GCOE Discussion Paper Series
Global COE Program

Human Behavior and Socioeconomic Dynamics
Discussion Paper No.79

How do high school graduates in Japan compete
for regular, full time jobs? An empirical analysis
based upon an internet survey of the youth

Kenn Ariga, Masako Kurosawa, Fumio Ohtake, and Masaru Sasaki

August 2009GCOE Secretariat
Graduate School of Economics

OSAKA UNIVERSITY

1-7 Machikaneyama, Toyonaka, Osaka, 560-0043, Japan

How do high school graduates in Japan compete
for regular, full time jobs? An empiricalanalysis based upon an internet survey of theyouth
Kenn Arigay, Masako Kurosawa, Fumio Ohtake, and Masaru Sasaki
August 26, 2009

Abstract
We use a survey of the Japanese youth within 10 year after high school
graduation to investiage the impacts of the academic and social skills on
their success in the job market. We …nd three major factors account for thejob market outcome immediately after school: school characteristics andjob placement services, academic performance, and social skills, including
the negative impacts of problematic behaviors at the school. Second, when
we run a Probit regression on whether or not the surveyed individuals holdregular, full time job, we …nd the persistent but declining (over age) impact of the job placement immediately after school. Moreover, we …nd theimpact of variables pertaining to the sociall skills remain signi…cant evenafter controling for the job placement outcome after school, whereas othervariables such as GPA or attributes of highschools are largely irrelevantto the current employment status.

1

Introduction

In much of the postwar years in Japan, the job market for new school graduates
had been institutionally separated from the rest of the labor market. Two
institutional features are critical in shaping the separation of the market for the
new school graduates. First of all, virtually all the …rms in Japan, except forvery small (say, less than 10 employees), adopted dual recruiting system, one forthe new school graduates, and the other for those already in the labor market.
The division between the two are both procedural as well as substantial. The
recruitment of new school leavers are controlled by the personnel department.
As such, they are not strictly based upon existing vacancies. Rather, their
Very preliminary, do not quote withtout authors’written permission. Comments welcome.
Mailing address: Institute of Economic Research, Kyoto University, Yoshida Honmachi, Sakyo-ku, Kyoto 6068501 Japan. ariga@kier.kyoto-u.ac.jp
y Corresponding author.

1

recruiting policy is guided by long term plan of the …rm, projecting the future
manpower demand within the …rm. On the other hand, those hires rather than
the new school leavers are ad hoc and virtually all the hires are based upon
immediate and existing vacant positions.
Syokugyo anteisyo, or, regional public employment o¢ ces jointly with regional high schools have been instrumental in organizing the market for the
new school graduates. Speci…cally, they make considerable e¤orts soliciting jobo¤ers from …rms within the region, organizing meetings with teachers in chargeof job placements, and in recent years, organizing "job fairs". Among otherthings, it is important to take note that those openings thus solicited are madeavailable only to the students in the last year of schools.
It is then not surprising that getting the …rst job through this channel ofschool placement o¢ ce and regional employment o¢ ce is by far the best andmost popular means by which school leavers obtained their …rst jobs immediately after the school. Genda and Kurosawa (2001) shows that indeed thatmatches created in this manner is on average of better quality, longer lasting
ones.
There is no doubt that years of long stagnation of the economy during the last
decade of the 20th century left visible scars on many segments of the economy.
The labor market was no exception. Ariga (2006) documents and analyzes thedeteriorations of match quality during the period in the market for the new highschool graduates.
This paper makes use of a unique survey of the youths we conducted in February 2008 . The survey is conducted using the internet and we corroborated
with an internet portal webcite which is very popular among the younger generations in Japan. We solicited participations to the survey among the memberof the portal site. One of the unique feature of the survey is the extensive list ofquestions on the last year at the high school. Since we limited the participants
to those who did not go to college, for a great majority of the participants, thelast year in high school is the last year before they entered the labor market.
We investigate two key questions pertaining to their attachment to the labormarket. First, we revisit the question asked in Genda and Kuroda on the factors
responsible for the success in their job placement immediately after the highschool. We do so by employing a much richer set of covariates than done in
the past. The second key question is how the impact of the …rst job (possibly)
erodes over time and how other factors in‡uence the current employment status.
Our main …ndings are in three holds.
First, we …nd three key contributing factors for the success in regular andfull time employment immediately after the school: high school characteristics
including the quality of job placement assistance, non-cognitive social skills,
and academic performance (in that order in terms of relative contributions).
Second, whether or not they obtained regular full time jobs immediately after
school does matter for the current employment status. The impact erodes overtime, and the e¤ect all but disappears after 10 years. Finally, non-cognitve social
skills continue to have impact on employment probability even after controlling
for the outcome of the job search immediately after school. On the other hand,2neither academic performance or other variables on their last year in high school
matters for the current employment.
The sequel of the paper is organized as follows. In Section 2, we introduce
the survey itself and characterize the sample individuals during their last years
in high school, as well as the current labor status and other attributes. Section3
reports the two main sets of empirical analysis, one on the factors responsible
for the success of regular full time employment immediately after the school,
and the other on the persistence of the impact of high school job placements on
the subsequent employment. Section 4 o¤ers a brief conclusion.

2

Internet Survey of the Youth in JapanIn February 2008, we surveyed 2329 individuals with age between 17 and 27(ISYJ: Internet Survey of the Youth in Japan). We solicited participants tothe survey through one of the internet cite called Goo, one of the most popular
portal among the youth in Japan. We restricted participants to be in the lastyear of high school or older, but not older than 27 of age. We excluded thoseeither currently enrolled or graduated from (either 2 year or 4 year) colleges
as our primary focus of the survey is on the subset of the young populationin Japan who are reputedly facing di¢ culties in school to work transitions.
We included graduates (or dropouts) from vocational colleges (Senmon-Gakko),
which typically entails two years of schooling. Although we do think some of theissues faced by our survey participants are shared also with college graduates,
our exclusion of college graduates is justi…ed by variety of data and studies
indicating the problems are far more severe among the youths without college
education. The English translation of the questionnaires used in the …rst andfollow up surveys are given in Appendix.

2.1

From school to work transition in Japan

After 9 years of compulsory education, 98% of junior high school graduates inJapan go to senior high schools. In the most recent years, more than 50% (52.8%
in 2008) of high school graduates go to either 2 year or 4 year colleges. Those
who start working immediately after high school is now a distinct minority,
comprising less than 20% (19.0% in 2008) of the total graduates. In 2008,
15.3% of high school graduates went to vocational colleges. Therefore even if
we limit to high school graduates who are not college bound, less than a half ofthem start working (excluding part time jobs) immediately after the school1 .
During the high growth era in Japan, job placements of high school graduates were supported and facilitated by the strong ties between high schools
and local employers, sometime mediated through regional public employmento¢ ces. Each school solicit and collect job openings for the graduating students
1 Up until early 1990s, the majority of high school graduates started working immediately
after graduation. Ariga (2006) looks into the factors responsible for the major changes during
the 1990s in the market for high school graduates.

3

towards the end of the …rst semester (July). The formal job exams are held in
early September and schools ask students to decide which job opening to apply
before the formal exam. Teachers in charge of job placements advises and makesuggestions on the application. For popular job openings, each school pre-screen
students so that by September, students will pick only one place to apply withthe approval and formal recommendations by the school master2 .
During the lost decade, the market for the new high school graduates shrank
its size to less than a half. In 1990, 35.2% of high school graduates obtained
regular, full time jobs, whereas in 2000, only 18.6% did. Since 1.8 million
students graduated in 1990, whereas only 1.4 million graduated high schools in
2000, the number of graduating students with regular full time jobs declined
to roughly 2/5th during this ten year period. The market base eroded during
the period both from the supply and the demand side. The period coincided
rapid decline in high school age population as the 1990 marked the peak of
the second baby boom population. The rapid increase in college enrollment
also took place at least in part due to the loosening of the regulation on theestablishment of colleges in the mid to late 1980s. Needless to say, the decade
long stagnation of the economy brought precipitous decline in the demand forhigh school graduates.
The population covered in our survey …nished high school between 1999 to2008. Roughly speaking, since the burst of the bubble in the early 1990, themarket for new high school graduates continued the decline and bottomed out
around 2003, but remained slow after that. Only in the last two to three years,
we …nd some mild recovery in the job openings for the new school graduates.
Not only the slow labor market but also the institutional changes made it
increasingly di¢ cult for high school graduates to land on a regular, full time
job3 immediately after graduation. As the share of college bound students increased, many high schools shifted their resources away from job placement to
preparation and assistance for college entrance exam. Vocational high schools4
lost popularity and many added new academic courses for college bound students. The slow labor market and gradual erosion of the institutional setting
assisting the transition jointly contributed to the overall decline of the market.
It is against this back ground that the sample youth …nished their high schools
and started their career in the labor market.
2 For these job openings posted through high school job placement services, the applicant
needs a formal letter of recommendation by school master. High schools typically apply strong
moral persuasion to ration the applicants to popular positions so that the number of applicantsfrom a school to each position matches the number of openings alloted to each school by theemployer.
3 A job is regular [Joyo] (as opposed to ad hoc [Rinji] ) if the employment contract iswithout …xed term.
4 In Japan, vocational high schools are on equal footing with regular high schools as the
pre-requisit for colleges.

