Discussion Papers In Economics
And BusinessLong-Term Consequences of Group Workin Japanese Public Elementary Schools
Kohei Kubota, Takahiro Ito, and Fumio Ohtake

Discussion Paper 19-11

August 2019

Graduate School of Economics
Osaka University, Toyonaka, Osaka 560-0043, JAPANLong-Term Consequences of Group Work in Japanese Public Elementary Schools *Kohei Kubota†, Takahiro Ito‡, and Fumio Ohtake§

Abstract
Using original web survey data, this study investigates the long-term consequences of theexperience of group work, which is a common teaching practice. We examined theconvention in the context of Japanese public elementary schools, which are considered tobe less susceptible to self-selection bias, in order to improve on the research conditionsof previous studies. The regression results show that the experience of group work is
negatively associated with annual income and financial assets. Furthermore, we find thatthe experience of group work does not relate to well-being and life satisfaction and thatthose who experienced group work attach higher satisfaction to human relationships andless satisfaction to household economic status. From the insignificant association between
group work and well-being/whole life satisfaction, it may be interpreted that the positive
association with satisfaction related to human relationships offsets the negative
association with satisfaction regarding one’s present economic status. We also show thatexperience of group work is negatively associated with cognitive skills but is positively
associated with altruistic and positive reciprocal behavior.
JEL classification D83, I21, I31, Z13
Keywords: Teaching practice, Annual income, Well-being, Cognitive skills, Noncognitive skills*

We thank Ryuichi Tanaka and numerous participants at the Kansai Labor Economics Workshop for theirhelpful comments. This study was supported by the Grant-in-Aid for Scientific Research A (Grant No.
26245041) and the Grant-in-Aid for Young Scientists B (Grant No. 15K17069) from the Japan Society for
the Promotion of Science and the Joint Usage/Research Center at ISER/Osaka University.
†
Corresponding author: Faculty of Commerce, Chuo University, 742-1 Higashinakano, Hachioji-shi,
Tokyo, 192-0393, Japan.
‡
Graduate School of International Cooperation Studies, Kobe University, 2–1 Rokkodai-cho, Nada-ku,
Kobe, 657-8501, Japan.
§
Graduate School of Economics, Osaka University, 1-7 Machikaneyama, Toyonaka, Osaka, 560-0043,
Japan.

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1. Introduction
New technology and automation have changed the content of jobs and the skills requiredin the workplace (see Autor, 2015; Frey and Osborne, 2017). Education in schools plays
an important role in adjusting to this rapid change in society, because cognitive and noncognitive skills are fundamental for learning. Active learning, which is believed topromote non-cognitive skills, is receiving tremendous attention. In fact, the Third Basic
Plan for the Promotion of Education1, established by the Japanese government in 2018,highlights both the need for an improvement in students’ non-cognitive skills toaccommodate the recent and future advances in artificial intelligence and robotics and theimportance of active learning in promoting subjective and interactive education in placeof cramming education. This statement is consistent with discussions in economics. For
example, Autor (2015) noted that “the interplay between machine and human
comparative advantage allows computers to substitute for workers in performing routine,
codifiable tasks while amplifying the comparative advantage of workers in supplying
problem-solving skills, adaptability, and creativity.” Problem-solving skills, adaptability,
and creativity are considered non-cognitive skills. However, it is not obvious whether
active learning is an effective practice in promoting students’ non-cognitive skills.
Furthermore, to the best of our knowledge, there is no existing study seeking to determine
the association between active learning and long-term outcomes.
Teaching practices and educational content are key instruments in school
education. Student outcomes are not only associated with educational content but alsowith teaching practices, such as the lecture method and group work. Studies on group

1

The Basic Education Promotion Plan is a plan formulated by the Japanese government to realize thephilosophy contained in the Basic Education Law and to promote educational policy.

2

work are ongoing in the fields of education and economics. In education research, manystudies focus on the correlation between group work and the attitudes or non-cognitive
skills of students.2 On the other hand, in economics research, many studies have devoted
attention to the causal impacts of group work on cognitive skills such as test scores.
This study investigates the long-term consequences of group work. The long-term
outcomes in which we are interested are annual income, financial assets, well-being, andsatisfaction. Well-being and satisfaction are better measurements in terms of representing
the comprehensive present life conditions of individuals.3 Education in schools is one of
the influential factors in developing the cognitive and non-cognitive skills of students. In
addition, these skills are associated with labor market outcomes (Heckman et al., 2006;
Lindqvist and Vestman, 2011; Deming, 2013). In this sense, school education has alifelong impact. However, few studies seek to understand the effects of group work after
graduation. We also explore channels related to the effects of group work. Furthermore,in keeping with previous papers and studies, we investigate the effects of group work on
cognitive and non-cognitive skills.
In this study, we used original web survey data collected in 2015. This data
includes several questionnaires about long-term outcomes and teaching practices. The
survey data has the advantage of evaluating the long-term consequences of group work.
Conversely, the survey data has some disadvantages. Questionnaires about teachingpractices are retrospective and questionnaires about cognitive and non-cognitive skills are2

The survey literature in education are，for example, Springer, et al. (1999), Prince (2004)，and Seidel andShavelson (2007).
3
Well-being refers to happiness in this study. Our survey asks the respondents about their level ofsatisfaction with several aspects of life. The concept of well-being is close to whole life satisfaction. The
results from regression are similar between them. The definitions of the two terms, along with the phrasingused in the questionnaire, are provided in Appendix A.

3

self-evaluated. Thus, the use of responses from retrospective and self-reported
questionnaires is always confronted with measurement errors. Although we recognize
these disadvantages, our focus is to evaluate the long-term consequences of group work,
which has not yet been addressed in the literature.
The endogeneity of teaching practices is a common empirical issue in estimating
the causal impacts of education in schools, because in many countries people can choosea school for their child to attend. Most of the existing literature seeking the causality ofgroup work employs individual fixed-effect models or school fixed-effect models toaccount for unobservable factors that could potentially associate with group work and its
outcomes (Schwerdt and Wuppermann, 2011; Van Klaveren, 2011; Algan et al., 2013;
Bietenbeck, 2014). This study uses Japanese educational conventions to mitigate thisconcern. As we will explain in section 3, Japanese public elementary schools are lesssusceptible to self-selection than previous studies have suggested.
Our primary findings are as follows: First, we endeavored to uncover the
association between the experience of group work and its long-term outcomes. The results
show that respondents who experienced group work earn 8.9% less annual income formales and 13.5% less annual income for females. Additionally, such respondents have
14.4% fewer financial assets. Further investigation into the effects of group work on
annual income indicate that the association between group work and annual income
comes from educational attainments, years of employment, and one’s choice to remain in
their hometown. Regression results for well-being and satisfaction indicate that theexperience of group work does not relate to well-being and life satisfaction and that those
who experienced group work attach higher satisfaction to human relationships and lesssatisfaction to household economic status. Next, we estimated the association between

