Disc
cussioon Papper Seeries

Univeersityy of Tokyyo

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

Are ThereT
IIncreasing Barrriers
to Inttergeneerationaal Mobility?
Trendss in Inteergenerrationall Class Reprodductionninn Conteemporaary Japaan

Hirosshi ISH
HIDA(Institutee of Social Sciences, UniversityUofo Tokyo)Feebruary 20
011
No.42

東京大学社
社会科学研究所
INSTITUTTE OF SOCIAAL SCIENCEE UNIVERSIITY OF TOKY
YO

Institute of Social Sciences
University of Tokyo
Panel Survey Project Discussion Paper Series No. 42
February 2011

Are There Increasing Barriers to Intergenerational Mobility?
Trends in Intergenerational Class Reproduction in Contemporary Japan
Hiroshi Ishida(Institute of Social Sciences, University of Tokyo)

I am grateful to the 2005 SSM Research Committee for permission to use the 2005
Social Stratification and Social Mobility Japan Survey (SSM) data, and to the Project ofJapanese Life Course Panel Survey for permission to use the Japanese Life Course
Panel Survey (JLPS). I thank the members of the 2005 SSM Research Committee for
their comments on earlier drafts of this paper. I am grateful to Yoshio Sugimoto andhis translation team at Trans Pacific Press for their assistance in the production of thisarticle. This paper is a revised version of my article, “Trends in Intergenerational Class
Inheritance,” Theories and Methods 23(2): 41–63 (in Japanese).

Are There Increasing Barriers to Intergenerational Mobility?
Trends in Intergenerational Class Reproduction in Contemporary Japan
Hiroshi Ishida(University of Tokyo)
Abstract
This study examines trends in intergenerational mobility using the method of survival
analysis. It focuses on the intergenerational inheritance of the upper white-collar classand of the non-skilled manual class, and uses four historical periods, rather than thesurvey years, as the unit of trend analysis in order to specify the effect of historicalcontext. The study examines separately the entry into the upper white-collar class orinto the non-skilled manual class at the time of first entry into the labor market andentry into the upper white-collar class or into the non-skilled manual class during the
career history.
The analysis reveals that entry into both the upper white-collar class and thenon-skilled manual class at the time of first labor market entry is strongly affected byclass origin. However, the effect of class origin does not vary by historical context. An
analysis predicting the hazard rate of entry into the upper white-collar class (or into thenon-skilled manual class), given that the person has not entered the class, presents a
different picture. The chances of reaching the upper white-collar class (or the
non-skilled manual class) through one’s occupational career are strongly affected byclass origin. Furthermore, the effect of class origin appears to be stronger in the mostrecent period of 1996–2005. In other words, the inheritance of the upper white-collar
class and of the non-skilled manual class seems to have increased in the most recent
period.

1

1.

INTRODUCTION
Intergenerational social mobility is a research area which sociologists haveaddressed over many years. Status inheritance between fathers’ and children’s social
positions has been widely used as an index to show the degree of social closure. In thecontext of industrialization thesis and modernization theories, prior studies focused onincreasing intergenerational mobility and social fluidity. Attention was focused on theextent to which the inter-regional movements from rural to urban areas and the
expansion of education have facilitated such mobility. However, in recent debate oversocial disparity in Japanese society, intergenerational mobility has been considered to bereflective of the social closure of the class system and the expansion of social inequality.
The way in which intergenerational social mobility is treated depends very much upon
the historical background of each period.
This paper contributes to this time-honored research area by examining threesets of issues. The first is methodological. This paper proposes a survival analysis
approach which is different from the conventional methods such as mobility table
analysis (including cross tabulations and log-linear models) and path analysis (often
used in status attainment models). Survival analysis has previously been used for
intra-generational mobility analysis, for instance, in the examination of occupational
career. However, this paper will show that this method can be employed forinvestigating mobility between generations. Second, this paper attempts to criticallyscrutinize the accumulated studies of intergenerational mobility and to demonstrate that
new analytical methods can shed light upon issues which earlier research has failed toaddress. Specifically, by treating occupational career paths as lines rather than dots, andby taking into account the time dimension, we will examine the process through whichchildren inherit their fathers’ classes and the degree to which historical context
influences class inheritance. Third, the new methodological strategy endeavors to makefresh observations about the so-called ‘social disparity’ issues which both Japanesescholars and mass media have frequently taken up since the latter half of the 1990s.
2. HYPOTHESIS OF INCREASING BARRIERS TO INTERGENERATIONAL
MOBILITY
Numerous studies on the trends of intergenerational mobility have been
conducted using the SSM data collected every ten years since1955. Hara and Seiyama(1999), Ishida (2000), Kanomata (2001) and others have pointed out that the patterns ofintergenerational mobility in postwar Japan have been stable, showing no substantialchanges. The association between class origin and class destination is consistently2strong for more than half a century since the end of the Second World War, with thepattern of association in Japan resembling those of other industrial countries (Ishida,
Golthorpe, and Erikson, 1991). However, some studies have raised questions about thestability of trends in intergenerational mobility.1 Sato (2000) highlighted the point inJapan as Unequal Society, which established itself as a best-seller at the beginning of
the 21st century.
Sato argues that conventional intergenerational mobility research suffers from afundamental lacuna: it regards the present occupation of each respondent from 20 to 69
years of age as his class destination. He points out that we should see occupational
career as a line rather than a point at the time of the survey (see also Yasuda, 1971 forsimilar ideas). Father’s principal occupation is usually used as the index of class origin.
As far as the respondent is concerned, his occupation at the age of 40 best corresponds
to this. In Sato’s judgment, using the long range from 20 to 69 years of age confounds
various historical factors. Focusing upon respondents from 40 to 59 years of age, one
can limit such influences. On this basis, Sato picks up males in this ten year age bracket
from the 1955 to 1995 SSM survey data so that he can identify each respondent’s
occupation at the age of 40. Using the class position at age 40, rather than his present
class, as the indicator of class destination, Sato then constructs new intergenerational
mobility tables and makes a fresh discovery: intergenerational mobility to upper
white-collar employees (professional and managerial class) has declined from 1985 to
1995, claiming that entry to this class has increasingly been closed. This is anobservation that is not revealed when the respondent’s present class is used as classdestination. Sato then concludes that, ‘while Japanese society was gradually opening
from the late 1960s to the early 1980s, it has been in the process of becoming moreclosed since the late 1980s’ (2000: 76). Based on these observations, he argues that the
reproduction of the intellectual elite class (upper white-collar employees) has
strengthened, leading to the emergence of an unequal society. In his interpretation,
postwar Japanese society until the first half of the 1980s was a society which ‘rewarded
hard workers in one way or another’ (2000: 87). In the 1990s, in contrast, Japan haschanged to a society in which social disparity based on class origin prevails and‘individual efforts are not rewarded’ (2000: 128).
To scrutinize Sato’s hypothesis about the emergence of an unequal society, themost important point for consideration is whether the reproduction of upper white-collar
employees has continued to strengthen or not. His hypothesis is given credence if their1

