Introduction

Satisfaction with one’s own life (SWL) varies over the lifespan, following critical life events (LEs) and across person groups (e.g., sociodemographic background). When experiencing potentially traumatic or critical LEs, the consequences for SWL are not uniform across individuals and instead different types of responses (e.g., resilience vs. decline) are seen (e.g., Galatzer-Levy et al., 2018; Infurna & Luthar, 2016; Sacchi et al., 2020). In the present study, we examine short-term and long-term consequences of a collective political and social event that affected a whole population on SWL. Further, we investigate several factors, such as sociodemographic background and internal migration, to explain interpersonal differences in SWL trajectories.

SWL is the conscious judgement of one’s life and about the extent to which one’s quality of life matches self-determined standards (Pavot & Diener, 1993). The majority of individuals show stable SWL but a small yet significant number of individuals show fluctuations in their SWL (Enste & Ewers, 2014). The set-point theory (Costa & McCrae, 1980; Headey, 2006; Headey & Wearing, 1989) explains that critical LEs can increase or decrease SWL for a short period of time after which SWL returns to the previous level or ‘set-point’. However, some critical LEs (e.g., unemployment) can have long-term effects on SWL (Anusic et al., 2014a; Clark et al., 2008; Kettlewell et al., 2020; Richter et al., 2020).

Galatzer-Levy and colleagues (2018) systematically reviewed SWL trajectory types following different types of life events. They found that most people showed resilient (i.e., high and stable) trajectories of SWL. Particularly high levels were seen following critical LEs (74%), while 64–65% of individuals showed this type of trajectory following a personal loss or the onset of a health condition. They reported mixed findings regarding the prevalence of the other types of trajectories: the recovery (i.e., an initially low level of SWL rises to an average level) trajectory type was seen second most frequently (23%), followed by the low stable (12%) and slowly decreasing (10%) trajectory types. Differences across event type were not seen for chronic or declining trajectories, but the recovery trajectory type was seen more following loss- (14%) and health-related LEs (18%) compared with other critical LEs (4%) (Galatzer-Levy et al., 2018).

Beyond the event type, other factors also affect SWL trajectories. Galatzer-Levy and colleagues (2018) found that individual characteristics were most frequently related to distress or well-being trajectories and Schräpler and colleagues (2019) reported that age, educational background, and divorce were differentially related to SWL trajectories. Similarly, Dunn and colleagues (2013) found that people with poor social support, younger age, and women were more likely to show stable low versus stable high trajectories. This suggests that factors, such as sociodemographic background, play a vital part in trajectories of SWL, yet these have rarely been examined in the literature.

The current study used a quasi-experimental setting by examining a collective critical LE experienced by a large part of a population, namely the German reunification. In 1989/1990, the former socialist East Germany was incorporated in the capitalist West Germany, which resulted in widespread political, societal, and health changes – particularly in East Germany – and as such has been classified as a critical collective LE (Eiroá Orosa, 2013; Pinquart & Silbereisen, 2004). Surrounding the reunification, an increase in negative employment-, finance- and health-related LEs was seen in East Germany (Hahm et al, 2024). This manifested in high unemployment rates (up to 40% by 1996) (Stöbel-Richter et al., 2014), increased divorce rates (Stöbel-Richter et al., 2014), as well as extreme social isolation (Barth et al., 1998; Meyer & Schulze, 1998). In the 30 years following the reunification, East Germans also showed greater instability in the life domains employment and family than their West German counterparts (Altweck et al., 2022). Further, between 1989 and 2018 approximately four million persons relocated internally (Heller et al., 2020). At first, East Germans moved, for example for employment opportunities or to reunite with family and friends, and after 1992, West Germans also moved to East Germany (Heller et al., 2020); the latter often reporting more positive migration experiences (Albani et al., 2007).

In relation to SWL, immediately following the reunification, a large gap was seen between East and West Germans, with the former reporting lower SWL (Priem et al., 2020). Then a steep increase in SWL was seen in East Germans until 1994, while the opposite trend was seen in West Germans (Enste & Ewers, 2014; Priem et al., 2020). While these differences nearly vanished recently, a slight gap remains (Kasinger et al., 2022; Petrunyk & Pfeifer, 2016; Priem et al., 2020). Wetzel and colleagues (2021) identified four distinct SWL trajectory types among East Germans between 1990 and 1994: Contrary to previous trajectory studies, the majority showed declining SWL (34%), followed by continuously low (25%), increasing (24%), and stable high SWL (17%). These trajectory types were significantly related to age, education, and employment at baseline (Wetzel et al., 2021).

