Discussion Paper Series

University of Tokyo

Institute of Social Science
Panel Survey
東京大学社会科学研究所 パネル調査プロジェクト
ディスカッションペーパーシリーズ

Social Inequality in Health in Japan

Hiroshi ISHIDA(Institute of Social Sciences, University of Tokyo)

October 2009No.27

東京大学社会科学研究所
INSTITUTE OF SOCIAL SCIENCE UNIVERSITY OF TOKYOInstitute of Social Sciences
University of Tokyo
Panel Survey Project Discussion Paper Series No.27
October 2009

Social Inequality in Health in Japan
Hiroshi Ishida(Institute of Social Sciences, University of Tokyo)

An earlier version of the paper was presented at the meeting of the International
Sociological Association, Research Committee on Social Stratification, in Beijin, China
in May 2009. I am grateful to Professor Bob Hauser, the discussant, and theparticipants of the meeting for helpful suggestions.

Social Inequality in Health in Japan
Hiroshi Ishida(University of Tokyo)
Abstract
This paper examines the relationship between job characteristics and workplace
conditions on the one hand and health-related outcomes on the other in Japan. It
analyzes the effect of changes in the work conditions and work arrangements on thesubjective health, activity restriction, and depression symptoms, using the Japanese Life
Course Panel Survey (JLPS). The first major conclusion from the analyses pertains to
the finding that there are substantial changes in health-related outcomes between shortperiods of time. Although the distributions of health-related outcomes are very similar
at two time points, it does not necessarily imply that the individuals did not experience
the change of health conditions. On the contrary, our results suggest that there aresubstantial changes in health conditions between the two waves. Depression
symptoms are most volatile, followed by self-reported health, and then activity
restriction. Even with the activity restriction, about 40 percent of individuals changed
their responses between the two waves.
The second major conclusion of this paper is that the effects of job
characteristics depend on different health-related outcomes. Self-reported health isaffected by a variety of job-related factors. Depression symptoms are also affected bya number of job-related characteristics. The atmosphere of helping each other and thecontrol over the pace of work are two important factors which affect both depression
and self-reported health. The change in income exerts an effect of reducing depression
symptoms, while the possibility of unemployment tends to increase depression.
Activity restriction is not much affected by job characteristics. In summary, all thesefindings suggest that the workplace conditions and job characteristics have profoundinfluence on the workers’ health.

11. INTRODUCTION
Japan is known as a country of longevity. The average life expectancy at birth has
remained the highest in the world, recording 78.4 years for men and 85.3 years for
women in 2002. The average healthy life expectancy is also highest in the world at
72.3 years for men and 77.7 years for women in Japan.１ The infant mortality rate fell
to four per thousand births in 2002, one of the lowest in the world (World HealthOrganization 2004). Japan’s long average lifespan is often attributed to healthy dietand the national health system. Japan has a universal health insurance system whichbegan in 1961. The system is believed to have created an excellent health and medical
service that was indicated as one of the best in the world by the World HealthOrganization (World Health Organization 2002). In principle, all Japanese citizens aresupposed to be enrolled in one of health insurance programs that ensure access to
medical care.２ The universal health insurance system is supposed to guarantee healthcare services to every citizen in the country, regardless of class, education, and income.
Because of the universal coverage of health care, it is not surprising to find that many
Japanese people assume equal access to health services and no apparent difference inhealth conditions by socio-economic ingredients.
The topic of socio-economic differentials in health has not received much attention
in the academic disciplines until very recently. The absence of studies on social
inequality of health, however, is closely related to the lack of empirical data to address
the issue of inequality in health. There are a number of studies on health by medical
profession and public health specialists. However, these studies concentrate mainly on
biological and environmental factors in explaining health outcomes and do not pay
direct attention to socio-economic factors. On the other hand, studies on social
stratification in Japan have accumulated surveys and analyses of the Japanese
stratification system. However, the surveys such as Social Stratification and Mobility
National Surveys (SSM) which produced collections of studies on Japanese
stratification did not include questions on health until 1995(see for example, Ishida
1993; Kosaka 1995; Sato 2000; Hara and Seiyama 2005). Only in 2005, did the SSM
survey include a few questions regarding the health of the respondents. Therefore,
national surveys which contained information on both health and socio-economic
factors were mainly restricted to government surveys conducted primarily by the
Ministry of Health, Labor, and Welfare.
Shibuya, Hashimoto, and Yano (2002) is one of few exceptions which utilized
these government surveys. Using the comprehensive survey of the living conditions ofpeople about health and welfare conducted by the former Ministry of Health and2

