Discussion Paper Series

University of Tokyo

Institute of Social Science
Panel Survey      !"""

" #*,)& &.#)(-"#* .1( ('*&)3'(.
( )#& -)&.#)( )(!#./#(& **,)" -#(! ." *(- # )/,- (& /,03

($# -"#5" (#0,-#.3 ) )%3)6
+/- &-5(#0,-#.4 &#,  ,/2&&- (#0,-#.3 ) ',#!6

March 2020
No.120  
INSTITUTE OF SOCIAL SCIENCE UNIVERSITY OF TOKYOInstitute of Social Sciences
University of Tokyo
Panel Survey Project Discussion Paper Series No. 120
March 2020

The Reciprocal Relationship between Unemployment and Social Isolation:
A Longitudinal Approach Using the Japanese Life Course Panel Survey
Kenji Ishida(University of Tokyo)
Jacques Wels
(Université libre de Bruxelles & University of Cambridge)

This research was supported by KAKENHI Grant-in-Aid for Specially Promoted
Research (Grant Numbers 25000001 and 18H05204) and Scientific Research (S) (Grant
Numbers 18103003 and 22223005) from the Japan Society for the Promotion of Science(JSPS). The research support in conducting the panel surveys was obtained from theInstitute of Social Science, University of Tokyo, and The Outsourcing, Inc. The
permission to use the panel data is obtained from the Management Committee of theJapanese Life Course Panel Surveys. Jacques Wels, an author of this paper, used the JLPS
datasets (PY090 and PM090) disseminated by Social Science Japan Data Archive(SSJDA), Center for Social Research and Data Archives, Institute of Social Science,
University of Tokyo.

The Reciprocal Relationship between Unemployment and Social Isolation:
A Longitudinal Approach Using the Japanese Life Course Panel Survey
Kenji Ishida(University of Tokyo)
Jacques Wels
(Université libre de Bruxelles & University of Cambridge)
Abstract
Even though Japan has lower unemployment rates than other industrial societies, it has
particularly increased for young and middle-aged people over the two last decades.
Similarly, social isolation is a problem in Japan and is thought to be a potential cause ofunemployment. The present study uses waves 1 (2007), 3 (2009) and 5 (2011) from theJapanese Life Course Panel Survey, a nationally representative data for Japanese people
aged 20 to 40. We perform a cross-lagged panel model with and without random
intercepts and control a set of socio-economic covariates. Results show that the seeming
reciprocal relationship between unemployment and social isolation is spurious after
controlling for covariates at an earlier life stage and random intercepts implying betweenindividual inherent traits. We conclude that it is challenging to overcome the initial socialand economic disadvantages in contemporary Japanese society.

1

1.

Background

Japan is a country where unemployment rate is relatively low compared with otherindustrial countries, e.g. among OECD members. According to the Labour Force Survey
produced by the Japanese government (which is the source of most OECD employmentrelated statistics in Japan), only 2.4% of the labour force was unemployed in 2019. This
figure was much lower than the OECD average, which is about 6% for the same year.
In the Japanese context, however, the seemingly low unemployment rate should
be regarded with caution, for at least two reasons. Firstly, Japanese unemployment rates
have risen substantially during the long-term recession that started in the early 1990s,
when Japan faced the late 1980s bursting of the bubble economy. The unemployment rate
was around 5% in the 2000s and at least about three times higher than during the periodof rapid economic growth, in the 1960s. Secondly, for young and middle-aged Japanese
workers today, unemployment is likely to occur in their career trajectories. Younger
workers are more likely to be unemployed than older ones. According to the labour force
survey, for example, unemployment rates reached about 9% for the 20-24 and 7% for 2529 aged workers in the 2000s. It is slightly higher for those in their 30s but near the
average.
Social isolation is also a problem that receives much attention in Japan. The 2018
Comprehensive Survey of Living Conditions by the Ministry of Health, Labour, andWelfare (MHLW) showed that 4.4% of the 20-44 did not have any contact to ask help
about things that worry them. Meanwhile, such a percentage was 4.9% for those who
were 45 years old or more. Concerning the social supports of any kind, however, there
are differences between young and older individuals. The same 2018 MHLW survey
reported the distribution of what bothered respondents the most. Young and middle-aged
respondents aged from 20 to 44 at the time of the survey mentioned their work as the top
concern, with 33% of them. It was 16% for the older people, but health related issues forthemselves and their families were the things that bothered them the most. These figuressuggest that social support that young and middle-aged people need is primarily about
work and that they may not obtain such support due to social isolation.
These figures, flowing from government surveys on unemployment and social
isolation, suggest that the two aspects should be somehow interrelated. However, how
unemployment affects isolation (and vice versa) and why such a relationship occurs is notentirely obvious. This study primarily aims to examine the existence of the vicious cycle.

2

2.

Previous Studies

The Impact of Unemployment and Social Isolation
Written in 1933, "Marienthal: The sociography of an unemployed community"(Lazarsfeld, Jahoda, and Zeisel, 1982) was one of the first pieces of work describing thesocial consequences of unemployment among which loneliness, isolation, hopelessness,and passivity were the most salient features. Almost one century later, the study of thesmall community of Marienthal is still accurate, demonstrating the universality of job loss
(Lobo, 2018) and its effects on social integration.
Unemployment goes beyond the loss of an income, and non-pecuniary effects of
unemployment are "much larger than the effect that stems from the associated loss of
income" (Winkelmann and Winkelmann, 1998). Social isolation remains a major social
issue, and a considerable number of researchers have carried out studies to assess to what
extent unemployment is associated with social isolation. For instance, analysing therelationship between unemployment and social participation in 26 countries andattempting to identify the role of national policies and attitudes as possible mediators,
Dieckhoff and Gash (2015) demonstrate that the unemployed are likely to be less sociallyengaged compared with employed respondents. The authors particularly underline therole played by poverty risks associated with unemployment in explaining social isolation:in countries where unemployment is associated with higher poverty risks, unemployedpeople have a lower social participation rate. The importance of such structural factors is
also demonstrated by Hammer (Hammer, 2000) in a study investigating the processes thatlead to the integration or social exclusion of unemployed youth comparing Denmark,Finland, Iceland, and Norway. The author finds that Danish youth unemployment had abetter economy, felt less isolated, and reported fewer mental health issues thanunemployment youth in the other selected countries.
Nevertheless, social isolation can also be a process, not only the outcome of aprocess (Silver, 2012). To better understand such a process, a longitudinal approach is
required. Using a longitudinal panel study to demonstrate the association between
unemployment and social support, Atkinson, Liem, and Liem (1986) have found thatunemployment is associated with a decline in spousal support, quality of the marital
relation and a decrease in the frequency of contact with network members for blue-collar
workers. The study demonstrates that unemployment affects marital and family support
mainly through the husband's psychological well-being. Meanwhile, social isolation
created by involuntary job loss is moderated by other factors, among which the area ofresidence might play a role. For instance, a higher level of social support is evidenced by3