See Grusky et al. (2008), Hashimoto (2006), Kanomata (2008), Miwa (2008a) forrecent studies based on the 2005 SSM data.
3

class closure in the 2005 data is similar to or greater than that in the 1995 data. Figure 1
plots the class inheritance rate (intergenerational reproduction rate) of upper
white-collar employees.2 The figure follows Sato’s calculation methods in constructing
intergenerational mobility tables. It cross-tabulates the occupation at the age of 40 of themale respondents between 40 and 59 years of age in the 2005 data by their fathers’
occupation.3 The right most value is based on the 2005 survey, and represents the logof odds ratios calculated from the mobility tables of the cohorts born between 1945 and1965. As to the cohorts before this period, Sato’s Figure 2.4 (2000: 58) is reproduced byusing a logarithmic scale on the Y-axis. From this figure, one can see that the SSM95
value considerably deviates from others and the SSM05 value approximately matches
the trend from SSM55 to SSM85. In other words, the general tendency is for theinheritance rate of upper white-collar employees to decline, and the increase observed in
1995 represents a deviation from the overall pattern.
A variety of questions have been raised about Sato’s frame of analysis andfindings. Seiyama (2000), for example, contends that Sato’s argument that Japan enteredinto a period of social inequality as a consequence of the strengthened reproduction of
the intellectual elite class is nothing but a ‘fiction’ based on a small sample and lacks
sufficient empirical evidence. Seiyama (2003) also challenges Sato’s claim that the
reproduction of the intellectual elite class is mediated through education anddemonstrated – using the same 1995 data – that class inheritance through education has
not increased. Hashimoto (2001) points out that the upper white-collar employees
whom Sato classifies as the intellectual elite actually include many managers of small or
medium-size enterprises. Hashimoto suggests that the level of reproduction of thecapitalist class has increased. The present study does not directly address thesequestions. Instead, it takes up a different problem about the interpretation of thehistorical period that was already pointed out by Ishida (2002) and shows how new
analytical methods will solve the problem.
Sato contends that social disparity has been enlarged since the latter half of the2

We cannot be certain that Figure 1 precisely reproduces Sato’s analysis because hedoes not show the details of variable constructions based on his proposed eight class
categories. However, we have done our best to reconstruct his Figure 2.4 (Sato, 2000:
58).

3

The analysis is based on two by two tables. They cross-tabulate (1) upper white-collar
employee and (2) other classes for the fathers’ class by the same two categories for the
respondents’ class. From these tables, the logarithmic odds ratios which show thestrength of relationship between fathers’ and respondents’ class were calculated fromthe surveys of the relevant years.
41980s. His argument is predicated on the comparison of mobility tables between the1985 survey and the 1995 survey with respect to the respondents’ occupations at the ageof 40. However, it is not necessarily the case that the respondents of the 1995 survey
arrived at their occupations at age 40 after 1985. It is true that the respondents’occupations in 1995 were influenced by the labor market conditions of the 1990s. Yet,the occupations at the age of 40 of those who were between 40 and 59 years old in 1995
cannot directly reflect the socio-economic circumstances in 1995. They reached their
40-year old occupations prior to the survey year (1995) – anytime between the 1950s
and the 1990s. Sato’s hypothesis and the historical period the data cover therefore do
not correspond with each other.
The mismatch can be explained through analyzing the 2005 SSM data. Of themale respondents who are between 40 and 59 years of age, 35% reported occupations in
the upper white-collar employee category at the age of 40. When we examined the age
when they reached the upper white-collar employee category, 27% were already in thisgroup in their teens and twenties and a majority (52%) reached it by age 34. In terms of
the years of arrival, 12% arrived in this group in the 1970s and 44% in the 1980s;
altogether 56% had reached this position by the 1980s. Thus, although these individuals
were surveyed in 2005, a majority were already in the upper white-collar category by
the 1980s. Indeed, approximately one-third of those male respondents who were 40 to
59 years of age in 2005 were already in this category at the beginning of their career, i.e.,at their first jobs.
Thus, two groups can be identified among those who had reached the upper
white-collar class at the age of 40. One consists of the people who entered this category
as soon as they secured their first job upon completion of school education. The othercomprises individuals who arrived in the class in question after promotion, transfer or
establishing a business during their occupational career. These two groups clearly differ
in their class entry mechanism, with one group achieving entry through transition fromschool and the other by mobility during occupational life. One should, therefore,
analyze these two groups separately. The first major contribution of this study is tofocus on the two groups in turn, and to pay heed to each groups’ particular occupational
paths.
The second contribution of this study is to attempt to examine the impact ofhistorical circumstances on intergenerational inheritance as precisely as possible with aview to testing Sato’s hypothesis that intergenerational mobility declined in a particularperiod. For this purpose, the present research divides the initial job entry time and theoccupational career of the respondents to the 2005 survey into decade blocks with a5

view to inspecting the class inheritance between generations in 1985–1995 (thetime-frame examined in Sato’s study) and in 1996–2005 (the decade which the SSM
2005 study can complement). In this way, one can see the movements of people in thelabor market in their corresponding historical period.
The third contribution of the present study is to use detailed data about theoccupational careers of the respondents up to the age of 40 from the time of their initial
employment and to take into account the timing of entry into the upper white-collar
class. This approach differs from Sato’s one-point focus on whether they were in theupper white-collar class at the age of 40. The proposed strategy is to trace theiroccupational careers as a line, not as a point, and calculate the probabilities of entry into
the upper white-collar class (more precisely, hazard rate) by using the methods ofsurvival analysis.
Finally, the present study scrutinizes not only the intergenerational
reproduction of the upper white-collar class but also that of the non-skilled manual
working class. In the recent debate on social disparity issues, attention is paid not only
to the exclusiveness of the most privileged class but also to the inheritance patterns of
the most underprivileged class, which faces the so-called vicious cycle of poverty. It istherefore meaningful to examine the latter class, who tends to fail to accumulate skills
across generations, on the basis of the 2005 data.
3. DATA, VARIABLES, AND THE METHODS OF ANALYSIS
The data which this paper utilizes are the Social Stratification and Social
Mobility (SSM) Japan Survey conducted in 2005. To allow accurate comparison withearlier SSM data, the data set under examination is confined to male respondents. In theanalysis of first job entries, those from 20 to 69 years of age are subject to investigation.
In the analysis of occupational career, the data from first employment to the age of 40
are examined. Data about occupational career are based on respondents’ retrospective
reports and include information about their movements in terms of employing firm,industry, firm size, employment status (such as managers, the self-employed andemployees), occupation and managerial position in the workplace.
Based on four survey questions in the questionnaire (employment status,
occupation, managerial status, and firm size), we constructed the six-category version of
the EGP class schema (Erikson, Goldthorpe and Portocarero 1979; Erikson andGoldthorpe 1992) for the respondents’ class and the fathers’ class: upper white-collar(professional and managerial) class,4 lower white-collar (clerical and sales) class, the4

The upper white-collar (professional and managerial) category roughly corresponds to6

self-employed class, farming class, skilled manual working class and semi- andnon-skilled manual working class. In the following survival analysis, those who are
unemployed, househusbands and students are included in the risk set, so we included aseparate category representing ‘the unemployed, househusbands and students’, in
addition to EGP class categories. As to education, three levels – middle and highschool, junior and technical college, and university level (both undergraduate andgraduate) – are distinguished on the basis of the SSM educational classification scheme.
The dependent variables are entry into the upper white-collar class and that into
the non-skilled manual class, which will be separately analyzed. First, the cases of entry
into these classes at the time of the first job will be studied. Then, focus will shift tocases in which entry is achieved through occupational career after the first job.5 In
order to capture the fluctuations of macroscopic economic conditions, four ten-year
blocks are distinguished in regard to the year of first job entry and the years ofoccupational career (from first job to 40 years of age): (1) 1975 or before, (2) 1976 to
1985, (3) 1986 to 1995, and (4) 1996 to 2005. The periodization is based on thefollowing justification. The decade from 1996 to 2005 constitutes the phase which only
the 2005 SSM data can address, with previous SSM data unable to examine. These ten
years are grouped together as a period when events which took place after the preceding
surveys, with the present study endeavoring to find out how this phase differs from theearlier periods. This decade witnessed the bursting of the so-called bubble economy andthe beginning of a long recession. The impact of the economic downturn is more vividly
recorded in the labor market indices such as employment rates of new graduates after
1995 than in changes to economic growth rates. The decade from 1986 to 1995 is theperiod in which the bubble economy reached its peak and began to stagnate. The years
from 1976 to 1985 were a decade of low economic growth following the oil shock ofNovember 1973. The fourth and final period before 1976 represents the postwar higheconomic growth phase. Occupational careers during these four decades will beanalyzed separately, and investigation will seek to determine if the relationship between
father’s class and son’s class differs depending upon the period.