4

2.2

ISYJ: A Brief Review

Table 1 shows the education background of the sample individuals by sex. The
majority (52%) of the sample are high school graduates, followed by 40% of
vocational college graduates. One unexpected result of the internet survey is
the heavy concentration of female, comprising 71% of the sample.
One of the distinguishing characteristics of ISYJ is a set of detailed questions
on various aspects of their lives during their senior year at high schools. Our
expectation is that these facets of their lives in the last year of high school shaped
and in‡uenced in important manners the subsequent successes and failures in
the labor market, and more generally, their success in the assimilation to thesociety outside their schools and homes.
2.2.1

How they spent the last year at high school

Then, let us begin the review of our survey by tabulations of responses to thequestions on their last year at the school, which is senior high school, for most of
our sampled individuals. Table 2 shows the type of high schools attended. Morethan 70% attended public high schools. Slightly more than 60% took academic
courses, and reminder were distributed over various vocational concentrations(commerce, industry-craft, agriculture, etc.). Even though none of the survey
participants went college, 18% of them took academic concentration courses
designed primarily for those taking college entrance examinations5 . The shareof college bound course is especially large among those enrolled at vocational
high schools6 .
Table 3 shows distribution of self reported GPA ( highest=5, lowest=1). As
can be seen from the table, there is no immediate association between high school
grades and subsequent education/career choice. As a matter of fact, if anything,
GPA is somewhat higher for those who did not go to vocational college after
high school. Table 4 tabulates subjects which they liked /hated. We listed 14
subjects most commonly o¤ered at high schools in Japan and asked to pick (anynumber of) favorite subjects, and those sample individuals hated. Table 4 shows
the variations across subjects. As expected, mathematics is the subject hated
most (53.4% of the sample hated the subject). The Japanese came out as themost popular: 41% said it was their favorite subject. Somewhat unexpectedly,mathematics is also the second most popular. Home economics and information
sciences are hated least, although they are not particularly popular either. We
construct the variable score which is simply the total number of favorite subjects
minus the sum of subjected they hated.
5 It is a common practice among high schools in Japan to divide the students in their senioryear into college bound and career track. Thus even if students are mixed as to their future
courses, each group are organized into separate classes with di¤erent curriculum.
6 This may sounds contradictory, but, in Japanese high schools, it is not at all uncommon that vocational high schools have classes alloted speci…cally for college bound academic
concentrations. Many vocaitonal high schools started such classes in 1990s to lure students
seeking college education as the vocational high school became unpopular as the share ofcollege bound students increased during the period.

5Table 5 o¤ers a summary of the major indicator of the variables relating to
the participants’self evaluation of the 3 years in high school. Roughly 30% of
the surveyed consider the time spent at high school enjoyable and rewarding,whereas 10% choose " I am glad that the time is over", and 20% choose "it was
uneventful and largely forgettable." Although the di¤erence is not large, thefemale respondents seem somewhat more positive than the male.
We run regressions for a simple ordered probit model to see important factors
contributing to the overall self assessment of the high school life. See Table
6. Many of the variables in the table have easily discernible impacts on theirself assessments. Higher GPA, taking parts in extracurricular activities, having
many favorite subjects are among the list of factors which contributed to enhancetheir assessments. Naturally, the experience of being absent from school for
extended period of time had clear negative impact. The same applies to repeatedexperiences of late or absent from school.
In the …rst block, we have a set of variables for high school characteristics and the curriculum. The results indicate the overall satisfaction levels arelower at day school, compared to other types (night schools, and correspondence
courses), and also lower at public, rather than private schools. Other attributes
such as academic or vocational, whether or not the majority of students go tocollege, etc., do not have signi…cant impacts.
In the second block, we list three variables measuring the academic performances, over all GPA (self assessed), average number of hours studied outsideschools (either at home or at Jyuku), and the variable score. As it turned out,
this variable consistently have signi…cant impact. On the other hand, GPA has
only marginally signi…cant impact.
In the third block, we have a set of variables related to the time allocation
when they were in the last year at high school. Two variables stand out as
the important determinants: timespent_friends, timespent_bukatsu, and timespent_otheradults are all highly signi…cant and exert positive impacts on self
evaluation. As is well known, the extracurricular activities, bukatsu, have uniqueand important place in Japanese high schools as they typically require commitment and a large amount of time devoted. For many, bukatsu is often thedominant part of their school life. The regression results support the thesisthat students heavily involved in bukatsu tend to have sense of achievement andsatisfaction.
In the fourth block, we have variables related to the friends. Naturally, those
who answered that they (at the moment of the survey) have many friends fromhigh-school days, view the high school years worthwhile. On the contrary, thosewith many friends before high school have the opposite views. Naturally, thosewith many of those friends still in constant touch view the high school days
more positively. Finally, their assessment of high school life is higher if they
had boy (-girl)friends.
As can be seen from the R2 statistics, much of the explanatory powers are
drawn in by variables in these four blocks, and those in personal characteristics
and family back ground add relatively little. Still some of the variables havesigni…cant coe¢ cients: Fincomeat15, index variable ranging from 1 to 10 for the6

family income at age 15, has positive impact, i.e., the students from more wellto do families …nd their high school life more valuable; for whatever the reasons,students with younger (older) father (mother) …nd that their high school years
was a worthwhile experience.
All in all, Table 6 o¤ers no big surprise: those in our survey place relatively
little weight on grades in evaluating their high school life. Interactions withfriends, times spent in extra curricular activities (bukatsu), whether or not they
had girl (boy) friends matter far more than their academic standing.
2.2.2

Their current employment status

One unintended outcome of using the internet portal as the basis of the survey
is the concentration of female, roughly 70% of the participants, which is close
to the member pro…le of the portal. Among male, 18% of them are married,
whereas roughly 40% of female participants are married.
Among the female, 80% of those currently working are still single, whereas 3
out of 4 non-working females with prior work experiences are married, indicating
many married female participants exited from the labor marker after marriage.
Not surprisingly, 80% of those married female with past work experiences havekids. 227 , or 3% of female participants are single mother, i.e., currently notliving with a partner and have kids.
In Table 7, we summarize the current labor market status: Among male,slightly more than the majority retain full time permanent jobs, and about
a quarter of them are working under di¤erent employment contracts. Among
female participants, only about 30% of them work as full time and permanent
employees. Roughly equal share of them work in other form of employments.
Overall, the attachment to the employment is still on shaky ground for many of
the sampled individuals. Given the participants ages, this may not be surprisingand comparable to those for the youth in Europe. On the other hand, there
are indications that tenuous link to the labor market for these youths in our
sample may di¤er in important ways from those found among youths in otherdeveloped countries.
For one thing, there is no apparent trend that over age their attachment to
the labor market becomes more …rm. Table 8 shows that except for the age
below 21 or 22, there does not seem to be any systematic age variations in theshare of full time permanent employees. Upon closer look, we …nd, however, theaverage is misleading. If we divide the sample across sex, for male, we do …nd
steady upward trend of the share of regular full time employees starting fromabout one third around at early 20 up to above 60% by late 20s, whereas among
the female samples, upward trend is virtually non existent, or very weak. The
highest share is found at age 21. Right hand panel of Table 8 reveals even morestriking heterogeneity. If the survey participants started o¤ their employment
career by getting a regular full time job immediately after …nishing school, theshare of regular, full time employment remains high above 80% for until early
7 Only one male has a kid and not living with a partner.

7

20’s and then the initial impact apparently erodes. By the end of their 20’s
only about a half of them retain the same status. For those who did not get
the full time permanent job after school, their share remains low for quite sometime but it gradually increases from mid 20’s. By the end of 20’s, their share
increased to about 35%, still short of the corresponding share among the …rst
group.
To sum up, two general observations can be made. First of all, the attachment to employment is not strong among the sample individuals in the survey,
even for male. The attachment is much weaker for female. Second, the employment is substantially more stable for those who obtained a regular full time job
immediately after school.

2.3

Key Questions

Base upon preliminary analysis of the survey data, in what follows, we confront
the two key questions.
1. What are the factors accounting for the successful transition from (high)school to work? How important are the cognitive and non-cognitive skills.
Are the family environments shape the ability to assimilate themselves tothe society in general, and to the working life, in particular?
2. How those who did well in the …rst job placement over years lost theadvantage? In other words, if they quit the …rst job, does the impact of
the …rst job evaporate? Conversely, what are the key factors responsible
for successful transition to the work for those who did not do well in themarket for the new entrants?

3

The Econometric Analysis

3.1

Results (1) Job placement at school

3.1.1

Main Results

Table 9A reports the main results. The probit estimate o¤ers a highly sharp
picture on what it takes for the high school students to be successful in getting
a stable job. Extreme left column shows the determinants of the probability oflanding on a regular full time job immediately after the school among those who
did not choose to continue school (to college or senmongakko). The dependent
variable, seisyain-syusyoku takes the value 1 if the respondent obtained a regularfull time job immediately after graduation, is zero otherwise. Slightly less thana half of the sample, about 1,100 respondents are covered in the regressions.
We categorize the variables into 8 blocks: school and curriculum attributes
and school placement services, labor market tightness conditions for new school
graduates, academic performance in the senior year at high schools, problematic
behaviors at schools, time spent on various extracurricular activities, types andnumber of friends, personal traits of the respondents, and family back ground.
8Equation 2-through 9 can be compared vis-a-vis the full speci…cations in equation 1 to obtain marginal contribution of respective group variables in pseudo
R2 : Their respective contributions are shown in parenthesis. As it turned out,
the largest contribution is found for school characteristics, followed by variables
in time allocation, and family background.
We start with variables in the school characteristics. We …nd signi…cantpositive impacts on the dependent variable by: zenjitsusei [day schooling, as
opposed to night-time or correspondence classes], koritsu [public, as opposed
to private schools], and if the respondents was in classes primarily for students
seeking jobs after school [ as opposed to those primarily for college bound or
mixed]. Not surprisingly, vocational high schools fare better in placements so
the impact of attending a hutsuka [academic track] high school is negative. The
impacts by school placement services are also highly signi…cant and quantitatively important: sinrosido counts number of various placement related activities which respondents recalled that their school organized, whereas sinromissed
counts those events which respondents did not attend. We also …nd the negative
impact from the time they needed to commute to their school [comtime].
Not surprisingly, the school grade and attendance matters. Once we control for GPA, none of the variables measuring other aspects of academic studies
really matter: whether or not any particular subject, e.g., math, was theirfavorite, or they hated it, does not have any signi…cant impact. The variable
hourstudy, measuring the hours of study at home or at jyuku actually carry negative e¤ects on the success in the job market, and some of them are marginallysigni…cant. The impacts of variables in problematic behaviors at schools arealso as predicted. Those who reported they were often late or missed schools[chikokukesseki ] tend to have lower chance of landing a regular full time job
after school. Similarly, extended period of absence [hutoukou] is also highly
signi…cant. On the other hand, ijime [bullying at school] does carry negativecoe¢ cient but never statistically signi…cant. The relative importance (seriousness) of extended absence, as opposed to bullying, is a well established fact
among school counselors, and our regression results con…rm their …nding in adi¤erent perspective.
The variables in the next two categories, time allocation and friends, areintended to capture the characteristics of the daily life in the last year at highschool. We …nd strong indication that those who are active at schools and sociable tended to fare signi…cantly better in job placements: friendsatHS, BFGF,
timespents_bukatsu are all highly signi…cant and positive, indicating that manyfriends at high school, having time spent on extracurricular school activities,
having boy (girl-) friends all contributed signi…cantly to the success in the job
market. Moreover, both part time work and doing house chores also carry
positive impacts. We …nd the negative impacts by variables representing thetendency that their primary interests and focus were outside the school. If they
had many friends from earlier periods [friends_beforeHS ], or at part time work,or they spent long time in their own hobby, or naraigoto [e.g., attending piano
lessons, English classes, outside schools], they are less likely to be successful inthe job market.
9

