The Costs and Benefits of Internal Migration in Japan: How Social Integration Mediates Migrant Happiness

The Costs and Benefits of Internal Migration in Japan: How Social Integration Mediates Migrant Happiness | 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 The Costs and Benefits of Internal Migration in Japan: How Social Integration Mediates Migrant Happiness Dionyssios Askitis This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8376986/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 While the psychosocial consequences of migration have been studied at large in the context of international migration, regionally specific movements–such as the U-turn (return migration to place of origin) and I-turn (unidirectional migration to places where migrants have no personal ties) migration patterns characteristic of internal mobility in Japan–have received surprisingly little attention. We collected data from a nationwide online survey (n = 2,500) and studied how past migration behavior relates to self-reported social integration and happiness in the present. Using factor-based mediation models, we show that I-turn and U-turn migrants reach similar average levels of happiness, but do so via opposing social integration pathways: I-turn migrants exhibit lower positive social integration (reducing happiness) but also lower negative social capital (increasing happiness), whereas U-turn migrants show the reverse pattern. These countervailing indirect effects largely cancel each other out at the level of the total effect of happiness, underscoring the importance of measuring both positive and negative facets of social integration when evaluating the well-being consequences of internal migration patterns. We discuss implications for theory in terms of internal migration movements, social integration and negative social capital research, and for policy with regards to integration and revitalization programs in contemporary Japan. 1. Introduction This article examines the social costs and benefits of characteristic patterns of internal migration in Japan and elucidates their relationship with well-being. Drawing on rational-choice theories of migration (e.g., De Jong and Fawcett’s value-expectancy model , 1981), migration decisions are commonly understood as attempts to satisfy core human needs in ways that ultimately increase migrants’ welfare or that of significant others. Beyond material incentives, social relationships and the prospect of social integration are often central motives for migration (DaVanzo, 1981 ; De Jong & Gardner, 1981 ; Haug, 2008 ). Yet some types of migration can carry unforeseen psychosocial costs that impair integration and subjective well-being. While the effects of international migration on social integration and well-being have been studied at large, few have empirically investigated the psychosocial consequences of internal migration in contemporary Japan. Historically, large cohorts have left their places of birth–in particular rural communities–for one of the metropolitan centers (the so-called “rural flight”) in pursuit of education or employment. Today, roughly 500,000 people move from local areas to the Tokyo metropolitan area alone in this fashion, annually (Statistics Bureau of Japan, 2020 ). Countermovements (“urban flight” and “lifestyle migration”) and return migration to one’s place of origin also take place after finishing education or entering retirement (Fielding, 2018 ; Japan Institute for Labor Policy and Training, 2016; Matanle, 2017 ). Internal migration in Japan is thus characterized by the stark contrast between the metropolitan regions, specifically Tokyo, and the rest of Japan, the recurrent seasonal workforce migration to these metropolitan centers (JILPT, 2016), as well as more generally by the distinction between urban and rural Japan. All of these migration patterns entail different integration trajectories and well-being outcomes: Return migrants face different social reintegration challenges than newcomers who relocate to communities where they lack prior ties. Yet, the long-term psychosocial costs and benefits of these return and unidirectional migration patterns have received relatively little scholarly attention thus far. In this study, we show that opposing dimensions of social integration–positive integration and negative social capital–operate to simultaneously mediate migrants’ happiness, and that this countervailing effect is particularly pronounced for return migrants compared to unidirectional migrants. By focusing on internal migration types in Japan and on multiple facets of social integration, the paper contributes empirical evidence on how internal migration translate into psychosocial outcomes and on the mechanisms that drive long-term migration success and community revitalization. 2. Current Research 2.1 Internal Migration in Japan While long-term “rural flight” and labor migration towards the Kanto and Kansai agglomerations remain a stable feature of Japan’s internal demography (Ishikawa, 2011 ; Matanle & Rausch, 2011 ), nominal migration rates have declined in recent years (Fielding, 2018 ). Alongside the overall demographic shrinkage of the workforce and the declining share of traditionally mobile youth, migration behavior is undergoing a cultural transformation: younger cohorts show reduced mobility (Fielding, 2018 ; Ishikawa & Fielding, 1998 ; JILPT, 2016) and, in some cases, lifestyle-driven moves away from urban centers (Klien, 2015 ; Sasaki, 2018 ). Japanese population research has formalized many of the recurring internal migration patterns–among them rural depopulation and (de-)urbanization–characterized by the flows between rural and urban communities. While terminology varies (see Wiltshire, 1979 ), U-turn migration typically denotes return from a (usually temporary and often metropolitan) residence in another place to one’s place of origin (Ishikawa, 2011 ; JILPT, 2016; Traphagan, 2020 ). This would include retirees returning to their hometown after retirement or graduates moving back to support family. J-turn describes migration from a metropolitan center back to a medium or large- city near one’s home prefecture rather than to the original community of birth (Fielding 2018 ; Wiltshire, 1979 ). I-turn , by contrast, refers to migration (from typically urban regions) to provincial areas where the migrants were not born in and typically lack pre-existing personal networks (Ishikawa, 2011 ; Klien, 2020 )–also motivated by lifestyle choices (Klien, 2015 ; Kutsuwada, 2017 ; Sasaki, 2018 ). L-turn denotes urban natives relocating to rural areas for marriage or partnership (Traphagan, 2020 ). While migration research has addressed contemporary migration patterns descriptively, the individual-level psychosocial mechanisms that drive or constrain these migration patterns remain underexplored in the Japanese context. To identify the mechanisms and formulate our own hypotheses with regards to them, it is necessary to draw on the broader international migration literature and adapt findings to the domestic patterns observed in Japan. 2.2 Psychosocial Drivers and Consequences of Internal Migration In migration research, migration decisions are commonly framed as microlevel cost-benefit evaluations (Nowok et al., 2013 ). The sociology of migration has integrated concepts such as “social capital” into this calculus: It argues that social networks both facilitate migration–by lowering costs and providing support in the process and at the migration destination–and constrain it, when ties at origin are stronger than those at the destination. Thus, social networks are both a driver and an outcome of migration and shape its direction and consequences (Haug, 2008 ). Although some internal migration patterns in Japan resemble international movements elsewhere, the underlying mechanisms can be context-specific. In Japan, community and family obligations frequently drive return migration. Qualitative work (e.g., Miserka, 2020 ) suggests that while U-turn migrants often benefit from pre-existing support networks and resources that ease (re-)integration, they tend to have more external migration commitments, including experiences of peer pressure. I-turners on the other hand, who relocate to areas without prior ties and resources, may rely more on personal agency when making the migration decision and approach their new environment with greater openness, which may be no less critical to successful integration than community ties (Miserka, 2020 ). Building on these observations, we hypothesize that return (U-turn) migration and unidirectional (I-turn) migration will show different mediation pathways to happiness: the two groups may achieve comparable levels of well-being, but via distinct combinations of positive integration and negative social capital. Lacking quantitative data to back up these findings, we rely on international studies on cross-national migration to form hypotheses on the social costs and benefits of various types of migration. Yet, empirical evidence from international longitudinal and cross-sectional studies is mixed. Microlevel panel research finds an initial dip in well-being prior to migration (Erlinghagen, 2016 ; Nowok et al., 2013 ) followed by a long-term return to baseline after moving (Nowok et al., 2013 ). While some cross-national studies find no immediate difference in well-being between migrants and locals (Engler et al., 2015 ; Erlinghagen et al., 2009 ), others find lower well-being among immigrants in destination countries (Bartram, 2011 ; Liao et al., 2023 ; Safi, 2010 ), although it was shown to be higher for emigrants in the country of origin (Bartram, 2013 ; Erlinghagen, 2012 ). For internal migrants, panel studies report positive long-term effects for certain groups (e.g., work migrants, Switek, 2016 ; urban-to-rural labor migration, Kopmann & Rehdanz, 2014 ) in terms of well-being, while cross-sectional Japanese studies find that only rural-urban migration significantly increases overall and domain-specific well-being (Kumagai et al., 2025 ), as well as higher well-being for I-turn migration to rural areas compared to U-turn and J-turn migrants (Sasaki, 2018 ). Social capital and integration consistently emerges as protective for migrants’ well-being in a recent meta-analysis (Galicia-Hernandez et al., 2025). Likewise–among internal migrants–long-distance migration imposes greater psychosocial costs than short-distance migration (Kopmann & Rehdanz, 2014 ; Nowok et al., 2013 ; Zheng et al., 2022 ), explaining their lower well-being; although this is not always the outcome in panel data (Nowok et al., 2013 ). In these instances, and in international research on refugees (Johnson et al., 2017 ), the literature suggests that social integration is a powerful mediator of well-being and that measuring its positive and negative dimensions is a promising avenue for understanding why different migration types produce different well-being outcomes. The present study therefore examines multiple facets of social integration as mediators of the relationship between migration type and happiness in Japan. 2.4 Methodological gaps Research on the costs and benefits of different patterns of migration remains limited for internal migration movements and particularly for the context of Japan. Existing studies frequently focus on urban-rural flows but tend to underrepresent rural respondents which can bias inferences about rural destinations. In addition, the heterogeneity of migration destinations and their respective trajectories, including economic, domestic, or lifestyle motives presents a challenge to research that is ideally modeled or controlled simultaneously. Finally, the disciplinary boundaries (e.g., micro-level individual economics versus meso-level sociological migration research) create conceptual and empirical gaps (Haug, 2008 ; Kopmann & Rehdanz, 2014 ). We address these gaps by (a) comparing social integration mechanisms across two common internal migration patterns in Japan (I-turn vs U-turn), and (b) controlling for a broad set of covariates that capture economic, demographic and lifecycle motives. 2.5 Hypotheses Drawing on value-expectancy and set-point perspectives on migration and well-being as well as on the distinction between positive and negative dimensions of social integration due to the mixed empirical evidence, we derive the following hypotheses. H1 (mean difference): Mean happiness does not differ substantially between I-turn and U-turn migrants (i.e., no large net total effect of migration type on happiness). H2 (mediation by social integration): The happiness levels across I-turn and U-turn migrants are partly explained by the quality of social integration associated with these types of migration, namely via two distinct mediation pathways: (a) a positive social-integration pathway and (b) a negative social-capital pathway. Specifically, we expect I-turn migrants to report lower positive social integration and also lower negative social capital than U-turn migrants. Since positive integration is positively associated with happiness and negative social capital is negatively associated with happiness, these two indirect effects may operate in opposite directions. H3 (generalization): A somewhat similar effect will be observable when contrasting all migrants with non-migrants (with migrants displaying a similar pattern to I-turn migrants), reflecting the role of social integration in explaining migration-related differences in happiness. 3. Methods 3.1 Data Data is collected via a nationwide online survey administered through a consumer monitoring company. This allowed access to a commercial monitor panel (2.2 million registered members), from which participants were randomly invited–stratified by population age and sex–and who entered on a first-come first-served basis until completed responses reached N = 2,500. The online format minimized input errors and allowed basic quality checks. No systematic response biases were detected. Missing values were concentrated on the income variable and were simulated using multiple imputations. Table 1 reports sample characteristics for the overall sample and by migration type. Table 1 Sample Characteristics between Migration Patterns Characteristic n Overall, N = 2,500 No Migration, n = 238 I-turn, n = 1,670 U-turn, n = 416 Sex, n (%) male 1,249 (50) 124 (52) 768 (46) 260 (62) female 1,251 (50) 114 (48) 902 (54) 156 (38) Age, M (SD, R = 20–79) 51.3 (16) 46.8 (16) 52.2 (16) 48.6 (14) Age group, n (%) 20s 308 (12) 53 (22) 194 (12) 50 (12) 30s 360 (14) 32 (13) 235 (14) 75 (18) 40s 479 (19) 46 (19) 297 (18) 100 (24) 50s 445 (18) 39 (16) 301 (18) 75 (18) 60s 446 (18) 45 (19) 286 (17) 75 (18) 70s 462 (18) 23 (10) 357 (21) 41 (10) Marital status, n (%) married 1,604 (64) 114 (48) 1,167 (70) 227 (55) single 590 (24) 108 (45) 297 (18) 136 (33) divorced 206 (8) 14 (6) 119 (7) 48 (12) widowed 87 (4) 2 (1) 76 (5) 5 (1) other 13 (1) 0 (0) 11 (1) 0 (0) Children, n (%) none 910 (36) 114 (48) 553 (33) 173 (42) one or more 1,590 (64) 124 (52) 1,117 (67) 243 (58) Income group, n (%) 1 - none 39 (2) 8 (3) 26 (2) 4 (1) 2 - less than ¥2 mil 233 (9) 17 (7) 156 (9) 37 (9) 3 - ¥2-¥3 mil 345 (14) 26 (11) 232 (14) 57 (14) 4 - ¥3-¥4 mil 376 (15) 35 (15) 255 (15) 57 (14) 5 - ¥4-¥6 mil 576 (23) 56 (23) 391 (23) 90 (22) 6 - ¥6-¥8 mil 377 (15) 41 (17) 254 (15) 63 (15) 7 - ¥8-¥10 mil 255 (10) 26 (11) 167 (10) 45 (11) 8 - ¥10-¥12 mil 124 (5) 9 (4) 81 (5) 24 (6) 9 - ¥12-¥15 mil 84 (3) 7 (3) 53 (3) 19 5) 10 - more than ¥15 mil 91 (4) 13 (5) 55 (3) 20 (5) 3.2 Ethics and Informed Consent As the present study is non-interventional, no ethical approval was required. In order to ensure informed consent, participants were informed that their participation was voluntary and retractable at any time. They received written information about the research purpose, procedures, data use and confidentiality protocols prior to the start of the survey. All respondents provided informed consent. Personal identifiers were anonymized at the point of collection and data was managed according to departmental data management guidelines. All participants received modest reimbursement for their time. 3.3 Variables Measures (items, response scales, and scale properties) are summarized in Table 2 . Japanese translations of existing scales were used where available. Whenever necessary, we developed short scales using expert review, translation and back-translation as well as a small pre-test (n = 100) on the same monitor panel. Rating scales are used unless otherwise specified. Where relevant, we report internal consistency (Cronbach’s alpha) and inter-item correlations. 3.3.1 Happiness Happiness is operationalized as an affect balance index adapted from the OECD well-being battery (2013). The index comprises one positive-affective item (happiness) and two negative affect items (anxiousness, depressiveness). The index is calculated by adding positive and the mean reverse-coded negative affect scores together. Cronbach’s alpha (α = .74) indicates acceptable internal consistency. Scale homogeneity is good at an inter-item correlation mean of r = .48 (median r = .46). 3.3.2 Social Integration We conceptualize social integration as comprising positive social capital and the absence of negative social capital at the individual level. The indicators we use are generalized trust, a support generator (Paulinger, 2012 ), communal social capital (Eriksson et al., 2011 ; Ziersch et al., 2009 ), negative aspects of social capital (adapted following Portes, 1998 ), network size (ISSP Research Group, 2019 ) 1 and contact intensity 2 . 