rural unemployment compared with urban unemployment (Gore, 1978). Looking at
marriage and parenthood, Klamijn (2012) shows using data from the Swiss Household
Panel that marriage does not affect weak ties but affects the nature of stronger ties,
parenthood is associated with more local relationships and divorce, and widowhood is
positively associated with contact and support. The study finds that unemployment isassociated with a reduced number of neighbours in the network with an increase in thenumber of friends.
The Reciprocal Relationship between Unemployment and Social Isolation
On the other side, much attention has been given to the role played by social
isolation and, more specifically, to the lack of social network, in explaining
unemployment. Inspired by Granovetter's weak-tie hypothesis (Granovetter, 1973),
Montgomery (1992) has demonstrated that the weak-tie implies an association between
network composition and a job seeker's minimum acceptable wage. Although, the weaktie is rarely associated with higher expected wages. His findings show that, on the one
hand, one should focus on the structure of the job seekers' network and, on the other hand,that social networks could have more potent effects on non-pecuniary aspects of work.
Using the 2001 International Social Survey Programme on social relations andsupport systems, Franzen and Hangartner (2006) show that the use of social networks infinding a job does not increase the monetary pay-off. Though, looking at a sample of
8,000 Swiss university graduates, they demonstrate that informal job-search channels are
beneficial for important non-monetary job characteristics such as jobs that are better
linked to educational degrees and better career perspectives. However, studies looking at
the reciprocal relationship between social isolation and unemployment are few. The
nature of the network also plays a role. Looking at unemployed people in Great Britain,
Sousounis and Lanot (Sousounis and Lanot, 2018) found that the probability of finding
work increases as the number of employed (versus unemployed) increases.
Comparing European countries using longitudinal and cross-sectional data fromthe European Community Household Panel, Gallie, Paugam and Jacobs have shown thatsocial exclusion "implies that there is a downward spiral in which labour market
marginality leads to poverty and social isolation, which in turn reinforces the risk of longterm unemployment" (Gallie, Paugam, and Jacobs, 2003). In the study, the authors firstlook at the impact unemployment could have on poverty risk and social isolation and thenlook at whether poverty and social isolation could affect the length of time before leaving
unemployment. Results show that there is a vicious circle of exclusion in whichunemployment reinforces poverty and, in return, poverty heightens long-term

4unemployment risks. However, the article does not find significant effects of
unemployment on social isolation or vice versa; according to the authors, social isolation
is much related to household structure and local sociability that vary across countries. The
article concludes that poverty remains the main challenge associated with unemployment.
Some other studies establish a link between social isolation and unemployment. For
instance, using qualitative surveys, Lindsay (2010) shows that long-term unemployedpeople in two areas of high unemployment in Glasgow struggle to access social networks
for a job search; long-term unemployment is associated with a general erosion of social
relations and communities.
Social Isolation and Unemployment in Japan
Japanese scholars have also been interested in looking at the association between
these two aspects. Such an interest has its origins in employment problems, especiallyamong young people, which have gain in intensity after the 1990s bubble burst which led
Japan to a prolonged recession, and reduced employment opportunities for the youngest
generations. In the 2000s, the problem of youth employment remained unsolved, and the
proportion of young unemployed people – known as NEET (Not in Education,
Employment, or Training) – has reached a size that is not negligible.
In exploring the background of the young unemployed, some discussions haveemerged that shed light on social relationships. A pioneering work by Genda andMaganuma (2004) pointed out that the young NEET were not confident with
communicating with others. Lack of social skills and adjustment to the work environment
could explain difficulties in finding a job. Given that isolated people are likely to lose
opportunities for social skill formation, social isolation may cause unemployment, andvice versa.
As long as the Japanese system for youth workers entering the labour market
worked well, social isolation and lack of social skills were not necessarily problematic.
Before the long-term recession, young people were able to start their first job as regular
employees soon after their school graduation (Ishida, 2005). Under such a configuration,
the impact of social networks - that connect people to various others (Granovetter, 1995)- was small, particularly because the central issue was career development within thecorporate organization. The changes that occurred in the 1990s, including lessstraightforward pathways to employment for those entering the labour market just after
graduation have increased the importance to be part of social network and to develop
social skills. In other words, social relationships have become more crucial for young
people's careers, particularly given the changing nature of the transition to employment

5

(Brinton, 2010).
Social networks can be one of the social skill-building sources for careers, butalso contribute more directly to the acquisition of job opportunities. In particular, strong
ties enable job seekers to obtain more information beneficial for job hunting in theJapanese labour market (Watanabe, 1991). Also, family and kin networks - a typical kindof strong ties - increase one's chance to get a regular job in case of involuntary job
turnover (Ishida, 2009).
Though social networks possibly play a role in finding job offers, unemployed
Japanese people, who would actually benefit the most from network resources, are in apoor situation about them. A recent analysis demonstrates that the joblessness is highly
associated with loss in social support (Nagayoshi, 2017). This validates what was foundin another study explicitly focusing on the youth unemployed: young unemployed tend
to have few social contacts (Hori, 2005).
3.

Empirical Issues and Hypotheses of the Study

The findings of previous studies in Japan and western societies suggest thatunemployment may increase the risk of social isolation and vice versa. While thisscholastic argument implies that there would be a reciprocal relationship between
unemployment and social isolation, such interdependency complicates the specificationof each path. Because the effect on one side possibly affects the other, misleading results
may occur if both effects are not identified simultaneously. None of the prior research has
yet tried to examine the reciprocal relationship between these two dimensions. It is oneof the original contributions of the present study.
Another original point of view of this study is to focus on the potential states of
unemployment and social isolation. In terms of robustly estimating these effects, thecorrelation of the latent traits of unemployment and isolation seems to be just a source ofthe simultaneous bias. However, which aspects are more critical in explaining theobserved reciprocal association is itself a question that is worth considering. If thecorrelation of the latent variables of interest is more determining than the effect of eachone, the policy implication for intervening the vicious cycle will be different from the one
in the case that the interdependent effects are decisive. The more considerable impact ofthe potential traits than the bi-directional effects implies a necessity of some policy
supports for the disadvantaged people at an earlier stage in the life course. Accordingly,
it is necessary to distinguish between- and within-individual differences and/or changes.
Based on these research concerns, the present study aims to examine the following two

6

hypotheses.
The first hypothesis is that there could be a ‘vicious cycle of unemployment andsocial isolation’ in which unemployment reinforces social isolation, and social isolation
reduces job opportunities. To test for such a hypothesis, we use a cross-lagged model thatlooks at the reciprocal relation between unemployment and social isolation.
The second hypothesis refers to social and life course background factors that
could affect both unemployment and social isolation. If it is possible to hold thishypothesis, the seeming vicious cycle comes from the individual difference in thepropensities of both unemployment and social isolation. Presuming the second hypothesis,
the cross-lagged effect in each endogenous variable would decrease after considering theindividual variations.
4.