Welfare, they found that people who lived in prefectures with higher medium income
are more likely to report good self-reported health than people who lived in prefectureswith lower medium income level. In addition, individual level characteristics affected
self-perceived health. Women, people with lower income, and older persons weremore likely to report ill-heath than men, people with higher income, and younger
persons. However, they did not assess the effect of social class or education on healthoutcome, due to limitation of the data set. Kojima (2003) used the same survey
conducted by the former Ministry of Health and Welfare and examined the relationshipbetween income and subjective health among the elderly. He concluded: “there is noclear tendency for those in ill-health to be concentrated among the poor elderly andthose in good-health to be concentrated among the wealthy elderly” (p. 89).
Yamazaki (1989) examined the mortality records of the administrative districts inlarge metropolitan areas and found that the districts with a high proportion of manuallabor workers and self-employed tend to show high mortality rate and the districts withhigh proportion of professional, managerial, clerical workers, and farmers tend to show
low mortality rate. Nakata (1999, 2001) reports based on his survey of elderly in anorthern city that occupational prestige and individual income affect subjective healthand depressive feelings.
Ishida (2004) presents the results of one of the first national surveys on health andsocial inequality. When he examined the onsets of chronic diseases, there was very little
difference in social class or income or education. People who were not employed(including those who were retired and those who were unemployed) were more likely tohave chronic diseases diagnosed by medical doctors, but among those who were
working there was virtually no difference by the kinds of occupation people were
engaged in.
More recently, there is an emerging body of literature that documents therelationship between socio-economic conditions and health outcomes in Japan, both byresearchers in medical profession and in the social sciences. Social Disparity and
Health (Kawakami, Kobayashi, and Hashimoto 2006), for example, is edited by aresearch team in the Medical School at the University of Tokyo. Tsutsumi (2006), oneof the chapters in the book, reports the relationship between occupation on the one handand job strain and hypertension on the other. Kondo (2005), an epidemiologist, showsthat education and income affect depression symptoms, subjective health, and the needfor long-term care among the elderly population in Japan. Ishida (2006) examines thenational survey of the elderly who were 65 years old or over and finds that although theonset of chronic health conditions and the visit to doctors are not affected by3socio-economic factors, there are clear differentials in physical discomfort, activity
restriction, depression, and subjective perception of health by social class and income.
Katase (2008) using the 2005 SSM survey claims that those with low education are
more likely to engage in health-risk behaviors (smoking and drinking) than thehighly-educated especially among men and that education is positively related withsubjective health. Kondo et al. (2008) claim that relative income deprivation is related
to poor self-reported health independently of absolute level of income in Japan.
Therefore, there is clearly an accumulation of the empirical studies on social inequalityof health in Japan.
This study departs from the previous studies conducted in Japan in two respects.
First, it uses panel survey to identify the effect of the socio-economic positions on
health-related outcomes. By taking full-advantage of the panel-type research design, it
focuses on the impact on health of the changes in socio-economic positions within theindividual, rather than the difference between individuals with varying socio-economic
positions. Second, the study incorporates a wide range of work-related conditions andworkplace arrangements, in addition to the typical socio-economic outcomes of
education, employment status, and income. These include indicators of autonomy andauthority at workplace, flexible work arrangements, relationship with co-workers, andopportunities for training and upgrading skills. None of the studies cited above about
health inequality in Japan used detailed measures of working conditions.
Research conducted in other nations has documented the effects of various
work-related conditions and attributes on health outcomes. Borg and Kristensen(2000) show that the changes in the self-reported health were affected not only by social
class but also by various work environment factors, including repetitive work, skill
discretion, job demands, social support, and job insecurity. Brand et al. (2007) reportthat physical and psychological job characteristics affect self-assessed health,cardiovascular and musculoskeletal health problems, and depression, and that job
characteristics mediate the association between socio-economic status and healthoutcomes. Niedhammer et al. (Niedhammer et al. 2008) take into accountpsychological demands, decision latitude, and social support, in addition to occupation,
work contract and occupational exposures, in assessing the impact on self-reported
health, absence by long sickness, and work injury. They report that all these work
factors except for psychological demands affect health outcomes and that occupational
differences in health were reduced after adjusting for work factors.
Following these examples, this study will assess the effect of various job
characteristics, in addition to education, employment status, and income, on health4conditions. Job characteristics include psychological and physical demands, social
support and relationship with co-workers, autonomy and authority at the workplace,training opportunities and skill upgrade, and flexibility and security. The key questionin this study is whether the changes in job characteristics affect the changes in healthconditions. The study will pay attention to the changes within the individual, taking
into account unobserved differences between individuals. By using the panel-typeresearch design, different statistical models which attempt to control for unobserved
heterogeneity will be employed to identify the effect of job characteristics on healthoutcomes.
2. DATA AND VARIABLES
The data set used in this paper comes from the Japanese Life Course Panel Surveys(JLPS). The first wave JLPS was conducted in Japan from January to April, 2007. It
consists of the youth panel (20 to 34 years old) and the middle-aged panel (35 to 40
years old). The 2007 JLPS sampled respondents from the population of men andwomen aged 20-34 (for the youth panel) and aged 35-40 (for the middle-aged panel)
residing in Japan in November 2006, using the electoral and resident registry.３ Because
the response rates among the youngest people (especially men) are known to be lower
than others, we stratified the sample by gender and age group, in addition to thegeographical region and city size. We sampled respondents separately for males andfemales from 20-24, 25-29, 30-34, and 35-40 age groups.
The sampled individuals were first contacted by mail and asked to take part in thesurvey. The enclosed letter explained that there would be a follow up after the initial
survey. This will reduce the response rate in the first wave, but it is hoped that thosepeople who agreed to participate will continue the survey, thereby increasing theretention rate. Those who agreed to take part in the survey, received the questionnaires
by mail, and the staff from a professional survey company visited the respondents tocollect the questionnaires. For the youth survey, 3367 respondents returned thequestionnaires and for the middle-aged survey 1433 respondents returned thequestionnaires. The response rates were 34.5% for the youth survey, and 40.4% for the
middle-aged survey.４
The second wave of JLPS was conducted from January to March, 2008. It
followed up all the respondents who returned the questionnaires in 2007. The initialinquiry mail was sent in December 2007, and the questionnaires were sent in January
2008. The staff from a professional survey company collected the questionnaires byvisiting respondents in January and February. For the youth survey, 2719 respondents

5returned the 2008 JLPS questionnaires, and for the middle-aged survey 1246
respondents returned the questionnaires. The retention rate was 80.1% for the youth
survey and 86.9% for the middle-aged survey. Because we are interested in the changeof health and work conditions across two waves, our analysis is restricted to the
respondents who completed the second wave. The youth survey and the middle-aged
survey are combined and analyzed together since the two surveys had identicalquestionnaires.
The JLPS asked a number of questions related to the respondents’ healthconditions. Three health-related outcomes are used in this paper. The first variable isself-reported health. The respondents were asked to report their present healthcondition: “how do you feel about your present health?” The responses were: “1 very
good,” “2 good,” “3 ordinary,” “4 not good,” and “5 bad.” The higher the score, theworse the self-perceived health condition. The second variable measures activity
restriction due to health. The respondents were asked: “were daily housework andactivities related to their job restricted because of health reasons in the last month?”
The following four-point scale is used to record the responses: “1 not at all,” “2seldom,” “3 sometimes,” and “4 always or almost always.” The third variable is ameasure of depression symptoms. The respondents were asked: “were you heavily
depressed during the last month?” and the responses were coded as: “1 not at all,” “2seldom,” “3 sometimes,” and “4 always or almost always.” All three variables arescored in the way that the highest score corresponds to the worst health conditions.
Socio-economic variables include the following: education, employment status,
and income. Education is measured by the attendance in higher education. The
original responses to the last school attended were six categories (years of schooling areindicated in parentheses): (1) junior high schools (9 years), (2) senior high schools (12years), (3) post-secondary vocational schools (13 years), (4) junior colleges andtechnical colleges (14 years), and (4) four-year universities including graduate school(16 years). The respondents who attended junior colleges, technical colleges,
universities, and graduate schools are given the score of one and zero otherwise forhigher education variable. Education variable was originally included in the analysis
as a years-of-schooling variable, but the difference between those who went to higher
education and those who did not seem to be the major threshold. Instead of using thelinear specification, this paper focuses on the difference between the two.
Employment status is based on the condition of employment at the time of thesurvey in 2007 and 2008. The following categories are used to distinguish
employment status: employer, full-time employee (base category), part-time and6temporary employee, self-employed and family worker, and not working. Because
those who did not work did not have responses to job characteristic variables, these
respondents were excluded from the analysis. Individual income is measured by theapproximate yen amount (in ten thousand). The respondents were asked to choose oneof 13 categories representing their individual income. The midpoints of each category
are used to estimate the individual income of the respondent in each category.
The JLPS contains rich questions about the working conditions, work
environments, and work arrangements. The following questions are asked about thecharacteristics of the workplace.５ If it applies to the workplace of the respondent, thescore of one is given to the variable, zero otherwise. Variable names are shown inparentheses.
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)

