The effect of attending college on one’s first job
With a focus on effect heterogeneity *Izumi Mori(The University of Tokyo)
Abstract
The increasing access to college in Japan underscores the importance of better understanding variation in
the effects of college attendance according to students’ social backgrounds. This study used the propensity
score method to estimate the causal effect of college attendance on one’s first job. Particular focus wasplaced on individuals who attended college despite a low likelihood of doing so due to theirsocioeconomic backgrounds. Using 2005 and 2015 Social Stratification and Social Mobility (SSM)
Survey data, the study applied the stratification-multilevel method to estimate the effect of college
attendance on one’s first job in a professional role. The results of the analysis showed that for both males
and females, individuals who had a low likelihood of attending college but who ultimately did so weremore likely to enter a professional role in their first job, once selectivity of college attendance was
accounted for. Possible mechanisms for this finding suggests that those who attend college against theodds tend to have a stronger motivation for learning and that, for this group, college is the only route toattain a professional job status unlike for those from advantageous family backgrounds who may attainprofessional status regardless of whether or not they attend university. In terms of theory, this study adds
to the literature on the heterogeneous effects of college attendance from a Japanese perspective,
particularly supporting the negative selection hypothesis where its outcome is a first job being in a
professional role. In terms of practice, the heterogeneity revealed in this study may lend support to policy
interventions that assist highly motivated individuals from disadvantaged backgrounds to attend college,
when this group may otherwise not be able to attend college without financial and institutional supports.
Keywords: access to college, heterogeneous effects, propensity score, Japan

1. Introduction
Over the past several decades, college enrollment rates have increased in Japan for both*

The study was supported by JSPS Grant-in-Aid for Specially Promoted Research (KAKENHI Grant
Number JP25000001). I would also like to thank the 2015 SSM Survey Management Committee forallowing me to use the SSM data.
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males and females. From the 1960s to the mid-1970s in particular, enrollment in four-year
universities and junior colleges rapidly increased, reaching over 40% for males and over
30% for females. In more recent years, the figure has reached over 50% for both genders.
Such educational expansion has led to greater diversity in the social backgrounds of students
who attend college, including those from less advantageous backgrounds who havetraditionally had more difficulty accessing college. In this context of increased diversity, it
has become more important to assess the extent to which colleges are actually effective inenhancing career outcomes for students of different social backgrounds.
Recent studies, particularly in the United States, have focused on heterogeneity in theeffects of attending college on various social and economic outcomes (Brand, Pfeffer, &Goldrick-Rab, 2014; Brand & Xie, 2010; Hout, 2012; Musick, Brand, & Davis, 2012). Thesestudies are based on the idea that the effects of college as a treatment may not be the samefor all groups when we consider selection bias, or preexisting student characteristics. Someof these studies have explored aspects such as the negative selection hypothesis, which posits
that the lower a person’s likelihood of advancing to college, the higher their returns from
attending college will be.
Building on this line of inquiry, this study examines the effect of attending college on
one’s first job – a direct causal outcome of college attendance in the Japanese context – witha focus on effect heterogeneity. Using the Social Stratification and Social Mobility (SSM)
Surveys, the study applies stratification-multilevel method based on propensity scores (Xie,
Brand, & Jann, 2012) to assess how the treatment effects may vary systematically according
to one’s likelihood of attending college. The results show that for both males and females,
the likelihood of a first job being in a professional role is greater for individuals who haveattended college despite a low likelihood of their doing so. The study then proposes several
auxiliary analyses to elaborate why such relationships were observed, and the findings arefurther discussed on this basis.

2. Background and effects of attending college
Although access to college has expanded in Japan, there remains inequality in who haseasier access to college education. A host of studies both within and outside of Japan haverevealed that students from a more advantageous background, including parentalsocioeconomic status and cultural resources, are more likely to attend college (e.g., Ishida,2007; Lareau, 2011; Ono, 2001; Sewell, Haller, & Portes, 1969). Studies have also shownthat the opportunity to receive college education varies by gender and area of residence
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(Buchmann & DiPrete, 2006; Byun, Irvin & Meece, 2015; Tsai & Xie, 2011).
Regarding the effects of attending college, researchers have examined various
positive effects on social and economic outcomes, including occupation, income, marriage,and health (e.g., Hout, 2012). In Japan, a major finding from analyses of the Social
Stratification and Social Mobility Surveys is that the relationship between education andoccupational attainment in postwar Japan has been stable over time (Hara & Seiyama, 1999;
Ishida & Miwa, 2011; Naoi & Fujita, 2008). Furthermore, recent investigations into theeffects of college attendance on first job and present occupation have shown that theinstitutional ranking of universities also matters in such scenarios (e.g., Hirasawa, 2011;
Nakanishi, 2000). Rather than economic indices such as income that are commonly used in
the field of economics, Japanese scholars in social stratification research have often focusedon occupation as a major outcome, an indicator that carries important social implications.
This study uses first job as a dependent variable. Due to its most direct causal link tocollege attendance, first job is considered the most appropriate candidate for inferring theeffects of attending college in the Japanese context. In Japan, there is strong tendency thatnew college graduates are recruited all at once. Moreover, in contrast to income and current
occupation, which are often missing for females who are not working because of marriage, it
is relatively easy to observe both genders in their first job after graduation. From the humancapital viewpoint, which is one of the theoretical perspectives that explain the link between
education and occupation, whether a person’s first job is in a professional role is anappropriate method of investigating the effects of college attendance (Becker, 1975; Bills,
2003). In general, professional jobs are attained after intense educational training to obtainknowledge and skills in and out of college.

