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

College Is Not the Great Equalizer in Japan:
An Alternative Approach to the OED Triangle

Sho FUJIHARA, Hiroshi ISHIDA(The University of Tokyo)

September 2021
No.146

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. 146
September 2021
College Is Not the Great Equalizer in Japan:
An Alternative Approach to the OED Triangle*

Sho Fujihara (University of Tokyo)
Hiroshi Ishida (University of Tokyo)*Earlier versions of this manuscript were presented at the XIX ISA World Congress ofSociology in Toronto, Canada in July 2018, and the ISA RC28 Spring Meeting in Frankfurt,
Germany in March 2019. We thank participants of the meetings. This research was supported
by KAKENHI Grant-in-Aid for Specially Promoted Research (Grant Numbers JP25000001
and JP18H05204) and Scientific Research (S) (Grant Numbers JP18103003 andJP22223005) from the Japan Society for the Promotion of Science (JSPS). The researchsupport in conducting the panel surveys was obtained from the Institute of Social Science,
University of Tokyo, and The Outsourcing, Inc. The research reported in this manuscript isbased on the analyses of the Social Stratification and Social Mobility (SSM) Surveys and theJapanese Life Course Panel Surveys (JLPS). The Research Ethics Review Board at theauthors’ institution approved the JLPS study. Permission to use the data from the 2015 SSM
Survey was obtained from the 2015 SSM Survey Management Committee. The 1995 and2005 SSM Surveys were obtained from the Social Science Japan Data Archives, the Center
for Social Research and Data Archives, The University of Tokyo. The permission to use thepanel data is obtained from the Management Committee of the Japanese Life Course Panel
Surveys.
1College Is Not the Great Equalizer in Japan:
An Alternative Approach to the OED Triangle

Sho Fujihara (University of Tokyo)
Hiroshi Ishida (University of Tokyo)

Abstract
This study examines whether the equalizing effect of a college education is found in Japanby using a novel approach to analyze the three-way association among class origin (O),destination (D), and education (E). Our approach estimates the differential causal effect ofcollege education (the ED association) by O after adjusting for a wide range of pretreatment
covariates that are likely to be associated with E and D. Our results indicate that college
education does not function as a great equalizer in Japan. There is no clear evidence tosuggest that the occupational returns to a college education are greater among those from lessadvantaged families than those from more advantaged families. We argue that the equalizing
effect of a college education is not apparent because of the specific institutional arrangementsof the educational system and the labor market in society.

2

1.

INTRODUCTION

Sociologists have long been concerned with the question of the role of education inintergenerational mobility (Blau and Duncan 1967; Ishida, Müller, and Ridge 1995).
Education acts as a mediator between class origin and class destination, while it also modifies
the relationship between origin and destination. The relationship among class origin (O),class destination (D), and education (E) is often discussed within the framework of the OED
triangle. As shown in Figure 1A, education mediates the influence of O on D, thereby
contributing to the reproduction of inequality between the two generations. At the same time,education can be seen as a vehicle of intergenerational mobility because variation in
education is not completely explained by social background characteristics; people from lessadvantaged origins may use education to move up the social ladder. The framework of the
OED triangle also allows us to examine the role of education as a modifier or a moderator in
the process of intergenerational mobility. Figure 1B shows how education can modify the
association between O and D. The extent of the OD association may vary with educationlevel: the influence of parental class on children’s class position may be weaker or stronger
depending on the level of education.
A number of studies in Europe and the United States have reported that college education
functions as a great equalizer in society because once people obtain a college education,social origin no longer seems to affect socioeconomic attainment (Breen 2004, 2010; Breenand Jonsson 2007; Breen and Luijkx 2007; Chetty et al. 2017; Hout 1984, 1988; Pfeffer andHertel 2015; Torche 2011; Vallet 2004). These studies are examples of education acting as amodifier, focusing on the OD association conditional on college education (E). They foundthat the OD association is absent or weaker among college-educated people than amongpeople with lower levels of education. This weaker OD association among college-educatedindividuals, together with the expansion of higher education, is thought to be a major
3explanatory factor for the decline in the overall association between O and D in some
European nations and the United States (Breen 2010; Breen and Jonsson 2007; Breen andMuller 2020; Pfeffer and Hertel 2015; Vallet 2004).
In this study, we take up the issue of the role of education in intergenerational mobility
in Japan. We will first discuss three directions of future development in the OED triangle
framework: interpretation, selection and causation, and societal variations. We will thenexamine whether the equalizing effect of a college education is found in Japan by using anovel approach to analyze the three-way association among O, D, and E. We argue that theequalizing effect of a college education is not evident in Japan and that the occupational
returns to college education are not greater among people from less advantaged families thanthose from more advantaged families. We focus on the Japanese institutional context within
which intergenerational mobility takes place and highlight its role in explaining why the
hypothesis of college education as an equalizer is not supported in Japan.

2.

ANALYTICAL FRAMEWORKInterpretation of the OED Triangle
A typical analytical strategy to studying the OED triangle is to focus on the OD association
within the levels of E, as shown in Figure 1B. To interpret the three-way interaction among
OEDs, this strategy examines whether the OD association varies by educational level (Breen
2004; Breen and Luijkx 2007; Hout 1984; 1988; Vallet 2004). The hypothesis that a college
education is the great equalizer predicts the following empirical observation: the OD
association is weaker among college graduates than among nongraduates.
Goldthorpe and Jackson (2008), however, proposed an alternative interpretation of the
OED interactions. As shown in Figure 1C, the same three-way interaction can be tested byexamining whether the ED association varies by O. This framework is similar to that of4

studies that investigate heterogeneous occupational or economic returns to education, whichhave been employed not only in sociology but also in economics (Brand and Xie 2010).
These studies have examined who benefits from college education and yielded different
results: individuals who are more likely to obtain a college degree receive more economic
benefits from college education (positive selection) (Tsai and Xie 2011), and those who areless likely to receive a college degree receive more benefits (negative selection) (Brand andXie 2010; Tsai and Xie 2008). Brand and Xie (2010) estimated the propensity score tocomplete college and examined the heterogeneous effect of college completion on wages bypropensity score strata using a hierarchical linear model. Their finding suggested the negative
selection.1
The college-as-the-great-equalizer hypothesis is consistent with the latter finding and canbe verified by the following observation: the ED association is stronger among those fromless advantaged social origins than those from more advantaged origins. Using empirical data,
we will test this observation.