Data and Method

Data
Data come from the Japanese Life Course Panel Surveys (JLPS). JLPS conducted its firstwave (Wave 1) in 2007 and has yearly follow-up surveys of the primary sample with aquestionnaire design that allows us to trace changes in social statuses and attitudes toward
a wide range of topics. The respondents stemmed from a stratified two-stage random
sampling design, and the sample was representative of the target population in Japan atfirst wave (Miwa, 2008). JLPS has two different samples, a youth and a middle-aged
sample (JLPS-Y and JLPS-M). The former consists of respondents aged 20 to 34 years
old in 2007. The respondents in the middle-aged sample were aged 35 to 40 years old.
These two samples have the same questionnaire, and it is entirely possible to analyse thetwo datasets together.
The response rates in JLPS-Y and JLPS-M at Wave 1 are 34.5% and 40.4%,
respectively. Those at Wave 2 (2008) and in the subsequent waves are around 80%. Given
the fact that the information about the degree of isolation from social support is available
in survey waves of odd numbers, we utilize the first, third, and fifth waves in this study.
We analyse the male and female samples separately and restrict the baseline sample only
to those who responded in all waves from Wave 1 to Wave 5. We also exclude students.
The numbers of cases for the male and female samples are respectively 957 and 1,269.
Variables
Endogenous Variables
In this study, we primarily focus on the following two endogenous variables:7

unemployment and the degree of isolation from social support networks. They are
endogenous in the sense that their states at the last time of the survey wave have effects
on the later ones each other.
The unemployment variable is defined as the employment status at each wave.
In this study, unemployment is defined as joblessness, i.e., regardless of the intention ofworking or any job search behaviour. Students are omitted from the analytical sample and,given the age-groups selected at the baseline (i.e., 20-40 years old), it does not containany retired respondents. By doing so, we use an extended definition of unemploymentthat does not only integrates respondents perceiving unemployment benefits only. We use
the employment status as a binary variable distinguishing those who are unemployed fromthose working at the time of the survey (reference category).
The other dependent variable is the degree of isolation from social support
networks. JLPS contains a social support module in waves 1, 3, and 5. It asks the
respondents to whom they would ask a favour about these four topics; (1) work or study,(2) job recommendation, (3) relationship issues with friends and spouse or partner, and(4) borrowing a significant amount of money. For each item, respondents can choose ‘noone’ in the case nobody could help with those issues. We regard the answer modality ‘noone in each item as the highest degree of isolation from social support networks.
Therefore, the variable of the social isolation ranges from 0 (the lowest degree) to 4 (thehighest degree).
There two ways to use this variable. One could use it as a continuous variable
containing five answer modalities. It can also be possible to use the variable as a binary
variable distinguishing those who benefit from some kind of social support (0-3) fromthose who are socially isolated in all aspects (4). We test both options in the present study
and confirmed similar results. This article only shows results for the continuous variable.
One limitation of the isolation variable is that those who do not need to ask anyone tohelp could choose the answer modality ‘no one’ because they do not need any support forthese matters. As Kalmijn (2012) points out, social contacts and supports not only comefrom social statuses but are also related to the need to contact someone or to get specificsupports. Therefore, a higher value within the social isolation scale could also indicatethat the respondent can easily afford anything by him- or herself. However, as his analysis
implies as well (Kalmijn 2012), it is an empirical matter what the seeming isolation meansfor each person. We will interpret associations of the social isolation degree andbackground variables from standpoints of both opportunity and necessity in the following
analyses.

8

Covariates
Concerning the second hypothesis in this article, we aim to demonstrate how theinitial life course states affect the individual-level variations on the degrees of risks ofunemployment and social isolation. Here, we explicitly focus on age, social background,respondents’ initial status attainment, household situations, and the past life course
experiences.
Because the age of the respondents ranges from 20 to 40 at the baseline (Wave1), it is necessary to control the effect of different life stages. To do so, we include age at
Wave 1 as a covariate explaining random variances of unemployment and social isolation
among individuals.
Social background is another major exogenous variable. In this paper, we use thefather’s occupational status as a marker of social origin. At Wave 1, the JLPS asked
respondents their father’s employment status, occupation, and the size of the firm they
worked when the respondents were 15 years old. The father’s status variable is composedof seven modalities; (1) professional, (2) managerial, (3) non-manual in the large-sized
firm, (4) self-employed, (5) other occupational statuses, (6) unemployed or no father atthat time, and (7) Do not know or Not answered (DK).
We also investigate the association between the early status attainment and latentrisks of the endogenous variables. We particularly pay attention to two kinds of variables:
the educational status and the first job. The educational status at Wave 1 includes threefolds: secondary (junior and senior high schools), post-secondary (technical college andtwo-year college), and tertiary (undergraduate and graduate degrees) education.
Respondents’ first job almost has the same structure as the father’s status, but managerial
and professional positions are combined because few respondents held a managerial
position at the first job.
The study also spots the household situation. It controls the total householdincome, the marital status, whether the respondent has a child or children at Wave 1. The
household income has six categories; (1) 150 million JPY or less, (2) 150-350 million
JPY, (3) 350-600 million JPY, (4) 600-850 million JPY, (5) 850 million JPY or more, (6)Do not know or Not answered (DK).
The marital status should be understood both as a social position and as a sourceof social networks. In Japan, under the male-breadwinner model, having a spouse will
make men work, but female quit a job (Zhou, 2015). The marital status also relates tosocial support, because one can expand family and kin networks through the spouse(Kalmijn, 2012). For a similar reason, having a child will increase social support ties with
one’s needs or opportunity for connecting with other parents’ networks (Kalmijn, 2012).

