clusters denoted by early family formation and working (nr. 1, 2, 6, \u0026amp; 10) were more likely to contain Czech women but less likely to contain French and West German women (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.05). Polish women were also more likely to belong to clusters 1 and 10 and East German women more likely to belong to clusters 2, 6, and 10 (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.05).\u003c/p\u003e\u003cp\u003eThe clusters denoted by later family formation and working (nr. 5 \u0026amp; 8) were more likely to contain West German, French, and Polish men but less likely to contain Czech and Polish women (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.05).\u003c/p\u003e\u003cp\u003eThe clusters denoted by other types of employment (nr. 7, 13, \u0026amp; 25) were more likely to contain West German and French women but less likely to contain Czech and Polish women (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.05).\u003c/p\u003e\u003cp\u003eCluster 4 (not married and working) was more likely to contain West German, French, and Polish men but less likely to contain East German, Czech, and Polish women (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.05).\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study aimed to investigate whether work and family trajectories differ between Eastern and Western Europe, focusing on gender differences shaped by the socio-political context of the European division between 1945 and 1990. Using sequence and cluster analysis, we compared life course typologies in selected countries across Eastern and Western Europe as well as East and West Germany. Our findings revealed distinct gender-specific trajectories, with fewer differences between Eastern and Western European men compared to women. Eastern European women experienced more continuous workforce participation, taking on the dual role of worker and caregiver. In contrast, Western European women were more likely to exit the workforce or work part-time after marriage or childbirth. The results also highlighted country-specific differences, with Poland showing a mixture of liberal and traditional influences.\u003c/p\u003e\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003eGendered life courses\u003c/h2\u003e\u003cp\u003eAs expected, the results showed significant gender differences in life course trajectories, particularly in employment patterns, which is in line with previous studies [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Men predominantly occupied the employment clusters, reflecting continuous full-time work, which is consistent with traditional expectations of men as primary \u0026lsquo;breadwinners\u0026rsquo;. Only a few employment clusters were dominated by women, mainly those also characterised by family foundation in their early 20s, aligning with the socialist image of the \u0026lsquo;working mother\u0026rsquo; [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. The \u0026lsquo;no employment\u0026rsquo; clusters were mainly made up of women, highlighting their association with family roles [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eNotably, individuals who transitioned into non-employment alongside marriage in their early 20s had the lowest levels of education, subjective household income, health, and QoL. This reflects previous findings that such life-course trajectories can limit one\u0026rsquo;s educational attainment and financial wellbeing [\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e, \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e], which is important for long-term health outcomes [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Given that these clusters are predominantly composed of women, they face particular social, economic and health disadvantages.\u003c/p\u003e\u003cp\u003eIn contrast, clusters denoted by \u0026lsquo;other\u0026rsquo; types of employment were largely male-dominated and were generally linked to higher education and income as well as better health and QoL. Additionally, they were more likely to live in a city as compared to rural areas or towns. This suggests that individuals in these clusters were likely pursuing higher degrees of education, which is associated with greater job flexibility, better career prospects, and improved health outcomes [\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e]. An exception was cluster 19, mainly comprised of women reporting other types of employment and early family foundation: They reported lower levels of education, income, and health (but not QoL) and were more likely to live in rural areas.\u003c/p\u003e\u003cp\u003eMen were more likely to be in clusters without any family responsibilities, while women dominated clusters involving work and caregiving after divorce or as single parents, with these clusters showing slightly below average levels of education and health. This aligns with existing literature, which shows that women\u0026rsquo;s life courses tend to be more diverse [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], but that certain work-family trajectories, such as being a single working mother, can negatively impact women\u0026rsquo;s health [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. However, these clusters did not differ much from the average regarding health and QoL in this sample, which might be due to other factors such as the socio-political context.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\u003ch2\u003eGender differences depending on socio-political context\u003c/h2\u003e\u003cp\u003eThe results demonstrate a clear interaction of gender and sociopolitical context regarding life course trajectories.