Sato’s upper white-collar employee category (Sato 2000).
5Preceding studies have examined the influence of macroscopic labor market
conditions, using variables such as the unemployment rate of the job entry year andthe size of the birth cohort at the time of 18 years of age (that is, the number of middle
school graduates three years before). The inclusion of these as control variables did
not show any significant effect. Therefore, they are excluded in the tables of thepresent study (Kurosawa and Genda 2001; Ishida 2005).
7

The methods of analysis used here are logit analysis and survival analysis. The
figures of intergenerational inheritance are based on recalculations after recoding thefather’s class and the son’s class into two categories. With regard to first jobs, themethod of binary logit analysis will be used, examining the presence or absence of theupper white-collar class membership and that of the non-skilled class membership. As
to the transition into the upper white-collar class and the non-skilled manual class,discrete-time survival analysis will be used.
Discrete-time survival analysis is applied to data about the same agents,
observable at multiple specifiable discrete time points, and examines the repeatedobservation of whether they have encountered a particular event or not at these points.6
The present study looks at whether or not each individual entered the upper white-collar
class or the non-skilled manual class at any time point from his first job until he is 40
years of age. It should be reiterated that the observed values here are based on
individuals’ recollections of occupational careers rather than on data which actually
traced his class at each time point. Assume that observations are made repeatedly at q
time points about n observed subjects (individuals). T is a discrete-time variable whose
values range from 1 to q. Another discrete-time variable is y it (i=1, ..., n) where it takes
the value of 1 when the ith subject encounters the event in question at the tth time point
and the value of 0 when it does not. The probability (the discrete-time hazard rate) of
the event occurring at the tth point on the condition that it did not occur at the (t-1)th
point is expressed as:pit= Pr ( y it = 1 |=y it-1 = 0, y it-2 = 0, y it-3 = 0, . . . , y i0 = 0)

Pr ( T i = t | T i ≥ t )A logarithmic transformation is performed on (pit) so that its observed values
can range from 0 to 1. The discrete-time hazard rate of the ith subject encountering theevent at the tth point can be written as a logit model as follows, where the probability isexpressed as k independent variables (including both time-fixed and time-varying
types): x it’= (xi1t,, xi2t,xi3t,, . . .,xikt):pit=[exp(αt + x it ’ [exp(αt + x it ’

The independent variables include both (1) time-fixed variables such as the6

For discrete-time survival analysis, see Allison (1984), Powers and Xie (2000), andYamaguchi (1991).
8

subject’s class origin and education (on the assumption that this is determined at thetime of first job acquisition) and (2) time-varying variables such as his class andemployer at the time of t-1. The major focus of analysis here is to measure the effects of
the respondent’s class origin and to inspect their historical changes. The dependent
variable is the discrete-time hazard rate of entry into the upper white-collar class (andthat of the non-skilled manual class) during the time between the first job to 40 years ofage. By taking the log-transformation, the parameters of the independent variables canbe interpreted as log odds ratios. It is well known that the odds ratios are not subject tomarginal distributions. This is an advantage which they share with the log linear models
used for the analysis of mobility tables. Survival analysis can handle the question ofidentifying the timing of entry into the class during the respondent’s career, an issue
which is often debated in mobility table analysis. Specification of the timing of mobility
enables researchers to include the above-mentioned four periods and use them as ahistorical context for analysis.
4.
ENTRY INTO THE UPPER WHITE-COLLAR CLASS AND THE
NON-SKILLED MANUAL CLASS AT THE TIME OF FIRST JOB
We first examine the differences between those who entered the upper
white-collar class at the point of their first job and those who did not using the 2005
SSM data. In the data set of male respondents from 20 to 69 years of age, 14% belong
to the upper white-collar class. The rate differs depending on the period: 9% for
1954–75, 18% for 1976–85, 19% for 1986–95, and 20% for 1996–2005. It is obviousthat this class has enlarged overall since 1976. Inspecting the finer categories of theclass reveals that some 90% are professional workers. This means that, when we refer tothe upper white-collar members who entered this class at the point of their first job, weare by and large talking about professionals who got their job immediately after
completing their school education.
Table 1 displays the result of binary logit analysis, where the dependent
variable is whether the first job is in the upper white-collar class or not. Model 1 uses
only the father’s class, the respondent’s age and the age squared as independent
variables. Men of the upper white-collar class origin are six times (e1.812) more likely toenter the same class as the first job than men of the non-skilled manual class origin. The
likelihood of entry into the upper white-collar class for the son of a lower white-collar
father is three time (e1.231) higher and for the son of a self-employed father it is twice(e0.858) higher than that of the non-skilled manual father. The age variable shows apositive impact and the square of age variable a negative effect, suggesting a tendency

9

for the probability of entry into the upper white-collar class to rise with the increase inage (up to approximately 60 years of age).7 This is due to the fact that older entrants
are likely to have higher education. Therefore, once education is controlled, the effect ofage disappears (see Model 4).
Model 2 introduces the period when the respondent entered the first job as acontrol variable. The effect of the father’s class remains strong and significant. By
reflecting the expansion of white-collar jobs after 1976, there is an increasing
probability of entry into the upper white-collar class, particularly in recent years.
However, the effects of the first job entry period variable appear to be somewhat
inflated, because they partially reflect the differences in the composition of educational
level among respondents between the periods of entry. The expansion of white-collar
jobs after 1976 tends to increase the probability of upper white-collar class entry,
particularly in recent years. Model 3 adds, as an independent variable, the interaction
effects between the father’s class (upper white-collar) and the year of the son’s entry
into the labor market. In comparison with the 1996–2005 entry (base category), the1986–1995 entry group shows the weaker effect of the father’s class (-0.301), but theeffect is not statistically significant. Based on the results of statistical analysis, one
cannot conclude that the father’s class influence is stronger in the latest 1996–2005
entry group than the earlier groups, and therefore one cannot claim that upper
white-collar class closure has enhanced in recent years.
Model 4 adds the education variable into analysis. Education increases theprobability of entry into the upper white-collar class at the point of the first job,regardless of class origin, age and entry year. In comparison with those with middle andhigh school education, the graduates of junior colleges and technical colleges are 8.5
times (e2.135) more likely to get into this class. The figure goes up to 9.2 times (e2.218)
when it comes to the graduates of universities both at undergraduate and postgraduatelevels. The values of the effects of the father’s class indicate its effects after the removalof the indirect effects through education. Among those who achieved class entry during
the 1996–2005 period at the time of their first job, those whose father was a member ofthe upper white-collar class are 2.8 times (e1.014) more likely to enter this class thanthose whose father is a member of the non-skilled manual class. The direct effect ofclass origin constitutes approximately two-thirds of the total effect of the father being a

7

By setting the partial differentiation of the quadratic equation of the age and thesquare-of-age variables to zero, the apex of the curve (the limit of the equation) was
calculated. The same method was used in the following calculations.
10

member of the upper white-collar class.8 The proportion of respondents who enteredthe upper white-collar class via education is smaller than that of those who achievedentry regardless of education.9 The interactions between the father’s class (upper
white-collar) and the year of entry are not statistically significant. This means that theeffect of the father’s class does not differ statistically between the 1996–2005 period
and the preceding periods. That is to say, one cannot conclude that the upper
white-collar class has become more exclusive.
Model 5 adds interaction effects of education and labor market entry time into
analysis. All interaction terms have positive coefficients, a finding that the effect ofeducation is smallest for the 1996–2005 period. However, none of them are statisticallysignificant, and therefore, we cannot conclude that these differences are present in the
population.
Next, we shift the focus of our attention to those who entered the non-skilled
manual class at the time of the first job, and compare this group with those who enteredother classes. In the 2005 SSM data (males from 20 to 69 years of age), 18% belonged
to the non-skilled manual class at the time of the first job. If we breakdown thepercentage by entry period, we find 18% in 1950–75, 17% in 1976–85, 15% in 1986–95,
and 22% in 1996–2005. The latest decade clearly shows a sharp upswing.
Table 2 displays the results of binary logit analysis with the dependent variablebeing whether or not the respondents were members of the non-skilled manual class at
the time of their first job. Model 1 uses only the father’s class, age and the square of age
as independent variables. The likelihood of entering the non-skilled manual class at thefirst job is some three times (e0.974) more for those whose father is in the same class thanfor those whose father belongs to the upper white-collar class. The figures are 1.8 times(e0.611) higher for those whose father is a member of the skilled manual class and 1.7
times (e0.548) higher for those whose father is of the farming class or lower white-collar
class than those whose father is upper white-collar class. Since the coefficient of the age
8

The impact of the father’s class being upper white-collar prior to controlling for
education is 1.537. Therefore, the proportion of the direct effect is 1.014/1.537=0.66.
In the following analysis, the same method is used for calculating direct effects.