9

The last type of covariates is the respondents’ past life course experiences. In thispaper, we use three variables, that are: experience(s) of crime victimization, experience(s)with any severe disease, and home atmosphere at 15 years old. While its effect isambiguous, exposure to crime is associated with socioemotional difficulties (Saegert andWinkel, 2014). This item is dichotomous and coded ‘1’ if one has been subject of crime
victimization and ‘0’ if not. Also, mental or physical health condition relates to thepropensity of employment (Pacheco, Page, and Webber, 2014), and this study utilizes thepast severe disease experience as an indicator of the health condition of an earlier life
course; this variable is a binary variable as well as the exposure to crime. Finally, we use
the home atmosphere at 15 years old as a circumstance of socialization because parenting
has an impact on children’s socio-emotional development (Shaffer, Suveg, Thomassin,
and Bradbury, 2012). This item has a four-point ordinal scale, from one to four, in whicha higher value indicates a warmer home environment at 15 years old.
Cross-lagged Panel Model
The present study uses a cross-lagged panel model to assess the interdependency
between unemployment and social isolation. With lagged endogenous variables, it ispossible to distinguish both the effect of unemployment on social isolation and the inverse
relationship from the entire association between them. Its methodological feature isappealing for social scientists who are interested in the dynamic process of social
phenomena. Especially in the life course research and development studies, scholars haveoften applied this model to various topics (for instance, see Lindwall, Larsman, andHagger (2011), and Yao and Zhong (2014) for some recent application of the cross-lagged
panel method).
In line with the advancement of research, however, there are somemethodological concerns about the standard cross-lagged panel model (Hamaker, Kuiper,
and Grasman, 2015). One issue is on the individual differences in endogenous variables.
In the standard approach, we assume the mean structure of endogenous variables of
constant. However, there can be differences among individuals. Unless distinguishing the
between-individual variances and the within-individual ones, we have a high risk ofobtaining inappropriate estimates (Mund and Nestlers 2019).
When ignoring the individual variation, it is impossible to investigate how it
emerges. Referring to the social background hypothesis in the present study, the betweenindividual differences in unemployment and social isolation may come from previous life
course situations. To test this thesis, we must consider the latent structure of endogenous
variables, which consists of their variances and covariance (Hamaker, Kuiper, and10

Grasman, 2015).
The cross-lagged panel model with random intercepts of endogenous variables
is an alternative for the standard approach. By considering the random variances of
unemployment and social isolation, and their covariance, we can estimate the crosslagged and autoregressive effects more accurately and examine the associations between
the time-invariant factors and random variances.
Another issue to be discussed is the covariance structure of residuals among
endogenous variables. The standard cross-lagged panel model controls the simultaneous
relationship between the residuals of dependent variables. Meanwhile, there are possiblyserial correlations between variables observed at different time points. In general
viewpoints from regression analysis, using a lagged dependent variable can cause a bias
stemming from a serial correlation of residuals (Schuurman et al., 2016). Due to thisconcern, there is an argument that the standard cross-lagged model would not beappropriate for investigating the dynamic relationship (Schuurman et al., 2016).
One option is to control the residual covariances of endogenous variables within
the cross-lagged panel model in order to consider these technical issues. In this paper, weestimate four types of residual covariance at the same time; (1) unemployment at t andsocial isolation at t+2, (2) social isolation at t and social isolation at t+2, (3)unemployment at t and t+2, (4) social isolation at t and t+2. It is possible to control thesecovariates with the structural equation model (SEM).

Figure 1. Study design (Cross-Lagged Panel Models)In this paper, we carry out the two types of the cross-lagged model, as shown in
Figure 1. Firstly, we perform the model without random intercepts of unemployment andsocial isolation (Model 1), which means the standard (classical) cross-lagged modeling.
In Model 1, we make the cross-lagged and autoregressive effects equal across survey

11waves. The characters “a,” “b,” “c,” and “d” in Figure 1 refer to the equality constraints.
We control the simultaneous and serial correlations of residuals in Model 1 and set theequality constraints among them across waves (“e,” “f,” “g,” “h,” and “i” in Figure 1).
Model 1 also considers the effects of the background covariates on the endogenous
variables at each wave. The letters, “j” and “k” means that the effects of covariates areequal through waves. “r” in the path diagrams refers to the residual term.
In Model 2, we add random intercepts of unemployment and social isolation to theModel 11. After investigating the model only with random intercepts, we include timeinvariant variables as background factors to explain the between-individual variations of
unemployment and social isolation. We perform these models by gender. Also, we use
robust standard errors for each parameter in the following analyses2.
Adjusting for Attrition EffectIn addition to the empirical model, a sample bias caused by the attrition process is
a serious problem when analyzing longitudinal data like JLPS. Attrition occurs in almost
all cases of longitudinal studies. We do not have to care about the attrition when it occurs
randomly. However, those who exit the panel often arise in some specific characteristics
and situations. Furthermore, we use a balanced sample, but it is difficult to assume thatwhether one can respond to all waves or not is random in this study. Without balancing
the propensity of remaining in or dropping out the panel among respondents, we may failto obtain accurate estimates in case that the propensity of one’s retention confounds
relationships among relevant variables.
There is no perfect measure to correct the attrition bias, but we need to consider it
as much as possible. In this paper, we run a logistic regression model on one’s retention.
The dependent variable took 1 when one responded in all five waves from Wave 1 to
Wave 5, and 0 otherwise. In the logistic regression model, we use 79 independent
variables and omit students at Wave 1 from the sample. With the inverse probability
Endogenous variables can change with time, and it is possible to estimate random
slopes of the time effect in the structural equation modeling (Mund and Nestler 2019).
In this paper, it was impossible to estimate models including random slope variances
due to non-convergence. However, authors did not regard this problem as a serious one
because they did not see any systematic change in endogenous variables over theselected sequence, as Table 4 and Figure 2 imply.
2 Because the unemployment variable is dichotomous, it is difficult to assume that its
error follows the normality and homoscedasticity. Using the nonlinear SEM(generalized SEM) is another approach, but estimation is more complex and unstablein the present study. We cannot deal with the simultaneous and serial correlation
among residuals, either. Authors recognize the methodological limitation in this paper
but rely on using the robust standard error as the second best solution (Savalei 2014).
1

12

weighting approach, weights for adjusting the propensity of retention are constructed forthe male and female samples based on the result of the logit model.
5.

Results

Descriptive Statistics
Adjusting Propensity for Retention in the Panel
Table 1 shows the result of the logistic regression models on the retention performed bygender. 50% of male respondents and 30% of female ones have dropped out of the sample
for the five years since Wave 1.
Table 1. Attrition and Retention of the Respondents in the JLPS dataset

Results of the logit model indicate tolerable performances. The logistic
regression model classified 68.39% of the male respondents and 71.93% of the female
ones. Also, we calculated the c-statistics (concordance statistics) for both results. Cstatistics is one of the indicators which evaluate the model fits of logistic regression
models (Hanley and McNeil 1982; Westreich et al. 2011). It ranges from 0.5 to the
maximum one of 1. The c-statistics of the male and female samples are respectively
0.75 and 0.76. These results indicate the intermediary levels about predicting thepropensity of retention or attrition.
Table 2 provides descriptive statistics for the time-invariant variables of interestof the unweighted and weighted samples. Unweighted samples are considerably deviant
from the ones which also include the cases of attrition. However, the data with inverse
probability weight for retention shows no statistically significant difference. In line withthis result, we perform the following cross-lagged models with the weighted data.

