{"paper_id":"2dff5a5e-6350-46c5-85b7-936999853a49","body_text":"Effects of internal migration on the life satisfaction of apprentices | 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 Effects of internal migration on the life satisfaction of apprentices Rafael Warkotsch, Nicolai Netz, Nico Stawarz, Alexandra Wicht This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7805231/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 Migration can have far-reaching implications for individuals’ life satisfaction (LS) trajectories. Previous research has mostly examined how migration influences LS using broad samples of the adult population. Moreover, it has not sufficiently considered the influence of other important life events preceding, accompanying, or following migration. Consequently, its conclusions are not readily transferable to individuals in different life stages and their age-specific coupled life events. To narrow this research gap, we examine how migrating within Germany during the transition to vocational education and training (VET) influences the LS trajectories of apprentices. We estimate fixed-effect panel regressions using data from the German National Educational Panel Study (NEPS). Beyond a substantially positive effect of entering VET, we find no significant average effect of VET-related migration on apprentices’ LS. Moreover, the pre-migration VET opportunity structures do not moderate the effect of VET-related migration on LS. However, VET-related migration geared towards the attainment of higher-status (versus lower-status) occupations positively influences apprentices’ LS in the short term. We observe the strongest positive effects of VET-related migration to urban (versus rural) regions. These results suggest that, beyond strictly VET-related factors, age-specific lifestyle factors, which become accessible by moving towards urban regions, moderate the effects of internal migration on LS. Overall, our study illustrates the need to consider the specificity of life stages and coupled life events when analysing the effects of migration on LS. Figures Figure 1 Figure 2 Figure 3 1 Introduction Migration can have far-reaching implications for individuals’ life satisfaction (LS) trajectories. A growing body of studies suggests that both international (cross-border) and internal (within-country) mobility can increase LS (Authors, 2024; Bartram, 2013 ; Erlinghagen et al., 2021 ; Guedes Auditor & Erlinghagen, 2021 ; Hendriks & Burger, 2021 ; Kratz, 2020 ; Melzer & Muffels, 2017 ; Nowok et al., 2013 ). These studies also illustrate that the effects of migration on LS vary depending on the motives for mobility, personal characteristics, and regional context features. While highly valuable, this body of research can be expanded for at least two reasons: First, the existing studies tend to use broad samples of the adult population. Consequently, their conclusions are not readily transferable to individuals in life stages such as childhood, youth, young adulthood, and old age. During middle adulthood, primary motives for migration include better job prospects, an improved housing situation, and family formation (Kley, 2011 ; Petzold, 2017 ; Walford & Stockdale, 2015 ). In earlier or later life stages, however, other motives and conditions frame mobility decisions (Coulter & Scott, 2015 ; Henkens et al., 2024 ; Switek & Easterlin, 2018 ). Second, previous studies have not sufficiently considered the potential LS effects of life events preceding, accompanying, or following migration. These events include union initiation or dissolution and transitions to other life stages, e.g., to new educational phases, the labour market, or retirement. Such events and transitions will arguably shape the extent to which migration can influence LS. For these reasons, research should more closely examine the effects of migration on LS considering the coupling of migration with age-specific life events characterising these life stages. Our study addresses this research gap by examining how internal migration within Germany during a key school-to-work transition affects LS trajectories. We focus on the transition to vocational education and training (VET), which is a significant entry point into the labour market for over half of young people in Germany (see section 2 for details). From a theoretical point of view, VET-related migration constitutes an interesting test case – which has, however, hardly been examined so far. On the one hand, VET-related migration resembles migration in other life stages. For instance, like economic migration in later adult life, it involves an employment-related investment decision based on personal preferences and regional opportunities (Thissen et al., 2010 ). On the other hand, it is characterised by the specificities of the (transition to the) VET life stage, which usually takes place during young adulthood (Schulenberg et al., 2004 ). What is specific about (migration at the transition to) the VET life stage? First, individuals are particularly prone to internal migration when transitioning to the VET life stage (; Geist & McManus, 2008 ; Horowitz & Entwisle, 2021 ; Schäfer & Just, 2018 ). Second, age-specific motives, constraints, and opportunities frame VET-related migration. It can be a necessity to secure a VET position by escaping regions with a scarce offer of suitable VET positions (Authors, 2023; S. Matthes & Ulrich, 2016 ), but also an opportunity to achieve a high occupational status in the long run (Authors, 2024; Waibel, 2019 ). Beyond such VET-related considerations, it can also help young adults gain independence from the parental household and fulfil lifestyle goals (Benson & Furstenberg, 2006 ). For young adults, in particular, moving towards the amenities of urban regions might boost LS (Schäfer & Just, 2018 ). Third, VET-related migration can be conceptualised as a coupled life event (Authors, 2023; Buchmann & Kriesi, 2011 ) because individuals need to find a VET position and leave their accustomed spatial environment. The few studies focussing on LS during the transition to the VET life stage – albeit without analysing the role of migration – report a surge in LS following the transition from school to VET (Authors, 2019; Reuter et al., 2022 ). At present, it is unclear whether internal migration has separate effects on LS in the face of such a major coupled life event. Considering the discussed similarities and differences between VET-related and other migration events raises the following questions: What are the standalone effects of internal migration during the transition to VET on apprentices’ LS? And which conditions moderate the size of these effects? We address these questions by combining a life course perspective with the theory of social production functions (SPF) and rational choice theory (RCT). These approaches allow us to derive hypotheses on how VET-related migration should influence LS on average, and on how this relationship might vary depending on certain conditions, including apprentices’ pre-migration VET opportunity structures, status attained through VET, and the type of destination region (rural versus urban). We test our hypotheses using individual-level data from the German National Educational Panel Study (NEPS), which we supplement with regional context data from the German Federal Employment Agency. To tackle selection into VET-related migration, we estimate fixed-effects (FE) panel regressions (Brüderl & Ludwig, 2015 ). We assess the sensitivity of our results to alternative modelling decisions through an exploratory multiverse analysis (Steegen et al., 2016 ). Additionally, we assess whether the potential moderating effects of the destination region depend on the pre-migration VET opportunity structures through split-sample analyses. We conclude by discussing the main contributions and limitations of our analyses, which illustrate the need to consider the specificity of coupled life events when examining the effects of internal migration on LS. 2 Contextualisation: Vocational education and training in Germany In Germany, VET is a widespread post-secondary education option. The number of yearly entrants to dual and school-based VET programmes usually ranges from about 650,000 (2005) to 735,000 (2020), thus even exceeding the number of yearly entrants to higher education (2005: 355,000; 2020: 490,000) (Autor:innengruppe Bildungsbericht, 2022, pp. 167, 206). To enter VET, students do not necessarily need an educational degree, although most have one (Autor:innengruppe Bildungsbericht, 2022, p. 168). German VET programmes usually last for two or three years (Protsch & Solga, 2016 ). Germany is a well-suited test case for our analysis, as both the decision to pursue VET (rather than other educational pathways) and the choice of migrating (as opposed to staying) constitute outcomes of a comparatively wide array of available options. This variety implies that VET-related migration can be used strategically to increase or maintain LS (for a broader theoretical discussion, see section 3). Moreover, Germany is characterised by a strong connection between its education system and the labour market compared to other countries, e.g., the US or France (DiPrete et al., 2017 ). It is also characterised by a pronounced regional clustering of economic branches (Kleinert et al., 2018 ). This regional clustering implies that VET-related migration may have a stronger bearing on LS than in countries with more evenly distributed educational and professional opportunities and where the linkages between the education system and the labour market are less pronounced. VET-related migration is fairly common among apprentices in Germany: 21.9 percent of apprentices enter VET in a regional labour market (RLM, see section 4.2 for details) different from their home RLM and 16.4 percent enter VET in their home RLM but outside their home district (Authors, 2023). 3 Theory and hypotheses From a life course perspective, individual life courses consist of different life stages (including education, employment, and retirement) and life events (e.g., access to different educational levels, labour market entry, or entry into retirement), which induce transitions between life stages (Bernardi et al., 2019 ). One major life event is school completion, which, in Germany, is usually followed by a transition to post-secondary education (for details, see section 2). The transition to post-secondary education is also highly relevant for migration research, as this transition is often coupled with migration, mostly within national borders (Authors, 2023; Bernardi et al., 2019 ; Brüderl et al., 2019 ; Coulter & Scott, 2015 ; Vidal & Lersch, 2021 ). This act of spatial mobility usually constitutes the first self-determined and long-term-oriented internal migration in the life course. As such, it may have a substantial bearing on young individuals’ LS trajectories. In the following, we draw on the theory of social production functions (SPF) and rational choice theory (RCT), which suggest that VET-related migration could, on average, lead to increases in apprentices’ LS (section 3.1). Additionally, we derive hypotheses on factors moderating the effects of VET-related migration on LS, which helps us carve out the specificity of this form of migration from a life course perspective. The discussed moderating factors include the regional VET opportunities before migration (section 3.2.1), the occupational status of the VET program (section 3.2.2), and the type of destination region (rural versus urban, section 3.2.3). 3.1 VET-related migration as a means to improve life satisfaction According to SPF theory, individuals strive to improve their subjective well-being (Buijs et al., 2021 ; Ormel et al., 1999 ), which we operationalise through overall LS. Following SPF theory, individuals can achieve this highest goal by reaching so-called (first-order) instrumental goals, which include stimulation/activation, comfort, status, behavioural confirmation, and affection. They can fulfil these instrumental goals by activities and endowments constituting so-called (second-order) means of production. We argue that VET-related migration can be considered an important means of production: It can grant access to various beneficial (third-order) activities and endowments fulfilling instrumental goals, thereby eventually increasing LS (Hendriks & Bartram, 2019 ). However, VET-related migration also tends to confront individuals with a trade-off, as mobile individuals usually need to abandon other means of production available at their current location. Rational choice theory (RCT) specifies that individuals should regard the option to migrate as an investment decision involving expected costs and benefits (Authors, 2016; Bernardi et al., 2019 ; Sjaastad, 1962 ). The costs of VET-related migration may involve monetary expenses (e.g., moving costs, Aassve et al., 2007 ) as well as non-monetary costs (e.g., the separation from family and friends or stress in adapting to new environments; Choy et al., 2021 ; Hendriks et al., 2016 ; Thissen et al., 2010 ). In the case of apprentices, the benefits of migration may encompass various activities and endowments, including not only access to a (better) VET position but also independence from the parental household and the possibility to develop a personal lifestyle (Schulenberg et al., 2004 ). According to both SPF theory and RCT, individuals should weigh the monetary and non-monetary costs and benefits of moving to different locations and not moving against each other, and eventually opt for the location promising the highest overall LS (Hendriks & Bartram, 2019 ). This implies that they should only migrate if they expect the benefits of migration to exceed its costs. In line with these thoughts, substantial evidence – mainly on economically motivated internal migration – shows that adults can increase their LS through migration (Authors, 2024; Bartram, 2013 ; Erlinghagen et al., 2021 ; Guedes Auditor & Erlinghagen, 2021 ; Hendriks & Burger, 2021 ; Kratz, 2020 ; Melzer & Muffels, 2017 ; Nowok et al., 2013 ). Moreover, first evidence suggests that residential relocations among young adults increase LS (Switek, 2016 , for the case of Swedish youth and Perales, 2017 , for the case of young adults in the UK and Australia). Based on these theoretical assumptions and empirical findings, it is plausible that school leavers opt for internal migration during the transition to VET only if they expect their mobility to increase their LS. If this was true, we would expect positive average effects of VET-related migration on the LS of apprentices (H1). 