3.3.3 Migration Types Respondents were assigned to their respective migration category (I-turn, U-turn, or non-migrant) on the basis of their reported place of birth, current residence and migration history (i.e., motives and trajectories). Category definitions are as follows (analytic cell sizes in parentheses): U-turn (“return migration”) is a form of relational migration based on one’s personal ties to a specific region or community. Operationalized as having been born in the current municipality and returning there after spending time elsewhere (n = 416). I-turn (“unidirectional migration”) is a form of autonomous migration into a region one has no immediate ties or history in. This is operationalized as not having been born in the current place of residence and having moved there for reasons of marriage, employment, personal preference or other (n = 1,670). Non-migrant indicates that no migration has taken place and that the person has grown up in their current place of residence (n = 238) which is contrasted with all migration categories (n = 2,086). A small group of respondents who moved back to their parents’ home after relocating (n = 176) were excluded because they do not fit the strict definitions above. The primary analyses therefore use an analytic sample of N = 2,324. 3.3.4 Control Variables We include a comprehensive set of covariates chosen for theoretical relevance based on De Jong and Fawcett’s value-expectancy theory 3 and prior empirical predictive power for well-being (Diener et al., 1999 ; Hommerich et al., 2022 ; Itaba, 2016 ; Moro-Egido et al., 2022 ; Tiefenbach and Kohlbacher, 2015 ). Thus, we control for the following variables: The demographic variables age, sex, marital status, parenthood, the socioeconomic variable household income and the psychological characteristic extraversion, as well as any aspects of social capital not modelled as mediators. Table 2 Items and Descriptive Statistics of Scales Scale / Items M SD Min Max Generalized trust: Overall, I believe that people can be trusted 2.56 0.75 1 4 Communal social capital (I): 2.93 0.91 1 5 - In my neighborhood people are willing to help each other 2.99 1.10 1 5 - There is a strong sense of community in my neighborhood 2.71 1.08 1 5 - The people in my neighborhood can be trusted 3.04 1.09 1 5 - I think I am accepted in my neighborhood 3.13 1.05 1 5 - If someone is kind to me, I feel indebted to that person 4.14 0.98 1 5 - I think I am contributing to my neighborhood and community 2.80 1.09 1 5 Support resources (I): How likely are you to get help from at least one person within a week? 6.30 2.42 0 10 - When you need legal advice 6.20 3.55 0 10 - When you want to know how to deal with the government or prefectural government 6.52 3.20 0 10 - When you become ill and need to be taken care of for an extended period of time 6.44 3.36 0 10 - When you need help with housework or yard work that you can't do by yourself 6.48 3.27 0 10 - When you need help making financial decisions 5.98 3.37 0 10 - When you want to meet someone you can enjoy your hobbies with 5.81 3.21 0 10 - When you need someone to refer you to a job 4.69 3.18 0 10 - When you are looking for someone to go with you to a neighborhood festival or other local event 5.51 3.38 0 10 - When you want to volunteer with someone, such as when organizing a festival 4.91 3.35 0 10 - When you need to borrow 120,000 yen for any reason 5.51 3.83 0 10 - When you want to go out to eat or have a drink with someone 6.85 3.17 0 10 - When you want to meet with someone to discuss personal or family issues 6.21 3.31 0 10 Negative social capital (I) 1.90 0.88 1 5 - Do your family members or relatives ever pressure you about your way of life and how you live? 2.07 1.14 1 5 - Do you sometimes feel that your family, relatives, or friends demand too much from you? 2.06 1.11 1 5 - Do you sometimes feel that your neighborhood or community demands too much from you? 1.58 0.90 1 5 Extraversion (I) 2.87 0.73 1 5 - I feel comfortable around people 2.76 1.04 1 5 - I make friends easily 3.12 1.13 1 5 - I am skilled in handling social situations 2.75 1.04 1 5 - I am the life of the party 3.45 1.13 1 5 - I know how to captivate people 3.44 1.04 1 5 - I have little to say (-) 2.64 1.06 1 5 - I keep in the background (-) 2.83 1.13 1 5 - I would describe my experiences as somewhat dull (-) 3.21 1.15 1 5 - I don't like to draw attention to myself (-) 2.58 1.07 1 5 - I don't talk a lot (-) 3.02 1.16 1 5 Affect balance (I) 6.26 2.19 0 10 - Happiness: How happy did you feel during the day yesterday? 6.11 2.56 0 10 - Depressiveness: How depressed did you feel during the day yesterday? (-) 3.12 2.89 0 10 - Anxiousness: How worried did you feel during the day yesterday? (-) 4.07 2.86 0 10 Note. I = Index. 3.4 Empirical Approach and Contrasts We test the mean differences in happiness across migration types (H1, H3) using OLS regressions and assess mediation by social integration measures using structural equation models (SEM) and bootstrap CIs (H2, H3). For both types of analyses, we develop independent orthogonal sum-to-zero contrasts, which we use throughout the statistical analysis: (a) migrant vs non-migrant (contrasting non-migration = -2 with I-turn = 1 and U-turn = 1), and (b) I-turn vs U-turn (contrasting I-turn = 1 with U-turn = -1, while neutralizing non-migration = 0). Contrast coding is defined so that coefficients represent the intended group comparisons and are orthogonal to each other. All models include the covariates listed above. Prior to analysis we inspected linearity of core variables contained in the model using Loess-smoothed scatterplots and examined residuals for approximate normality and homoscedasticity using residual plots and plotting them against predicted values. No violation of basic requirement was detected. Limitations are examined in the discussion. 4. Results 4.1 Multiple Linear Regression We estimated separate OLS regressions predicting happiness for the two pre-specified contrasts (migrant vs non-migrant; I-turn vs U-turn). Table 3 reports coefficients from models without and with covariate adjustment. Table 3 OLS Regressions Predicting Happiness Contrast a) Migration vs Non-migration Unadjusted Adjusted Estimate 0.85 0.22 SE .30 .26 95% CI 0.26, 1.44 -0.29, 0.77 p .004** .397 N 2324 2324 R 2 /Adjusted R 2 .003 .300 b) I-turn vs U-turn migration Estimate 0.30 -0.06 SE .23 .20 95% CI -0.15, 0.75 0.33, -0.45 p .204 .764 N 2324 2324 R 2 /Adjusted R 2 < .001 .300 Note. Unadjusted = only specified contrast. Adjusted = including covariates age, sex, marital status, parenthood, household income, extraversion, generalized trust, communal social capital, support resources, negative social capital, network size and contact intensity. *p < 0.05. **p < 0.01. ***p < 0.001. Consistent with previous bivariate findings, migration status significantly predicts happiness in models without covariates, but these effects disappear once socioeconomic and demographic covariates are included. Likewise, the I-turn vs U-turn contrast shows a positive point estimate (higher happiness among I-turn in the sample), but this difference is non-significant in both unadjusted and adjusted models (Table 3 ). 4.2 Mediation Analysis We test the power of social integration in explaining the non-significant association we find between the type of migration and happiness using a mediation analysis. We run an exploratory factor analysis of all social integration indicators and determine factor loadings on two latent factors (λ > 1), with generalized trust, communal social capital, support resources, network size and contact intensity loading on Factor 1 and negative social capital loading on Factor 2. Variance inflation factors were low (VIF ≤ 1.68), indicating no problematic multicollinearity among the mediators. Therefore, we modelled them jointly into a SEM. We estimated SEMs (lavaan) to (a) model the latent social integration factor, (b) include negative social capital as an observed mediator, and (c) obtain bootstrap confidence intervals for indirect effects using ML with 5,000 bootstrap replications with both of the mediators exhibiting quasi-interval or interval scale level due to scale length and/or indexing. For comparison and as a robustness check, we also estimate a full parallel SEM entering all six indicators as manifest mediators (Appendix: Table 6 ). Model fit is reported using standard indices. 4.2.1 Migration vs Non-Migration First, we contrast the mediation effect between migration and non-migration behavior (see Table 4 ). The factor-based SEM (Factor 1 plus observed negative social capital as mediators) fits substantially better than the full parallel SEM (see Appendix: Table 6 ). Fit indices for the factor model show mixed performance (CFI = 0.808, RMSEA = 0.080, SRMR = 0.046). Table 4 Mediation Summary for Migration vs Non-Migration → Social Integration → Happiness (Factor-based SEM) Effect Estimate SE 95% CI p Std. est. Indirect: Social integration (factor) Indirect: Negative social capital (observed) -0.23 -0.06 .16 .08 -0.55, 0.09 -0.21, 0.10 .161 .481 − .016 − .004 Total indirect -0.29 .19 -0.66, 0.08 .122 − .020 Direct 0.37 .28 -0.17, 0.94 .187 .026 Total effect 0.09 .29 -0.46, 0.66 .759 .006 Path a: Migration type → Social integration (factor) -0.05 .03 -0.11, 0.02 .151 − .036 b: Social integration (factor) → Happiness 4.71 .50 3.84, 5.80 .000*** .442 a: Migration type → Negative social capital 0.04 .06 -0.08, 0.16 .479 .015 b: Negative social capital → Happiness -1.28 .09 -1.46,-1.10 .000*** − .257 Note. N = 2324. Estimator/CIs (bootstrap) = ML/5000. Indirect = ACME (a*b); direct = ADE (c’); total effect = c. Social integration (factor) = Generalized trust, communal social capital, support resources, network size, contact intensity. Covariates include age, sex, marital status, parenthood, household income, extraversion. b = controlled for other mediators and migration type. + p < .10. *p < .05. **p < .01. ***p < .001. After adjustment for covariates, the total effect of migration (migrant vs non-migrant) on happiness (c-path) is non-significant. Migration status does not significantly predict Factor 1 or negative social capital (a-paths), whereas both mediators significantly predict happiness (b-paths). Consequently, the indirect effects (a*b) are not significant for either mediator, and the direct effect between migration type and happiness (c’-path) remains non-significant (Table 4 ). In the full parallel SEM (Appendix: Table 6 ), only communal social capital shows a marginal indirect effect at the 10% significance level. 4.2.2 I-turn vs U-turn Migrants Second, we test for the presence of a mediation effect between I-turn and U-turn migration behavior using our factor-based SEM design (see Table 5 ). ML estimates show a similar model fit (CFI = 0.807, RMSEA = 0.081, SRMR = 0.047) that again improves on the full parallel SEM (see Appendix: Table 7 ). Table 5 Mediation Summary for I-turn vs U-turn Migration → Social Integration → Happiness (Factor-based SEM) Effect Estimate SE 95% CI p Std. est. Indirect: Social integration (factor) Indirect: Negative social capital (observed) -0.41 0.23 .12 .06 -0.66,-0.17 0.10, 0.36 .001** .000*** − .036 .020 Total indirect -0.18 .14 -0.47, 0.10 .215 − .016 Direct 0.21 .21 -0.20, 0.61 .314 .018 Total effect 0.03 .22 -0.40, 0.45 .895 .003 Path a: Migration type → Social integration (factor) -0.09 .02 -0.14,-0.04 .001** − .082 b: Social integration (factor) → Happiness 4.73 .50 3.84, 5.79 .000*** .442 a: Migration type → Negative social capital -0.18 .05 -0.27,-0.08 .000*** − .079 b: Negative social capital → Happiness -1.28 .09 -1.46,-1.09 .000*** − .257 Note. N = 2324. Estimator/CIs (bootstrap) = ML/5000. Indirect = ACME (a*b); direct = ADE (c’); total effect = c. Social integration (factor) = Generalized trust, communal social capital, support resources, network size, contact intensity. Covariates include age, sex, marital status, parenthood, household income, extraversion. b = controlled for other mediators and migration type. + p < .10. *p < .05. **p < .01. ***p < .001. Table 6 Mediation Summary for Migration vs Non-Migration → Social Integration → Happiness (Full Parallel SEM) Effect Estimate SE 95% CI p Std. est. Indirect: Social integration - Generalized trust - Communal social capital - Support resources - Negative social capital - Network size - Contact intensity 0.02 -0.05 -0.04 -0.05 -0.00 -0.00 .03 .03 .05 .07 .01 .01 -0.04, 0.08 -0.11, -0.01 -0.14, 0.06 -0.20, 0.09 -0.02, 0.01 -0.02, 0.01 .514 .055 + .477 .481 .898 .593 .001 − .004 − .003 − .004 − .000 − .000 Total indirect -0.13 .12 -0.35, 0.10 .272 − .009 Direct 0.05 .12 -0.19, 0.29 .690 .008 Total effect -0.08 .16 -0.40, 0.24 .630 − .001 Path a: Migration pattern → Generalized trust b: Generalized trust → Happiness 0.03 0.58 .05 .12 -0.06, 0.13 0.34, 0.82 .502 .000*** .01 .10 a: Migration pattern → Communal social capital b: Communal social capital → Happiness -0.16 0.32 .06 .11 -0.29, -0.04 0.11, 0.53 .010* .003** − .05 .07 a: Migration pattern → Support resources b: Support resources → Happiness -0.11 0.35 .15 .04 -0.40, 0.18 0.27, 0.43 .473 .000*** − .01 .20 a: Migration pattern → Negative social capital b: Negative social capital → Happiness 0.04 -1.19 .06 .09 -0.08, 0.16 -1.37, -1.01 .479 .000*** .01 − .24 a: Migration pattern → Network size b: Network size → Happiness -0.40 0.00 .65 .01 -1.78, 0.78 -0.02, 0.02 .535 .807 − .01 .00 a: Migration pattern → Contact intensity b: Contact intensity → Happiness -0.11 0.04 .09 .06 -0.29, 0.07 -0.07, 0.15 .252 .042* − .02 .01 Note. N = 2324. Estimator/CIs (bootstrap) = ML/5000. Indirect = ACME (a*b); direct = ADE (c’); total effect = c. Covariates include age, sex, marital status, parenthood, household income, extraversion. b = controlled for other mediators and migration type. + p < .10. *p < .05. **p < .01. ***p < .001. Model fit: CFI = 0.800, RMSEA = 0.114, SRMR = 0.042. Table 7 Mediation Summary for I-turn vs U-turn Migration → Social Integration → Happiness (Full Parallel SEM) Effect Estimate SE 95% CI p Std. est. Indirect: Social integration - Generalized trust - Communal social capital - Support resources - Negative social capital - Network size - Contact intensity -0.01 -0.07 -0.04 0.21 -0.00 -0.01 .02 .03 .04 .06 .01 .01 -0.05, 0.04 -0.13,-0.02 -0.12, 0.04 0.10, 0.33 -0.03, 0.02 -0.04, 0.02 .835 .015* .327 .000*** .827 .487 .000 − .006 − .004 .019 .000 − .001 Total indirect 0.09 .09 -0.10, 0.27 .355 .008 Direct 0.05 .12 -0.19, 0.29 .690 .008 Total effect 0.14 .15 -0.17, 0.44 .379 .015 Path a: Migration pattern → Generalized trust b: Generalized trust → Happiness -0.01 0.583 .04 .12 -0.08, 0.07 0.34, 0.82 .831 .000*** − .004 .101 a: Migration pattern → Communal social capital b: Communal social capital → Happiness -0.21 0.32 .05 .11 -0.30,-0.13 0.11, 0.53 .000*** .003** − .092 .067 a: Migration pattern → Support resources b: Support resources → Happiness -0.11 0.35 .12 .04 -0.34, 0.11 0.27, 0.43 .321 .000*** − .019 .196 a: Migration pattern → Negative social capital b: Negative social capital → Happiness -0.18 -1.19 .05 .09 -0.27,-0.08 -1.37, -1.01 .000*** .000*** − .079 − .242 a: Migration pattern → Network size b: Network size → Happiness -1.13 0.00 .56 .01 -2.25,-0.06 -0.02, 0.02 .042* .807 − .049 .005 a: Migration pattern → Contact intensity b: Contact intensity → Happiness -0.24 0.04 .08 .06 -0.39,-0.09 -0.07, 0.15 .002** .460 − .067 .014 Note. N = 2324. Estimator/CIs (bootstrap) = ML/5000. Indirect = ACME (a*b); direct = ADE (c’); total effect = c. Covariates include age, sex, marital status, parenthood, household income, extraversion. + p < .10. *p < .05. **p < .01. ***p < .001. b = controlled for other mediators and migration type. Model fit: CFI = 0.813, RMSEA = 0.110, SRMR = 0.042. With covariates included, the total (c-path) and direct (c’-path) effects of migration type on happiness are non-significant. However, the migration contrast significantly predicts both the latent social integration factor (Factor 1) and the observed negative social capital index (a-paths), and both mediators in turn significantly predict happiness (b-paths). As a result, both indirect effects (a*b) are statistically significant: Factor 1 produces a negative indirect while negative social capital produces a positive indirect. These opposing indirects counteract each other, explaining the near-zero total effect on happiness (Table 5 ). In the full parallel SEM reported in the Appendix, communal social capital emerged as the most influential individual manifest mediator for the Factor 1 effect (a*b = -0.07, 95%-CI[-0.13, -0.02], p = .015), which is the only positive social integration mediator that is significant. 