[FIGURE 1 ABOUT HERE]

Selection and Causation in the OED Triangle
Similar to other disciplines in social science research, sociological studies have been
concerned with the question of causality (Elwert and Winship 2014; Gangl 2010; J. H.
Goldthorpe 2001; Morgan and Winship 2014; Sobel 1996; Winship and Mare 1992; Winshipand Morgan 1999). Although many previous studies have revealed that the OD association
is weaker when E is higher, i.e., social mobility is more prevalent among those who are highly
educated, it is not clear whether these observed patterns are produced by the causal effect ofeducation on intergenerational mobility. Zhou (2019) pointed out that the observed
5association reflects the selection process such that individuals from low social backgrounds
but with high ability and motivation enter university. Zhou found that when such a selection
process was considered by a weighting technique called residual balancing, intergenerational
income mobility among college graduates became very similar to that among high school
graduates. Karlson (2019) also examined whether the selectivity of college graduates
explained higher levels of social mobility for college graduates by using the inverse
probability weighting (IPW) technique to control for selectivity based on a set of observed
characteristics. Karlson found that even after considering the selection processes, the OD
associations measured by the regression coefficient of parents’ socioeconomic index (SEI)and family income on those of children among college graduates did not change, supportingthe college-as-the-great-equalizer hypothesis. Fiel (2020) also found that after consideringboth measurement errors and the selection process, intergenerational income mobility amonghigh school dropouts is significantly lower than that among those with more education, an
observation consistent with the college-as-the-great-equalizer hypothesis.
The process of selection into education is important in understanding how OED
associations arise and in estimating the causal effect of education on destination. In addition
to socioeconomic backgrounds, academic performance is one of the most important
determinants of educational attainment. Previous studies have included variables related toboth socioeconomic backgrounds and academic performance in the model estimating theprobability of college completion for the propensity scores and inversed probability weights(Brand and Xie 2010; Karlson 2019; Zhou 2019). However, further consideration must be
given to the fact that, as has been demonstrated by research in social stratification and thesociology of education, these not only independently affect educational attainment but also
have interaction effects (Bernardi and Boado 2014; Boudon 1998; Gambetta 1987; John H.
Goldthorpe 1996; Holm, Hjorth-Trolle, and Jæger 2019; Hout 2012). The effect of academic
6performance on educational attainment is stronger for students from less advantaged social
origins than for those from advantaged social origins. That is, students from advantagedsocial origins are likely to obtain higher levels of education even when their academic
performance is poor, while students from less advantaged social origins tend to leave
education if their academic performance is poor. Furthermore, it is possible that college
graduates from less advantaged backgrounds are more likely to possess certain characteristics(higher cognitive ability and motivation) than those from more advantaged backgrounds(Mare 1980; Torche 2011). Because these characteristics are likely to affect socioeconomic
outcomes (D), the causal effect of college education for those from less advantaged
backgrounds may be overestimated. Such a selection process of educational attainment has
not been sufficiently considered in estimating the propensity score or the weight to correct
for the selection (Brand and Xie 2010; Karlson 2019; Zhou 2019). Therefore, we will include
interaction effects that have been reported to be critical in previous research and test thetheory on the process of selection (Li 2013).
Based on the above arguments, we propose the analytical framework of the OED that
estimates the heterogeneous causal effect of college education (E) on destination (D) bysocial origins (O) after controlling for a wide range of pretreatment covariates and theirinteractions that are likely to be associated with both E and D. Although this approach is very
similar to that of Brand and Xie (2010), what should be emphasized here is that instead ofusing the propensity score to examine the heterogeneity of the economic returns to education,
we consider the modification of the causal effect of education on destinations by directly
measured social origins, which is more in line with the framework of the OED triangle insocial mobility research. Moreover, to maintain continuity with previous studies of social
mobility investigating OED interactions, this study focuses on occupational mobility rather
than income mobility. We define individuals’ educational attainment as a treatment, their
7

occupational attainment as an outcome, and their parental occupation as an effect modifier(see below for details of our methods).

Societal Variations
We seek to examine societal variations in the way social background, education, andsocioeconomic attainment are related. There are many studies of OED interactions in the US(Bloome, Dyer, and Zhou 2018; Chetty et al. 2017; Hauser and Logan 1992; Hout 1984;
1988; Karlson 2019; Pfeffer and Hertel 2015; Rytina 1992; Semyonov and Roberts 1989;
Torche 2011; Witteveen and Attewell 2020; Zhou 2019). These studies used a variety ofmeasures of origin and destination, such as class, SEI, occupational prestige, wage, andincome. On the whole, whatever measures of origin and destination are used, there is atendency that the higher the level of education is, the weaker the origin-destination
relationship. However, it should be noted that some recent studies have shown inconsistent
results (Forster, de Werfhorst, and Leopold 2021; Witteveen and Attewell 2020; Zhou 2019).
In addition, there have been various developments in this research topic, such as comparisonsbased on the selectivity of universities (Chetty et al. 2017; Klein 2021), comparisons between
college graduates and those who attended graduate schools (Torche 2011; Witteveen and
Attewell 2020), and comparisons of fields of study and selectivity within graduate schools(Torche 2018). Studies of European societies also found that the OD associations are weaker
for those who obtained higher levels of education in the UK (Breen 2010; Breen and Luijkx
2007; Bukodi and Goldthorpe 2019), France (Vallet 2004), Germany (Breen 2010; Breenand Luijkx 2007), Sweden (Breen 2010; Breen and Jonsson 2007), and Spain (Gil-Hernández,
Marqués-Perales, and Fachelli 2017).
There are few studies of OED interactions in societies outside Europe and the UnitedStates. Chung and Park (2019) showed that the OD associations were weaker for those who
8obtained a bachelor’s degree or higher than for those who finished high school or less.
However, Torche and Ribeiro (2010) found that the OD associations were not weaker amongpeople with higher levels of education in Brazil. These findings indicate that the tendency
that the OD associations are weaker for those with higher levels of education is not universal,
and social and institutional contexts may be of importance in understanding the various
results (Zhou and Xie 2019). Studies of the associations between origin and education(educational inequality) have mainly focused on the educational system (Erikson 1996;
Shavit and Müller 1998). In particular, when looking at the relationship between educationand destination, we often focus on the institutional characteristics of the education system
and labor market (Bol et al. 2019). On the other hand, studies of the relationship between OD
emphasize commonalities or similarities of industrial societies rather than differences anddissimilarities (Erikson and Goldthorpe 1992; Featherman, Jones, and Hauser 1975; Jonsson
et al. 2009).
When considering the interactions of OEDs, we need to pay attention to the educational
system, the structure of the labor market, and the linkage between education and the labormarket. In the next section, we will describe the Japanese institutional context in which social
background, education, and socioeconomic attainment are associated.

3.

THE JAPANESE CONTEXT: Bringing Institutions Back In

The Japanese educational system, like those of many other industrial nations, has undergone
a dramatic expansion of secondary education and tertiary education. However, the expansion
was not linear. The enrollment rate in two-year junior colleges and four-year universities
increased sharply in the 1960s and early 1970s, stagnated for two decades at approximately
37% and then increased again after the mid-1990s, reaching 59% in 2020. Despite theexpansion, there was no clear evidence of increasing equality in access to higher levels of9education when education is measured in relative terms (Aramaki 2000; Hara and Seiyama
2005; Ishida 2007; Kondo 2000). Fujihara and Ishida (2016), for example, report that whenyears of school is used as the measure of absolute level of educational attainment, the equalityof educational opportunities by social background was increased in postwar Japan. People
from less advantaged backgrounds benefited most from the upgrading of education. However,when education was measured as a relative value by taking into account the quality of
institutions, there was a trend of increasing inequality. During the period of the second
expansion of education, nonselective institutions of higher education expanded while highly
selective institutions remained stable in size and continued to enjoy the advantaged positionin the educational distribution and the labor market. The relative value of educational
attainment for students in nonselective institutions tended to diminish following the
expansion of the higher education sector. Since people from advantaged backgrounds had
better access to highly selective institutions, the relative gap in educational attainment bypeople from different backgrounds tended to increase.
The Japanese labor market and class structure exhibit dramatic transformation during thepostwar period. Late and rapid industrialization changed Japan from an agrarian society inthe 1950s to a mature industrial society in the 1980s. As in other industrial nations, thefarming class shrunk rapidly while the professional-managerial class expanded, thereby
producing absolute upward mobility. The pace of transformation of the class structure was
faster in Japan than in early industrializing nations. Previous studies report that the Japaneseclass structure has not become more fluid or open during the postwar period despite the rapidchanges in class structure and the expansion of the educational system (Hara and Seiyama
2005; Ishida and Miwa 2009). Ishida (2018) showed that the association between class origin
and class destination has been stable throughout the postwar period. The strength ofassociation represented by uniform difference parameters showed stability and10