9Here, the education variable takes only three values: high school, junior college, anduniversity. These are simple vertical classifications. However, more refinedclassifications are desirable. For instance, other educational institutions like technicalcolleges ought to be considered. Distinctions between undergraduate and postgraduate
education at the university level as well as the ranking of universities can also be
considered. With the inclusion of these variables, the indirect effects of education will
increase and its direct effects will decline.
11

variable is negative and the squared age variable is positive, the younger people are
more likely to enter the non-skilled manual class at the first job, with the probability
going down up to approximately 42 years of age. Similar to Table 1, this tendency
reflects the fact that the older class entrants are more likely to have higher educational
levels. When education is controlled, the age effects disappear (see Model 4).
Model 2 adds the years of entry to the first job to the independent variable set.
While the effect of the father’s class hardly changes from Model 1, the new variables
suggest that class entry is more likely in the earlier periods and less likely in more
recent decades, a pattern reflective of the fact that there are more people with higher
educational level in recent years. An inspection of marginal distributions shows that the
proportions of non-skilled manuals is 22% in 1996–2005 and 17% in preceding periods,
a pattern indicative of the influence of the different compositions of education indifferent periods. In Model 3, interactions between the father’s class (non-skilled
manual) and the year of class entry are added. For all periods, their coefficients arenegative, indicating that the father’s class had a greater influence in 1996–2005(benchmark decade) than in the preceding periods. In particular, the strength of thefather’s class influence in this decade is significantly higher than that in the oldest
period, 1955–1975 (at 10% significance level). During 1996–2005, those with
non-skilled manual fathers recorded entry into the same class six times (e1.858) more
likely than those with upper white-collar fathers. In preceding periods (1995 or before),the likelihood of entry amounts to approximately twice (e0.853). 10 These observations
point to the possibility that the intergenerational closure of the most disadvantaged
group, the non-skilled manual class, is increasing.
Model 4 adds the education variable into the independent variable list. The
higher educational level (especially with university undergraduate and postgraduatelevels) decreases the probability of first-job entry into the non-skilled manual class,regardless of class origin, age, and year of entry. For example, those with university
education are approximately six times (e1.752) more unlikely to get into this class thanthose with middle or high school education only. Once education is controlled, theinfluence of the father’s class diminishes and becomes not statistically significant, withthe exception of the non-skilled manual class. This means that the tendency for thosewith lower white-collar, farming and skilled manual class origins to record first-job
class entry into the non-skilled manual class can be attributable mostly to the relatively
10

The value was obtained after all the entrants before 1995 are lumped together andcompared with the corresponding group in 1996–2005 (Coefficient=0.853, pvalue=0.131).
12

low educational level of the former class groups. In contrast, it is confirmed that, in thecase of sons whose class origin is the non-skilled manual class, class origin tends to be
reproduced at the first job, regardless of their education. Model 5 further adds theinteractions between education and entry year to the independent variable set. The
coefficients of the interaction variables are negative for the graduates of junior colleges
and positive for university graduates. This means that, during the decade of 1996–2005,
the effects of junior college education were strongest and those of university education
were weakest in comparison with other periods. However, none of the values are
statistically significant.
Here are some points of summary of this section. Figure 2 displays a succinct
picture of class entry into the upper white-collar class and into the non-skilled manualclass. In this figure, we dichotomized both the first job and the father’s class between
the upper white-collar class and others as well as between the non-skilled manual classand others. Based on this dichotomized variable approach, we estimated a model which
includes father’s class, age, squared age, entry year, and interaction between father’sclass and entry year. Therefore, the values of the figure differ slightly from those of
Tables 1 and 2. From the figure, it is clear that the log odds ratios are generally high forboth classes, indicating that the intergenerational class reproduction tendency is salient.
Looking at the trend over time, one can see that class entrants during the latest decade,1996–2005, show a greater influence of their father’s class than those during otherperiods. In particular, for the non-skilled manual class, the level of influence shows asteep upswing, with the difference between the 1996–2005 period and the pre-1976
period being statistically significant. A tentative conclusion here is that the influence ofthe father’s class (namely, the degree of intergenerational class inheritance) is observed
consistently across the four periods with some upsurge, though it is not statisticallysignificant. One cannot make conclusive remarks because the sample size of the
1996–2005 entrants is small. Though it is possible that the class closures have increased
during 1996–2005 for the two classes under consideration (particularly the non-skilled
manual class), the foregoing analysis shows no clear-cut evidence to support the
proposition.11
11

One should be cautious about drawing definitive conclusions, because the number of
persons who recorded class entry is limited in the 2005 SSM data. I have repeated asimilar analysis using the Japanese Life Course Panel Survey conducted in 2007,
which was targeted at both males and females, with two groups – those from 20 to 34
years of age (sample size 3,367) and those from 35 to 40 (sample size 1,433) (fordetails of the survey, see Ishida, Miwa and Oshima 2008; Miwa 2008b). The appendix
figure (Figure 4) shows the result of this study. The samples included only those who
13

5.
ENTRY INTO THE UPPER WHITE-COLLAR CLASS AND THE
NON-SKILLED MANUAL CLASS AFTER THE FIRST JOBThis section deals with those who did not achieve the upper white-collar class(or the non-skilled manual class) upon their first job. The analysis will focus upon
whether or not they arrived at the upper white-collar class or the non-skilled manualclass during their occupational career. We begin with the analysis of the upper
white-collar class.12 Here the method of survival analysis will be used to include those‘censored’ individuals who had not yet arrived in this class by the time of observation.
Survival analysis is capable of considering two types of information simultaneously:
whether respondents entered the upper white-collar class or not and, if they did, at whatage. The unit of analysis is what is called the spell or the segment of the person-year. In
other words, the employment situation of each respondent at a particular age constitutes
a unit. For instance, 13 person-year spells will be built into analysis for a respondent
who started his first job at the age of 18 and was 30 years of age at the time of thesurvey. Similarly, 19 person-year spells will be subjected to analysis for a respondent
who was first employed at the age of 22 and arrived at a managerial position at the ageof 40 at the time of the survey. In all of these cases, the employment position of each
respondent at each age should constitute the unit of analysis.13
The important point in survival analysis is the concept of risk set which focuses
were below 40 years of age at the time of the survey. The number of first-job entrants
prior to 1986 was quite limited. Therefore, a comparison was made only between those
who entered in 1986–1995 and those in 1996–2005. The figure indicates that thedegree of intergenerational mobility of both the upper white-collar class and thenon-skilled manual class declined slightly though the differences are not statisticallysignificant.
12

Precisely speaking, those who entered the upper white-collar class at the first job andmoved to other classes are included in the risk set whenever they moved out of theupper white collar class. For example, the respondent who was a ship captain (which isin a professional category) at his first job and became an advertising agent (which
belongs to a category of other service jobs) is subject to analysis at the time when hestopped being a ship captain (spell). Those who fall into the risk set in this way
constitute approximately 2% of all spells. Some respondents recorded multiple entries
into the upper white-collar class and constitute some 5% of all respondents. They areincluded in the risk set at the point when they moved out of the upper white-collar
class.