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group work and cognitive and non-cognitive skills. We used a subjective grade evaluation
at the age of 15 to measure cognitive skills. The results show that those who experienced
group work are more likely to have earned lower grades on average. We further
investigated the distributional associations of experiencing group work and found that thenegative association is larger in the lower-grade group and insignificant in the highergrade group. Turning to the results for non-cognitive skills, we found that those who
experienced group work are more likely to exhibit altruistic and reciprocal behavior. The
results obtained by dividing the sample according to grades earned show that theassociations between group work and altruism and reciprocity are higher in the lowergrade subsample than in the higher-grade subsample.
The rest of the paper is organized as follows. Section 2 reviews the previous
literature on group work, and section 3 presents the empirical framework. Here, we
provide our empirical specifications and explain the Japanese public elementary school
system. Section 4 introduces the data, and section 5 reports the estimation results. Section
6 concludes this paper.

2. Previous Literature
Active learning has substituted for lecture-style learning, and has received increasing
attention in recent years (Prince, 2004). It is difficult to define active learning because it
includes learning through cooperation, collaboration, problem-solving, and so forth.
Group work in classroom settings can be seen as one type of active learning because it isa teaching practice that also utilizes cooperative learning, collaborative learning, andproblem-based learning.

5

Estimating the causal impact of group work is not straightforward because ofvarious confounding factors. Self-selection into a school that conducts an attractive
teaching practice could create a bias related to the estimated impact of the teachingpractice. To resolve this issue, previous literature has employed a fixed-effect modelutilizing the variation between subjects within students. Schwerdt and Wuppermann(2011) estimated the impacts of teaching practices on student test scores using the 2003
wave of the Trends in International Mathematics and Science Study (TIMSS) for students
in the U.S. in their second year of secondary education. The results showed that a 10%
shift from problem-solving to lecture-style presentation resulted in an increase in student
achievement of about 1% from the standard deviation. Van Klaveren (2011) also
estimated the effects of lecture-style teaching on test scores, obtaining information fromthe 2003 wave of the TIMSS for students in the Netherlands. In contrast to Schwerdt and
Wuppermann (2011), Van Klaveren (2011) found no relationship between the proportionof time that teachers spend on lecture-style teaching and student test results. These studies
indicate that group work has no positive effect on student test scores, and the impacts ofa given teaching style differ from country to country. To determine why this is so, it isnecessary to accumulate empirical evidence from various countries.
The above studies focused on test scores as a proxy for cognitive skills reflecting
student outcomes. Test scores can measure one aspect of cognitive skills. Bietenbeck(2014) estimated the effects of teaching styles on cognitive skills as measured by selfreported questionnaires. He also utilized the fixed-effect model and the 2007 wave of the
TIMSS for students in the U.S., showing that different teaching practices promote
different cognitive skills in students. In particular, lecture-style teaching fosters factual
knowledge and competency in routine problem-solving skills. In contrast, group work

6

promotes reasoning skills. The results of Bietenbeck (2014) suggest that an evaluation ofteaching practices depends on the outcome of the given practice.
In contrast to cognitive skills, Algan et al. (2013) focused on non-cognitive skills
as the student outcome. They employed a fixed-effect model that uses the variation
between students within schools to identify the effects of teaching styles on beliefs about
cooperation among students. They found that group work was associated with pro-social
beliefs, while lecture-style teaching was associated with the opposite.
The previous literature that we surveyed estimated the effects of teachingpractices on outcomes that were measured at a time when the teaching practice wasconducted. To the best of our knowledge, there are no studies focusing on the long-term
consequences of teaching practices. The evaluation of teaching styles differs by country
and outcomes. Furthermore, the effects of teaching styles could differ according to thelength of time that is being evaluated, namely, short-term or long-term. One contributionof this study is the estimation of the long-term effects of group work using annual income,financial wealth, well-being, and satisfaction as long-term outcomes. We also investigate
the association between group work and cognitive and non-cognitive skills. Our analysis
has an advantage over previous literature, particularly in our investigation into theassociation between group work and cognitive and non-cognitive skills and how theydiffer according to the distribution of cognitive skills.

3. Empirical Framework
3.1.Empirical Specification

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We estimated the association between the experience of group work (GWi) and
respondent outcomes (Outcomei) based on the following equation:(1)

Outcomei = α + βGWi + Xiγ + εi

where i indexes individuals, Xi is the vector of controls that were determined before
experiencing group work, and εi is an unobserved component affecting Outcomei; we
assumed that E[εi] = 0, and α, β, and γ are the parameters to be estimated. Outcomei are
long-term outcomes (annual income, amount of financial assets, well-being, andsatisfaction), cognitive skills (academic achievement at the age of 15), and non-cognitive
skills (social preferences such as altruism, reciprocity, and beliefs about trust andcooperation).4
Xi includes individual characteristics (birth cohort dummies, birth month
dummies, gender dummy, dummy for “do not remember”), family characteristics (parents’
education dummies, dummies for the number of books at home, dummies for living with
grandparents at the age of 12, and dummies for the number of siblings at the age of 15),
schoolcharacteristics(dummies

for

class

size),

community

characteristics(city/ward/county dummies at the age of 12), and teacher characteristics. We used the
responses to the following three statements to capture teaching quality: (1) There wasclassroom disorder; (2) The teacher intervened when bullying occurred; and (3) The
teacher did not use corporal punishment. The descriptive statistics of Xi are provided inTable A of Appendix B.

4

The wordings for the questions we used is presented in Appendix A.