3.2 The moderating role of regional VET opportunities, status attainment, and destination regions SPF theory posits that individuals’ actions should aim at improving their life satisfaction (Buijs et al., 2021 ; Ormel et al., 1999 ). At the transition to VET, factors like family formation, improving housing conditions, or immediate wage gains are likely less relevant for LS improvements than during later life stages. Instead, training- and lifestyle-related factors should be more relevant (Geist & McManus, 2008 ; Vilhelmson & Thulin, 2016 ), such as overcoming local shortages in the regional VET supply, attaining a high status through high-quality VET, and moving to an urban region. As elaborated in the following, the effects of VET-related migration might vary depending on the aforementioned motives. 3.2.1 Regional VET opportunities The VET opportunity structure of the region in which pupils live upon school completion may influence their quality of life by shaping their chances of finding a VET position (S. Matthes & Ulrich, 2016 ). If VET supply is low, finding an adequate position is more difficult (Jost et al., 2019 ; Kleinert & Jacob, 2013 ; S. Matthes & Ulrich, 2016 ). Importantly, school leavers can cope with such shortages by extending their search radius and, eventually, by becoming spatially mobile (Authors, 2023; Kropp et al., 2007 ). This implies a compromise between fulfilling their occupational VET aspirations and accepting regionally available opportunities (S. Matthes & Ulrich, 2016 ). To fulfil their instrumental goals of behavioural confirmation and affection, likely provided by their familiar social environment (Ormel et al., 1999 ), school leavers can sacrifice specific occupational goals and stay. Alternatively, they can accept the social costs of leaving their home region and thereby attain status, comfort, and confirmation through a suitable VET position, thus increasing their overall LS by obtaining a VET position that meets their aspirations (Etzel & Nagy, 2021 ). On the one hand, migrating school leavers from regions with worse VET opportunity structures could be less satisfied because they are more likely to have to accept potentially high social or monetary costs to fulfil their VET preferences (Eckelt & Schauer, 2019 ). Coming from a poor VET opportunity structure, they face more difficulties when searching for VET positions and pressure to migrate. School leavers from regions with better VET opportunity structures, on their part, should require more benefits for them to leave their advantageous region, implying that their migration should result in greater LS gains. According to this reasoning, the effects of VET-related migration on LS might be smaller for apprentices from regions with worse VET opportunity structures (H2a). On the other hand, this hypothesis does not account for the possibility of a migration-related shift in the cost-benefit evaluation: If VET opportunities are scarce in the home region, leaving this region may be experienced as overcoming a major disadvantage. The feeling of successfully overcoming this disadvantage may yield a benefit not experienced by apprentices from advantageous regions. It could thus be a unique benefit for individuals who left poor VET opportunity structures. This leads us to an equally plausible competing hypothesis: The effects of VET-related migration on LS might be larger for apprentices from regions with worse VET opportunity structures (H2b). 3.2.2 Status attainment The occupational status attained through VET may also serve as a motivating factor for VET-related migration and have a significant influence on LS. As achieving a high status can be an important goal for school leavers (Authors, 2022, 2023; Protsch & Solga, 2016 ), they may be willing to migrate if they are otherwise unable to obtain a high-status occupation (Fielding, 1992 ). Apprentices who secure high-status VET positions through migration are arguably more likely to fulfil their occupational goals and, thus, to perceive migration as beneficial. Therefore, the effects of VET-related migration on LS might be larger, the higher the status of the attained VET occupation is (H3). 3.2.3 Type of destination region The decision for VET-related migration may not only be motivated by strictly VET-related reasons but also by broader lifestyle considerations. Leaving the home region allows young adults to detach from their parental household and to live in an environment they have chosen for themselves, so as “to ‘become someone else’ and grow as a person” (Vilhelmson & Thulin, 2016 , p. 283). This important step on the way to adulthood could also increase LS (Kins & Beyers, 2010 ; Otte & Baur, 2008 ; Schulenberg et al., 2004 ). Especially if apprentices move towards regions offering more life stage-relevant amenities, they should experience benefits in multiple life domains and thereby increase their LS. Besides more abundant VET and labour market opportunities, urban regions tend to offer more opportunities to take part in social and cultural life and to establish new social contacts than rural regions (Gans, 2017 ). In line with these thoughts, empirical evidence shows that young adults are particularly likely to migrate towards urban regions (Busch, 2016 ; Schäfer & Just, 2018 ). Against this background, VET-related migration to urban regions might have larger positive effects on LS than VET-related migration to rural regions (H4). 4 Analytic strategy 4.1 Data We test our hypotheses using data from starting cohort 4 (version 14.0.0) of the German National Educational Panel Study (NEPS Network, 2024 ). This nationally representative longitudinal survey gathers rich information on 16,425 respondents who were grade 9 students in German schools during the school year 2010/11. The schools were randomly selected from the total population of regular schools offering lower secondary education. Within each school, respondents were sampled in up to two randomly selected classes. Between 2010 and 2022, the respondents were invited to participate in fourteen survey waves. The survey waves covered different life stages, from attending school in the teenage years via the transition to post-secondary education to its completion. Thus, the data include students from all educational tracks who experienced different transitions to VET, either directly after obtaining a low or medium secondary degree (after grade 9 or 10), or after obtaining the Abitur , the highest general educational degree in Germany (after grade 12 or 13), or indirectly after an intermittent employment, prevocational training, or other gap activity. We applied some sample restrictions to improve the precision of our estimates: First, we excluded 9,725 individuals who did not enter VET at any point. We also excluded 42 individuals who obtained another professional qualification (e.g., a university degree) before their first VET spell and 17 individuals who were misclassified as apprentices in waves 1 or 2 (when they were still enrolled in secondary school). Furthermore, we only included observations until the (successful or unsuccessful) completion of their first VET spell. After applying these sample restrictions, the full sample contains 6,641 individuals. The analytical sample used in the main analyses comprises 17,788 observations for 2,797 individuals after listwise deletion (for details, see sections A1 and A3.1 in the online appendix). 4.2 Operationalisation Life satisfaction. Across waves, our dependent variable was measured by asking respondents how satisfied they were overall with their life on a scale from 0 (completely unsatisfied) to 10 (completely satisfied). We used monthly available episode data on individuals’ educational trajectories and matched the dates of their VET phases with the interview dates of the general annual surveys. We defined baseline LS as respondents’ mean LS more than one year before entering VET and excluded observations relating to the year before entering VET. This reduces the possibility that any positive or negative anticipation of leaving school and entering training biases the baseline measurement. We estimated the effects of VET-related migration using dummies for each training year (first, second, and third year). VET-related migration. To measure VET-related migration, we constructed a variable indicating whether apprentices migrated across regional labour markets (RLMs). We used a classification distinguishing 141 RLMs by Kosfeld and Werner ( 2012 ), which was designed to capture regions with minimal commuting prevalence between them and maximal commuting prevalence within them. Thus, it identifies spatial units that contain relatively homogeneous labour markets regarding internal migration (Authors, 2019). We defined VET-related migration as a change of the primary location of residence from one RLM to another, with the new location of residence being the same as the training location. We defined staying as keeping the primary location of residence in the same administrative district (NUTS-3), with the training location being in the same district as the residence. These definitions provide clearly defined treatment (migrants) and reference groups (stayers), excluding individuals who opted for other forms of spatial mobility (e.g., long-distance commuting). According to our definition, 18.2 percent (N = 508) of apprentices in our sample are VET-related migrants (see Table A1 in the online appendix). Poor regional VET opportunity structure. To capture the regional VET opportunity structure before entering VET, we constructed a measure that specifically targets the VET market. Using data from the Federal Employment Agency, we divided the number of registered vacant VET positions in the aspired occupational segment (B. Matthes et al., 2015 ) within the RLM of residence one year before entering VET by the size of the age cohort from 15 to 24 in the same RLM and year. While the number of vacancies represents the supply of training positions, the cohort size approximates the number of potential competitors for these vacancies. The constructed scale thus reflects the potential competition for VET positions in the aspired occupation. We inverted the scale to align with our theoretical reasoning and for ease of interpretation. The inverted scale ranges from − 0.13 (a favourable regional VET opportunity structure with 0.13 vacant positions per potential competitor) to 0 (a poor VET opportunity structure with no vacant position). The variable is z -standardised in our models. Status attainment. To measure the status of the trained occupation, we used the International Socio-Economic Index of Occupational Status (ISEI-08). Higher values indicate occupations with a higher status. We z-standardised this variable as well. Type of destination region. To distinguish between rural and urban destination regions, we used a classification of settlement structure types on the district level (NUTS-3) by the Federal Institute for Research on Building, Urban Affairs and Spatial Development (BBSR, 2024 ). We coded the categories “large cities” and “urbanized districts” as “urban” and the categories “rural districts with agglomeration tendencies” and “scarcely populated rural districts” as “rural”. Control variables. To consider age and period effects, we further controlled for age groups (below 16 years, 16 to 17 years, 18 years and older), grouped survey waves (waves 1/2, 3/4, 5/6, 7/8, 9/10, 11, 12/13/14) , and the calendar months in which the data were collected. Distributions of the variables discussed above are reported in Tables A1 and A2 in the online appendix. 4.3 Methods We apply linear fixed-effects (FE) regressions (Brüderl & Ludwig, 2015 ). These models allow us to control for time-invariant heterogeneity, thereby improving the approximation of causal effects of VET-related migration on LS. By splitting the VET period into years using dummy variables (first, second, and third year), we model LS trajectories over time. LS more than one year before entering VET constitutes the reference baseline measurement. We estimate the difference in LS trajectories between migrants and stayers by interacting VET-related migration with the aforementioned year dummies. \\(\\:L{S}_{it}={\\beta\\:}_{t}T+{\\gamma\\:}_{t}T\\bullet\\:{M}_{i}+{\\alpha\\:}_{i}+\\:{\\text{ϵ}}_{\\text{i}\\text{t}}\\) ( 1 ) In equation ( 1 ), \\(\\:{M}_{i}\\) denotes a person-level dummy variable for VET-related migration, \\(\\:T\\) the dummies for each training year, \\(\\:L{S}_{it}\\) the outcome variable for each person at each time point T, \\(\\:{{\\alpha\\:}}_{\\text{i}}\\) the person-level fixed effect, and \\(\\:{\\text{ϵ}}_{\\text{i}\\text{t}}\\) the residual. \\(\\:{\\beta\\:}_{t}\\) denotes the time-fixed effect, while \\(\\:{\\gamma\\:}_{t}\\) is the coefficient of interest for each time point. Moreover, we modelled interactions between time points, VET-related migration, and the additional moderator variables (VET opportunity structure, occupational status, destination region) to estimate the LS trajectories of specific groups of apprentices: \\(\\:L{S}_{it}={\\beta\\:}_{t}T+{\\gamma\\:}_{t}T\\bullet\\:{M}_{i}+{{\\delta\\:}_{1\\text{t}}\\text{T}\\:\\bullet\\:{\\text{X}}_{\\text{i}}+\\:{\\delta\\:}_{2\\text{t}}\\text{T}\\bullet\\:{\\text{M}}_{\\text{i}}\\bullet\\:{X}_{\\text{i}}+{\\alpha\\:}_{i}+\\:\\text{ϵ}}_{\\text{i}\\text{t}}\\) ( 2 ) In equation ( 2 ), \\(\\:{\\text{X}}_{\\text{i}}\\) represents the person-level moderator variables (VET opportunity structure, VET status, type of destination region) and \\(\\:{\\delta\\:}_{1\\text{t}}\\) and \\(\\:{\\delta\\:}_{2\\text{t}}\\) represent the coefficients of the main effects and interaction effects with VET-related migration for each time point. We estimated all models using cluster-robust standard errors on the person level. 