Does retirement affect the mental and general health of the older Japanese workforce?
A four waves follow-up using the Japanese Study of Ageing and Retirement (JSTARRIETI).
Dr Jacques Wels
FNRS, Centre Metices
University of Cambridge, Department of sociology
Wels.Jacques@ulb.ac.beTokyo, the 5th of August 2018
Abstract
This research paper uses data from the Japanese Study of Ageing and Retirement (JSTARRIETI) to assess the association between retirement transition and change in general health(self-perceived health) and mental health of the older workforce aged 50 and over in Japan. The
scientific literature provides mixed evidence of the impact of late career transitions on healthand highlights the need to account for the heterogeneity of the older workforce. The case ofJapan is particularly relevant to assess as it is among the OCDE countries where the effective
age of retirement is the highest. Though, little is known about the older Japanese workforce.
The article uses longitudinal data from the Japanese Study of Ageing and Retirement (JSTARRIETI) that contains four waves (2007, 2009, 2011, 2013). Only the population working at thebaseline is selected. Two health indicators are selected for this study: the self-perceived health(SPH) and a post-calculated mental health index (MHI). A latent growth modeling (LGM) is
used to assess the short-term association between employment status and health at the baseline(intercept) and the evolution of health over time (slope), after controlling for fixed and timevarying covariates. Maximum likelihood estimations are calculated to control for attrition.
Results show that, compared with respondents remaining in employment, those who stop
working and do not benefit from pension or disability benefits have a poorer SPH and MHI at
the baseline. However, the slope of the LGM indicates that, compared with those who remain
in work, their health tends to improve over time. Direct post-retirement health outcomes tend
to be negative but there is a health adjustment mechanism so that health outcomes improve overtime. The study estimates that a two-waves period (i.e. four years) is enough to recover fromthe initial fall down. The paper concludes that one must consider the long-term effects of
employment transitions rather than short-term, as they raise some causation problems.
Acknowledgement
This working paper was written following a scientific stay funded by the Japan Institute forLabour Policy and Training (JILPT) in April, May and June 2019. During this scientific stay, I
received some supports from many members of the JILPT and I would like to thank them fortheir very warm hospitality. I would also like to say a special thank you to Kubo Eriko for hervery kind support during my stay at the JILPT. Additionally, I would like to thank the RIETI for
providing the JSTAR data, Prof Glenda Roberts (Waseda University) and Prof Hashimoto
Hideki (University of Tokyo).

1

Summary
Abstract ...................................................................................................................................... 1
Acknowledgement ...................................................................................................................... 1
Introduction ................................................................................................................................ 3
1.

2.

3.

Occupational health and late career transitions .................................................................. 4
1.1.

Accounting for the heterogeneity of the older workforce .......................................... 4

1.2.

Late career transitions specificities in Japan .............................................................. 6

Data and methods ............................................................................................................... 8
2.1.

JSTAR-RIETI ............................................................................................................. 8

2.2.

Dependent variables ................................................................................................... 9

2.3.

Variables of interest .................................................................................................... 9

2.4.

Covariates ................................................................................................................... 9

2.5.

Models specification................................................................................................. 10

Results .............................................................................................................................. 11
3.1.

Descriptive statistics ................................................................................................. 11

3.2.

OLS and Logit in wave 2007 (baseline) ................................................................... 15

3.3.

Latent Growth Curve ................................................................................................ 18

Limitations................................................................................................................................ 20
Conclusion ................................................................................................................................ 20
References ................................................................................................................................ 22
Tables and figures ..................................................................................................................... 26

2

Introduction
The sustainability of the State Pension System is a major concern in the context of an ageing
population and declining birth rate in most industrialized countries. In Japan, the International
Monetary Fund has underlined the benefits of raising the eligibility age to the basic pension
scheme to 69 by 2030 (Kashiwase et al. 2012) and the Japanese Government is paving the way
to raise pension age to 70, but discussions are also about reducing the replacement rate (Kitao
2017). To give a few well-known figures about the demographic challenge Japan faces, theshare of the dependent population calculated by the OCDE (i.e. the ratio between the population
aged 65+ and the population aged 15-64) was 5.3 per cent in 1955, 10.3 per cent in 1985 and25.1 per cent in 2013. Even though the share of the population aged 65 and more has increasedin most OECD countries, Japan is the only country in which an exponential curve can beobserved. As a political consequence, most advanced economies have reformed their pension
systems to make them sustainable under current forecast. Even though pension reforms are notalways efficient in retaining the older workforce, the older workers’ effective age of retirement
has nevertheless increased (Wels 2016), raising questions about the well-being of the ageing
workforce and the health impact of late career transitions. To give some recent figures, figure 1
exhibits the effective age of retirement as calculated by the OECD (for a critical approach ofthese estimates, plese read: Wels 2016). It can be clearly observed that, compared to otherOECD members, Japan has one of the highest effective age of retirement, both for men (70.6
years of age) and women (69.3 years of age), just after Korea.
Figure 1 Effective age of retirement in 2017 in OECD countries.