5. Discussion Contrasting migrants with non-migrants, we find no systematic effect of migration status on happiness once socioeconomic and demographic covariates are included, a result that aligns with prior work in cross-national migration (Engler et al., 2015 ; Erlinghagen et al., 2009 ). Consistent with expectations and prior meta-analytic evidence (Galicia-Hernandez et al., 2025), higher positive social integration is associated with greater happiness, whereas negative social capital is associated with lower happiness. However, migrants as a general category do not differ significantly from non-migrants on either of these mediators in our adjusted models. Accordingly, there is no evidence that migration (vs non-migration) affects happiness via these social integration pathways, and H3 is rejected. Echoing Nowok et al., ( 2013 ), one plausible explanation is that migrants’ happiness returns to a set-point over time, narrowing differences with non-migrants. Methodologically, the heterogeneity of the migrant category (various motives and opposing mediator pathways, see below) also speaks for disaggregating migration types when assessing psychosocial consequences. Comparing I-turn and U-turn migrants, mean happiness does not differ significantly, confirming H1. This indicates that differences lie not in the average level of well-being but in the pathways to well-being (and perhaps the type of well-being experienced). The mediation analysis reveals two notable opposing mechanisms: I-turn migrants report lower positive social integration than U migrants–and because positive integration predicts happiness–this pathway yields a negative indirect effect for I-turn. At the same time, I-turn migrants exhibit lower negative social capital than U-turn migrants–and since negative social capital reduces happiness–this produces a positive indirect effect. Both indirects are statistically significant and of opposing polarity, which explains the near-zero total effect in terms of happiness levels in the sample 4 and confirms H2. Consistent with prior international studies (Zheng et al., 2022 ), the indirect effects remain statistically significant after adjusting for a broad set of covariates, indicating the mediation effect of social integration is robust to those controls. The pattern we find is best characterized as “inconsistent” (or countervailing) mediation rather than simple full mediation or “suppression”. Mediating pathways that operate in opposite directions and thereby cancel at the level of the total effect point to a variant of an “inconsistent mediation model” (e.g., Kenny, 2025 ) as opposed to, e.g., the absence of a significant direct effect and the presence of significant indirect effects, which would suggest a “suppression” or “indirect-only” mediation (Zhao et al., 2010 ) 5 . The value of this finding lies in that it reveals how I-turn migrants and U-turn migrants can show similar happiness levels yet differ markedly in the social processes that produce that happiness. Distinguishing positive and negative facets of social integration is therefore essential: Focusing only on positive resources or on an undifferentiated “absence” of social capital would have obscured these opposing mechanisms. Our findings resonate with Portes ( 1998 ) and later work (e.g., Villalonga-Olives & Kawachi, 2017 ) emphasizing that social ties can generate both resources and demands–obligations, conformity, pressures, or exclusion–that differentially affect well-being. Qualitative studies of return migration in Japan (e.g., Miserka, 2020 ) similarly describe the family- and community-embedded character of U-turns, which can supply communal resources but also impose burdens. In this respect, we find that communal social capital emerges as a plausible gatekeeper of successful return migration in our auxiliary full parallel SEM: it supplies belonging, reciprocity, trust, and local support that benefit some returnees while simultaneously carrying social obligations that may reduce autonomy or generate stress for others, perhaps functioning as a mediator of other social resources itself. Finally, our results highlight more general measurement issues. Many studies of the social capital, social integration and well-being of migrants neglect dedicated negative scales other than unipolar measures that register “absence” rather than active harm. Including the negative social capital was crucial here for uncovering opposing mediation pathways. Likewise, our operationalization of the emotional outcome as affect balance with both positive and negative affect components better captures the nuanced emotional consequences of migration than positive-only metrics (see Huppert, 2009 ; Keyes, 2002 ; Ryff et al., 2006 ; Zhao & Tay, 2023 ). 5.1 Limitations The cross-sectional nature of the data precludes causal claims. Although migration history was measured retrospectively, psychosocial variables (such as social integration and well-being indicators) were measured at interview (i.e., after migration for most respondents), and some covariates in the model are stable attributes (e.g., extraversion, age, sex, children), we cannot rule out reverse causation or recall bias. In addition, self-reported measures raise the usual concerns about common-method variance and reporting bias. Sampling via an online commercial panel may have introduced selection bias. Although recruitment used age-based cutoffs for “offline” demographics, stratification by age and sex-and random sampling within strata, the sample is not a probability sample of the Japanese population. We were unable to weight cases to full population benchmarks because the required selection-frame parameters were unavailable. Our operationalization of migration types trades precision for analytical tractability. Collapsing diverse migration motivations and trajectories into binary I/U-turn categories simplifies complex pathways and may attenuate or underestimate heterogeneity in effects on well-being such as those found in studies that also report urban-rural flows (e.g., Sasaki, 2018 ). While smaller, migration-specific subgroups could show stronger mediation patterns, they were infeasible given the current sample size. Thus, in our present coding of migration types, we privilege the presence of pre-existing ties in migrants’ destinations and the resulting relationality/autonomy over the geospatial characteristics of their origin or destination. The model fit limitations merit some caution. Comparative fit was suboptimal for some specifications, suggesting imperfect approximation of the covariance structure (e.g., small Factor 1 mediator loadings). Consequently, weaker parameter estimates should be interpreted cautiously. To address this, we present sensitivity checks (bootstrap stability) and alternative model specifications (parallel manifest SEM) in the Appendix. These robustness analyses identify which results are stable across specifications. 5.2 Conclusion To our knowledge, this study is the first to empirically examine the social costs and benefits of common internal migration patterns in Japan with regards to their mediation of migrants’ well-being. We document an inconsistent (countervailing) mediation effect of positive and negative aspects of social integration on happiness between I-turn and U-turn migrants: I-turn migrants show lower positive social integration (which reduces happiness) but also lower negative social capital (which increases happiness), with both effects largely canceling each other out at the level of the total effect, explaining similar happiness levels among I-turn and U-turn migrants. This pattern was not repeated for general migration. The finding was robust to a broad set of covariates and could only be clarified through our use of both positive and negative facets of social capital instead of one-dimensional or unipolar indicators. The results contribute to debates about the dualistic nature of social ties (Portes, 1998 ; Villalonga-Olives & Kawachi, 2017 ). The findings also extend recent qualitative work on Japanese internal migration (Miserka, 2020 ) and add to quantitative international studies on internal (Kopmann & Rehdanz, 2014 ; Zheng et al., 2022 ) and international migration (Johnson et al., 2017 ) that previously identified a mediating role of social integration on well-being by showing how communal resources and obligations operate as opposing forces for different types of migration. Practically, the findings suggest that revitalization policies or interventions that aim to support migrants’ well-being should equally be attentive to potential burdens and obligations that can accompany close local ties than to strengthen positive communal support only. Future research should (a) test these mechanisms in longitudinal data to establish causal direction, (b) expand the range of well-being outcomes (short- vs long-term affect, domain-specific well-being, and distress), and (c) use larger, geographically representative samples so that geospatial characteristics and finer migration motives can be modelled without aggregation. Declarations Compliance with Ethical Standards Clinical trial number: not applicable. Ethics Approval As the present study is non-interventional, no ethical approval was required at the time of data collection. Informed Consent In order to ensure informed consent, participants were informed that their participation was voluntary and retractable at any time. They received written information about the research purpose, procedures, data use and confidentiality protocols prior to the start of the survey. All respondents provided informed consent. Personal identifiers were anonymized at the point of collection and data was managed according to departmental data management guidelines. All participants received modest reimbursement for their time. Funding This work was supported by [ANONYMOUS] and, in part, [ANONYMOUS]. Author Contribution I am the sole author of the manuscript. Acknowledgement I want to thank my colleague Stefan Hundsdorfer (University of Vienna, Department of Sociology) for his collaboration with regard to the survey design. 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Include anyone you chat with, talk to, or text, either face-to-face, by phone, internet or any other communication device.” Operationalized as “How often do you meet with relatives who do not live with you? at least once a week, weekly, at least once a month, monthly, at least once a year, never” The value-expectancy theory states that migration decisions are indirectly affected by individual and household factors, specifically demographic and socioeconomic variables, as well as social and cultural norms, personality factors and the opportunity structure. I-turn migrants show similar happiness levels (M = 6.31, SD = 2.19) to U-turn migrants (M = 6.24, SD = 2.14) with no significant difference (-0.07, SE = .12, p = .533). Both these types of mediation traditionally were not considered “proper” mediation effects (Baron & Kenny, 1986 ), however in recent years they have become increasingly accepted as variants of mediation that have proven their value in uncovering otherwise invisible associations (Rucker et al., 2011 ; Zhao et al., 2010 ). Additional Declarations No competing interests reported. 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. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8376986","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":595233810,"identity":"e9fa2de0-48f2-421f-ba5e-feb01e88a9e0","order_by":0,"name":"Dionyssios Askitis","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5ElEQVRIiWNgGAWjYFACNoYDDAYMcgg+OzFaDhgwGANZjA1gPjMRWoDWMCQ2wLQwENLC338s8fCHgrr07dKHjz/4uGObPB8zA+OHjzm4tUjcSDsAdNjh3J19aYmNM8/cNmxjZmCWnLkNjzU32BuAWg7kbjjDY9jM23abEaiFjZkXjxb588dBWurSDaBa7AlqMTgAdhhzAkxLIkEthjfSEg6cMThsuLOHLXHmzLbbyW3MjM14/SJ3/pjxh4o/dfLmPMwHPnxsu207v7354IeP+LwPdyGCCY0fUrSMglEwCkbBKEAFAGMWVUFKb61xAAAAAElFTkSuQmCC","orcid":"","institution":"University of Vienna","correspondingAuthor":true,"prefix":"","firstName":"Dionyssios","middleName":"","lastName":"Askitis","suffix":""}],"badges":[],"createdAt":"2025-12-16 14:08:24","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8376986/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8376986/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104028920,"identity":"a904bb75-0f37-4176-a025-3115b1246ce9","added_by":"auto","created_at":"2026-03-05 22:24:16","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1390647,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8376986/v1/db8597f1-0ca0-4c5f-8c9c-b8b5f88026d3.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"The Costs and Benefits of Internal Migration in Japan: How Social Integration Mediates Migrant Happiness","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThis article examines the social costs and benefits of characteristic patterns of internal migration in Japan and elucidates their relationship with well-being. Drawing on rational-choice theories of migration (e.g., De Jong and Fawcett\u0026rsquo;s \u003cem\u003evalue-expectancy model\u003c/em\u003e, 1981), migration decisions are commonly understood as attempts to satisfy core human needs in ways that ultimately increase migrants\u0026rsquo; welfare or that of significant others. Beyond material incentives, social relationships and the prospect of social integration are often central motives for migration (DaVanzo, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e1981\u003c/span\u003e; De Jong \u0026amp; Gardner, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e1981\u003c/span\u003e; Haug, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Yet some types of migration can carry unforeseen psychosocial costs that impair integration and subjective well-being.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003eWhile the effects of international migration on social integration and well-being have been studied at large, few have empirically investigated the psychosocial consequences of internal migration in contemporary Japan. Historically, large cohorts have left their places of birth\u0026ndash;in particular rural communities\u0026ndash;for one of the metropolitan centers (the so-called \u0026ldquo;rural flight\u0026rdquo;) in pursuit of education or employment. Today, roughly 500,000 people move from local areas to the Tokyo metropolitan area alone in this fashion, annually (Statistics Bureau of Japan, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Countermovements (\u0026ldquo;urban flight\u0026rdquo; and \u0026ldquo;lifestyle migration\u0026rdquo;) and return migration to one\u0026rsquo;s place of origin also take place after finishing education or entering retirement (Fielding, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Japan Institute for Labor Policy and Training, 2016; Matanle, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Internal migration in Japan is thus characterized by the stark contrast between the metropolitan regions, specifically Tokyo, and the rest of Japan, the recurrent seasonal workforce migration to these metropolitan centers (JILPT, 2016), as well as more generally by the distinction between urban and rural Japan.\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eAll of these migration patterns entail different integration trajectories and well-being outcomes: Return migrants face different social reintegration challenges than newcomers who relocate to communities where they lack prior ties. Yet, the long-term psychosocial costs and benefits of these return and unidirectional migration patterns have received relatively little scholarly attention thus far. In this study, we show that opposing dimensions of social integration\u0026ndash;positive integration and negative social capital\u0026ndash;operate to simultaneously mediate migrants\u0026rsquo; happiness, and that this countervailing effect is particularly pronounced for return migrants compared to unidirectional migrants. By focusing on internal migration types in Japan and on multiple facets of social integration, the paper contributes empirical evidence on how internal migration translate into psychosocial outcomes and on the mechanisms that drive long-term migration success and community revitalization.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e"},{"header":"2. Current Research","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Internal Migration in Japan\u003c/h2\u003e \u003cp\u003eWhile long-term \u0026ldquo;rural flight\u0026rdquo; and labor migration towards the Kanto and Kansai agglomerations remain a stable feature of Japan\u0026rsquo;s internal demography (Ishikawa, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Matanle \u0026amp; Rausch, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), nominal migration rates have declined in recent years (Fielding, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Alongside the overall demographic shrinkage of the workforce and the declining share of traditionally mobile youth, migration behavior is undergoing a cultural transformation: younger cohorts show reduced mobility (Fielding, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Ishikawa \u0026amp; Fielding, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e1998\u003c/span\u003e; JILPT, 2016) and, in some cases, lifestyle-driven moves away from urban centers (Klien, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Sasaki, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eJapanese population research has formalized many of the recurring internal migration patterns\u0026ndash;among them rural depopulation and (de-)urbanization\u0026ndash;characterized by the flows between rural and urban communities. While terminology varies (see Wiltshire, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e1979\u003c/span\u003e), \u003cem\u003eU-turn\u003c/em\u003e migration typically denotes return from a (usually temporary and often metropolitan) residence in another place to one\u0026rsquo;s place of origin (Ishikawa, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; JILPT, 2016; Traphagan, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). This would include retirees returning to their hometown after retirement or graduates moving back to support family. \u003cem\u003eJ-turn\u003c/em\u003e describes migration from a metropolitan center back to a medium or large- city near one\u0026rsquo;s home prefecture rather than to the original community of birth (Fielding \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Wiltshire, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e1979\u003c/span\u003e). \u003cem\u003eI-turn\u003c/em\u003e, by contrast, refers to migration (from typically urban regions) to provincial areas where the migrants were not born in and typically lack pre-existing personal networks (Ishikawa, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Klien, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2020\u003c/span\u003e)\u0026ndash;also motivated by lifestyle choices (Klien, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Kutsuwada, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Sasaki, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). \u003cem\u003eL-turn\u003c/em\u003e denotes urban natives relocating to rural areas for marriage or partnership (Traphagan, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWhile migration research has addressed contemporary migration patterns descriptively, the individual-level psychosocial mechanisms that drive or constrain these migration patterns remain underexplored in the Japanese context. To identify the mechanisms and formulate our own hypotheses with regards to them, it is necessary to draw on the broader international migration literature and adapt findings to the domestic patterns observed in Japan.