nonsignificant trendless fluctuation from 1955 to 2015. Unlike many European nations thatexhibited increasing social fluidity in the 20th century (Breen 2004; Breen and Müller 2020),
Japanese society showed no sign of the trend of societal openness.
Another important feature of educational expansion is that it occurred along withpopulation decline. The 18-year-old population has been steadily declining since 1992, when
the second stage of educational expansion took place. Consequently, the absolute number ofstudents in junior colleges and universities has not increased since the mid-1990s. In otherwords, there was no clear oversupply of the college-educated population, and thesocioeconomic returns to education were thus stable despite educational expansion(Kambayashi, Kawaguchi, and Yokoyama 2008; Kawaguchi and Mori 2016).
Throughout the postwar period, youth unemployment has remained low in Japan, at least
compared to that of most other industrial nations. This fact is often ascribed to the successfultransition from school to first job among Japanese youth (Kariya and Rosenbaum 1987;
Rosenbaum and Kariya 1989). The transition from school to work among high school
students in Japan is regulated by the rules set forth by the national government (Brinton 2008).
The procedure and schedule for recruiting students who are still in high school are determinednationwide by the government. Employers wishing to hire high school students must fill out
the job posting forms and submit them to the Public Employment Security Office. The forms
are then distributed to high schools by the office. High school students apply for jobs
allocated to schools, and the school staff ranks students and nominates the appropriate
candidates for job interviews that begin on the specific date. Students nominated by highschools are only allowed to take job interviews, and employers usually honor
recommendations by high schools because there are long-term semiformal agreements
between particular schools and particular employers. Employers tend to hire students from
specific high schools based on the past history of recruitment and to trust that these schools
11nominate students appropriate for them. Schools have delegated authority to select students
within schools and nominate them for particular employers. High schools play an active rolein matching students to jobs, and most students begin working immediately following school
graduation in April (Brinton 2008). Because access to assistance provided by high schools is
not related to social origin, mediation by school institutions is likely to weaken theassociation between social origin and first job (Ishida 2011).
The labor market for college graduates is different from that for high school graduates(Chiavacci 2005; Oshima 2012). Similar to high school students, university students begin
looking for jobs while they are still in school, and they begin their employment immediately
after graduation. An important institutional characteristic of the labor market for college
graduates is that employers set up special recruitment tracks only for college students who
are looking for a job before graduation. Similar to high school students, these students do notcompete for a job with college graduates who have already been in the labor market. However,unlike high school students, university students are on their own to search for job openings
on the web and fill out entry forms. Employers screen the entry forms and invite applicants
for personal interviews. Although university placement offices offer assistance to students,
they are not involved in the selection process. Employers do not delegate the selection to
universities. Because of the less active role of university institutions, family background islikely to affect the chances of getting interviews and interview performance and demeanor(Jackson, Goldthorpe, and Mills 2005; Rivera and Tilcsik 2016).
The linkage between education and the labor market is heavily influenced by Japaneseemployment relations, which are often characterized by in-house job training, limited
external hiring, internal promotion, and long-term commitment (Kambayashi and Kato 2017;
Ono 2010; Shimizutani and Yokoyama 2009). Because of the emphasis on internal training
and promotion, Japanese firms prefer to hire college seniors over those who are already12

working in the labor market and set up special recruitment tracks and schedules targeted atcollege seniors every year (Kariya and Honda 2010; Nakamura 1993). However, it isimportant to note that men benefited from Japanese employment relations, but women did
not always do so (Osawa 2011). Some Japanese women left employment following marriage
and child bearing, although the extent of withdrawal has become less common in recent years.
When these women re-entered the labor market, they were not assisted by schools and often
occupied part-time employment that is outside the Japanese employment relations. Therefore,
with regard to first occupational attainment, both men and women took advantage of school
assistance to have a smooth transition to work. However, some women withdraw from thelabor market, and their occupational attainments following re-entry into the labor market are
different from those experienced by men.
In summary, the labor market is highly segregated by educational credentials. The smoothtransition from school to work and the special recruitment track for untrained students
constitute critical institutional features of the occupational attainment process in Japan. The
difference in the role played by school institutions (high schools and universities) is importantin understanding how social origins influence occupational destinations in a different way
for high school graduates and college graduates.

4.

METHODS

4.1 Statistical Models
In examining the OED interactions, we apply the analytical framework of the effectmodification by pretreatment covariates. This method is often employed in epidemiology toevaluate heterogeneous causal effects by pretreatment variables (M. Á. Hernán, Brumback,and Robins 2000; M. A. Hernán and Robins 2019; Robins, Hernán, and Brumback 2000;
VanderWeele 2009; VanderWeele and Robins 2007). Let 𝐴 be a treatment variable and 𝑌13

be an outcome variable. In this study, 𝐴 indicates college graduation (1: college graduates,
0: high school graduates), and 𝑌 indicates occupational attainment, such as prestige and SEI.
Let 𝑌 ! denote the potential outcome for an individual if treatment 𝐴 had been set to 𝑎,
𝑌 !"# be the occupational attainment that would have been observed under 𝑎 = 1 (college
graduation) and 𝑌 !"$ be the occupational attainment under 𝑎 = 0 (high school
graduation). Let E[𝑌 !"# ] indicate the mean counterfactual outcome had all individuals in
the population graduated from college and E[𝑌 !"$ ] indicate the mean counterfactual
outcome had all individuals graduated from high school. Here, we are concerned with a meandifference, E[𝑌 !"# ] − E[𝑌 !"$ ] , as the average causal effect. We applied the marginal
structural mean model to estimate the average causal effect, which can be written as follows:
E[𝑌 ! ] = 𝛽$ + 𝛽# 𝑎(1)

We are interested in 𝛽# in equation (1) because the parameter 𝛽# equals the causal
estimand of interest: E[𝑌 !"# ] − E[𝑌 !"$ ] . However, the outcomes 𝑌 ! in this model arecounterfactual and unobserved, and thus, we cannot fit this model to actual data. Instead, byweighting, we create pseudo-population data in which there is no confounding by a set ofcovariates 𝐿; that is, the measured covariates are balanced between the treatment and control
groups (treatment variable is independent of the measured covariates) and then fit thefollowing model:
E[𝑌|𝐴] = 𝜃$ + 𝜃# 𝐴

(2)

If the conditional exchangeability 𝑌 ! ⫫ 𝐴|𝐿 as well as positivity and consistency hold true,
the estimated parameter 𝜃4# is a consistent estimator of the (homogenous) causal effect 𝛽#(M. A. Hernán and Robins 2019). To estimate the weight, we use the entropy balancing
technique (Hainmueller 2012). Similar to the propensity score (PS) weight with logistic
regression, this method also estimates a weight to create the pseudo-population but allows us
to achieve a high level of covariate balance rather than PS weighting.
14