8Note that differences in regional and generational tendencies are controlled bycommunity and birth cohort fixed effects. Since our regression controls for birth year andbirth month, the effect of instructional time is implicitly controlled. Therefore, as
discussed in section 2, the negative (positive) association between group work andacademic outcomes can be interpreted as the positive (negative) association between
lecture-style teaching and academic outcomes.
Our empirical model accounts for omitted variable bias. The omitted variableviolates the conditional mean independent assumption, E[εi | WGi] = E[εi], which is
required to estimate the causal effects of group work. The potential omitted variables inour empirical model can be divided into individual-, school-, and teacher-level variables.
First, we discuss the omitted variables at the individual level. The bias caused by selfselection into schools can be seen as an individual omitted variable issue. For example,
parents who are enthusiastic about education select high-quality schools and we observethat their children obtain high test scores. In this case, we cannot identify the effects ofeducation provided by a particular school on a child’s test score because his/her parents,
who were enthusiastic about education, provided him/her with a high-quality education.
As we will explain in the next section, self-selection is considered to be less serious in
our sample.
The bias caused by using retrospective questionnaires can also be seen in thefollowing type of individual-level omitted variable: the case of individual memory. Let
us consider the case that people with good memories are more likely to not only remember
the teaching practice that was used in elementary school but also achieve good grades andearn higher salaries later in life. In this case, the effects of group work on annual income
should exhibit an upward bias because the unobservable individual memory positively

9correlates to both the experience of group work and annual income. To resolve thisconcern, we added the dummy variable of “do not remember” to our empirical model.
Nonetheless, it should be noted that controlling for “do not remember” does not guarantee
a causal relationship because a retrospective response could still correlate to unobserved
individual characteristics.
Next, we consider the school-level omitted variable. Tanaka and Ishizaki (2017)
estimated the effects of teaching styles on student test scores. They employed a school
fixed-effect model and used Japanese micro-data. Their results showed that thecoefficients of teaching practices on test scores that used the model without a school fixed
effect are similar to those obtained using the school fixed-effect model. This suggests that
school-level unobservable variables do not correlate to teaching practices and test scores,
and therefore the school-level omitted variable bias is not considered to be subversive tothis study.
Finally, we discuss the teacher-level omitted variable. If a high-quality teacher
selects the group work teaching practice and it correlates to student outcomes, then wecannot identify the effects of group work on student outcomes. Algan et al. (2013) also
discussed the teacher-level omitted variable bias and noted the difficulty in identifying
the causal effects of group work. While they admitted the difficulty, they showed that theteacher-level omitted variable bias is not as impactful as previously thought. If a highquality teacher selects the group work method, then group work should positively
correlate to both cognitive and non-cognitive skills because of the skill of the teacher.
Algan et al. examined whether group work positively associates with reading test scoresand showed that it does not significantly associate with those particular test scores. Our

10empirical model includes proxy variables for teacher quality, which we explained above
in order to temper the impact of the teacher-level omitted variable.

3.2.Japanese Public Elementary School System
The educational contents of Japanese public schools are essentially stipulated in the
School Curriculum Guidelines of the Ministry of Education, Culture, Sports, Science, andTechnology. The contents stipulate the subjects, content, textbooks, and instructionaltime, among other elements. Therefore, it is believed that most Japanese people think thatthe same educational content is provided at every public elementary school. However,teaching practices are not stipulated in the School Curriculum Guidelines, and, in such a
circumstance, teaching practices could differ from school to school (and from teacher toteacher).5
The public elementary school in which a child is enrolled in Japan depends ontheir place of residence. However, people are less likely to move in order to select specificpublic elementary schools. There are at least four reasons for this: First, as previously
mentioned, all public elementary schools provide uniform educational content because of
the School Curriculum Guidelines, which naturally leads residents to think that the sameteaching practices and the same lessons are conducted at every public elementary school.
Second, the parents of those targeted in our survey could not access information regarding
each school’s quality. It was difficult for parents to know which public school provides
high-quality education in terms of academic achievement because school-level test scores

For example, an article by Mainichi Newspaper published on February 14, 2017 reports that “Despite pastguidelines focused mainly on the contents of each subject, new guidelines emphasize that ‘What kind of
qualities and skills should we foster’ through learning subjects and the goals are described in detail.” Thisis an indication that past guidelines did not prescribe teaching methods.
https://mainichi.jp/articles/20170215/k00/00m/040/130000c
511were not made available to the public before 2014. Third, an alternative to public
elementary schools is scarcely available for the majority of Japanese people, with the
exception of those living in big cities. The ratio of public elementary schools (= numberof public elementary schools/number of all elementary schools) was 99.3% in 1955 and98.5% in 2015. The ratio of students at public elementary schools (= number of students
at public elementary schools/number of all students at elementary schools) was 99.3% in
1955 and 98.3% in 2015. While both ratios have somewhat declined, almost all Japanese
students attend public elementary schools. Finally, it is common for parents who are
enthusiastic about education to utilize a supplementary private school or a private teacher
instead of moving to select a specific public elementary school for their child.
Research has been conducted on self-selection into Japanese public schools. 6
Although some evidence shows that people self-select in to public school, the cohorts ofthese studies are different from our sample cohort, which comprises an older cohort thanin previous studies. For instance, our youngest respondents were born in 1990 and enteredpublic elementary school in 1996. It was difficult to access the scores school-level tests
taken before 1996, as municipalities were only allowed to publish the school-level
outcomes of national tests taken after 2014, and the respondents in our sample were born

6

Asai et al. (2016) showed counter-evidence of self-selection into school. Through the use of the
Employment Structure Survey 2002, they reported that the proportion of mothers with children under six
years of age who indicated that they moved to another locations for child-rearing purposes and for theirchildren’s education was only 8.7%. This is the highest percentage reported in existing research, largely
because this figure also includes mothers who moved to another location for child-rearing purposes. In
addition, as reported in Ito et al. (2015), land values differ significantly from town to town in the Tokyo
metropolitan area, but the difference in school zones in a town does not influence land values. This implies
that people are less concerned with school zones. On the other hand, there is evidence in support of selfselection into school. Yoshida et al. (2009) showed that the introduction of the school choice program forjunior high school in Adachi ward has caused student sorting. This suggests that parents potentially want
to select a junior high school. Furthermore, Kuroda (2018) found that test scores in public elementary
schools have a positive impact on the cost of rent for family apartment housing in Matsue city.

12before 1990. The parents of respondents were therefore unable to access the school-level
national test scores.
In summary, the regression results that examine the situation in Japanese public
elementary schools and the controlling for observables is considered to suffer less fromself-selection bias, and, therefore, our empirical results show an association that is close
to causality.

4. Data
4.1.The Survey
To investigate the educational experiences of respondents and the determinants of theirpreferences and beliefs, we carried out an original survey in February 2015. The survey
was conducted online through a Japanese market research company under the authors’
direction. 7 To ensure sufficient variation among the past educational experiences of
respondents, we employed quota sampling based on age, gender, and prefecture (sevenage categories, two genders, and forty-seven regions), and obtained 18,235 survey
responses. In the analysis, we used a sampling weight so that our sample’s age-genderprefecture distribution was proportional to the actual age-gender-prefecture distribution
in Japan. The actual distribution of the Japanese population was calculated from the 2014
Basic Resident Resister.