Most of variables in personal characteristics do not appear to be signi…cantin the regressions. There is some indication that risk aversion tended to havenegative impact [risk ] but evidence is not very strong. Male students on average had somewhat higher probability of the success, but the impact is relatively
small. Although variables in family background category jointly contribute signi…cantly in explaining the outcome, most of individual variables are not significant. We …nd, however signi…cant impacts of their mothers. [If mother is deador older (relative to their own age), they have lower probability of success].
We take away three strong messages from these regression analysis. Firstof all, what type of schools they attended do matter highly signi…cantly totheir success in the job market after graduation. This is consistent with theimportant role of Japanese high schools in the job placements. Second, we …ndimportant impacts of social skills as proxyed by the activeness and strong ties
to fellow students. This is also con…rmed in the negative impacts of indicatorvariables representing isolation in schools. Finally, we …nd the work experienceeven house chores does have signi…cant positive impact.
Figures 1 through 3 show the box plots of the predicted probability conditional on the value of three variables, GPA, timespent_bukatsu, and sinro-sidou.
Measured in the median (50% tile), those with highest GPA(=5) have 60% probability of landing a regular full time job after the school as opposed to about 30%
with the lowest GPA. The impact is much larger for sinrosido variable. If theyattended a school which organized all six types of placement meetings/seminars,
roughly the predicted median probability is 75%, as opposed to about 20% ifthey attended a school which organized none. Figure 2 shows the impact ofparticipation in various extracurricular activities. Those who are most active
has a median probability about 65%, as opposed to 30% for those who did notparticipate at all.
3.1.2

Correcting for Selectivity Biases

Our analysis so far assumed exogeneity by the decision of sample individuals in
the last year at high school on whether or not they continue to college or othereducation institutions after high school. Clearly, the assumption is dubious at
best. As we already indicated, our analysis so far focused on sub-samples who
decided not to continue schooling after high school. About half of the sample
individuals in our survey did not choose to search for job after school either
because they went to technical (vocational) college [ typically 2 year term] orother types of schools after high school, although our survey excluded by design
those who went to college.
To correct for the selectivity bias introduced by the endogeneity of this decision, we modi…ed the base line regression model to include Heckman correction
term. At the extreme right column of Table , we show the probit estimationof our base line speci…cation with Heckman correction for the selectivity bias.
In Table 10, we show the selection equation. We notice …rst that the selectivity correction retain the qualitative features of the base line results, whereassome of the variables, especially those in family background blocks now become10statistically signi…cant.
This makes sense as these variable perform as good predictor of the decisionon whether or not to enter the job market after school:

3.2

Results (2) Job stability

3.2.1

Main Results

The second question on the success in the job search at high school is its impact
on the subsequent employment. We ask how persistent the impact of the successor the failure is on the employment in later years.
Regression results (1) and (9) in Table 11 report our main results. Our left
hand side variable is a dummy equal to unity if the sample individual currently
holds a regular full time (seisyain) job. To represent the impact of the outcomeof the job search during the high school, we use seisyainsyusyoku, which is unity
is they obtained a seisyain job immediately after the graduation. The second
variable is the interaction of the variable and the years since the graduation fromhigh school. In equations (1)-(8), the entire samples are used for regressions,whereas in equations (9)-(16), we only used samples who did not go to vocational
colleges (senmon gakko). The two groups of regressions are qualitatively similar.
We …nd the high school job placement outcome to have consistently positive
impact on the probability that they currently hold a seisayin job. The negative
impact of the second variable shows that the impact declines over age. In the…rst set of regressions that we use all the sample, the estimated coe¢ cients
indicate that the impact disappears after 10 years. Figure 4 illustrates the
positive but declining impact of the success in the job search during the lastyear in school. The Box plot also shows the heterogeneity in the probability
increases over age within each group. In the second set of regressions, we limit
the sample to those who attended vocational college after high school. The
cross product term become insigni…cant although the qualitative feature of theregressions remain largely the same. The impact of age itself has positive andsigni…cant for female, and the impact is even larger for female, suggesting thesample individual gradually assimilates into the society and …nd stable jobs
as they age. Not surprisingly, the impact of marriage has diagonally di¤erente¤ects on male (positive and signi…cant) and female (negative and signi…cant).
In equations through (2)-(8) [(9) -(16)] we checked if the major variables representing high school characteristics, academic and social performances at highschool, and family background have any impact beyond those through seisyainsyusyoku. Overall, most of the variables are insigni…cant. None of the variablesin the school characteristics group except only for the positive impact of school
being a day school (not night or correspondence course), and the school is perceived as the mixed students (those college bound and on career tracks). The
same is true for academic performance, although in one regression GPA carries
marginally signi…cant positive impact. Similarly, none of the variables in family
background or personal traits enter signi…cantly in the regressions. The only
exception is a dummy variable for father jobless or retired, which carry a nega-

11

tive signi…cant coe¢ cient. On the other hand, the variables in the problematic
behaviors at high school continue to have strong and signi…cant impacts oncurrent employment, even after controlling for the job placement outcome after
the school. Problems in assimilations and being punctual at schools apparently
carry its impact well beyond the high school and high school job placements.
Although the impacts are smaller and less signi…cant, the time allocation variables continue to play important role in the current employment. The timespent on extracurricular activities continue to be signi…cant and positive on thecurrent regular full time employment. Part time job experiences also continue
to have positive and signi…cant impact as they did for the job placement immediately after school. The over all picture is similar for group of variables related
to friends. Those who have many friends at part time job has signi…cantly lower
probability being at regular full time work. The impact of having friends fromclam school now have positive and signi…cant impact (the coe¢ cient is positivein job placement at school regression but not signi…cant). having boy friend orgirl friend at high school continue to have positive signi…cant impact.
The underlying message seems clear. School characteristics, personal traits,
and academic performance are all important determinants of job placement
outcome at high school, whereas they are largely irrelevant in the current employment status. On the other hand, a variety of variables pertaining to aspectsof social skills continue to play signi…cant role in the determination of current
employment type and status.
3.2.2

Dealing with the endogeneity of the …rst job outcome: themaximum likelihood estimation method

The key explanatory variable, seisyainsyusyoku, is an endogenous variable. Consider a recursive structure in which seisyainsyusyoku, and the current job status,
seisyain are determined.
yi
si
xi
sei

= Z 1i 1 + 1 sei += I yi y= Z 0i 0 + "2
= I xi x

ei (age2s18) + "1wherein we assume"1 ; "2 ~N (0; )
2

=

2
1
1 2

1 2
2
2

The system of equations given above cannot be estimated using a conventional
statistical package routine. Since sei is itself a binary random variable, y i isnecessarily correlated with xi even if two error terms, "1 ; "2 are mutually12independent. One idea is to use an IV method for sei in the …rst equation. Thisis problematic if "1 ; "2 are correlated because what we can do is to have a xbi
1i
orthogonal to " ; which is not a prediction of se : Bivariate probit cannot do thejob either because the …rst equation includes the left hand side variable in thesecond equation.
Here we estimate the parameters in the system using maximum likelihood.
Let
p11

pr(si = 1; sei = 1)

= pr

p10

= pr
Z
=

Z 1i 1 +Z"

1x Z 0i
i

=
p01
=

=

Z
"2

Z

+ 1 + 2 (agex Z 0i 018)"1 ; "2 d"1 "2 ;y (Z 1ii1+y Z 1ii1

pr(s = 1; se = 0)
Z x Z 0i 0 Z

1 + 2 (age 18))

"1 ; "2 d"1 "2 ;pr(si = 0; se = 1)ZZ y (Z 1i 1 + 1 + 2 (age 18))x Z 0i
i

p00

y

+"

1i 1

y

0

18) + "1x

1 + 2 (age0i 0
2"1 ; "2 d"1 "2 ;0pr(s = 1; sei = 0)
Z x Z 0i 0 Z y Z 1i 1

"1 ; "2 d"1 "2 :The construction of the log-likelihood is then straight forward. Against
the set of data on si ; sei with matched observations of explanatory variables
Z 0i ; Z 1i ; we haveX
log L [ ] =I i si ; sei log prkm ( )iwherein I i is an indicator vector variable taking [1 1] [1 0] [01] or [0 0], depending upon the value vector of si ; sei ; and prkm is one of the corresponding
probability de…ned above, and is the set of parameters to be estimated:
;

3.2.3

0

;

1

;

1;

2 ; y; x

Estimation results using cmp.ado

Luckily, there is a program that can be implemented and executed in STATA
called cmp.ado that performs exactly the above maximum likelihood estimation13

(and more). We used this program for the estimation. The results are shownin Table 12.
The key …ndings in Table 10 remain intact in this full maximum likelihood
estimation: The impact of seisyain-syusyoku remains highly signi…cant in all
the speci…cations when the variable is treated as endogenous; The impact of thevariable with the age also signi…cant and negative; and, the impacts of some ofthe variables in the problematic behaviors and friends groups remain signi…cantfor the determination of the current employment status8 .

3.3

Comparisons with the …ndings in the literature

In the analysis above, we largely con…rm earlier …ndings in Genda and Kurosawa(2001) and Kondo (2007). The regular and full time employment immediately
after school does have a persistent and quantitatively important e¤ect on thesubsequent employment of the youth.
Ours also o¤er additional …ndings. First of all, as emphasized in the recent
literature such as Carneiro and Heckman (2005) and Heckman and Rubinstein(2001), we …nd signi…cant impact of non-cognitive skills on the probability of
regular full time employment after school. Moreover, the impact of non-cognitive
skills remain for the current employment even after controlling for the outcomeof the job placement immediately after school. On the other hand, our results
indicate academic skills in high schools do not have such impacts on the current employment once we control for the job search outcome immediately after
school. It seems possible to interpret the contrasting results in terms of information dissemination. Academic records are readily available information for
all the potential employers, whereas information relevant to the non-cognitive
skills are much more di¢ cult to collect. After all, for example, whether or not a
person is open minded, friendly or sociliaze, cannot be known from a c.v. Thenit makes sense that information not readily available to the employer remains
important in the subsequent employment whereas publish available informationsuch as academic records does not9 .
Our estimation results indicate the impact of the regular full time employment after school persists for a long time. Although the estimates vary somewhat across speci…cations and estimation methods, it is fair to say that the
impact lasts at least ten years after graduation, and possibly somewhat longer,
which is also the …nding in Esteban-Pretel, Nakajima, and Tanaka (2009).
Genda, Kondo and Ohta (forthcoming) compares the impact of the entering
the job during a recession in Japan and the United States. They …nd that the
8 Given that the product of age and seisyainsyusyoku is also endogenous, we also ran
regressions separately for 4 age groups (age<22,22-23,24-25,26-27). The estimated coe¢ cienton seisyainsyusyoku declines monotonically over age groups: it is .957 for the youngest group,
followed by .262, .151, and .055. Except for the last one, the coe¤cients are all signi…cant at
5% con…dence interval.
9 See Farber and Gibbons (1996). Our …ndins may also re‡ect the large share of employment
in the service and retail sector, as emphasized in Borghans ter Weel and Weinberg (2006).
They argue the growing importance of non-cognitive skills (people skills) re‡ect the increased
share of the service sector jobs.