3. Heterogeneous effects of college attendance
As previously noted, access to university remains unequal despite the recent expansion inhigher education in Japan. If we consider this situation, there may be a variation in the gains
from advancing to college, depending on one’s social background. Relative risk aversion
theory, as proposed by Breen and Goldthorpe (1997), suggests that socially advantaged
parents and their children expect to pursue education so that their children can avoid
downward mobility. Similarly, Kikkawa (2006) suggests that college-graduated parents hopethat their children will also proceed to college so that they can “at least avoid” experiencing
downward educational mobility. As these theories suggest, students from advantageous
social and educational backgrounds who are more likely to attend college may do so based
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on a rather passive motivation, which is to avoid downward social mobility, so that they canretain at least the same level of educational and occupational status as their parents.
On the other hand, when students from less advantageous backgrounds who are lesslikely to attend college, actually do end up attending, it may be inferred that they have astronger motivation and objectives that would prompt them to pursue college attendance“against the odds.” Compared to high school graduates who start working immediately after
high school, going to college requires significant financial and opportunity costs, which
would make it a crucial decision for those at the marginal likelihood of attending college.
Therefore, if such people do proceed to college, the benefits they obtain from attending
college may increase, compared to those of similar backgrounds who do not proceed tocollege. In other words, once selection bias due to preexisting characteristics is considered,those with a higher propensity to attend college may attain some level of socioeconomic
status in any case, even when they do not proceed to college; for those from moreadvantaged backgrounds, going to college may not be the sole ticket to success, while thosewith a lower propensity to attend college who actually do end up attending may try to makethe most out of their degrees, compared to those who do not attend college in the same
situation.

4. Method
4.1 Analytic strategy
This study uses propensity scores to estimate the causal effect of college attendanceon one’s first job. By using this approach, it is possible to estimate the average treatment
effect by using a combination of actual outcomes and “potential outcomes” that are definedas counterfactuals of the observed variables (Guo & Fraser, 2009; Hoshino, 2009; Morgan &Winship, 2007; Rosenbaum & Rubin, 1983). The propensity scores are defined as aconditional probability of assignment to treatment given the observed covariates. They are atype of balancing score that are predicted with covariates and summarized into asingle-dimensional scale. This study uses logistic regression to generate these propensity
scores. When using this approach to estimate causal effects, the selection of covariates
becomes important. It is important to select covariates that influence both the assignment
into treatment (in this case, college attendance) and the outcome (first job being in a
professional role). By removing the effects of these covariates, it becomes possible toestimate the causal effects of the treatment on the outcome variable.
Based on this approach, this study takes the further step of investigating the
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heterogeneity of the effect. In this case, the stratification-multilevel method proposed by Xie
and his colleagues is applicable (Xie, Brand, & Jann, 2012). This method utilizes propensity
score strata divided at fixed intervals, which enables a more systematic investigation ofheterogeneity. In the first step, propensity scores for attending university were created using
observed data. Next, strata were created so that the covariates were equal across thetreatment group (college attendees) and the control group (non-attendees) within eachstratum. This step was supported by the “pscore” command in Stata. The final number ofstrata was confirmed after testing that there were no significant differences in the meanvalues of the covariates within each stratum to the point where significant differences for all
intra-stratum covariates were eliminated (Becker & Ichino, 2002).
Next, the propensity score strata were used to estimate the treatment effect for eachstratum. This can be done by comparing the outcome variables of the treatment and control
groups, or by fitting a regression model to each stratum and correcting any imbalances in theremaining covariates (Xie et al., 2012). The analysis in this study followed the latter method,whereby a logistic regression analysis of the outcome variable (a dummy variable forwhether the first job was in a professional role) was performed for each stratum. Because thecovariates were well balanced within each stratum when the initial propensity scores werecreated, the only independent variable included in the regression model was the treatment
variable (college attendance). This process was therefore equivalent to comparing theoutcome variable for the treatment and control groups.
Finally, the treatment effects estimated for each stratum were modeled at “leveltwo” of a multilevel model and a conclusion was reached as to whether any systematic
trends were discernible in terms of the heterogeneity of effect. In particular, the examinationof whether there was a positive or negative relationship between the results and the size ofthe propensity score for each stratum based on fitting the results to a linear model has boththeoretical and empirical significance (Brand & Xie, 2010). The above stratificationmultilevel method, including the creation of graphs to illustrate the relationship between
propensity scores for each stratum, was supported by the “hte” user command in Stata (Jann,
Brand, & Xie, 2010).
4.2 Data and measures
This study analyzed the 2005 and 2015 cycles of the Social Stratification and Social
Mobility (SSM) Surveys in Japan. The sample was restricted to respondents born on or after
1960, so that they were 18 years old after the mid-1970s when college enrollment had
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experienced a rapid increase. Because mechanisms for advancing to college and gaining
employment may differ according to gender, the analysis was performed separately for males
and females. The academic background for study subjects was set at “high school graduate
or higher.” Subjects whose highest level of education was “junior high school” and thosewith “no record” or an “unclear record” were excluded. For males, individuals who
graduated from a four-year university were defined as college attendees, and effects were
estimated by comparing these subjects with high school graduates. In addition, because therelationship of “specialized vocational high school” and “junior college graduates” touniversity and high school graduates was unclear for males, these individuals were removed
from the analysis for males. Considering that a certain number of males advanced tograduate school, only males older than 25 were considered in the analysis. In light of the factthat the enrollment rate in four-year universities for females expanded later than for males,
females who attended either a two-year (junior college) or four-year university were treatedas college attendees. Females older than 23 were included in the analysis.
After reviewing the related literature, the following covariates were selected.
 Attended College: For males, Highest level of education “University” or “Graduate
School”=1,

Otherwise=0.