7

The survey company is MyVoice Communications Inc. It started conducting Internet surveys in 1998 andhad more than one million registered survey panelists as of November 2013. It has a rigorous data quality
control system that obtains highly reliable data. For instance, all registrants are strictly checked byexamining their registration information, and about one-fourth of new registrants are eliminated beforehand
because of inconsistent responses or double registrations. In addition, the survey panelists are regularly
monitored, and the number of surveys in which one panelist can participate is controlled (average frequency
in one year is about 13).

13In our empirical analysis, we restricted the sample to graduates of Japanese public
elementary schools and Japanese public junior high schools to manage the self-selection
issue. Our empirical specifications used resident information at the city/ward/county levelat the age of 12. A sample of missing resident information was excluded for analysis.
After the restriction, the sample used in the analysis consisted of 15,429 respondents.

4.2.Group Work
We used the following statement to measure individual experiences of group work:“There was a task in which students worked together as a group.” The answers for thisstatement are “Yes,” “No,” and “Do not remember.” The dummy variable for group worktakes the value one if respondents chose “Yes” and takes zero if otherwise. We shouldnote that our questionnaire does not assess detailed aspects of the group work reported byrespondents, such as the size of the work groups or the frequency and duration with whichgroups met. Furthermore, as discussed in the section 3.1, bias can arise in the completionof retrospective questionnaires. Therefore, measurement of group work per se could
correlate with unobserved factors.
We found that there were regional variations in the distribution of respondents
reporting group work, which can be seen in Figure 1. While group work revealed
variations in terms of birth cohort and region, our empirical results presented theconsequences of group work after eliminating the birth cohort and regional effects, as ourregression controlled for birth cohort and city/ward/county level residence at the age of
12.
Table 1 presents the proportions of respondents of a certain age who experienced
group work by gender. We found that younger respondents were more likely to have14

experienced group work and that the proportion of males who experienced group work
was lower than females for all age range. The proportion of males who answered “Yes”
is 66.4% and the proportion of females is 73.5%, making for a gender difference of 7.1%.8
This seems to be an imbalance of treatment. However, the imbalance can be explained by
the gender difference in those who answered with “Do not remember.” The last column
reports the fractions in which the numerators are “Yes” and the denominators are “Yes +No.” The proportion of males is 81.6% and that for females it is 88.1%. The genderdifference is 6.5%, suggesting that the part of 9.5% gender imbalance between those who
experienced group work can be accounted for by the gender difference in those who
indicated that they “Do not remember.” Furthermore, Table 1 shows that “Do notremember” responses not only differ by gender but also by age. The proportion of males
between the ages of 25 to 29 who answered with “Do not remember” is much higher thanfemales of the same age range, while the proportion of males in the age ranges of 35 to
39, 40 to 44, and 50 to 54 are at the same level as the females of those age ranges.
Controlling for both gender and age difference further reduces the gender imbalance ofgroup work. This is an indication that a great part of the imbalance between thoseanswered with “Do not remember” is due to differences between both age and gender.
Nonetheless, an unexplained gender imbalance remains. We resolved this imbalance of

8

The result of testing the difference is provided in Column (1) in Table B of Appendix B. The dependent
variable in Table B is a dummy variable that takes one if the respondent answers “Yes” and takes zero if
he/she answers “No” or “Do not remember.” The controls are age dummies and city/ward/county dummies.
Column (1) in Table B reports the results of the linear probability model, indicating that males are 6.8%
less likely to answer “Yes” than females.

15treatment by adding various controls and the dummy of “Do not remember” to ourregressions.9

4.3.Outcomes
We identified three major outcomes. The first were long-term outcomes: namely, annual
income, financial assets, well-being, and satisfaction. The second were cognitive skills
measured by means of retrospective answers about academic grades in the third grade ofjunior high school (eight grade in the American education system). The third were noncognitive skills measured using self-reported answers about altruism, trust, reciprocity,and cooperation. The descriptive statistics of the outcomes are shown in Table 2.10

4.3.1. Long-Term Outcomes
Our long-term outcomes can be divided into present economic status and wellbeing/satisfaction. Economic status outcomes consist of annual income and financial
assets. The analysis for annual income used a sample that was split according to gender,
since there are distinct gender differences in the labor market.
We compared economic statuses in our data with those in national representative
data to check for consistency. The average male annual income in our data was 5,250(thousand yen) which is similar to the 5,210 (thousand yen) that was reported as theaverage male salary by the Statistical Survey of Actual Status for Salary in the Private

9

The primary findings in this study do not change if we exclude the sample of those who are in the age
ranges 25–29 and 55–59. The gender difference between those who answered with “Do not remember” isoutstanding in these age ranges.
10Turning to the control variables, we found that females, younger people, and people who have parents
with more years of schooling were more likely to experience group work from the mean comparison
between “Yes” and “No.”16

Sector 2015. The regular employment rate for men according to our data was 86%, whilethat reported by the Labor Force Survey 2015 was 77%. One reason for the difference
might be that the definition of regular employment rate as used by the Labor Force Survey
does not include executives, self-employed workers, and family-employed workers while
our definition does. The annual income for working males who did not experience group
work is 754,000 (yen) higher than for working males who experienced group work. Thisdifference is significant at a level of 1%. Working females who did not experience group
work also had a higher annual income, by 275,000 (yen), than their group work-familiar
counterparts. This difference was also significant at a level of 1 percent level. As we haveshown, one’s experience of group work differs by region and cohort. These are nonnegligible confounders that bring the different results between simple mean comparison
tests and regression with controls for region and cohort variables. We will present
regression results in a later section.
The mean amount of financial assets for households of two or more persons in ourdata was 1,219 (thousand yen). On the basis of the National Survey of Family Income andExpenditure 2014 (NSFIE), the national representative data showed an amount of
1,565,000 (yen). The mean amount of financial assets for our data was lower than that of
the NSFIE. This difference might owe to our data targeting a sample range from the agesof 25 to 59, while the NSFIE did not restrict the sample by age. Thus, the exclusion ofolder people from the sample who would have more financial assets than younger people
might account for the difference between our data and that of the NSFIE. The financial
assets for those who did not experience group work is 3,595,000 (yen) higher than for
those who experienced group work. This difference is significant at a level of 1%.

17

The degree of well-being was measured on a scale from 1 to 11 with the following
question: “Overall, to what degree are you currently feeling happy? Using a scale from
1–11 where 1 is very unhappy and 11 is very happy, how do you rate your current levelof happiness?” We used eight questions about life satisfaction, covering the following
aspects: (1) Whole life; (2) Present economic status; (3) Relationship with family; (4)Relationship with spouse; (5) Relationship with friends; (6) Living area; (7) Leisure life;and (8) Work. The answers were measured on a scale of “1. Not satisfied” to “5. Satisfied.”
The distributions of satisfaction are provided in Figure 2. The distributions of present
economic status and work skewed right more than other satisfactions, indicating that alarge portion of respondents were not satisfied with their economic status and work. Table
2 reports that the differences in satisfaction with whole life between those who
experienced group work and those who did not are not significant while the differencesin satisfaction with present economic status, relationship with family, relationship withspouse, relationship with friends, and work are significant.