14impact persists and far more important for high school graduates than the college graduates in Japan, whereas in the United States, they …nd modest impact
on college graduate subsequent earnings but no discernible persistent e¤ects onhigh school graduates. Their …ndings are consistent with the view that theunique feature of the job market for high school graduates in Japan is responsible for the persistent impact of the …rst job. They consider the information
advantage provided by school based hiring system discourages the …rm fromhiring those who left out in the school based system.
Our own …ndings, however, indicate that the school based pre-screening system may have some built-in-bias. The high school pre-screening system is verymuch grade based merit rating, thus the pre-screening is based primarily on academic skills. To the extent that non-cognitive skills are important, the system
may well be systematically biased, which can only be adjusted by subsequent
employment and turnovers.

4

Conclusion

In line with the earlier …ndings by Genda and Kurosawa (2001), we …nd stronge¤ects of job placement assistance at high school on the success of the job search
immediately after schools. We also con…rm that the academic performance isalso an important determinant in the job search during the last year of theschool. Based upon our survey, however, our analysis strongly indicate that
non-academic skills are equally, if not more, important. Moreover, even though
the impact of academic performance is limited to the success of the job searchduring the school, the impact of non-academic, social skills captured in our
survey questions on high school life continue and remain important even for
those in the late 20’s.
Whatever the underlying reasons, the success or the failure of the job searchduring the last year in high school remain persistent for extended period oftime. Our estimate suggests that even 10 years after high school graduation,
the impact is still signi…cant in explaining the current employment status.
.

15

References[1] Ariga, K. (2007),‘Kosotusya no Rodo Shhijyo (Market for High School
Graduates in Japan)’, in F. Hayashi (ed.) : Keizai-Seido no Bunseki to
Sekkei (Analyses and Design of Japanese Economic Institutions), KeisoSyobou, 2007
[2] Ariga, K. (2005) ‘From Golden Eggs to Rotten Apples: Changing Landscape of the Market for New High School Graduates in Japan’2004, revised
September 2005
[3] Borghans, L., B. ter Weel, and B.A. Weinberg (2006), "People People:
Social Capital and the Labor Market Outcomes of Under-represented
Groups," NBERWP 11985[4] Carneiro, P., and J. Heckman (2005), "Human Capital Policy," in Heckman
and Kruger (eds.) Ineuqality in America MIT Press[5] Esteban-Pretel, J., R. Nakajima, and R. Tanaka (2009), “Are contingent
Jobs Dead Ends or Stepping Stones to Regular Jobs? Evidence from a
Structural Estimation,” RIETI Discussion Paper 09-E002[6] Farber, H., Gibbons, R. (1996). “Learning and wage dynamics”. Quarterly
Journal of Economics 111, 1007–1048.
[7] Genda, Y., and M. Kurosawa (2001), "Transition from school to work inJapan," Journal of the Japanese and International Economies, 15(4): 465488[8] Genda, Y. ,A. Kondo and S. Ohta (2009), "Long-term e¤ects of a recession
at labor market entry in Japan and the United States , forthcoming in
Journal of Human Resources.
[9] Heckman,J. and Y. Rubinstein (2001), "The Importance of Noncognitive
Skills: Lessons from the GED Testing Program," The American Economic
Review, Vol. 91(2):145-149[10] Kondo, A., "Does the First Job Really Matter? State Dependency in
Employment Status in Japan," Journal of the Japanese and International
Economies, 21(3): 379-402[11] Postlewaite, A., and D. Silverman (2006), "Non-Cognitive Skills, Social
Success, and Labor Market Outcomes,"[12] Roodman, D. 2009, "Estimating fully observed recursive mixed-processmodels with cmp.ado," Working Paper 168. Center for Global Development. Washington, DC

16

Table 1 Educational Attainment
FemaleMale
Junior Highschool
50
Senior High School879
56
Some education at Technica
Technical Colleges
677
1662

35
340
43
249
667

Total
85
3.65%
1,219 52.34%
99
4.25%
926 39.76%
2329

Table 2 Types of Highschool
TracksAcademic

50
487
160
697

College bound
Mixed
Career
Total

PublicVocational

230
702
75
1007

Table 3 GPA

GPA (self reported)
1(lowest)23
4
5 (highest)

Vocational
Did not go tocollegevocational
graduates andcollegedropouts
14.03
12.00
12.55
14.05
28.71
33.56
21.90
21.56
22.81
18.83

Total

280
1189
235
1704

PrivateAcademic Vocational

13
117
37
167

135
216
22
373

Total

148
333
59
540

Table 4 Favorite Subjects
Subjects
Japanese
Social Science
Philosophy
Mathematics
Sciences
EnglishHealth & Sports
Fine Arts
Home Economics
Information Science

Favorite Neutral
Hated
41.2
39.6
19.2
23.8
42.6
33.6
10.6
73.3
16.1
27.7
18.9
53.4
19.7
47.1
33.2
20.3
39.2
40.5
19.4
62.8
17.9
24.2
67.1
8.7
22.1
72.7
5.2
23.7
73.1
3.2

Table 5 Self evaluation of High school Life
Female3 Years in highschool was?
Highly rewarding and enjoyable
It was ok
It was uneventful and forgettable
Glad that it is over

33.33
40.91
17.15
8.6

Male
27.59
42.58
21.29
8.55

Total
31.69
41.39
18.33
8.59

Table 6 Ordered Probit Regression on Self-evaluation of High School Years
GroupsVariables
zenjitsusei
koritsu
hutsukaschools and curriculum

sinrocourse1
sinrocourse2
gakkohyoka1
gakkohyoka2
GPA

academic performancesscore
hoursstudy
chikokukesseki

problematic behaviors

ijimehutokotimespent_friends
timespent_parents
timespent_otheradults
timespent_tv
timespent_game
timespent_mobile
timespent_pc

time allocation

timespent_hobby
timespent_volunteer
timespent_bukatsu
timespent_sports
timespent_study
timespent_naraigoto
timespent_arubaito
timespent_chores
Robust standard errors in parent*** p<0.01, ** p<0.05, * p<0.1

A

B

C

D

selfevalselfevalselfevalselfevalE
selfeval

0.087-0.086-0.247-0.253-0.253(0.090)(0.092)(0.095)***(0.095)*** (0.100)**-0.068-0.138-0.140-0.130-0.122(0.056)(0.058)** (0.060)** (0.060)** (0.062)**-0.074-0.047-0.028-0.026-0.035(0.055)(0.056)(0.057)(0.057)(0.059)
0.093
0.094
0.072
0.051
0.064
(0.092)(0.095)(0.096)(0.096)(0.100)
0.055
0.080
0.089
0.074
0.072
(0.073)(0.075)(0.075)(0.075)(0.078)-0.023-0.039-0.041-0.031-0.006(0.081)(0.082)(0.083)(0.083)(0.087)
0.041
0.041
0.054
0.063
0.103
(0.066)(0.066)(0.066)(0.067)(0.069)
0.015
0.027
0.033
0.038
0.040
(0.021)(0.021)(0.021)(0.022)* (0.022)*
0.072
0.043
0.032
0.031
0.039
(0.013)***(0.013)***(0.013)***(0.013)*** (0.014)***
0.026
0.022
0.010
0.008
0.012
(0.011)** (0.016)(0.016)(0.015)(0.016)-0.095-0.129-0.153-0.162-0.160(0.026)***(0.027)***(0.028)***(0.028)*** (0.029)***-0.068-0.107-0.067-0.058-0.054(0.047)(0.048)** (0.049)(0.050)(0.052)-0.434-0.376-0.288-0.286-0.264(0.069)***(0.071)***(0.073)***(0.073)*** (0.075)***
0.200
0.116
0.122
0.114
(0.024)***(0.025)***(0.025)*** (0.026)***-0.032-0.014-0.011-0.032(0.020)(0.021)(0.021)(0.021)
0.086
0.064
0.064
0.069
(0.022)***(0.022)***(0.022)*** (0.022)***-0.025-0.036-0.032-0.031(0.027)(0.027)(0.027)(0.029)-0.0280.003-0.009-0.006(0.017)(0.018)(0.019)(0.020)
0.107
0.025
0.027
0.021
(0.020)***(0.022)(0.023)(0.024)-0.025-0.024-0.031-0.026(0.017)(0.017)(0.019)(0.020)
0.014
0.024
0.025
0.028
(0.019)(0.019)(0.019)(0.020)-0.0040.003-0.0000.011(0.043)(0.044)(0.044)(0.046)
0.119
0.101
0.099
0.101
(0.016)***(0.016)***(0.016)*** (0.017)***-0.019-0.023-0.036-0.047(0.039)(0.041)(0.041)(0.043)-0.008
0.008
0.013
0.007
(0.022)(0.023)(0.023)(0.023)0.006-0.006-0.0040.004(0.033)(0.034)(0.034)(0.035)
0.023
0.014
0.014
0.020
(0.019)(0.020)(0.020)(0.021)0.006-0.003-0.001-0.012(0.020)(0.020)(0.021)(0.021)