Given the scarcity of research on the heterogeneity of SWL trajectories following collective socio-political events such as the German reunification and the predominant focus on the impact of baseline sociodemographic characteristics, our study expands the analysis to both East and West Germans in a short-term (5 years) and long-term (30 years) perspective, while considering internal migration as well as changes in sociodemographic characteristics as predictors. Therefore, the following hypotheses are investigated:

H1

Different types of SWL trajectories after the reunification can be identified in the short-term and long-term perspective, with the majority showing stable SWL.

H2

Internal migration is associated with more change and less stability in life satisfaction.

H3

Trajectory types are associated with sociodemographic characteristics at baseline (i.e., sex, age, education, marital status, income, employment status) and changes over time (i.e., becoming not employed, separation/divorce, change in income).

Methodology

Data and Ethics

To investigate the hypotheses, data from the German Socio-Economic Panel (GSOEP version 36) was used. GSOEP is an ongoing, annual representative longitudinal study of private households from 1984 until present, using random-route sampling (Goebel et al., 2019). Data was first collected in West Germany and directly after reunification, recruitment of East German participants started. To model short-term-trajectories, data from five waves (1990–1994) were used, and data from 30 waves (1990–2019) were used to model long-term trajectories.

The sample selection procedure is presented in Fig. 1. From the baseline sample of individuals who participated in 1990 (N = 19,666), individuals younger than 18 years at baseline were excluded. Furthermore, participants with less than 50% responses for SWL over time within the time span of 1990–2019 and 1990–1994 were excluded, as were participants with incomplete data for the predictors at baseline in 1990. As a result, the current analyses were run on a sample of 5,548 participants.

Compared with individuals excluded from the initial sample of N = 19,666, the analysis sample had a lower percentage of male participants (p < .001), was older (p < .001), more highly educated (p < .001), more likely employed (p < .001), more likely married (p < .001), and had a slightly lower household income. Furthermore, included participants had experienced unemployment and divorce/separation more frequently between 1990 and 2019 and 1990–1994 (all p < .001). Finally, the analysis sample had a higher percentage of persons that remained in East Germany and a lower percentage of persons that remained in West Germany between 1990 and 2019 and 1990–1994 (all p < .001). For more details, see Online Supplementary Material 1.

This study is a secondary analysis of the GSOEP data; therefore, no additional ethical approval was needed. In the primary studies, all human studies were approved by the appropriate ethics committee and have therefore been performed in accordance with the ethical standards laid down in the 1964 Declaration of Helsinki and its later amendments or comparable ethical standards; written informed consent was obtained from each subject (Goebel et al., 2019; Liebig et al., 2021). The current study protocols were approved by the German Institute for Economic Research (DIW Berlin).

Fig. 1
figure 1

Sample selection procedure

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Variables and Measures

Satisfaction with Life (SWL)

SWL was measured annually using a single item (“How satisfied are you with your life, all things considered?”), with a possible range from 0 (completely dissatisfied) to 10 (completely satisfied) (Schimmack, 2009).

Independent variables

Baseline Sociodemographic Characteristics

We included sex (male [reference group], female), age (in years), education (International Standard Classification of Education (ISCED)-97 (Organisation for Economic Co-operation and Development [OECD], 1999); low: level 0–2, medium: level 3–4, high: level 5–6 [reference group]), employment status (employed [reference group], not employed), net household income (in Euro (€); converted from Deutsche Mark), and marital status (married [reference group], not married) at baseline.

Short- and Long-term Changes in Sociodemographic Characteristics

Since employment status, household income, and marital status are prone to change over time, changes over a short- and long-term period (5 years: 1990–1994, 30 years: 1990–2019) were considered in the present study. Regarding employment status and marital status, only negative changes or transitions (i.e., becoming not employed, becoming divorced/separated) were considered. Regarding ‘become not employed’, the variable was coded as ‘yes’, if at least one transition from employed to not employed was observed within the available measurements of each individual. A similar approach was used for ‘become divorced/separated’, i.e., if at least one transition from married to not married (separated/divorced) was observed. For change in household income, the difference was calculated for the short-term and long-term period by subtracting the baseline measurement (1990) from the final measurement (short-term: 1994; long-term: 2019). If no data for household income for 1994 or 2019 was available, the last available measurement was used as the final measurement for each individual.