4.3.2. Cognitive Skills
Our measurements for cognitive skills reflected the responses to the retrospective
question about academic grades earned in the third grade of junior high school. We asked
questions about grades in six areas in the survey: (1) Language; (2) Social studies; (3)Mathematics; (4) Science; (5) English (foreign language); and (6) Average of all subjects.
The answers are measured on a scale from “1. In the lower rank,” “2. Rather low,” “3. In
the middle,” “4. Rather high,” to “5. In the higher rank.” The distribution of these isshown in Figure 3, indicating that all the variables are skew left. This suggest that themeasure seems to have a cognitive bias. This bias is consistent with the above-average

18effect (see Svenson, 1981; Dunning et al., 1989), which is one of cognitive bias that leads
people to overestimate their positive qualities and underestimate their weaknesses. 11 In
addition to these variables, we constructed a new variable that consisted of the averageof the five main subjects. We called the composite variable Main5. Table 2 reveals thatthe academic grades of those who did not experience group work are higher than thegrades of those who did experience group work, except in language and English classes.12

4.3.3. Non-Cognitive Skills
Our interests in terms of non-cognitive skills were altruism, trust, positive reciprocity,
negative reciprocity, and two cooperation variables. These were measured by means ofself-reported survey questionnaires. Employing a questionnaire to measure preferences is
supported by Falk et al. (2016), who showed that a questionnaire can succeed in
predicting the preferences revealed in incentivized choice experiments. The variables are
self-reported answers and are measured on a scale of “1. Strongly disagree,” “2. Rather
disagree,” “3. Neither,” “4. Rather agree,” and “5. Strongly agree.” The statement on
altruism was as follows: “I feel happy when I do a good deed that I think is for others(such as picking up trash in a park).” We used following statement for trust: “In general,
you can trust most people.” The distributions of altruism and trust are provided in Figure
4. More than half (55%) of the respondents answered that they agreed with the altruism
statement, while 34% of respondents answered that they agreed with the trust statement.

11

If cognitive bias equalizes the bias for all respondents regarding direction and size, cognitive bias will beof no consequence to OLS regression.
12
The standard deviation and coefficient of variation are provided in Table C of Appendix B. The tableshows that grade inequality in the group of those who experienced group work was larger for all variables.
The results imply that conducting group work could widen the inequality of academic achievements.

19

We used the positive and negative reciprocity variables proposed by Perugini etal. (2003). Positive reciprocity consisted of the averaged answers to the following threequestions: “If someone does me a favor, I am prepared to return it”; “I go out of my way
to help somebody who has been kind to me before”; and “I am ready to undergo personal
costs to help someone who has helped me before.” Negative reciprocity consists of theaveraged answers to the following three questions: (1) “If somebody offends me, I will
offend him/her back”; (2) “If somebody puts me in a difficult position, I will do the same
to him/her”; and (3) “If I suffer a serious wrong, I will take revenge as soon as possible,
no matter the cost.”
The statements on cooperation in the questionnaires were as follows: “Working
as a group results in greater achievements than working individually (cooperation
outcome)” and “I am more satisfied when I achieve a goal by cooperating with others
than if I do so only by myself (cooperation satisfaction).” Figure 4 shows the distributionof cooperation variables, revealing that 43% of respondents agreed with the statement oncooperation outcomes and that 46% of respondents agreed with that of cooperation
satisfaction. Table 2 shows that people who experience group work are more likely to
exhibit altruistic and reciprocal behavior and feel satisfaction with cooperation.

5. Empirical Results
5.1.Long-Term Outcomes
5.1.1. Annual Income and Financial Assets
The estimation results for the basic specification in Equation (1) using annual incomes
and financial assets are reported in Table 3. We estimated the model using ordinary leastsquares (OLS) and adjusted the sampling weight to make our observations proportional20

to the overall Japanese population distribution. The numbers in parentheses are HuberWhite robust standard errors. The first and second columns of Table 3 provide results
using a log of the annual income for men and women, respectively. For men, thecoefficient of group work is −0.089 and significant at a 5% level. This means that people
who did not experience group work earned 8.9% more than those who did.
For female regression, we employed the control function approach to address the
sample selection issue regarding employment decisions.13 Our exclusion variable was adummy variable that selected one if the respondent’s mother worked at the respondent’s
age of twelve and zero if otherwise. This variable is considered to correlate with work
decisions but not with one’s marginal productivity in the labor market. In the first stage,
we regress using the working dummy on the exclusion variable, and, in the second stage,
we regress log annual income on the group work dummy, controls, maternal work status
at age 12, and a third-order polynomial of fitted probability estimated in the first stage.14
The second column reports that the coefficient of group work is −0.135 and is significant
at a 5% level. The negative association between annual income and the experience ofgroup work is larger for women than for men.
The third and fourth columns display results using a log of financial assets for theentire sample, restricting the portion of the sample who were not living with their parents.

The control function approach is surveyed in Wooldridge (2015). Heckman’s two-step estimator is the
typical approach for dealing with sample selection bias. However, Heckman’s two-step estimator, whichtakes decisions regarding participation in the labor market into account, is often criticized for being
sensitive to the functional form assumption. The control function estimator is also a two-step estimator.
The first step is a semiparametric estimator of the selection parameters using exclusion variables and thesecond step utilizes a least squares regression on independent variables and approximating functions of firststage parameters. The difference between Heckman’s two-step estimator and the control function estimator
lie in the first-stage parameters that are estimated through a distribution-free method rather than by a probit
model and in the use of a non-parametric approximation in the second-stage regression rather than the
inverse Mills’ ratio. The main results do not change if we employ Heckman’s two-step estimator.
14
The main results do not change if we employ the second or forth order polynomial function.
13

21

The questionnaire about financial assets inquired into the entire household’s financial
assets. It is difficult to separate a respondent’s financial assets from that of his/her parents
if he/she lives with them. Therefore, we reported not only the results for the entire sample
but also the results related to the restriction of the portion of the sample who were notliving with their parents. The results showed that the amount of financial assets for people
who have experienced group work was lower by 14.4% when using the whole sample
and was lower by 15.6% when using the sample of people not living with their parents.
In summary, the experience of group work in public elementary schools was negatively
associated with long-term economic consequences. Next, we briefly analyze and discuss
the paths of the effect of group work on economic outcomes.