For

females,

Highestlevelofeducation“JuniorCollege/Two-year University,” “University,” or “Graduate School”=1, Otherwise=0.
 Professional First Job: First job was in a professional role=1, Other=01.
 Female: Male=0, Female=1.
 Birth Year: Birth year of respondent (60–94).
 Number of Siblings at Age 15: Number of siblings (including self) at age 15 (1-13).
 Economic Status at Age 15: Family circumstances at age 15 (1=Poor to 5=Wealthy).
 Family Possessions at Age 15: Sum of the following 18 items: owns house, bath,children’s room(s), study desk, lounge suite, piano, television, radio, video player,
refrigerator,

microwave

oven,

telephone,camera,literature/encyclopedia

set,computer/word processor, car, work of art/antique (0-18).
 Number of Books at Age 15: The mid-point of the category for the number of books atage 15, divided by 10 (e.g., 11–25 books = 18 books = 1.8). To preserve the sample size,
the missing values (approximately 10%) were uniformly recorded as 1.2 after
considering their correlation with other variables (0.5-85).
 National or Private Junior High School: Junior high school was a national or private
school=1, public school=0.
1First job being in a managerial role was dropped due to missing cases during the analysis.
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 Academic Achievement at Year 3 of Junior High School: Self-rated level of academic
achievement at year 3 of junior high school (1=Bottom to 5=Top).
 Parents’ Education: Number of years of education for mother or father, whichever ishigher (6–18).
 Father’s Job – Professional/Management: Father employed in a professional/technical ormanagement role when respondent was aged 15.
 Father’s Job – Non-Manual: Father employed in an office administration, sales, service,or security job when respondent was aged 15=1, Other=0.
 Father’s Job – Manual: Father employed in a manual job when respondent was aged
15=1, Other=0.
 Father’s Job – Other: Father employed in any other type of job when respondent wasaged 15=1, Other=0.
 Single-Parent Family at Age 15: If at least one parent was absent or not alive when
respondent was aged 15=1, Other=0. In creating the categories, information onfather’s/mother’s occupation, and age of death of father/mother was used (Inaba, 2011).
 Vocational Track at High School: Subjects offered at respondent’s school were other thanregular courses, science and mathematics courses, languages courses, or arts courses=1,
Other=1.
 High School Rank: Proportion of respondent’s peers at the same high school who
advanced to study at university or junior college/two-year university (1=Almost None to
5=Almost All).
 Large City at Age 15: At age 15, respondent lived in any part of Tokyo or Osaka, agovernment designated city, or the urban areas of Saitama, Chiba, Kanagawa, Aichi,
Kyoto, Hyogo, or Okayama=1, Other=0 (Aramaki, 2011).

5. Results
Table 1 presents the pre-matching mean values for the covariates using the propensity
scores for college attendees and non-attendees by gender. A statistically significant
difference (t-test) between the treatment group (college attendees) and control group(non-attendees) was observed for all variables. Both male and female college attendees
exhibited the characteristics of being slightly younger, having fewer siblings, and having a
wealthier home environment at age 15. A higher proportion of college attendees had attended
national or private junior high schools. They also demonstrated better academic performancein the third year of junior high school, and had parents with longer years of education and
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higher occupational status. A lower percentage of college attendees lived in single-parent
families, and there was a strong trend for them to attend high schools with high university
matriculation rates. Furthermore, a high proportion of students who went on to college
studied regular subjects in high schools as opposed to vocational ones, and lived in a major
city at age 15.
Table 1. Means of precollege covariates
Male
Variables
No College College
Birth Year
71.58
72.62
Number of Siblings at Age 15
2.48
2.37
Economic Status at Age 15
3.05
3.33
Family Possessions at Age 15
12.36
13.72
Number of Books at Age 15
6.83
15.28
National or Private Junior High School
0.03
0.08
Academic Achievement at Year 3 of
2.73
3.75
Junior High School
Parent Education
11.60
13.49
Father's Job 0.05
0.25
Professional/Management
Father's Job - Non-Manual
0.25
0.34
Father's Job - Manual
0.56
0.29
Father's Job - Other
0.14
0.12
Single-Parent Family at Age 15
0.08
0.04
Vocational Track at High School
0.51
0.06
High School Rank
2.28
4.04
Large City at Age 15
0.36
0.51
N
1079
898
***p<.001, **p<.01, *p<.05

**
**
***
***
***
***

FemaleNo College College
71.91
73.55
2.50
2.34
3.08
3.40
12.61
14.50
7.02
14.88
0.03
0.10

***
***
***
***
***
***

***

2.94

3.66

***

***

11.73

13.50

***

***

0.07

0.24

***

***
***

0.23
0.56
0.13
0.09
0.38
2.69
0.39
1545

0.33
0.30
0.13
0.04
0.09
4.10
0.50
1237

***
***

***
***
***
***

***
***
***
***

Table 2 presents the results of the logistic regression analysis, which was conductedin the process of creating the propensity scores. Generally, the results are in line with thosereported in the existing literature. This paper does not interpret each coefficient presented inthis table in detail. The important point is that these propensity scores were created as aunidimensional measurement that includes all of the possible covariate information.
In addition, Appendices A and B present the mean values for the covariates for each
propensity score strata by gender. Test results are not included, but show no differences
between college attendees and non-attendees for all covariates in all strata at the .01