70

65

Korea
Japan
New Zealand
IcelandChile
MexicoUnited States
IsraelPortugal
TurkeyEstonia
SwedenSwitzerland
Australia
Ireland
NorwayUnited Kingdom
OECD-27
CanadaGermany
FinlandDenmark
Netherlands
Lithuania
LatviaSpain
Austria
Italy
Czech Republic
Luxembourg
FranceSlovenia
Hungary
Belgium
PolandGreeceSlovak Republic

60

Legend: • = women;  = men
Source : OCDE & LSE, using methods described in Keese (n.d.).

The current demographic context together with the recent development of longitudinal datasets
have led to an increasing – but still sparse – number of studies focusing on the association
between employment trajectories, extension of the working life and older workers’ health. (One3should note that this issue is relatively new: pensions systems have not been initially
implemented to prevent health deterioration over the life course. The increasing length of theworking life has consequences that goes beyond the access to social rights).
Using longitudinal data from the Japanese Study of Ageing and Retirement (JSTAR-RIETI),
this working paper discusses to what extent late career transitions have an impact on older
workers’ self-perceived and mental health. The working paper is divided in three sections. The
first section aims to present the main recent findings on this matter. The second section presents
the methodological features of the study and describes the dataset. Results are presented in thethird section of the paper. Finally, we briefly describe the limitations of the research andsummarize the main findings flowing from this study.
1. Occupational health and late career transitions
1.1. Accounting for the heterogeneity of the older workforce
The impact of late career transitions on health is subject to a considerable – but still relatively
new – amount of research using longitudinal methods. Among the different possible transitions,many studies have focused on the impact of the transition from work to retirement,
unemployment and inactivity on health (Graetz 1993; Calvo 2006; Waddell and Burton 2006;
Rice et al. 2011; Burton-jeangros et al. 2015) but also on work or family care histories (Cornaand Sacker 2013; Wahrendorf 2014; Benson et al. 2017). So far, results vary from one study toanother. Some research has found a negative association between retirement transitions andpost-retirement health while some other studies have demonstrated the health benefits ofretiring.
For instance, Di Gessa and Grundy (2014) have demonstrated that engagement in paid work
contributes to maintain health in later life compared to people leaving the labour market.
Similarly, using cross-sectional data Siegrist and Wahrendorf (2014) have shown that continued
participation in socially productive activities improves prospective quality of life in early old
age and Alavinia and Burdof (2008) have demonstrated that poor health, chronic diseases, andlifestyle factors are associated with being out of the labor market. Similarly, looking at fulltime workers aged 50 and over, Moon et al. (2012) show that retirement is associated withelevated odds of having cardiovascular disease. Using the Health and Retirement Survey, Vosset al. (2018) found that late-career unemployment has no significant effect on self-reported
physical health but is associated with lower levels of mental health. Similarly, taking a lifecourse perspective, Wahrendorf et al. (2018) have shown, for France, that adverse employment

4histories and years out of work are associated with poor health at the old age. Conversely, acertain number of studies have underlined the positive impact of retiring, particularly for thoseworking in poor work environment. Using the French GASEL dataset, Westerlund et al. (2009)
have shown that workers’ self-perceived health is substantially relieved by retirement for allgroups of workers (apart from those with ideal working conditions). Similarly, focusing on
sleep disturbance, Vahtera et al. (2009) provide strong evidence for a substantial and sustained
post-retirement decrease in sleep disturbances. Using Belgian data from the SHARE, I foundthat respondents moving to retirement are more likely to present a better self-perceived health,depression level and quality of life compared to people increasing or keeping the same workingtime level, after controlling for the age and other socio-economic variables (Wels 2018a).
As a matter of fact, the association between retirement transitions and health needs to benuanced and to account for factors that affect the retirement decision. For instance, Schuring etal. (2015) show that the level of education plays a role in explaining both early retirement
decision and post-retirement health. While the health of low-educated workers partly prompts
early retirement and economic inactivity, these exit routes prevent further deterioration of theirhealth. The opposite relation is observed for high educated workers as early retirement has anadverse effect on self-reported health. Slightly different finding was reported by König et al.
(2018) using Swedish data: as lower educated retirees are more likely to stop working forphysical reasons, this leads to poor post-retirement health. The same kind of result is observed
when looking at the occupational class (see, for instance, Virtanen et al., 2017). But other factors
can be taken into consideration. For instance, the generosity of the Welfare State plays a role inexplaining the change in health following work exit. Comparing 16 European countries,
Richardson et al. (2018) found that national expenditure on in-kind benefits is associated withmore favourable wellbeing change outcomes after leaving the labour market. The nature of thetransition, whether voluntary or involuntary, also affects health. Using the Swedish
Longitudinal Occupational Survey of Health, Hyde et al. (2015) show that, compared tovoluntary employment exit, involuntary exit is associated with a higher risk of reporting major
depression and becoming newly prescribed anti-depressant medication. Finally, collective
bargaining and trade union membership may also play a role in explaining older workers’ healthvariations. Using the Health and Retirement Study for the United States, I have shown that
unionized workers are less likely than non-unionized workers to experience a negative change
in self-perceived health and depression level during the different types of late career transitions(Wels 2018b). Unionized workers are also more likely to retire earlier compared with non-5