5.1.2. Why Does the Experience of Group Work Negatively Associate with Annual
Income?
The negative association between group work and annual income implies that theexperience of group work also associates with various subsequent decisions relating toannual income, such as years of education. The experience of group work could have an
impact on a person’s schooling as it is connected to academic achievement and, as a result,annual income. We cannot examine all possible paths relating to annual income. To
disentangle the mechanism of the negative association between group work and annual
income, we estimated equation (2), which included three variables, Zi, related to annual
income. Zi is the vector of the dummies for respondent educational attainments, thenumber of years working in the present company, and for the choice to remain in one’s
hometown; one was selected if the place of residence from the ages of 15 to 18 was thesame as the place of present residence and zero if otherwise.

22

(2)

Outcomei = α2 + β2GWi + Xiγ2+ Ziδ + εi

The experience of group work could be associated with income through the variables, Zi.
To estimate the extent to which the inclusion of these variables changed the coefficientof group work, β2, we determined that the factors explained the coefficient of group work
based on the estimation results of equation (2).
Table 4 shows the estimation results of Equation (2). Panel A provides the results
for men and Panel B reports the results for women. Column (1) in Panel A shows that thecoefficient of group work was −0.080, as estimated from equation (1). Column (2) reportsthat the coefficient of group work was −0.066, representing the estimate when
additionally controlling for a respondent’s education. Adding the dummy for education
lowered the negative association between annual income and the experience of group
work by 17.5%, which was accounted for by education. In the same manner, controlling
for the number of years at the present company and remaining in one’s hometown also
lowers the negative association by 31.3% and 18.8%, respectively. Finally, column (5)provides the results for controlling all factors. The coefficient of group work was −0.029,
which is statistically insignificant at a level of 10%. These results indicate that theexperience of group work associates with educational attainment, the number of years at
the present company, and decisions regarding places of residence; these three factors areable to explain 63.8% of the negative association between the experience of group workand annual income.
Next, we turn to the results for women in Panel B. The portion of the coefficientof group work explained by education was 18.2%, which is at the same level as the result

23

for men. However, the portion of the coefficients of group work explained by the numberof years at the present company and the number of years living in one’s hometown was
19.5% and 2.6%, respectively. Those magnitudes are smaller than the male results.
Nonetheless, the negative associations between the experience of group work and annual
income became insignificant after controlling for the three factors for both the male andfemale results.
The results in Table 4 imply that the experience of group work is associated withyears of education, the number of years working at the present company, and decisions
related to one’s place of residence. To validate our results, we estimated that the models
for those dependent variables are the following four variables: the dummy variable forliving with one’s parents, the public employee dummy, the self-employed dummy, andthe company executive dummy. The results are summarized in Table 5. 15 The control
variables are the same as those of Table 3. The male results showed that the experienceof group work was negatively associated with the number of years working at one’s
present company and being a public employee, while it was positively associated withremaining in one’s hometown, being self-employed, and being a company executive. The
female results differ from the male results. For women, the experience of group work was
negatively associated with years of schooling.
Men who experience group work might elect to continue living in their hometown
and are more likely to work in the public sector of their hometown or succeed the familybusiness from their parents. The shorter duration of working years might represent achange in employment made for the purpose of returning to one’s hometown. These are

15

Table 5 provides results by employing the linear probability models from OLS. These are similar to theresults employing the logit or probit model.

24consistent with the results in the succeeding subsections, which show that those who
experienced group work feel satisfaction in their relationship with family and friends andare more likely to exhibit altruistic and reciprocal behavior. The experience of group work
encourages altruistic and reciprocal behavior, and, as the results suggest, those who
experience group work might elect to live in their hometown.
We introduce the interpretations of the gender difference in the coefficients ofgroup work on years of schooling. First, the gender difference might reflect theassociation between the experience of group work and altruistic, family-oriented, or local
preferences. Altruistic females are more likely to be nursery teachers, nurses, or care
workers. These jobs are favored by altruistic people and do not require college oruniversity degrees. Second, females who have such altruistic, family-oriented, or local
preferences might prefer to engage in housework, volunteer activities, or activities for the
local community. These activities do not need any of the skills or knowledge that are
acquired at college or university. As a result, females who experience group work are lesslikely to go on to college or university.”

5.1.3. Well-Being and Satisfaction
The estimation results using well-being and satisfaction as outcome variables are reported
in Table 6.16 The dependent variables were standardized in the regression analysis. We
found that the experience of group work does not statistically associate with well-being
and whole life satisfaction. Turning to several aspects of life satisfaction, the experienceof group work was negatively correlated with satisfaction in one’s present economic

16

The main results do not change if we estimate the ordered logit model.

25

status, while it was positively correlated with satisfaction in relationships with one’s
family and friends. The negative correlation is compatible with the results of the negative
association between the experience of group work and economic outcomes. From theinsignificant association between group work and well-being/whole life satisfaction, it
may be interpreted that the positive association with satisfaction related to humanrelationships offsets the negative association with satisfaction regarding one’s present
economic status.

5.2.Cognitive Skills
We found that there was a negative association between group work and years ofeducation, as shown in Table 5. This might be a result of group work lowering cognitive
skills. As mentioned in section 2, this interpretation is consistent with the evidence
provided in the previous literature that group work does not increase a student’s testscores. This evidence implies that group work is negatively associated with years ofeducation through the decline of cognitive skills. This section examines this prediction
and provides an analysis related to the association between the experience of group workand cognitive skills.
Our measurements of cognitive skills were derived from the responses provided
to the item on the self-reported questionnaire that asked respondents to list their gradesfor each subject in the third grade of junior high school. We restricted the sample torespondents who attended public elementary schools and public junior high schools.