13

In constructing the person-year file, a program specified in Ishida (2002) was used toenable comparison with the analysis of the 1995 SSM survey. Tokio Yasuda provided
the SPSS syntax to build the person-year file from the 2005 SSM data. The 2005
Survey Research Group appreciates his generous offer of sharing the syntax.
14

upon who should be included as objects of analysis: in the present case, respondents
who have the risks (or chances) of reaching the upper white-collar class. Those whoarrived at this class at the first job are eliminated from the risk set. In other words, all
the situations where the respondents have not reached this class are included in the risk
set. Therefore, the self-employed and family workers are included in the set, as arethose who temporarily became jobless persons, househusbands or students during thecourse of their occupational career. They are considered to have the risk of moving into
the upper white-collar class.14 Furthermore, the occupational career from the first job(from 15 years of age in the youngest cases) to the job at 40 years old is subjected toanalysis. Age 40 is selected because, in the intergenerational mobility debate, whether
the upper white-collar class status is achieved by age 40 is often the central issue in theliterature as discussed above, and this age also corresponds to the father’s main
occupation.15
When we examined those respondents who were not members of the upper
white-collar class at their first jobs but reached this class at or before the age of 40, they
are mainly holders of managerial posts, with professionals constituting less than 20%.16
In other words, an overwhelming majority is managers who attained their managerial
positions through promotions, job changes, or inaugurated businesses; those withprofessional positions are a small minority. This means that the upper white-collar classmembers, either at their current position or at the age of 40, are made up primarily of
two groups: (1) professionals who have been members of this class since their first job
and (2) managers who entered this class during their occupational career.
Table 3 exhibits the results of survival analysis of entry into the upper
white-collar class from other classes. The table excludes those who were already in thisclass in their first job and includes only those who entered it during their occupational
career. The independent variables include the father’s class (using the category of thenon-skilled manual as the benchmark), age at the spell (person-year), the square of age
at the spell, the spell year (1996–2005 as the reference), interaction between the father’sclass (upper white-collar) and the spell (1996–2005 as the reference), education (middle
14

However, when these groups were excluded from the risk set and only employees areincluded, the same results were obtained.

15

When the age range is extended to 60 years of age, the results reported in this section
hold.

16

More precisely, an extremely small number of respondents (approximately 2%) weremembers of the upper white-collar class at their first jobs but later moved into otherclasses.
15and high school as the reference), the class in the previous spell (non-skilled manualclass as the reference), and change in the place of employment from the previous spell.
Model 1 uses the father’s class, age at the spell (person-year), its square and thespell year dummy variables as the independent variables. Those whose father belongs to
the upper white-collar class are 2.6 times (e0.971) more likely to enter the same class thanthose whose father is a member of the non-skilled manual class and 2.3 times (e0.839)
more likely than those whose father belongs to the lower white-collar class. Age and its
square together show that the probability of entry into the upper white-collar class rises
with age up to 33–34 years but reaches a limit and declines thereafter. The dummy
variable of the spell year compares the likelihood of entry into the white-collar class in
the 1986–1995 period, the 1976–1985 period, and the period prior to 1996 with that in
the 1996–2005 period (the base category). The comparison reveals no statisticallysignificant differences between the 1996–2005 period and the others in terms of thelikelihood of entry into the upper white-collar class. In each period, various age groups(up to 40) are included, and the result suggests that there is no clear tendency for the
probability of entry into the upper white-collar class to be higher in the more recent
period.17
Model 2 attempts to demonstrate one of the most important points of this study.
It tries to examine if the influence of the father’s class (upper white-collar) differs,
depending upon the period. In comparison with the years 1996–2005, the father’s classinfluences in the three preceding periods are consistently smaller in a statisticallysignificant way. Since there are no statistically significant differences between the three
periods before 1996–2005 with respect to the influence of the father’s class, one cangenerate Model 3 by lumping together those three periods into one. The influence of thefather’s class in 1996–2005 is 1.435, implying that those whose father’s class is upper
white-collar are 4.2 times (e1.435) more likely to enter this class than those with
non-skilled manual fathers. During the lumped period from 1950 to 1995, the former are
2.2 times (e0.809) more likely to get into the upper white-collar class than the latter.18
These observations suggest that the degree of social closure (the extent of classinheritance) of the upper white-collar class has risen in the 1996–2005 period.
Model 4 adds education to Model 3. The model shows that education has
effects independent of the father’s class, the age of the respondent and the decade. Even17

No statistically significant results were obtained with the introduction of each year as
a dummy variable.

18

The effect of the upper white-collar class during the 1950–1995 period was
calculated by adding the 1996–2005 effect (1.435) to the interaction effect (-0.626).
16

if the father’s class is controlled, those with junior and technical college education are
2.8 times (e1.046) more likely to join the upper white-collar class than those with middle
or high school education only. The effect climbs to 3.2 times (e1.148) for those with
undergraduate and postgraduate university education. The effect of the father’s classhere is direct after the exclusion of the indirect effect via education. As discussed in theexamination of Model 3, even when education is introduced as an independent variable,
the direct effect of the father’s class differs significantly between the decade 1996–2005
and the years prior to that. The direct effect of the father’s class during 1996–2005 is
1.012. This means that those whose fathers are members of the upper white-collar classare 2.8 times (e1.012) more likely to enter the class than those with non-skilled manualclass fathers, even if the sons have the same level of education. In the prior period from
1950–1995, the likelihood is only 1.4 times (e0.348) greater.
Moreover, one can observe differences between the two periods in the
proportion of the direct effect of the father’s class which does not go through education.
During 1996–2005, some 70% of the effects of the father’s class remain even after the
introduction of education. However, the corresponding figure is only 40% for the period
from 1950–1995.19 In other words, when the father belonged to the upper white-collar
class, the main mechanism of his son arriving at the same class was through educationin 1950–1995. In contrast, in 1996–2005, the main mechanism was without going
through education. The mechanism of intergenerational inheritance of the professionaland managerial class has changed. In the latest decade in comparison with the period
before, not only has the social closure of this class increased but the pattern of classinheritance has also become more direct, with the weight of the path of going througheducational attainment declining.
Model 5 adds two further variables – the class prior to entry into the upper
white-collar class in the previous year and the place of employment – to Model 4. The
inspection of the two newly added variables clearly reveals that belonging to either the
lower white-collar class or unemployed in the previous year increases the likelihood ofentry into the upper white-collar class while belonging to the self-employed or thefarming class decreases the likelihood. As to the place of employment, job changes tend
to facilitate one’s entry into the upper white-collar class. Even if other factors remain
constant, those who have changed their place of employment or started a new enterpriseare 10 times (e2.396) more likely than those who have stayed in the same job to get into
the upper white-collar class. Approximately 7% of all the spells recorded the changes in
19