5 Results 5.1 Effects of VET-related migration on the life satisfaction of apprentices Based on SPF theory and RCT, we hypothesised that VET-related migration had positive average effects on LS (H1). To test hypothesis H1, we first turn to a description of the LS trajectories of migrants and stayers. Figure 1 shows their mean LS trajectories (for multivariate results see Table A3 in the online appendix). For both groups, it unveils a very large increase in LS upon entering VET of about 0.7 points on the LS scale, followed by a small decrease towards the end of VET. Moreover, migrating apprentices begin at slightly lower levels of LS on average but catch up during the VET phase. Considering their similar shape and convergence over time, the curves suggest only small differences in the LS trajectories of migrants and stayers. Data source: NEPS SC4, N (individuals, total) = 2,797, N (stayers) = 2,289, N (migrants) = 508 Using FE models, we observe no significant or substantial differences in LS during the VET phase compared to the baseline levels between migrants and stayers (Fig. 2 ). Thus, internal migration does not provide a general benefit in the face of the large satisfaction increase associated with leaving school and entering VET. Therefore, we must reject hypothesis H1. Note The full regression models are available in Table A3 in the online appendix. Data Source: NEPS SC4, N (individuals) = 2,797, N (observations) = 17,788 5.2 The moderating role of regional VET opportunities, status attainment, and destination regions 5.2.1 Regional VET opportunities In section 3.2.1, we came to conflicting hypotheses regarding the moderating role of poor regional VET opportunity structures in the home regions. The influence of such opportunity structures may be negative if school leavers migrate out of necessity to find a suitable VET position (H2a). On the other hand, it may be positive if apprentices experience migration as a means to overcome regional disadvantages (H2b). Panel (a) in Fig. 3 shows the effect of a poor VET opportunity structure in the home region for internally migrating apprentices. Our results reveal that migrants from regions with poorer VET opportunities experience slightly higher LS gains than those with better VET opportunities. As none of the estimates are significant, however, we must reject both hypotheses H2a and H2b. Nonetheless, because all the effects are positive, we reject hypothesis H2a with more certainty. Note The full regression models are available in Table A3 in the online appendix. Data Source: NEPS SC4, N (individuals) = 2,797, N (observations) = 17,788 5.2.2 Status attainment Hypothesis H3 stated that VET-related migration could have larger effects for apprentices attaining high-status occupations. Indeed, the results in panel (b) of Fig. 3 indicate a small positive effect in the first training year of attaining a VET position with a higher ISEI score. This benefit (0.18 points, SE = 0.075, p = 0.016), however, decreases and loses significance in the second and third training years. 5.2.3 Type of destination region Based on the assumption that urban regions offer attractive amenities for young adults, we expected larger LS gains for migrating apprentices moving to urban regions (H4). Sustaining this hypothesis, panel (c) in Fig. 3 shows a large initial increase in LS for this group (0.56 points, SE = 0.160, p = 0.001). In the second and third training years, the effect decreases and becomes statistically insignificant. However, this is mainly due to the large uncertainty of the effect estimation. The point estimates remain substantially positive. These results indicate that migrating to urban regions indeed offers large benefits for apprentices. 5.3 Sensitivity analyses To assess the sensitivity of our results, we carried out a comprehensive exploratory multiverse analysis and split sample analyses. Multiverse analysis inspects how sensitive the results are to alternative modelling decisions (Steegen et al., 2016 ). The alternative specifications relate to the selected sample, operationalisation of key variables, and missing value handling. Section A3 of the online appendix summarises all results. Our results are sensitive to the model specification in only two ways: First, the moderation by the occupational status of VET position is significant only in models using complete cases. Still, the point estimates have the same direction in models using multiple imputations. This finding likely reflects inflated standard errors resulting from the multiple imputation procedure. Because the direction of the first-year effect is always positive, our conclusion still holds that reaching a higher occupational status through migration slightly increases LS in the first year. Second, the operationalisation of destination regions matters. The models yield smaller, yet still substantial effects if less densely populated urbanised districts are not coded as urban regions. This may indicate that migration to urbanised districts, not to the largest cities, drives the positive effects of migration to urban regions. To test whether the positive effects of VET-related migration to urban destination regions merely reflect an improvement in occupational opportunities, we repeated our analyses using split samples for apprentices in rural and apprentices in urban regions (for details, see section A3.4 of the online appendix). The results hint at differences between occupational and personal motivations for VET-related migration. The first-year effect of occupational status on LS is only significant in the group of apprentices migrating to rural regions, indicating that this group foregoes urban amenities in favour of attaining higher occupational status. Conversely, the effect of occupational status on LS is always insignificant for apprentices migrating to urban regions. This indicates that apprentices who strive for a high occupational status are willing to forego urban amenities and that apprentices who value urban amenities are less sensitive to occupational status as a means of production of LS. 6 Discussion and conclusion 6.1 Main findings and implications Prior research on the effects of migration on life satisfaction (LS) has mostly used broad samples of the adult population, implying that its conclusions are not readily transferable to individuals in life stages such as childhood, youth, young adulthood, and old age. Moreover, it has not sufficiently considered potential life events preceding, accompanying, or following migration, implying that the extent to which migration affects LS beyond these events is not fully understood. To narrow this research gap, we examined the effects of internal migration on LS during the transition to vocational education and training (VET). Given that migration decisions at this transition are likely driven by regional opportunity structures and lifestyle preferences, we explored the moderating influence of pre-migration regional VET opportunities, the attained occupational status, and the type of destination region. We developed our hypotheses by applying theories of social production functions (SPF) and rational choice (RCT) to the specific case of VET-related migration. We tested our hypotheses using large-scale longitudinal data from the German NEPS and applying fixed-effects (FE) panel regression models. Our results support the claim that research on the effects of migration on LS should consider the specificities of life course stages and coupled life events as well as relevant regional moderating factors. They reveal that leaving school and entering VET substantially enhance LS – a finding which aligns with previous research on German apprentices (Authors, 2019; Reuter et al., 2022 ). In the face of this major LS surge, we did not find a significant standalone effect of VET-related migration on LS. This does not align with our hypothesis H1 and various studies reporting positive average effects of migration on LS for the broader adult population (Erlinghagen et al., 2021 ; Kratz, 2020 ; Melzer & Muffels, 2017 ; Nowok et al., 2013 ). Our study highlights the importance of considering the circumstances of the transition from school to VET. As expected, we found that VET-related and lifestyle factors moderate the effects of migration: German apprentices experienced higher LS gains from migration, the higher the occupational status of their attained VET position was (supporting hypothesis H3, albeit only for apprentices’ first training year). Furthermore, migration to urban (as opposed to rural) regions led to large improvements in LS in the first training year (supporting hypothesis H4). These LS improvements may be explained by the importance of urban amenities for young adults and their lifestyle aspirations (Busch, 2016 ; Otte & Baur, 2008 ; Schäfer & Just, 2018 ). Contrary to hypotheses H2a and H2b, we did not find differential effects depending on the pre-migration regional VET opportunity structure. This may indicate that attaining a VET position in the first place is most relevant for German apprentices from any region. On average, the training opportunities in the home region neither make apprentices dissatisfied with having to move, nor do they instil satisfaction as a result of overcoming a poor opportunity structure. As stated above, our study reveals that the moderated positive effects of VET-related migration on LS (observed for migration towards high-status positions and urban regions) are significant only in the first year after entering VET. This finding fits the notion of the hedonic treadmill (Brickman & Campbell, 1971 ). According to the hedonic treadmill, individuals will increase their aspirations (or focus on other goals) after attaining a desirable goal (e.g., attaining a high-status occupation or migrating towards urban amenities). The life course perspective additionally highlights that the transition to adulthood is usually accomplished during the VET life stage, which tends to transform individual objectives and aspirations (Benson & Furstenberg, 2006 ). Another possible explanation for these effects diminishing over time is the potentially limited ability of individuals to predict their future satisfaction with gains in specific life domains (Hendriks & Bartram, 2019 ; Murphy, 1992 ). This should be true especially for young adults, who are often confronted for the first time with far-reaching decisions, e.g., about entering a new educational stage or migrating. For example, relocating to urban regions may initially seem highly desirable to young adults, and even increase their expectations regarding quality of life (Hanell, 2022 ). Later, however, they may realise that their expectations may have been too high. Difficulty in fulfilling initial expectations may result from goal attainment requiring substantial resources in costly urban regions (Otte & Baur, 2008 ), which young adults usually do not have. It may also result from migrating apprentices initially overestimating their ability to socially integrate into their new social environment (Hendriks et al., 2016 ). Such gaps between expectations and lived experiences may explain the observed re-decline in LS after VET-related migration. In summary, German apprentices experience a significant increase in LS upon entering VET. VET-related internal migration, however, does not generally impact LS at this transition. A reason could be the coupling of VET entry and the migration event. Arguably, successful entry into VET is the most relevant aspect for young individuals’ LS at this point in life, apparently independently of a potential migration event. Still, our findings indicate that internal migration can be a means for achieving higher LS under certain circumstances: Migration can lead to substantial gains in LS when apprentices attain high-status occupations and especially when they move to urban areas. 6.2 Limitations and further research Our study has some limitations, which suggest ways forward for research on the effects of migration on LS. First, the large standard errors for the point estimates of the effects of urban destination regions suggest that some regional differences were not captured by our measures. Thus, further research could examine differences between regions in more detail, e.g., regarding the availability of specific amenities, housing prices, or the occupational and socio-economic structure. Future research could also consider differences between countries regarding the features of national education systems and the possibility or necessity to migrate. Such analyses would show whether our theoretical claims are transferable to other institutional settings. Second, we estimated heterogeneous effects of VET-related migration on LS depending on the regional VET opportunities, the attained status, and the type of destination region. While these factors are central to understanding the impact of migration on LS, other potential moderators, such as social origin, personality traits, and pre-VET educational attainment, may also moderate this impact. Further research could address how such individual-level moderators shape LS effects of migration. Third, VET is only one educational pathway that young adults can choose after secondary education in Germany. The second most prominent pathway is higher education. Individuals transitioning to VET and higher education, respectively, differ regarding their social origin and educational attainment (Autor:innengruppe Bildungsbericht, 2022). Moreover, the spatial distribution of VET versus higher education differs. This implies that our conclusions are not readily transferable to individuals choosing educational pathways other than VET. The findings of Reuter et al. ( 2022 ) and Authors (2019) suggest similar developments of LS among school leavers entering VET and higher education. What requires further attention, however, is the standalone effect of migration on LS during the transition to higher education. Fourth, our data do not permit us to neatly separate the effects of migrating from the effects of gaining independence from the parents through other means. A conceivable test would be to compare apprentices who stay within the parental household, apprentices who leave the parental household but stay within its proximity, and apprentices who migrate to a different region. Furthermore, the consideration of migration motives could further elucidate the mechanisms explaining the LS effects of migration. Fifth, our data do not allow us to comprehensively consider changes in LS during the time leading up to VET-related migration. From a theoretical point of view, it would be interesting to additionally examine anticipation effects of internal migration preceding the transition to VET. Similarly, future research could study possible anticipation effects of the transition into the labour market (or higher education), which takes place at the end of the VET life stage. It is plausible that, depending on training success and regional opportunities, some apprentices may perceive the approaching end of VET as positive (especially if they expect to smoothly transition into the labour market), while others may expect difficulties. Furthermore, graduating from VET presents apprentices with the option to return to their home region, stay in their region of training, or migrate onwards (Bijwaard & Wahba, 2023 ). All these options entail further life events having the potential to influence LS trajectories. Despite the discussed limitations and open questions, our study highlights the need for life stage-specific theoretical and empirical modelling, thereby considering coupled life events (for a similar argumentation with regard to international migration, see Authors, 2023). As our study has illustrated, the coupling of migration with other life events can substantially shape the effects of migration on LS. This may, for example, also apply to transitions into higher education and the labour market. Consequently, future research on the effects of migration should acutely consider the timing in the life course when migration occurs and relevant factors moderating its effects. Declarations Funding This analysis is part of the research project ***, funded by the *** under grant number ***, and part of the junior research group ***, funded by the ***. Author Contribution R.W. contributed to: conceptualization (main contributor), methodology (main contributor), software, formal analysis, writing (original draft, main contributor), writing (review & editing, main contributor), visualization (sole contributor); overall contribution: 50%. N.N. contributed to: conceptualization, methodology, writing (original draft), writing (review & editing), supervision, project administration, funding acquisition; overall contribution: 25%.N.S. contributed to: conceptualization, methodology, writing (review & editing), project administration, funding acquisition; overall contribution: 15%.A.W. contributed to: conceptualization, methodology, writing (review & editing); overall contribution: 10%. Acknowledgement We thank Tobias Koberg for his support in processing the NEPS data and Daniel Klein for his valuable remarks on our imputation procedure. Data Availability This paper uses data from National Educational Panel Study (NEPS), Starting Cohort 4 - Grade 9, https://doi.org/10.5157/NEPS:SC4:14.0.0, available at the Leibniz Institute for Educational Trajectories (LIfBi). 