26

The estimation results are provided in Table 7.17 The coefficient of group workon Main5 was −0.076 and significance was at a level of 5%. When examining the resultsfor each subject, we found that associations between the experience of group work andgrades differed according to different subjects. We found a statistically significant
negative association between the experience of group work and Mathematics, Science,and English, and found an insignificant negative association for Language and Social
Studies. It can be interpreted that the association may be strong for subjects where anunderstanding of the content is required at the elementary school level. The final row inTable 7 also shows the significant negative association between the experience of group
work and All.
The results of Table 7 are consistent with previous studies have shown that group
work does not have a positive effect on academic achievement. This might be due to thefact that group work sacrifices learning about a broad range of topics for learning deeply
about a specific topic, a method that devotes far more time to a single topic than in lecturestyle teaching. Indeed, there is evidence showing that lecture-style teaching has positiveeffects on student academic achievement. This might result from the fact that lecturestyle teaching can convey a greater amount of text book content to students. Thus, we
speculate that group work teaching has a stronger association with non-cognitive skills
while lecture-style teaching is more strongly associated with cognitive skills.
Since instructional time in Japanese public elementary school is restricted by the
School Curriculum Guidelines, an increase in group work teaching might correspond toa decrease in lecture-type, one-to-one teaching. Schools and teachers, then, are faced with

While we did not report the coefficients of the dummy for “do not remember,” the coefficients arenegative and significant for all grades. The dependent variables were standardized in the regression analysis.
The main results do not change if we estimate the ordered logit model.
1727

a trade-off between group work teaching and lecture-style teaching. For example, if
schools or teachers place a great deal of importance on the cultivation of students’ noncognitive skills, they would select group work teaching while sacrificing their cognitive
skills. Conversely, if schools emphasize improvement in academic achievement, theywould select lecture-style teaching while sacrificing their non-cognitive skills. In cases
wherein the outcome is academic achievement, the negative effects of group work can beseen as being equal to the positive effects of lecture-style teaching. Therefore, under the
trade-off situation, it is hard to identify the effects of group work when the instructionaltime of lecture-style is fixed. In our results in Table 7, the associations between group
work and long-run outcomes include the possibility that less lecture-style teaching resultsin fewer years of education and other long-run outcomes.
Next, we examined whether the association between the experience of group workand grades change according to the distribution of grades. We employed the quantile
regression model to assess the distributional association, and the quantiles were 10%,25%, 50%, 75%, and 90%. Since the variation in grade variables totaled five except for
Main5, we estimated the model by the following two steps: In the first step, we obtained
the residuals by regressing Likert scale variables on the independent variables in Table 3
without group work and a dummy for “do not remember” by OLS. In the second step, weestimated the quantile regression models by using the dependent variables that wereresiduals obtained in the first step and by setting the independent variables as group workand a dummy for “do not remember.”
The results of the quantile regression are provided in Table 8. We reported only
the coefficients of group work and robust standard errors in parentheses. We found thatthe coefficients of group work in the 10% quantile were the smallest of all quantiles for28

all outcomes. The coefficient of group work on Main5 in the 10% quantile was −0.175
and the significance level was at 5%, while the coefficient in the 90% quantile was −0.026
and insignificant. Examining the results using All, the coefficient of group work in the
10% quantile was −0.244 and the significance level was at 1%; the coefficient in the 90%
quantile was −0.003 and insignificant. Similar results were obtained for Social Studies,
Mathematics, and Science. These results imply that the negative association between theexperience of group work and academic achievement is more significant for the students
who are ranked lower in the grade, while the negative association is not observed for the
students who are ranked the highest. It can be interpreted that unmotivated students
underperform in group work. The students with lower grades underperform and, as aresult, their grades will continue to deteriorate. At the very least, it can be seen that
positive peer effects on academic achievement are not observed in group work. 18
Furthermore, this implies that employing group work could widen the inequality ofacademic achievements.

5.3.Non-Cognitive Skills
Algan et al. (2013) showed that the experience of group work promotes cooperation
among students. We found a positive association between group work and satisfaction
with human relationships. These results imply that group work is associated with noncognitive skills. This section reexamines this relationship.
Our non-cognitive skill measures were altruism, trust, positive reciprocity,
negative reciprocity, cooperation outcomes, and cooperation satisfaction. The regression

18

This can be interpreted as a negative peer effect in classroom. Epple and Romano (2011) and Sacerdote(2011) survey peer effects in education.

29

results are reported in Table 9.19 We found that respondents who experienced group work
were more likely to be altruistic, positive, and reciprocal. We also found that respondents
who experienced group work were more likely to be satisfied with cooperation and, in
contrast, the coefficient of the cooperation outcome was not significant. If we regard
cooperation outcome as an academic outcome and regard cooperation satisfaction as asatisfaction with human relationships, this contrasting result is consistent with our results:(1) group work negatively associates with grades; and (2) group work positively
associates with outcomes relating to human relationships.
We found a negative association between the experience of group work andcognitive skills in the lower-grade group. Does the association between the experience ofgroup work and non-cognitive skills differ according to respondent academic
achievements? Table 10 shows how the estimation results are split according to
respondent grades (Main5). We split our sample into a quartile subsample using Main5.20
The positive association between the experience of group work and positive reciprocity
was the largest in the lowest quartile subsample. The coefficient of group work on
altruism in the below 25 percentile subsample was 0.167, while the coefficient of group
work in the above 75 percentile subsample was −0.053. The coefficient of group work on
positive reciprocity in the first quartile was 0.324, while that in the fourth quartile was
0.169. Examining the results of cooperation satisfaction, the coefficient of group work in
the fourth quartile was positive and significant at 5% level, while that in the first quartile
was negative and insignificant. This result implies that higher-grade respondents actively

19

The dependent variables were standardized in the regression analysis. The main results do not change if
we estimate the ordered logit model.
20
The number of observations for quartile subsample are 6,022 (1st quartile), 2,074 (2nd quartile), 3,959(3rd quartile), and 3,371 (4th quartile). This is because the distribution of Main5 may have some spikes.
The distribution is provided in Figure A of Appendix B.

30

joined group work and were therefore satisfied with cooperation outcomes. On the other
hand, lower-grade respondents might have been more likely to underperform duringgroup work and did not actively join in the work.

6. Conclusion
This article studies the long-term consequences of experiencing group work at public
elementary schools. To the best of our knowledge, this is the first study to employ longterm outcomes such as annual income, financial assets, well-being, and satisfaction in
evaluating the association of group work. The regression results showed that theexperience of group work has pros and cons. The positive aspect of group work is its
cultivation of altruistic and reciprocal behavior. On the other hand, its negative aspect isthe negative association between the experience of group work and long-term economic
outcomes such as annual income and financial assets.
We mention some reasons for caution in using our results and some challenges
for future research. First, our results did not present a causal relationship between group
work experience and student outcomes. The assumption of no-self-selection into public
elementary school cannot be tested by data. Identifying rigorous causality is a task for
future research. Second, this article did not seek the mechanism of association between
group work and student outcomes. Why does group work negatively associate withcognitive ability? One interpretation is that group work teaching crowds out lecture-type
teaching that significantly improves students’ cognitive abilities. Negative peer effects,
which result from interactions between students, can also be interpreted. Previous papers
have discussed the importance of peer effects in education, but a straightforward31

attribution of impact on the part of peer effects cannot be made because of the selfselection and reflection problem as discussed in Manski (1993). Interactions withunmotivated students could have a negative impact on other students. It will beworthwhile to clarify the mechanism of negative associations between group work andcognitive skills in future research. Finally, our study did not tend to the content andimplementation time of group work. In reality, these differ according to school, teacher,grade, and subject. Further research is required to clarify the conditions for the effective
implementation of group work.