unionized workers. In essence, it can be assumed that working life prolongation may have bothadverse and beneficial effects.
1.2. Late career transitions specificities in Japan
Studies looking specifically at Japan are few in number. Looking at the case of post-retirement
health of older Japanese men using the National Survey of Japanese Elderly, Okamoto et al.
(2018) find – after controlling for economic, sociodemographic and health data – that remaining
in employment (versus not being employed) has positive effect on life expectancy, reducescognitive decline and reduces the risk of early diabetes and stroke. They also find that selfemployed workers tend to have longer life expectancies compared with employed workers.
Similarly, using data from the Japan Gerontological Evaluation Study, Shiba et al. (2017) lookat the association between retirement and mental health and the impact of social participation.
They find that respondents who transitioned to retirement experience increased depressive
symptoms. Men who were continuously retired report increased depressive symptoms whereas
moving back to work is associated with reduced depressive symptoms. Using the same dataset,
Amemiya et al. (2019) particularly focus on the impact of the socioeconomic status
on improvement in functional ability among people aged 60 and over. Their study finds that ahigh level of education (13 years or more) is associated with functional ability improvement,
both for men and women. Interestingly, the number of years of education is the only statisticallysignificant predictor: neither income nor the type of occupation have a statistically significant
impact. Using the Komo-Ise study (a prospective cohort of community-dwelling residents aged
44–77 years living in two areas in Gunma prefecture), (Miyawaki et al. 2019) analyse theassociation between information caregiving and mortality. Even though informal caregiving
activities are seen as linked to high stress levels, the study does not find any statisticallysignificant association with increased risk of death (all causes) except for female caregivers
from the lowest-income group.
But a consensual view on this matter has not been reached. Using the JSTAR-RIETI dataset,
Hashimoto (2015) analyses to what extent leaving paid work may affect health, functions, andLifestyle Behaviour. He finds limited impact of transitioning from paid work to retirement andunderlines the need to account for the heterogeneity of the population. The study shows alimited impact on cognitive function, mobility, smoking behaviour, body mass index,
psychological distress, hypertension, fruit intake and social participation to voluntary services.
Though, some particular segments of the population are more vulnerable than others. That isparticularly the case of former while-collar male workers or older women with unsecured jobs.
6

He concludes that “the heterogeneity of the population at retirement age should be consideredto specify causal pathways and policy implications of health impacts after leaving paid work
more effectively” (p.1).
Late career transitions have some specificities in Japan that one should particularly account for.
Firstly, mandatory retirement ages fixed by the employers raise some issues. Under Japaneselaw, the mandatory retirement age cannot be under 60 years old (in comparison, most liberalcountries such as the United Kingdom, the United States or New Zealand do not havemandatory retirement ages anymore, or just in some very particular sectors of activity (Lain
2017)) and, if the mandatory retirement age is under 65, the employer is required to provide
continuous employment up to age 65 (Hamaguchi 2017). Since. 2013, ‘re-employed’ workers
could work until 65 if they want to do so under a status of ‘skilled partner’. In 2015, themandatory retirement age was set to 60 years old by 81.8 per cent of the corporations (Kodama
2015, p. 7). Hence, rather than extending mandatory retirement age, most companies make useof the re-employment system. One major problem caused by re-employment is that workers
might work under more precarious status than prior retirement such as non-regular employment
and contract work.
Secondly, there is an important gender divide within the labour market, that also affects the oldage. Many Japanese women interrupts their career following childbirth. This does not apply toa minority: 74 per cent of female university graduates experience period away from work (Zhou2015). Though, flexible work arrangements are not widespread and job interruption could leadto some situations in which it is hard for women to go back in the labour market. This contrasts
with women’s expectations. 60 per cent of women in childrearing think it would be ideal towork after childbirth but there is a gap between women’s expectations and the reality (Zhou2015). Consequently, post-50 female employment rates are lower than male employment rates(this study finds a 25-percentage points difference between men and women aged 50 and over).
Thirdly, flexible work arrangements are not very popular in Japan. However, late careertransitions have something to do with working time modulations. Comparing Japan and theUnited States, Usui et al. (2016) observe that Japanese men aged 60-74 tend to adjust theirworking time after beginning to receive a public pension: “Men who were employees at 54
gradually move to part-time work or retire after beginning to receive pension benefits; those
who continue working tend to be underemployed”. Self-employed men however do not reduce
working time, do not retirement and tend to be overemployed. Similarly, Kajitani (2011) findsthat Japanese elderly males prefer to work for relatively few hours as they grow older and that7

working time reduction can maintain older workers’ health.