\u003c/p\u003e\u003cp\u003eWomen in Eastern European countries, particularly the Czech Republic, exhibited more continuous workforce participation compared to their Western European counterparts. This reflects the influence of socialist regimes that promoted gender equality in the labour market [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. These women often experienced early marriage and childbirth alongside steady employment, supported by state policies such as childcare provisions. However, this also contributed to the \u0026lsquo;double burden\u0026rsquo; of balancing work and caregiving responsibilities [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. The results indicate that this burden primarily posed health risks for single or divorced women. A seemingly modern cluster of women re-entering the labour market after leaving due to family formation emerged in both Poland and the Czech Republic, a pattern also identified in recent studies (e.g., 6). However, East German women were also overrepresented in some clusters characterised by continuous employment \u0026ndash; 2, 6, and 10 \u0026ndash; highlighting the strong norm of female workforce participation in the former East Germany. Compared to the more male-dominated employment clusters, especially cluster 10 showed lower levels of education, income, health, and QoL, suggesting the continued impact of a \u0026lsquo;double burden\u0026rsquo; as well. In contrast, East German men barely differed from men in other countries.\u003c/p\u003e\u003cp\u003eDespite both Poland and the Czech Republic falling in the middle of the traditional-liberal spectrum regarding gender roles [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e], some differences emerged. Czech women followed a stereotypically socialist trajectory, while Polish women demonstrated a mixture of liberal and traditional influences. Some Polish women reflected the socialist image of the \u0026lsquo;working mother\u0026rsquo;, maintaining continuous employment or only temporarily leaving the labour market, while others permanently exited the workforce during early family formation, which was linked to lower education, subjective household income, and health. It is noteworthy, that Polish men and women generally showed the lowest levels of education, income, and health in this study. These results are possibly due to Poland\u0026rsquo;s unique cultural context, where socialist policies coexisted with traditional religious norms, influencing both gender roles and life course patterns [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/p\u003e\u003cp\u003eFrance and West Germany exhibited more traditional patterns, where women\u0026rsquo;s employment was shaped more strongly by family responsibilities, often exiting the workforce or transitioning to part-time employment after marriage or childbirth (Meurs \u0026amp; Pora, 2020; Spellerberg, 1996). These patterns align with earlier research showing that traditional gender roles in capitalist societies emphasized women\u0026rsquo;s domestic responsibilities [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e], which were reinforced by state and religious institutions [\u003cspan additionalcitationids=\"CR33 CR34\" citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eIn general, men\u0026rsquo;s life course patterns were relatively consistent across countries and socio-political systems, primarily reflecting continuous full-time work, which is consistent with previous studies [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\u003ch2\u003eStrengths and Limitations\u003c/h2\u003e\u003cp\u003eThis study offers important insights into gendered life courses by providing a comparative analysis of Eastern and Western Europe during a historically significant period, including the unique case of East and West Germany. Using sequence and cluster analysis, it captures the complexity of work, family, and partnership patterns over time. Analysing men and women separately highlights gender-specific differences that are often hidden in broader studies. Another strength is the relatively large sample sizes for most countries, which allow for meaningful cross-national comparisons. However, there are several limitations. First, the sample size for East Germany was smaller than for other countries, which may have limited the ability to detect more differences between the former East Germany and the other countries. Future research should include larger samples from East Germany to provide more robust comparisons. Additionally, the use of retrospective survey data introduces potential recall bias, as participants may have difficulty accurately recalling life events from several decades ago. Lastly, the study also focused on a limited number of countries in Eastern and Western Europe, which may limit the generalizability of the findings, as we saw differing results even within the socialist and capitalist country groups. Future studies should include a broader range of countries to capture a more comprehensive view of life course diversity across Europe.\u003c/p\u003e\u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study contributes to research on gendered life course trajectories by considering the socio-political context of the socialist Eastern and capitalist Western parts of Europe during the division from 1945 to 1990. The findings underscore the significant role of socio-political context in shaping these trajectories, with Eastern European women experiencing more continuous work patterns, while Western European women were more likely to face career interruptions after family formation. Still existing health disparities in older age, particularly lower outcomes in Poland, suggest additional factors beyond life courses that warrant further research. Future studies should explore these influences to better understand the long-term effects of different life course patterns.\u003c/p\u003e"},{"header":"Declarations","content":"\n\u003ch3\u003eData availability\u003c/h3\u003e\n\u003cp\u003eThe data analysed in this study are from the SHARE‑ERIC (Survey of Health, Ageing and Retirement in Europe). Access to the micro-data is granted free of charge for scientific use, subject to registration and the Conditions of Use stipulated by SHARE-ERIC. Researchers wishing to access the data can register as a user directly with the SHARE Research Data Center: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://share-eric.eu/\u003c/span\u003e\u003cspan address=\"https://share-eric.eu/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eGuriev S, Melnikov N (2018) Happiness convergence in transition countries. J Comp Econ 46(3):683\u0026ndash;707. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.jce.2018.07.003\u003c/span\u003e\u003cspan address=\"10.1016/j.jce.2018.07.003\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNikolova M (2016) Minding the happiness gap: Political institutions and perceived quality of life in transition. Eur J Political Econ 45:129\u0026ndash;148. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.ejpoleco.2016.07.008\u003c/span\u003e\u003cspan address=\"10.1016/j.ejpoleco.2016.07.008\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHenretta JC (2007) Early childbearing, marital status, and women's health and mortality after age 50. J Health Soc Behav 48(3):254\u0026ndash;266. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1177/002214650704800304\u003c/span\u003e\u003cspan address=\"10.1177/002214650704800304\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eStavrova O, Fetchenhauer D, Schl\u0026ouml;sser T (2012) Cohabitation, Gender, and Happiness. J Cross-Cult Psychol 43(7):1063\u0026ndash;1081. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1177/0022022111419030\u003c/span\u003e\u003cspan address=\"10.1177/0022022111419030\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHeller A, Altweck L, Hahm S, Michalski N (2024) The (fe-)male breadwinner? Beliefs about gender roles in East Germany, in \u003cem\u003eThirty years after the Berlin Wall: German Unification and Transformation Research. Routledge.\u003c/em\u003e, A. Heller and P. Schmidt, Eds\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAltweck L et al (2023) ., Even Now Women Focus on Family, Men on Work: An Analysis of Employment, Marital, and Reproductive Life-Course Typologies in Relation to Change. Appl Res Qual Life 18(3):1205\u0026ndash;1223 (in En;en). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s11482-022-10130-3\u003c/span\u003e\u003cspan address=\"10.1007/s11482-022-10130-3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. in Health-Related Quality of Life\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMayer KU (2004) Whose Lives? How History, Societies, and Institutions Define and Shape Life Courses. Res Hum Dev 1:161\u0026ndash;187. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1207/s15427617rhd0103_3\u003c/span\u003e\u003cspan address=\"10.1207/s15427617rhd0103_3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMills M (2007) Individualization and the Life Course: Toward a Theoretical Model and Empirical Evidence. In: Howard C (ed) in Contested Individualization. Palgrave Macmillan US, New York, pp 61\u0026ndash;79\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBraun M, Scott J, Alwin DF (1994) Economic necessity or self-actualization? Attitudes toward women's labour-force participation in East and West Germany. Eur Sociol Rev 10(1):29\u0026ndash;47. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/oxfordjournals.esr.a036314\u003c/span\u003e\u003cspan address=\"10.1093/oxfordjournals.esr.a036314\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMeurs D, Pora P (2020) Gender Equality on the Labour Market in France: A Slow Convergence Hampered by Motherhood. Ecostat 510:109\u0026ndash;130. 510-511-51210.24187/ecostat.2019.510t.1990\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePlomien A (2008) From socialism to capitalism: women and their changed relationship with the labor market in Poland. In: Heather (ed) in Globalization, uncertainty and women's careers: An international comparison. Elgar, Cheltenham, UK, pp 247\u0026ndash;274\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMatysiak A, Steinmetz S (2008) Finding Their Way? Female Employment Patterns in West Germany, East Germany, and Poland. Eur Sociol Rev 24(3):331\u0026ndash;345. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/esr/jcn007\u003c/span\u003e\u003cspan address=\"10.1093/esr/jcn007\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eElder GH, Johnson MK, Crosnoe R (2003) The Emergence and Development of Life Course Theory, in \u003cem\u003eHandbooks of Sociology and Social Research, Handbook of the life course\u003c/em\u003e, J. T. Mortimer and M. J. Shanahan, Eds., New York: Springer, pp. 3\u0026ndash;19. [Online]. Available: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://​link.springer.com​/​chapter/​10.1007/​978-​0-​306-​48247-​2_​1\u003c/span\u003e\u003cspan address=\"https://​link.springer.com​/​chapter/​10.1007/​978-​0-​306-​48247-​2_​1\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMitchell BA (2003) Life course theory. In: Ponzetti JJ (ed) in The International Encyclopedia of marriage and Family Relationships. Macmillan Reference, New York\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMoen P, Robison J, Dempster-McClain D (1995) Caregiving and Women's Well-being: A Life Course Approach. J Health Soc Behav 36(3):259. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.2307/2137342\u003c/span\u003e\u003cspan address=\"10.2307/2137342\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHagqvist E, G\u0026aring;din KG, Nordenmark M (2017) Work\u0026ndash;Family Conflict and Well-Being Across Europe: The Role of Gender Context, (in En;en), \u003cem\u003eSoc Indic Res\u003c/em\u003e, vol. 132, no. 2, pp. 785\u0026ndash;797. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s11205-016-1301-x\u003c/span\u003e\u003cspan address=\"10.1007/s11205-016-1301-x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGlavin P, Schieman S (2012) Work\u0026ndash;Family Role Blurring and Work\u0026ndash;Family Conflict. Work Occup 39(1):71\u0026ndash;98. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1177/0730888411406295\u003c/span\u003e\u003cspan address=\"10.1177/0730888411406295\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCreary SJ, Gordon JR, Conflict R, Overload R, Strain R (2016) In: Shehan CL, Duncan M (eds) in Wiley Blackwell encyclopedias in social science, The Wiley Blackwell encyclopedia of family studies. West Sussex, Malden, MA, Oxford: Wiley Blackwell,, Chichester, pp 1\u0026ndash;6\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWahrendorf M, Zaninotto P, Hoven H, Head J, Carr E (2018) Late Life Employment Histories and Their Association With Work and Family Formation During Adulthood: A Sequence Analysis Based on ELSA. journals Gerontol Ser B 73(7):1263\u0026ndash;1277. Psychological sciences and social sciences10.1093/geronb/gbx066\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMadero-Cabib I, Fasang AE (2016) Gendered work\u0026ndash;family life courses and financial well-being in retirement. Adv life course Res 27:43\u0026ndash;60. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.alcr.2015.11.003\u003c/span\u003e\u003cspan address=\"10.1016/j.alcr.2015.11.003\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLyu X, Fan Y (2020) Research on the relationship of work family conflict, work engagement and job crafting: A gender perspective. Curr Psychol. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s12144-020-00705-4\u003c/span\u003e\u003cspan address=\"10.1007/s12144-020-00705-4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003evan der Meer PH (2014) Gender, Unemployment and Subjective Well-Being: Why Being Unemployed Is Worse for Men than for Women. Soc Indic Res 115(1):23\u0026ndash;44. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s11205-012-0207-5\u003c/span\u003e\u003cspan address=\"10.1007/s11205-012-0207-5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eEngels M et al (2019) ., Gendered work-family trajectories and depression at older age. Aging Ment Health 23(11):1478\u0026ndash;1486. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1080/13607863.2018.1501665\u003c/span\u003e\u003cspan address=\"10.1080/13607863.2018.1501665\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRubiano Matulevich EC, Viollaz M Gender Differences in Time Use: Allocating Time between the Market and the Household: World Bank Policy Research Working Paper No. 8981, 2019. [Online]. Available: https://​ssrn.com​/​abstract=3437824\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTomczyk S, Altweck L, Schmidt S (2021) How is the way we spend our time related to psychological wellbeing? A cross-sectional analysis of time-use patterns in the general population and its associations with wellbeing and life satisfaction. BMC Public Health\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePaul KI, Moser K (2009) Unemployment impairs mental health: Meta-analyses. J Vocat Behav 74(3):264\u0026ndash;282. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.jvb.2009.01.001\u003c/span\u003e\u003cspan address=\"10.1016/j.jvb.2009.01.001\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMusick K, Meier A, Flood S (2016) How Parents Fare. Am Sociol Rev 81(5):1069\u0026ndash;1095. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1177/0003122416663917\u003c/span\u003e\u003cspan address=\"10.1177/0003122416663917\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKr\u0026auml;mer MD, Rodgers JL (2020) The impact of having children on domain-specific life satisfaction: A quasi-experimental longitudinal investigation using the Socio-Economic Panel (SOEP) data. J Pers Soc Psychol. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1037/pspp0000279\u003c/span\u003e\u003cspan address=\"10.1037/pspp0000279\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003evan Hedel K et al (2016) ., Work-Family Trajectories and the Higher Cardiovascular Risk of American Women Relative to Women in 13 European Countries. Am J Public Health 106(8):1449\u0026ndash;1456. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.2105/AJPH.2016.303264\u003c/span\u003e\u003cspan address=\"10.2105/AJPH.2016.303264\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMcKetta S, Prins SJ, Platt J, Bates LM, Keyes K (2018) Social sequencing to determine patterns in health and work-family trajectories for U.S. women, 1968\u0026ndash;2013, \u003cem\u003eSSM - population health\u003c/em\u003e, vol. 6, pp. 301\u0026ndash;308. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.ssmph.2018.10.003\u003c/span\u003e\u003cspan address=\"10.1016/j.ssmph.2018.10.003\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFritzell S et al (2012) ., Does non-employment contribute to the health disadvantage among lone mothers in Britain, Italy and Sweden? Synergy effects and the meaning of family policy. Health Place 18(2):199\u0026ndash;208. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.healthplace.2011.09.007\u003c/span\u003e\u003cspan address=\"10.1016/j.healthplace.2011.09.007\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eD\u0026ouml;rfler S, Wernhart, and, Georg (2016) Die Arbeit von M\u0026auml;nnern und Frauen: eine Entwicklungsgeschichte der geschlechtsspezifischen Rollenverteilung in Frankreich, Schweden und \u0026Ouml;sterreich, [Online]. Available: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://​nbn-resolving.org​/​urn:​nbn:​de:​0168-​ssoar-​57291-​9\u003c/span\u003e\u003cspan address=\"https://​nbn-resolving.org​/​urn:​nbn:​de:​0168-​ssoar-​57291-​9\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eColvin KR (2012) Solidarity or Suspicion: Gender, Enfranchisement, and Popular Culture in Liberation France, \u003cem\u003ejowh\u003c/em\u003e, vol. 24, no. 2, pp. 89\u0026ndash;114. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1353/jowh.2012.0016\u003c/span\u003e\u003cspan address=\"10.1353/jowh.2012.0016\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eVeil M (2005) Geschlechterbeziehungen im deutsch-franz\u0026ouml;sischen Vergleich\u0026ndash;ein Blick auf Familien-und Arbeitsmarktpolitik. In: Achcar G, Simon D, Veil M (eds) in Geschlechterbeziehungen im deutsch-franz\u0026ouml;sischen Vergleich\u0026ndash;ein Blick auf Familien-und Arbeitsmarktpolitik. Wissenschaftszentrum Berlin f\u0026uuml;r Sozialforschung (WZB), Berlin\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNebe K (2020) #FamilienLeben \u0026ndash; 50 Jahre wissenschaftliche Beratung f\u0026uuml;r eine nachhaltige Familienpolitik. Sozialer Fortschritt 69:8\u0026ndash;9. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3790/sfo.69.8-9.529\u003c/span\u003e\u003cspan address=\"10.3790/sfo.69.8-9.529\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSpellerberg A (1996) Frauen zwischen Familie und Beruf, in \u003cem\u003eWohlfahrtsentwicklung im vereinten Deutschland: Sozialstruktur, sozialer Wandel und Lebensqualit\u0026auml;t\u003c/em\u003e, W. Zapf and R. Habich, Eds., Berlin: Edition Sigma, pp. 99\u0026ndash;120. [Online]. Available: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://​hdl.handle.net​/​10419/​122772\u003c/span\u003e\u003cspan address=\"http://​hdl.handle.net​/​10419/​122772\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWanat E (2023) Frauen in Polen zwischen Rechtskonservatismus und Feminismus, \u003cem\u003ePolen-Analysen\u003c/em\u003e, no. 307, pp. 2\u0026ndash;7. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.31205/PA.307.01\u003c/span\u003e\u003cspan address=\"10.31205/PA.307.01\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRobila M, Krishnakumar A (2004) The Role of Children in Eastern European Families. Child Soc 18(1):30\u0026ndash;41. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/chi.773\u003c/span\u003e\u003cspan address=\"10.1002/chi.773\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRoth K (2004) \u003cem\u003eArbeit im Sozialismus - Arbeit im Postsozialismus: Erkundungen zum Arbeitsleben im \u0026ouml;stlichen Europa\u003c/em\u003e. M\u0026uuml;nster: LIT, [Online]. Available: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://​www.h-net.org​/​reviews/​showrev.php​?​id=​19957\u003c/span\u003e\u003cspan address=\"http://​www.h-net.org​/​reviews/​showrev.php​?​id=​19957\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eB\u0026ouml;rsch-Supan A et al (2013) ., Data Resource Profile: the Survey of Health, Ageing and Retirement in Europe (SHARE). Int J Epidemiol 42(4):992\u0026ndash;1001. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/ije/dyt088\u003c/span\u003e\u003cspan address=\"10.1093/ije/dyt088\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAntonova L, Aranda L, Pasini G, Trevisan E (2014) Migration, family history and pension: The second release of the SHARE Job Episodes Panel\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBrugiavini A, Orso CE, Genie MG, Naci R, Pasini G (2020) SHARE Job Episodes Panel\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eB\u0026ouml;rsch-Supan A (2022) Survey of Health, Ageing and Retirement in Europe (SHARE) Wave 3 - SHARELIFE\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBergmann M, Scherpenzeel A, B\u0026ouml;rsch-Supan A (2019) SHARE Wave 7 Methodology: Panel innovations and life histories. MEA, Max Planck Institute for Social Law and Social Policy, Munich\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eB\u0026ouml;rsch-Supan A (2022) Survey of Health, Ageing and Retirement in Europe (SHARE) Wave 2\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBorrat-Besson C, Ryser V-A, Gon\u0026ccedil;alves J (2015) An evaluation of the CASP-12 scale used in the Survey of Health, Ageing and Retirement in Europe (SHARE) to measure Quality of Life among people aged 50+\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eConde-Sala JL, Portellano-Ortiz C, Calv\u0026oacute;-Perxas L, Garre-Olmo J (2017) Quality of life in people aged 65\u0026thinsp;+\u0026thinsp;in Europe: associated factors and models of social welfare-analysis of data from the SHARE project (Wave 5). Qual Life Res 26(4):1059\u0026ndash;1070. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s11136-016-1436-x\u003c/span\u003e\u003cspan address=\"10.1007/s11136-016-1436-x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eOliver A, Sentandreu-Ma\u0026ntilde;\u0026oacute; T, Tom\u0026aacute;s JM, Fern\u0026aacute;ndez I, Sancho P (2021) Quality of Life in European Older Adults of SHARE Wave 7: Comparing the Old and the Oldest-Old. J Clin Med 10(13). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/jcm10132850\u003c/span\u003e\u003cspan address=\"10.3390/jcm10132850\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCore Team R \u003cem\u003eR: A language and environment for statistical computing.\u003c/em\u003e [Online]. Available: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://​www.r-project.org​/​\u003c/span\u003e\u003cspan address=\"https://​www.r-project.org​/​\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eThe jamovi (2022) \u003cem\u003eproject\u003c/em\u003e, [Online]. Available: https://​www.jamovi.org​/\u0026amp;#8203\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWickham H, Fran\u0026ccedil;ois R, Henry L, M\u0026uuml;ller K (2023) \u003cem\u003edplyr: A Grammar of Data Manipulation\u003c/em\u003e, [Online]. Available: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://​dplyr.tidyverse.org​/​\u003c/span\u003e\u003cspan address=\"https://​dplyr.tidyverse.org​/​\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWickham H et al (2019) ., Welcome to the Tidyverse. JOSS 4(43):1686. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.21105/joss.01686\u003c/span\u003e\u003cspan address=\"10.21105/joss.01686\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRevelle W (2023) \u003cem\u003epsych: Procedures for Psychological, Psychometric, and Personality Research\u003c/em\u003e, [Online]. Available: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://​personality-project.org​/​r/​psych/​\u003c/span\u003e\u003cspan address=\"https://​personality-project.org​/​r/​psych/​\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHarrell FE Jr, Dupont C (2023) \u003cem\u003eHmisc: Harrell Miscellaneous\u003c/em\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLong JA (2020) \u003cem\u003eCRAN: Contributed Packages\u003c/em\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFox J, Weisberg S (2011) In: Angeles L (ed) An R companion to applied regression, 2 edn. Sage, London, New Delhi, Singapore, Washington DC\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGabadinho A, Ritschard G, M\u0026uuml;ller NS, Studer M (2011) Analyzing and Visualizing State Sequences in R with TraMineR. J Stat Soft 40(4). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.18637/jss.v040.i04\u003c/span\u003e\u003cspan address=\"10.18637/jss.v040.i04\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eStuder M (2013) WeightedCluster Library Manual: A practical guide to creating typologies of trajectories in the social sciences with R. 24. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.12682/LIVES.2296-1658.2013.24\u003c/span\u003e\u003cspan address=\"10.12682/LIVES.2296-1658.2013.24\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eM\u0026uuml;llner D (2013) fastcluster: Fast Hierarchical, Agglomerative Clustering Routines for R and Python. J Stat Soft 53(9). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.18637/jss.v053.i09\u003c/span\u003e\u003cspan address=\"10.18637/jss.v053.i09\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eEbbert D (2019) \u003cem\u003e_chisq.posthoc.test: A Post Hoc Analysis for Pearson's Chi-Squared Test for Count Data_\u003c/em\u003e, [Online]. Available: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://​cran.r-project.org​/​package=chisq.posthoc.test\u003c/span\u003e\u003cspan address=\"https://​cran.r-project.org​/​package=chisq.posthoc.test\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLenth RV et al (2023) emmeans: Estimated marginal means, aka least-squares means, [Online]. Available: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://​cran.r-project.org​/​web/​packages/​emmeans/​index.html\u003c/span\u003e\u003cspan address=\"https://​cran.r-project.org​/​web/​packages/​emmeans/​index.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRipley B, Venables W (2016) \u003cem\u003ePackage 'nnet'\u003c/em\u003e, [Online]. Available: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://​staff.fmi.uvt.ro​/~​daniela.zaharie/​dm2019/​RO/​lab/​lab3/​biblio/​nnet.pdf\u003c/span\u003e\u003cspan address=\"https://​staff.fmi.uvt.ro​/~​daniela.zaharie/​dm2019/​RO/​lab/​lab3/​biblio/​nnet.pdf\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGauthier J-A, Widmer ED, Bucher P, Notredame C (2010) Sociol Methodol 40(1):1\u0026ndash;38. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1111/j.1467-9531.2010.01227.x\u003c/span\u003e\u003cspan address=\"10.1111/j.1467-9531.2010.01227.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. 1. Multichannel Sequence Analysis Applied to Social Science Data\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePollock G (2007) Holistic trajectories: a study of combined employment, housing and family careers by using multiple-sequence analysis. J Royal Stat Society: Ser (Statistics Society) 170(1):167\u0026ndash;183. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1111/j.1467-985X.2006.00450.x\u003c/span\u003e\u003cspan address=\"10.1111/j.1467-985X.2006.00450.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAbbott A (1995) Sequence Analysis: New Methods for Old Ideas. Annu Rev Sociol 21(1):93\u0026ndash;113. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1146/annurev.so.21.080195.000521\u003c/span\u003e\u003cspan address=\"10.1146/annurev.so.21.080195.000521\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWard JH (1963) Hierarchical Grouping to Optimize an Objective Function. J Am Stat Assoc 58(301):236\u0026ndash;244. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1080/01621459.1963.10500845\u003c/span\u003e\u003cspan address=\"10.1080/01621459.1963.10500845\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHennig C, Liao TF (2010) Comparing latent class and dissimilarity based clustering for mixed type variables with application to social stratification\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eStuder M (2021) Validating Sequence Analysis Typologies Using Parametric Bootstrap. Sociol Methodol 51(2):290\u0026ndash;318. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1177/00811750211014232\u003c/span\u003e\u003cspan address=\"10.1177/00811750211014232\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMcMunn A et al (2015) ., De-standardization and gender convergence in work\u0026ndash;family life courses in Great Britain: A multi-channel sequence analysis. Adv life course Res 26:60\u0026ndash;75. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.alcr.2015.06.002\u003c/span\u003e\u003cspan address=\"10.1016/j.alcr.2015.06.002\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFirat M, Visser M, Kraaykamp G (2023) Work-family trajectories across Europe: differences between social groups and welfare regimes. Front Sociol 8:1100700. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fsoc.2023.1100700\u003c/span\u003e\u003cspan address=\"10.3389/fsoc.2023.1100700\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZajacova A, Lawrence EM (2024) The Relationship Between Education and Health: Reducing Disparities Through a Contextual Approach. Annu Rev Public Health 39(1):273\u0026ndash;289. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1146/annurev-publhealth-031816-044628\u003c/span\u003e\u003cspan address=\"10.1146/annurev-publhealth-031816-044628\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1. \u003c/strong\u003eSample description\u0026nbsp;\u003c/p\u003e\n\u003ctable style=\"width: 603px;\"\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 179px;\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 65px;\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 122px;\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 86px;\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eM/n\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 110.954px;\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eSD /\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e%\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 179px;\"\u003e\n\u003cp\u003eCountry, \u003cem\u003en\u003c/em\u003e(%)\u003cstrong\u003e\u0026nbsp;\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 65px;\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 122px;\"\u003e\n\u003cp\u003eCzech\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWest Germany\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eEast Germany\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFrance\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePoland\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 86px;\"\u003e\n\u003cp\u003e3997\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e2483\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e761\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e3311\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e3405\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 110.954px;\"\u003e\n\u003cp\u003e28.6\u003c/p\u003e\n\u003cp\u003e17.8\u003c/p\u003e\n\u003cp\u003e5.5\u003c/p\u003e\n\u003cp\u003e23.7\u003c/p\u003e\n\u003cp\u003e24.4\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 