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significance level, with only two minor exceptions2. As a result, eight strata were formed forboth males and females. The figures present the mean value of the propensity score and thenumber of cases included for each stratum. According to Xie et al. (2012), a minimum of 20
cases for the treatment and control groups in each stratum is desirable. The relevant
estimates of the likelihood of attending university yielded the unsurprising result that strata
with lower propensity scores had more non-attendees and strata with higher propensity
scores had more attendees.
Table 2. Logit models predicting college attendance for the generation of estimatedpropensity scores
Male
Variables
Birth Year
Number of Siblings at Age 15
Economic Status at Age 15
Family Possessions at Age 15
Number of Books at Age 15
National or Private Junior High School
Academic Achievement at Year 3 ofJunior High School
Parent Education
Father's Job Professional/Management
Father's Job - Non-Manual
Father's Job - Manual (Reference)
Father's Job - Other
Single-Parent Family at Age 15
Vocational Track at High School
High School Rank
Large City at Age 15
Constant
Likelihood Ratio X2
p>X2
2
Pseudo (McFadden's) RN

Coef.
-0.007-0.142 †0.047
0.088 ***
0.003
0.389

FemaleS.E.
(0.009)(0.079)(0.088)(0.027)(0.004)(0.304)Coef.
-0.010-0.112 †0.072
0.144 ***
0.004
0.538 *

S.E.
(0.007)(0.063)(0.070)(0.023)(0.003)(0.227)0.644 *** (0.072)

0.512 *** (0.061)

0.131 *** (0.032)

0.136 *** (0.024)

1.050 *** (0.229)

0.471 **

(0.168)0.407 **

0.263 *

(0.120)(0.151)0.268(0.220)-0.578 †(0.307)-1.210 *** (0.182)
0.645 *** (0.061)
0.082(0.130)-6.441 *** (0.735)
1136.380
0.000
0.417
1977

0.439 *
(0.175)-0.466 †(0.239)-0.650 *** (0.138)
0.685 *** (0.050)-0.046(0.101)-7.244 *(0.582)1253.230
0.000
0.328
2782

***p<.001, **p<.01, *<.05, †<.10

Table 3 presents the results, by gender and for each propensity score stratum, of the2

Number of books at age 15 did not balance in block 1 for males, and vocational track for high school
did not balance in block 4 for females. However, even after including these covariates again into themodel after matching, the results remained very similar from what is presented in the current paper.
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logistic regression analysis of the dummy variable that represented whether attending college
resulted in a first job that was in a professional role. For both males and females, logit
regression coefficients were higher in the lower propensity groups. As propensity scores
increased, the value of the coefficients tended to decrease, although the trend was moderate
for males compared to females. Figures 1 and 2 present the relationship between strata
expressed as Level 2 slopes by plotting the values of the coefficients. The slope was -0.193
for males and -0.298 for females, both fitting a downward sloping line.
Table 3. Heterogeneous effects of college attendance on first job being in a professional role
Male
Level-1 Slopes
P-score Stratum 1 [0.0-0.1]
P-score Stratum 2 [0.1-0.2]
P-score Stratum 3 [0.2-0.4]
P-score Stratum 4 [0.4-0.6]
P-score Stratum 5 [0.6-0.7]
P-score Stratum 6 [0.7-0.8]
P-score Stratum 7 [0.8-0.9]
P-score Stratum 8 [0.9-1.0]
Level-2 Slope***p<.001, **p<.01, *<.05, †<.10

Female

Coef.

S.E.

Coef.

S.E.

2.957 ***
1.487 **
1.379 ***
1.290 **
1.707 **
0.428
1.666 **
0.854
-0.193 *(0.531)(0.576)(0.401)(0.407)(0.640)(0.537)(0.552)(0.597)(0.085)1.383 **
0.979 **
0.662 **
0.422 †
0.690 *
-0.423-0.535-0.615-0.298 ***(0.453)(0.338)(0.227)(0.237)(0.328)(0.294)(0.350)(0.659)(0.062)210-1Treatment Effect3

4

slope of linear trend (s.e.) = -0.193 (0.085)

1

2

3

4
5
Propensity Score Strata
95% CI
linear trend

6

7

8

TE within strata

Figure 1. Heterogeneous effects of college attendance on first job being in a professional role(male)
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2
1
0
-1
-2

Treatment Effectslope of linear trend (s.e.) = -0.298 (0.062)

1

2

3

4
5
Propensity Score Strata
95% CI
linear trend

6

7

8

TE within strata

Figure 2. Heterogeneous effects of college attendance on first job being in a professional role(female)Tables 4 and 5 present the mean values for the actual percentages of males andfemales whose first job was of a professional nature for each propensity score stratum. Of
the 1006 males that did not attend college, only 6% entered a professional role in their firstjob. Of the 888 males that did attend college, 35% entered some type of professional role intheir first job. Similarly, of the 1449 females that did not attend college, 17% entered a
professional role in their first job, and 33% of the 1218 females that attended college entereda professional role in their first job. For both males and females, if they attended college, thelikelihood of entering a professional job was approximately 30% regardless of the propensity
score stratum. On the other hand, if they did not attend college, their likelihood of entering aprofessional job became higher as their propensity to attend college increased.