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Bundesinstitut für Bevölkerungsforschung. https://nbn-resolving.org/urn:nbn:de:0168-ssoar-64970-0 Walford, N., & Stockdale, A. (2015). Lifestyle and internal migration. In D. Smith, N. Finney, K. Halfacree, & N. Walford (Eds.), Internal migration. Geographical perspectives and processes (pp. 99–111). Routledge. Footnotes This hypothesis is also plausible considering that staying in rural areas may lead to stigmatisation of young adults (Pedersen & Gram, 2018 ), which might negatively influence their LS. Due to a large time gap between the collection of waves 10 and 11, we did not group them together. Because waves 12 to 14 roughly cover the period of the COVID-19 pandemic, we chose to not group wave 11 together with waves 12 to 14 to better control for the effect of the pandemic on LS. With about 0.4 scale points, the increase remains large for all apprentices in the first training year even when controlling for unobserved time-constant heterogeneity, age, and period effects (model (1) in Table A3 in the online appendix). Additional Declarations No competing interests reported. Supplementary Files Appendixblinded20250114.docx 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. 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17:46:51\",\"extension\":\"png\",\"order_by\":1,\"title\":\"Figure 1\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":228983,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eLife satisfaction trajectories of stayers and migrants (means)\\u003c/p\\u003e\\n\\u003cp\\u003eData source: NEPS SC4, N (individuals, total) = 2,797, N (stayers) = 2,289, N (migrants) = 508\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"floatimage1.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7805231/v1/360d050d0f6ddae31a44b759.png\"},{\"id\":95946313,\"identity\":\"46ab51c2-2a68-4e09-9766-5683fbda161b\",\"added_by\":\"auto\",\"created_at\":\"2025-11-14 17:46:51\",\"extension\":\"png\",\"order_by\":2,\"title\":\"Figure 2\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":180218,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eEffects of VET-related migration on apprentices’ life satisfaction: Results of fixed-effects regressions (average marginal effects and 95% confidence intervals)\\u003c/p\\u003e\\n\\u003cp\\u003eNote: The full regression models are available in Table A3 in the online appendix.\\u003c/p\\u003e\\n\\u003cp\\u003eData Source: NEPS SC4, N (individuals) = 2,797, N (observations) = 17,788\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"floatimage2.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7805231/v1/e95f40d494f6dc774bb06086.png\"},{\"id\":95946311,\"identity\":\"f9a6b631-47d5-4969-9913-5242baa4ff0b\",\"added_by\":\"auto\",\"created_at\":\"2025-11-14 17:46:51\",\"extension\":\"png\",\"order_by\":3,\"title\":\"Figure 3\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":457396,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eModeration of the effects of VET-related migration on apprentices’ life satisfaction: Results of fixed-effects regressions (average marginal effects and 95% confidence intervals)\\u003c/p\\u003e\\n\\u003cp\\u003eNote: The full regression models are available in Table A3 in the online appendix.\\u003c/p\\u003e\\n\\u003cp\\u003eData Source: NEPS SC4, N (individuals) = 2,797, N (observations) = 17,788\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"floatimage3.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7805231/v1/96283d22644c399f17cf88d3.png\"},{\"id\":96255806,\"identity\":\"1560be3a-0529-4674-8c20-06da37a0c4f5\",\"added_by\":\"auto\",\"created_at\":\"2025-11-19 07:49:03\",\"extension\":\"pdf\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":1713431,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"manuscript.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7805231/v1/fac99157-fa89-4869-b93d-d97a8ee35fd1.pdf\"},{\"id\":95946317,\"identity\":\"39238dbc-bd2f-491c-afe5-3e927e6731ec\",\"added_by\":\"auto\",\"created_at\":\"2025-11-14 17:46:51\",\"extension\":\"docx\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":1155474,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"Appendixblinded20250114.docx\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7805231/v1/c2c6c154a47cc2fe5d994264.docx\"}],\"financialInterests\":\"No competing interests reported.\",\"formattedTitle\":\"Effects of internal migration on the life satisfaction of apprentices\",\"fulltext\":[{\"header\":\"1 Introduction\",\"content\":\"\\u003cp\\u003eMigration can have far-reaching implications for individuals\\u0026rsquo; life satisfaction (LS) trajectories. A growing body of studies suggests that both international (cross-border) and internal (within-country) mobility can increase LS (Authors, 2024; Bartram, \\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e2013\\u003c/span\\u003e; Erlinghagen et al., \\u003cspan citationid=\\\"CR19\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e; Guedes Auditor \\u0026amp; Erlinghagen, \\u003cspan citationid=\\\"CR24\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e; Hendriks \\u0026amp; Burger, \\u003cspan citationid=\\\"CR27\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e; Kratz, \\u003cspan citationid=\\\"CR37\\\" class=\\\"CitationRef\\\"\\u003e2020\\u003c/span\\u003e; Melzer \\u0026amp; Muffels, \\u003cspan citationid=\\\"CR41\\\" class=\\\"CitationRef\\\"\\u003e2017\\u003c/span\\u003e; Nowok et al., \\u003cspan citationid=\\\"CR44\\\" class=\\\"CitationRef\\\"\\u003e2013\\u003c/span\\u003e). These studies also illustrate that the effects of migration on LS vary depending on the motives for mobility, personal characteristics, and regional context features.\\u003c/p\\u003e\\u003cp\\u003eWhile highly valuable, this body of research can be expanded for at least two reasons: First, the existing studies tend to use broad samples of the adult population. Consequently, their conclusions are not readily transferable to individuals in life stages such as childhood, youth, young adulthood, and old age. During middle adulthood, primary motives for migration include better job prospects, an improved housing situation, and family formation (Kley, \\u003cspan citationid=\\\"CR35\\\" class=\\\"CitationRef\\\"\\u003e2011\\u003c/span\\u003e; Petzold, \\u003cspan citationid=\\\"CR49\\\" class=\\\"CitationRef\\\"\\u003e2017\\u003c/span\\u003e; Walford \\u0026amp; Stockdale, \\u003cspan citationid=\\\"CR62\\\" class=\\\"CitationRef\\\"\\u003e2015\\u003c/span\\u003e). In earlier or later life stages, however, other motives and conditions frame mobility decisions (Coulter \\u0026amp; Scott, \\u003cspan citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e2015\\u003c/span\\u003e; Henkens et al., \\u003cspan citationid=\\\"CR29\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e; Switek \\u0026amp; Easterlin, \\u003cspan citationid=\\\"CR57\\\" class=\\\"CitationRef\\\"\\u003e2018\\u003c/span\\u003e). Second, previous studies have not sufficiently considered the potential LS effects of life events preceding, accompanying, or following migration. These events include union initiation or dissolution and transitions to other life stages, e.g., to new educational phases, the labour market, or retirement. Such events and transitions will arguably shape the extent to which migration can influence LS. For these reasons, research should more closely examine the effects of migration on LS considering the coupling of migration with age-specific life events characterising these life stages.\\u003c/p\\u003e\\u003cp\\u003eOur study addresses this research gap by examining how internal migration within Germany during a key school-to-work transition affects LS trajectories. We focus on the transition to vocational education and training (VET), which is a significant entry point into the labour market for over half of young people in Germany (see section 2 for details). From a theoretical point of view, VET-related migration constitutes an interesting test case \\u0026ndash; which has, however, hardly been examined so far. On the one hand, VET-related migration resembles migration in other life stages. For instance, like economic migration in later adult life, it involves an employment-related investment decision based on personal preferences and regional opportunities (Thissen et al., \\u003cspan citationid=\\\"CR58\\\" class=\\\"CitationRef\\\"\\u003e2010\\u003c/span\\u003e). On the other hand, it is characterised by the specificities of the (transition to the) VET life stage, which usually takes place during young adulthood (Schulenberg et al., \\u003cspan citationid=\\\"CR53\\\" class=\\\"CitationRef\\\"\\u003e2004\\u003c/span\\u003e).\\u003c/p\\u003e\\u003cp\\u003eWhat is specific about (migration at the transition to) the VET life stage? First, individuals are particularly prone to internal migration when transitioning to the VET life stage (; Geist \\u0026amp; McManus, \\u003cspan citationid=\\\"CR23\\\" class=\\\"CitationRef\\\"\\u003e2008\\u003c/span\\u003e; Horowitz \\u0026amp; Entwisle, \\u003cspan citationid=\\\"CR30\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e; Sch\\u0026auml;fer \\u0026amp; Just, \\u003cspan citationid=\\\"CR52\\\" class=\\\"CitationRef\\\"\\u003e2018\\u003c/span\\u003e). Second, age-specific motives, constraints, and opportunities frame VET-related migration. It can be a necessity to secure a VET position by escaping regions with a scarce offer of suitable VET positions (Authors, 2023; S. Matthes \\u0026amp; Ulrich, \\u003cspan citationid=\\\"CR40\\\" class=\\\"CitationRef\\\"\\u003e2016\\u003c/span\\u003e), but also an opportunity to achieve a high occupational status in the long run (Authors, 2024; Waibel, \\u003cspan citationid=\\\"CR61\\\" class=\\\"CitationRef\\\"\\u003e2019\\u003c/span\\u003e). Beyond such VET-related considerations, it can also help young adults gain independence from the parental household and fulfil lifestyle goals (Benson \\u0026amp; Furstenberg, \\u003cspan citationid=\\\"CR5\\\" class=\\\"CitationRef\\\"\\u003e2006\\u003c/span\\u003e). For young adults, in particular, moving towards the amenities of urban regions might boost LS (Sch\\u0026auml;fer \\u0026amp; Just, \\u003cspan citationid=\\\"CR52\\\" class=\\\"CitationRef\\\"\\u003e2018\\u003c/span\\u003e). Third, VET-related migration can be conceptualised as a coupled life event (Authors, 2023; Buchmann \\u0026amp; Kriesi, \\u003cspan citationid=\\\"CR11\\\" class=\\\"CitationRef\\\"\\u003e2011\\u003c/span\\u003e) because individuals need to find a VET position and leave their accustomed spatial environment. The few studies focussing on LS during the transition to the VET life stage \\u0026ndash; albeit without analysing the role of migration \\u0026ndash; report a surge in LS following the transition from school to VET (Authors, 2019; Reuter et al., \\u003cspan citationid=\\\"CR51\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e). At present, it is unclear whether internal migration has separate effects on LS in the face of such a major coupled life event.\\u003c/p\\u003e\\u003cp\\u003eConsidering the discussed similarities and differences between VET-related and other migration events raises the following questions: What are the standalone effects of internal migration during the transition to VET on apprentices\\u0026rsquo; LS? And which conditions moderate the size of these effects?\\u003c/p\\u003e\\u003cp\\u003eWe address these questions by combining a life course perspective with the theory of social production functions (SPF) and rational choice theory (RCT). These approaches allow us to derive hypotheses on how VET-related migration should influence LS on average, and on how this relationship might vary depending on certain conditions, including apprentices\\u0026rsquo; pre-migration VET opportunity structures, status attained through VET, and the type of destination region (rural versus urban). We test our hypotheses using individual-level data from the German National Educational Panel Study (NEPS), which we supplement with regional context data from the German Federal Employment Agency. To tackle selection into VET-related migration, we estimate fixed-effects (FE) panel regressions (Br\\u0026uuml;derl \\u0026amp; Ludwig, \\u003cspan citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e2015\\u003c/span\\u003e). We assess the sensitivity of our results to alternative modelling decisions through an exploratory multiverse analysis (Steegen et al., \\u003cspan citationid=\\\"CR55\\\" class=\\\"CitationRef\\\"\\u003e2016\\u003c/span\\u003e). Additionally, we assess whether the potential moderating effects of the destination region depend on the pre-migration VET opportunity structures through split-sample analyses. We conclude by discussing the main contributions and limitations of our analyses, which illustrate the need to consider the specificity of coupled life events when examining the effects of internal migration on LS.