13

Table 2. Descriptive Statistics of Relevant Variables
Unweighted sample
Male
Mean/Proportion

Weighted sample
FemaleS.D.

Mean/Proportion

Male
S.D.

Mean/Proportion

FemaleS.D.

Mean/Proportion

S.D.

Unemployment at t

0.048

0.214

0.292

0.455

0.054

0.227

0.266

0.442

Degree of isolation from support at t

0.737

1.006

0.556

0.742

0.755

1.029

0.554

0.766

Age at Wave1

33.1374.942

32.8835.336

32.3965.266

32.425

5.497

Professional

0.107

0.309

0.091

0.287

0.092

0.289

0.085

0.279

Managerial

0.07

0.255

0.087

0.283

0.091

0.288

0.087

0.282

Non-manual in large-sized firm

0.079

0.271

0.076

0.265

0.068

0.252

0.072

0.259

Self-employed

0.258

0.438

0.246

0.431

0.245

0.431

0.242

0.428

Unemployed/No father

0.041

0.198

0.038

0.191

0.043

0.204

0.039

0.194

DK

0.01

0.102

0.017

0.131

0.012

0.107

0.017

0.131

Secondary

0.313

0.464

0.288

0.453

0.328

0.47

0.309

0.462

Undergraduate or more

0.452

0.498

0.249

0.433

0.443

0.497

0.234

0.424

Father's status at 15 years old(Ref: Other occupations)

Educational status at Wave1(Ref: Post secondary)First job (Ref: Other occupations)
Professional/Managerial

0.213

0.41

0.214

0.411

0.197

0.398

0.212

0.409

Non-manual in large-sized firm

0.082

0.274

0.183

0.387

0.07

0.255

0.165

0.372

Self-employed

0.027

0.163

0.009

0.097

0.031

0.173

0.008

0.091

DK

0.077

0.267

0.073

0.261

0.073

0.261

0.077

0.267

150 million JPY or less

0.016

0.124

0.02

0.139

0.017

0.13

0.02

0.142

350-600 million JPY

0.285

0.452

0.287

0.452

0.28

0.449

0.254

0.435

600-850 million JPY

0.19

0.393

0.139

0.346

0.17

0.376

0.141

0.348

850 million JPY or more

0.162

0.369

0.158

0.365

0.144

0.352

0.159

0.365

DK

0.23

0.421

0.281

0.449

0.269

0.444

0.301

0.459

Unmarried

0.442

0.497

0.389

0.488

0.491

0.5

0.422

0.494

Divorce/Widowed

0.021

0.143

0.039

0.195

0.03

0.171

0.039

0.193

Having children at Wave1

0.441

0.497

0.51

0.5

0.404

0.491

0.476

0.5

Crime Victimization Experience

0.053

0.225

0.042

0.2

0.059

0.236

0.043

0.203

Experience having got a heavy disease

0.241

0.428

0.199

0.4

0.232

0.422

0.199

0.4

Home atmosphere at 15 years old

3.09

0.805

3.108

0.831

3.074

0.843

3.1

0.842

Household income (Ref: 150-350 million JPY)

Marital status at Wave1 (Ref: Married)

Table 3. Unemployment Rate and Average of Degree of Isolation from Social Support
Networks each Wave

Table 3 provides some descriptive figures about unemployment rates in Wave 1,
3, and 5 and the degree of complete social isolation by the type of social need. About five
percent of the male sample was unemployed across the three waves against about twentyfive percent within the female sample. It appears that the most frequently cited item ofsocial isolation is about asking for job introduction: 40% of the female sample and 36%of the male sample declared not having any kind of support for such a matter.

14

Estimates from Cross-lagged Panel Models
Results from the cross-lagged panel models are shown in Tables 4 (for men) and
5 (for women). Each table exhibits the results for the different models mentioned above.
Model 1a looks at the cross-lagged association between unemployment and social
isolation without considering the effects of covariates and without random intercepts.
Model 1b controls the set of covariates but does not include random intercepts. Model 2adoes not control the covariates but includes random intercepts. Finally, Model 2b accounts
for both the set of covariates and the random intercepts.
Table 4. Cross-lagged Panel Models in the Male Sample (n = 957)
Model 1aModel 1bModel 2aModel 2b

Uemp. atIsolation

Uemp. atIsolation

Uemp. atIsolation

Uemp. att+2

at t+2t+2

at t+2t+2

at t+2t+2

at t+2

Exogenous variables

Coef.

Coef.

Coef.

Coef.

Coef.

Coef.

Coef.

Coef.

Unemployment at t

0.411 **

0.665 ***

0.278

0.366 †

0.533

2.246

-0.446-0.181Degree of isolation from support at t

0.024 **

0.663 ***

0.013 †

0.629 ***

0.005

0.056

-0.015

0.127

Unemp. at

Isolation at

Unemp. at

Isolation ateach wave

each wave

each wave

each wave

L(Unemp.)

L(Isolation)

L(Unemp.)

L(Isolation)

Coef.

Coef.

Coef.

Coef.

Coef.

0.003 **

0.366 †

0.005 *

0.016 *

Professional

0.002-0.0460.000-0.021

Managerial-0.008-0.089-0.015-0.176 †Non-manual in large-sized firm

0.016

-0.0890.019-0.135

Self-employed

0.007-0.0300.007-0.028

Unemployed/No father

0.032

0.020

0.053

0.075

DK

0.004

0.184

0.033

0.301

Coef.

Coef.

Age at Wave1

Coef.

Isolation

Father's status at 15 years old(Ref: Other occupations)

Educational status at Wave1(Ref: Post secondary)
Secondary-0.007

0.052

0.001

0.107

Undergraduate or more

-0.020 †-0.019-0.036-0.054

Professional/Managerial-0.012

0.095 †

0.000

0.181 *

Non-manual in large-sized firm

-0.0170.130 †

-0.0240.133

Self-employed-0.051 **-0.068-0.088 **-0.176

DK-0.009-0.017-0.015-0.019150 million JPY or less0.118 †

0.122

0.201 †

0.323

350-600 million JPY

0.007

0.029

0.023

0.133

600-850 million JPY-0.013-0.038-0.0130.042

850 million JPY or more

-0.002-0.075-0.003-0.061DK

0.031 †

-0.014

0.056

0.110

Unmarried

0.033 †

0.046

0.069

0.168

Divorce/Widowed

0.128

0.160

0.243

0.492

Having children at Wave1-0.011-0.081-0.018-0.112

Crime Victimization Experience-0.003-0.052-0.010-0.052

Experience having got a heavy disease

0.052 **

0.091 *

0.084 **

0.204 *

Home atmosphere at 15 years old-0.004-0.082 **-0.018-0.135 **0.021 ***

0.483 ***First job (Ref: Other occupations)Household income (Ref: 150-350 million JPY)

Marital status at Wave1 (Ref: Married)

Variances of random intercepts

0.013 ***

Covariance of two random intercepts

0.03 *

0.04 **

0.031

0.018

SRMR

0.146

0.018

*** p < 0.001, ** p < 0.01, * p < 0.05, † p < 0.1 (Two-tailed test)
Note1) SRMR refers to " Standardized Root Mean Squared Residual."
Note2) Standard errors in all of the models are robust ones.
Note3) Other variances, covariances, and intercepts are not shown in this table.