Table 6 Ordered Probit Regression on Self-evaluation of High School Years(continued)
GroupsVariablesAB

selfevalselfevalCD
selfevalE
selfevalselfeval

0.632
0.632
0.644
(0.046)*** (0.046)*** (0.048)***-0.088-0.093-0.102friends_beforeHS(0.039)** (0.039)** (0.041)**
0.026
0.024
0.035
friends_atarubaito(0.038)(0.038)(0.039)
0.105
0.101
0.065
friends_atjyuku(0.066)(0.067)(0.069)
0.042
0.041
0.036
friendsintouch(0.010)*** (0.010)*** (0.011)***
0.249
0.265
0.260
BFGF
(0.058)*** (0.059)*** (0.061)***
0.013
0.011
whendidyoudoHW(0.018)(0.019)0.014
0.013
whendidyouplanHW(0.015)(0.016)-0.001-0.001risk(0.001)(0.001)-0.025-0.029boldness(0.010)** (0.011)***
0.004
0.001
bmi
(0.006)(0.006)0.022
0.037
naturalLH(0.078)(0.078)-0.168-0.063naturalboth(0.152)(0.150)-0.003-0.001age(0.010)(0.010)
0.074
0.100
male
(0.064)(0.066)0.037
Fincomeat15(0.015)**0.059
parentsmarriedalive.
(0.072)0.092
motherdead(0.193)-0.022
fatherageat18(0.006)***0.016
motherageat18(0.007)**-1.603-0.889-0.440-0.545-0.735Constant(0.148)*** (0.180)*** (0.186)** (0.353)(0.461)-0.731
0.068
0.611
0.511
0.334
cut1
(0.146)*** (0.179)(0.185)*** (0.352)(0.459)
0.429
1.328
1.976
1.881
1.712
cut2
(0.146)*** (0.181)*** (0.188)*** (0.352)*** (0.459)***friends_atHSfriends

Personal characteristics

Family background

Observations
2244
2244
2244
2244
2126
R-squared
0.03120.08560.138
0.14060.1439Robust standard errors i*** p<0.01, ** p<0.05,

Table 7 Current Employment Statusworkingfull time,
regularmale
femaletotal

not working

full time, adpart time temporary
hoc

53
29.5
36.1

6.3
9.4
8.5

6.7
8.9
8.2

10.5
7.2
8.1

Enrolled inhousewives,
Vocational
etc
College
6.3
7.7
3.7
23.8
4.5
19.2

Table 8 The Share of surveyed individuals with regular fulltime jobs
Gender

Age
20
21
22
23
24
25
26
27
28
total

male
0.227
0.313
0.429
0.524
0.594
0.634
0.465
0.719
0.9
0.532

Seisyainsyusyoku
female
Yes
No
0.292
0.778
0.135
0.35
0.824
0.128
0.286
0.538
0.216
0.362
0.607
0.306
0.297
0.512
0.3
0.314
0.492
0.333
0.277
0.526
0.219
0.291
0.453
0.362
0.28
0.476
0.362
0.295
0.527
0.263

total
0.261
0.339
0.325
0.4
0.374
0.404
0.327
0.401
0.457
0.362

NEET
9.5
17.5
15.3

Table 9 Ordered Probit Regression on Regular Full Time Job after School(1)Groups

VARIABLES
zenjitsusei
koritsu
hutsukasinrocourse1

SchoolCharacteristics(.0908)sinrocourse2
gakkohyoka1
gakkohyoka2
comtimesinrosido
sinromissed

Labor market(.003)

kyujinkyusyokubairitu
prefvar
GPA

academic
performance(.0072)score
hoursstudy
chikokukesseki

Problematic
behaviors at
school (.0123)

ijimehutokotimespent_hobby
timespent_volunteer
timespent_sports

time allocation
at high school(.0291)timespent_study
timespent_bukatsu
timespent_naraigoto
timespent_arubaito
timespent_chores
friends_atHSfriends_beforeHS
friends_atarubaito

Friends (.012)
friends_atjyuku
friendsintouch
BFGF
hwdelayriskboldness
Personal
Characteristics(.0068)bmi
naturalLH
naturalboth
male
Fincomeat15
parentsmarriedalive
motherdead

Familybackground(.0176)

fatherageat18
motherageat18
jiei
sengyosyufu
fatherinactive

Observations
R-Squared
Robust standard errors in parentheses*** p<0.01, ** p<0.05, * p<0.1

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

seisyainsyusyoku
0.280
0.293
0.269
0.317
0.273
0.284
0.274
0.287
(0.062)***(0.060)*** (0.064)*** (0.055)*** (0.059)*** (0.060)*** (0.063)*** (0.058)***
0.093
0.089
0.084
0.110
0.115
0.104
0.102
0.114
(0.047)**(0.047)*(0.047)* (0.046)** (0.046)** (0.046)** (0.047)**(0.045)**-0.173-0.174-0.182-0.183-0.176-0.173-0.175-0.183(0.041)***(0.041)*** (0.041)*** (0.041)*** (0.040)*** (0.041)*** (0.041)*** (0.040)***-0.237-0.248-0.229-0.260-0.199-0.245-0.226-0.233(0.062)***(0.060)*** (0.062)*** (0.060)*** (0.065)*** (0.061)*** (0.062)*** (0.060)***-0.198-0.206-0.199-0.202-0.180-0.207-0.193-0.209(0.045)***(0.045)*** (0.045)*** (0.045)*** (0.045)*** (0.045)*** (0.045)*** (0.043)***
0.038
0.040
0.016
0.035
0.027
0.023
0.043
0.038
(0.063)(0.063)(0.062)(0.062)(0.062)(0.063)(0.063)(0.060)-0.015-0.013-0.024-0.021-0.026-0.022-0.016-0.005(0.045)(0.044)(0.044)(0.044)(0.043)(0.044)(0.044)(0.042)-0.001-0.001-0.001-0.001-0.001-0.001-0.001-0.001(0.001)(0.001)*(0.001)(0.001)* (0.001)*(0.001)*(0.001)(0.001)*
0.063
0.059
0.067
0.066
0.059
0.061
0.062
0.059
(0.014)***(0.014)*** (0.014)*** (0.014)*** (0.014)*** (0.014)*** (0.014)*** (0.013)***-0.059-0.064-0.071-0.068-0.052-0.064-0.054-0.057(0.030)*(0.030)** (0.030)** (0.031)** (0.030)*(0.030)** (0.030)*(0.029)**
0.229
0.222
0.224
0.218
0.261
0.220
0.226
0.224
(0.079)*** (0.074)***(0.079)*** (0.077)*** (0.077)*** (0.077)*** (0.078)*** (0.075)***0.001-0.014
0.002
0.004
0.004
0.001
0.001
0.003
(0.012)(0.011)(0.012)(0.012)(0.012)(0.012)(0.012)(0.011)
0.046
0.061
0.045
0.059
0.044
0.046
0.045
0.042
(0.016)*** (0.014)*** (0.016)***(0.015)*** (0.015)*** (0.015)*** (0.015)*** (0.015)***-0.004-0.007-0.003-0.0050.004-0.003-0.005(0.009)(0.008)(0.009)(0.009)(0.009)(0.009)(0.008)-0.022-0.031-0.022-0.024-0.017-0.017-0.023-0.024(0.015)(0.014)** (0.014)(0.014)* (0.011)(0.014)(0.014)(0.014)*-0.051-0.063-0.050-0.066-0.055-0.053-0.056-0.058(0.020)**(0.019)*** (0.020)** (0.019)***(0.020)*** (0.020)*** (0.020)*** (0.019)***-0.029-0.020-0.021-0.027-0.026-0.022-0.035-0.036(0.038)(0.035)(0.037)(0.037)(0.037)(0.038)(0.038)(0.036)-0.133-0.247-0.134-0.139-0.147-0.143-0.135-0.126(0.048)*** (0.041)*** (0.047)*** (0.048)***(0.047)*** (0.047)*** (0.048)*** (0.047)***-0.039-0.027-0.042-0.037-0.046-0.041-0.039-0.034(0.013)*** (0.013)** (0.013)*** (0.013)*** (0.013)***(0.013)*** (0.013)*** (0.013)***-0.069-0.049-0.067-0.064-0.075-0.072-0.065-0.070(0.031)**(0.030)*(0.031)** (0.031)** (0.031)**(0.031)** (0.031)**(0.031)**
0.049
0.027
0.048
0.053
0.048
0.044
0.058
0.044
(0.037)(0.033)(0.037)(0.037)(0.037)(0.037)(0.036)(0.034)
0.013
0.012
0.013
-0.000
0.016
0.011
0.014
0.015
(0.018)(0.018)(0.018)(0.014)(0.018)(0.018)(0.018)(0.018)
0.049
0.063
0.051
0.050
0.053
0.052
0.049
0.042
(0.013)*** (0.012)*** (0.013)*** (0.012)*** (0.012)***(0.012)*** (0.012)*** (0.012)***-0.063-0.047-0.066-0.064-0.054-0.057-0.066-0.063(0.029)**(0.026)*(0.029)** (0.029)** (0.029)*(0.029)*(0.029)**(0.028)**
0.037
0.034
0.038
0.036
0.035
0.028
0.034
0.038
(0.015)**(0.014)** (0.015)*** (0.015)** (0.015)**(0.013)** (0.014)**(0.014)***
0.031
0.030
0.029
0.031
0.025
0.032
0.027
0.031
(0.015)**(0.014)** (0.015)*(0.015)** (0.015)*(0.015)** (0.015)*(0.014)**
0.054
0.106
0.049
0.052
0.068
0.078
0.050
0.056
(0.034)(0.031)*** (0.033)(0.034)(0.033)** (0.033)**(0.033)(0.032)*
0.029
0.036
0.026
0.027
0.033
0.021
0.032
0.034
(0.029)(0.028)(0.029)(0.029)(0.029)(0.029)(0.029)(0.028)-0.093-0.104-0.094-0.089-0.098-0.072-0.090-0.088(0.028)*** (0.027)*** (0.028)*** (0.028)*** (0.028)*** (0.027)***(0.028)*** (0.027)***
0.075
0.034
0.075
0.064
0.089
0.070
0.059
0.077
(0.060)(0.056)(0.060)(0.061)(0.061)(0.056)(0.060)(0.058)-0.009-0.010-0.008-0.011-0.008-0.011-0.010-0.009(0.008)(0.007)(0.007)(0.008)(0.007)(0.007)(0.007)(0.007)
0.078
0.069
0.075
0.073
0.064
0.095
0.073
0.073
(0.042)*(0.039)*(0.042)*(0.042)* (0.042)(0.039)**(0.041)*(0.040)*-0.055-0.052-0.053-0.064-0.071-0.055-0.047-0.043(0.040)(0.038)(0.040)(0.040)(0.039)* (0.039)(0.040)(0.039)
0.002
0.001
0.002
0.002
0.002
0.002
0.002
0.001
(0.001)*(0.001)(0.001)*(0.001)** (0.001)** (0.001)*(0.001)**(0.001)-0.002-0.006-0.002-0.003-0.001-0.005-0.001-0.002(0.008)(0.008)(0.008)(0.008)(0.008)(0.008)(0.008)(0.008)-0.001-0.002-0.001-0.001-0.002-0.000-0.001-0.001(0.004)(0.004)(0.004)(0.004)(0.004)(0.004)(0.004)(0.004)
0.085
0.058
0.075
0.078
0.091
0.111
0.068
0.061
(0.062)(0.058)(0.061)(0.061)(0.062)(0.058)*(0.061)(0.061)-0.042-0.029-0.035-0.017-0.058-0.031-0.028-0.034(0.103)(0.100)(0.106)(0.108)(0.106)(0.100)(0.100)(0.099)
0.079
0.069
0.087
0.068
0.091
0.036
0.069
0.082
(0.048)*(0.044)(0.048)*(0.048)(0.047)* (0.044)(0.047)(0.045)*-0.015-0.016-0.015-0.015-0.015-0.016-0.013-0.014(0.010)(0.009)*(0.009)(0.009)(0.010)(0.010)*(0.009)(0.010)
0.039
0.034
0.046
0.031
0.061
0.024
0.041
0.042
(0.052)(0.050)(0.052)(0.051)(0.051)(0.052)(0.051)(0.052)-0.223-0.182-0.213-0.221-0.239-0.202-0.210-0.222(0.113)**(0.106)*(0.111)*(0.111)** (0.107)** (0.107)*(0.119)*(0.113)**-0.002-0.004-0.003-0.002-0.003-0.002-0.003-0.002(0.004)(0.004)(0.004)(0.004)(0.004)(0.004)(0.004)(0.004)-0.012-0.013-0.012-0.013-0.012-0.012-0.012-0.013(0.005)**(0.005)*** (0.005)** (0.005)*** (0.005)** (0.005)** (0.005)** (0.005)***-0.040-0.035-0.036-0.046-0.027-0.045-0.038-0.036(0.044)(0.042)(0.044)(0.043)(0.044)(0.043)(0.043)(0.043)0.002-0.005
0.004
0.011
0.002
-0.021-0.008-0.003(0.042)(0.039)(0.041)(0.041)(0.041)(0.040)(0.041)(0.041)-0.031-0.028-0.019-0.036-0.017-0.039-0.038-0.033(0.052)(0.049)(0.052)(0.052)(0.052)(0.051)(0.052)(0.052)
1022
1022
1027
1022
1022
1022
1022
1022
1077
0.288
0.19720.285
0.28080.27570.25890.276
0.28120.2704