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Table 4. Proportion for the first job being in a professional role, by propensity score strata(male)

Stratum 1
Stratum 2Stratum 3
Stratum 4Stratum 5Stratum 6Stratum 7
Stratum 8
Total

No College
Proportion N
0.03
(416)
0.04
(179)
0.07
(174)
0.09
(116)
0.07
(42)
0.17
(30)
0.12
(34)
0.27
(15)
0.06
(1006)

College
Proportion N
Odds Ratio
0.36
(22)
19.24
0.17
(35)
4.42
0.24
(70)
3.97
0.26
(94)
3.63
0.30
(94)
5.52
0.23
(115)
1.53
0.41
(191)
5.29
0.46
(267)
2.35
0.35
(888)
8.69

Table 5. Proportion for the first job being in a professional role, by propensity score strata(female)Stratum 1
Stratum 2Stratum 3
Stratum 4Stratum 5Stratum 6Stratum 7
Stratum 8
Total

No College
Proportion N
0.09
(390)
0.12
(326)
0.20
(336)
0.20
(199)
0.23
(80)
0.41
(69)
0.44
(39)
0.60
(10)
0.17
(1449)

College
Proportion N
Odds Ratio
0.28
(29)
3.99
0.27
(59)
2.66
0.32
(140)
1.94
0.28
(202)
1.52
0.37
(120)
1.99
0.31
(178)
0.65
0.31
(260)
0.59
0.45
(230)
0.54
0.33
(1218)
2.43

Additionally, Tables 4 and 5 show the odds ratios on each percentage. The odds ratio
indicates the degree of difference between the number of male or female college attendees
who gained professional employment versus the number of male or female non-attendees
who gained professional employment. The odds ratio for males is 8.69, which is higher thanthe 2.43 for females. This suggests that, on average, the proportion of male college attendees
entering a professional job in comparison to male non-college attendees is higher for males
than for females. It is also noteworthy that for females, the proportion entering a professional
job is higher for non-college attendees than for college attendees in the higher strata (stratum
6-8), which is also apparent from the odds ratios that are smaller than 1. We need to examine
further why it can be the case that females who did not attend college showed a higher
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percentage of entering a professional job if they had a higher likelihood of attending college.

6. Auxiliary analyses
In this section, we will further examine the mechanisms of how and why theheterogeneity observed for both genders in the previous section occurred. The auxiliary
analyses proceed as follows. First, the details of the professional job are described. Second,as part of possible explanations for the heterogeneous trend, one’s college major, values
placed on attending college, and proportion attending technical schools are examined bystrata and gender.
Examining the specific details of professions based on the strata generated by the
analysis, many male professionals had entered the fields of engineering, research, andteaching. A large number of female professionals were nurses, early childhood educators, orteachers. However, even among professionals, there was a tendency for doctors, dentists, andpharmacists to come from strata with high propensity scores for both genders. Among
females in the low propensity strata, nurses were a major profession for non-college
attendees, whereas early childhood educators and teachers (kindergarten to high school)
were major professions for college attendees. For females, individuals who did not attend
university despite a high propensity to do so were able to enter a professional job at the sameor even a higher rate as college graduates. This may be because some females in the subject
group, such as nurses or other health workers and nursery school teachers, were able to findwork in a professional role by attending a technical schools or other lectures and seminars,
thereby independently gaining qualifications without attending college.
In addition to examining the details of professions, the distribution of the first job for
both professional and non-professional jobs by college-attendance status and propensity
strata for both genders are summarized in Appendices C and D.
As for the possible mechanisms why those with a lower propensity are more likely
to enter a professional role in their first job, two scenarios are proposed. The first is that forindividuals who attend college despite a low likelihood of so doing, they may have astronger motivation and objectives in attending college considering that they do so “against
the odds.” We will examine this scenario through two indicators: one is college major andthe other is values on attending college. The second scenario is that among those with alower propensity to attend college, whether or not they attend college tends to make a soleand crucial difference to whether or not they obtain a professional job. However, among
those with a higher propensity to attend college, they may obtain a professional job through
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other means such as having other training opportunities elsewhere, even when they do notattend college. Technical schools are one such example that will be examined below.
Table 6 shows the distribution of college majors by propensity score strata amongcollege attendees for both genders. Lower propensity males are slightly more likely to major
in social science. Within the social science category, the further details (table not shownhere) reveal that there are approximately 40% business and economics majors among thelower strata (strata 1-2) compared to approximately 20% among the higher strata (strata 7-8),
suggesting that lower propensity males may tend to seek more practical expertise whenattending college. We also see more agricultural majors among the lower propensity than thehigher propensity males. Although medicine and health is also an applied and practical field,
no male in the lower strata majored in these subjects, possibly because it is a costly field inwhich to study. For females, there are more home economics and education majors, which
are applied and practical fields, among the lower strata. In the higher strata, there are morehumanities majors (particularly literature), which could be regarded as an example of a
non-applied field.
Table 6. Distribution of college majors by propensity score strata (%, college attendees only)

Male
Stratum 1-2 Stratum 7-8
College Major(N=54)(N=451)
Humanities
5.6
6.0
Social Sciences
55.6
39.5
Natural Sciences
0.0
3.6
Engineering
22.2
31.0
Agriculture
11.1
3.6
Medicine and Health
0.0
6.2Home Economics
0.0
0.0
Education
3.7
7.1
Art
0.0
1.3
Other
1.9
1.8
Total
100.0
100.0