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Psychosocial Drivers and Consequences of Internal Migration\u003c/h2\u003e \u003cp\u003eIn migration research, migration decisions are commonly framed as microlevel cost-benefit evaluations (Nowok et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). The sociology of migration has integrated concepts such as \u0026ldquo;social capital\u0026rdquo; into this calculus: It argues that social networks both facilitate migration\u0026ndash;by lowering costs and providing support in the process and at the migration destination\u0026ndash;and constrain it, when ties at origin are stronger than those at the destination. Thus, social networks are both a driver and an outcome of migration and shape its direction and consequences (Haug, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2008\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAlthough some internal migration patterns in Japan resemble international movements elsewhere, the underlying mechanisms can be context-specific. In Japan, community and family obligations frequently drive return migration. Qualitative work (e.g., Miserka, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) suggests that while U-turn migrants often benefit from pre-existing support networks and resources that ease (re-)integration, they tend to have more external migration commitments, including experiences of peer pressure. I-turners on the other hand, who relocate to areas without prior ties and resources, may rely more on personal agency when making the migration decision and approach their new environment with greater openness, which may be no less critical to successful integration than community ties (Miserka, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Building on these observations, we hypothesize that return (U-turn) migration and unidirectional (I-turn) migration will show different mediation pathways to happiness: the two groups may achieve comparable levels of well-being, but via distinct combinations of positive integration and negative social capital.\u003c/p\u003e \u003cp\u003eLacking quantitative data to back up these findings, we rely on international studies on cross-national migration to form hypotheses on the social costs and benefits of various types of migration. Yet, empirical evidence from international longitudinal and cross-sectional studies is mixed. Microlevel panel research finds an initial dip in well-being prior to migration (Erlinghagen, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Nowok et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) followed by a long-term return to baseline after moving (Nowok et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). While some cross-national studies find no immediate difference in well-being between migrants and locals (Engler et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Erlinghagen et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2009\u003c/span\u003e), others find lower well-being among immigrants in destination countries (Bartram, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Liao et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Safi, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2010\u003c/span\u003e), although it was shown to be higher for emigrants in the country of origin (Bartram, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Erlinghagen, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFor internal migrants, panel studies report positive long-term effects for certain groups (e.g., work migrants, Switek, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; urban-to-rural labor migration, Kopmann \u0026amp; Rehdanz, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) in terms of well-being, while cross-sectional Japanese studies find that only rural-urban migration significantly increases overall and domain-specific well-being (Kumagai et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), as well as higher well-being for I-turn migration to rural areas compared to U-turn and J-turn migrants (Sasaki, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSocial capital and integration consistently emerges as protective for migrants\u0026rsquo; well-being in a recent meta-analysis (Galicia-Hernandez et al., 2025). Likewise\u0026ndash;among internal migrants\u0026ndash;long-distance migration imposes greater psychosocial costs than short-distance migration (Kopmann \u0026amp; Rehdanz, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Nowok et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Zheng et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), explaining their lower well-being; although this is not always the outcome in panel data (Nowok et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). In these instances, and in international research on refugees (Johnson et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), the literature suggests that social integration is a powerful mediator of well-being and that measuring its positive and negative dimensions is a promising avenue for understanding why different migration types produce different well-being outcomes. The present study therefore examines multiple facets of social integration as mediators of the relationship between migration type and happiness in Japan.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Methodological gaps\u003c/h2\u003e \u003cp\u003eResearch on the costs and benefits of different patterns of migration remains limited for internal migration movements and particularly for the context of Japan. Existing studies frequently focus on urban-rural flows but tend to underrepresent rural respondents which can bias inferences about rural destinations. In addition, the heterogeneity of migration destinations and their respective trajectories, including economic, domestic, or lifestyle motives presents a challenge to research that is ideally modeled or controlled simultaneously. Finally, the disciplinary boundaries (e.g., micro-level individual economics versus meso-level sociological migration research) create conceptual and empirical gaps (Haug, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Kopmann \u0026amp; Rehdanz, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). We address these gaps by (a) comparing social integration mechanisms across two common internal migration patterns in Japan (I-turn vs U-turn), and (b) controlling for a broad set of covariates that capture economic, demographic and lifecycle motives.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Hypotheses\u003c/h2\u003e \u003cp\u003eDrawing on value-expectancy and set-point perspectives on migration and well-being as well as on the distinction between positive and negative dimensions of social integration due to the mixed empirical evidence, we derive the following hypotheses.\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eH1 (mean difference): Mean happiness does not differ substantially between I-turn and U-turn migrants (i.e., no large net total effect of migration type on happiness).\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eH2 (mediation by social integration): The happiness levels across I-turn and U-turn migrants are partly explained by the quality of social integration associated with these types of migration, namely via two distinct mediation pathways: (a) a positive social-integration pathway and (b) a negative social-capital pathway. Specifically, we expect I-turn migrants to report lower positive social integration and also lower negative social capital than U-turn migrants. Since positive integration is positively associated with happiness and negative social capital is negatively associated with happiness, these two indirect effects may operate in opposite directions.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eH3 (generalization): A somewhat similar effect will be observable when contrasting all migrants with non-migrants (with migrants displaying a similar pattern to I-turn migrants), reflecting the role of social integration in explaining migration-related differences in happiness.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"3. Methods","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Data\u003c/h2\u003e \u003cp\u003eData is collected via a nationwide online survey administered through a consumer monitoring company. This allowed access to a commercial monitor panel (2.2\u0026nbsp;million registered members), from which participants were randomly invited\u0026ndash;stratified by population age and sex\u0026ndash;and who entered on a first-come first-served basis until completed responses reached \u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2,500. The online format minimized input errors and allowed basic quality checks. No systematic response biases were detected. Missing values were concentrated on the income variable and were simulated using multiple imputations. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e reports sample characteristics for the overall sample and by migration type.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSample Characteristics between Migration Patterns\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003en\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOverall,\u003c/p\u003e \u003cp\u003e\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2,500\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNo Migration,\u003c/p\u003e \u003cp\u003e\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;238\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eI-turn,\u003c/p\u003e \u003cp\u003e\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1,670\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eU-turn,\u003c/p\u003e \u003cp\u003e\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;416\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003emale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,249 (50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e124 (52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e768 (46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e260 (62)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003efemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,251 (50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e114 (48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e902 (54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e156 (38)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, M (SD, R\u0026thinsp;=\u0026thinsp;20\u0026ndash;79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e51.3 (16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e46.8 (16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e52.2 (16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e48.6 (14)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge group, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e20s\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e308 (12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e53 (22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e194 (12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e50 (12)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e30s\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e360 (14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e32 (13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e235 (14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e75 (18)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e40s\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e479 (19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e46 (19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e297 (18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e100 (24)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e50s\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e445 (18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e39 (16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e301 (18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e75 (18)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e60s\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e446 (18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e45 (19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e286 (17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e75 (18)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e70s\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e462 (18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23 (10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e357 (21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e41 (10)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarital status, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003emarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,604 (64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e114 (48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,167 (70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e227 (55)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003esingle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e590 (24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e108 (45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e297 (18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e136 (33)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003edivorced\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e206 (8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14 (6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e119 (7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e48 (12)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ewidowed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e87 (4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2 (1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e76 (5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5 (1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eother\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13 (1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11 (1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChildren, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003enone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e910 (36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e114 (48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e553 (33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e173 (42)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eone or more\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,590 (64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e124 (52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,117 (67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e243 (58)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIncome group, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1 - none\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e39 (2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8 (3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e26 (2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4 (1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2 - less than \u0026yen;2 mil\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e233 (9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17 (7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e156 (9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e37 (9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3 - \u0026yen;2-\u0026yen;3 mil\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e345 (14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26 (11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e232 (14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e57 (14)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4 - \u0026yen;3-\u0026yen;4 mil\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e376 (15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e35 (15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e255 (15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e57 (14)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5 - \u0026yen;4-\u0026yen;6 mil\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e576 (23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e56 (23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e391 (23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e90 (22)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6 - \u0026yen;6-\u0026yen;8 mil\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e377 (15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e41 (17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e254 (15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e63 (15)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7 - \u0026yen;8-\u0026yen;10 mil\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e255 (10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26 (11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e167 (10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e45 (11)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8 - \u0026yen;10-\u0026yen;12 mil\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e124 (5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9 (4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e81 (5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e24 (6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9 - \u0026yen;12-\u0026yen;15 mil\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e84 (3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7 (3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e53 (3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e19 5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10 - more than \u0026yen;15 mil\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e91 (4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13 (5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e55 (3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e20 (5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Ethics and Informed Consent\u003c/h2\u003e \u003cp\u003eAs the present study is non-interventional, no ethical approval was required. In order to ensure informed consent, participants were informed that their participation was voluntary and retractable at any time. They received written information about the research purpose, procedures, data use and confidentiality protocols prior to the start of the survey. All respondents provided informed consent. Personal identifiers were anonymized at the point of collection and data was managed according to departmental data management guidelines. All participants received modest reimbursement for their time.