179px;\"\u003e\n\u003cp\u003eSex, \u003cem\u003en\u003c/em\u003e(%)\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e \u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 65px;\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 122px;\"\u003e\n\u003cp\u003eMan\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWoman\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 86px;\"\u003e\n\u003cp\u003e6308\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e7649\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 110.954px;\"\u003e\n\u003cp\u003e45.2\u003c/p\u003e\n\u003cp\u003e54.8\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 179px;\"\u003e\n\u003cp\u003eAge, \u003cem\u003eM\u003c/em\u003e(\u003cem\u003eSD\u003c/em\u003e)\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 65px;\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 122px;\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 86px;\"\u003e\n\u003cp\u003e68.7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 110.954px;\"\u003e\n\u003cp\u003e8.14\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 179px;\"\u003e\n\u003cp\u003eEducation (years), \u003cem\u003eM\u003c/em\u003e(\u003cem\u003eSD\u003c/em\u003e)\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 65px;\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 122px;\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 86px;\"\u003e\n\u003cp\u003e11.6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 110.954px;\"\u003e\n\u003cp\u003e3.56\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 179px;\"\u003e\n\u003cp\u003eSubjective household income\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 65px;\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 122px;\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 86px;\"\u003e\n\u003cp\u003e3.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 110.954px;\"\u003e\n\u003cp\u003e0.94\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 179px;\"\u003e\n\u003cp\u003eLocation\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 65px;\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 122px;\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 86px;\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 110.954px;\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 179px;\"\u003e\n\u003cp\u003e\u0026nbsp;\u0026nbsp; city\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 65px;\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 122px;\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 86px;\"\u003e\n\u003cp\u003e2461\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 110.954px;\"\u003e\n\u003cp\u003e20.0\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 179px;\"\u003e\n\u003cp\u003e\u0026nbsp;\u0026nbsp; town\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 65px;\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 122px;\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 86px;\"\u003e\n\u003cp\u003e4995\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 110.954px;\"\u003e\n\u003cp\u003e40.6\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 179px;\"\u003e\n\u003cp\u003e\u0026nbsp;\u0026nbsp; rural\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 65px;\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 122px;\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 86px;\"\u003e\n\u003cp\u003e4849\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 110.954px;\"\u003e\n\u003cp\u003e39.4\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 179px;\"\u003e\n\u003cp\u003eSelf-rated health, \u003cem\u003eM\u003c/em\u003e(\u003cem\u003eSD\u003c/em\u003e)\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 65px;\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 122px;\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 86px;\"\u003e\n\u003cp\u003e2.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 110.954px;\"\u003e\n\u003cp\u003e0.97\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 179px;\"\u003e\n\u003cp\u003eQuality of life, \u003cem\u003eM\u003c/em\u003e(\u003cem\u003eSD\u003c/em\u003e)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 65px;\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 122px;\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 86px;\"\u003e\n\u003cp\u003e36.9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 110.954px;\"\u003e\n\u003cp\u003e6.20\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 179px;\"\u003e\n\u003cp\u003eMarital status, \u003cem\u003en\u003c/em\u003e(%)\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 65px;\"\u003e\n\u003cp\u003e18 years\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 122px;\"\u003e\n\u003cp\u003eNot married\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMarried \u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 86px;\"\u003e\n\u003cp\u003e13155\u003c/p\u003e\n\u003cp\u003e802\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 110.954px;\"\u003e\n\u003cp\u003e94.3\u003c/p\u003e\n\u003cp\u003e5.7\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 179px;\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 65px;\"\u003e\n\u003cp\u003e30 years\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 122px;\"\u003e\n\u003cp\u003eNot married\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMarried\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 86px;\"\u003e\n\u003cp\u003e1911\u003c/p\u003e\n\u003cp\u003e12046\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 110.954px;\"\u003e\n\u003cp\u003e13.7\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;86.3\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 179px;\"\u003e\n\u003cp\u003eChildren, \u003cem\u003en\u003c/em\u003e(%)\u003cstrong\u003e\u0026nbsp;\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 65px;\"\u003e\n\u003cp\u003e18 years\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 122px;\"\u003e\n\u003cp\u003eNone\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOne\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTwo plus\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 