over-time work (overt) – almost everyday people do over-time work
labor shortage (labshort) – there is chronicle labor shortage
deadline (deadl) – always chased at the deadline
help each other (helpo) – there is an atmosphere of helping each otherindependent work (indepw) – most work is done independently
coordinating work (coordw) – most work is done by coordinating with each otherguidance to juniors (guidej) – there is an atmosphere of seniors guiding juniors
transfer (transf) – there is a mechanism for moving positions based on people’s
preference(9) advice to young people (advicey) – there is a designated advisor for giving advice
to young workers(10) advice for future work (advicef) – there are opportunities for getting advice about
the future work

The following questions are asked about autonomy and authority at the workplace,training opportunities, flexibility, and security. When the respondent believes that itstrongly applies or applies to his/her work, a score of one is given to the variable, zerootherwise.
(11) determine pace (pace) – I could determine the pace of my work(12) decide work pattern (decide) – I could decide my work pattern(13) decide subordinate’s work (boss) – I could decide the subordinate’s work(14) opportunities for training (train) – I have opportunities for training(15) opportunities for upgrading skills (upskill) – I have opportunities for upgrading my7

job skills(16) flexibility in work (flex) – I could arrange my work schedule to fit the needs forchild-care, housework, and study(17) insecurity in work (unsecure) – there is a possibility that I could be unemployed ina yearFinally, the respondent’s gender and age and age square are included as control
variables.
3. ANALYSIS
Distribution of Health-related Outcomes and Other Variables
Table 1 presents descriptive statistics for the variables used in this paper. The first setof variables presents the distributions of three health-related outcomes. First, withregard to the self-reported health, 15 percent of our respondents reported that theirhealth was “very good,” 34 percent “good,” 39 percent “ordinary,” 11 percent “notgood,” and 1 percent “bad” at wave 1. The distribution of self-rated health changed
very little between wave 1 (2007) and wave 2 (2008). Second, almost 70 percent of
our respondents had no activity restriction due to health conditions while 13 percent
experienced some restriction at wave 1. The proportion of those who had restriction
seems to have increased slightly from wave 1 to wave 2. Third, respondents withdepression symptoms (sometimes and always) amount to 35 percent at wave 1, and the
proportion increased slightly to 37 percent at wave 2. These one-way distributions ofhealth outcomes show little changes between the two waves.
There are significant correlations among these the health-related variables.
However, these correlations are not exceptionally high: between self-reported health andactivity restriction (r=.287 at wave 1 and r=.291 at wave2), between self-reported healthand depression (r=.284 at wave 1 and r=.264 at wave2), and between activity restriction
and depression (r=.265 at wave 1 and r=.297 at wave2). These results suggest thatthese variables are related but tap different aspects of respondent’s health condition.
The second set of variables pertains to socio-economic ones. The distribution of
education shows that half of the respondents attended institutions of higher education.
Since there is a rapid increase in the attendance rate to higher education (especiallyfour-year universities) beginning in 1990s, these young respondents clearly benefited
from the expansion of the higher education sector. Education variable is fixed and did
not change between the two waves. As to the employment status, about 60 percent of
the respondents are full-time employees and 30 percent part-time employees. Women8are much more likely to engage in part-time work than men, since the proportion ofpart-time work reaches 48 percent among women while it is only 16 percent amongmen. There are very few employers and self-employed/family workers among our
respondents. As to the individual income, we see slight increase in the average levelof income from 260 thousand yen to 274 thousand yen.
The third set of variables relate to job characteristics. There are seventeen
detailed job characteristics that are considered in this paper. Among the characteristicsof the workplace, we learn that some characteristics are more prevalent than others.
For example, overtime work and labor shortage are reported by more than 30 percent of
our respondents at wave 1, while features related to advice (whether there is a
designated advisor for young workers and whether there are opportunities for getting
advice for future work) are reported by less than ten percent of our respondents.
Cooperation and coordination with co-workers seem to be a prevalent feature since over
40 percent of the respondents report that there is an atmosphere of helping each otherand that their work is done by coordinating with each other. With regard to autonomy
and authority, over 60 percent of the respondents replied that they could determine thepace of their work and almost half replied that they could decide their work pattern.
Training and opportunities for upgrading job skills seem to be prevalent among our
respondents, and about half of the respondents report that their work allows flexibility.
The prospect of losing their job is reported by some 13 percent of respondents.
When we compare the distribution of job characteristics between the two waves,there is no substantial change. If anything, the proportion of favorable job
characteristics (such as helping each other, coordinating work, determining pace, andflexibility) seemed to have increased slightly, while the proportion of unfavorable job
characteristics (such as overtime and labor shortage) seemed to have decreased slightly.
Changes in Health-related Outcomes
Table 2 reports the cross-tabulation of health conditions at wave 1 and those at wave 2.
It shows how much health outcomes changed between 2007 and 2008 within theindividual. There are substantial changes in health conditions between the two waves.
Table 2a shows the respondents’ responses to self-reported health at two waves. The
numbers on the main diagonal indicate the cases in which the same responses wereobserved at two time points. The row percentages of those in the main diagonals, as
shown in the second row, are not very high. Among those who reported “very good”
health at wave 1, only 52 percent reported the same response at wave 2, and about athird (35%) reported “good.” Among those who reported “bad” health at wave 1, only9