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Variables\u003c/h2\u003e \u003cp\u003eMeasures (items, response scales, and scale properties) are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Japanese translations of existing scales were used where available. Whenever necessary, we developed short scales using expert review, translation and back-translation as well as a small pre-test (n\u0026thinsp;=\u0026thinsp;100) on the same monitor panel. Rating scales are used unless otherwise specified. Where relevant, we report internal consistency (Cronbach\u0026rsquo;s alpha) and inter-item correlations.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e \u003ch2\u003e3.3.1 Happiness\u003c/h2\u003e \u003cp\u003eHappiness is operationalized as an affect balance index adapted from the OECD well-being battery (2013). The index comprises one positive-affective item (happiness) and two negative affect items (anxiousness, depressiveness). The index is calculated by adding positive and the mean reverse-coded negative affect scores together. Cronbach\u0026rsquo;s alpha (α\u0026thinsp;=\u0026thinsp;.74) indicates acceptable internal consistency. Scale homogeneity is good at an inter-item correlation mean of \u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.48 (median \u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.46).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003ch2\u003e3.3.2 Social Integration\u003c/h2\u003e \u003cp\u003eWe conceptualize social integration as comprising positive social capital and the absence of negative social capital at the individual level. The indicators we use are generalized trust, a support generator (Paulinger, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), communal social capital (Eriksson et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Ziersch et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2009\u003c/span\u003e), negative aspects of social capital (adapted following Portes, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e1998\u003c/span\u003e), network size (ISSP Research Group, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2019\u003c/span\u003e)\u003csup\u003e1\u003c/sup\u003e and contact intensity\u003csup\u003e2\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e \u003ch2\u003e3.3.3 Migration Types\u003c/h2\u003e \u003cp\u003eRespondents were assigned to their respective migration category (I-turn, U-turn, or non-migrant) on the basis of their reported place of birth, current residence and migration history (i.e., motives and trajectories). Category definitions are as follows (analytic cell sizes in parentheses):\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003e \u003cem\u003eU-turn\u003c/em\u003e (\u0026ldquo;return migration\u0026rdquo;) is a form of relational migration based on one\u0026rsquo;s personal ties to a specific region or community. Operationalized as having been born in the current municipality and returning there after spending time elsewhere (n\u0026thinsp;=\u0026thinsp;416).\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cem\u003eI-turn\u003c/em\u003e (\u0026ldquo;unidirectional migration\u0026rdquo;) is a form of autonomous migration into a region one has no immediate ties or history in. This is operationalized as not having been born in the current place of residence and having moved there for reasons of marriage, employment, personal preference or other (n\u0026thinsp;=\u0026thinsp;1,670).\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cem\u003eNon-migrant\u003c/em\u003e indicates that no migration has taken place and that the person has grown up in their current place of residence (n\u0026thinsp;=\u0026thinsp;238) which is contrasted with all migration categories (n\u0026thinsp;=\u0026thinsp;2,086).\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eA small group of respondents who moved back to their parents\u0026rsquo; home after relocating (n\u0026thinsp;=\u0026thinsp;176) were excluded because they do not fit the strict definitions above. The primary analyses therefore use an analytic sample of \u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2,324.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section3\"\u003e \u003ch2\u003e3.3.4 Control Variables\u003c/h2\u003e \u003cp\u003eWe include a comprehensive set of covariates chosen for theoretical relevance based on De Jong and Fawcett\u0026rsquo;s value-expectancy theory\u003csup\u003e3\u003c/sup\u003e and prior empirical predictive power for well-being (Diener et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e1999\u003c/span\u003e; Hommerich et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Itaba, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Moro-Egido et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Tiefenbach and Kohlbacher, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Thus, we control for the following variables: The demographic variables age, sex, marital status, parenthood, the socioeconomic variable household income and the psychological characteristic extraversion, as well as any aspects of social capital not modelled as mediators.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eItems and Descriptive Statistics of Scales\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eScale / Items\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eM\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMin\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMax\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGeneralized trust: \u003cem\u003eOverall, I believe that people can be trusted\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCommunal social capital (I):\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003e- In my neighborhood people are willing to help each other\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003e- There is a strong sense of community in my neighborhood\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003e- The people in my neighborhood can be trusted\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003e- I think I am accepted in my neighborhood\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003e- If someone is kind to me, I feel indebted to that person\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003e- I think I am contributing to my neighborhood and community\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSupport resources (I): \u003cem\u003eHow likely are you to get help from at least one person within a week?\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003e- When you need legal advice\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003e- When you want to know how to deal with the government or prefectural government\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003e- When you become ill and need to be taken care of for an extended period of time\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003e- When you need help with housework or yard work that you can't do by yourself\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003e- When you need help making financial decisions\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003e- When you want to meet someone you can enjoy your hobbies with\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003e- When you need someone to refer you to a job\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003e- When you are looking for someone to go with you to a neighborhood festival or other local event\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003e- When you want to volunteer with someone, such as when organizing a festival\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003e- When you need to borrow 120,000 yen for any reason\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003e- When you want to go out to eat or have a drink with someone\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003e- When you want to meet with someone to discuss personal or family issues\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNegative social capital (I)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003e- Do your family members or relatives ever pressure you about your way of life and how you live?\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003e- Do you sometimes feel that your family, relatives, or friends demand too much from you?\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003e- Do you sometimes feel that your neighborhood or community demands too much from you?\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExtraversion (I)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003e- I feel comfortable around people\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003e- I make friends easily\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003e- I am skilled in handling social situations\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003e- I am the life of the party\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003e- I know how to captivate people\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003e- I have little to say\u003c/em\u003e (-)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003e- I keep in the background\u003c/em\u003e (-)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003e- I would describe my experiences as somewhat dull\u003c/em\u003e (-)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003e- I don't like to draw attention to myself\u003c/em\u003e (-)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003e- I don't talk a lot\u003c/em\u003e (-)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAffect balance (I)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e- Happiness: \u003cem\u003eHow happy did you feel during the day yesterday?\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e- Depressiveness: \u003cem\u003eHow depressed did you feel during the day yesterday?\u003c/em\u003e (-)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e- Anxiousness: \u003cem\u003eHow worried did you feel during the day yesterday?\u003c/em\u003e (-)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003cem\u003eNote.\u003c/em\u003e I\u0026thinsp;=\u0026thinsp;Index.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Empirical Approach and Contrasts\u003c/h2\u003e \u003cp\u003eWe test the mean differences in happiness across migration types (H1, H3) using OLS regressions and assess mediation by social integration measures using structural equation models (SEM) and bootstrap CIs (H2, H3). For both types of analyses, we develop independent orthogonal sum-to-zero contrasts, which we use throughout the statistical analysis: (a) migrant vs non-migrant (contrasting non-migration = -2 with I-turn\u0026thinsp;=\u0026thinsp;1 and U-turn\u0026thinsp;=\u0026thinsp;1), and (b) I-turn vs U-turn (contrasting I-turn\u0026thinsp;=\u0026thinsp;1 with U-turn = -1, while neutralizing non-migration\u0026thinsp;=\u0026thinsp;0). Contrast coding is defined so that coefficients represent the intended group comparisons and are orthogonal to each other. All models include the covariates listed above.\u003c/p\u003e \u003cp\u003ePrior to analysis we inspected linearity of core variables contained in the model using Loess-smoothed scatterplots and examined residuals for approximate normality and homoscedasticity using residual plots and plotting them against predicted values. No violation of basic requirement was detected. Limitations are examined in the discussion.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Results","content":"\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\n\u003ch2\u003e4.1 Multiple Linear Regression\u003c/h2\u003e\n\u003cp\u003eWe estimated separate OLS regressions predicting happiness for the two pre-specified contrasts (migrant vs non-migrant; I-turn vs U-turn). Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e reports coefficients from models without and with covariate adjustment.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003ctable id=\"Tab3\" style=\"width: 397px;\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eOLS Regressions Predicting Happiness\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth style=\"width: 205px;\" align=\"left\"\u003e\n\u003cp\u003eContrast\u003c/p\u003e\n\u003c/th\u003e\n\u003cth style=\"width: 221.144px;\" colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth style=\"width: 51.7523px;\" align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 205px;\" align=\"left\"\u003e\n\u003cp\u003ea) Migration vs Non-migration\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 87px;\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eUnadjusted\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 175.896px;\" colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eAdjusted\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 205px;\" align=\"left\"\u003e\n\u003cp\u003eEstimate\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 87px;\" align=\"left\"\u003e\n\u003cp\u003e0.85\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 175.896px;\" colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.22\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 205px;\" align=\"left\"\u003e\n\u003cp\u003eSE\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 87px;\" align=\"left\"\u003e\n\u003cp\u003e.30\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 175.896px;\" colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e.26\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 205px;\" align=\"left\"\u003e\n\u003cp\u003e95% CI\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 87px;\" align=\"left\"\u003e\n\u003cp\u003e0.26, 1.44\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 175.896px;\" colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e-0.29, 0.77\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 205px;\" align=\"left\"\u003e\n\u003cp\u003ep\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 87px;\" align=\"left\"\u003e\n\u003cp\u003e.004**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 175.896px;\" colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e.397\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 205px;\" align=\"left\"\u003e\n\u003cp\u003eN\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 87px;\" align=\"left\"\u003e\n\u003cp\u003e2324\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 175.896px;\" colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e2324\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 205px;\" align=\"left\"\u003e\n\u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e/Adjusted R\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 87px;\" align=\"left\"\u003e\n\u003cp\u003e.003\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 175.896px;\" colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e.300\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 205px;\" align=\"left\"\u003e\n\u003cp\u003eb) I-turn vs U-turn migration\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 87px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 175.896px;\" colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 205px;\" align=\"left\"\u003e\n\u003cp\u003eEstimate\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 87px;\" align=\"left\"\u003e\n\u003cp\u003e0.30\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 175.896px;\" colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e-0.06\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 205px;\" align=\"left\"\u003e\n\u003cp\u003eSE\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 87px;\" align=\"left\"\u003e\n\u003cp\u003e.23\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 175.896px;\" colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e.20\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 205px;\" align=\"left\"\u003e\n\u003cp\u003e95% CI\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 87px;\" align=\"left\"\u003e\n\u003cp\u003e-0.15, 0.75\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 175.896px;\" colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.33, -0.45\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 205px;\" align=\"left\"\u003e\n\u003cp\u003ep\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 87px;\" align=\"left\"\u003e\n\u003cp\u003e.204\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 175.896px;\" colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e.764\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 205px;\" align=\"left\"\u003e\n\u003cp\u003eN\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 87px;\" align=\"left\"\u003e\n\u003cp\u003e2324\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 175.896px;\" colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e2324\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 205px;\" align=\"left\"\u003e\n\u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e/Adjusted R\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 87px;\" align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 175.896px;\" colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e.300\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 467.896px;\" colspan=\"4\" align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eNote.