15

0.558 ***

Table 5. Cross-lagged Panel Models in the Female Sample (n = 1269)
Model 1aModel 1bModel 2aModel 2b

Uemp. atIsolation

Uemp. atIsolation

Uemp. atIsolation

Uemp. att+2

at t+2t+2

at t+2t+2

at t+2t+2

at t+2

Exogenous variables

Coef.

Coef.

Coef.

Coef.

Coef.

Coef.

Coef.

Coef.

Unemployment at t

0.611 ***

0.014

0.431 ***

0.092 †

0.346 †

0.507 **

-0.072 †-0.004Degree of isolation from support at t

0.008

0.674 ***

0.033 *

0.621 ***

0.172 **

0.899 ***

-0.042

0.127

Unemp. at

Isolation at

Unemp. at

Isolation ateach wave

each wave

each wave

each wave

L(Unemp.)

L(Isolation)

L(Unemp.)

L(Isolation)

Coef.

Coef.

Age at Wave1

Coef.

Coef.

-0.007 ***0.009 **

Coef.

Coef.

Isolation

Coef.

Coef.

-0.011 ***0.010 *

Father's status at 15 years old(Ref: Other occupations)
Professional-0.004

0.055

0.001

0.080

Managerial

0.005-0.031-0.003-0.058Non-manual in large-sized firm

-0.021

0.008-0.0300.009

Self-employed-0.0040.042-0.013

0.062

Unemployed/No father-0.0440.041-0.069 †

0.052

DK

0.003

-0.0120.015-0.015

Secondary-0.055 **0.057 †

-0.066 **0.073

Undergraduate or more

-0.0130.035-0.0050.075 †

Professional/Managerial-0.056 **-0.033-0.076 **-0.077 †Non-manual in large-sized firm

0.009

0.035

0.027

0.062

Self-employed

0.061-0.1040.076-0.152

DK-0.059 *0.117 *

-0.076 †0.151 †

Educational status at Wave1(Ref: Post secondary)First job (Ref: Other occupations)Household income (Ref: 150-350 million JPY)
150 million JPY or less

0.129 *

0.018

0.236 *

0.039

350-600 million JPY

0.020-0.012-0.003-0.114600-850 million JPY-0.018-0.094 *-0.062-0.207 **850 million JPY or more

-0.042 †-0.063-0.085 *-0.156 *

DK

0.027

-0.051

0.015-0.169 *Unmarried-0.189 ***0.121 **

-0.266 ***0.157 *

Divorce/Widowed-0.168 ***0.082-0.284 ***0.060

Having children at Wave1

0.041-0.0300.120 **

0.036

Crime Victimization Experience

0.038

0.149 *

0.068

0.268 *

Experience having got a heavy disease

0.052 **

0.011

0.077 **

0.023

Home atmosphere at 15 years old-0.005-0.067 ***-0.019-0.104 ***0.060 ***

0.247 ***

Marital status at Wave1 (Ref: Married)

Variances of random intercepts

0.040 ***

Covariance of two random intercepts-0.06 *

0.01

0.008

0.017

SRMR

0.066

0.028

0.048 ****** p < 0.001, ** p < 0.01, * p < 0.05, † p < 0.1 (Two-tailed test)
Note1) SRMR refers to " Standardized Root Mean Squared Residual."
Note2) Standard errors in all of the models are robust ones.
Note3) Other variances, covariances, and intercepts are not shown in this table.

What can be observed when looking at Model 1a is that there is a positive andstatistically significant cross-lagged association between unemployment and social
isolation for the male sample. The effect of unemployment at baseline on social isolation
at follow-up is positive and statistically significant. However, unemployment at baseline
is not associated with social isolation at follow-up for the female sample. The same typeof association is observed when looking at the impact of social isolation at baseline on
unemployment at follow-up: isolation from social support is slightly associated with
unemployment for the male sample but the association is almost non-existent for thefemale sample.
Model 1b shows what happens when introducing the set of covariates. The crosslagged coefficients are not significant at a 5 per cent level for the male sample, and the16magnitude of the estimates decreases compared to the Model 1a. By contrast, the crosslagged effects for the female sample increase after controlling the individual covariates,
though the effect of unemployment on the subsequent isolation is still insignificant. The
way between-individual factors affect the cross-lagged effects is different between men
and women. According to the result, however, it is adequate to consider the individual
variations about the variables of interest.
Finally, Model 2a and 2b run the same analysis but integrating the variancecomponents of unemployment and social isolation. It can be observed that the crosslagged association between unemployment and social isolation loses its significance.
Model 2a still show significant cross-lagged effects for the female sample. In Model 2bwhich is the most rigorous3, however, there is no significant cross-lagged coefficient for
both male and female. The series of analyses indicate the first hypothesis does not hold,but the second one does4.
6.

Conclusion

The results of a series of analyses do not support the first hypothesis but seem to confirm
the second one to some extent. The seeming reciprocal relationship between
unemployment and social isolation turned out to be spurious after distinguishing the
between-individual level latent traits. Instead, individual variations improve the model fit,
and they explain the observed relationship much better than the bi-directional paths of
unemployment and social isolation. Unemployment is not a direct cause of social
isolation nor vice versa, but these results indicate that some people are likely to experience
both unemployment and social isolation simultaneously. It can be possible to interpretthat they have a double disadvantage. The propensity of the disadvantage seems to bestem from states and events at an earlier life course stage, though they do not fully explain
the propensity.
These results suggest that it would be challenging to overcome the initial socialand economic disadvantages in contemporary Japanese society. Japan has maintained asystem that makes it difficult for career development to occur through inter-firm mobility,
which is a career opportunity in a fluid labour market. Japanese career development relies
The cross-lagged panel model with a random intercept separates stable betweenperson differences from within-person fluctuations (Hamaker et al., 2015; Usami et al.,2019).
4 In addition to the cross-lagged panel models in Table 5 and Table 6, we also performthe same model with considering missing values and show the results in Appendix
table 1 and 2. We obtained the almost same results.
317

on skill formation based on a within-firm mobility through rearrangements andpromotions. Its system has attracted (mainly male) workers and motivated them to keep
working in the same company for a very long time with little mobility from one company
to another, though this tendency is slowly weakening (Yu, 2010). As well, social networks
are workplace-centric, and it is unlikely for Japanese workers to expand their networks.
Under such a configuration, the first chance, which is the transition from school to work,
is consequently the last one.
One plausible policy implication is to develop further support for new graduates.
Japanese schools have already developed a system of job recommendation, which iscalled ‘institutional linkage’ (Rosenbaum and Kariya, 1989). While searching for asustainable career development system which provides the second chances to Japanese
workers, it is essential to strengthen and actively make use of the existing system for
dealing with the confronting problems.
REFERENCES
Abe A (2012) Poverty and Social Exclusion of Women in Japan. Japanese Journal ofSocial Security Policy 9: 61–82.
Atkinson T, Liem R., and Liem JH (1986) The Social Costs of Unemployment:
Implications for Social Support. Journal of Health and Social Behaviour 27(4): 317–
331.
Brinton MC (2010) Lost in Transition: Youth, work, and instability in postindustrial