2. Data and methods
2.1. JSTAR-RIETIJSTAR is a longitudinal dataset that currently contains four waves released every two years(currently: 2007, 2009, 2011, 2013). One of the specificities of the JSTAR dataset is that thebaseline sample (2007) contains respondents aged 50 to 75 living in five municipalities ineastern area of Japan (Hidehiko et al. 2009): Takikawa in Hokkaido, Sendai in the Tohoku area,
Adachi Ward within Tokyo, Kanazawa in Hokuriki and Shirakawa in the Chubu area. The
baseline survey contains 4,200 individuals. One should assume, however, that JSTAR does notcontain information about rural Japan, which may cause problems when comparing estimateswith results obtained using other international surveys. (One should carefully pay attention tothis specificity when carrying cross-national comparisons involving the JSTAR dataset).
However, some efforts have been made to increase the number of cities. The 2011 wave
includes two additional cities (Tosu and Naha) and there are ten cities in the 2013 wave (Chofu,
Hiroshima and Tondabayashi).
Table 1 Sample Attrition in the original RIETI-JSTAR sample
Wave
Year
Adachi (足立区)
Kanazawa (金沢市)
Sendai (仙台市)
Takikawa (滝川市)
Shirakawa (白川村)
Total
Attrition (as percentage ofprevious wave)

1st wave
2007
868
1,011
908
570
806
4,163

2d wave
2009
590
723
611
456
711
3,091

3rd wave
2011
430
549
475
386
638
2,478

4th wave
2013
363
486
384
344
576
2,153

25.8

19.8

13.1

4th / 1st
41.8
48.1
42.3
60.4
71.5
51.7

The original sample was composed of 4,163 respondents. The attrition rate was 25.8 per centbetween wave 1 and wave 2, 19.8 per cent between wave 2 and wave 3 and 13.1 per centbetween wave 3 and wave 4. Interestingly, the highest sample attrition between the baseline andwave 4 is in Shirakawa and the lowest attrition is in Adachi. JSTAR-RIETI provides a countrylevel sample weight that is based on census data and accounts for age and householdcomposition, the type of employment and regional attributes (information about themunicipality level are not publicly available).

8

2.2. Dependent variables
Health is measured using two variables: the self-perceived health (SPH) and a mental healthindex (MHI). Both variables are used as numeric (see table 1), which facilitates theinterpretation of the coefficients. The MHI is post calculated based on the answer to twentyquestions (Felt unusual in some way, Had no appetite, Felt depressed and could not be consoledby family or friends, Felt I could do anything a normal person could do, Could not concentrateon what I was doing, Felt depressed, Something that is normally effortless was difficult to do,Felt the future was bright, Felt that my life so far has been a failure, Felt frightened, Could not
sleep well, Felt happy, Felt more taciturn than usual, Felt lonely, People around me seemed cold
to me, Felt happy, Cried or felt like crying, Felt sad, Felt that people around me disliked me,
Didn't feel like doing anything) measuring mental health problems frequencies on a Likert scale(Not at all, 1~2 days a week, 3~4 days a week, 4~5 days or more) coded from 1 to 4. The IMH
sums up the answers to these questions and provides an index coded from 0 (very good) to 80(very poor). SPH simply uses the variable “Overall, how is your current health?” and is codedfrom (1) ‘good’ to (5) ‘not good’.
2.3. Variables of interest
As this study looks at retirement as a process, the paper distinguishes several employment
statuses based on both self-declared information and whether or not respondents receive
pension benefits, and the nature of these benefits. Four categories are distinguished: (a)respondents who are working and do not receive any kind of social benefits, (b) respondents
who are working but receive state pension benefits, (c) respondents who are not working, and(d) respondents who are not working and receive disability benefits.
2.4. Covariates
The paper controls for several fixed covariates (information collected in wave 2007): a
quadratic function of the age at the baseline, the highest level of education achieved(Elementary to middle school, High School, Junior college, Vocational school, University,
Master, PhD), the seniority (i.e. the number of years since the current position started), themartial status (married or common law spouse, not married and no common law spouse), thenumber of children, the type of business (18 types), the type of employment (Full-time
employee, Part-time employee, Temporary worker, contract worker, own or dependent
business, Helping in dependent business, side job at home, or other) and the post calculated
working time and incomes per week. The study also controls for an index of job dissatisfaction