The effect of education is constant regardless of the period. Interactions between
education and the period are not statistically significant.
17the place of employment from the previous year to the current one. Among those whoachieved entry into the upper white-collar class, some one-third had changed jobs(including those who established new companies) and two-thirds had stayed and beenpromoted at the same place of employment. Notably, those who changed jobs havedistinctly higher chances of entering the upper white-collar class. Once the class in theprevious year and the change in the place of employment are controlled, the difference
between the periods in the effect of the father’s class weakens but remains statisticallysignificant. The general observation that the social closure of the upper white-collar
class is higher in 1996–2005 than the previous periods still stands.
Next, we take up entry into the non-skilled manual class.20 This class differsfrom the upper white-collar class in two respects. In the first place, there is no clear-cut
difference between the two paths of entry – those who entered the class at their first job
and those who entered during their occupational career – unlike the upper white-collar
class in which the first group is dominated by professionals and the latter by managers.
Nevertheless, we can specify that for those who entered the class at their first job, 35%
are production process workers (mainly working in factories) and 40% are laborers(such as earthwork, construction and road-cleaning workers).Transport workers (such astruck drivers) constitute only 13% of this category. Of those who arrived in the
non-skilled manual class during their occupational career, 22% are production
processing workers, 31% are laborers and 37% are transport workers. Although thedifference between the two groups of transport workers is considerable, this is perhaps
attributable to the fact that many of them did not possess drivers’ licenses at the time
when they left school, could not obtain jobs in the transport sector, and initially had towork in factories or construction sites as manual workers.
The second difference between the two classes is that, in the case of the upper
white-collar class, only a very small number of people exited from this class after the
first job and joined the risk set, while in the case of the non-skilled manual class, this arelatively common occurrence. Of all spells in the risk set, some 10% were in the
non-skilled manual class initially at their first job and moved out of this class later.
Among the events which mark entering the non-skilled manual class during an
occupational career, 17% were in the class in the first job, then exited it, and finally
returned to it. In this way, no conspicuous difference is present between the two groups
in this class – the first job entry type and the post-progression entry type – recording
more frequent entry and exit movements than the upper white-collar class.
20

Among those at the age of 40, approximately 13% are non-skilled manual classmembers.
18

Table 4 shows the results of survival analysis of entry into the non-skilled
manual class during an occupational career from other classes as the starting point. The
independent variables are the same as those used in Table 3, except that the upper
white-collar class is used as the base reference category for the father’s class and theclass in the previous spell.
The independent variables of Model 1 are the father’s class, age at theperson-year, its square, and the spell (dummy). From the table, it is obvious that theeffect of the father’s class is strong. It is 2.7 times (e1.006) more likely for the sons of
non-skilled manual fathers to get into the fathers’ class than the sons of upper
white-collar fathers and almost twice (e0.612) as likely for the sons of skilled manual or
farming class fathers. The age and age-squared variables suggest that the probability ofyounger people entering the non-skilled manual class is high and declines with age until
57 years or so, though the probability increases thereafter. Once the class origin and age
variables are controlled, people in the older cohort are less likely to arrive at this class.
Model 2 investigates how the effect of the father’s class (non-skilled manual)differs, depending upon the period. In comparison with the 1996–2005 decade, it issmall in the 1986–1995 period as well as in the 1976–1985 period in a statisticallysignificant way. No significant difference is found between 1996–2005 period and theyears before 1976. In Model 3, the years from 1976 to 1995 are lumped together to form
the 1976–1995 period, and its interaction with the father’s class is inspected. The modelshows that the effect of the father’s class during the most recent 1996–2005 period
increases from that in 1976–1995 in a statistically significant fashion. The effect ofintergenerational inheritance of the father’s class (non-skilled manual) in 1996–2005 is
1.444. The sons of non-skilled manual fathers are 4.2 times (e1.444) more likely to get
into the non-skilled class than those of upper white-collar fathers. In 1976–1995, theeffect of the father’s class is much smaller, with the corresponding degree of likelihood
being only approximately twice (e0.669). In short, the analysis suggests that the extent ofsocial closure (intergenerational inheritance) of the non-skilled manual class has
doubled in the last decade.
Model 4 demonstrates that education has statistically significant effects. Even
when other variables are controlled, the graduates of junior and technical colleges are
2.3 times (e0.849) less likely to get into the non-skilled manual class than those of middle
and high schools. The likelihood of not entering the non-skilled manual class increases
to 4 times (e1.396) for those who have completed undergraduate or postgraduate
university education. The effect of the father’s class (the inheritance of the non-skilled
manual class) is different between 1996–2005 and 1976–1995 in a statistically19significant way, even after the introduction of education as an independent variable.
Moreover, between these periods, the proportions of the direct effects of the father’sclass are different, a pattern similar to that found in the analysis of entries into the upper
white-collar class. In 1996–2005, 75% of the effects of the father’s class (inheritance ofthe non-skilled manual class) are direct, without going through the educational route,whereas in 1976–1995 the direct effect does not reach 50%. In other words, in theearlier period, sons of non-skilled manual fathers were likely to arrive at the same classprimarily because of their low levels of education. In the most recent period, in contrast,
the intergenerational reproduction of the non-skilled manual class tended to continue
regardless of the sons’ educational level.
Finally, Model 5 attempts to examine the effect of the class prior to entry into
the non-skilled manual class and that of changes in the place of employment. It showsthat, in cases where the class in the previous year is upper white-collar (benchmark
category), the probability of entering the non-skilled manual class is low. In contrast, theprobability is particularly high in cases where the class in the previous year is either
farming or jobless. With respect to the jobless group, a notable point should be
mentioned. About one-third of the respondents who got a non-skilled manual job at first,
then moved into a different class and went back into the initial class had a spell ofjoblessness along the way, a pattern that suggests that joblessness forms an important
pathway to the non-skilled manual class. This has to do with the fact that the joblesspeople in the male sample tend to include, in large numbers, involuntarily unemployedpersons. To that extent, changes in the place of employment greatly facilitates entry into
the non-skilled manual class. One can comprehend the effect of this route, once one
considers the fact that changes in the place of employment include not only cases where
people moved from one job to another or started a new enterprise but also those who
left their jobs against their will. Model 5 also demonstrates that, in controlling thechanges in the place of employment, the effect of the father’s class (inheritance of thenon-skilled manual class) is greatly increased. This is attributable to the fact that themembers of the non-skilled manual class generally are less likely to change their placeof employment than those of other classes. In the 1996–2005 decade, the effect of thefather’s class being non-skilled manual is 1.352. This means that those whose classorigin is non-skilled manual class, are 4 times (e1.352) more likely to enter this class thanthose whose class origin is upper white-collar. The effect of the father’s class(inheritance of the blue-collar class) is very high both in the group that changed theirplace of employment and in the group that did not. In contrast, in the 1976–1995 period,a similar effect of the father’s class is not discernible. In other words, in the model that20

controls the variable on changes in the place of employment, the observation that thenon-skilled manual class is more closed in 1996–2005 than in the preceding period does
stand.
6.

CONCLUDING REMARKSThis article analyzed the trends of intergenerational mobility by using themethod of survival analysis, with a focus upon the reproduction (intergenerational
inheritance) of the upper white-collar class and the non-skilled manual class. The study
avoided using the survey year as the unit of trend analysis but distinguished four periods
with respect to the time point of first-job entry and that of class entry via occupational
career in an attempt to clarify the historical context. Focusing upon different paths ofentry into the class, two patterns were identified: (1) the cases of class entry at the timeof the first job upon graduating from or leaving school and (2) the cases of class entry
after attaining promotion, making job changes or starting a business during an
occupational career. For these two types, separate analyses were performed. As to thesecond type, considerations were made for the censored cases in which observations
were terminated before the respondents had arrived at the upper white-collar class or the
non-skilled manual class.
Among those respondents who were members of the upper white-collar class at
the age of 40 (40–59 years of age at the survey year of 2005), a little over one-third had
already entered this class at the time of their first job. These respondents arrived at thisclass not by accumulating occupational skills during a career but at the stage oftransition from school to occupational life. Furthermore, entry into this class at the firstjob was strongly influenced by the father’s class. However, there was no statisticallysignificant difference in the degree of this influence between four periods. In comparing
the period from the latter half of the 1990s to the present – when the social disparity
issue has been hotly debated – with the preceding periods, no evidence was found thatthe influence of the father’s class has increased and that entry into the upper
white-collar class has increasingly closed with respect to entry at the first-job. The
conclusion on this point is that the influence of the father’s class (intergenerational
inheritance of the upper white-collar class) was consistently high and did not record
significant changes in this respect.
However, a different conclusion is drawn with respect to the respondents whoachieved entry into the upper white-collar class through their occupational career. They
make up two-thirds of the members of this class at the age of 40. There is evidence thatthe occupational career paths of these respondents were strongly influenced by the21