Short- and Long-Term Internal Migration

Internal migration was operationalised as the change in the region of residence (East/West Germany) between the baseline (1990) and final measurement (i.e., short-term: 1994; long-term: 2019), resulting in four groups: West German non-migrants [reference], East German non-migrants, East-West migrants, West-East migrants. To use as much data as possible, the last available measurement of region of residence was used as the final measurement for each individual.

Statistical Analysis

All analyses were conducted using R version 4.2.1 (R Core Team, 2022). The following packages were used: lcmm (version 2.0.2) for growth mixture modelling (Proust-Lima et al., 2017), nnet for multinomial regressions (Ripley & Venables, 2016), ggplot2 (Wickham, 2016) and cowplot (Wilke, 2020) to visualise the results, Table 1 (Rich, 2021) to create descriptive tables and compareGroups (Subirana et al., 2014) to conduct comparisons by class.

Growth Mixture Modelling (GMM)

GMM was applied to detect classes of short-term and long-term trajectories of SWL following the reunification.

For the short-term trajectories, time was modelled in three different ways: linear, quadratic, and as a categorical variable to capture the greatest variability. For the long-term trajectories, time was modelled as a linear and quadratic variable. These models were then calculated with different constraints: (1) fixed intercept and fixed slope mean for each class; (2) random intercept and fixed slope mean for each class with (2a) the same intercept variance for all classes and (2b) different intercept variances for each class (2b); (3) random intercept and random slope mean with (3a) the same intercept and slope (co-)variance for all classes and (3b) different intercept and slope (co-)variances for each class. Full information maximum likelihood estimation was used to handle missing outcome data (SWL).

All models were run with 10 sets of starting values and 30 iterations to identify a replicable Log-Likelihood maximum that was unlikely to be at a local maximum. The starting values of models with > 1 class were derived from the 1-class-model. The best-fitting model was then fully fitted with a maximum of 500 iterations.

Model selection was based on: (1) the Bayesian Information Criterion (BIC) or sample-size adjusted BIC (SABIC) since the Akaike Information Criterion (AIC) often leads to an over-estimation of classes (Nylund et al., 2007), (2) entropy, class size, and mean posterior probability for each class (minimum > 0.70) (Nagin, 2005), (3) the Lo-Mendell-Rubin adjusted likelihood ratio test (LMR-LRT) which compares a model with k classes with a model with k-1 classes (Lo et al., 2001), and finally, (4) an at least small improvement of model fit, as indicated by the likelihood increment percentage per parameter (LIPpp) (small: > 0.02, medium: > 0.10, large: > 0.30) (Grimm et al., 2021). All models were run for an increasing number of classes until non-convergence was reached or fit indices did not improve substantially.

Multinomial Logistic Regressions (MLR)

For the best models for both short- and long-term, MLR were applied to analyze contributing factors of class membership. As a first step, internal migration status was introduced into the regression (model 1). Subsequently, sociodemographic variables at baseline were included (age, sex, education, net household income, marital status, employment status) (model 2). As the final step, changes in sociodemographic variables (become not employed, become divorced/separated, net household income change) were added (model 3). Age was divided by 10 and net household income (change) was divided by 1,000 for the analysis to increase the interpretability of the regression coefficients (odds ratios). The odds ratios indicate the chance to belong to a specific class compared to the reference with the change of one unit (e.g. 10 years, 1,000€) or in in relation to a reference group (e.g., East vs. West).

Results

Sample Characteristics

Detailed information regarding sociodemographic baseline characteristics and sociodemographic changes, grouped by internal migration status, is provided in Table 1. For both the short-term and long-term perspective, the migrant groups (M = 31.56–35.88 years) were younger than the non-migrant groups (M = 41.00–41.54 years) and less likely to be married (East-West/West-East: 46.9–60.8%; West/East: 67.0–78.2%).