\u003c/em\u003e Unadjusted\u0026thinsp;=\u0026thinsp;only specified contrast.\u003c/p\u003e\n\u003cp\u003eAdjusted\u0026thinsp;=\u0026thinsp;including covariates age, sex, marital status, parenthood,\u003c/p\u003e\n\u003cp\u003ehousehold income, extraversion, generalized trust, communal social\u003c/p\u003e\n\u003cp\u003ecapital, support resources, negative social capital, network size and\u003c/p\u003e\n\u003cp\u003econtact intensity.\u003c/p\u003e\n\u003cp\u003e*p\u0026thinsp;\u0026lt;\u0026thinsp;0.05. **p\u0026thinsp;\u0026lt;\u0026thinsp;0.01. ***p\u0026thinsp;\u0026lt;\u0026thinsp;0.001.\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eConsistent with previous bivariate findings, migration status significantly predicts happiness in models without covariates, but these effects disappear once socioeconomic and demographic covariates are included. Likewise, the I-turn vs U-turn contrast shows a positive point estimate (higher happiness among I-turn in the sample), but this difference is non-significant in both unadjusted and adjusted models (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\n\u003ch2\u003e4.2 Mediation Analysis\u003c/h2\u003e\n\u003cp\u003eWe test the power of social integration in explaining the non-significant association we find between the type of migration and happiness using a mediation analysis. We run an exploratory factor analysis of all social integration indicators and determine factor loadings on two latent factors (\u0026lambda;\u0026thinsp;\u0026gt;\u0026thinsp;1), with generalized trust, communal social capital, support resources, network size and contact intensity loading on Factor 1 and negative social capital loading on Factor 2. Variance inflation factors were low (VIF \u0026le; 1.68), indicating no problematic multicollinearity among the mediators. Therefore, we modelled them jointly into a SEM.\u003c/p\u003e\n\u003cp\u003eWe estimated SEMs (lavaan) to (a) model the latent social integration factor, (b) include negative social capital as an observed mediator, and (c) obtain bootstrap confidence intervals for indirect effects using ML with 5,000 bootstrap replications with both of the mediators exhibiting quasi-interval or interval scale level due to scale length and/or indexing. For comparison and as a robustness check, we also estimate a full parallel SEM entering all six indicators as manifest mediators (Appendix: Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e). Model fit is reported using standard indices.\u003c/p\u003e\n\u003cdiv id=\"Sec19\" class=\"Section3\"\u003e\n\u003ch2\u003e4.2.1 Migration vs Non-Migration\u003c/h2\u003e\n\u003cp\u003eFirst, we contrast the mediation effect between migration and non-migration behavior (see Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e). The factor-based SEM (Factor 1 plus observed negative social capital as mediators) fits substantially better than the full parallel SEM (see Appendix: Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e). Fit indices for the factor model show mixed performance (CFI\u0026thinsp;=\u0026thinsp;0.808, RMSEA\u0026thinsp;=\u0026thinsp;0.080, SRMR\u0026thinsp;=\u0026thinsp;0.046).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab5\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eMediation Summary for Migration vs Non-Migration \u0026rarr; Social Integration \u0026rarr; Happiness (Factor-based SEM)\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eEffect\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eEstimate\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eSE\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e95% CI\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003ep\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eStd. est.\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIndirect: Social integration (factor)\u003c/p\u003e\n\u003cp\u003eIndirect: Negative social capital (observed)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.23\u003c/p\u003e\n\u003cp\u003e-0.06\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.16\u003c/p\u003e\n\u003cp\u003e.08\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.55, 0.09\u003c/p\u003e\n\u003cp\u003e-0.21, 0.10\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.161\u003c/p\u003e\n\u003cp\u003e.481\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026minus;\u0026thinsp;.016\u003c/p\u003e\n\u003cp\u003e\u0026minus;\u0026thinsp;.004\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTotal indirect\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.29\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.19\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.66, 0.08\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.122\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026minus;\u0026thinsp;.020\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDirect\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.37\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.28\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.17, 0.94\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.187\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.026\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTotal effect\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.09\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.29\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.46, 0.66\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.759\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.006\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePath\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ea: Migration type \u0026rarr; Social integration (factor)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.05\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.03\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.11, 0.02\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.151\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026minus;\u0026thinsp;.036\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eb: Social integration (factor) \u0026rarr; Happiness\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4.71\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.50\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.84, 5.80\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.000***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.442\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ea: Migration type \u0026rarr; Negative social capital\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.04\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.06\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.08, 0.16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.479\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.015\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eb: Negative social capital \u0026rarr; Happiness\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-1.28\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.09\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-1.46,-1.10\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.000***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026minus;\u0026thinsp;.257\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"6\" align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eNote.\u003c/em\u003e N\u0026thinsp;=\u0026thinsp;2324. Estimator/CIs (bootstrap)\u0026thinsp;=\u0026thinsp;ML/5000.\u003c/p\u003e\n\u003cp\u003eIndirect\u0026thinsp;=\u0026thinsp;ACME (a*b); direct\u0026thinsp;=\u0026thinsp;ADE (c\u0026rsquo;); total effect\u0026thinsp;=\u0026thinsp;c.\u003c/p\u003e\n\u003cp\u003eSocial integration (factor)\u0026thinsp;=\u0026thinsp;Generalized trust, communal social capital, support resources, network size, contact intensity.\u003c/p\u003e\n\u003cp\u003eCovariates include age, sex, marital status, parenthood, household income, extraversion. b\u0026thinsp;=\u0026thinsp;controlled for other mediators and migration type.\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e+\u003c/sup\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;.10. *p\u0026thinsp;\u0026lt;\u0026thinsp;.05. **p\u0026thinsp;\u0026lt;\u0026thinsp;.01. ***p\u0026thinsp;\u0026lt;\u0026thinsp;.001.\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eAfter adjustment for covariates, the total effect of migration (migrant vs non-migrant) on happiness (c-path) is non-significant. Migration status does not significantly predict Factor 1 or negative social capital (a-paths), whereas both mediators significantly predict happiness (b-paths). Consequently, the indirect effects (a*b) are not significant for either mediator, and the direct effect between migration type and happiness (c\u0026rsquo;-path) remains non-significant (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e). In the full parallel SEM (Appendix: Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e), only communal social capital shows a marginal indirect effect at the 10% significance level.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec20\" class=\"Section3\"\u003e\n\u003ch2\u003e4.2.2 I-turn vs U-turn Migrants\u003c/h2\u003e\n\u003cp\u003eSecond, we test for the presence of a mediation effect between I-turn and U-turn migration behavior using our factor-based SEM design (see Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e). ML estimates show a similar model fit (CFI\u0026thinsp;=\u0026thinsp;0.807, RMSEA\u0026thinsp;=\u0026thinsp;0.081, SRMR\u0026thinsp;=\u0026thinsp;0.047) that again improves on the full parallel SEM (see Appendix: Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003ctable id=\"Tab6\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eMediation Summary for I-turn vs U-turn Migration \u0026rarr; Social Integration \u0026rarr; Happiness (Factor-based SEM)\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eEffect\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eEstimate\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eSE\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e95% CI\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003ep\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eStd. est.\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIndirect: Social integration (factor)\u003c/p\u003e\n\u003cp\u003eIndirect: Negative social capital (observed)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.41\u003c/p\u003e\n\u003cp\u003e0.23\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.12\u003c/p\u003e\n\u003cp\u003e.06\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.66,-0.17\u003c/p\u003e\n\u003cp\u003e0.10, 0.36\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.001**\u003c/p\u003e\n\u003cp\u003e.000***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026minus;\u0026thinsp;.036\u003c/p\u003e\n\u003cp\u003e.020\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTotal indirect\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.18\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.14\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.47, 0.10\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.215\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026minus;\u0026thinsp;.016\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDirect\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.21\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.21\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.20, 0.61\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.314\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.018\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTotal effect\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.03\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.22\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.40, 0.45\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.895\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.003\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePath\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ea: Migration type \u0026rarr; Social integration (factor)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.09\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.02\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.14,-0.04\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.001**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026minus;\u0026thinsp;.082\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eb: Social integration (factor) \u0026rarr; Happiness\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4.73\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.50\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.84, 5.79\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.000***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.442\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ea: Migration type \u0026rarr; Negative social capital\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.18\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.05\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.27,-0.08\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.000***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026minus;\u0026thinsp;.079\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eb: Negative social capital \u0026rarr; Happiness\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-1.28\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.09\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-1.46,-1.09\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.000***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026minus;\u0026thinsp;.257\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"6\" align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eNote.\u003c/em\u003e N\u0026thinsp;=\u0026thinsp;2324. Estimator/CIs (bootstrap)\u0026thinsp;=\u0026thinsp;ML/5000.\u003c/p\u003e\n\u003cp\u003eIndirect\u0026thinsp;=\u0026thinsp;ACME (a*b); direct\u0026thinsp;=\u0026thinsp;ADE (c\u0026rsquo;); total effect\u0026thinsp;=\u0026thinsp;c.\u003c/p\u003e\n\u003cp\u003eSocial integration (factor)\u0026thinsp;=\u0026thinsp;Generalized trust, communal social capital, support resources, network size, contact intensity.\u003c/p\u003e\n\u003cp\u003eCovariates include age, sex, marital status, parenthood, household income, extraversion.\u003c/p\u003e\n\u003cp\u003eb\u0026thinsp;=\u0026thinsp;controlled for other mediators and migration type.\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e+\u003c/sup\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;.10. *p\u0026thinsp;\u0026lt;\u0026thinsp;.05. **p\u0026thinsp;\u0026lt;\u0026thinsp;.01. ***p\u0026thinsp;\u0026lt;\u0026thinsp;.001.\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\n\u003ctable id=\"Tab4\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eMediation Summary for Migration vs Non-Migration \u0026rarr; Social Integration \u0026rarr; Happiness (Full Parallel SEM)\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eEffect\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eEstimate\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eSE\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e95% CI\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003ep\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eStd. est.