38 percent remained “bad” at wave 2 and half shifted to “not good” category. If wecompute the proportion of individuals who did not change their responses between thetwo waves, it is only 53 percent of all individual cases. The remaining 47 percent
changed their response. Among those who changed responses, about half (22 %)improved their self-rated health and the other half (25%) worsened their self-reported
health. Furthermore, among those who changed responses, 85 percent changed
response to adjacent category (such as between “very good” and “good”), and theremaining 15 percent experienced two-step changes. Individuals, therefore, seem tomake small changes in reporting their health.
Table 2b presents the cross-tabulation of the responses to activity restriction at
wave 1 and 2. Among those who had no activity restriction at wave 1, three-fourths(74%) remain in the same category after a year. In contrast, respondents who were inother categories at wave 1 tend to experience change of their health conditions; only
about 30 percent remain in the same category. Among those who always or almost
always had restrictions in daily activities due to health problems at wave 1, only 31
percent remained in the same category while about a half (49%) experienced two or
three-step changes to either “seldom” or “not at all” categories. It is possible that someof these people had physical injuries at wave 1 and later recovered from the injuries.
The proportion of individuals who did not change their health conditions with respect toactivity restriction is 60 percent, so the remaining 40 percent changed their conditions.
However, it should be noted that the respondents who did not have any restriction at all
are less likely to change their conditions than those who were in other categories atwave 1. With respect to the direction of change, among those who changed responses
58 percent of those did so toward the direction of worsening their health, while 42
percent changed toward the direction of improving their health.
Table 2c shows the cross-tabulation of the responses to the depression question at
wave 1 and 2. The proportion of individuals who did not experience change indepression question is 47 percent, so more than a half (53%) of all individuals
experienced change in depression symptoms. Among those who experienced change,
54 percent changed toward the direction of worsening depression while 44 percent
toward the direction of improving the depression.
In summary, despite the fact that the distributions of health outcomes are very
similar between wave 1 and wave 2, it does not necessarily imply that individuals did
not experience a change of health conditions. On the contrary, our results suggest that
there are substantial changes in health conditions between the two waves. Depression
symptom is most volatile, followed by self-reported health, and then activity restriction.

10

Even with the activity restriction, about 40 percent of individuals changed their
responses between the two waves. In the next section, we will examine what factors
account for these changes in health outcomes.
Determinants of Health-related Outcomes
Table 3 presents the results of predicting the self-reported health. Four different
models are fitted to the data. The first model is the fixed effects model and can be
written as:

yit = αi + Xit β

+ εit

(1)

where i stands for individual and t stands for time or panel wave. αi is a set of intercept
for the ith individual, and it is treated as a parameter to be estimated for eachcross-section observation i. Xit is the i th observation on explanatory variables, and εitdenotes the disturbance term.
The important feature of the fixed effects model is that we allow for arbitrarycorrelation between explanatory variables and unobserved unit (individual) effect(Wooldridge 2002), and the main advantage of using the panel data is “the ability toremove a time-invariant unobservable” (Lee 2002, p. 16). The parameters of the fixed
effects model uses information on the effect of the changes in the explanatory variableson the changes in the dependent variable, so that the parameters of the fixed effects
model are not affected by the unobserved heterogeneity bias.
The second model is the random effects model and can be written as:

yit =

a +

Xit β

+ θi + εit(2)where θi denotes the unit (individual) effects.

The key feature of the random effects
model is that the unobserved θi are assumed to be uncorrelated with explanatory
variables and the unit effects θi are treated as a random effect. The random effects
models are prevalent in sociological research (Halaby 2004). The attraction of therandom effects model is that it can include time-invariant explanatory variables so thatvariation between individuals may be assessed at the same time as the change within theindividual. Two versions of the random effects models are considered in the paper.
Model (2) includes only time-variant explanatory variables, and Model (3) includes
both time-variant and time-invariant explanatory variables. Model (2) is shown for the
purpose of comparison with Model (1) and (3).

11

Finally, the Hausman and Taylor (HT) estimation model is introduced. The
model can be written as:yit =Xit β + Zi γ

+ θi + εit(3)where Zi are cross-sectional time-invariant variables. Hausman and Taylor (1981) split
X and Z into two sets of variables: X = [ X1 ; X2 ] and Z = [ Z1 ; Z2 ]. The majoradvantage of this model is that we are able to break down X and Z variables into two
components: those which are correlated with θi and those which are independent of θi .
X1 and Z1 are assumed to be exogenous and are not correlated with θi and εit . In
contrast, X2 and Z2 are endogenous because they are correlated with θi but not with εit .
The fixed effects model would sweep θi and “remove the [heterogeneity] bias, but in theprocess it would also remove the Zi and hence the Within [fixed effects] estimator will
not give an estimate of γ (Baltagi 2005, p. 126). The HT estimation will include
time-variant variables while relaxing the assumption of the random effects model ontime-invariant variables. The HT estimation, “which involves mixing estimators thathave the desirable properties of fixed effects for time-varying explanatory variables
with random effects estimators for time-invariant explanatory variables, goes to theheart of the resistance many researchers have shown to fixed effects estimation”(Halaby
2005, p. 530). Because the individual unit effects are likely to be correlated with sometime-variant explanatory variables, the HT estimation has a practical appeal. The HT
estimation will allow the researchers to take advantage of the fixed effects model (i.e.,removing the heterogeneity bias) while retaining the ability to identify the parameters of
the time-invariant variables (i.e., estimating the unit individual effects).
Table 3 Model (1) presents the estimators of the fixed effects model for the
self-reported health. The model does not distinguish between time-variant exogenous
and time-variant endogenous since there is no time-invariant variable included in theequation.６ Among the job characteristics, the following variables indicate significant
effects: (1) helpo - when the respondent’s workplace becomes more cooperative
inducing the atmosphere of helping each other, there is an improvement in the
self-reported health (negative sign); (2) advicef – when the respondent’s workplacechanges in the way that there are opportunities for getting advice about future work,
self-reported health tend to improve; (3) decide and pace – when the respondent’s
workplace changed and he/she is now able to decide his/her work pattern and pace ofwork, there is an improvement in the self-reported health; and (4) deadl – when the
respondent’s workplace becomes always chased at the deadline, his/her self-reported