9

that sums up information collected on a Likert scale ((1) strongly agree, (2) somewhat agree,(3) don't really agree, (4) strongly disagree) on whether or not the job includes physical labor,
workload and time pressure, job discretion, colleagues support, appropriate evaluation by
manager and pay satisfaction. The natural logarithm of the index is used to ensure that thevariable is normally distributed. Finally, two dummy variables are included to control for theexact year when respondents retired (2008 versus 2007, and 2009 versus 2007).
2.5.Models specification
We use a latent growth curve (latent growth modeling - LGM) to examine the association
between SPH and MHI and employment status. This is a multivariate approach (Masyn et al.
2013) in which intra individual change is captured by the measurement model for the growth
factors, and interindividual differences are captured by the structural model (i.e. the mean andvariance/covariance structure of the growth factors). The linear latent growth curve contains
two latent factors, an intercept and a slope. The dependent variable (𝑌𝑡𝑖 ) is the observed
outcome Y for individual i (from 1 to n) at time t (T is the number of waves). The intercept(random intercept factor) is the expected outcome on y for respondent i at time score 0 (i.e. thefirst wave). The slope (random linear slope factor) is the change in the expected outcome on
the dependent variable for respondent i for one-unit increase in time so that the slope can beinterpreted as the linear rate of change in the dependent variable. In this study, we select only
the population in employment in wave 2007 (N= 2,129) and pick up wave 2009 as the baseline
to assess for the change in employment status between 2007 and 2009. The model controls forcovariates at the baseline and time-changing covariates in order to control for change in
employment status in waves 2011 and 2013. For each health indicator, three models are tested.
The first model only includes the change in employment status in wave 2009 and does notcontrol for fixed nor time-varying covariates. The second money includes fixed covariates.
Finally, the third model controls both for fixed and time-varying covariates. Attrition iscontrolled using maximum likelihood methods.
The general feature of the model is shown in figure 2 where ‘ES’ is the employment status,‘GR’ is the general health and ‘V’ is the set of covariates. ‘i’ is the random intercept factor and‘s’ is the random linear slope
Figure 2 Latent growth curve model including a time-varying predictor

10

V
s
i
It.

GH(2009)i

ES(2007)s

ES(2009)GH(2011)

GH(2013)tv

tv

ES(2011)

ES(2013)

The model is performed using the R-package Lavaan. Attrition and missing data are controlledusing likelihood estimations.

3. Results
The results section is divided in three subsections. The first subsection presents somedescriptive statistics about the variables of interest. The second subsection shows the results ofan OLS and an Ordered logit regression for the baseline only (2007), not using longitudinal
data. Finally, the third subsection shows the results of the LGM.
3.1. Descriptive statistics
The ageing workforce is characterized by an important gender divide. Figure 2 exhibits thechange in employment participation across waves. Only respondents in employment wereselected at the baseline (in 2007), so the employment participation is 100 per cent. The
employment participation at baseline was 69.5 per cent for men and 44.7 per cent for women.
From wave 2007 to wave 2009, the employment participation decreased by 14.7 percentage
points for men and 16.1 percentage points for women. It decreased respectively by 7.1 and 10.3
percentage points from 2009 to 2011 and by 5.8 and 8.8 percentage points between 2011 and2013.
Figure 3 Employment participation in waves 2007, 2009, 2010 and 2013 per gender, in percent.

11

Source: JSTAR-RIETI, waves2007-13, author’s calculation. Excluding missing data.

Looking only at respondents remaining in employment in waves 2009, 2011 and 2013, figure
3 shows the evolution of the average working time for men and women separately. As for theemployment participation, the average working time at baseline is higher for men (43.9 hours
a week) than for women (32.3 hours). In both cases, the average working time declines fromone wave to another. In 2013, the male average working time was 40.1 hours (an average
reduction of 3.8 hours a week) and the female average working time was 30.6 (a reduction by
1.7 hours).
Figure 4 Average working time in waves 2007, 2009, 2010 and 2013 per gender, in hours per week.

Source: JSTAR-RIETI, waves2007-13, author’s calculation. Excluding missing data.

Table 2 calculates the association between general health in wave 2007 and twelve health12conditions that were diagnosed or for which the respondent was advised to seek care in waves
2007, 2009, 2011 and 2013 using an ordered logit regression (not controlling for other factors).
The table also shows the prevalence of these conditions. Not surprisingly, high blood pressure
concerns 26.4 per cent of the original sample (workers, aged 50 to 75). Respectively 10.8, 8.8;
8.3 and 7.4 per cent of the sample was affected by hyperlipidemia, ulcer and stomach
conditions, diabetes and heart problems. Only a small share (0.9 per cent) of the population was
diagnosed with depression, which might indicate an under-diagnosis of depressive symptoms.
The association between general health – which is self-rated on a Likert scale – and healthconditions – that can be considered as some objective measurements of health conditions (butcould be subject to under- or overdiagnosis) – show positive estimates when looking at wave
2007. In other words, there is a strong and statistically significant relationship between bothvariables. That is particularly true for diabetes, stroke or lung conditions.