Table 10 Probit regression with Heckman Correction
Variable Groupzenjitsusei
koritsu
hutsukasinrocourse1
sinrocourse2
gakkohyoka1
School Characteristics
gakkohyoka2
comtimesinrosido
sinromissed
kyujinbairitsu
toshibu
GPA
Academic Performancehoursstudy
chikokukesseki

Problematic Behaviors

ijimehutokotimespent_hobby
timespent_volunteer
timespent_sports
timespent_study

Time Allocation

timespent_bukatsu
timespent_naraigoto
timespent_arubaito
timespent_chores
friends_atHSfriends_beforeHS
friends_atarubaito

Friends

friends_atjyuku
friendsintouch
BFGF
hwdelayriskboldness

Personal Traits

Base Csse

Variablesbmi
naturalLH
naturalboth
male
Fincomeat15
parentsmarriedalive
motherageat18

Probit with Heckman Correction

seisyainsyusyoku

seisyainsyusyoku selection model

0.288(0.060)***0.095(0.047)**-0.173(0.041)***-0.247(0.060)***-0.212(0.044)***0.049(0.063)-0.005(0.044)-0.001(0.001)0.060(0.014)***-0.061(0.030)**0.017(0.019)-0.015(0.045)0.046(0.015)***-0.021(0.014)-0.050(0.020)**-0.020(0.038)-0.133(0.048)***-0.041(0.013)***-0.063(0.031)**
0.047(0.036)0.012(0.018)0.050(0.012)***-0.067(0.029)**0.039(0.015)***0.028(0.015)*0.053(0.034)0.024(0.029)-0.091(0.028)***0.072(0.059)-0.010(0.008)0.074(0.042)*-0.055(0.040)0.002(0.001)*-0.002(0.008)-0.001(0.004)0.073(0.062)-0.048(0.103)0.082(0.048)*-0.015(0.009)0.067(0.046)-0.013(0.004)***0.844(0.215)***0.288(0.125)**-0.421(0.113)***-0.722(0.200)***-0.544(0.124)***0.156(0.162)-0.005(0.119)-0.004(0.002)*0.163(0.036)***-0.164(0.080)**0.044(0.049)-0.039(0.121)0.130(0.041)***-0.056(0.038)-0.113(0.054)**-0.014(0.099)-0.313(0.137)**-0.105(0.036)***-0.193(0.083)**0.140(0.092)0.042(0.049)0.115(0.033)***-0.162(0.073)**0.069(0.038)*0.102(0.040)**0.106(0.090)0.093(0.079)-0.257(0.075)***0.149(0.147)-0.032(0.020)0.199(0.112)*-0.152(0.107)0.004(0.002)*-0.003(0.021)0.001(0.011)
0.144(0.160)-0.057(0.307)0.288(0.123)**-0.020(0.031)
0.245(0.138)*-0.038(0.011)***

fatherageat18
Family Backgrounds

jieisengyosyufu
fatherinactive
numsibling

Constant

0.510(0.705)athrhoObservations
Both probit model and selection equations include age dummmies
Robust standard errors in parentheses*** p<0.01, ** p<0.05, * p<0.1

0.012(0.030)-0.091(0.073)-0.007(0.023)-0.086(0.016)***-0.312(0.094)***-0.004(0.009)-0.003(0.008)-0.068(0.073)0.036(0.065)0.133(0.089)0.122(0.041)***(0.238)**1.010(0.368)***-0.190(0.315)

1027

1938

1938

Table 11 Probit Regressions on the Regular Full Time Jobs
(1)
VARIABLES
0.706(0.073)***-0.056seisyainsyusyoku*(age-18)(0.014)***
0.043
age
(0.010)***-0.211male(0.294)0.012
male*age(0.015)0.377
married(0.104)***-0.568female*married(0.047)***-0.224
Middle school graduate(0.051)***-0.155High School Graduates(0.043)***-0.014toshibu(0.035)seisyainsyusyoku2

Base specifications

Current residence

zenjitsusei
koritsu
hutsukasinrocourse1
sinrocourse2
School characteristcs
gakkohyoka1
gakkohyoka=academic career
track mixed
comtimesinrosido
sinromissed
GPA
academic performancehoursstudy
chikokukesseki
problematic behaviors at highschool

ijime
hutoko

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

(10)

(11)

(12)

(13)

(14)

(15)

(16)

seisyainatt
0.681
0.668
0.654
0.644
0.639
0.655
0.611
0.513
0.459
0.440
0.404
0.388
0.363
0.385
0.360
(0.079)***(0.081)***(0.085)***(0.088)***(0.090)***(0.088)***(0.097)***(0.084)***(0.092)***(0.094)***(0.100)***(0.103)***(0.105)***(0.106)***(0.108)***-0.057-0.056-0.056-0.056-0.057-0.059-0.049-0.022-0.020-0.019-0.018-0.018-0.017-0.019-0.013(0.015)***(0.015)***(0.015)***(0.015)***(0.015)***(0.015)***(0.016)***(0.013)* (0.014)(0.013)(0.014)(0.014)(0.014)(0.014)(0.015)
0.048
0.048
0.050
0.049
0.051
0.050
0.045
(0.011)***(0.011)***(0.011)***(0.011)***(0.011)***(0.011)***(0.012)***-0.213-0.207-0.235-0.199-0.215-0.223-0.303-0.523-0.553-0.547-0.554-0.532-0.537-0.556-0.560(0.312)(0.316)(0.305)(0.320)(0.313)(0.310)(0.281)(0.139)***(0.139)***(0.141)***(0.139)***(0.149)***(0.144)***(0.139)***(0.141)***
0.012
0.012
0.013
0.012
0.014
0.015
0.019
0.033
0.036
0.036
0.036
0.034
0.036
0.039
0.040
(0.016)(0.016)(0.016)(0.016)(0.016)(0.016)(0.016)(0.014)** (0.015)** (0.015)** (0.015)** (0.016)** (0.015)** (0.016)** (0.017)**
0.334
0.338
0.330
0.325
0.321
0.320
0.289
0.461
0.392
0.404
0.409
0.403
0.379
0.383
0.333
(0.105)***(0.106)***(0.108)***(0.110)***(0.110)***(0.109)***(0.115)** (0.122)***(0.123)***(0.123)***(0.124)***(0.127)***(0.126)***(0.121)***(0.129)***-0.568-0.570-0.566-0.570-0.576-0.571-0.560-0.604-0.598-0.602-0.600-0.603-0.601-0.596-0.570(0.049)***(0.049)***(0.050)***(0.050)***(0.049)***(0.049)***(0.052)***(0.056)***(0.059)***(0.058)***(0.058)***(0.059)***(0.058)***(0.057)***(0.061)***-0.188(0.066)***-0.141-0.138-0.114-0.096-0.092-0.097-0.093-0.095-0.100-0.098-0.079-0.069-0.069-0.072-0.069(0.044)***(0.044)***(0.045)** (0.046)** (0.046)** (0.046)** (0.048)* (0.061)(0.063)(0.063)(0.065)(0.068)(0.070)(0.068)(0.068)
0.003
0.007
0.007
-0.002
0.003
0.010
0.025
0.022
0.028
0.031
0.034
0.021
0.024
0.027
0.032
(0.036)(0.037)(0.037)(0.037)(0.037)(0.037)(0.040)(0.039)(0.041)(0.042)(0.042)(0.042)(0.042)(0.042)(0.045)
0.097
0.105
0.058
0.060
0.053
0.045
0.035
0.141
0.151
0.110
0.111
0.096
0.091
0.072
(0.055)* (0.055)* (0.062)(0.062)(0.062)(0.063)(0.065)(0.056)** (0.056)***(0.063)* (0.063)* (0.065)(0.065)(0.068)
0.035
0.033
0.019
0.005
0.013
0.013
0.030
-0.009-0.013-0.030-0.049-0.037-0.037-0.009(0.043)(0.044)(0.044)(0.046)(0.046)(0.046)(0.047)(0.050)(0.051)(0.053)(0.054)(0.054)(0.055)(0.056)-0.010-0.0080.007
0.000-0.0030.000-0.001-0.031-0.029-0.007-0.012-0.014-0.014-0.011(0.042)(0.043)(0.043)(0.044)(0.044)(0.043)(0.045)(0.048)(0.048)(0.049)(0.050)(0.051)(0.050)(0.051)
0.055
0.046
0.075
0.052
0.024
0.016
0.030
0.012
-0.0030.030
0.006-0.028-0.027-0.015(0.069)(0.069)(0.071)(0.072)(0.073)(0.072)(0.075)(0.079)(0.078)(0.081)(0.081)(0.081)(0.081)(0.084)
0.019
0.016
0.023
0.022
0.008
0.002
0.026
0.008
0.006
0.012
0.013
-0.003-0.0080.015(0.046)(0.046)(0.046)(0.047)(0.048)(0.048)(0.050)(0.049)(0.049)(0.049)(0.050)(0.050)(0.051)(0.052)-0.052-0.046-0.061-0.044-0.038-0.043-0.062-0.041-0.035-0.041-0.036-0.025-0.031-0.031(0.057)(0.058)(0.058)(0.060)(0.060)(0.060)(0.062)(0.064)(0.065)(0.066)(0.067)(0.068)(0.068)(0.070)-0.055-0.053-0.053-0.045-0.042-0.049-0.061-0.095-0.093-0.090-0.086-0.075-0.079-0.097(0.045)(0.045)(0.046)(0.046)(0.047)(0.047)(0.048)(0.048)** (0.048)* (0.049)* (0.049)* (0.051)(0.050)(0.052)*-0.001-0.001-0.001-0.001-0.001-0.001-0.001-0.001-0.001-0.001-0.001-0.001-0.001-0.001(0.001)(0.001)(0.001)(0.001)(0.001)(0.001)(0.001)(0.001)(0.001)(0.001)(0.001)(0.001)(0.001)(0.001)
0.014
0.010
0.010
0.010
0.006
0.005
0.002
0.002
-0.002-0.002-0.002-0.005-0.008-0.010(0.013)(0.013)(0.013)(0.014)(0.014)(0.014)(0.015)(0.014)(0.014)(0.014)(0.015)(0.015)(0.015)(0.016)-0.044-0.039-0.047-0.043-0.033-0.037-0.016-0.050-0.045-0.048-0.045-0.037-0.042-0.014(0.029)(0.030)(0.030)(0.030)(0.030)(0.030)(0.031)(0.034)(0.034)(0.034)(0.034)(0.035)(0.034)(0.036)
0.024
0.015
0.022
0.022
0.023
0.025
0.027
0.011
0.021
0.024
0.026
0.023
(0.014)* (0.015)(0.015)(0.015)(0.016)(0.016)(0.015)* (0.017)(0.017)(0.017)(0.017)(0.018)
0.003
0.002
0.009
0.006
0.008
0.012
0.005
0.001
0.011
0.009
0.012
0.019
(0.009)(0.009)(0.013)(0.013)(0.013)(0.013)(0.010)(0.010)(0.014)(0.014)(0.014)(0.014)-0.039-0.046-0.056-0.059-0.052-0.065-0.068-0.071-0.077-0.070(0.019)** (0.020)** (0.020)***(0.020)***(0.021)**(0.022)***(0.022)***(0.023)***(0.023)***(0.024)***-0.080-0.070-0.059-0.058-0.058-0.056-0.041-0.039-0.033-0.027(0.036)** (0.036)* (0.037)(0.037)(0.038)(0.040)(0.041)(0.041)(0.041)(0.042)-0.116-0.103-0.079-0.076-0.080-0.108-0.096-0.077-0.066-0.071(0.046)** (0.047)** (0.049)(0.049)(0.051)(0.050)** (0.052)* (0.054)(0.054)(0.056)