Table 1 Sociodemographic characteristics at baseline (1990) as well as short- and long-term changes in sociodemographic characteristics (N = 5,548)
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Furthermore, East German non-migrants and East-West migrants were more likely to have completed high education (East/East-West: 33.0–36.9%; West/West-East: 16.1–21.9%), were more likely employed in 1990 (East/East-West: 77.5–83.7%; West/West-East: 62.5–63.6%), but had a lower household income in 1990 (East/East-West: M = 998.89–1,104.48€; West/West-East: 1,971.93–2,161.78€).

Regarding short-term changes in sociodemographic factors, East-West migrants showed the highest rates of being not employed (46.3%) and separation/divorce (15.0%) as well as the highest increase in household income (M = 976.66€), whereas the West German non-migration group showed the lowest rates of being not employed (18.8%) and separation/divorce (5.2%) as well as the lowest household income increase (M = 259.54€). Concerning long-term changes, West-East migrants were most likely to have become not employed (78.1%), closely followed by East-West migrants (77.7%) and East German non-migrants (74.0%). East-West migrants were most likely to become divorced/separated long-term (36.2%), whereas West-East migrants had the largest household income increase (M = 2,108.56€), closely followed by East-West migrants (M = 1,994.21€).

Classes of SWL Trajectories

The four-class-solutions with random intercepts and fixed slopes (model 2a) represented the best match between statistical fit indicators, parsimony, and meaningful interpretability for both the short- and long-term perspective. For details on LCMM model selection, see Online Supplementary Material 2. Graphical plots of mean SWL trajectories for the final solutions are depicted in Fig. 2 (for individually observed trajectories within the groups, see Online Supplementary Material 3, SM3 Fig 1).

Fig. 2
figure 2

Short- and long-term trajectories of satisfaction with life (SWL) for the four-class solution. Note Solid, coloured lines indicate predicted trajectories, dashed lines the mean observed values. Shaded areas around lines indicate 95% confidence intervals for predicted values

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For both the short-term and long-term perspective, the best models revealed similar classes of trajectories in terms of the overall trends: (1) stable SWL, with a continuous mean value between 6.96 and 7.33 (short-term: 86.7%, long-term: 62.3%); (2) improving SWL, with a strong increase from 2.64 to about 5.62 in 1991, followed by a continuous slower increase to about 6.47 for the short-term perspective (2.5%) and a continuous increase from 5.57 to 7.45 for the long-term perspective (16.4%); (3) declining-improving SWL, with strong decline from 6.64 to 3.43 in 1991, followed by consistent improvement to 5.99 for the short-term perspective (5.2%), and a U-shaped trajectory with 7.42 as the highest value and 4.73 as the lowest value for the long-term-perspective (9.4%); (4) declining SWL, with a continuous decline from 7.09 in 1990 to about 3.84 in 1993, followed by stability for the short-term perspective (5.6%) and a continuous decline from 7.71 to 3.53 for the long-term perspective (11.9%).

Individuals with consistently high values in SWL were most common, with a higher prevalence in the short-term perspective with about 87% compared to 62% in the long-term perspective. Following this, unstable trajectories were more prevalent long-term. Increasing SWL was the rarest class five years after reunification with only 2.5%, but the second most frequent class 30 years later with about 16%. Additionally, stable short-term trajectories generally corresponded to a higher likelihood for stable long-term trajectories (for further details, see Online Supplementary Material 3, SM3 Fig 2).

Factors Associated with Trajectory Classes of SWL

Pairwise Comparisons of Trajectory Classes

The short-term trajectory classes differed significantly regarding all variables except sex, age, and educational level. Employment at baseline was least likely in individuals with improving SWL (58.3%) and most likely in the decline-improvement class (79.0%). Individuals with improving SWL were least likely to be married at baseline (58.3%). The stable SWL group had the highest baseline household income (M = 1658.91€). The declining-improving and declining classes were significantly more likely to become not employed than the stable or improving classes (43.4%/47.9% vs. 23.4%/25.9%). Those with stable SWL had the lowest divorce/separation rates (4.1%), whereas the group with declining SWL had the smallest income increase (M = 232.04€). The stable class had the highest proportion of West German non-migrants (65.9%), whereas the group with declining-increasing SWL had the highest proportion of East German non-migrants (74.5%) and East-West migrants (2.4%).