FemaleStratum 1-2 Stratum 7-8(N=87)(N=488)
13.8
27.7
17.2
17.8
0.0
1.0
3.5
3.7
1.2
1.6
3.5
8.4
24.1
12.7
28.7
16.0
5.8
8.0
2.3
3.1
100.0
100.0

Table 7 shows the mean values for objectives of attending college, in response to
the questionnaire item: “Those without clear objectives should not go to college.” Onaverage, non-college attendees have higher values than college attendees in both genders. By
strata among non-college attendees, those in the lower strata of both genders are more likely
to agree with such an idea. However, among college attendees, this trend is observed only
14

―180―

among females.
Table 7. Mean “college objectives” by propensity score strata

No College
Stratum 1
Stratum 2Stratum 3
Stratum 4Stratum 5Stratum 6Stratum 7
Stratum 8
Total

Male

FemaleNo College
CollegeCollege

Mean

N

Mean

N

Mean

N

Mean

N

2.90
3.03
2.88
2.56
2.71
2.64
2.94
2.36
2.85

(248)
(114)
(125)
(79)
(28)
(22)
(18)
(11)
(645)

2.54
2.30
2.51
2.34
2.13
2.56
2.28
2.22
2.32

(13)
(27)
(43)
(64)
(53)
(75)
(128)
(172)
(575)

2.91
2.75
2.75
2.74
2.62
2.78
2.35
2.57
2.77

(213)
(190)
(188)
(136)
(47)
(50)
(26)
(7)
(857)

2.40
2.88
2.44
2.47
2.59
2.42
2.31
2.16
2.39

(15)
(32)
(89)
(116)
(78)
(121)
(173)
(166)
(790)

Note: The values are means of responses to the item: "Those without clear
objectives should not go to college" (Strongly Agree=4, Agree=3, Disagree=2,
Strongly Disagree=1)."The item is only available in 2015 SSM, not in 2005 SSM.

Table 8 shows the proportion of technical school attendance by propensity score
strata. For both male and female non-college attendees, on average, approximately 30%
attended technical schools across all strata. Among male and female college attendees,
approximately 5% attended technical schools in addition to four-year or two-year
universities. In the non-attendees groups, the proportion of technical school attendees
increased as the propensity to attend college increased for both genders. These facts suggestthat technical schools may provide an alternative route to a professional job other than
through four-year or two-year universities, especially for females.

15

―181―

Table 8. Proportions of technical school attendance by propensity score strata

Male

No College
Proportion N
Stratum 1
0.14 (418)Stratum 2
0.24 (180)Stratum 3
0.45 (175)
Stratum 4
0.48 (116)Stratum 50.50(42)Stratum 6
0.58(31)Stratum 7
0.41(34)
Stratum 8
0.60
(15)
Total
0.29 (1011)

FemaleCollege

Proportion

0.09
0.06
0.04
0.05
0.08
0.07
0.07
0.02
0.05

N
(22)
(35)
(70)
(96)
(96)
(117)
(193)
(269)
(898)

No College
Proportion N
0.19 (393)0.26 (329)0.40 (340)0.52 (200)
0.54
(82)
0.71
(69)
0.70
(40)
0.73
(11)
0.36 (1464)

College

Proportion

0.07
0.03
0.03
0.05
0.05
0.06
0.03
0.07
0.05

N
(29)
(60)
(140)
(203)
(122)
(181)
(267)
(235)
(1237)

7. Discussion
This study used propensity scores to estimate the causal effect of university attendanceon one’s first job. Particular focus was placed on individuals who attended university despite
a low likelihood of so doing. The results of the analysis showed that for both males andfemales, individuals who had a low likelihood of attending university, but who did ultimately
do so, were more likely to enter a professional role in their first job, once selectivity ofcollege attendance was taken care of. The trend was more straightforward for females thanfor males.
This study attempted to further elaborate on the reasons for such findings, and found thatthose with a lower likelihood of attending college but who ultimately did so were slightly
more likely to major in more applied and practical fields than their higher propensity
counterparts. These low propensity groups also had slightly stricter views on college
attendance, and considered that those without clear objectives should not attend college. This
may suggest that individuals who attend college against the odds tend to have clearer
learning objectives in college. Due to a stronger motivation to gain skills and expertisethrough their college career, they may have more opportunity to enter a professional job thanwould have been the case if they had not attended college. The study also found that among
these “against the odds” groups, the proportion of technical school attendance increased as
the propensity to attend college increased. This suggests that individuals with a higher
propensity have alternative routes to enter a professional job, one of which is possibly byattending technical schools. For those with a lower propensity to attend college, however,college tends to be the only route to success, which may be another reason why those with a
16