Table 10 Probit Regressions on the Regular Full Time Jobs (continued)
(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

VARIABLES

timespent_volunteer
timespent_sports
timespent_study
tiem allocation at highshooltimespent_bukatsu
timespent_naraigoto
timespent_arubaito
timespent_chores
friends_atHSfriends_beforeHS
friends_atarubaito
friends
friends_atjyuku
friendsintouch
BFGF
hwdelayriskboldness
Personal traitsbmi
naturalLH
naturalboth
Fincomeat15
motherdead
fatherageat18
motherageat18
jiei
sengyosyufu
fatherinactive

Observations

(10)

(11)

(12)

-0.037-0.039-0.039-0.041(0.013)*** (0.013)*** (0.013)*** (0.014)***-0.008-0.008-0.007-0.006(0.028)(0.028)(0.029)(0.030)0.001-0.008-0.013-0.016(0.030)(0.030)(0.030)(0.032)-0.010-0.010-0.009-0.011(0.017)(0.017)(0.018)(0.018)
0.019
0.016
0.015
0.021
(0.012)* (0.012)(0.012)(0.012)*
0.036
0.034
0.034
0.039
(0.026)(0.026)(0.026)(0.027)
0.040
0.038
0.037
0.040
(0.013)*** (0.014)*** (0.015)** (0.015)***-0.018-0.020-0.021-0.018(0.015)(0.015)(0.015)(0.016)
0.038
0.036
0.028
(0.032)(0.032)(0.034)
0.023
0.022
0.024
(0.028)(0.029)(0.030)-0.062-0.063-0.064(0.030)** (0.030)** (0.031)**
0.124
0.131
0.112
(0.058)** (0.058)** (0.058)*
0.008
0.008
0.008
(0.007)(0.007)(0.008)
0.086
0.079
0.067
(0.040)** (0.041)* (0.043)
0.035
0.031
(0.041)(0.042)0.001
0.002(0.001)(0.001)*-0.008-0.004(0.008)(0.008)-0.002-0.001(0.005)(0.005)-0.024-0.050(0.058)(0.058)0.106
0.126(0.132)(0.142)0.017(0.011)0.020(0.124)0.002(0.004)0.002(0.005)0.021(0.047)0.014(0.041)-0.101(0.044)**timespent_hobby

family background

(9)

(13)

(14)

(15)

(16)

-0.036(0.015)**-0.012(0.029)0.005(0.033)-0.018(0.020)0.027(0.013)**0.045(0.029)0.035(0.014)**-0.022(0.016)-0.036(0.015)**-0.014(0.030)-0.001(0.033)-0.017(0.020)0.021(0.014)0.045(0.029)0.033(0.016)**-0.026(0.017)0.066(0.036)*-0.011(0.033)-0.073(0.035)**0.102(0.063)0.009(0.008)0.094(0.046)**-0.037(0.015)**-0.015(0.030)-0.007(0.034)-0.015(0.020)0.020(0.013)0.044(0.030)0.034(0.016)**-0.026(0.017)0.061(0.037)*-0.015(0.033)-0.073(0.035)**0.110(0.063)*0.008(0.008)0.078(0.047)*0.063(0.047)0.001(0.001)-0.011(0.009)-0.007(0.006)-0.058(0.061)
0.049(0.161)-0.039(0.015)**-0.010(0.031)-0.002(0.037)-0.019(0.020)0.025(0.014)*0.046(0.031)
0.032(0.017)*-0.025(0.017)0.062(0.039)-0.011(0.035)-0.069(0.036)*0.074(0.063)0.009(0.009)0.067(0.048)0.068(0.049)0.001(0.001)-0.008(0.009)-0.005(0.006)-0.054(0.064)0.060(0.171)0.013(0.012)0.105(0.132)0.001(0.004)0.004(0.006)-0.007(0.053)0.048(0.047)-0.173(0.043)***

755

755

755

715

seisyainatt

990

935

935

935

935

935

935

882

810

755

755

755

Table 12 Maximum Likelihood Estimation of Recursive Probit Model
VARIABLES
seisyainsyusyoku
seiageage
maleage
Base sepcification for
marriedseisyainatt
femalemarried
SCHD1
SCHD2
toshibuzenjitsusei
koritsu
hutsukasinrocourse1
sinrocourse2
School Characteristics

gakkohyoka1
gakkohyoka2
comtimesinrosido
sinromissed
kyujinbairitsu
GPA

Academic performancehoursstudy
chikokukesseki

Problematic behaviors

ijimehutokotimespent_hobby
timespent_volunteer
timespent_sports
timespent_study

Time Allocation

timespent_bukatsu
timespent_naraigoto
timespent_arubaito
timespent_chores
friends_atHSfriends_beforeHS
friends_atarubaito