12health tends to worsen.
It is important to notice that these effects of job characteristics are estimated after
controlling for the heterogeneity bias. These effects pertain to the effect of the changes
in the job characteristics within the individual, and unobserved differences between
individuals (unit effects) are removed from the model.
Table 3 Models (2) and (3) are the random effects model. Model (2) includes
time-variant explanatory variables, and Model (3) includes both time-variant andtime-invariant (gender and education) variables in the equation. By comparing Model(2) and (3), the addition of the two time-invariant variables did not change the estimatesof job characteristics, and these two variables did not contribute to eliminating theheterogeneity bias. At the bottom of the table, we show the results of the Hausman
tests by comparing the estimates of the fixed effects model and the random effects
model. Large values of the Hausman chi-square statistics will imply that theassumption of no correlation between the unit effects and the time-variant explanatory
variables is rejected and hence the fixed effects model is preferred. Hausman tests areroutinely used in econometrics analyses, while they are not popular in sociologicalresearch (Halaby 2005). The results of the Hausman tests lead us to reject theassumption of the null hypothesis, and the estimates from the random effects model are
subject to heterogeneity bias.
By comparing the estimates of the fixed effects and the random effects models,there are two noticeable differences. Income and labor shortage (labshort) coefficientsare significant in the random effects model, while they are not significant in the fixed
effects model. This finding implies that the estimates of the random effects model tend
to overestimate the “true” effect of the change because they confound the effect of thechange in the time-variant explanatory variable with the effect of the difference between
individuals. For example, the effect of income (-0.134) of the random effects model isabout twice of the size of the effect of income (-0.065) of the fixed effects model
because the effect of the random effects model include both the effect of the change inincome between the two waves within the individual (i.e., increased income leads toimproved self-reported health) and the effect of individual differences in income (i.e.,
people with higher income tend to have better self-reported health than those with lower
income). Similarly, the effect of the labor shortage of the random effects model
includes both the effect of the change of the workplace condition in which there isshortage of labor and the effect of difference between respondents who work in aworkplace with short labor supply and those who do not.
Finally, Model (4) shows the estimates of the HT estimation model. Time-variant

13and time-invariant variables are each divided into two components: exogenous variables
which are independent of θi and endogenous variables which are correlated with θi.
HT model provides the estimates of the time-invariant variables in addition totime-variant explanatory variables, by allowing some of time-invariant variables to beendogenous. The Hausman test statistics indicate that the assumption of null
hypothesis is not rejected, thereby preferring the HT model to the fixed effects model.
The parameters of job characteristics variables are very similar between those of the
fixed effects model and those of the HT model, and the coefficients which aresignificant are the same in the two models. By relaxing the assumption of the random
effects model, the coefficients of income and labor shortage are no longer significant.
The advantage of HT model over the fixed effects model is that the former provides
estimates for the time-invariant effects, that is, the unit individual effects. Both genderand education are not significant at 5 percentage level, but they are significant at 10
percentage level. Males tend to show lower self-reported health score than females,and people with higher education tend to show higher self-reported health score thanthose without higher education.
Table 4 presents the results of fitting various models predicting activity restriction.
When we compare four models using the Hasuman statistics, we arrive at theconclusion that the fixed effects model is our preferred model. The assumption of thenull hypothesis of no correlation for the random effects model and for the HT model isrejected at 5 percent significance level.
Using the fixed effects model as our representation, we find the following effects
of job characteristics on activity restriction: (1) guidej – when the respondent’s
workplace is changed to have the atmosphere of seniors guiding juniors, the respondentis less likely to experience activity restriction; and (2) labshort – when the respondent’s
workplace changes to experience chronicle labor shortage, he/she is more likely tosuffer from activity restriction.
These two variables are the only factors affecting activity restriction. In otherwords, activity restriction is not much affected by job characteristics and workplace
conditions. Because the analysis of the effect of job characteristics requires that the
respondent is working at the time of the survey, those with severe activity restriction arelikely to be out of the labor market and excluded from our analysis.
Table 5 presents the results of predicting depression symptoms. The comparisonof four models suggests (1) that the null hypothesis used in the random effects model isrejected and the fixed effects model is preferred over the random effects model, and (2)the Hausman test is not significant for the HT model and the HT model is preferred over14

the fixed effects model. With regard to the effects of job characteristics, the following
coefficients of the HT model are significant: (1) helpo – when the workplace changed to
an atmosphere of helping each other, the depression symptoms are reduced; (2) advicey– when the respondent’s workplace changes in the way that advisors to young workers
are provided, depression tends to improve; (3) income – when the respondent’s income
increased, there is a tendency for the reduction in depression; (4) pace – when the
respondent is able to decide his/her pace of work, there is an improvement in the
depression symptoms; and (5) when the respondent becomes to feel that there is apossibility of unemployment in a year, there is a tendency for increased depression
symptoms.
The comparison of the estimates of the fixed effects model and the random effects
model suggests that the effects of overtime and deadline in the random effects model are
primarily the result of the differences in these two job characteristics between
individuals. Because the magnitude of these two effects is much smaller in the fixed
effects model, the effects of the random effects model picked up the following effect:(1) the respondents who have a job in the workplace where people do over-time work
almost everyday are more likely to suffer from depression than those who do not workin such a place; and (2) the respondents who have a job in the workplace where workers
are always chased at the deadline are more likely to suffer from depression than those
who do not work in such a place.
4. DISCUSSION
This paper examined the relationship between job characteristics and workplace
conditions on the one hand and health-related outcomes on the other. We identified
three different health-related outcomes and examined the effects of various job-related
variables separately for each outcome. The first major conclusion from the analyses
pertains to the finding that there are substantial changes in health-related outcomes
between short periods of time. Although the distributions of health-related outcomesare very similar at two points in time, it does not necessarily imply that the individuals
did not experience a change in health conditions. On the contrary, our results suggestthat there are substantial changes in health conditions between the two waves.
Depression symptom is most volatile, followed by self-reported health, and then activity
restriction. Even with the activity restriction, about 40 percent of individuals changed
their responses between the two waves.
The second major conclusion of this paper is that the effects of job characteristics
depend on the different health-related outcomes. Self-reported health is affected by a15