―182―

lower propensity to attend college were more likely to enter a professional job compared totheir non-college attendee counterparts.
Although there have been few attempts to examine the heterogeneous effects of college
attendance according to students’ social backgrounds in Japan, Ishida (2017) has done so byusing propensity score matching to examine the effect of higher education on occupational
outcomes. In particular, the study found that for both males and females, cumulativeadvantage and disadvantage continue to exist after one’s educational attainment; the benefitsof higher education on one’s first job are the same regardless of one’s likelihood of attending
higher education. Ishida’s findings contradict with the present study’s findings with regards
to whether the effect heterogeneity exists in college attendance in Japan, which is a crucial
difference in findings that needs to be investigated further. Although the two studies usedsimilar approaches to examine the effect of attending college on one’s first job, possible
difference in the estimated effects may be due to various factors such as the datasets used,treatment variable, covariates, and the use of propensity scores3. Future study is needed to
scrutinize such difference in findings to in order to continue the discussion on the effectheterogeneity of higher education in the Japanese context.
The present study has both theoretical and practical implications. In terms of theory, it
adds to the literature on the heterogeneous effects of college attendance from a Japaneseperspective, particularly supporting the negative selection hypothesis where its outcome is a
first job being in a professional role. In terms of practice, the heterogeneity revealed in thisstudy may lend support to policy interventions that assist highly motivated individuals fromdisadvantaged backgrounds to attend college, when this group may otherwise not be able toattend college without financial and institutional supports. Such an intervention based on theeffect heterogeneity may help to achieve both greater efficiency and equality in theopportunity to access college.
One limitation of this research includes the possible influence of unobserved variables.
Although it is a common challenge in any regression-based analyses, propensity score
methods in particular depend on the strongly ignorable treatment assumption, meaning thatthe assignment for a treatment condition does not depend on the outcome of interest. Thus,
inclusion of variables that influence both college attendance and the first job is crucial for
3

Ishida (2017) used Japanese Life Course Panel Surveys (JLPS) for young and middle-aged cohorts thatwere conducted between 2008 and 2015. The outcome variable was whether one’s first job was in a
professional/ managerial role. Ishida’s treatment variable was higher education (two-year junior college,four-year university, and graduate school), whereas the current study dropped junior college for males.
Possible causes of difference may also include the process of estimating propensity scores as well as themaking of strata based on propensity scores.
17

―183―

obtaining the unbiased college effect estimates in this study. Therefore, if, for example, there
is a difference in ability between college attendees and non-attendees such that the former
has a higher level of ability, then the effect of attending college could be overestimated.
Although this study attempted to address motivation, which may in part be an example ofnon-cognitive ability, as a relevant factor in explaining the heterogeneous effects of college
attendance, further study is needed to consider such possible factors that are currently
unobserved in the existing surveys.

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―187―

21

Birth Year
Number of Siblings at Age 15
Economic Status at Age 15
Family Possessions at Age 15
Number of Books at Age 15
National or Private Junior HighAcademic Achievement at Year
2.44
2.41
2.69
2.83
2.95
2.86
3.18
3.27
3.33
3.48
3.45
3.61
4.12
3.88
4.53
4.46
3 of Junior High School
Parent Education
10.85 11.23 11.92 12.09 12.07 11.91 12.59 12.44 12.57 12.31 12.81 13.10 13.26 13.71 15.20 15.06
Father's Job 0.01
0.05
0.04
0.00
0.09
0.06
0.11
0.10
0.07
0.09
0.10
0.14
0.21
0.21
0.33
0.54
Professional/Management
Father's Job - Non-Manual
0.17
0.27
0.28
0.29
0.26
0.30
0.39
0.39
0.48
0.47
0.42
0.38
0.38
0.35
0.47
0.28
Father's Job - Manual
0.68
0.59
0.53
0.63
0.51
0.49
0.41
0.40
0.29
0.29
0.29
0.33
0.32
0.31
0.07
0.09
Father's Job - Other
0.14
0.09
0.14
0.09
0.14
0.16
0.09
0.11
0.17
0.15
0.19
0.15
0.09
0.13
0.13
0.09
Single-Parent Family at Age 15
0.10
0.05
0.09
0.00
0.06
0.07
0.04
0.07
0.05
0.06
0.03
0.05
0.00
0.02
0.07
0.02
Vocational Track at High School 0.80
0.95
0.52
0.49
0.25
0.17
0.06
0.05
0.00
0.00
0.00
0.00
0.00
0.01
0.00
0.00
High School Rank
1.56
1.50
2.16
2.03
2.63
2.61
3.16
3.23
3.71
3.81
4.35
4.12
4.35
4.54
4.67
4.84
Large City at Age 15
0.26
0.23
0.38
0.37
0.45
0.47
0.53
0.43
0.50
0.46
0.48
0.61
0.38
0.54
0.67
0.55
Propensity Score
.05
.05
.15
.15
.29
.30
.50
.51
.65
.66
.75
.76
.85
.86
.93
.95
N
418
22
180
35
175
70
116
96
42
96
31
117
34
193
15
269
440
215
245
212
138
148
227
284
N (Total for Each Stratum)

Variables

Stratum 1
Stratum 2Stratum 3
Stratum 4Stratum 5Stratum 6Stratum 7
Stratum 8[.0-.1][.1-.2][.2-.4][.4-.6][.6-.7][.7-.8][.8-.9][.9-1.0]
No
No
No
No
No
No
No
No
CollegeCollegeCollegeCollegeCollegeCollegeCollegeCollegeCollegeCollegeCollegeCollegeCollegeCollegeCollegeCollege70.54 71.41 72.71 74.31 72.33 72.13 72.92 73.07 73.14 71.75 73.42 72.82 72.29 72.46 74.20 72.81
2.57
2.32
2.39
2.51
2.39
2.27
2.32
2.46
2.38
2.65
2.23
2.25
2.65
2.32
2.33
2.32
2.92
3.18
3.14
3.00
3.14
3.10
3.09
3.27
3.38
3.17
3.19
3.21
3.32
3.34
3.73
3.55
11.83 12.64 12.79 12.86 12.54 12.93 13.20 12.76 13.93 13.13 12.81 13.52 14.44 13.85 14.00 14.68
3.97
9.72
6.63
8.81
7.23
9.56
8.28
9.97 10.33
8.59
9.18 11.91 22.33 15.07 35.31 23.96
0.02
0.00
0.03
0.00
0.05
0.04
0.03
0.01
0.05
0.07
0.06
0.03
0.06
0.12
0.07
0.14