13

Table 2 Association between general health at baseline and health conditions for which respondents have been
diagnosed or advised to seek care in waves 2007, 2009, 2011 and 2013. Ordered logit model (estimates are in log
odds, including 95% CI).
Condition
Blood pressure

Per cent
26.4

Hyperlipidaemia

10.8

Ulcer

8.8

Diabetes

8.3

Heart

7.4

Liver

3.6

Joint

3.1

Cancer2.8

Bladder

2.6

Stroke1.6

Depression

0.9

Lung

0.9

2007
0.60***(0.43, 0.78)
0.54***(0.29, 0.78)
0.25*(-0.03, 0.53)
1.19***(0.89, 1.48)
1.06***(0.76, 1.36)
0.71***(0.27, 1.15)
0.86***(0.40, 1.32)
0.67***(0.20, 1.15)
0.94***(0.45, 1.43)
1.79***(1.12, 2.46)
0.80*(-0.05, 1.66)
1.43***(0.54, 2.32)

2009
0.49***(0.29, 0.69)
0.42***(0.14, 0.71)-0.002(-0.33, 0.33)
0.70***(0.36, 1.04)
0.37**(0.02, 0.73)
0.03(-0.46, 0.53)
0.31(-0.23, 0.86)
0.19(-0.37, 0.76)
0.40(-0.16, 0.95)
0.58*(-0.11, 1.27)
0.12(-0.83, 1.07)
0.30(-0.56, 1.17)

2011
0.25**(0.03, 0.48)-0.01(-0.32, 0.30)
0.003(-0.36, 0.37)
0.49***(0.12, 0.85)
0.43**(0.05, 0.82)
0.02(-0.51, 0.55)
0.36(-0.22, 0.95)
0.37(-0.25, 0.99)
0.69**(0.08, 1.29)
0.44(-0.28, 1.15)
0.04(-1.03, 1.12)
0.38(-0.67, 1.42)

2013
0.31***(0.08, 0.55)
0.29*(-0.04, 0.62)-0.09(-0.46, 0.29)
0.44**(0.05, 0.82)
0.58***(0.17, 0.99)
0.65**(0.09, 1.22)
0.70**(0.10, 1.30)
0.14(-0.50, 0.77)
0.50(-0.13, 1.13)
0.26(-0.44, 0.97)
0.79(-0.70, 2.29)
1.10**(0.01, 2.18)

Note: General health is coded from ‘1.Good’ to ‘5.Not Good’. Health issues are coded 0 when no healthissue was diagnosed and 1 when the health issue has been diagnosed. Thus, positive log odds (>0) indicatethat there is a positive association between the specified health issue and the level of general health (thehealth issue is associated with lower general health). The model does not apply longitudinal weights anddoes not control for non-response and attrition. Significance levels: ‘***’: 0.001; ‘**’: 0.05; ‘*’: 0.1, ‘.’:>0.1.

14

3.2. OLS and Logit in wave 2007 (baseline)
First, we perform an ordered probit regression and an ordered least square regression using datafrom the first wave (2007). This is a synchronic approach that looks at the association between
the type of professional status (working, temporarily not working or not working) and thegeneral health. The model controls for age (a quadratic function of age), level of education,
gender, number of children and marital status. Data are weighted using the 2007 JSTAR
weights.
Table 3 Association between general health and employment status in wave 2007. Ordered logit and ordinary
least square weighted regressions.
Ordered Logit-0.05(-1.06, 0.96)
0.001(-0.02, 0.02)-0.01(-9.55, 9.53)-3.69(-9.72, 2.35)-3.73(-11.61, 4.15)-3.93***(-5.44, -2.42)-3.92***(-4.08, -3.77)-3.43***(-3.44, -3.42)-4.63***(-4.64, -4.61)-3.75***(-6.08, -1.42)-4.11***(-5.21, -3.02)-0.06(-5.59, 5.47)
0.49(-3.80, 4.79)-0.46***(-0.55, -0.37)-0.72(-3.10, 1.65)

Age
Age_square
Gender : Male
Educ : Elementary to middle school
Educ : High School
Educ : Junior college
Educ : Master
Educ : Other
Educ : PhD
Educ : University
Educ : Vocationalschool
Number of children
Marstat : Not Married
Empl : Temporarily not working
Empl : In employment

OLS
0.03
(-0.09, 0.14)-0.0001(-0.001, 0.001)-0.02(-0.10, 0.07)-1.08(-5.38, 3.22)-1.15(-5.45, 3.15)-1.35(-5.65, 2.96)-1.20(-5.52, 3.13)-0.56(-5.05, 3.94)-1.70(-6.22, 2.83)-1.16(-5.47, 3.14)-1.53(-5.84, 2.77)-0.07***(-0.10, -0.03)
0.51***(0.43, 0.60)-0.41(-0.91, 0.09)-0.75***(-0.84, -0.67)

General health is a categorical variable containing five answer modalities: 1. good, 2. Fairly
good, 3. average, 4. not very good, 5. not good. It can be treated as an ordered variable or as anumeric variable. Table 3 shows the results from an ordered logit regression (using general
health as an ordered variable) and from an ordinary least square regression (using general healthas a numeric variable). The variable of interest is the employment status for which the reference
category is ‘not working’ (i.e. retired).