Usually, the marginal structural model includes only treatment variables and does notinclude covariates if we want to estimate the average treatment effect. However, if we want
to investigate the heterogeneous causal effect (effect modification), the marginal structuralmodel includes the subset of the covariates 𝐿. The marginal structural mean model for effectmodification can be written as follows:
E[𝑌 ! |𝑉] = 𝛽$ + 𝛽# 𝑎 + 𝛽% 𝑉𝑎 + 𝛽& 𝑉

(3)where 𝑉 is one of the sets of covariates 𝐿 and an effect modifier of college education on
occupational attainment. In this study, 𝑉 is social origin (parental prestige or SEI). If 𝛽% is
not zero, there is the effect modification, and the causal effect of education varies by thecovariate. Because this model also cannot be fitted to actual data, we fit the following modelto the pseudo-population data created by the stabilized weights from the entropy balancing
technique, as mentioned above (𝐿 includes 𝑉):
𝐸 [𝑌|𝐴, 𝑉] = 𝜃$ + 𝜃# 𝐴 + 𝜃% 𝑉𝐴 + 𝜃& 𝑉

(4)

We applied the linear regression model by weighted least squares and estimated theparameters.

4.2 Data
We use the data from the Social Stratification and Social Mobility (SSM) Surveys and theJapanese Life Course Panel Surveys (JLPS), both of which are nationally representative
surveys of Japan. The SSM surveys are repeated cross-sectional surveys and have been
conducted in Japan every ten years since 1955. We used the 1995, 2005, and 2015 SSM
datasets, and the respondents were men and women aged 20 to 64 years old at the time of thesurveys. The JLPS includes longitudinal surveys started in 2007. The respondents wereextracted from male and female Japanese residents aged 20 to 40 years in 2006 (born between
1966 and 1986). Additional respondents of the same cohort were added in 2010. We used the15data from the 2007 to 2019 surveys. Altogether, after omitting the cases with missing values
for the treatment and outcome variables, as described below, there were 4,678 and 4,665
male respondents for the first and current occupations, respectively, and 5,741 and 4,712
female respondents for the first and current occupations, respectively. We used multiple
imputation (M = 160) for the missing values of the pretreatment covariates by using
multivariate imputation by chained equations (Van Buuren 2018).

4.3 Variables
The outcome variables (𝑌) are the occupational status of the respondents’ first job and thatof current job, as measured by two occupational scales: occupational prestige (Nakao andTreas 1994; Tsuzuki 1998) and the SEI (Fujihara 2020; Hauser and Warren 1997). Since theoccupational status scores are measured for multiple years, the average of the available scores
over the past five years is used for the current occupation status of the SSM respondents, andthe average of available scores over the survey periods (2007-2019) is used for the JLPS
respondents.
The treatment variable (𝐴) is the attainment of college education, which distinguishes
those with only a high school education from those with college education. Because of thelimitation of sample size, the variables for college education were created separately for men
and women (Yamamoto and Brinton 2010): 𝐴 = 1 for male respondents who graduatedfrom four-year colleges or more, and 𝐴 = 0 for those who graduated from high school anddid not go on to higher education; and 𝐴 = 1 for female respondents who graduated fromfour-year or junior colleges or more, and 𝐴 = 0 for those who graduated from high school
and did not go on to higher education. Since we are interested in the effects of college
compared to high school, individuals with other educational backgrounds, such as secondary

16

school graduates, professional training school graduates, and those who dropped out highschool or university, were excluded from the analysis.
The effect modifier variable (𝑉) is the father’s occupation, which is used as a central
measure of social origin. For ease of interpretation, the scores of the father’s occupation are
centered at the mean. To estimate the causal effect of college education, we need to specify
a set of pretreatment covariates predicting college completion, which may also be directly
associated with occupational attainment independent of college completion. There are twomain types of pretreatment covariates ( 𝐿 ) that affect both college completion andoccupational attainment: social background and academic achievement.
The pretreatment covariates related to individuals’ social background are the father’s
education, the mother’s education (Ishida 2007), number of siblings and birth order. The
pretreatment covariates also include the father’s occupation, as mentioned above. Because
the SSM and JLPS lack information on academic ability or cognitive test scores, we usedself-reported grades in 9th grade (self-reported grades relative to the cohort in the 3rd year of
junior high school, from bottom to top), high school rank, and high school track (academic
or vocational) as proxies for academic ability. The high school rank is based on the
respondent’s assessment of the proportion of his or her classmates who attended college after
high school graduation, and the values are standardized for each cohort. The Japanese highschool system has been highly stratified, and students are allocated into diverse types of highschools according to their academic performance (Kariya and Rosenbaum 1987). Thus, therank of high school the respondent attended is a good proxy of academic ability andmotivation, especially in Japan. Previous studies indicated that self-reported grades in 9th
grade and high school rank are accurate measures (Yamamoto and Brinton 2010). We also
used birth cohort and the survey year as pretreatment covariates.

17

There may be an interaction effect of social origin and academic performance on theprobability of entering college (Bernardi and Boado 2014; Boudon 1998; Gambetta 1987;
John H. Goldthorpe 1996): the effect of social origin on college attendance is weaker for
those with higher academic ability. In other words, the effect of academic performance atschool on college attendance is weaker for those from more advantageous social origins thanfor those from less advantageous ones. Thus, we included the interaction terms in estimating
the weights.
Although previous studies included not only socioeconomic background variables but
also social-psychological variables such as educational aspirations, motivation, and otherpersonal traits (Brand and Xie 2010; Karlson 2019; Zhou 2019), these variables were notavailable in our datasets. Despite these limitations, essential variables such as those used in
the analysis of educational attainment are included. Descriptive statistics are shown in Table
1.

[TABLE 1 ABOUT HERE]

5.

RESULTS

5.1 Selection into College Graduation
The column labeled “Before Weighting” in Figure 2 shows the distributions of the
pretreatment covariates by treatment status (college graduation or high school graduation)
before weighting. Thus, this indicates baseline differences in social origin and academic
performance between college graduates and high school graduates. Because of space
limitations, we show the results of the analysis for men using occupational prestige as ameasure of social origin. College graduates differ in many respects from high school
graduates. The levels of fathers’ occupational prestige, fathers’ and mothers’ education, and18

durable goods ownership at the age of 15 among college graduates tend to be higher thanthose among high school graduates. College graduates have fewer siblings and are more
likely to have a higher birth order than high school graduates. The self-rated GPA at juniorhigh school and the high school rank were higher for college graduates than for high school
graduates. College graduates were more likely to attend academic high schools than
vocational schools. Therefore, college graduates tend to be from more advantageous social
backgrounds and to have higher academic performance than high school graduates.
The column labeled “After Weighting” in Figure 2 shows the distributions of the
pretreatment covariates by treatment status after weighting. The distributions of the
pretreatment covariates were highly balanced between the treatment and control groups.