Friends

friends_atjyuku
friendsintouch
BFGF
hwdelayriskboldness

Personal Traitsbmi
naturalLH
naturalboth
male
Fincomeat15

Family Backgroundparentsmarriedalive
motherageat18

atanhrho_12

EQ1
EQ2
EQ3
EQ4
EQ5
EQ6
EQ7
seisyainsyu
seisyainsy
seisyainsy
seisyainsy
seisyainsy
seisyainsy
seisyainsy
seisyainatt
seisyainatt
seisyainatt
seisyainatt
seisyainatt
seisyainattsyoku
usyokuusyokuusyokuusyokuusyokuusyoku
2.615
2.546
2.160
1.952
1.697
1.726
1.805
(0.405)***(0.419)***(0.481)***(0.511)***(0.534)***(0.540)***(0.588)***-0.140-0.138-0.140-0.137-0.127-0.130-0.127(0.050)***(0.051)***(0.054)***(0.055)**(0.057)**(0.057)**(0.059)**
0.076
0.077
0.078
0.073
0.073
0.073
0.078
(0.039)*(0.039)**(0.042)*(0.044)*(0.045)(0.045)(0.047)
0.045
0.044
0.042
0.049
0.053
0.057
0.059
(0.054)(0.054)(0.058)(0.059)(0.061)(0.061)(0.064)
1.084
1.114
1.226
1.249
1.175
1.194
1.101
(0.319)***(0.323)***(0.343)***(0.354)***(0.365)***(0.368)***(0.378)***-2.389-2.431-2.597-2.661-2.656-2.647-2.550(0.353)***(0.358)***(0.373)***(0.383)***(0.391)***(0.394)***(0.407)***-0.273-0.250-0.111-0.407-0.323-0.368-0.452(0.284)(0.286)(0.301)(0.342)(0.362)(0.365)(0.419)-0.161-0.166-0.166-0.136-0.139-0.154-0.136(0.151)(0.153)(0.160)(0.166)(0.168)(0.170)(0.175)0.118-0.0440.119-0.0450.125-0.0510.088-0.0520.082-0.0540.094-0.0540.148-0.054(0.108)(0.116)(0.109)(0.116)(0.114)(0.117)(0.117)(0.118)(0.119)(0.118)(0.120)(0.118)(0.125)(0.118)
0.791
0.796
0.805
0.801
0.793
0.793
0.797
(0.202)***(0.203)***(0.208)***(0.209)***(0.209)***(0.209)***(0.209)***
0.228
0.232
0.261
0.271
0.276
0.277
0.274
(0.123)*(0.123)*(0.126)**(0.126)**(0.126)**(0.126)**(0.126)**-0.452-0.455-0.468-0.470-0.468-0.468-0.469(0.109)***(0.109)***(0.111)***(0.112)***(0.112)***(0.112)***(0.112)***-0.561-0.569-0.604-0.615-0.620-0.620-0.620(0.194)***(0.195)***(0.199)***(0.199)***(0.199)***(0.199)***(0.199)***-0.448-0.455-0.492-0.502-0.509-0.509-0.507(0.118)***(0.119)***(0.120)***(0.120)***(0.120)***(0.120)***(0.120)***
0.085
0.090
0.120
0.130
0.136
0.136
0.133
(0.157)(0.158)(0.161)(0.161)(0.161)(0.161)(0.161)-0.097-0.092-0.053-0.038-0.024-0.024-0.031(0.115)(0.115)(0.118)(0.119)(0.119)(0.119)(0.120)-0.004-0.004-0.003-0.003-0.003-0.003-0.003(0.002)*(0.002)*(0.002)*(0.002)(0.002)(0.002)(0.002)
0.127
0.129
0.139
0.141
0.143
0.143
0.142
(0.036)***(0.036)***(0.036)***(0.037)***(0.037)***(0.037)***(0.037)***-0.118-0.119-0.122-0.119-0.118-0.118-0.119(0.077)(0.077)(0.079)(0.079)(0.079)(0.079)(0.079)
0.075
0.075
0.071
0.069
0.067
0.067
0.068
(0.049)(0.049)(0.050)(0.050)(0.051)(0.051)(0.051)
0.112
0.041
0.105
0.021
0.109
0.048
0.107
0.060
0.107
0.064
0.107
0.065
0.107
(0.039)*** (0.043)(0.039)***(0.046)(0.040)***(0.048)(0.040)***(0.049)(0.040)***(0.049)(0.040)***(0.052)(0.040)***-0.037-0.002-0.038-0.014-0.0450.005-0.048-0.007-0.050-0.002-0.0500.010-0.049(0.035)(0.027)(0.036)(0.029)(0.037)(0.040)(0.037)(0.041)(0.037)(0.041)(0.037)(0.043)(0.037)-0.150-0.148-0.171-0.122-0.196-0.121-0.217-0.120-0.236-0.120-0.216-0.120(0.052)***(0.052)***(0.062)***(0.053)** (0.064)***(0.053)** (0.065)***(0.053)** (0.067)***(0.053)** (0.070)***(0.053)**-0.016-0.014-0.1610.010-0.1240.007-0.1140.008-0.0900.008-0.1200.008(0.095)(0.096)(0.111)(0.098)(0.115)(0.099)(0.116)(0.099)(0.117)(0.099)(0.121)(0.099)-0.366-0.366-0.323-0.342-0.340-0.352-0.304-0.360-0.270-0.361-0.280-0.357(0.130)***(0.130)***(0.150)** (0.135)** (0.154)** (0.135)***(0.158)* (0.135)***(0.160)* (0.135)***(0.173)(0.135)***-0.113-0.113-0.111-0.076-0.106-0.086-0.106-0.090-0.106-0.101-0.106(0.034)***(0.034)***(0.035)***(0.041)* (0.035)***(0.041)** (0.035)***(0.042)** (0.035)***(0.044)** (0.035)***-0.135-0.136-0.131-0.043-0.126-0.060-0.124-0.062-0.124-0.044-0.125(0.079)*(0.080)*(0.082)(0.092)(0.082)(0.095)(0.082)(0.096)(0.082)(0.098)(0.082)
0.086
0.087
0.096
0.024
0.097
0.010
0.100
-0.0100.100-0.0200.100(0.087)(0.088)(0.090)(0.099)(0.090)(0.103)(0.090)(0.103)(0.090)(0.106)(0.090)
0.030
0.032
0.037
-0.0570.041-0.0490.040-0.0430.040-0.0310.040(0.047)(0.047)(0.048)(0.055)(0.049)(0.057)(0.049)(0.058)(0.049)(0.060)(0.049)
0.129
0.129
0.128
0.071
0.124
0.062
0.124
0.061
0.124
0.068
0.124
(0.032)***(0.032)***(0.032)***(0.039)* (0.033)***(0.039)(0.033)***(0.040)(0.033)***(0.044)(0.033)***-0.134-0.137-0.1640.154-0.1800.144-0.1820.142-0.1820.140-0.181(0.073)*(0.074)*(0.075)** (0.083)* (0.074)** (0.084)* (0.074)** (0.085)* (0.074)** (0.087)(0.074)**
0.104
0.104
0.100
0.088
0.094
0.082
0.094
0.085
0.094
0.078
0.094
(0.037)***(0.037)***(0.038)***(0.040)** (0.038)** (0.046)* (0.038)** (0.047)* (0.038)** (0.048)(0.038)**
0.066
0.067
0.074
-0.0510.079-0.0530.079-0.0550.079-0.0360.079(0.037)*(0.037)*(0.038)* (0.045)(0.038)** (0.046)(0.038)** (0.046)(0.038)** (0.048)(0.038)**
0.139
0.139
0.132
0.128
0.163
0.124
0.144
0.124
0.108
0.124
(0.089)(0.086)(0.086)(0.089)(0.089)(0.105)(0.089)(0.106)(0.089)(0.112)
0.084
0.083
0.084
0.084
-0.0200.083-0.0280.083-0.0080.084(0.077)(0.077)(0.079)(0.079)(0.093)(0.079)(0.094)(0.079)(0.097)(0.079)-0.251-0.250-0.237-0.231-0.194-0.223-0.196-0.223-0.184-0.224(0.072)***(0.072)***(0.074)***(0.074)***(0.094)** (0.074)***(0.095)** (0.074)***(0.099)* (0.074)***
0.200
0.199
0.181
0.176
0.250
0.168
0.266
0.168
0.218
0.168
(0.143)(0.144)(0.147)(0.148)(0.188)(0.147)(0.189)(0.147)(0.191)(0.147)-0.022-0.022-0.024-0.0250.035-0.0260.033-0.0260.035-0.026(0.019)(0.019)(0.019)(0.019)(0.023)(0.019)(0.023)(0.019)(0.024)(0.019)
0.195
0.195
0.188
0.183
0.245
0.176
0.204
0.177
0.225
0.175
(0.106)*(0.107)*(0.109)*(0.110)* (0.132)* (0.110)(0.135)(0.110)(0.140)(0.110)-0.098-0.102-0.132-0.142-0.1490.151-0.1500.197-0.149(0.104)(0.105)(0.106)(0.106)(0.106)(0.128)(0.106)(0.133)(0.106)
0.004
0.004
0.004
0.004
0.004
0.002
0.004
0.003
0.003
(0.002)*(0.002)*(0.002)(0.002)(0.002)(0.003)(0.002)(0.003)(0.002)-0.011-0.011-0.008-0.007-0.006-0.038-0.006-0.026-0.006(0.020)(0.020)(0.021)(0.021)(0.021)(0.024)(0.021)(0.025)(0.021)-0.001-0.001-0.000-0.0000.000-0.0200.000-0.0130.000(0.011)(0.011)(0.011)(0.011)(0.011)(0.016)(0.011)(0.017)(0.011)
0.085
0.087
0.101
0.106
0.113
-0.2200.113-0.1890.112(0.155)(0.156)(0.160)(0.160)(0.161)(0.187)(0.160)(0.192)(0.160)-0.133-0.129-0.112-0.108-0.105-0.037-0.1050.021-0.106(0.288)(0.289)(0.293)(0.293)(0.293)(0.346)(0.293)(0.358)(0.293)-1.0000.210-0.9690.211-0.9650.224-1.0940.224-1.1260.221-1.1890.221-1.2190.220(1.323)(0.119)*(1.334)(0.119)* (1.414)(0.120)* (1.452)(0.121)* (1.487)(0.121)* (1.495)(0.121)* (1.559)(0.121)*-0.027-0.028-0.031-0.033-0.034-0.0340.035-0.034(0.024)(0.024)(0.025)(0.025)(0.025)(0.025)(0.031)(0.025)
0.145
0.145
0.150
0.153
0.153
0.153
0.040
0.152
(0.117)(0.117)(0.120)(0.120)(0.120)(0.120)(0.153)(0.120)-0.031-0.031-0.034-0.034-0.034-0.0340.003-0.034(0.010)***(0.010)***(0.010)***(0.010)***(0.010)***(0.010)***(0.014)(0.010)***seisyainatt-0.637(0.175)***-0.588(0.178)***-0.250(0.175)-2.7000.382(0.997)***(0.662)
1057
1057
.
.

-2.098(1.075)*1057.

-0.127(0.181)-0.014(0.183)-0.010(0.187)-0.070(0.231)

rho_12
Constant-2.551(0.973)***Observations
1057
R-squared.
*** p<0.01, ** p<0.05, * p<0.1
Standard errors in parentheses

0.348(0.658)
1057
.

0.395
(0.677)1057.

-1.965(1.134)*
1057
.

0.420
(0.680)1057.

-2.221(1.165)*
1057
.

0.446
(0.680)1057.

-1.684(1.258)
1057
.

0.446
(0.681)1057.

-2.592(1.527)*
1011
.

0.448
(0.680)1011.

Figure 1 Box Plot of Predicted Probaility

0.2

Pr(seisyainsyusyoku)
.4
.6
.8

1

Across GPA(=1 low to =5 high)

1

2

3

4

5

Figure 2 Box Plot of Predicted Probaility

0.2

Pr(seisyainsyusyoku)
.4
.6
.8

1

Across Bukatsu(=0 low to =4 high)

0

1

2

3

4

Figure 3 Box Plot of Predicted Probaility

0.2

Pr(seisyainsyusyoku)
.4
.6
.8

1

Across Sinrosido(=0 low to =6 high)

0

1

2

3

4

5

6

Figure 4 Box Plot of Predicted Probaility

0.2

Pr(seisyainatt)
.4
.6

.8

1

Across Seisyain-syusyoku and age

20 21 22 23 24 25 26 27

20 21 22 23 24 25 26 27

01