\\u003c/p\\u003e\"},{\"header\":\"2 Contextualisation: Vocational education and training in Germany\",\"content\":\"\\u003cp\\u003eIn Germany, VET is a widespread post-secondary education option. The number of yearly entrants to dual and school-based VET programmes usually ranges from about 650,000 (2005) to 735,000 (2020), thus even exceeding the number of yearly entrants to higher education (2005: 355,000; 2020: 490,000) (Autor:innengruppe Bildungsbericht, 2022, pp. 167, 206). To enter VET, students do not necessarily need an educational degree, although most have one (Autor:innengruppe Bildungsbericht, 2022, p. 168). German VET programmes usually last for two or three years (Protsch \\u0026amp; Solga, \\u003cspan citationid=\\\"CR50\\\" class=\\\"CitationRef\\\"\\u003e2016\\u003c/span\\u003e).\\u003c/p\\u003e\\u003cp\\u003eGermany is a well-suited test case for our analysis, as both the decision to pursue VET (rather than other educational pathways) and the choice of migrating (as opposed to staying) constitute outcomes of a comparatively wide array of available options. This variety implies that VET-related migration can be used strategically to increase or maintain LS (for a broader theoretical discussion, see section 3). Moreover, Germany is characterised by a strong connection between its education system and the labour market compared to other countries, e.g., the US or France (DiPrete et al., \\u003cspan citationid=\\\"CR16\\\" class=\\\"CitationRef\\\"\\u003e2017\\u003c/span\\u003e). It is also characterised by a pronounced regional clustering of economic branches (Kleinert et al., \\u003cspan citationid=\\\"CR34\\\" class=\\\"CitationRef\\\"\\u003e2018\\u003c/span\\u003e). This regional clustering implies that VET-related migration may have a stronger bearing on LS than in countries with more evenly distributed educational and professional opportunities and where the linkages between the education system and the labour market are less pronounced.\\u003c/p\\u003e\\u003cp\\u003eVET-related migration is fairly common among apprentices in Germany: 21.9 percent of apprentices enter VET in a regional labour market (RLM, see section 4.2 for details) different from their home RLM and 16.4 percent enter VET in their home RLM but outside their home district (Authors, 2023).\\u003c/p\\u003e\"},{\"header\":\"3 Theory and hypotheses\",\"content\":\"\\u003cp\\u003eFrom a life course perspective, individual life courses consist of different life stages (including education, employment, and retirement) and life events (e.g., access to different educational levels, labour market entry, or entry into retirement), which induce transitions between life stages (Bernardi et al., \\u003cspan citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e2019\\u003c/span\\u003e). One major life event is school completion, which, in Germany, is usually followed by a transition to post-secondary education (for details, see section 2).\\u003c/p\\u003e\\u003cp\\u003eThe transition to post-secondary education is also highly relevant for migration research, as this transition is often coupled with migration, mostly within national borders (Authors, 2023; Bernardi et al., \\u003cspan citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e2019\\u003c/span\\u003e; Br\\u0026uuml;derl et al., \\u003cspan citationid=\\\"CR9\\\" class=\\\"CitationRef\\\"\\u003e2019\\u003c/span\\u003e; Coulter \\u0026amp; Scott, \\u003cspan citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e2015\\u003c/span\\u003e; Vidal \\u0026amp; Lersch, \\u003cspan citationid=\\\"CR59\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e). This act of spatial mobility usually constitutes the first self-determined and long-term-oriented internal migration in the life course. As such, it may have a substantial bearing on young individuals\\u0026rsquo; LS trajectories.\\u003c/p\\u003e\\u003cp\\u003eIn the following, we draw on the theory of social production functions (SPF) and rational choice theory (RCT), which suggest that VET-related migration could, on average, lead to increases in apprentices\\u0026rsquo; LS (section 3.1). Additionally, we derive hypotheses on factors moderating the effects of VET-related migration on LS, which helps us carve out the specificity of this form of migration from a life course perspective. The discussed moderating factors include the regional VET opportunities before migration (section 3.2.1), the occupational status of the VET program (section 3.2.2), and the type of destination region (rural versus urban, section 3.2.3).\\u003c/p\\u003e\\u003cdiv id=\\\"Sec4\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003e3.1 VET-related migration as a means to improve life satisfaction\\u003c/h2\\u003e\\u003cp\\u003eAccording to SPF theory, individuals strive to improve their subjective well-being (Buijs et al., \\u003cspan citationid=\\\"CR12\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e; Ormel et al., \\u003cspan citationid=\\\"CR45\\\" class=\\\"CitationRef\\\"\\u003e1999\\u003c/span\\u003e), which we operationalise through overall LS. Following SPF theory, individuals can achieve this highest goal by reaching so-called (first-order) instrumental goals, which include stimulation/activation, comfort, status, behavioural confirmation, and affection. They can fulfil these instrumental goals by activities and endowments constituting so-called (second-order) means of production. We argue that VET-related migration can be considered an important means of production: It can grant access to various beneficial (third-order) activities and endowments fulfilling instrumental goals, thereby eventually increasing LS (Hendriks \\u0026amp; Bartram, \\u003cspan citationid=\\\"CR26\\\" class=\\\"CitationRef\\\"\\u003e2019\\u003c/span\\u003e). However, VET-related migration also tends to confront individuals with a trade-off, as mobile individuals usually need to abandon other means of production available at their current location.\\u003c/p\\u003e\\u003cp\\u003eRational choice theory (RCT) specifies that individuals should regard the option to migrate as an investment decision involving expected costs and benefits (Authors, 2016; Bernardi et al., \\u003cspan citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e2019\\u003c/span\\u003e; Sjaastad, \\u003cspan citationid=\\\"CR54\\\" class=\\\"CitationRef\\\"\\u003e1962\\u003c/span\\u003e). The costs of VET-related migration may involve monetary expenses (e.g., moving costs, Aassve et al., \\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e2007\\u003c/span\\u003e) as well as non-monetary costs (e.g., the separation from family and friends or stress in adapting to new environments; Choy et al., \\u003cspan citationid=\\\"CR14\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e; Hendriks et al., \\u003cspan citationid=\\\"CR28\\\" class=\\\"CitationRef\\\"\\u003e2016\\u003c/span\\u003e; Thissen et al., \\u003cspan citationid=\\\"CR58\\\" class=\\\"CitationRef\\\"\\u003e2010\\u003c/span\\u003e). In the case of apprentices, the benefits of migration may encompass various activities and endowments, including not only access to a (better) VET position but also independence from the parental household and the possibility to develop a personal lifestyle (Schulenberg et al., \\u003cspan citationid=\\\"CR53\\\" class=\\\"CitationRef\\\"\\u003e2004\\u003c/span\\u003e). According to both SPF theory and RCT, individuals should weigh the monetary and non-monetary costs and benefits of moving to different locations and not moving against each other, and eventually opt for the location promising the highest overall LS (Hendriks \\u0026amp; Bartram, \\u003cspan citationid=\\\"CR26\\\" class=\\\"CitationRef\\\"\\u003e2019\\u003c/span\\u003e). This implies that they should only migrate if they expect the benefits of migration to exceed its costs.\\u003c/p\\u003e\\u003cp\\u003eIn line with these thoughts, substantial evidence \\u0026ndash; mainly on economically motivated internal migration \\u0026ndash; shows that adults can increase their LS through migration (Authors, 2024; Bartram, \\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e2013\\u003c/span\\u003e; Erlinghagen et al., \\u003cspan citationid=\\\"CR19\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e; Guedes Auditor \\u0026amp; Erlinghagen, \\u003cspan citationid=\\\"CR24\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e; Hendriks \\u0026amp; Burger, \\u003cspan citationid=\\\"CR27\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e; Kratz, \\u003cspan citationid=\\\"CR37\\\" class=\\\"CitationRef\\\"\\u003e2020\\u003c/span\\u003e; Melzer \\u0026amp; Muffels, \\u003cspan citationid=\\\"CR41\\\" class=\\\"CitationRef\\\"\\u003e2017\\u003c/span\\u003e; Nowok et al., \\u003cspan citationid=\\\"CR44\\\" class=\\\"CitationRef\\\"\\u003e2013\\u003c/span\\u003e). Moreover, first evidence suggests that residential relocations among young adults increase LS (Switek, \\u003cspan citationid=\\\"CR56\\\" class=\\\"CitationRef\\\"\\u003e2016\\u003c/span\\u003e, for the case of Swedish youth and Perales, \\u003cspan citationid=\\\"CR48\\\" class=\\\"CitationRef\\\"\\u003e2017\\u003c/span\\u003e, for the case of young adults in the UK and Australia).\\u003c/p\\u003e\\u003cp\\u003eBased on these theoretical assumptions and empirical findings, it is plausible that school leavers opt for internal migration during the transition to VET only if they expect their mobility to increase their LS. \\u003cem\\u003eIf this was true, we would expect positive average effects of VET-related migration on the LS of apprentices (H1).\\u003c/em\\u003e\\u003c/p\\u003e\\u003c/div\\u003e\\u003cdiv id=\\\"Sec5\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003e3.2 The moderating role of regional VET opportunities, status attainment, and destination regions\\u003c/h2\\u003e\\u003cp\\u003eSPF theory posits that individuals\\u0026rsquo; actions should aim at improving their life satisfaction (Buijs et al., \\u003cspan citationid=\\\"CR12\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e; Ormel et al., \\u003cspan citationid=\\\"CR45\\\" class=\\\"CitationRef\\\"\\u003e1999\\u003c/span\\u003e). At the transition to VET, factors like family formation, improving housing conditions, or immediate wage gains are likely less relevant for LS improvements than during later life stages. Instead, training- and lifestyle-related factors should be more relevant (Geist \\u0026amp; McManus, \\u003cspan citationid=\\\"CR23\\\" class=\\\"CitationRef\\\"\\u003e2008\\u003c/span\\u003e; Vilhelmson \\u0026amp; Thulin, \\u003cspan citationid=\\\"CR60\\\" class=\\\"CitationRef\\\"\\u003e2016\\u003c/span\\u003e), such as overcoming local shortages in the regional VET supply, attaining a high status through high-quality VET, and moving to an urban region. As elaborated in the following, the effects of VET-related migration might vary depending on the aforementioned motives.\\u003c/p\\u003e\\u003cdiv id=\\\"Sec6\\\" class=\\\"Section3\\\"\\u003e\\u003ch2\\u003e3.2.1 Regional VET opportunities\\u003c/h2\\u003e\\u003cp\\u003eThe VET opportunity structure of the region in which pupils live upon school completion may influence their quality of life by shaping their chances of finding a VET position (S. Matthes \\u0026amp; Ulrich, \\u003cspan citationid=\\\"CR40\\\" class=\\\"CitationRef\\\"\\u003e2016\\u003c/span\\u003e). If VET supply is low, finding an adequate position is more difficult (Jost et al., \\u003cspan citationid=\\\"CR31\\\" class=\\\"CitationRef\\\"\\u003e2019\\u003c/span\\u003e; Kleinert \\u0026amp; Jacob, \\u003cspan citationid=\\\"CR33\\\" class=\\\"CitationRef\\\"\\u003e2013\\u003c/span\\u003e; S. Matthes \\u0026amp; Ulrich, \\u003cspan citationid=\\\"CR40\\\" class=\\\"CitationRef\\\"\\u003e2016\\u003c/span\\u003e). Importantly, school leavers can cope with such shortages by extending their search radius and, eventually, by becoming spatially mobile (Authors, 2023; Kropp et al., \\u003cspan citationid=\\\"CR38\\\" class=\\\"CitationRef\\\"\\u003e2007\\u003c/span\\u003e). This implies a compromise between fulfilling their occupational VET aspirations and accepting regionally available opportunities (S. Matthes \\u0026amp; Ulrich, \\u003cspan citationid=\\\"CR40\\\" class=\\\"CitationRef\\\"\\u003e2016\\u003c/span\\u003e). To fulfil their instrumental goals of behavioural confirmation and affection, likely provided by their familiar social environment (Ormel et al., \\u003cspan citationid=\\\"CR45\\\" class=\\\"CitationRef\\\"\\u003e1999\\u003c/span\\u003e), school leavers can sacrifice specific occupational goals and stay. Alternatively, they can accept the social costs of leaving their home region and thereby attain status, comfort, and confirmation through a suitable VET position, thus increasing their overall LS by obtaining a VET position that meets their aspirations (Etzel \\u0026amp; Nagy, \\u003cspan citationid=\\\"CR20\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e).\\u003c/p\\u003e\\u003cp\\u003eOn the one hand, migrating school leavers from regions with worse VET opportunity structures could be less satisfied because they are more likely to have to accept potentially high social or monetary costs to fulfil their VET preferences (Eckelt \\u0026amp; Schauer, \\u003cspan citationid=\\\"CR17\\\" class=\\\"CitationRef\\\"\\u003e2019\\u003c/span\\u003e). Coming from a poor VET opportunity structure, they face more difficulties when searching for VET positions and pressure to migrate. School leavers from regions with better VET opportunity structures, on their part, should require more benefits for them to leave their advantageous region, implying that their migration should result in greater LS gains. \\u003cem\\u003eAccording to this reasoning, the effects of VET-related migration on LS might be smaller for apprentices from regions with worse VET opportunity structures (H2a).\\u003c/em\\u003e\\u003c/p\\u003e\\u003cp\\u003eOn the other hand, this hypothesis does not account for the possibility of a migration-related shift in the cost-benefit evaluation: If VET opportunities are scarce in the home region, leaving this region may be experienced as overcoming a major disadvantage. The feeling of successfully overcoming this disadvantage may yield a benefit not experienced by apprentices from advantageous regions. It could thus be a unique benefit for individuals who left poor VET opportunity structures. This leads us to an equally plausible competing hypothesis: \\u003cem\\u003eThe effects of VET-related migration on LS might be larger for apprentices from regions with worse VET opportunity structures (H2b).