[FIGURE 2 ABOUT HERE]

5.2 Homogenous Causal Effect of College Graduation
Before investigating the heterogeneous causal effect of college education on occupational
attainment by parental occupation, we show the causal effects without the interaction term
between education and social origin in Table 2. We used two different indicators ofoccupational attainment (prestige and SEI) for the first and current occupations and two
analytical models. The first model is the linear regression model (Equation 2) with robust
standard errors that does not use any weights and thus estimates the total association between
college education and occupational attainment (column labeled “Before Weighting”). The
second model is the linear regression model (Equation 2) with the weights estimated by theentropy balancing technique (column labeled “After Weighting”). The result before
weighting indicates that for both men and women, college education is associated with eachtype of status of first and current occupations. After weighting, the coefficients decrease, but
19there still exist substantive and significant effects of college education on occupational
attainment. Because the pretreatment covariates are highly balanced between college andhigh school graduates (see Figure 2), the estimated coefficients can be interpreted as thecausal effect of college education on occupational attainment.

[TABLE 2 ABOUT HERE]

5.3 Heterogeneous Causal Effect of College Graduation
Table 3 shows the results of the regression analysis predicting the respondents’ occupational
attainment from Equation 4 for men and women. For each type of occupation, we report theresults of the regression models before and after weighting. To examine the heterogeneous
causal effect, we focus upon the interaction term between college education and fathers’
occupational scale. Because the scores of the father’s occupation were centered at the mean,
the coefficient for college education (𝜃4# ) indicates the causal effect of college education for
those whose father’s occupational score was at the mean.
Let us begin with men. First, we examine occupational prestige at first job (the first twocolumns of the upper panel in Table 3). Before weighting, the coefficient for the interaction
between the father’s occupational prestige and college education ( 𝜃4% ) is positive andsignificant (0.091). However, pretreatment variables such as social origin and academic
performance may produce such an association. We adjusted for the confounders of educationand occupation by weighting. After weighting, the coefficient for the interaction changed
little (0.110), indicating that the causal effect of college graduation on occupational prestige
at first job differs by the father’s occupational prestige. The higher the father’s occupationalprestige, the stronger the causal effect of education on the prestige of first occupation.
However, when we examine the results for SEI (the second and third columns of the upper
20

panel in Table 3), the coefficient of the interaction term for first occupation was small andnot significant before weighting (−0.008). It became slightly larger and positive after
weighting but was not significant (0.019).
We conducted the same analysis for the current occupation (the last four columns of theupper panel in Table 3). For the columns on occupational prestige, the coefficient for theinteraction term was 0.078 before weighting, but it was 0.203 after weighting. The same
pattern was not observed for the SEI of the current occupation (the last two columns of theupper panel in Table 3): the coefficient for the interaction term was -0.018 before weighting
and 0.038 after weighting (both were not significant).
The results from the analysis of the prestige score are consistent with the positive
selection hypothesis, but those from the analysis of SEI support neither the positive selection
hypothesis nor the negative selection hypothesis. In summary, the overall analysis amongmen did not show any support for the negative selection hypothesis.
Next, we move to the analysis of women. First, we examine occupational prestige at firstjob (the first two columns of the lower panel in Table 3). The coefficients for the interaction
term were 0.064 before weighting and 0.109 after weighting, and both were significant. The
result for SEI of first occupation (the third and fourth columns of the lower panel in Table3) shows that the coefficients for the interaction term were 0.032 and not significant before
weighting and 0.099 and significant after weighting. Among women, the causal effect ofeducation on the status of first occupation was heterogeneous by the father’s occupational
status. The causal effect of college graduation on prestige and SEI of the first occupation was
greater among women whose fathers had higher status occupations than among those whose
fathers had lower status occupations.
However, for the current occupation (the last two columns of the lower panel in Table3), the coefficients for the interaction term were small and not significant both before and21

after weighting, suggesting that the causal effect of college completion on current occupation
is homogenous across social origin.
The results from the analysis of the first occupation are consistent with the positive
selection hypothesis, but those from the analysis of current occupation support neither the
positive selection hypothesis nor the negative selection hypothesis. Similar to the results
among men, the women’s results do not support the negative selection hypothesis.

[TABLE 3 ABOUT HERE]

Figure 3 provides a graphical representation of the heterogeneous returns to college
education by the father’s occupation before and after weighting among men. The dotted line
indicates the average occupational scores for high school graduates, and the solid line
indicates the scores for college graduates. The gaps between the two lines represent thedifference in occupational attainment between high school graduates and college graduates,that is, the effect of college education. When the two lines are parallel to each other, theeffects of college education on occupational outcomes are the same across different levels of
the father’s occupational status. When SEI is used to measure the first and the current
occupational status, the two lines are parallel to each other: the effect of college education isapproximately the same for people from different social backgrounds. However, when the
two lines diverge as the father’s occupational status increases, the gaps between the two lines
are larger for those from the more advantaged background than for those from the lessadvantaged background. This pattern implies positive selection. We find this pattern for firstand current occupational prestige. It is also worth noting that the pattern of positive selection
is more pronounced after controlling for covariates by weighting. In other words, after
controlling for social background and academic performance, the apparent returns to college
22education for people from less advantaged backgrounds are reduced, while the same returns
for people from more advantaged backgrounds are expanded. Further inspection of Figure 3
suggests that the increasing gaps between the two lines come from the fact that the dottedlines for high school graduates are almost flat. This finding implies that among high school
graduates, an increase in a father’s occupational prestige does not always produce an increasein the son’s occupational prestige.
Figure 4 provides the same graphical representation of the heterogeneous returns tocollege education among women. With regard to the first occupation for both prestige and
SEI, the pattern shows diverging gaps as the father’s occupational status increases, especiallyafter weighting. The effect of college education appears to be much larger for women fromadvantaged backgrounds than for those from disadvantaged backgrounds. The dotted line
representing high school graduates seems to be flat, indicating that the father’s occupational
status does not seem to increase the daughters’ occupational status. This observation was also
found among men. We will argue later in the discussion section that the weak association
between the father’s and the respondent’s occupational status is probably explained by theinstitutional arrangement of high schools assisting students in finding jobs. With regard tocurrent occupation, the two lines are parallel to each other, indicating that the returns tocollege education are homogeneous across women with different social backgrounds.
Finally, there is the third pattern in which the gaps between the two lines decrease as thefather’s occupational status increases, and this pattern is consistent with the negative
selection hypothesis where the returns to college education are larger among those from theless advantaged backgrounds than those from the more advantaged backgrounds. None of
the figures we presented thus far exhibit this pattern. What is unique about Japanese societyis that the association between college education and occupational attainment does not
become weaker as the father’s occupation becomes more advantageous.
23

[FIGURES 3 & 4 ABOUT HERE]

6.