86px;\"\u003e\n\u003cp\u003e13566\u003c/p\u003e\n\u003cp\u003e355\u003c/p\u003e\n\u003cp\u003e36\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 110.954px;\"\u003e\n\u003cp\u003e\u0026nbsp;97.2\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;2.5\u003c/p\u003e\n\u003cp\u003e.3\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 179px;\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 65px;\"\u003e\n\u003cp\u003e30 years\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 122px;\"\u003e\n\u003cp\u003eNone\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOne\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTwo plus \u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 86px;\"\u003e\n\u003cp\u003e2713\u003c/p\u003e\n\u003cp\u003e3732\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e7512\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 110.954px;\"\u003e\n\u003cp\u003e\u0026nbsp;19.4\u003c/p\u003e\n\u003cp\u003e26.7\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;53.8\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 179px;\"\u003e\n\u003cp\u003eWork, \u003cem\u003en\u003c/em\u003e(%)\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e \u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 65px;\"\u003e\n\u003cp\u003e18 years\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 122px;\"\u003e\n\u003cp\u003eWork\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNone \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOther\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 86px;\"\u003e\n\u003cp\u003e7964\u003c/p\u003e\n\u003cp\u003e770\u003c/p\u003e\n\u003cp\u003e5223\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 110.954px;\"\u003e\n\u003cp\u003e57.1\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;5.5\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;37.4\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 179px;\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 65px;\"\u003e\n\u003cp\u003e30 years\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 122px;\"\u003e\n\u003cp\u003eWork\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNone \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOther\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 86px;\"\u003e\n\u003cp\u003e11933\u003c/p\u003e\n\u003cp\u003e1744\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e280\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 110.954px;\"\u003e\n\u003cp\u003e85.5\u003c/p\u003e\n\u003cp\u003e12.5\u003c/p\u003e\n\u003cp\u003e2.0\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003cem\u003eNote\u003c/em\u003e. Missing values for education years: 1182(8.5 %), self-related health: 15(0.1%), quality of life: 2008(16.8%) and subjective household income: 1381(10.9%)\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[{"identity":"db63f231-6475-4a36-82ad-2d57f634b87f","identifier":"10.13039/501100002347","name":"Bundesministerium für Bildung und Forschung","awardNumber":"01UJ1911DY","order_by":0}],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"University of Greifswald","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Employment, family, gender norms, well-being, life course, trajectories, SHARE","lastPublishedDoi":"10.21203/rs.3.rs-8052975/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8052975/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eObjective. \u003c/strong\u003eThis study explored how employment and family life courses in young adulthood and their relationship with subjective well-being (SWB) in older age. We focus on life trajectories between 1945 and 1990 under contrasting capitalist and former socialist countries, examining differences by sex and country.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods. \u003c/strong\u003eWe used\u003cstrong\u003e \u003c/strong\u003edata from the Survey of Health, Ageing and Retirement in Europe (SHARE) for participants 50+ in East/West Germany, Poland, Czech Republic, and France. Participants were surveyed on health outcomes and retrospectively reconstructed their life courses across employment and family domains. We used sequence and cluster analysis for states between 18 and 30 years, for the domains employment, marital status, and children. Descriptive comparisons across clusters were made using \u003cem\u003et\u003c/em\u003e-tests, ANOVA and Games-Howell post-hoc-tests, as well as chi-square tests and Bonferroni post-hoc-tests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults.\u003c/strong\u003e We identified distinct gender-specific trajectories, with fewer differences between Eastern and Western European men compared to women. Eastern European women reported more continuous workforce participation, while Western European women were more likely to exit the workforce or work part-time after family formation. Differences in SWB in later life varied across countries and sex as well as life courses – e.g., early marriage, single parenthood, and long-term unemployment were consistently related to lower SWB across countries.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions. \u003c/strong\u003eThis study underscores how socio-political contexts shape work and family life courses. Despite differing policies, some groups are at risk of consistently experiencing lower SWB. This suggests that additional factors beyond life courses warrant attention in respect to health disparities in older age.\u003c/p\u003e","manuscriptTitle":"From East to West: Gendered Patterns of Work-Family Life Courses in Formerly Divided Europe","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-11 17:10:45","doi":"10.21203/rs.3.rs-8052975/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"4c4f105b-bf7b-47e0-a520-737a598c80e8","owner":[],"postedDate":"November 11th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":57588823,"name":"Psychology"},{"id":57588824,"name":"Health Economics \u0026 Outcomes Research"},{"id":57588825,"name":"Sociology"}],"tags":[],"updatedAt":"2025-11-11T17:10:45+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-11 17:10:45","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8052975","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8052975","identity":"rs-8052975","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}