\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIndirect: Social integration\u003c/p\u003e\n\u003cp\u003e- Generalized trust\u003c/p\u003e\n\u003cp\u003e- Communal social capital\u003c/p\u003e\n\u003cp\u003e- Support resources\u003c/p\u003e\n\u003cp\u003e- Negative social capital\u003c/p\u003e\n\u003cp\u003e- Network size\u003c/p\u003e\n\u003cp\u003e- Contact intensity\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e0.02\u003c/p\u003e\n\u003cp\u003e-0.05\u003c/p\u003e\n\u003cp\u003e-0.04\u003c/p\u003e\n\u003cp\u003e-0.05\u003c/p\u003e\n\u003cp\u003e-0.00\u003c/p\u003e\n\u003cp\u003e-0.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e.03\u003c/p\u003e\n\u003cp\u003e.03\u003c/p\u003e\n\u003cp\u003e.05\u003c/p\u003e\n\u003cp\u003e.07\u003c/p\u003e\n\u003cp\u003e.01\u003c/p\u003e\n\u003cp\u003e.01\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e-0.04, 0.08\u003c/p\u003e\n\u003cp\u003e-0.11, -0.01\u003c/p\u003e\n\u003cp\u003e-0.14, 0.06\u003c/p\u003e\n\u003cp\u003e-0.20, 0.09\u003c/p\u003e\n\u003cp\u003e-0.02, 0.01\u003c/p\u003e\n\u003cp\u003e-0.02, 0.01\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e.514\u003c/p\u003e\n\u003cp\u003e.055\u003csup\u003e+\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003e.477\u003c/p\u003e\n\u003cp\u003e.481\u003c/p\u003e\n\u003cp\u003e.898\u003c/p\u003e\n\u003cp\u003e.593\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e.001\u003c/p\u003e\n\u003cp\u003e\u0026minus;\u0026thinsp;.004\u003c/p\u003e\n\u003cp\u003e\u0026minus;\u0026thinsp;.003\u003c/p\u003e\n\u003cp\u003e\u0026minus;\u0026thinsp;.004\u003c/p\u003e\n\u003cp\u003e\u0026minus;\u0026thinsp;.000\u003c/p\u003e\n\u003cp\u003e\u0026minus;\u0026thinsp;.000\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTotal indirect\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.13\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.12\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.35, 0.10\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.272\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026minus;\u0026thinsp;.009\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDirect\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.05\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.12\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.19, 0.29\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.690\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.008\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTotal effect\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.08\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.40, 0.24\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.630\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026minus;\u0026thinsp;.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePath\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ea: Migration pattern \u0026rarr; Generalized trust\u003c/p\u003e\n\u003cp\u003eb: Generalized trust \u0026rarr; Happiness\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.03\u003c/p\u003e\n\u003cp\u003e0.58\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.05\u003c/p\u003e\n\u003cp\u003e.12\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.06, 0.13\u003c/p\u003e\n\u003cp\u003e0.34, 0.82\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.502\u003c/p\u003e\n\u003cp\u003e.000***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.01\u003c/p\u003e\n\u003cp\u003e.10\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ea: Migration pattern \u0026rarr; Communal social capital\u003c/p\u003e\n\u003cp\u003eb: Communal social capital \u0026rarr; Happiness\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.16\u003c/p\u003e\n\u003cp\u003e0.32\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.06\u003c/p\u003e\n\u003cp\u003e.11\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.29, -0.04\u003c/p\u003e\n\u003cp\u003e0.11, 0.53\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.010*\u003c/p\u003e\n\u003cp\u003e.003**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026minus;\u0026thinsp;.05\u003c/p\u003e\n\u003cp\u003e.07\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ea: Migration pattern \u0026rarr; Support resources\u003c/p\u003e\n\u003cp\u003eb: Support resources \u0026rarr; Happiness\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.11\u003c/p\u003e\n\u003cp\u003e0.35\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.15\u003c/p\u003e\n\u003cp\u003e.04\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.40, 0.18\u003c/p\u003e\n\u003cp\u003e0.27, 0.43\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.473\u003c/p\u003e\n\u003cp\u003e.000***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026minus;\u0026thinsp;.01\u003c/p\u003e\n\u003cp\u003e.20\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ea: Migration pattern \u0026rarr; Negative social capital\u003c/p\u003e\n\u003cp\u003eb: Negative social capital \u0026rarr; Happiness\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.04\u003c/p\u003e\n\u003cp\u003e-1.19\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.06\u003c/p\u003e\n\u003cp\u003e.09\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.08, 0.16\u003c/p\u003e\n\u003cp\u003e-1.37, -1.01\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.479\u003c/p\u003e\n\u003cp\u003e.000***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.01\u003c/p\u003e\n\u003cp\u003e\u0026minus;\u0026thinsp;.24\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ea: Migration pattern \u0026rarr; Network size\u003c/p\u003e\n\u003cp\u003eb: Network size \u0026rarr; Happiness\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.40\u003c/p\u003e\n\u003cp\u003e0.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.65\u003c/p\u003e\n\u003cp\u003e.01\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-1.78, 0.78\u003c/p\u003e\n\u003cp\u003e-0.02, 0.02\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.535\u003c/p\u003e\n\u003cp\u003e.807\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026minus;\u0026thinsp;.01\u003c/p\u003e\n\u003cp\u003e.00\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ea: Migration pattern \u0026rarr; Contact intensity\u003c/p\u003e\n\u003cp\u003eb: Contact intensity \u0026rarr; Happiness\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.11\u003c/p\u003e\n\u003cp\u003e0.04\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.09\u003c/p\u003e\n\u003cp\u003e.06\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.29, 0.07\u003c/p\u003e\n\u003cp\u003e-0.07, 0.15\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.252\u003c/p\u003e\n\u003cp\u003e.042*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026minus;\u0026thinsp;.02\u003c/p\u003e\n\u003cp\u003e.01\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"6\" align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eNote.\u003c/em\u003e N\u0026thinsp;=\u0026thinsp;2324. Estimator/CIs (bootstrap)\u0026thinsp;=\u0026thinsp;ML/5000.\u003c/p\u003e\n\u003cp\u003eIndirect\u0026thinsp;=\u0026thinsp;ACME (a*b); direct\u0026thinsp;=\u0026thinsp;ADE (c\u0026rsquo;); total effect\u0026thinsp;=\u0026thinsp;c.\u003c/p\u003e\n\u003cp\u003eCovariates include age, sex, marital status, parenthood, household income, extraversion.\u003c/p\u003e\n\u003cp\u003eb\u0026thinsp;=\u0026thinsp;controlled for other mediators and migration type.\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e+\u003c/sup\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;.10. *p\u0026thinsp;\u0026lt;\u0026thinsp;.05. **p\u0026thinsp;\u0026lt;\u0026thinsp;.01. ***p\u0026thinsp;\u0026lt;\u0026thinsp;.001.\u003c/p\u003e\n\u003cp\u003eModel fit: CFI\u0026thinsp;=\u0026thinsp;0.800, RMSEA\u0026thinsp;=\u0026thinsp;0.114, SRMR\u0026thinsp;=\u0026thinsp;0.042.\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u0026nbsp;\u003c/div\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003ctable id=\"Tab7\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eMediation Summary for I-turn vs U-turn Migration \u0026rarr; Social Integration \u0026rarr; Happiness (Full Parallel SEM)\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eEffect\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eEstimate\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eSE\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e95% CI\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003ep\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eStd. est.\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIndirect: Social integration\u003c/p\u003e\n\u003cp\u003e- Generalized trust\u003c/p\u003e\n\u003cp\u003e- Communal social capital\u003c/p\u003e\n\u003cp\u003e- Support resources\u003c/p\u003e\n\u003cp\u003e- Negative social capital\u003c/p\u003e\n\u003cp\u003e- Network size\u003c/p\u003e\n\u003cp\u003e- Contact intensity\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e-0.01\u003c/p\u003e\n\u003cp\u003e-0.07\u003c/p\u003e\n\u003cp\u003e-0.04\u003c/p\u003e\n\u003cp\u003e0.21\u003c/p\u003e\n\u003cp\u003e-0.00\u003c/p\u003e\n\u003cp\u003e-0.01\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e.02\u003c/p\u003e\n\u003cp\u003e.03\u003c/p\u003e\n\u003cp\u003e.04\u003c/p\u003e\n\u003cp\u003e.06\u003c/p\u003e\n\u003cp\u003e.01\u003c/p\u003e\n\u003cp\u003e.01\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e-0.05, 0.04\u003c/p\u003e\n\u003cp\u003e-0.13,-0.02\u003c/p\u003e\n\u003cp\u003e-0.12, 0.04\u003c/p\u003e\n\u003cp\u003e0.10, 0.33\u003c/p\u003e\n\u003cp\u003e-0.03, 0.02\u003c/p\u003e\n\u003cp\u003e-0.04, 0.02\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e.835\u003c/p\u003e\n\u003cp\u003e.015*\u003c/p\u003e\n\u003cp\u003e.327\u003c/p\u003e\n\u003cp\u003e.000***\u003c/p\u003e\n\u003cp\u003e.827\u003c/p\u003e\n\u003cp\u003e.487\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e.000\u003c/p\u003e\n\u003cp\u003e\u0026minus;\u0026thinsp;.006\u003c/p\u003e\n\u003cp\u003e\u0026minus;\u0026thinsp;.004\u003c/p\u003e\n\u003cp\u003e.019\u003c/p\u003e\n\u003cp\u003e.000\u003c/p\u003e\n\u003cp\u003e\u0026minus;\u0026thinsp;.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTotal indirect\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.09\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.09\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.10, 0.27\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.355\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.008\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDirect\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.05\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.12\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.19, 0.29\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.690\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.008\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTotal effect\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.14\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.15\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.17, 0.44\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.379\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.015\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePath\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ea: Migration pattern \u0026rarr; Generalized trust\u003c/p\u003e\n\u003cp\u003eb: Generalized trust \u0026rarr; Happiness\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.01\u003c/p\u003e\n\u003cp\u003e0.583\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.04\u003c/p\u003e\n\u003cp\u003e.12\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.08, 0.07\u003c/p\u003e\n\u003cp\u003e0.34, 0.82\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.831\u003c/p\u003e\n\u003cp\u003e.000***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026minus;\u0026thinsp;.004\u003c/p\u003e\n\u003cp\u003e.101\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ea: Migration pattern \u0026rarr; Communal social capital\u003c/p\u003e\n\u003cp\u003eb: Communal social capital \u0026rarr; Happiness\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.21\u003c/p\u003e\n\u003cp\u003e0.32\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.05\u003c/p\u003e\n\u003cp\u003e.11\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.30,-0.13\u003c/p\u003e\n\u003cp\u003e0.11, 0.53\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.000***\u003c/p\u003e\n\u003cp\u003e.003**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026minus;\u0026thinsp;.092\u003c/p\u003e\n\u003cp\u003e.067\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ea: Migration pattern \u0026rarr; Support resources\u003c/p\u003e\n\u003cp\u003eb: Support resources \u0026rarr; Happiness\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.11\u003c/p\u003e\n\u003cp\u003e0.35\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.12\u003c/p\u003e\n\u003cp\u003e.04\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.34, 0.11\u003c/p\u003e\n\u003cp\u003e0.27, 0.43\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.321\u003c/p\u003e\n\u003cp\u003e.000***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026minus;\u0026thinsp;.019\u003c/p\u003e\n\u003cp\u003e.196\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ea: Migration pattern \u0026rarr; Negative social capital\u003c/p\u003e\n\u003cp\u003eb: Negative social capital \u0026rarr; Happiness\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.18\u003c/p\u003e\n\u003cp\u003e-1.19\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.05\u003c/p\u003e\n\u003cp\u003e.09\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.27,-0.08\u003c/p\u003e\n\u003cp\u003e-1.37, -1.01\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.000***\u003c/p\u003e\n\u003cp\u003e.000***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026minus;\u0026thinsp;.079\u003c/p\u003e\n\u003cp\u003e\u0026minus;\u0026thinsp;.242\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ea: Migration pattern \u0026rarr; Network size\u003c/p\u003e\n\u003cp\u003eb: Network size \u0026rarr; Happiness\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-1.13\u003c/p\u003e\n\u003cp\u003e0.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.56\u003c/p\u003e\n\u003cp\u003e.01\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-2.25,-0.06\u003c/p\u003e\n\u003cp\u003e-0.02, 0.02\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.042*\u003c/p\u003e\n\u003cp\u003e.807\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026minus;\u0026thinsp;.049\u003c/p\u003e\n\u003cp\u003e.005\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ea: Migration pattern \u0026rarr; Contact intensity\u003c/p\u003e\n\u003cp\u003eb: Contact intensity \u0026rarr; Happiness\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.24\u003c/p\u003e\n\u003cp\u003e0.04\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.08\u003c/p\u003e\n\u003cp\u003e.06\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.39,-0.09\u003c/p\u003e\n\u003cp\u003e-0.07, 0.15\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.002**\u003c/p\u003e\n\u003cp\u003e.460\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026minus;\u0026thinsp;.067\u003c/p\u003e\n\u003cp\u003e.014\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"6\" align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eNote.\u003c/em\u003e N\u0026thinsp;=\u0026thinsp;2324. Estimator/CIs (bootstrap)\u0026thinsp;=\u0026thinsp;ML/5000.\u003c/p\u003e\n\u003cp\u003eIndirect\u0026thinsp;=\u0026thinsp;ACME (a*b); direct\u0026thinsp;=\u0026thinsp;ADE (c\u0026rsquo;); total effect\u0026thinsp;=\u0026thinsp;c.\u003c/p\u003e\n\u003cp\u003eCovariates include age, sex, marital status, parenthood, household income, extraversion. \u003csup\u003e+\u003c/sup\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;.10. *p\u0026thinsp;\u0026lt;\u0026thinsp;.05. **p\u0026thinsp;\u0026lt;\u0026thinsp;.01. ***p\u0026thinsp;\u0026lt;\u0026thinsp;.001.\u003c/p\u003e\n\u003cp\u003eb\u0026thinsp;=\u0026thinsp;controlled for other mediators and migration type.\u003c/p\u003e\n\u003cp\u003eModel fit: CFI\u0026thinsp;=\u0026thinsp;0.813, RMSEA\u0026thinsp;=\u0026thinsp;0.110, SRMR\u0026thinsp;=\u0026thinsp;0.042.\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eWith covariates included, the total (c-path) and direct (c\u0026rsquo;-path) effects of migration type on happiness are non-significant. However, the migration contrast significantly predicts both the latent social integration factor (Factor 1) and the observed negative social capital index (a-paths), and both mediators in turn significantly predict happiness (b-paths). As a result, both indirect effects (a*b) are statistically significant: Factor 1 produces a negative indirect while negative social capital produces a positive indirect. These opposing indirects counteract each other, explaining the near-zero total effect on happiness (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e). In the full parallel SEM reported in the Appendix, communal social capital emerged as the most influential individual manifest mediator for the Factor 1 effect (a*b = -0.07, 95%-CI[-0.13, -0.02], p\u0026thinsp;=\u0026thinsp;.015), which is the only positive social integration mediator that is significant.