variety of job-related factors. Changes in the workplace conditions and environments
regarding the atmosphere of helping each other, getting advice for future work, beingchased at the deadline affect the self-reported health. The ability to control work pace
and work pattern seems to improve self-reported health.
Depression symptoms are also affected by a number of job-related characteristics.
The atmosphere of helping each other and the control over the pace of work are two
important factors which affect both depression and self-reported health. The change inincome exerts an effect of reducing depression symptoms, while the possibility of
unemployment tends to increase depression.
Activity restriction is not much affected by job characteristics. The only factors
which had significant effects are the changes in the workplace conditions regarding theatmosphere of seniors guiding juniors and chronicle labor shortage. The guidance
tends to reduce the experience of activity restriction, while labor shortage tends toincrease the likelihood of activity restriction. As argued above, the lack of extensive
effects of job characteristics on activity restriction may be due to the fact that only
respondents working at the time of the survey are included in the analysis and thosewith severe activity restriction who are likely to be out of the labor market are excluded
from our analysis.
In summary, all these findings suggest that the workplace conditions and job
characteristics have profound influence on the workers’ health.

16

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17

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18

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19

Table 1 Descriptive Statistics
Health-related variables
Self-reported Health
1 very good
2 good3 ordinary
4 not good
5 bad
Activity restriction
1 not at all
2 seldom
3 sometimes
4 always/almost always
Depression
1 not at all
2 seldom
3 sometimes
4 always/almost always

2007

2008

14.8
34.4
39.3
10.7
0.9

14.0
35.0
37.6
12.1
1.3

69.0
17.5
10.2
3.3

63.8
19.1
12.8
4.3

35.4
30.1
26.0
8.5

33.6
29.3
27.2
9.9

1.5
47.7
13.7
37.2
50.8
15.2

1.5
47.7
13.7
37.2
50.8
15.2

Socio-economic variables
Education
middle school
high school/ vocational schools
junior college/technical college
university/ graduate school
higher education attendance
average years of schooling
Employment status
employerfull-time employee
part-time employee
self-employed, family worker
Incomeaverage income

1.6
59.1
31.3
6.5

1.9
60.7
30.6
6.7

260.0

273.5

Job characteristic variables
Overtime (overt)
Labor shoratge (labshort)
Deadline (deadl)
Help each other (helpo)
Independent work (indepw)
Coordinating work (coordw)
Guidance to juniors (guidej)
Transfer (transf)
Advice to young people (advicey)
Advice for future work (advicef)
Determine pace (pace)
Decide work pattern (decide)
Decide subordinate's work (boss)
Opportunities for training (train)
Opportunities for upgrading skills (upskill)
Flexibility in work (flex)
Insecurity in work (unsecure)

40.2
30.9
17.7
41.6
28.8
46.5
33.2
10.3
3.5
7.9
61.3
47.7
20.8
47.0
60.8
48.2
12.6

39.9
26.8
17.3
43.8
30.0
48.8
34.4
11.7
4.0
9.0
63.6
49.3
21.7
49.8
60.6
52.3
13.0

Gendermale
female

47.9
52.1

47.9
52.1

Age
20-24 in 2007
25-29 in 2007
30-34 in 200735-40 in 2007
average age

19.4
20.8
28.3
31.5
30.8

19.4
20.8
28.3
31.5
31.8

20

Table 2 Changes in Health Outcomes between Wave 1 (2007) and Wave 2 (2008)a) Self-reported health
Wave 1 (2007)
1 very good
2 good3 ordinary
4 not good
5 bad
Total

Wave 2 (2008)
1 very good
301
51.6
175
12.9
74
4.8
3
0.7
0
0.0
553
14.1

2 good
201
34.5
719
53.2
414
26.8
37
8.8
4
11.8
1375
35.0

3 ordinary 4 not good
72
9
12.3
1.5
392
61
29.0
4.5
858
194
55.5
12.5
158
195
37.7
46.5
0
17
0.0
50.0
1480
476
37.6
12.1

5 bad
0
0.0
5
0.4
6
0.4
26
6.2
13
38.2
50
1.3

b) Activity restriction
Wave 2 (2008)
1 not at all 2 seldom3 sometimes 4 always/
almost always
1 not at all
1993
425
229
55
73.8
15.7
8.5
2.0
2 seldom
317
197
133
37
46.3
28.8
19.4
5.4
3 sometimes
147
108
113
36
36.4
26.7
28.0
8.9
4 always/almost always
43
20
27
40
33.1
15.4
20.8
30.8
Total
2500
750
502
168
63.8
19.1
12.8
4.3

Wave 1 (2007)

Total
2702
100
684
100
404
100
130
100
3920
100

c) Depression symptom
Wave 2 (2008)
1 not at all 2 seldom3 sometimes 4 always/
almost always
1 not at all
801
342
195
39
58.2
24.8
14.2
2.8
2 seldom
313
462
323
71
26.8
39.5
27.6
6.1
3 sometimes
169
289
423
135
16.6
28.4
41.6
13.3
4 always/almost always
26
51
117
139
7.8
15.3
35.1
41.7
Total
1309
1144
1058
384
33.6
29.4
27.2
9.9
Wave 1 (2007)

Total
1377
100
1169
100
1016
100
333
100
3895
100

Note: The first row shows cell count, and the second row shows row percentage.