\\u003c/em\\u003e\\u003c/p\\u003e\\u003c/div\\u003e\\u003cdiv id=\\\"Sec7\\\" class=\\\"Section3\\\"\\u003e\\u003ch2\\u003e3.2.2 Status attainment\\u003c/h2\\u003e\\u003cp\\u003eThe occupational status attained through VET may also serve as a motivating factor for VET-related migration and have a significant influence on LS. As achieving a high status can be an important goal for school leavers (Authors, 2022, 2023; Protsch \\u0026amp; Solga, \\u003cspan citationid=\\\"CR50\\\" class=\\\"CitationRef\\\"\\u003e2016\\u003c/span\\u003e), they may be willing to migrate if they are otherwise unable to obtain a high-status occupation (Fielding, \\u003cspan citationid=\\\"CR21\\\" class=\\\"CitationRef\\\"\\u003e1992\\u003c/span\\u003e). Apprentices who secure high-status VET positions through migration are arguably more likely to fulfil their occupational goals and, thus, to perceive migration as beneficial. \\u003cem\\u003eTherefore, the effects of VET-related migration on LS might be larger, the higher the status of the attained VET occupation is (H3).\\u003c/em\\u003e\\u003c/p\\u003e\\u003c/div\\u003e\\u003cdiv id=\\\"Sec8\\\" class=\\\"Section3\\\"\\u003e\\u003ch2\\u003e3.2.3 Type of destination region\\u003c/h2\\u003e\\u003cp\\u003eThe decision for VET-related migration may not only be motivated by strictly VET-related reasons but also by broader lifestyle considerations. Leaving the home region allows young adults to detach from their parental household and to live in an environment they have chosen for themselves, so as \\u0026ldquo;to \\u0026lsquo;become someone else\\u0026rsquo; and grow as a person\\u0026rdquo; (Vilhelmson \\u0026amp; Thulin, \\u003cspan citationid=\\\"CR60\\\" class=\\\"CitationRef\\\"\\u003e2016\\u003c/span\\u003e, p. 283). This important step on the way to adulthood could also increase LS (Kins \\u0026amp; Beyers, \\u003cspan citationid=\\\"CR32\\\" class=\\\"CitationRef\\\"\\u003e2010\\u003c/span\\u003e; Otte \\u0026amp; Baur, \\u003cspan citationid=\\\"CR46\\\" class=\\\"CitationRef\\\"\\u003e2008\\u003c/span\\u003e; Schulenberg et al., \\u003cspan citationid=\\\"CR53\\\" class=\\\"CitationRef\\\"\\u003e2004\\u003c/span\\u003e).\\u003c/p\\u003e\\u003cp\\u003eEspecially if apprentices move towards regions offering more life stage-relevant amenities, they should experience benefits in multiple life domains and thereby increase their LS. Besides more abundant VET and labour market opportunities, urban regions tend to offer more opportunities to take part in social and cultural life and to establish new social contacts than rural regions (Gans, \\u003cspan citationid=\\\"CR22\\\" class=\\\"CitationRef\\\"\\u003e2017\\u003c/span\\u003e). In line with these thoughts, empirical evidence shows that young adults are particularly likely to migrate towards urban regions (Busch, \\u003cspan citationid=\\\"CR13\\\" class=\\\"CitationRef\\\"\\u003e2016\\u003c/span\\u003e; Sch\\u0026auml;fer \\u0026amp; Just, \\u003cspan citationid=\\\"CR52\\\" class=\\\"CitationRef\\\"\\u003e2018\\u003c/span\\u003e). \\u003cem\\u003eAgainst this background, VET-related migration to urban regions might have larger positive effects on LS than VET-related migration to rural regions (H4).\\u003c/em\\u003e\\u003ca class=\\\"FNLink\\\" href=\\\"#Fn1\\\" id=\\\"#FNLinkFn1\\\"\\u003e\\u003c/a\\u003e\\u003c/p\\u003e\\u003c/div\\u003e\\u003c/div\\u003e\"},{\"header\":\"4 Analytic strategy\",\"content\":\"\\u003cdiv id=\\\"Sec10\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003e4.1 Data\\u003c/h2\\u003e\\u003cp\\u003eWe test our hypotheses using data from starting cohort 4 (version 14.0.0) of the German National Educational Panel Study (NEPS Network, \\u003cspan citationid=\\\"CR43\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e). This nationally representative longitudinal survey gathers rich information on 16,425 respondents who were grade 9 students in German schools during the school year 2010/11. The schools were randomly selected from the total population of regular schools offering lower secondary education. Within each school, respondents were sampled in up to two randomly selected classes.\\u003c/p\\u003e\\u003cp\\u003eBetween 2010 and 2022, the respondents were invited to participate in fourteen survey waves. The survey waves covered different life stages, from attending school in the teenage years via the transition to post-secondary education to its completion. Thus, the data include students from all educational tracks who experienced different transitions to VET, either directly after obtaining a low or medium secondary degree (after grade 9 or 10), or after obtaining the \\u003cem\\u003eAbitur\\u003c/em\\u003e, the highest general educational degree in Germany (after grade 12 or 13), or indirectly after an intermittent employment, prevocational training, or other gap activity.\\u003c/p\\u003e\\u003cp\\u003eWe applied some sample restrictions to improve the precision of our estimates: First, we excluded 9,725 individuals who did not enter VET at any point. We also excluded 42 individuals who obtained another professional qualification (e.g., a university degree) before their first VET spell and 17 individuals who were misclassified as apprentices in waves 1 or 2 (when they were still enrolled in secondary school). Furthermore, we only included observations until the (successful or unsuccessful) completion of their first VET spell. After applying these sample restrictions, the full sample contains 6,641 individuals. The analytical sample used in the main analyses comprises 17,788 observations for 2,797 individuals after listwise deletion (for details, see sections A1 and A3.1 in the online appendix).\\u003c/p\\u003e\\u003c/div\\u003e\\u003cdiv id=\\\"Sec11\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003e4.2 Operationalisation\\u003c/h2\\u003e\\u003cp\\u003e\\u003cem\\u003eLife satisfaction.\\u003c/em\\u003e Across waves, our dependent variable was measured by asking respondents how satisfied they were overall with their life on a scale from 0 (completely unsatisfied) to 10 (completely satisfied).\\u003c/p\\u003e\\u003cp\\u003eWe used monthly available episode data on individuals\\u0026rsquo; educational trajectories and matched the dates of their VET phases with the interview dates of the general annual surveys. We defined baseline LS as respondents\\u0026rsquo; mean LS more than one year before entering VET and excluded observations relating to the year before entering VET. This reduces the possibility that any positive or negative anticipation of leaving school and entering training biases the baseline measurement. We estimated the effects of VET-related migration using dummies for each training year (first, second, and third year).\\u003c/p\\u003e\\u003cp\\u003e\\u003cem\\u003eVET-related migration.\\u003c/em\\u003e To measure VET-related migration, we constructed a variable indicating whether apprentices migrated across regional labour markets (RLMs). We used a classification distinguishing 141 RLMs by Kosfeld and Werner (\\u003cspan citationid=\\\"CR36\\\" class=\\\"CitationRef\\\"\\u003e2012\\u003c/span\\u003e), which was designed to capture regions with minimal commuting prevalence between them and maximal commuting prevalence within them. Thus, it identifies spatial units that contain relatively homogeneous labour markets regarding internal migration (Authors, 2019). We defined VET-related migration as a change of the primary location of residence from one RLM to another, with the new location of residence being the same as the training location. We defined staying as keeping the primary location of residence in the same administrative district (NUTS-3), with the training location being in the same district as the residence. These definitions provide clearly defined treatment (migrants) and reference groups (stayers), excluding individuals who opted for other forms of spatial mobility (e.g., long-distance commuting). According to our definition, 18.2 percent (N\\u0026thinsp;=\\u0026thinsp;508) of apprentices in our sample are VET-related migrants (see Table A1 in the online appendix).\\u003c/p\\u003e\\u003cp\\u003e\\u003cem\\u003ePoor regional VET opportunity structure.\\u003c/em\\u003e To capture the regional VET opportunity structure before entering VET, we constructed a measure that specifically targets the VET market. Using data from the Federal Employment Agency, we divided the number of registered vacant VET positions in the aspired occupational segment (B. Matthes et al., \\u003cspan citationid=\\\"CR39\\\" class=\\\"CitationRef\\\"\\u003e2015\\u003c/span\\u003e) within the RLM of residence one year before entering VET by the size of the age cohort from 15 to 24 in the same RLM and year. While the number of vacancies represents the supply of training positions, the cohort size approximates the number of potential competitors for these vacancies. The constructed scale thus reflects the potential competition for VET positions in the aspired occupation. We inverted the scale to align with our theoretical reasoning and for ease of interpretation. The inverted scale ranges from \\u0026minus;\\u0026thinsp;0.13 (a favourable regional VET opportunity structure with 0.13 vacant positions per potential competitor) to 0 (a poor VET opportunity structure with no vacant position). The variable is \\u003cem\\u003ez\\u003c/em\\u003e-standardised in our models.\\u003c/p\\u003e\\u003cp\\u003e\\u003cem\\u003eStatus attainment.\\u003c/em\\u003e To measure the status of the trained occupation, we used the International Socio-Economic Index of Occupational Status (ISEI-08). Higher values indicate occupations with a higher status. We z-standardised this variable as well.\\u003c/p\\u003e\\u003cp\\u003e\\u003cem\\u003eType of destination region.\\u003c/em\\u003e To distinguish between rural and urban destination regions, we used a classification of settlement structure types on the district level (NUTS-3) by the Federal Institute for Research on Building, Urban Affairs and Spatial Development (BBSR, \\u003cspan citationid=\\\"CR4\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e). We coded the categories \\u0026ldquo;large cities\\u0026rdquo; and \\u0026ldquo;urbanized districts\\u0026rdquo; as \\u0026ldquo;urban\\u0026rdquo; and the categories \\u0026ldquo;rural districts with agglomeration tendencies\\u0026rdquo; and \\u0026ldquo;scarcely populated rural districts\\u0026rdquo; as \\u0026ldquo;rural\\u0026rdquo;.\\u003c/p\\u003e\\u003cp\\u003e\\u003cem\\u003eControl variables.\\u003c/em\\u003e To consider age and period effects, we further controlled for age groups (below 16 years, 16 to 17 years, 18 years and older), grouped survey waves (waves 1/2, 3/4, 5/6, 7/8, 9/10, 11, 12/13/14)\\u003ca class=\\\"FNLink\\\" href=\\\"#Fn2\\\" id=\\\"#FNLinkFn2\\\"\\u003e\\u003c/a\\u003e, and the calendar months in which the data were collected.\\u003c/p\\u003e\\u003cp\\u003eDistributions of the variables discussed above are reported in Tables A1 and A2 in the online appendix.\\u003c/p\\u003e\\u003c/div\\u003e\\u003cdiv id=\\\"Sec12\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003e4.3 Methods\\u003c/h2\\u003e\\u003cp\\u003eWe apply linear fixed-effects (FE) regressions (Br\\u0026uuml;derl \\u0026amp; Ludwig, \\u003cspan citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e2015\\u003c/span\\u003e). These models allow us to control for time-invariant heterogeneity, thereby improving the approximation of causal effects of VET-related migration on LS. By splitting the VET period into years using dummy variables (first, second, and third year), we model LS trajectories over time. LS more than one year before entering VET constitutes the reference baseline measurement. We estimate the difference in LS trajectories between migrants and stayers by interacting VET-related migration with the aforementioned year dummies.\\u003c/p\\u003e\\u003cp\\u003e\\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"No\\\" id=\\\"Taba\\\" border=\\\"1\\\"\\u003e\\u003ccolgroup cols=\\\"2\\\"\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e\\u003ctbody\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e\\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:L{S}_{it}={\\\\beta\\\\:}_{t}T+{\\\\gamma\\\\:}_{t}T\\\\bullet\\\\:{M}_{i}+{\\\\alpha\\\\:}_{i}+\\\\:{\\\\text{ϵ}}_{\\\\text{i}\\\\text{t}}\\\\)\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e( 1 )\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003c/tbody\\u003e\\u003c/colgroup\\u003e\\u003c/table\\u003e\\u003c/div\\u003e\\u003c/p\\u003e\\u003cp\\u003eIn equation ( 1 ), \\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:{M}_{i}\\\\)\\u003c/span\\u003e\\u003c/span\\u003e denotes a person-level dummy variable for VET-related migration, \\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:T\\\\)\\u003c/span\\u003e\\u003c/span\\u003e the dummies for each training year, \\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:L{S}_{it}\\\\)\\u003c/span\\u003e\\u003c/span\\u003e the outcome variable for each person at each time point T, \\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:{{\\\\alpha\\\\:}}_{\\\\text{i}}\\\\)\\u003c/span\\u003e\\u003c/span\\u003e the person-level fixed effect, and \\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:{\\\\text{ϵ}}_{\\\\text{i}\\\\text{t}}\\\\)\\u003c/span\\u003e\\u003c/span\\u003e the residual. \\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:{\\\\beta\\\\:}_{t}\\\\)\\u003c/span\\u003e\\u003c/span\\u003e denotes the time-fixed effect, while \\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:{\\\\gamma\\\\:}_{t}\\\\)\\u003c/span\\u003e\\u003c/span\\u003e is the coefficient of interest for each time point.\\u003c/p\\u003e\\u003cp\\u003eMoreover, we modelled interactions between time points, VET-related migration, and the additional moderator variables (VET opportunity structure, occupational status, destination region) to estimate the LS trajectories of specific groups of apprentices:\\u003c/p\\u003e\\u003cp\\u003e\\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"No\\\" id=\\\"Tabb\\\" border=\\\"1\\\"\\u003e\\u003ccolgroup cols=\\\"2\\\"\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e\\u003ctbody\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e\\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:L{S}_{it}={\\\\beta\\\\:}_{t}T+{\\\\gamma\\\\:}_{t}T\\\\bullet\\\\:{M}_{i}+{{\\\\delta\\\\:}_{1\\\\text{t}}\\\\text{T}\\\\:\\\\bullet\\\\:{\\\\text{X}}_{\\\\text{i}}+\\\\:{\\\\delta\\\\:}_{2\\\\text{t}}\\\\text{T}\\\\bullet\\\\:{\\\\text{M}}_{\\\\text{i}}\\\\bullet\\\\:{X}_{\\\\text{i}}+{\\\\alpha\\\\:}_{i}+\\\\:\\\\text{ϵ}}_{\\\\text{i}\\\\text{t}}\\\\)\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e( 2 )\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003c/tbody\\u003e\\u003c/colgroup\\u003e\\u003c/table\\u003e\\u003c/div\\u003e\\u003c/p\\u003e\\u003cp\\u003eIn equation ( 2 ), \\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:{\\\\text{X}}_{\\\\text{i}}\\\\)\\u003c/span\\u003e\\u003c/span\\u003e represents the person-level moderator variables (VET opportunity structure, VET status, type of destination region) and \\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:{\\\\delta\\\\:}_{1\\\\text{t}}\\\\)\\u003c/span\\u003e\\u003c/span\\u003e and \\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:{\\\\delta\\\\:}_{2\\\\text{t}}\\\\)\\u003c/span\\u003e\\u003c/span\\u003e represent the coefficients of the main effects and interaction effects with VET-related migration for each time point.