There were significant differences between the long-term trajectory classes regarding all variables except sex and marital status. Individuals with declining SWL were significantly older (M = 45.89 years). Furthermore, individuals in the improving group were most likely highly educated (27.0%) and employed at baseline (74.0%), whereas the lowest percentages of high education (17.0%) and employment (60.7%) were found in the declining class. The stable SWL group had the highest (M = 1693.80€), whereas the improving class had the lowest household income at baseline (M = 1382.18€). The improving and declining-improving classes were significantly more likely to become not employed than the stable or declining classes (73.0%/71.0% vs. 63.4%/62.1%). The group with stable SWL had a significantly lower rate of divorce/separation (21.7%). The improving SWL group showed the highest household income change (M = 1693.80€), whereas the declining class showed the lowest change (M = 523.23€). In terms of migration status, those with increasing SWL had the lowest proportion of West German non-migrants (40.3%) and the highest proportions of East German non-migrants (54.7%) and East-West migrants (4.8%).

For more details on descriptive statistics and pairwise comparisons of the identified classes, see Online Supplementary Material 4.

Results of multinomial regressions

Multinomial logistic regression was applied to identify factors associated with individual class memberships within the four-class models for both the short-term and long-term perspective (see Figs. 3 and 4; Online Supplementary Material 5).

The multinomial regression revealed significant effects of the migration status for the short-term perspective (R2model 1 = 0.070), which slightly decreased but remained significant after introducing demographic characteristics at baseline and changes (model 3): Compared with West-German non-migrants, East German non-migrants had a 1.6 to 4.7 times higher chance for unstable, changing trajectories, with the largest difference concerning decline-improvement. The chance for decline-improvement was also nearly three times higher for East-West migrants. The addition of sociodemographic variables at baseline (model 2) only explained a comparatively small additional percentage of variance (1.6%). The chances of belonging to the class with improving SWL were significantly higher for those who were not employed and not married at baseline, whereas each increment in baseline household income of 1,000€ was associated with a 36% lower chance of belonging to this class. Higher household income at baseline was also associated with lower chances for decline-improvement and decline. The addition of sociodemographic changes (model 3) explained an additional percentage of 4.1% of variance. Becoming not employed and divorced/separated were associated with higher chances for decline-improvement and decline. Furthermore, the chance for improving SWL was significantly higher for those who became divorced/separated. Additionally, a positive income change of 1,000€ was associated with 27% higher chance of improvement and a 42% lower chance for decline.

Regarding the long-term perspective, significant and consistent associations of migration status were mainly found for the improving class, with a lower percentage of explained variance compared to the short-term perspective (R2model 1 = 0.045). Again, these effects slightly decreased but remained significant after introducing demographic characteristics at baseline and changes (model 3): Compared with West-German non-migrants, East German non-migrants and East-West migrants had 2.6 and 3.3 times higher chances for improvement, respectively. However, a marginally significant association between West-East migration and a higher chance for decline-improvement emerged in model 3. The addition of sociodemographic variables (model 2) explained a small but higher additional percentage of variance (2.9%) compared to the short-term perspective. In contrast to the short-term model, being not employed increased the chances for all unstable groups by 33 to 49% compared to the stable group, and marital status was not associated with class membership at all. An increment in baseline household income of 1,000€ was associated with a 40% lower chance for decline-improvement and a 30% lower chance for decline. Additionally, an increase in age by 10 years was associated with a 13% lower chance of decline-improvement, but a 12% higher chance for decline. The addition of sociodemographic changes (model 3) explained an additional percentage of 3.7% of variance, which was comparable to the short-term model. Becoming not employed and divorced/separated were significantly associated with higher chances for improvement and decline-improvement, but not decline. Additionally, a positive income change of 1,000€ was associated with a 30% lower chance for decline-improvement and a 27% lower chance for decline, but not improvement.