21

Total
583
100
1352
100
1546
100
419
100
34
100
3934
100

Tabel 3 OLS Fixed Effects, GLS Random Effects, and GLS Hausman/Taylor Estimatesfor Self-reported HealthModel (1)Model (2)Model (3)Model (4)
FE
RE
RE
HT
Time variant exogenous
employ-0.4050.008
0.000-0.123
part-0.077-0.064 *-0.055-0.055
semp
0.306
0.058
0.026
-0.083
helpo-0.080 **-0.085 **-0.083 **-0.086 **indepw-0.013-0.027-0.022-0.011
coopw-0.038-0.032-0.034-0.038
guidej-0.008-0.031-0.033-0.012transf0.054-0.011-0.001
0.014
advicey-0.026
0.010
0.009
0.010
advicef-0.119 *-0.107 **-0.106 **-0.100 *decide-0.069 *-0.071 **-0.070 **-0.074 **Time variant endogenous
age-0.0550.001
0.002-0.080
age2
0.002
0.000
0.000
0.002
income-0.065-0.139 *-0.134 *-0.091
overt-0.030-0.017-0.010-0.027
labshort
0.043
0.089 **
0.089 **
0.044
deadl
0.093 *
0.127 **
0.129 **
0.092 *
pace
-0.063 *-0.083 **-0.076 **-0.061 *boss-0.0270.023
0.007-0.023
train
0.016
0.004
0.006
0.014
upskill-0.038-0.050 *-0.048 *-0.040flex-0.050-0.084 **-0.076 **-0.048
unsecure
0.017
0.061 *
0.057
0.017
Time invariant exogenous
male
0.054
0.066
Time invariant endogenous
education-0.189 **-0.492constant
Wald chi-sq
Hausman chi-squaredf
p-value

2.721

2.489 **

2.560 **

3.452 **

222.69
41.35
23
0.011

277.98
37.29
23
0.030

134.60
12.24
10
0.269

Note: The coefficients for income are multiplied by 1000.
* siignificant at 5%; ** significant at 1%

22

Tabel 4 OLS Fixed Effects, GLS Random Effects, and GLS Hausman/Taylor Estimatesfor Activity Restriction
Model (1)Model (2)Model (3)Model (4)
FE
RE
RE
HT
Time variant exogenous
employ0.027-0.001
0.005-0.125
part-0.050-0.008-0.0190.015
semp-0.0200.039
0.050-0.058helpo-0.019-0.035-0.040-0.031indepw
0.021
0.011
0.014
0.022
coopw
-0.028-0.033-0.039-0.046
guidej-0.117 **-0.061 *-0.064 **-0.057 *transf0.033-0.018-0.022-0.002
advicey
0.030
0.038
0.035
0.034
advicef-0.085-0.037-0.030-0.032
decide-0.021-0.036-0.033-0.036Time variant endogenous
age
0.087
0.036
0.033
-0.079
age2
0.000
0.000
0.000
0.002
income0.108-0.295 **-0.193 **
0.066
overt
-0.016-0.049 *-0.041 +-0.012labshort
0.089 *
0.067 **
0.064 **
0.081 *
deadl
-0.0340.025
0.028-0.032
pace
0.051
0.000
0.004
0.057
boss
0.003
0.039
0.049 +
0.001
train
0.037
0.028
0.029
0.049
upskill-0.062-0.004-0.001-0.061 *
flex
0.011
0.029
0.025
0.015
unsecure-0.0060.125 **
0.130 **
-0.005Time invariant exogenous
male-0.126 **-0.163 **Time invariant endogenous
education-0.029-0.516constant
Wald chi-sq
Hausman chi-squaredf
p-value-1.3200.948 **

1.069 **

2.444 *

108.02
78.14
23
0.00

131.88
73.78
23
0.00

91.81
23.44
10
0.01

Note: The coefficients for income are multiplied by 1000.
* siignificant at 5%; ** significant at 1%

23

Tabel 5 OLS Fixed Effects, GLS Random Effects, and GLS Hausman/Taylor Estimatesfor Depression
Model (1)Model (2)Model (3)Model (4)
FE
RE
RE
HT
Time variant exogenous
employ
0.206
0.066
0.077
-0.026
part-0.057
0.035
0.023
0.044
semp
0.260
0.126 *
0.142 *
0.033
helpo
-0.055-0.056 *-0.061 *-0.069 *indepw-0.033
0.000
0.004
0.003
coopw
-0.049-0.006-0.014-0.020
guidej-0.056-0.048 +-0.052 +-0.021transf-0.073-0.070 +-0.073 +-0.079 +advicey
0.146 +
0.102
0.098
0.127 *
advicef0.013-0.035-0.027-0.019decide-0.042-0.045-0.041-0.053 +Time variant endogenous
age
0.154
-0.052 +-0.056 *0.035
age2-0.001
0.001
0.001
0.000
income-0.211-0.257 **-0.140 *-0.249 *
overt
0.017
0.056 *
0.066 *
0.026
labshort-0.0200.030
0.026-0.024deadl-0.012
0.092 **
0.097 **
-0.007pace-0.112 **-0.117 **-0.115 **-0.113 **
boss
0.049
0.049
0.060 +
0.043
train
0.031
0.032
0.031
0.041
upskill
0.022-0.042-0.039
0.017
flex
0.007
-0.034-0.042
0.006
unsecure
0.156 **
0.234 **
0.238 **
0.161 **
Time invariant exogenous
male-0.154 **-0.108 **Time invariant endogenous
education-0.028-0.118constant
Wald chi-sq
Hausman chi-squaredf
p-value-1.0803.284 **

3.437 **

1.468

222.19
52.74
23
0.00

247.16
53.57
23
0.00

96.93
15.98
10
0.10

Note: The coefficients for income are multiplied by 1000.
* siignificant at 5%; ** significant at 1%

24

１

Healthy life expectancy is computed from life expectancy, but includes an adjustment
for time spent in poor health. Healthy life expectancy measures the equivalent numberof years in full health that a newborn child is expected to live based on the present levelof mortality rates and the distribution of health states in the population (World HealthOrganization 2004).
２
People who are receiving pubic assistance (low income families) are exempt from thepayment of premium for the national health insurance.
３
Because of the change in the rules regarding the access to official registries, bothelectoral and resident registries are used so that we could assure sampling from each
district. The usual procedure is try to access the electoral registry and, if it is notpossible, the resident registry was used instead.
４
The response rate is computed by the number of cases which returned thequestionnaires divided by the number of sample attacked. Among those who wereoriginally sampled, we excluded those who were dead, or had no correct address, or
moved out the address sampled.
５
The 2007 JLPS asked respondents who worked at the time of the survey to report thejob characteristics about the current work, and asked respondents who did not work at
the time of the survey to report the job characteristics about the last job they held. The
2008 JLPS asked the job characteristics to only those who worked at the time of thesurvey. Therefore, the respondents who did not work at wave 2 are excluded from theanalysis although the respondents who did not work at wave 1 but had previous job
were included in the analysis.
６ The distinction between the time-variant exogenous and the time-variant endogenous
applies only to the HT estimation model.