\u003c/p\u003e\n\u003c/div\u003e\n\u003c/div\u003e"},{"header":"5. Discussion","content":"\u003cp\u003eContrasting migrants with non-migrants, we find no systematic effect of migration status on happiness once socioeconomic and demographic covariates are included, a result that aligns with prior work in cross-national migration (Engler et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Erlinghagen et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Consistent with expectations and prior meta-analytic evidence (Galicia-Hernandez et al., 2025), higher positive social integration is associated with greater happiness, whereas negative social capital is associated with lower happiness. However, migrants as a general category do not differ significantly from non-migrants on either of these mediators in our adjusted models. Accordingly, there is no evidence that migration (vs non-migration) affects happiness via these social integration pathways, and H3 is rejected. Echoing Nowok et al., (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), one plausible explanation is that migrants\u0026rsquo; happiness returns to a set-point over time, narrowing differences with non-migrants. Methodologically, the heterogeneity of the migrant category (various motives and opposing mediator pathways, see below) also speaks for disaggregating migration types when assessing psychosocial consequences.\u003c/p\u003e \u003cp\u003eComparing I-turn and U-turn migrants, mean happiness does not differ significantly, confirming H1. This indicates that differences lie not in the average level of well-being but in the pathways to well-being (and perhaps the type of well-being experienced). The mediation analysis reveals two notable opposing mechanisms: I-turn migrants report lower positive social integration than U migrants\u0026ndash;and because positive integration predicts happiness\u0026ndash;this pathway yields a negative indirect effect for I-turn. At the same time, I-turn migrants exhibit lower negative social capital than U-turn migrants\u0026ndash;and since negative social capital reduces happiness\u0026ndash;this produces a positive indirect effect. Both indirects are statistically significant and of opposing polarity, which explains the near-zero total effect in terms of happiness levels in the sample\u003csup\u003e4\u003c/sup\u003e and confirms H2. Consistent with prior international studies (Zheng et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), the indirect effects remain statistically significant after adjusting for a broad set of covariates, indicating the mediation effect of social integration is robust to those controls.\u003c/p\u003e \u003cp\u003eThe pattern we find is best characterized as \u0026ldquo;inconsistent\u0026rdquo; (or countervailing) mediation rather than simple full mediation or \u0026ldquo;suppression\u0026rdquo;. Mediating pathways that operate in opposite directions and thereby cancel at the level of the total effect point to a variant of an \u0026ldquo;inconsistent mediation model\u0026rdquo; (e.g., Kenny, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) as opposed to, e.g., the absence of a significant direct effect and the presence of significant indirect effects, which would suggest a \u0026ldquo;suppression\u0026rdquo; or \u0026ldquo;indirect-only\u0026rdquo; mediation (Zhao et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2010\u003c/span\u003e)\u003csup\u003e5\u003c/sup\u003e. The value of this finding lies in that it reveals how I-turn migrants and U-turn migrants can show similar happiness levels yet differ markedly in the social processes that produce that happiness. Distinguishing positive and negative facets of social integration is therefore essential: Focusing only on positive resources or on an undifferentiated \u0026ldquo;absence\u0026rdquo; of social capital would have obscured these opposing mechanisms.\u003c/p\u003e \u003cp\u003eOur findings resonate with Portes (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e1998\u003c/span\u003e) and later work (e.g., Villalonga-Olives \u0026amp; Kawachi, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) emphasizing that social ties can generate both resources and demands\u0026ndash;obligations, conformity, pressures, or exclusion\u0026ndash;that differentially affect well-being. Qualitative studies of return migration in Japan (e.g., Miserka, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) similarly describe the family- and community-embedded character of U-turns, which can supply communal resources but also impose burdens. In this respect, we find that communal social capital emerges as a plausible gatekeeper of successful return migration in our auxiliary full parallel SEM: it supplies belonging, reciprocity, trust, and local support that benefit some returnees while simultaneously carrying social obligations that may reduce autonomy or generate stress for others, perhaps functioning as a mediator of other social resources itself.\u003c/p\u003e \u003cp\u003eFinally, our results highlight more general measurement issues. Many studies of the social capital, social integration and well-being of migrants neglect dedicated negative scales other than unipolar measures that register \u0026ldquo;absence\u0026rdquo; rather than active harm. Including the negative social capital was crucial here for uncovering opposing mediation pathways. Likewise, our operationalization of the emotional outcome as affect balance with both positive and negative affect components better captures the nuanced emotional consequences of migration than positive-only metrics (see Huppert, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Keyes, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Ryff et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Zhao \u0026amp; Tay, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003e5.1 Limitations\u003c/h2\u003e \u003cp\u003eThe cross-sectional nature of the data precludes causal claims. Although migration history was measured retrospectively, psychosocial variables (such as social integration and well-being indicators) were measured at interview (i.e., after migration for most respondents), and some covariates in the model are stable attributes (e.g., extraversion, age, sex, children), we cannot rule out reverse causation or recall bias. In addition, self-reported measures raise the usual concerns about common-method variance and reporting bias.\u003c/p\u003e \u003cp\u003eSampling via an online commercial panel may have introduced selection bias. Although recruitment used age-based cutoffs for \u0026ldquo;offline\u0026rdquo; demographics, stratification by age and sex-and random sampling within strata, the sample is not a probability sample of the Japanese population. We were unable to weight cases to full population benchmarks because the required selection-frame parameters were unavailable.\u003c/p\u003e \u003cp\u003eOur operationalization of migration types trades precision for analytical tractability. Collapsing diverse migration motivations and trajectories into binary I/U-turn categories simplifies complex pathways and may attenuate or underestimate heterogeneity in effects on well-being such as those found in studies that also report urban-rural flows (e.g., Sasaki, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). While smaller, migration-specific subgroups could show stronger mediation patterns, they were infeasible given the current sample size. Thus, in our present coding of migration types, we privilege the presence of pre-existing ties in migrants\u0026rsquo; destinations and the resulting relationality/autonomy over the geospatial characteristics of their origin or destination.\u003c/p\u003e \u003cp\u003eThe model fit limitations merit some caution. Comparative fit was suboptimal for some specifications, suggesting imperfect approximation of the covariance structure (e.g., small Factor 1 mediator loadings). Consequently, weaker parameter estimates should be interpreted cautiously. To address this, we present sensitivity checks (bootstrap stability) and alternative model specifications (parallel manifest SEM) in the Appendix. These robustness analyses identify which results are stable across specifications.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec23\" class=\"Section2\"\u003e \u003ch2\u003e5.2 Conclusion\u003c/h2\u003e \u003cp\u003eTo our knowledge, this study is the first to empirically examine the social costs and benefits of common internal migration patterns in Japan with regards to their mediation of migrants\u0026rsquo; well-being. We document an inconsistent (countervailing) mediation effect of positive and negative aspects of social integration on happiness between I-turn and U-turn migrants: I-turn migrants show lower positive social integration (which reduces happiness) but also lower negative social capital (which increases happiness), with both effects largely canceling each other out at the level of the total effect, explaining similar happiness levels among I-turn and U-turn migrants. This pattern was not repeated for general migration. The finding was robust to a broad set of covariates and could only be clarified through our use of both positive and negative facets of social capital instead of one-dimensional or unipolar indicators.\u003c/p\u003e \u003cp\u003eThe results contribute to debates about the dualistic nature of social ties (Portes, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e1998\u003c/span\u003e; Villalonga-Olives \u0026amp; Kawachi, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The findings also extend recent qualitative work on Japanese internal migration (Miserka, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) and add to quantitative international studies on internal (Kopmann \u0026amp; Rehdanz, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Zheng et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) and international migration (Johnson et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) that previously identified a mediating role of social integration on well-being by showing how communal resources and obligations operate as opposing forces for different types of migration. Practically, the findings suggest that revitalization policies or interventions that aim to support migrants\u0026rsquo; well-being should equally be attentive to potential burdens and obligations that can accompany close local ties than to strengthen positive communal support only.\u003c/p\u003e \u003cp\u003eFuture research should (a) test these mechanisms in longitudinal data to establish causal direction, (b) expand the range of well-being outcomes (short- vs long-term affect, domain-specific well-being, and distress), and (c) use larger, geographically representative samples so that geospatial characteristics and finer migration motives can be modelled without aggregation.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eCompliance with Ethical Standards\u003c/h2\u003e \u003cp\u003eClinical trial number: not applicable.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eEthics Approval\u003c/strong\u003e \u003cp\u003eAs the present study is non-interventional, no ethical approval was required at the time of data collection.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eInformed Consent\u003c/strong\u003e \u003cp\u003eIn order to ensure informed consent, participants were informed that their participation was voluntary and retractable at any time. They received written information about the research purpose, procedures, data use and confidentiality protocols prior to the start of the survey. All respondents provided informed consent. Personal identifiers were anonymized at the point of collection and data was managed according to departmental data management guidelines. All participants received modest reimbursement for their time.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis work was supported by [ANONYMOUS] and, in part, [ANONYMOUS].\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eI am the sole author of the manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eI want to thank my colleague Stefan Hundsdorfer (University of Vienna, Department of Sociology) for his collaboration with regard to the survey design.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe data that provides the foundation for this study is presently under license at the Department of East Asian Studies at the University of Vienna and can only be made available with the permission of the department via a license agreement.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBaron, R. M., \u0026amp; Kenny, D. A. (1986). 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Evidence From Internal Migrants in China. \u003cem\u003eFrontiers in Public Health\u003c/em\u003e, \u003cem\u003e10\u003c/em\u003e. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fpubh.2022.913553\u003c/span\u003e\u003cspan address=\"10.3389/fpubh.2022.913553\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZiersch, A. M., Baum, F., Darmawan, I. G. N., Kavanagh, A. M., \u0026amp; Bentley, R. J. (2009). Social Capital and Health in Rural and Urban Communities in South Australia. \u003cem\u003eAustralian and New Zealand Journal of Public Health\u003c/em\u003e, \u003cem\u003e33\u003c/em\u003e(1), 7\u0026ndash;16. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/j.1753-6405.2009.00332.x\u003c/span\u003e\u003cspan address=\"10.1111/j.1753-6405.2009.00332.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Footnotes","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003e Operationalized as \u0026ldquo;Please indicate about how many people do you have contact with on a typical weekday irrespective of whether you know them or not. Include anyone you chat with, talk to, or text, either face-to-face, by phone, internet or any other communication device.\u0026rdquo;\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e Operationalized as \u0026ldquo;How often do you meet with relatives who do not live with you? at least once a week, weekly, at least once a month, monthly, at least once a year, never\u0026rdquo;\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e The value-expectancy theory states that migration decisions are indirectly affected by individual and household factors, specifically demographic and socioeconomic variables, as well as social and cultural norms, personality factors and the opportunity structure.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e I-turn migrants show similar happiness levels (M\u0026thinsp;=\u0026thinsp;6.31, SD\u0026thinsp;=\u0026thinsp;2.19) to U-turn migrants (M\u0026thinsp;=\u0026thinsp;6.24, SD\u0026thinsp;=\u0026thinsp;2.14) with no significant difference (-0.07, SE\u0026thinsp;=\u0026thinsp;.12, p\u0026thinsp;=\u0026thinsp;.533).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e Both these types of mediation traditionally were not considered \u0026ldquo;proper\u0026rdquo; mediation effects (Baron \u0026amp; Kenny, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1986\u003c/span\u003e), however in recent years they have become increasingly accepted as variants of mediation that have proven their value in uncovering otherwise invisible associations (Rucker et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Zhao et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2010\u003c/span\u003e).\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":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-8376986/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8376986/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eWhile the psychosocial consequences of migration have been studied at large in the context of international migration, regionally specific movements\u0026ndash;such as the U-turn (return migration to place of origin) and I-turn (unidirectional migration to places where migrants have no personal ties) migration patterns characteristic of internal mobility in Japan\u0026ndash;have received surprisingly little attention.\u003c/p\u003e \u003cp\u003eWe collected data from a nationwide online survey (n\u0026thinsp;=\u0026thinsp;2,500) and studied how past migration behavior relates to self-reported social integration and happiness in the present. Using factor-based mediation models, we show that I-turn and U-turn migrants reach similar average levels of happiness, but do so via opposing social integration pathways: I-turn migrants exhibit lower positive social integration (reducing happiness) but also lower negative social capital (increasing happiness), whereas U-turn migrants show the reverse pattern. These countervailing indirect effects largely cancel each other out at the level of the total effect of happiness, underscoring the importance of measuring both positive and negative facets of social integration when evaluating the well-being consequences of internal migration patterns. We discuss implications for theory in terms of internal migration movements, social integration and negative social capital research, and for policy with regards to integration and revitalization programs in contemporary Japan.\u003c/p\u003e","manuscriptTitle":"The Costs and Benefits of Internal Migration in Japan: How Social Integration Mediates Migrant Happiness","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-23 10:52:12","doi":"10.21203/rs.3.rs-8376986/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"4d4332aa-57d4-46c8-a636-3f2d7fd20fed","owner":[],"postedDate":"February 23rd, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-03-05T22:24:00+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-23 10:52:12","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8376986","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8376986","identity":"rs-8376986","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}