CONCLUSION AND DISCUSSION

College education is widely known to open doors to prestigious occupations and higher
paying jobs. However, it has not been clear whether the benefits of higher education vary
among individuals of different social backgrounds in Japan. This study represents the firstsystematic account to test college as the great equalizer hypothesis in Japan: socioeconomic
returns to a college education are greatest among people from less advantaged backgrounds,
and a college degree serves to level the playing field by promoting greater intergenerational
mobility for disadvantaged people in Japan.
Our results indicate that a college education does not function as the great equalizer inJapan. There is no clear evidence to suggest that the occupational returns to college education
are greater among those who come from less advantaged families than those from moreadvantaged families, as was predicted by the hypothesis of college as the great equalizer(Torche 2011) or the negative selection hypothesis (Brand and Xie 2010). Among men, after
controlling for pretreatment covariates, the causal effect of college completion on
occupational attainment was homogeneous when SEI was used to measure occupational
attainment, and the causal effect appeared to be even smaller for those from less advantaged
families when occupational prestige was used. The reason for the declined returns to college
education among the less advantaged comes from selection based on academic performance.2
Those who attend college from less advantaged origins tend to be high-performing students.
Once we control for academic performance, the apparent benefits of college education for
those from less advantaged families are reduced. Our results also imply that controlling for
pretreatment covariates increases the returns to college education for those from more24

advantaged families because their benefits of college education seem to be greater when
academic ability is held constant.
Among women, the causal effect of college education on current occupational attainment
remained homogenous across levels of the father’s occupation, while the causal effect on
first occupational attainment appears to show the increased level of returns for women from
more advantaged backgrounds.3 These results were obtained regardless of the measurementof occupational attainment. In summary, among both men and women, there was no clear
evidence for greater equalizing benefits of college education for people from disadvantaged
backgrounds. Our evidence is more consistent with the homogenous returns to college
education or greater returns for people from more advantaged families than negative selection,
where the returns are greater for those from disadvantaged families.
The argument about college education as the great equalizer implied that the association
between social origins and occupational attainment is absent or much weaker among collegeeducated individuals than among those with lower education (Hout 1984; 1988; Torche 2011).
In some European nations and the United States, once people have completed college
education, they have access to a high-skilled labor market, and their attainments are notgreatly affected by their social origins because labor markets for college-educated individuals
are more meritocratic than those for less-educated individuals (Breen and Jonsson 2007). On
the other hand, among people with little education, the association between social origin andattainment is strongest. People who have limited educational resources, such as high school
dropouts in the U.S., have to rely on familial resources to advance in the labor market (Fiel2020). Advantaged families use familial resources to compensate for their children’s lack of
educational resources (Bernardi and Ballarino 2016).
However, the same phenomena were not found in Japan. Why does Japan lack evidenceof education as the great equalizer? Two complimentary mechanisms may be at work in
25

Japan. First, social background continues to play an important role in allocating occupational
positions among the college-educated in Japan. College education does not seem to eliminate
the effect of social origins because of the particular institutional features of the labor market
for fresh college graduates in Japan. When college graduates enter the labor market, they
compete in the labor market specifically reserved for college seniors who are looking for a
job following graduation (Arita 2020; Kariya and Honda 2010). College seniors do notcompete with college graduates who have already been in the labor market and are looking
for a job after graduation because large Japanese firms prefer recruiting fresh school
graduates, training them on the job, and promoting them internally over hiring from theexternal labor market and head-hunting managers. The competition among college seniors
means little variation in the skills they possess. Employers focus on the trainability ofprospective employees rather than hands-on skills that may be put into practice immediately
in the company. The selection is based on personal interviews, and interview performanceand demeanor are known to be related to family background (Jackson, Goldthorpe, and Mills
2005; Rivera and Tilcsik 2016). This leaves space for family resources to be effectively
mobilized. Family resources, including social capital and the network of parents, may be used
to establish connections with employers. Although college students can invest in upgrading
their qualifications and skills, such as English proficiency, in college, the chances of upgrades
are affected by family economic resources. Because of the preference of large Japanese firms
to recruit college seniors and train them internally, the influence of family backgroundcharacteristics continues to surface in the selection process and makes a difference ineventual outcomes.
Second, among individuals with less education (high school graduates), our analyses
suggest that the effects of social origins on occupational attainment were not larger than thoseamong college graduates. This outcome is probably related to the institutional context in
26

which high school students find jobs in Japan. As briefly discussed at the beginning of thisstudy, high schools play a critical role in the process of matching students to jobs (Brinton2008). Employers delegate the selection of students to schools, and only students who arerecommended by high school can proceed to the interview stage. Schools normally use
meritocratic criteria (grades and attendance records) in the selection process, without any
reference to social background (Kariya and Rosenbaum 1987; Rosenbaum and Kariya
1989).4 Furthermore, the use of school assistance is not related to social background (Ishida2011). All students, regardless of their social origins, have access to the assistance provided
by high schools. The institutionalized linkage mediated by high schools contributed to
weakening the association between social origins and occupational outcomes among highschool graduates in Japan.5
Thanks to these two complementary mechanisms, the returns to college education (thatis, the difference in occupational attainment between high school graduates and college
graduates) for people from less advantaged backgrounds were not greater than those from
more advantaged backgrounds in Japan. The absence in Japan of what researchers in the U.S.
and Europe have portrayed as the great equalizing effect of college education is likely tocome from the cross-national difference in the institutional context of the labor market forcollege graduates and high school graduates in Japan. This study, therefore, highlights theimportance of the institutional arrangements of the educational system and the labor market
in explaining the relationship among class origin, class destination, and education.
Our results also have implications for the lack of a trend of increasing societal fluidity
and openness in postwar Japan. We already know from previous studies that the equality ofeducational opportunities has not increased in postwar Japan. Together with this lack of
educational equalization trend, the absence of the equalizing effect of college education27

means that the expansion of the educational system has not weakened the association between
class origin and destination. The stability in the OD association is consistent with our finding.
What are the policy implications that can be drawn from this study? Our study indicatedthat educational expansion alone does not always emancipate people from the influence ofsocial background. Instead, among high school graduates, school institutions play an activerole in reducing the effect of social origin in the occupational attainment process. The successfound at the high school level may be extended to the college level. Indeed, some
nonselective institutions offer their students assistance in the job search process because thesestudents often suffer in the free labor market when competing with students from more
selective institutions (Oshima 2012). Government agencies such as the Public Employment
Security Office may play a more active role in the job search for college graduates. The key
idea is that people who lack familial resources should be able to rely on institutions whose
access is not restricted by social background.
Finally, there are a number of future directions that can be taken from this study. First,
due to the limitation of the datasets, we were not able to control for a wide range of covariates.
The lack of a more accurate measure of cognitive ability is probably the most serious concern.
The inclusion of the measures of motivations and expectations is also likely to explain theassociation between education and occupational attainment. Second, it is possible to extend
the analyses to income mobility instead of occupational mobility. We did not havemeasurement of income in the parental generation, but if such a measure is available, we
could replicate the analyses using different outcomes (monetary returns). Third, our approachof bringing in the institutional context within which occupational attainment takes place canbe extended to cross-national research. Societies have differing arrangements of transition
from school to work and labor market structures. By paying more attention to the institutional
context, we should be able to understand how and why college education functions as the28

great equalizer in a particular society but not in others and to examine the different roles
played by family background and institutions in occupational attainment within society.

Notes
1.

For further discussions, see Breen, et al. (2015) and Zhou and Xie (2016).

2.

We repeated the analysis using social background factors alone as covariates and using
academic performance factors alone as covariates to estimate the weights. When we usedacademic performance factors alone, we obtained results very similar to those reported
in Table 3 and Figures 3 and 4. In other words, academic performance factors largely
contributed to the result with weighting. Please see appendix for details.

3.

It should be noted that the current occupations for some women are the occupations after
re-entering the labor market following marriage or childbirth.

4.

In the 2015 SSM dataset, more than half of high school graduates obtained their first job
through the assistance of schools.

5.

Our datasets contained very few high school dropouts, so we could not test whether the
association between social origins and occupational attainment is stronger among highschool dropouts, a finding reported in the U.S. (Fiel 2020).