15

Interestingly, similar trends are observed in the OLS and Ordered Logit models, but significancelevels are different. The odds of having a poor general health are 0.46 units lower for those who
are temporarily not working compared to respondents who are permanently not working in theordered model. When looking at the OLS regression, those who are temporarily not working
have, on average, a lower general health, by 0.41 units. These coefficients are pretty similar,
but the confidence levels are not: the coefficient is significant at 99 per cent in the ordered logit
model and is not significant at 90 per cent in the OLS model. By comparison, the coefficientsthat are observed for the association between being in employment (versus being permanently
out of the labor market) and general health are -0.72 (not significant) in the ordered model and-0.75 (significant at 99 per cent) in the OLS model.
In fact, the benefits of using an ordered version of the outcome variable (general health) insteadof keeping it as a numeric variable are unclear. Everything seems to indicate that general healthcould be used as a numeric outcome.
Tables 4 and 5 exhibits some descriptive results about health and employment variables. It can
be clearly observed, looking at the Kurtosis, Skewness and Shapiro test, that SPH and MHI are
normally distributed. For both SPH and MHI, the mean is slightly higher than the median,
indicating the presence of some extreme values. Looking at the employment status, one
observes that the baseline sample is composed of 62 per cent of respondents that were working
and 38 per cent of respondents combining work and pension. From 2007 to 2009, 15.2 per centof the sample left the labor market (0.2 per cent moved to a disability scheme) – that is the
population this study is analyzing.
Table 4 Descriptive statistics for self-perceived health (SPH) and mental health index (MHI)

MeanMedianKurtosis
Skewness
Shapiro

2009
2.35
2
-0.72
0.10
0.87***

SPH
2011
2.43
3
-0.51
0.08
0.88***

2013
2.41
2
-0.56
0.19
0.89***

MHI
2011
7.22
6
3.36
1.38
0.89***

2009
6.36
4
4.80
1.75
0.84***

2013
6.91
6
7.61
2.01
0.84***

Table 5 Distribution of employment statuses from wave 2007 (baseline) to wave 2013, in percentage of the total
population at the baseline.
Empl.
Work
Work and pension
Not working
Disability(2007)(62.0)(38.0)
(–)
(–)

16

2009
70.3
14.4
15.2
0.2

2011
33.4
42.7
23.5
0.4

2013
4.7
57.6
37.4
0.3

17

3.3. Latent Growth Curve
Table 6 shows the results flowing from LGM. Only a selected number of variables is shown in
the table to keep it tidy. The top of the table shows the results for the random intercept factors
and the bottom shows the random linear slopes.
The random intercept factor for those who were not working in wave 2009, compared withthose who kept working (reference category) clearly indicates that there is a direct association
between leaving the labor market and health. The SPH of those who retired is higher by 0.33
units in models 2 and 3 (statistically significant at 99 per cent), compared with those who did
not. In other words, retirement is associated with an increase on the SPH scale (that is codedfrom ‘good’ to ‘not good’). Model 1 shows the same type of coefficient but is not statisticallysignificant. Same conclusion can be made when looking at the MHI. In model 3 (that controls
for fixed and time-varying covariates) the MHI of those who retired is, on average, higher of
1.06 units (significant at 90 per cent). As for SPH, there is a direct detrimental effect of retiring
on mental health.
What is interesting is that the opposite association can be observed when looking at the random
linear slopes. The slope for SPH is negative by 0.15 units (statistically significant at 99 per cent)for those who left the labor market in 2009. Put in another way, compared with those who stayed
within the labor market, those who retired tend to have a positive change in SPH over time.
One unit increase in time (i.e. two years, in this case) is associated with a lower SPH, by 0.15.
Therefore, two units increase in time (i.e. four years) equals -0.30. Similarly, the slope for MHI
is negative by -0.72 (significant at 90 per cent) which means that two units increase in time isassociated with a lower MHI of about -1.4 units for those who retired compared with those who
kept working.
These results indicate two things. On the one hand, retiring is associated with higher SPH and
MHI values (SPH and MHI are more likely to be negative for those who retire) when looking
at the short-term change in employment status. On the other hand, the long-term effects that are
observed with the latent curve in waves 2011 and 2013 indicate that, compared with those who
remain within the labor market, those who retire have more positive SPH and MHI slopes.