Japan. Lost in Transition: Youth, Work, and Instability in Postindustrial Japan.
Cambridge: Cambridge University Press.
Dieckhoff M, Gash V (2015) Unemployed and alone? unemployment and social
participation in Europe. International Journal of Sociology and Social Policy 35: 67–
90.
Gallie D, Paugam S, and Jacobs S (2003) Unemployment, Poverty and Social Isolation:
Is there a vicious circle of social exclusion? European Societies 5(1): 1–32.
Genda Y, Maganuma M (2004) NEET: Freeter demo naku Shitsugyosha demo naku

(NEET: Neither freeter nor Unemployed). Tokyo: Gentosha. (in Japanese)
Gore S (1978) The Effect of Social Support in Moderating the Health Consequences ofUnemployment. Journal of Health and Social Behavior 19(2): 157–165.
Granovetter MS (1973) The Strength of Weak Ties. American Journal of Sociology 78(6):1360–1380.
Granovetter MS (1995) Getting a Job: A Study of Contacts and Careers (2nd edition).

18Chicago: University of Chicago Press.
Hamaker EL, Kuiper RM, and Grasman, RPPP (2015) A critique of the cross-lagged
panel model. Psychological Methods 20(1): 102–116.
Hammer T (2000) Mental health and social exclusion among unemployed youth in
Scandinavia: A comparative study. International Journal of Social Welfare 9(1): 53–
63.
Hanley JA, McNeil BJ (1982) The Meaning and Use of the Area under a Receiver
Operating Characteristic (ROC) Curve. Radiology 143(1): 29–36.
Hori Y (2005) Situations of Social Network of Jobless Youth and Assistance Measures.

Japan Labor Review 48: 26–48.
Ishida H (2005) Stratification and the Labor Market in the Stage of Extended
Adolescence. Journal of Educational Sociology 76: 41-57. (in Japanese)
Ishida M (2009) The Effects of Social Networks on Job Hunting: Status Attainment and
Safety Net. Japanese Sociological Review 60(2): 279-296. (in Japanese)
Kalmijn M (2012) Longitudinal analyses of the effects of age, marriage, and parenthoodon social contacts and support. Advances in Life Course Research 17(4): 177-190.
Lazarsfeld P, Jahoda, M, and Zeisel H (1982) Les chômeurs de Marienthal. Paris: Les
Editions de Minuit.
Lindsay C (2010) In a Lonely Place? Social Networks, Job Seeking and the Experienceof Long-Term Unemployment. Social Policy and Society 9(1): 25–37.
Lindwall M, Larsman P, and Hagger MS (2011) The Reciprocal Relationship Between
Physical Activity and Depression in Older European Adults: A Prospective CrossLagged Panel Design Using SHARE Data. Health Psychology 30(4): 453–462.
Lobo F (2018) Unemployment and leisure: the Marienthal legacy. World Leisure Journal
60(2): 75–93.
Miwa S (2008) Sample Bias and Unit Nonresponse Cases for JLPS-2007. JLPS

Discussion Paper Series 10: 1-17. (in Japanese)Montgomery JD (1992) Job Search and Network Composition: Implications of theStrength-Of-Weak-Ties. American Sociological Review 57(5): 586–596.
Mund M, Nestler S (2019) Beyond the Cross-Lagged Panel Model: Next-generation
statistical tools for analyzing interdependencies across the life course. Advances inLife

CourseResearch

41:

1-11

(accessed

02

June

2020,

https://doi.org/10.1016/j.alcr.2018.10.002).
Nagayoshi K. (2017). Gender Differences in Determinants of Loss of Social Supports:
Fixed Logit Model for Impacts of Poverty and Unemployment. Sociological Theory andMethods 32(1): 114-126. (in Japanese)

19Pacheco G, Page D, and Webber DJ (2014) Mental and Physical Health: Re-Assessing
the Relationship with Employment Propensity. Work, Employment and Society 28(3):407–429.
Rosenbaum JE, Kariya T (1989) From High School to Work: Market and Institutional
Mechanisms in Japan. American Journal of Sociology 94(6): 1334–1365.
Savalei V (2014) Understanding Robust Corrections in Structural Equation Modeling.

Structural Equation Modeling 21(1):149–160.
Schuurman NK, Ferrer E, de Boer-Sonnenschein M, and Hamaker EL (2016) How tocompare cross-lagged associations in a multilevel autoregressive model. Psychological

Methods 21: 206–221.
Shaffer A, Suveg C, and Thomassin K, and Bradbury LL (2012) Emotion Socialization in
the Context of Family Risks: Links to Child Emotion Regulation. Journal of Child andFamily Studies 21(6): 917–24.
Silver H (2012) The Process of Social Exclusion: The Dynamics of an Evolving Concept.

SSRN Electronic Journal (October). https://doi.org/10.2139/ssrn.1629282
Sousounis P, Lanot G (2018) Social networks and unemployment exit in Great Britain.

International Journal of Social Economics 45(8): 1205–1226.
Usami S, Murayama K, and Hamaker EL (2019) A Unified Framework of Longitudinal
Models to Examine Reciprocal Relations. Psychological Methods 24(5): 637-657.
Westreich D, Cole SR, Funk MJ, Brookhart MA, and Stürmer T (2011) The Role of the
C-Statistic in Variable Selection for Propensity Score Models. Pharmacoepidemiology

and Drug Safety 20(3): 317–320.
Yao MZ, Zhong ZJ (2014) Loneliness, social contacts and Internet addiction: A crosslagged panel study. Computers in Human Behavior 30(January): 164–170.
Yu W (2010) Enduring an Economic Crisis: The Effect of Macroeconomic Shocks on
Intragenerational Mobility in Japan. Social Science Research 39(6): 1088–1107.