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38

E

E

O
(A) OED triangle

D

O

E

D

(B) A typical interpretation of
the three-way interaction (OD by E)OD(C) An alternative interpretation of
the three-way interaction (ED by O)

Figure 1. OED Triangle (A) and Two Analytic Strategies (B and C)

Figure 2. The distributions of pretreatment covariates between high school graduates(white) and college graduates (gray) before and after weighting
Note: The scale for father’s occupations is prestige.

Figure 3. The heterogeneous effect of college education on the status of first andcurrent occupations for men
Note: The ribbons represent the 95% confidence intervals (CIs). Because multiple imputation (M =160) was used for the pretreatment variables, multiple lines and confidence intervals are displayed
after weighting.

Figure 4. The heterogeneous effect of college education on the status of first andcurrent occupations for women
Note: The ribbons represent the 95% confidence intervals (CIs). Because multiple imputation (M =160) was used for the pretreatment variables, multiple lines and confidence intervals are displayed
after weighting.

Table 1. Descriptive statistics
Men
First
occupation
N = 4,678

Current
occupation
N = 4,665

Women
First
Current
occupation
occupation
N = 5,741
N = 4,712

Occupational Status
Prestige50.7 (8.3)
53.0 (8.9)
50.9 (6.6)
SEI
50.4 (8.3)
52.3 (9.2)
50.3 (6.9)
Father's Occupational Status
Prestige52.0 (9.2)
52.0 (9.2)
52.0 (9.0)
SEI
48.8 (10.5)
48.9 (10.6)
49.1 (10.3)
Father's education
JH
39.5%
39.4%
34.9%
HS
38.1%
38.2%
40.4%
JC/Univ
22.4%
22.4%
24.7%
Mother's education
JH
38.2%
38.0%
34.0%
HS
49.4%
49.4%
52.1%
JC
6.3%
6.3%
8.2%
Univ
6.2%
6.2%
5.8%
Resources at 15 years old
0.0 (0.9)
0.0 (0.9)
0.2 (0.9)
Siblings
1.8 (1.1)
1.8 (1.1)
1.8 (1.1)
Birth Order
2.0 (1.1)
2.0 (1.1)
1.9 (1.1)
GPA at 9th Grade (ref.: Low)
Low
6.7%
6.6%
3.1%
Somewhat Low
15.5%
15.1%
12.8%
Middle
39.4%
39.5%
45.4%
Somewhat High
23.6%
23.8%
25.3%
High
14.9%
15.0%
13.4%
High school rank
-0.1 (1.1)-0.1 (1.1)
0.1 (1.0)
High school track (General)
Vocational
36.7%
36.6%
28.7%
General
63.3%
63.4%
71.3%
Birth cohort
1926-35
1.0%
1.0%
0.7%
1936-45
7.1%
7.1%
5.4%
1946-55
18.7%
18.7%
17.0%
1956-65
19.0%
18.9%
19.4%
1966-75
32.0%
31.9%
32.7%
1976-85
18.9%
19.0%
21.3%
1986-95
3.4%
3.4%
3.5%
SurveySSM1995
13.2%
13.2%
13.2%
SSM2005
29.1%
28.8%
27.2%
JLPS2007
22.5%
23.0%
21.4%
JLPS2010
4.2%
4.3%
5.2%
SSM2015
31.0%
30.7%
33.0%
Note: Mean (standard deviation) and percent are computed from the original data set.

50.1 (7.2)
49.0 (7.6)
51.8 (8.8)
48.9 (10.3)
35.1%
40.7%
24.2%
33.5%
52.2%
8.2%
6.0%
0.2 (0.9)
1.8 (1.1)
1.9 (1.1)
3.3%
12.7%
45.1%
25.4%
13.4%
0.1 (1.0)
28.9%
71.1%
0.6%
5.1%
17.0%
18.1%
33.2%
22.2%
3.8%
10.8%
25.9%
25.2%
6.0%
32.1%

Men
College
Constant
N
Women
College
Constant
N

Table 2. The homogenous causal effect of college education on the status of first and current occupationsFirst occupation
Current occupation
Prestige
SEI
Prestige
SEI
BeforeAfter
BeforeAfter
BeforeAfter
BeforeAfter
Weighting
Weighting
Weighting
Weighting
Weighting
Weighting
Weighting
Weighting
6.932***(0.289)46.839***(0.130)4,678

6.025***(0.596)46.864***(0.290)4,678

10.138***(0.261)45.377***(0.142)
4,678

8.131***(0.556)45.720***(0.318)4,678

7.966***(0.277)46.329***(0.148)4,665

6.847***(0.557)46.379***(0.302)
4,665

9.528***(0.260)45.609***(0.168)4,665

7.442***(0.607)46.290***(0.363)4,665

6.710***(0.254)46.864***(0.148)5,741

5.696***(0.399)47.146***(0.233)5,741

7.189***(0.251)46.640***(0.146)
5,741

5.955***(0.421)46.963***(0.225)5,741

6.947***(0.276)46.666***(0.164)4,712

4.636***(0.449)47.724***(0.282)4,712

7.255***(0.274)46.519***(0.166)4,712

4.305***(0.457)48.022***(0.282)
4,712

Source: SSM1995-2015 and JLPS2007-2019.
Note: Robust standard errors are in the parentheses.
† p < 0.1; * p < 0.05; ** p < 0.01; *** p < 0.001

Men
College

Table 3. The heterogeneous causal effect of college graduation on the status of first and current occupations by father’s occupation
First occupation
Current occupation
Prestige
SEI
Prestige
SEI
BeforeAfter
BeforeAfter
BeforeAfter
BeforeAfter
Weighting
Weighting
Weighting
Weighting
Weighting
Weighting
Weighting
Weighting

Father's Occupation
College X F’s Occupation
Constant
N
Women
College
Father's Occupation
College X F’s Occupation
Constant
N

6.281***(0.300)0.044*(0.021)
0.091**(0.035)46.979***(0.153)
4,678

6.025***(0.588)0.064(0.042)0.110†(0.060)46.864***(0.287)4,678

9.284***(0.289)0.104***(0.019)-0.008(0.029)45.783***(0.159)4,678

8.131***(0.550)0.099**(0.038)0.019(0.056)45.720***(0.312)
4,678

6.864***(0.291)0.117***(0.025)0.078*(0.035)46.702***(0.177)4,665

6.847***(0.551)0.030(0.047)0.203**(0.063)46.379***(0.304)4,665

8.400***(0.293)0.142***(0.022)-0.018(0.030)46.167***(0.187)4,665

7.442***(0.599)0.122**(0.044)0.038(0.063)46.290***(0.359)4,665

6.221***(0.263)0.048*(0.020)0.064*(0.030)46.998***(0.160)5,741

5.696***(0.397)-0.014(0.026)
0.109**(0.039)47.146***(0.233)5,741

6.362***(0.267)0.089***(0.018)0.032(0.027)46.964***(0.156)
5,741

5.955***(0.417)0.034(0.024)0.099*(0.043)46.963***(0.226)5,741

6.231***(0.283)0.107***(0.021)
0.028(0.032)46.968***(0.177)4,712

4.636***(0.446)0.104***(0.031)
0.005(0.048)47.724***(0.280)4,712

6.083***(0.291)0.140***(0.021)
0.017(0.031)47.048***(0.186)4,712

4.305***(0.450)0.142***(0.032)-0.016(0.048)48.023***(0.277)
4,712

Source: SSM1995-2015 and JLPS2007-2019.
Note: Robust standard errors in parentheses.
† p < 0.1; * p < 0.05; ** p < 0.01; *** p < 0.001