Appendix A. Mean covariate values by propensity score strata (male)―188―

22

Birth Year
Number of Siblings at Age 15
Economic Status at Age 15
Family Possessions at Age 15
Number of Books at Age 15
National or Private Junior HighAcademic Achievement at Year
2.57
2.55
2.82
2.85
3.05
3.09
3.23
3.26
3.51
3.59
3.71
3.64
4.03
3.93
4.36
4.46
3 of Junior High School
Parent Education
10.70 10.28 11.54 11.86 11.87 12.25 12.85 12.65 12.99 12.66 13.70 13.46 13.74 14.19 15.50 15.48
Father's Job 0.02
0.07
0.03
0.07
0.08
0.05
0.15
0.15
0.11
0.18
0.22
0.20
0.28
0.25
0.64
0.54
Professional/Management
Father's Job - Non-Manual
0.15
0.17
0.20
0.15
0.24
0.30
0.35
0.33
0.37
0.33
0.42
0.40
0.40
0.40
0.18
0.28
Father's Job - Manual
0.69
0.72
0.64
0.63
0.56
0.54
0.39
0.40
0.41
0.38
0.28
0.27
0.15
0.19
0.18
0.06
Father's Job - Other
0.14
0.03
0.14
0.15
0.13
0.11
0.13
0.12
0.11
0.11
0.09
0.14
0.18
0.15
0.00
0.12
Single-Parent Family at Age 15
0.14
0.10
0.08
0.03
0.07
0.06
0.03
0.06
0.04
0.06
0.06
0.03
0.03
0.04
0.00
0.01
Vocational Track at High School 0.70
0.59
0.43
0.55
0.22
0.22
0.14
0.05
0.02
0.02
0.06
0.04
0.00
0.02
0.00
0.00
High School Rank
1.78
1.86
2.33
2.33
2.94
2.99
3.59
3.61
4.07
4.10
4.28
4.48
4.78
4.73
4.91
4.93
Large City at Age 15
0.32
0.28
0.37
0.27
0.39
0.41
0.44
0.44
0.52
0.49
0.45
0.49
0.38
0.58
0.82
0.60
Propensity Score
.06
.07
.15
.15
.29
.30
.50
.51
.65
.66
.74
.75
.84
.85
.93
.93
N
393
29
329
60
340
140
200
203
82
122
69
181
40
267
11
235
422
389
480
403
204
250
307
246
N (Total for Each Stratum)

Variables

Stratum 1
Stratum 2Stratum 3
Stratum 4Stratum 5Stratum 6Stratum 7
Stratum 8[.0-.1][.1-.2][.2-.4][.4-.6][.6-.7][.7-.8][.8-.9][.9-1.0]
No
No
No
No
No
No
No
No
CollegeCollegeCollegeCollegeCollegeCollegeCollegeCollegeCollegeCollegeCollegeCollegeCollegeCollegeCollegeCollege70.65 73.38 71.36 71.32 72.34 73.04 72.93 73.18 73.83 71.82 75.23 73.94 73.13 73.79 73.27 75.10
2.58
2.41
2.52
2.50
2.47
2.47
2.36
2.35
2.45
2.44
2.38
2.25
2.18
2.33
1.73
2.23
2.88
3.00
3.07
3.10
3.16
3.12
3.19
3.29
3.39
3.25
3.48
3.33
3.28
3.47
3.91
3.83
11.30 11.72 12.47 12.73 13.26 13.11 13.77 13.88 14.32 14.25 14.77 14.51 14.68 15.12 16.00 16.08
4.25
4.93
5.48
4.87
6.67
7.56
8.74 10.96 11.32 11.76 14.15 11.89 19.93 16.58 37.96 28.39
0.02
0.00
0.02
0.00
0.01
0.02
0.06
0.03
0.06
0.06
0.10
0.06
0.05
0.16
0.18
0.25

Appendix B. Mean covariate values by propensity score strata (female)Appendix C. Distribution of first job by propensity score strata (male, %)Lower Propensity(Stratum 1-2)
No College College
First Job
(N=595)(N=57)
Professional/Technical
3.4
24.6
Clerical
9.8
22.8
Sales
11.9
22.8
Service
4.9
7.0
Police/Security
3.9
0.0
Manual work
65.7
22.8
Other
0.5
0.0
Total
100.0
100.0

Higher Propensity(Stratum 7-8)
No College College(N=458)(N=49)
16.3
44.1
20.4
23.6
16.3
19.4
14.3
2.6
6.1
1.1
26.5
9.2
0.0
0.0
100.0
100.0

Appendix D. Distribution of first job by propensity score strata (female, %)Lower Propensity(Stratum 1-2)
No College
CollegeFirst Job
(N=716)(N=88)
Professional/Technical
10.3
27.3
Clerical
39.3
44.3
Sales
17.6
12.5
Service
13.4
9.1
Manual work
19.4
6.8
Other
0.0
0.0
Total
100.0
100.0

―189―

Higher Propensity(Stratum 7-8)
No College
College(N=49)(N=490)
46.9
37.6
34.7
43.3
4.1
10.8
12.2
4.5
2.0
3.7
0.0
0.2
100.0
100.0

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