20

Appendix: Results of the Models Considering Missing Values
Appendix Table 1. Cross-lagged Panel Models in the Male Sample with Full Information
Maximum Likelihood Estimation (n = 1109)
Model 1aModel 1bModel 2aModel 2b

Uemp. atIsolation

Uemp. atIsolation

Uemp. atIsolation

Uemp. att+2

at t+2t+2

at t+2t+2

at t+2t+2

at t+2

Exogenous variables

Coef.

Coef.

Coef.

Coef.

Coef.

Coef.

Coef.

Coef.

Unemployment at t

0.414 **

0.574 ***

0.282 †

0.263

0.297

1.017

-0.362-0.266

Degree of isolation from support at t

0.021 *

0.643 ***

0.007

0.601 ***

-0.0220.045-0.0280.133

Unemp. at

Isolation at

Unemp. at

Isolation ateach wave

each wave

each wave

each wave

L(Unemp.)

L(Isolation)

L(Unemp.)

L(Isolation)

Coef.

Coef.

Coef.

Coef.

Isolation

Coef.

Coef.

0.003 **

0.008 *

Professional

0.014-0.044

0.021

0.011

Managerial-0.006-0.107-0.017-0.201 *Non-manual in large-sized firm

0.034 †

-0.122 *0.040-0.184 *Self-employed

0.006-0.0150.003-0.020

Unemployed/No father

0.021

0.009

0.033

0.038

DK

0.059

0.074

0.088

0.146

Age at Wave1

Coef.

Coef.

0.004 **

0.014 *

Father's status at 15 years old(Ref: Other occupations)

Educational status at Wave1(Ref: Post secondary)
Secondary-0.005

0.030

0.003

0.071

Undergraduate or more

-0.016-0.020-0.027-0.056

Professional/Managerial-0.0190.090 †

-0.0110.162 †

Non-manual in large-sized firm

-0.027 †0.111 †

-0.035

0.109

Self-employed-0.054 **-0.051-0.086 **-0.142

DK-0.009-0.011-0.014-0.011150 million JPY or less

0.114 †

0.084

0.176 †

0.236

350-600 million JPY-0.002

0.030

0.006

0.111

600-850 million JPY-0.016-0.030-0.021

0.015

850 million JPY or more

-0.011-0.106-0.018-0.111

DK

0.024-0.006

0.046

0.087

Unmarried

0.037 *

0.071

0.071

0.188 †

Divorce/Widowed

0.116

0.140

0.216

0.419

Having children at Wave1-0.013-0.076-0.021-0.099Crime Victimization Experience-0.006-0.072-0.014-0.104

Experience having got a heavy disease

0.053 **

0.107 *

0.086 ***

0.220 **

Home atmosphere at 15 years old-0.010-0.088 **-0.026-0.149 **0.547 ***

0.462 ***First job (Ref: Other occupations)Household income (Ref: 150-350 million JPY)

Marital status at Wave1 (Ref: Married)

Variances of random intercepts

0.019 ***

Covariance of two random intercepts

0.04 †*** p < 0.001, ** p < 0.01, * p < 0.05, † p < 0.1 (Two-tailed test)
Note1) Standard errors in all of the models are robust ones.
Note2) Other variances, covariances, and intercepts are not shown in this table.

21

0.549 ***

0.04 ***

Appendix Table 2. Cross-lagged Panel Models in the Female Sample with Full Information
Maximum Likelihood Estimation (n = 1479)
Model 1aModel 1bModel 2aModel 2b

Uemp. atIsolation

Uemp. atIsolation

Uemp. atIsolation

Uemp. att+2

at t+2t+2

at t+2t+2

at t+2t+2

at t+2

Exogenous variables

Coef.

Coef.

Coef.

Coef.

Coef.

Coef.

Coef.

Coef.

Unemployment at t

0.593 ***

0.027

0.431 ***

0.080 †

0.217

0.426 †

-0.0620.021

Degree of isolation from support at t

0.012

0.670 ***

0.032 *

0.622 ***

0.151 †

1.052 ***

-0.033

0.132

Unemp. at

Isolation at

Unemp. at

Isolation ateach wave

each wave

each wave

each wave

L(Unemp.)

L(Isolation)

L(Unemp.)

L(Isolation)

Coef.

Coef.

Coef.

Coef.

Coef.

Coef.

Coef.

-0.007 ***0.008 **

-0.010 ***0.011 **

Age at Wave1

Coef.

Isolation

Father's status at 15 years old(Ref: Other occupations)
Professional-0.0080.056-0.0080.070

Managerial-0.006-0.041-0.019-0.069Non-manual in large-sized firm

-0.0240.000-0.0350.000

Self-employed-0.0080.048 †

-0.0200.070

Unemployed/No father-0.0380.047-0.0520.072

DK-0.0290.010-0.023

0.037

Secondary-0.056 **0.054 †

-0.069 **0.075 †

Undergraduate or more

-0.0210.034-0.0140.074 †

Professional/Managerial-0.049 **-0.038-0.069 **-0.078 *Non-manual in large-sized firm

0.012

0.021

0.026

0.040

Self-employed

0.105-0.121

0.125-0.174

DK-0.071 **0.062-0.101 **0.067

150 million JPY or less

0.090

-0.0780.141-0.134350-600 million JPY

0.016-0.024-0.003-0.118 †

600-850 million JPY-0.017-0.116 **-0.056-0.230 **850 million JPY or more

-0.041 †-0.068-0.080 *-0.161 *

DK

0.023

-0.0480.011-0.165 *Unmarried-0.192 ***0.094 *

-0.273 ***0.127 *

Divorce/Widowed-0.193 ***0.025-0.322 ***-0.018

Having children at Wave1

0.027-0.031

0.091 *

0.010

Crime Victimization Experience

0.048

0.160 *

0.095 †

0.288 *

Experience having got a heavy disease

0.056 **

0.003

0.077 **

0.007

Home atmosphere at 15 years old-0.010-0.066 ***-0.024 *-0.100 ***0.331 ***

0.240 ***

Educational status at Wave1(Ref: Post secondary)First job (Ref: Other occupations)Household income (Ref: 150-350 million JPY)

Marital status at Wave1 (Ref: Married)

Variances of random intercepts

0.051 ***

Covariance of two random intercepts-0.04 †*** p < 0.001, ** p < 0.01, * p < 0.05, † p < 0.1 (Two-tailed test)
Note1) Standard errors in all of the models are robust ones.
Note2) Other variances, covariances, and intercepts are not shown in this table.

220.004 ***

0.01





S 2006

 "

2009
2015
2020

2016
B 2016
2004

2010
2017

# 

2014
2018

C 2013
A 2015
2018
2024

2006
2006

2008

   " # $%%!% 

https://csrda.iss.u-tokyo.ac.jp/panel/