18

Table 6 Latent Growth Modelling
SPH
Model 20.333***
0.030
0.743
-0.124*0.001*-0.067
1.091***-0.101Model 30.331***
0.033
0.789
-0.130*0.001*-0.083
1.107***-0.094

Intercept

Not working
Working and pension
Disability
Age
Age^square
Wage (baseline)
Job dissatisfaction
Gender (female)

Model 10.365
0.011
1.009*
–
–
–
–
–

Slope

Not working
Working and pension
Disability
Age
Age^square
Wage (baseline)
Job dissatisfaction
Gender (female)-0.0750.026-0.657**–––––-0.0880.001-0.4510.070-0.001-0.046-0.283**0.013-0.151***-0.015-0.7640.059-0.001-0.046-0.274**0.005

Not working
Working and pension
Disability
Age
Age^square
Wage (baseline)
Job dissatisfaction
Gender (female)

Model 10.460
-0.264-1.017
–
–
–
–
–

MHI
Model 1
0.984*
0.064-1.032-0.5360.004
0.046
3.739***
1.758***

Model 1
1.057*
0.048-1.705-0.6340.005
0.341
3.504**
1.758***

Intercept

Not working-0.244-0.407-0.717*
Working and pension
0.294
0.300
0.187
Disability
0.662
0.250
4.315
Age
–
-0.149-0.423Age^square
–
0.001
0.003
Wage (baseline)–-3.415*-3.276*
Job dissatisfaction
–
0.677
0.689
Gender (female)–-0.211-0.227
Note: LGM assessing the impact of the change in employment status from wave 2007 to wave 2009. 2009 is
selected as the baseline. Slopes are calculated for waves 2011 and 2013. The reference category for employment
statuses is ‘working’. Model 1 only controls for the employment status. Model 2 controls for the variable ofinterest and fixed covariates. Model 3 controls for the variable of interest, fixed and time-varying covariates.
Data are weighted using 2007 JSTAR weights and attrition and missing values is controlled using maximum
likelihood estimations. Results are produced using the R-package Lavaan. Source: the Japanese Study of Agingand Retirement (JSTAR-RIETI).
Slope

19

Unfortunately, no significant result is observed for the other employment status (except for
those who moved to a disability scheme in SPH, model 1 – which is consistent with what could
have been expected). Two covariates are of particular interest. First, job dissatisfaction is highly
associated with negative SPH and MHI at the baseline. The random linear slopes do notcompensate such a negative effect. Second, female respondents are much more likely to have aworst MHI than men at the baseline (significant at 99 per cent). But what is particularly relevant
is the intensity of the coefficients: gender and job dissatisfaction have a much higher impact onworkers’ health than employment transitions and this also indicates some policy needs.
Limitations
This study has some important limitations.
First, JSTAR does not contain information about rural Japan and this could have an impact on
the overall results as labor markets, income levels and quality of life might be expected to bedifferent in part of Japan that are not covered by the study.
Second, given the small sample size, it is not possible to disaggregate the different types ofpension benefits that can be received after the retirement age. The role of private pensions,occupational pensions and widow pensions remain to be investigated.
Third, JSTAR will soon provide retrospective data about labor market participation which will
enlighten the gender gap and allow an in-depth comparison of the impact of late careertransitions for female workers. Finally, the LGM estimates flowing from this study could be
improved using more waves – this will be done when new JSTAR waves will be released.
Conclusion
The study shows the need to consider potential health adjustments when looking at late careertransitions. The notion of heath adjustment may refer to two perspectives. On the one hand, thelongitudinal methods that are used for assessing the association between employmenttransitions and change in health struggle to deal with causation issues. It is difficult to knowwhether the change in health following the retirement transition is due to the end of theprofessional activity or whether that is the health that explains retirement. Looking at short- andlong-term variations is a way to tackle such an issue. On the other hand, health adjustment canrefer to the change in health that is observed at individual level. A hypothesis flowing from thisstudy could be that there are negative effects of retirement on health when looking at a shorttime period but that these effects fade away over time.

20

21

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Tables and figures
FiguresFigure 1 Effective age of retirement in 2017 in OECD countries. ............................................. 3
Figure 2 Latent growth curve model including a time-varying predictor ................................ 10
Figure 3 Employment participation in waves 2007, 2009, 2010 and 2013 per gender, in percent.
.................................................................................................................................................. 11
Figure 4 Average working time in waves 2007, 2009, 2010 and 2013 per gender, in hours per
week.......................................................................................................................................... 12
TablesTable 1 Sample Attrition in the original RIETI-JSTAR sample ................................................. 8
Table 2 Association between general health at baseline and health conditions for which
respondents have been diagnosed or advised to seek care in waves 2007, 2009, 2011 and 2013.
Ordered logit model (estimates are in log odds, including 95% CI). ....................................... 14
Table 3 Association between general health and employment status in wave 2007. Ordered logit
and ordinary least square weighted regressions. ...................................................................... 15
Table 4 Descriptive statistics for self-perceived health (SPH) and mental health index (MHI).................................................................................................................................................. 16
Table 5 Distribution of employment statuses from wave 2007 (baseline) to wave 2013, inpercentage of the total population at the baseline. ................................................................... 16
Table 6 Latent Growth Modelling ............................................................................................ 1926