father’s class (upper white-collar). More importantly, it is quite probable that suchinfluence was strengthened during the decade 1996–2005, suggesting that the classclosure of this class (intergenerational class inheritance) was enhanced in recent years.
Furthermore, during this decade, the mechanism of class inheritance appears to havechanged; the weight of direct class inheritance from the father to the son without themediating path of education has increased.
As to the non-skilled manual class, similar, almost parallel, conclusions can be
drawn. One can observe that the impact of the father’s class (inheritance of thenon-skilled manual class) on first-job class entry has increased in recent years. Yet,
there is no evidence to suggest that intergenerational mobility was more closed in
1996–2005 than in the preceding periods in a statistically significant fashion. However,with regard to the respondents who entered the non-skilled manual class during theiroccupational career, there is evidence to suggest that class inheritance was strengthened
during the 1996–2005 decade. Similar to the intergenerational class reproduction
mechanism manifest in the upper white-collar analysis, the weight of direct classinheritance from fathers to sons without the mediation of education increased.
Figure 3 plots the temporal trends of the intergenerational inheritance throughthe occupational career of the upper white-collar class and that of the non-skilled
manual class.21 The figure shows that the inheritance rates (log odds ratios) of theupper white-collar class are roughly constant up to the decade 1986–1995. However, theratio of the 1996–2005 period shows an upward swing which is statistically significant,an observation that appears to be in tune with the recent argument that the level of social
disparity has increased. Likewise, the inheritance rate of the non-skilled manual class
has increased from 1976–1995 to 1996–2005 in a statistically significant way. One must,however, refrain from leaping to the conclusions of the ‘increasing disparity argument’on the basis of these results. Nevertheless, the data set under consideration includes
information about the years after the mid-1990s, which the preceding SSM surveys
could not provide. The latest data set suggests that social mobility into the upper
white-collar class has declined for those who attempt to enter the class through theaccumulation of occupational career – such as intra-company promotions,inter-company transfers, establishing new businesses, etc. The inheritance rates of the21

The values shown in Figure 3 are the log odds ratios of the current generations’ entry
into the upper white-collar class (or into the non-skilled manual class) up to the age of
40 between those who came from the same class origin and those who did not. The
model included such variables as age at the entry year, its square, entry year (dummy)
and interaction between class origin and entry year. Therefore, the values in this figure
differ slightly from those of Tables 3 and 4.
22

upper white-collar class have been consistently high throughout the post-World War II
era. There is no change between the 1986–1995 period and earlier periods, thecomparison on which Sato’s argument (2000) is based. Importantly, the present study
has shown that the symptoms of the increased intergenerational class inheritance and theassociated expansion of social disparity are present in the latest decade underobservation, 1996–2005. With respect to the non-skilled manual class, the mostdisadvantaged class, the signs of increased social disparity are observable only during
the latest period since the mid-1990s. Careful attention ought to be paid to whether therising trends of the inheritance rates of these classes continue in the future or not.
One may get the impression that the observed tendency for the reproduction of
the upper white-collar class and the non-skilled manual class in recent years isinconsistent with other research results (Miwa and Ishida, 2008; Ishida and Miwa,
2008) based upon the analysis of mobility tables. These studies did not find any
evidence to suggest that the level of class inheritance has increased from 1995 to 2005.
They used survey data conducted every ten years from 1955 to 2005 as the unit of trend
analysis, analyzed mobility tables based on the present occupations of males from 30 to
64 years of age and concluded that the intergenerational mobility patterns, including theinheritance rates of the upper white-collar and non-skilled manual classes, werebasically stable, with no evidence of increased social closure in these classes. However,while sharing these studies’ research focus on intergenerational mobility, the present
study differs from them in terms of the objects of investigation, research methods andobjectives. The mobility table research predicated on the present jobs of people atdifferent ages aims to grasp the overall picture of society at the time point of eachsurvey and to study the strengths of openness and closure of a society as a whole. The
data sets at two time points, ten years apart between 1995 and 2005, include the samebirth cohorts as the objects of study, with the exception of the oldest age group in their
60s in 1995 and the youngest in their 20s in 2005. This merely reflects the simple factthat the people who make up Japanese society in 1995 and those in 2005 considerably
overlap. Therefore, it is not surprising that the intergenerational mobility patterns in the
1995 survey and those in the 2005 survey show similarities. The present study useddifferent analytical methods to scrutinize the class entry of respondents after the
mid-1990s, subjects and a time span that the 1995 survey was unable to investigate.
Despite the same research focus on intergenerational mobility, it is quite conceivablethat different studies produce different research outcomes.
The 2005 SSM survey provides data about occupational history during thepost-1995 period when the social disparity issue attracted public attention. It is an23

important mission for SSM researchers to use this precious data set to find answers to
the issue of whether social disparity with respect to intergenerational mobility has
enlarged or not and whether the mechanisms that produce social disparity have changed.
This paper is an attempt to provide a fresh perspective by using an analytical method
which differs from the conventional approaches. Admittedly, this paper shed light only
on one aspect of intergenerational inheritance, namely the reproduction of the upper
white-collar class and the non-skilled manual class. However, the potential of theanalytical method used in this paper is arguably not restricted to studies of theintergenerational inheritance of these two classes. It is hoped that a comprehensive
picture of intergenerational mobility can be developed by making maximum use of themerits of survival analysis.

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(in Japanese)25

Table 1 Logit analysis of entry into the upper white-collar class at the first job
Model 1Model 2Father's class (Reference: Non-skilled manual)Upper white-collar
1.812 **
1.600 **
Lower white-collar
1.231 **
1.061 **
Self-employed
0.858 **
0.782 *
Farming0.293
0.361
Skilled manual
0.510
0.610 +
Age
0.120 **
0.452 **
Age squared-0.001 **-0.004 **Years of entry at the first job (Reference: 1996–2005）
1986–1995-1.865 **1976–1985-3.328 **1975 and before-5.187 **Interaction between the father's class and the year of first-job entry
Upper white-collar*1986–1995
Upper white-collar*1976–1985
Upper white-collar*1975 and before
Education (Reference: Middle and high school)
Juninor and techinical college
University
Interaction between education and the year of first-job entry
Junior and technical college*1986–1995
Junior and technical college*1976–1985
Junior and technical college*1975 and before
University*1986–1995
University*1976–1985
University*1975 and before
Constant-4.315 **-16.610 **Model 3Model 4Model 51.537 **
1.066 **
0.805 *
0.427
0.612 +
0.452 **
-0.004 **1.014 *
0.642 +
0.526
0.554
0.646 +
0.147 +
-0.001

1.126 *
0.618 +
0.518
0.571
0.635 +
0.162 *
-0.001-1.754 **-3.382 **-5.286 **-0.480-1.205 *-2.233 **-1.275 +-1.684 *-3.134 **-0.3010.144
0.325-0.3770.000-0.035-0.527-0.041-0.2232.135 **
2.218 **

0.746
1.496 **

-6.381 **

1.413
1.351
1.596
0.930
0.357
0.954
-5.167 *-16.509 **-2Log Likelihood
1660.009
1576.594
1573.680
Cox & Snell R-square
0.052
0.087
0.088
Number of cases
2230
2230
2230
** significant at 1% * significant at 5% ＋ significant at 10%261387.997
0.161
2230