Fig. 3
figure 3

Predictors of short-term trajectories of satisfaction with life (SWL) (1990–1994) from regression model 3 for the four-class solution of GMM (model 2a: random intercept, fixed slope) modelling time as categorical variable. Note. Stable SWL class is used as reference group. Odds ratios and 95%-confidence intervals are depicted. Reference group for each categorical variable is listed after vertical line. Age was divided by 10 and net household income (change) was divided by 1,000 to increase the interpretability of the odds ratios. Values above 1 indicate higher chances of belonging to the non-reference group (i.e., improvement, decline-improvement, decline) compared to the group with stable SWL with an increase of one unit (e.g. 10 years, 1,000€) or in relation to a reference group (e.g., East vs. West)

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Fig. 4
figure 4

Predictors of long-term trajectories of satisfaction with life (SWL) (1990–2019) from regression model 3 for the four-class solution of GMM (model 2a: random intercept, fixed slope) modelling time as quadratic variable. Note Stable SWL class is used as reference group. Odds ratios and 95%-confidence intervals are depicted. Reference group for each categorical variable is listed after vertical line. Age was divided by 10 and net household income (change) was divided by 1,000 to increase the interpretability of the odds ratios. Values above 1 indicate higher chances of belonging to the non-reference group (i.e., improvement, decline-improvement, decline) compared to the group with stable SWL with an increase of one unit (e.g. 10 years, 1,000€) or in relation to a reference group (e.g., East vs. West)

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Fig. 5 depicts the predicted probabilities of short- and long-term trajectory classes by migration group based on the final regression model (model 3). Overall, the majority within all migration groups showed stable trajectories (short-term: 79.9–91.0%; long-term: 57.7–66.7%). Regarding short-term trajectories, East German non-migrants showed the highest rates of unstable, changing trajectories, with a higher percentage of declining-improving SWL compared to the other groups (9.0%). East-West migrants showed the second highest percentage of declining-improving SWL. Concerning long-term trajectories, East German non-migrants and East-West migrants showed the highest percentages for increasing SWL (23.2% and 27.2%), whereas West-East migrants had the highest likelihood for declining-improving SWL (16.0%).

Fig. 5
figure 5

Predicted probabilities of short-term (ST) and long-term (LT) trajectory classes by migration status based on regression model 3. Note Percentages < 3% not displayed

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Discussion

The present study examined trajectories of SWL following a collective, critical LE (the German reunification) and was able to replicate previous findings, which showed the heterogeneity of responses following such events (Galatzer-Levy et al., 2018). We examined a short-term (5 years) and long-term (30 years) period following the event and, in both cases, found similar types of trajectories, namely stable, improvement, declining then improving, and declining. Also, sociodemographic factors (e.g., age, employment status, and household income) and internal migration status were predictors of trajectory type.

Trajectory types of SWL have typically been examined following potentially traumatic (Sacchi et al., 2020) and health-related (Dunn et al., 2013) events, but less so following other critical LEs (Infurna & Luthar, 2016). The German reunification provided quasi-experimental conditions, whereby a large proportion of the population experienced the same critical LE. We were able to replicate previous findings (e.g., Galatzer-Levy et al., 2018) identifying the stable trajectory type as the most common, whereas the fluctuating trajectories (improvement, declining then improving, declining) were less common.

As we examined SWL trajectories 5 as well as 30 years following the reunification, we were able to show that the amount of time elapsed since an event is essential. In the short-term perspective, 80–91% of the sample reported stable trajectories while in the long-term this was only the case for 57–67%. Instead, the fluctuating trajectories were more frequent in the long-term perspective. It is noteworthy, that the recovery trajectories were nearly non-existent in the short-term perspective (3%) but were the second most frequent class in the long-term perspective (16%); whereas the declining trajectories merely doubled in frequency from 11 to 21%. This is particularly noteworthy as most studies examined short periods of time (2–10 years) following an event (e.g., Bonanno et al., 2012; Dunn et al., 2013; Sacchi et al., 2020), in which particular trajectory types, such as improvement or recovery, may not yet be visible. Our findings contradict those of Wetzel and colleagues (2021), who found that the majority of East Germans experienced declining SWL 5 years post-reunification, which is likely due to methodological differences (e.g., including both East and West Germans, while using inner migration status as a predictor).

The type of SWL trajectory was significantly associated with sociodemographic background. Individuals who were not employed at baseline, became not employed or divorced/separated after the event, and with a lower household income were less likely to belong to the stable class. Becoming not employed or divorced/separated in the examined period was particularly relevant for short-term declines in SWL, but was also related to long-term improvement in SWL, which underlines the recovery approach following negative event experiences proposed by the set-point theory (Costa & McCrae, 1980; Headey, 2006; Headey & Wearing, 1989). Instead, not being employed at baseline was more relevant long-term than short-term, which underlines findings of the chronic, negative consequences of job loss (Altweck et al., 2021; McKee-Ryan et al., 2005).