125東京大学社会科学研究所パネル調査プロジェクトについて
労働市場の構造変動、急激な少子高齢化、グローバル化の進展などにともない、日本社会における就業、結婚、家族、教育、意識、ライフスタイルのあり方は大きく変化を遂げようとしている。これからの日本社会がどのような方向に進むのかを考える上で、現在生じている変化がどのような原因によるものなのか、あるいはどこが変化してどこが変化していないのかを明確にすることはきわめて重要である。
本プロジェクトは、こうした問題をパネル調査の手法を用いることによって、実証的に解明することを研究課題とするものである。このため社会科学研究所では、若年パネル調
査、壮年パネル調査、高卒パネル調査の３つのパネル調査を実施している。

本プロジェクトの推進にあたり、以下の資金提供を受けた。記して感謝したい。

文部科学省・独立行政法人日本学術振興会科学研究費補助金
基盤研究 S：2006 年度～2010 年度
厚生労働科学研究費補助金
政策科学推進研究：2004 年度～2006 年度
奨学寄付金
株式会社アウトソーシング（代表取締役社長・土井春彦、本社・静岡市）：2006 年度～2008 年度

東京大学社会科学研究所パネル調査プロジェクト
ディスカッションペーパーシリーズについて
東京大学社会科学研究所パネル調査プロジェクトディスカッションペーパーシリーズは、
東京大学社会科学研究所におけるパネル調査プロジェクト関連の研究成果を、速報性を重視し暫定的にまとめたものである。

東京大学社会科学研究所パネル調査プロジェクト ディスカッションペーパーシリーズ

No.1

山本耕資

標本調査における性別・年齢による層化の効果：100 万人シミュレーション（2007 年 4 月発行）

No.2

石田浩
三輪哲
山本耕資
大島真夫

仕事・健康・希望：「働き方とライフスタイルの変化に関する調査（JLPS）2007」の結果から（2007 年 12 月発行）

No.3

中澤渉

性別役割分業意識の日英比較と変動要因：British Household Panel
Survey を用いて（2007 年 12 月発行）

No.4

戸ヶ里泰典

大規模多目的一般住民調査向け東大健康社会学版 SOC3 項目スケー
ル：(University of Tokyo Health Sociology version of the SOC3 scale:
SOC3-UTHS)の開発（2008 年 1 月発行）

No.5

戸ヶ里泰典

20～40 歳の成人男女における健康保持・ストレス対処能力 sense ofcoherence の形成・規定にかかわる思春期及び成人期の社会的要因に関する研究（2008 年 1 月発行）

No.6

田辺俊介
相澤真一

職業・産業コーディングマニュアルと作業記録（2008 年 2 月発行）

No.7

中澤渉

若年層における意識とライフスタイル：JLPS と BHPS における日英の家事労働と性役割意識の比較（2008 年 3 月発行）

No.8

深堀聰子

若者の働くこと・結婚すること・子どもをもつことに関わる意識
高卒パネル（JLPS-H）と NELS による日米比較（2008 年 3 月発行）

No.9

戸ヶ里泰典

若年者の婚姻および就業形態と健康状態、健康関連習慣との関連性の検討（2008 年 3 月発行）

No.10

三輪哲

働き方とライフスタイルの変化に関する全国調査 2007 における標本特性と欠票についての基礎分析（2008 年 3 月発行）

No.11

安藤理

公共政策支持の規定要因～公共事業と所得再分配に着目して～（2008 年 4 月発行）

No.12

長尾由希子

若年男女における性別役割分業意識の変化とその特徴：高校生のパネル調査から（2008 年 4 月発行）

No.13

伊藤秀樹

高校生の自信と卒業後の揺らぎ（2008 年 4 月発行）

No.14

相澤真一

誰が仕事をやめたがっているのか：重要なのは職場環境か、それとも家庭か？（2008 年 6 月発行）

No.15

元治恵子

若年層のキャリアデザイン・ライフデザインの変化―高校在学時から高卒 3 年目への変化―（2008 年 6 月発行）

No.16

橋本摂子

性別役割意識の揺らぎをたどる（1）―結婚アスピレーションから見た行動規範と現状追認の距離―（2008 年 6 月発行）

No.17

石田浩

世代間移動の閉鎖性は上昇したのか（2008 年 11 月発行）

No.18

石田浩
三輪哲
村上あかね

結婚・健康・地域：「働き方とライフスタイルの変化に関する調査（JLPS）2008」の結果から（2008 年 12 月発行）

No.19

SawakoChange in Living Arrangement of Unmarried Adults with Parents and
SHIRAHASE Income Inequality in Japan with Comparative Perspective（2009 年 2 月発行）

No.20

WataruInequality of Opportunities for Access to Universities among the Japanese
NAKAZAWA Young People: Focused on the Scholarship Loan Program（2009 年 2 月発行）

No.21

Hiroshi
ISHIDA

Educational Attainment and Social Background（2009 年 2 月発行）

No.22

大島真夫

大学就職部の斡旋機能とその効果（2009 年 3 月発行）

No.23

中澤渉

職業的地位の変容に関する基礎分析ら（2009 年 3 月発行）

No.24

戸ヶ里泰典

ストレス対処能力概念 Sense of Coherence の抑うつ傾向ならびに心理社会的な職場環境との因果関係の検証―構造方程式モデリングを用いた検討（2009 年 4 月発行）

No.25

戸ヶ里泰典

若年成人男女における慢性疾患の有病率の分布と就業、婚姻との関連の検討―自己報告を国際疾病分類（ICD-10）に基づいて分類した
データより（2009 年 5 月発行）

No.26

大島真夫

誰が大学就職部を利用するのか（2009 年 9 月発行）

JLPS wave1 と wave2 の比較か

No.27

Hiroshi
ISHIDA

Social Inequality in Health in Japan（2009 年 10 月発行）

東京大学社会科学研究所 パネル調査プロジェクト
http://ssjda.iss.u-tokyo.ac.jp/panel/