\\u003c/p\\u003e\\u003cp\\u003eWe estimated all models using cluster-robust standard errors on the person level.\\u003c/p\\u003e\\u003c/div\\u003e\"},{\"header\":\"5 Results\",\"content\":\"\\u003cdiv id=\\\"Sec14\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003e5.1 Effects of VET-related migration on the life satisfaction of apprentices\\u003c/h2\\u003e\\u003cp\\u003eBased on SPF theory and RCT, we hypothesised that VET-related migration had positive average effects on LS (H1). To test hypothesis H1, we first turn to a description of the LS trajectories of migrants and stayers. Figure\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e shows their mean LS trajectories (for multivariate results see Table A3 in the online appendix). For both groups, it unveils a very large increase in LS upon entering VET of about 0.7 points on the LS scale, followed by a small decrease towards the end of VET.\\u003ca class=\\\"FNLink\\\" href=\\\"#Fn3\\\" id=\\\"#FNLinkFn3\\\"\\u003e\\u003c/a\\u003e Moreover, migrating apprentices begin at slightly lower levels of LS on average but catch up during the VET phase. Considering their similar shape and convergence over time, the curves suggest only small differences in the LS trajectories of migrants and stayers.\\u003c/p\\u003e\\u003cp\\u003e\\u003c/p\\u003e\\u003cp\\u003eData source: NEPS SC4, N (individuals, total)\\u0026thinsp;=\\u0026thinsp;2,797, N (stayers)\\u0026thinsp;=\\u0026thinsp;2,289, N (migrants)\\u0026thinsp;=\\u0026thinsp;508\\u003c/p\\u003e\\u003cp\\u003eUsing FE models, we observe no significant or substantial differences in LS during the VET phase compared to the baseline levels between migrants and stayers (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e). Thus, internal migration does not provide a general benefit in the face of the large satisfaction increase associated with leaving school and entering VET. Therefore, we must reject hypothesis H1.\\u003c/p\\u003e\\u003cp\\u003e\\u003c/p\\u003e\\u003cp\\u003e\\u003cstrong\\u003eNote\\u003c/strong\\u003e\\u003cp\\u003eThe full regression models are available in Table A3 in the online appendix.\\u003c/p\\u003e\\u003c/p\\u003e\\u003cp\\u003eData Source: NEPS SC4, N (individuals)\\u0026thinsp;=\\u0026thinsp;2,797, N (observations)\\u0026thinsp;=\\u0026thinsp;17,788\\u003c/p\\u003e\\u003c/div\\u003e\\u003cdiv id=\\\"Sec15\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003e5.2 The moderating role of regional VET opportunities, status attainment, and destination regions\\u003c/h2\\u003e\\u003cdiv id=\\\"Sec16\\\" class=\\\"Section3\\\"\\u003e\\u003ch2\\u003e5.2.1 Regional VET opportunities\\u003c/h2\\u003e\\u003cp\\u003eIn section 3.2.1, we came to conflicting hypotheses regarding the moderating role of poor regional VET opportunity structures in the home regions. The influence of such opportunity structures may be negative if school leavers migrate out of necessity to find a suitable VET position (H2a). On the other hand, it may be positive if apprentices experience migration as a means to overcome regional disadvantages (H2b). Panel (a) in Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e shows the effect of a poor VET opportunity structure in the home region for internally migrating apprentices. Our results reveal that migrants from regions with poorer VET opportunities experience slightly higher LS gains than those with better VET opportunities. As none of the estimates are significant, however, we must reject both hypotheses H2a and H2b. Nonetheless, because all the effects are positive, we reject hypothesis H2a with more certainty.\\u003c/p\\u003e\\u003cp\\u003e\\u003c/p\\u003e\\u003cp\\u003e\\u003cstrong\\u003eNote\\u003c/strong\\u003e\\u003cp\\u003eThe full regression models are available in Table A3 in the online appendix.\\u003c/p\\u003e\\u003c/p\\u003e\\u003cp\\u003eData Source: NEPS SC4, N (individuals)\\u0026thinsp;=\\u0026thinsp;2,797, N (observations)\\u0026thinsp;=\\u0026thinsp;17,788\\u003c/p\\u003e\\u003c/div\\u003e\\u003cdiv id=\\\"Sec17\\\" class=\\\"Section3\\\"\\u003e\\u003ch2\\u003e5.2.2 Status attainment\\u003c/h2\\u003e\\u003cp\\u003eHypothesis H3 stated that VET-related migration could have larger effects for apprentices attaining high-status occupations. Indeed, the results in panel (b) of Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e indicate a small positive effect in the first training year of attaining a VET position with a higher ISEI score. This benefit (0.18 points, SE\\u0026thinsp;=\\u0026thinsp;0.075, p\\u0026thinsp;=\\u0026thinsp;0.016), however, decreases and loses significance in the second and third training years.\\u003c/p\\u003e\\u003c/div\\u003e\\u003cdiv id=\\\"Sec18\\\" class=\\\"Section3\\\"\\u003e\\u003ch2\\u003e5.2.3 Type of destination region\\u003c/h2\\u003e\\u003cp\\u003eBased on the assumption that urban regions offer attractive amenities for young adults, we expected larger LS gains for migrating apprentices moving to urban regions (H4). Sustaining this hypothesis, panel (c) in Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e shows a large initial increase in LS for this group (0.56 points, SE\\u0026thinsp;=\\u0026thinsp;0.160, p\\u0026thinsp;=\\u0026thinsp;0.001). In the second and third training years, the effect decreases and becomes statistically insignificant. However, this is mainly due to the large uncertainty of the effect estimation. The point estimates remain substantially positive. These results indicate that migrating to urban regions indeed offers large benefits for apprentices.\\u003c/p\\u003e\\u003c/div\\u003e\\u003c/div\\u003e\\u003cdiv id=\\\"Sec19\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003e5.3 Sensitivity analyses\\u003c/h2\\u003e\\u003cp\\u003eTo assess the sensitivity of our results, we carried out a comprehensive exploratory multiverse analysis and split sample analyses. Multiverse analysis inspects how sensitive the results are to alternative modelling decisions (Steegen et al., \\u003cspan citationid=\\\"CR55\\\" class=\\\"CitationRef\\\"\\u003e2016\\u003c/span\\u003e). The alternative specifications relate to the selected sample, operationalisation of key variables, and missing value handling. Section A3 of the online appendix summarises all results. Our results are sensitive to the model specification in only two ways: First, the moderation by the occupational status of VET position is significant only in models using complete cases. Still, the point estimates have the same direction in models using multiple imputations. This finding likely reflects inflated standard errors resulting from the multiple imputation procedure. Because the direction of the first-year effect is always positive, our conclusion still holds that reaching a higher occupational status through migration slightly increases LS in the first year. Second, the operationalisation of destination regions matters. The models yield smaller, yet still substantial effects if less densely populated urbanised districts are not coded as urban regions. This may indicate that migration to urbanised districts, not to the largest cities, drives the positive effects of migration to urban regions.\\u003c/p\\u003e\\u003cp\\u003eTo test whether the positive effects of VET-related migration to urban destination regions merely reflect an improvement in occupational opportunities, we repeated our analyses using split samples for apprentices in rural and apprentices in urban regions (for details, see section A3.4 of the online appendix). The results hint at differences between occupational and personal motivations for VET-related migration. The first-year effect of occupational status on LS is only significant in the group of apprentices migrating to rural regions, indicating that this group foregoes urban amenities in favour of attaining higher occupational status. Conversely, the effect of occupational status on LS is always insignificant for apprentices migrating to urban regions. This indicates that apprentices who strive for a high occupational status are willing to forego urban amenities and that apprentices who value urban amenities are less sensitive to occupational status as a means of production of LS.\\u003c/p\\u003e\\u003c/div\\u003e\"},{\"header\":\"6 Discussion and conclusion\",\"content\":\"\\u003cdiv id=\\\"Sec21\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003e6.1 Main findings and implications\\u003c/h2\\u003e\\u003cp\\u003ePrior research on the effects of migration on life satisfaction (LS) has mostly used broad samples of the adult population, implying that its conclusions are not readily transferable to individuals in life stages such as childhood, youth, young adulthood, and old age. Moreover, it has not sufficiently considered potential life events preceding, accompanying, or following migration, implying that the extent to which migration affects LS beyond these events is not fully understood. To narrow this research gap, we examined the effects of internal migration on LS during the transition to vocational education and training (VET). Given that migration decisions at this transition are likely driven by regional opportunity structures and lifestyle preferences, we explored the moderating influence of pre-migration regional VET opportunities, the attained occupational status, and the type of destination region. We developed our hypotheses by applying theories of social production functions (SPF) and rational choice (RCT) to the specific case of VET-related migration. We tested our hypotheses using large-scale longitudinal data from the German NEPS and applying fixed-effects (FE) panel regression models.\\u003c/p\\u003e\\u003cp\\u003eOur results support the claim that research on the effects of migration on LS should consider the specificities of life course stages and coupled life events as well as relevant regional moderating factors. They reveal that leaving school and entering VET substantially enhance LS \\u0026ndash; a finding which aligns with previous research on German apprentices (Authors, 2019; Reuter et al., \\u003cspan citationid=\\\"CR51\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e). In the face of this major LS surge, we did not find a significant standalone effect of VET-related migration on LS. This does not align with our hypothesis H1 and various studies reporting positive average effects of migration on LS for the broader adult population (Erlinghagen et al., \\u003cspan citationid=\\\"CR19\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e; Kratz, \\u003cspan citationid=\\\"CR37\\\" class=\\\"CitationRef\\\"\\u003e2020\\u003c/span\\u003e; Melzer \\u0026amp; Muffels, \\u003cspan citationid=\\\"CR41\\\" class=\\\"CitationRef\\\"\\u003e2017\\u003c/span\\u003e; Nowok et al., \\u003cspan citationid=\\\"CR44\\\" class=\\\"CitationRef\\\"\\u003e2013\\u003c/span\\u003e).\\u003c/p\\u003e\\u003cp\\u003eOur study highlights the importance of considering the circumstances of the transition from school to VET. As expected, we found that VET-related and lifestyle factors moderate the effects of migration: German apprentices experienced higher LS gains from migration, the higher the occupational status of their attained VET position was (supporting hypothesis H3, albeit only for apprentices\\u0026rsquo; first training year). Furthermore, migration to urban (as opposed to rural) regions led to large improvements in LS in the first training year (supporting hypothesis H4). These LS improvements may be explained by the importance of urban amenities for young adults and their lifestyle aspirations (Busch, \\u003cspan citationid=\\\"CR13\\\" class=\\\"CitationRef\\\"\\u003e2016\\u003c/span\\u003e; Otte \\u0026amp; Baur, \\u003cspan citationid=\\\"CR46\\\" class=\\\"CitationRef\\\"\\u003e2008\\u003c/span\\u003e; Sch\\u0026auml;fer \\u0026amp; Just, \\u003cspan citationid=\\\"CR52\\\" class=\\\"CitationRef\\\"\\u003e2018\\u003c/span\\u003e).\\u003c/p\\u003e\\u003cp\\u003eContrary to hypotheses H2a and H2b, we did not find differential effects depending on the pre-migration regional VET opportunity structure. This may indicate that attaining a VET position in the first place is most relevant for German apprentices from any region. On average, the training opportunities in the home region neither make apprentices dissatisfied with having to move, nor do they instil satisfaction as a result of overcoming a poor opportunity structure.\\u003c/p\\u003e\\u003cp\\u003eAs stated above, our study reveals that the moderated positive effects of VET-related migration on LS (observed for migration towards high-status positions and urban regions) are significant only in the first year after entering VET. This finding fits the notion of the hedonic treadmill (Brickman \\u0026amp; Campbell, \\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e1971\\u003c/span\\u003e). According to the hedonic treadmill, individuals will increase their aspirations (or focus on other goals) after attaining a desirable goal (e.g., attaining a high-status occupation or migrating towards urban amenities). The life course perspective additionally highlights that the transition to adulthood is usually accomplished during the VET life stage, which tends to transform individual objectives and aspirations (Benson \\u0026amp; Furstenberg, \\u003cspan citationid=\\\"CR5\\\" class=\\\"CitationRef\\\"\\u003e2006\\u003c/span\\u003e). Another possible explanation for these effects diminishing over time is the potentially limited ability of individuals to predict their future satisfaction with gains in specific life domains (Hendriks \\u0026amp; Bartram, \\u003cspan citationid=\\\"CR26\\\" class=\\\"CitationRef\\\"\\u003e2019\\u003c/span\\u003e; Murphy, \\u003cspan citationid=\\\"CR42\\\" class=\\\"CitationRef\\\"\\u003e1992\\u003c/span\\u003e). This should be true especially for young adults, who are often confronted for the first time with far-reaching decisions, e.g., about entering a new educational stage or migrating. For example, relocating to urban regions may initially seem highly desirable to young adults, and even increase their expectations regarding quality of life (Hanell, \\u003cspan citationid=\\\"CR25\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e). Later, however, they may realise that their expectations may have been too high. Difficulty in fulfilling initial expectations may result from goal attainment requiring substantial resources in costly urban regions (Otte \\u0026amp; Baur, \\u003cspan citationid=\\\"CR46\\\" class=\\\"CitationRef\\\"\\u003e2008\\u003c/span\\u003e), which young adults usually do not have. It may also result from migrating apprentices initially overestimating their ability to socially integrate into their new social environment (Hendriks et al., \\u003cspan citationid=\\\"CR28\\\" class=\\\"CitationRef\\\"\\u003e2016\\u003c/span\\u003e). Such gaps between expectations and lived experiences may explain the observed re-decline in LS after VET-related migration.