Appendix
We repeated the analysis using social background factors alone as covariates (Analysis 1) and using
academic performance factors alone (Analysis 2) as covariates to estimate the weights. In Analysis 1, theacademic ability variables were not included in estimating the weights by the entropy balancing technique.
Therefore, these variables were not balanced between high school and college graduates. Table A1 reports
the estimated coefficients, and Figures A3 and A4 are graphical representations of the heterogeneous
returns to college education by father’s occupation.
In Analysis 2, the variables related to social background were not included in estimating the weights
by the entropy balancing technique. Therefore, these variables were not balanced between high school andcollege graduates. Table A2 reports the estimated coefficients. Figures A3 and A4 are graphicalrepresentations of the heterogeneous returns to college education by father’s occupation.
When we used academic performance factors alone (Analysis 2), we obtained results (Table A2 andFigures A3 and A4) very similar to those reported in Table 3 and Figures 3 and 4.

The Results of Analysis 1
Table A1. The heterogeneous causal effect of college graduation on the status of first and current occupations by father’s occupation (adjusted forsocial background factors and not adjusted for academic ability)
First occupation
Prestige
SEI
BeforeAfter
BeforeAfter
Weighting
Weighting
Weighting
Weighting
Men
College
Father's Occupation
College X F's Occupation
Constant
N
Women
College
Father's Occupation
College X F's Occupation
ConstantN

Current occupation
Prestige

SEI

BeforeWeighting

After
Weighting

BeforeWeighting

After
Weighting

6.281***(0.300)0.044*(0.021)
0.091**(0.035)46.979***(0.153)
4,678

6.694***(0.346)0.039(0.026)0.057(0.040)46.741***(0.171)
4,678

9.284***(0.289)0.104***(0.019)-0.008(0.029)45.783***(0.159)4,678

9.525***(0.331)0.083***(0.022)-0.032(0.035)45.645***(0.183)
4,678

6.864***(0.291)0.117***(0.025)0.078*(0.035)46.702***(0.177)4,665

7.445***(0.333)
0.098**(0.032)0.077†(0.043)46.422***(0.193)4,665

8.400***(0.293)0.142***(0.022)-0.018(0.030)46.167***(0.187)4,665

8.893***(0.340)0.128***(0.026)-0.033(0.036)45.974***(0.216)4,665

6.221***(0.263)0.048*(0.020)0.064*(0.030)46.998***(0.160)5,741

6.849***(0.340)0.024(0.024)0.071*(0.035)46.793***(0.195)5,741

6.362***(0.267)0.089***(0.018)0.032(0.027)46.964***(0.156)
5,741

7.385***(0.357)0.060**(0.020)0.049(0.037)46.594***(0.183)5,741

6.231***(0.283)0.107***(0.021)
0.028(0.032)46.968***(0.177)4,712

5.954***(0.372)0.123***(0.027)0.001(0.041)47.288***(0.224)4,712

6.083***(0.291)0.140***(0.021)
0.017(0.031)47.048***(0.186)4,712

6.005***(0.367)0.141***(0.025)0.010(0.040)47.319***(0.210)
4,712

Source: SSM1995-2015 and JLPS2007-2019.
Note: Robust standard errors in parentheses.
† p < 0.1; * p < 0.05; ** p < 0.01; *** p < 0.001

Figure A1. The heterogeneous effect of college education on the status of first and current
occupations for men (adjusted for social background factors alone and not adjusted foracademic ability)
Note: The ribbons represent the 95% confidence intervals (CIs). Because multiple imputation (M = 160)
was used for the pretreatment variables, multiple lines and confidence intervals are displayed after
weighting.

Figure A2. The heterogeneous effect of college education on the status of first and current
occupations for women (adjusted for social background factors alone and not adjusted foracademic ability)
Note: The ribbons represent the 95% confidence intervals (CIs). Because multiple imputation (M = 160)
was used for the pretreatment variables, multiple lines and confidence intervals are displayed after
weighting.

The Results of Analysis 2
Tables A2. The heterogeneous causal effect of college graduation on the status of first and current occupations by father’s occupation (adjusted
for academic ability factors alone and not adjusted for social background)First occupation
Prestige
SEI
BeforeAfter
BeforeAfter
Weighting
Weighting
Weighting
Weighting
Men
College
Father's Occupation
College X F's Occupation
Constant
N
Women
College
Father's Occupation
College X F's Occupation
ConstantN

Current occupation
Prestige

SEI

BeforeWeighting

After
Weighting

BeforeWeighting

After
Weighting

6.281***(0.300)0.044*(0.021)
0.091**(0.035)46.979***(0.153)4,678

5.710***(0.470)0.037(0.029)0.059(0.053)46.733***(0.219)4,678

9.284***(0.289)0.104***(0.019)-0.008(0.029)45.783***(0.159)4,678

7.931***(0.450)0.072**(0.026)-0.019(0.044)46.016***(0.247)4,678

6.864***(0.291)0.117***(0.025)0.078*(0.035)46.702***(0.177)4,665

6.146***(0.452)0.090*(0.036)0.102†(0.055)46.792***(0.259)4,665

8.400***(0.293)0.142***(0.022)-0.018(0.030)46.167***(0.187)4,665

7.180***(0.461)0.115***(0.031)-0.009(0.047)46.430***(0.282)4,665

6.221***(0.263)0.048*(0.020)0.064*(0.030)46.998***(0.160)5,741

5.622***(0.380)-0.004(0.024)0.107**(0.039)47.189***(0.221)
5,741

6.362***(0.267)0.089***(0.018)0.032(0.027)46.964***(0.156)5,741

5.813***(0.380)0.040.
(0.022)0.083*(0.039)46.991***(0.205)5,741

6.231***(0.283)0.107***(0.021)
0.028(0.032)46.968***(0.177)4,712

4.419***(0.407)0.121***(0.030)-0.009(0.045)47.719***(0.253)
4,712

6.083***(0.291)0.140***(0.021)
0.017(0.031)47.048***(0.186)4,712

4.377***(0.411)0.106***(0.028)0.021(0.044)47.801***(0.244)
4,712

Source: SSM1995-2015 and JLPS2007-2019.
Note: Robust standard errors in parentheses.
† p < 0.1; * p < 0.05; ** p < 0.01; *** p < 0.001

Figure A3. The heterogeneous effect of college education on the status of first andcurrent occupations for men (adjusted for academic ability factors alone and not
adjusted for social background)
Note: The ribbons represent the 95% confidence intervals (CIs). Because multiple imputation (M =160) was used for the pretreatment variables, multiple lines and confidence intervals are displayed
after weighting.

Figure A4. The heterogeneous effect of college education on the status of first andcurrent occupations for women (adjusted for academic ability factors alone and not
adjusted for social background)
Note: The ribbons represent the 95% confidence intervals (CIs). Because multiple imputation (M =160) was used for the pretreatment variables, multiple lines and confidence intervals are displayed
after weighting.

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、中学生親子パネル調査の４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
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～2008 年度

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ディスカッションペーパーシリーズについて
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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
https://csrda.iss.u-tokyo.ac.jp/panel/