1382.972
0.163
2230

Table 2 Logit analysis of entry into non-skilled manual class at the first job
Model 1Model 2Model 3
Father's class (Reference: Upper white-collar)
Lower whie-collar
0.548 *
0.534 *
0.533 *
Self-employed
0.216
0.173
0.164
Farming
0.517 **
0.444 *
0.423 *
Skilled manual
0.611 **
0.539 *
0.541 *
Non-skilled manual
0.974 **
0.926 **
1.858 **
Age
-0.077 *-0.171 **-0.167 **Age squared
0.001 *
0.002 **
0.001 **
Years of entry at the first job (Reference: 1996–2005）
1986–1995
0.221
0.292
1976–1985
0.894 +
0.927 +
1975 and before
1.067 +
1.159 +
Interaction between the father's class and the year of first-job entryNon-skilled manual*1986–1995-0.963
Non-skilled manual*1976–1985-0.777
Non-skilled manual*1975 and before-1.164 +Education (Reference: Middle and high school)
Juninor and techinical college
University
Interaction between education and the year of first-job entry
Junior and technical college*1986–1995
Junior and technical college*1976–1985
Junior and technical college*1975 and before
University*1986–1995
University*1976–1985
University*1975 and before
Constant
0.209 **
3.231 +
1.876

Model 4Model 50.255
-0.242-0.1360.033
1.390 *
0.049
0.000

0.273
-0.239-0.1320.043
1.328 +
0.054
0.000

-0.724 +-0.781-1.160 +-0.754 +-0.793-1.190-1.030-0.878-1.219 +-0.951-0.820-1.147-0.702 +-1.752 **0.460-1.951 **-5.757 **-1.545-1.030-2.142
0.332
0.076
0.308
-7.322 **-2Log Likelihood
2014.1722007.486
2004.337
Cox & Snell R-square
0.014
0.017
0.018
Number of cases
2230
2230
2230
** significant at 1% * significant at 5% ＋ significant at 10%

1910.868
0.059
2230

27

1906.929
0.060
2230

Table 3 Survival analysis of entry into the upper white-collar class via occupational career
Model 1Model 2Father's class (Reference: Non-skilled manual)Upper white-collar
0.971 **
1.436 **
Lower white-collar
0.839 **
0.842 **
Self-employed
0.215
0.203
Farming-0.197-0.225Skilled manual
0.224
0.233
Age at spell year
0.536 **
0.533 **
Age squared (at spell year
-0.008 **-0.008 **Spell year (Reference: 1996–2005）
1986–1995
0.147
0.375 *
1976–1985-0.1370.092
1975 and before-0.143
0.054
Interaction between the father's class and the spell year
Upper white-collar*1986–1995-0.663 *Upper white-collar*1976–1985-0.668 *Upper white-collar*1975 and before-0.535 +Upper white-collar*1995 and before
Education (Reference: Middle and high school)
Juninor and techinical college
University
Class prior to entry (Reference: Non-skilled manual)
Lower whie-collar
Self-employed
Farming
Skilled manual
Jobless
Change in the place of employment
Constant-12.330 **-13.000 **Model 3Model 4Model 51.435 **
0.841 **
0.201
-0.2290.233
0.534 **
-0.008 **1.012 **
0.592 **
0.059
-0.171
0.295
0.439 **
-0.007 **0.820 **
0.489 *
0.242
0.110
0.363 +
0.512 **
-0.007 **0.366 *
0.083
0.073

0.343 *
0.124
0.236

0.331 *
0.220
0.373 *

-0.626 **-0.664 **-0.516 *1.046 **
1.148 **

0.778 **
0.702 **

-10.276 **1.437 **
-1.086 **-1.524 *0.154
1.198 **
2.396 **
-11.904 **5194.355
0.010
34248
539

4529.565
0.029
34248
539

-12.386 **-2Log Likelihood
5331.727
5324.207
5324.473
Cox & Snell R-square
0.006
0.006
0.006
spells
34248
34248
34248
events539
539
539** significant at 1% * significant at 5% ＋ significant at 10%28

Table 4 Survival analysis of entry into the non-skilled manual class via occupational career
Model 1Model 2Father's class (Reference: Upper white-collar)
Lower whie-collar
0.203
0.205
Self-employed0.319 +
0.319 +
Farming
0.639 **
0.641 **
Skilled manual
0.612 **
0.609 **
Non-skilled manual
1.006 **
1.444 **
Age at spell year
-0.231 **-0.227 **Age squared (at spell yea
0.002 +
0.002 +
Spell year (Reference: 1996–2005）
1986–1995-0.112
0.027
1976–1985-0.387 *-0.2541975 and before-0.688 **-0.642 **Interaction between the father's class and the spell year
Non-skilled manual*1986–1995-0.752 +Non-skilled manual*1976–1985-0.807 +Non-skilled manual*1975 and before-0.227
Non-skilled manual*1976–1995
Education (Reference: Middle and high school)
Juninor and techinical college
University
Class prior to entry (Reference: Upper white-collar)
Lower whie-collar
Self-employed
Farming
Skilled manual
Jobless
Change in the place of employment
Constant-0.105-0.881Model 3Model 4Model 50.205
0.319 +
0.642 **
0.609 **
1.444 **
-0.227 **0.002 +

0.016
0.027
0.241
0.206
1.090 **
-0.152 *
0.001

0.142
0.134
0.282
0.075
1.352 **
-0.0640.001

0.031-0.258-0.642 **0.027-0.300 +-0.734 **0.114-0.151-0.537 *-0.227-0.775 *-0.225-0.804 *-0.525-1.344 **-0.849 *-1.396 **-0.976 *-1.312 **-2.509 **1.321 **
1.462 **
2.081 **
1.328 **
1.960 **
6.657 **
-2.631 *-0.491-2Log Likelihood
4277.280
4271.557
4271.594
Cox & Snell R-square
0.006
0.007
0.007
spells
35864
35864
35864
events413
413
413** significant at 1% * significant at 5% ＋ significant at 10%294203.915
0.008
35864
413

1975.738
0.068
35864
413

Figure 1 Intergenerational inheritance of the upper white-collar
class by birth cohort (SSM)

Log odds ratio

2.5
2
1.5
1
0.5
0
1896-1915（SSM55)1906-1925（SSM65)

1916-1935（SSM75)

1926-1945（SSM85)

1936-1955（SSM95)

1946-1965（SSM05)Birth cohort

Figure 2 Temporal trends of intergenerational inheritance rate of theupper white-collar class and the non-skilled manual class at the firstjob (SSM)
Upper white-collar
Non-skilled manualLog odds ratio

2
1.5
1
0.5
0
1975 or before

1976-1985
Year of entry

30

1986-1995

1996-2005

Figure 3 Temporal trends of intergenerational inheritance rate of theupper white-collar class and the non-skilled manual class up to the ageof 40Upper white-collar
Non-skilled manual

1.4
Log odds ratio

1.2
1
0.8
0.6
0.4
0.2
0
1975 or before

1976-1985

1986-1995

Spell year31

1996-2005

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。
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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

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No.8

深堀聰子

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No.9

戸ヶ里泰典

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No.10

三輪哲

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No.11

安藤理

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No.12

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伊藤秀樹

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No.14

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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
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
三輪哲（JLPS）2008」の結果から（2008 年 12 月発行）
村上あかね

No.19

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No.20

WataruInequality of Opportunities for Access to Universities among the Japanese
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No.21

Hiroshi
ISHIDA

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No.22

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No.23

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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

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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

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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
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No.26

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No.27

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ISHIDA

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No.28

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母親の就労が思春期の子どもの行動・学業に及ぼす効果：Propensity
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No.29

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ットモデルによるアスピレーション分析（2009 年 12 月発行）

No.30

石田浩
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田辺俊介
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No.31

中澤渉

男女間のメンタルヘルスの変動要因の違いに関する分析（2010 年 2
月発行）

No.32

平沢和司

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行）

No.33

長尾由希子

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No.34

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No.35

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No.36

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No.37

三輪哲

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No.38

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No.39

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No.40

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No.41Hiroshi
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Social Inequality among Japanese Youth: Education, Work, and Marriage
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No.42

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東京大学社会科学研究所 パネル調査プロジェクト
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