\\u003c/p\\u003e\\u003cp\\u003eIn summary, German apprentices experience a significant increase in LS upon entering VET. VET-related internal migration, however, does not generally impact LS at this transition. A reason could be the coupling of VET entry and the migration event. Arguably, successful entry into VET is the most relevant aspect for young individuals\\u0026rsquo; LS at this point in life, apparently independently of a potential migration event. Still, our findings indicate that internal migration can be a means for achieving higher LS under certain circumstances: Migration can lead to substantial gains in LS when apprentices attain high-status occupations and especially when they move to urban areas.\\u003c/p\\u003e\\u003c/div\\u003e\\u003cdiv id=\\\"Sec22\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003e6.2 Limitations and further research\\u003c/h2\\u003e\\u003cp\\u003eOur study has some limitations, which suggest ways forward for research on the effects of migration on LS. First, the large standard errors for the point estimates of the effects of urban destination regions suggest that some regional differences were not captured by our measures. Thus, further research could examine differences between regions in more detail, e.g., regarding the availability of specific amenities, housing prices, or the occupational and socio-economic structure. Future research could also consider differences between countries regarding the features of national education systems and the possibility or necessity to migrate. Such analyses would show whether our theoretical claims are transferable to other institutional settings.\\u003c/p\\u003e\\u003cp\\u003eSecond, we estimated heterogeneous effects of VET-related migration on LS depending on the regional VET opportunities, the attained status, and the type of destination region. While these factors are central to understanding the impact of migration on LS, other potential moderators, such as social origin, personality traits, and pre-VET educational attainment, may also moderate this impact. Further research could address how such individual-level moderators shape LS effects of migration.\\u003c/p\\u003e\\u003cp\\u003eThird, VET is only one educational pathway that young adults can choose after secondary education in Germany. The second most prominent pathway is higher education. Individuals transitioning to VET and higher education, respectively, differ regarding their social origin and educational attainment (Autor:innengruppe Bildungsbericht, 2022). Moreover, the spatial distribution of VET versus higher education differs. This implies that our conclusions are not readily transferable to individuals choosing educational pathways other than VET. The findings of Reuter et al. (\\u003cspan citationid=\\\"CR51\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e) and Authors (2019) suggest similar developments of LS among school leavers entering VET and higher education. What requires further attention, however, is the standalone effect of migration on LS during the transition to higher education.\\u003c/p\\u003e\\u003cp\\u003e Fourth, our data do not permit us to neatly separate the effects of migrating from the effects of gaining independence from the parents through other means. A conceivable test would be to compare apprentices who stay within the parental household, apprentices who leave the parental household but stay within its proximity, and apprentices who migrate to a different region. Furthermore, the consideration of migration motives could further elucidate the mechanisms explaining the LS effects of migration.\\u003c/p\\u003e\\u003cp\\u003eFifth, our data do not allow us to comprehensively consider changes in LS during the time leading up to VET-related migration. From a theoretical point of view, it would be interesting to additionally examine anticipation effects of internal migration preceding the transition to VET. Similarly, future research could study possible anticipation effects of the transition into the labour market (or higher education), which takes place at the end of the VET life stage. It is plausible that, depending on training success and regional opportunities, some apprentices may perceive the approaching end of VET as positive (especially if they expect to smoothly transition into the labour market), while others may expect difficulties. Furthermore, graduating from VET presents apprentices with the option to return to their home region, stay in their region of training, or migrate onwards (Bijwaard \\u0026amp; Wahba, \\u003cspan citationid=\\\"CR7\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e). All these options entail further life events having the potential to influence LS trajectories.\\u003c/p\\u003e\\u003cp\\u003eDespite the discussed limitations and open questions, our study highlights the need for life stage-specific theoretical and empirical modelling, thereby considering coupled life events (for a similar argumentation with regard to international migration, see Authors, 2023). As our study has illustrated, the coupling of migration with other life events can substantially shape the effects of migration on LS. This may, for example, also apply to transitions into higher education and the labour market. Consequently, future research on the effects of migration should acutely consider the timing in the life course when migration occurs and relevant factors moderating its effects.\\u003c/p\\u003e\\u003c/div\\u003e\"},{\"header\":\"Declarations\",\"content\":\"\\u003ch2\\u003eFunding\\u003c/h2\\u003e\\u003cp\\u003eThis analysis is part of the research project ***, funded by the *** under grant number ***, and part of the junior research group ***, funded by the ***.\\u003c/p\\u003e\\u003ch2\\u003eAuthor Contribution\\u003c/h2\\u003e\\u003cp\\u003eR.W. contributed to: conceptualization (main contributor), methodology (main contributor), software, formal analysis, writing (original draft, main contributor), writing (review \\u0026amp; editing, main contributor), visualization (sole contributor); overall contribution: 50%. N.N. contributed to: conceptualization, methodology, writing (original draft), writing (review \\u0026amp; editing), supervision, project administration, funding acquisition; overall contribution: 25%.N.S. contributed to: conceptualization, methodology, writing (review \\u0026amp; editing), project administration, funding acquisition; overall contribution: 15%.A.W. contributed to: conceptualization, methodology, writing (review \\u0026amp; editing); overall contribution: 10%.\\u003c/p\\u003e\\u003ch2\\u003eAcknowledgement\\u003c/h2\\u003e\\u003cp\\u003eWe thank Tobias Koberg for his support in processing the NEPS data and Daniel Klein for his valuable remarks on our imputation procedure.\\u003c/p\\u003e\\u003ch2\\u003eData Availability\\u003c/h2\\u003e\\u003cp\\u003eThis paper uses data from National Educational Panel Study (NEPS), Starting Cohort 4 - Grade 9, https://doi.org/10.5157/NEPS:SC4:14.0.0, available at the Leibniz Institute for Educational Trajectories (LIfBi). From 2008 to 2013, NEPS data were collected as part of the Framework Program for the Promotion of Empirical Educational Research funded by the German Federal Ministry of Education and Research (BMBF). As of 2014, NEPS is carried out by the Leibniz Institute for Educational Trajectories (LIfBi) at the University of Bamberg in cooperation with a nationwide network.\\u003c/p\\u003e\"},{\"header\":\"References\",\"content\":\"\\u003col\\u003e\\u003cli\\u003e\\u003cspan\\u003eAassve, A., Davia, M., Iacovou, M., \\u0026amp; Mazzuco, S. (2007). Does leaving home make you poor? 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Migration intentions of rural youth in the Westhoek, Flanders, Belgium and the Veenkoloni\\u0026euml;n, The Netherlands. \\u003cem\\u003eJournal of Rural Studies\\u003c/em\\u003e, \\u003cem\\u003e26\\u003c/em\\u003e(4), 428\\u0026ndash;436. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1016/j.jrurstud.2010.05.001\\u003c/span\\u003e\\u003cspan address=\\\"10.1016/j.jrurstud.2010.05.001\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eVidal, S., \\u0026amp; Lersch, P. (2021). 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Environment as a multifaceted migration motive: Meanings and interpretations among a group of young adults in Sweden. \\u003cem\\u003ePopulation Space and Place\\u003c/em\\u003e, \\u003cem\\u003e22\\u003c/em\\u003e, 276\\u0026ndash;287. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1002/psp.1924\\u003c/span\\u003e\\u003cspan address=\\\"10.1002/psp.1924\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eWaibel, S. (2019). \\u003cem\\u003eDoes spatial mobility in young adulthood matter? Indirect and direct effects of spatial mobility during education on occupational status\\u003c/em\\u003e (BiB Working Paper 1/2019). Bundesinstitut f\\u0026uuml;r Bev\\u0026ouml;lkerungsforschung. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://nbn-resolving.org/urn:nbn:de:0168-ssoar-64970-0\\u003c/span\\u003e\\u003cspan address=\\\"https://nbn-resolving.org/urn:nbn:de:0168-ssoar-64970-0\\\" targettype=\\\"URL\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eWalford, N., \\u0026amp; Stockdale, A. (2015). Lifestyle and internal migration. In D. Smith, N. Finney, K. Halfacree, \\u0026amp; N. Walford (Eds.), \\u003cem\\u003eInternal migration. Geographical perspectives and processes\\u003c/em\\u003e (pp. 99\\u0026ndash;111). Routledge.\\u003c/span\\u003e\\u003c/li\\u003e\\u003c/ol\\u003e\"},{\"header\":\"Footnotes\",\"content\":\"\\u003col\\u003e\\u003cli\\u003e\\u003cspan\\u003e This hypothesis is also plausible considering that staying in rural areas may lead to stigmatisation of young adults (Pedersen \\u0026amp; Gram, \\u003cspan citationid=\\\"CR47\\\" class=\\\"CitationRef\\\"\\u003e2018\\u003c/span\\u003e), which might negatively influence their LS.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003e Due to a large time gap between the collection of waves 10 and 11, we did not group them together. Because waves 12 to 14 roughly cover the period of the COVID-19 pandemic, we chose to not group wave 11 together with waves 12 to 14 to better control for the effect of the pandemic on LS.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003e With about 0.4 scale points, the increase remains large for all apprentices in the first training year even when controlling for unobserved time-constant heterogeneity, age, and period effects (model (1) in Table A3 in the online appendix).\\u003c/span\\u003e\\u003c/li\\u003e\\u003c/ol\\u003e\"}],\"fulltextSource\":\"\",\"fullText\":\"\",\"funders\":[],\"hasAdminPriorityOnWorkflow\":false,\"hasManuscriptDocX\":true,\"hasOptedInToPreprint\":true,\"hasPassedJournalQc\":\"\",\"hasAnyPriority\":false,\"hideJournal\":true,\"highlight\":\"\",\"institution\":\"\",\"isAcceptedByJournal\":false,\"isAuthorSuppliedPdf\":false,\"isDeskRejected\":\"\",\"isHiddenFromSearch\":false,\"isInQc\":false,\"isInWorkflow\":false,\"isPdf\":false,\"isPdfUpToDate\":true,\"isWithdrawnOrRetracted\":false,\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"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\":\"\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-7805231/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-7805231/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003cp\\u003eMigration can have far-reaching implications for individuals\\u0026rsquo; life satisfaction (LS) trajectories. Previous research has mostly examined how migration influences LS using broad samples of the adult population. Moreover, it has not sufficiently considered the influence of other important life events preceding, accompanying, or following migration. Consequently, its conclusions are not readily transferable to individuals in different life stages and their age-specific coupled life events. To narrow this research gap, we examine how migrating within Germany during the transition to vocational education and training (VET) influences the LS trajectories of apprentices. We estimate fixed-effect panel regressions using data from the German National Educational Panel Study (NEPS). Beyond a substantially positive effect of entering VET, we find no significant average effect of VET-related migration on apprentices\\u0026rsquo; LS. Moreover, the pre-migration VET opportunity structures do not moderate the effect of VET-related migration on LS. However, VET-related migration geared towards the attainment of higher-status (versus lower-status) occupations positively influences apprentices\\u0026rsquo; LS in the short term. We observe the strongest positive effects of VET-related migration to urban (versus rural) regions. These results suggest that, beyond strictly VET-related factors, age-specific lifestyle factors, which become accessible by moving towards urban regions, moderate the effects of internal migration on LS. Overall, our study illustrates the need to consider the specificity of life stages and coupled life events when analysing the effects of migration on LS.\\u003c/p\\u003e\",\"manuscriptTitle\":\"Effects of internal migration on the life satisfaction of apprentices\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2025-11-14 17:46:46\",\"doi\":\"10.21203/rs.3.rs-7805231/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"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\":\"2f4d1472-19b7-420e-9e27-e9d7f011f1fd\",\"owner\":[],\"postedDate\":\"November 14th, 2025\",\"published\":true,\"recentEditorialEvents\":[],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"posted\",\"subjectAreas\":[],\"tags\":[],\"updatedAt\":\"2026-03-05T11:26:42+00:00\",\"versionOfRecord\":[],\"versionCreatedAt\":\"2025-11-14 17:46:46\",\"video\":\"\",\"vorDoi\":\"\",\"vorDoiUrl\":\"\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-7805231\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-7805231\",\"identity\":\"rs-7805231\",\"version\":[\"v1\"]},\"buildId\":\"XKTyCvWXoU3ODBz1xrDgd\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}