In contrast, factors like age, sex, and education were not related to trajectory membership, although younger age, male sex, and higher education are often associated with lower SWL loss (Altweck et al., 2021; McKee-Ryan et al., 2005). Our findings also contradict those of Dunn and colleagues (2013), who examined a health-related event and found that people with poor social support, younger age, and women were more likely to be in the constant low versus constant high trajectory type. This discrepancy may however be explained by the different types of events examined (i.e., health-related vs. other critical life events).

Finally, internal migration was also predictive of class membership. Studies showed that following the reunification, East Germans were more negatively affected (Barth et al., 1998; Bohley et al., 2016; Hahm et al., 2024) across different life domains (e.g., unemployment, financial, social isolation). Indeed, we found that in the short-term perspective, regional differences played a greater role than migration, as East Germans were less likely to belong to the stable trajectories than West Germans. However, we should refrain from drawing excessively negative conclusions, as in the long-term perspective East Germans – irrespective of migrant status – were most likely to show improvement trajectories. This indicates that the negative consequences of this collective LE were most visible in the short-term perspective and were then compensated for in the long-term. This also reflects the literature reporting that both living conditions (Krause, 2019) as well as differences in SWL (Kasinger et al., 2022; Petrunyk & Pfeifer, 2016; Priem et al., 2020) nearly equalised in modern East and West Germany. The increased chances of improving long-term SWL trajectories among East Germans might also reflect a baseline effect, suggesting that when starting from lower SWL values, the situation is more likely to improve over time. Finally, while the German reunification is generally seen as a transformative and critical event for East Germans (Eiroá Orosa, 2013; Pinquart & Silbereisen, 2004), we found that West Germans were also affected. Particularly in the long-term perspective, the highest proportion of declining trajectories was found in this group.

To better understand the implications of this study, its strengths, limitations, and directions for future research are outlined. A strength of our study is that we investigated trajectory types during two different time periods. Our findings demonstrated that certain types of trajectories only emerged after a significantly longer time period, which may otherwise be missed in short time periods that are generally considered (Bonanno et al., 2012; Dunn et al., 2013). Also, unlike previous research, which primarily focused on health-related or potentially traumatic events (Juengst et al., 2015; Sacchi et al., 2020), we examined a collective, critical LE and were able to replicate common SWL trajectories. A limitation of our research is, that we were unable to analyse SWL before the reunification, as this data was not available for all migration samples and so cannot draw any conclusions regarding causality of the reunification event. Future studies should address this when exploring other critical, mass events (e.g., the global financial crisis 2008). Furthermore, the findings may be skewed due to a selection bias by including older, more educated as well as more likely employed and married individuals with different rates of changes in sociodemographic characteristics compared to the original sample. Also, in the analyses we assumed that the German reunification was a critical negative event for the entire German sample. Although existing studies highlight the overwhelmingly negative consequences (e.g., mass unemployment) of the reunification, particularly for East Germans (Altweck et al., 2021; Hahm et al., 2024), other findings report both positive and negative evaluations of the reunification (Hahm et al., 2023; Roether & Fischer-Cyrulies, 1999; Wiethoff et al., 1999). Lastly, the migrant samples were small, likely due to drop-out effects, and so generalisation of the findings should be viewed with caution.

Conclusions

By examining a collective LE, the German reunification, we managed to replicate common SWL trajectories and demonstrated that trajectory membership is influenced by time after the event, sociodemographic factors, and internal migration. The four distinct trajectories – stable, improvement, decline followed by improvement, and declining – were found in both the short- and long-term perspective, with the stable trajectory as the most common. East Germans, both non-migrants and migrants, were more likely to experience positive and negative changes in SWL, reflecting the post-reunification challenges they were confronted with, but also their resilience and adaptability in response to their changing living conditions. Becoming not employed, divorce/separation, and a lower household income was related to a lower likelihood of stable SWL trajectories. These findings have noteworthy implications for health research and policies as they highlight the long-term effects of collective LEs on individuals’ SWL trajectories which we can extrapolate to similar modern, collective events like the financial crisis 2008 or the COVID-19 pandemic.