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34

(%)
0.75
0.70
0.65
0.60

Whole sample

0

400km

Figure 1. Regional Distribution of Those Who Experienced Group Work
Note: The figures in Figure 1 show the proportions of those who answered “Yes” to thefollowing statement, “There was a task in which students worked together as a group.”

35

Figure 2. Distribution of Satisfaction
Note: These measurements of life satisfaction reflect the responses provided to thefollowing question, as applied to various aspects of life: “How satisfied are you with eachof the following?”: (1) Whole life; (2) Present economic status; (3) Relationship withfamily; (4) Relationship with spouse; (5) Relationship with friends; (6) Living area; (7)Leisure life; and (8) Work. The answers were measured on a scale of “1. Not satisfied”
to “5. Satisfied.”36Figure 3. Distribution of Academic Achievement in the Third Grade of Junior HighSchoolNote: These measurements of cognitive skills reflect the responses provided to a
retrospective question about academic grades earned in the third grade of junior highschool. The subjects are (1) Language; (2) Social studies; (3) Mathematics; (4) Science;(5) English; and (6) Average of all subjects. The answers were measured according to thefollowing scale: “1. In the lower rank,” “2. Rather low,” “3. In the middle,” “4. Rather
high,” and “5. In the higher rank.”37

Figure 4. Distribution of Non-Cognitive Skills
Note: These measurements of non-cognitive skills reflect the responses provided to thefollowing statements: 1) Altruism is: “I feel happy when I do a good deed that I think isbeneficial for others (such as picking up trash in a park)”; 2) Trust is: “In general, you
can trust most people”; 3) Coop_outcome is: “Working as a group results in greaterachievements than working individually”; 4) Coop_satisfaction is: “I am more satisfied
when I achieve a goal by cooperating with others than if I do so only by myself.” Theanswers were measured according to the following scale: “1. Strongly disagree,” “2.
Rather disagree,” “3. Neither,” “4. Rather agree,” and “5. Strongly agree.”38

Table 1. Distribution of Group Work by Birth Year
Birth year
Panel A. Male
−1959
1960−64
1965−69
1970−74
1975−79
1980−84
1985−
Total
Panel B. Female−1959
1960−64
1965−69
1970−74
1975−79
1980−84
1985−
Total

Obs.

Yes

Group workNo
Do not remember

1,166
1,171
1,209
1,202
1,156
1,112
463
7,479

0.574
0.667
0.639
0.641
0.671
0.739
0.743
0.664

0.194
0.147
0.173
0.148
0.154
0.112
0.102
0.149

0.232
0.186
0.188
0.211
0.175
0.148
0.154
0.187

0.747
0.819
0.787
0.813
0.814
0.868
0.879
0.816

1,101
1,224
1,282
1,241
1,249
1,163
687
7,947

0.660
0.696
0.721
0.693
0.721
0.814
0.890
0.735

0.152
0.121
0.110
0.096
0.103
0.067
0.031
0.099

0.188
0.182
0.169
0.211
0.176
0.120
0.079
0.166

0.813
0.852
0.867
0.878
0.875
0.924
0.966
0.881

39

Yes/(Yes + No)

Table 2. Descriptive Statistics of OutcomesObs.
Sample of working people
Male
Annual income (ten thousand yen)
Years of schoolingYears working for present company
Remaining in hometown
Public employee dummy
Self-employed dummy
Company executive dummy
FemaleAnnual income (ten thousand yen)
Years of schoolingYears working for present company
Remaining in hometown
Public employee dummy
Self-employed dummy
Company executive dummy
Whole sample
Financial assets (ten thousand yen)
Whole sample
Two or more persons in household
Not living with parents
Well-being and Satisfaction
Well-being
Satisfaction with
Whole life
Present economic status
Relationship with family
Relationship with spouse
Relationship with friends
Living area
Leisure life
Work

Group workNo
Do notrememberYes − No

Mean

Yes

6,552 525.0
7,285 14.74
6,459 13.89
7,479 0.49
6,459 0.10
6,459 0.11
6,459 0.04

511.8
14.71
13.26
0.50
0.09
0.12
0.05

587.2
14.92
15.58
0.46
0.15
0.09
0.03

521.6
14.69
14.83
0.49
0.10
0.10
0.03

−75.4
−0.21
−2.32
0.03
−0.06
0.02
0.02

[11.01]***[0.079]***[0.370]***[0.017]*[0.011]***[0.011]**[0.008]**5,243 241.7
7,761 13.86
4,862 8.51
7,947 0.56
4,862 0.06
4,862 0.13
4,862 0.02

239.7
13.86
8.41
0.56
0.06
0.12
0.02

267.2
14.05
9.27
0.57
0.07
0.12
0.02

234.4
13.76
8.48
0.57
0.06
0.16
0.01

−27.5
−0.19
−0.85
−0.01
0.00
0.00
0.00

[10.09]***[0.077]**[0.376]**[0.019][0.012][0.015][0.007]

9,704 1,219 1186.2 1545.7
8,154 1,266 1227.6 1590.2
7,661 1,127 1084.9 1453.9

1093.91162.61036.4-359.5 [64.46]***-362.6 [70.83]***-369.0 [66.31]***

15,426

7.33

7.40

7.36

7.06

0.04 [0.050]

15,426
15,426
14,494
11,101
15,426
15,426
15,426
13,378

3.29
2.73
3.58
3.58
3.30
3.45
3.37
3.03

3.31
2.71
3.62
3.63
3.33
3.48
3.39
3.03

3.33
2.85
3.53
3.46
3.27
3.45
3.36
3.08

3.20
2.75
3.47
3.45
3.18
3.36
3.30
2.98

-0.02
-0.14
0.09
0.17
0.06
0.02
0.03
-0.05

40

[0.027][0.030]***[0.026]***[0.032]***[0.024]**[0.027][0.025][0.028]*

Table 2. Descriptive Statistics of Outcomes (continued)
Obs.

Group work
Mean YesNo

Do notrememberYes − No

Whole sample
Cognitive skills
Language
3.44
0.03 [0.027]
15,426 3.61 3.65 3.62
Social studies
3.31 −0.16 [0.031]***
15,426 3.43 3.44 3.59
Mathematics
3.33 −0.15 [0.029]***15,426 3.46 3.46 3.61
Science3.38 −0.06 [0.029]**
15,426 3.52 3.54 3.60
English (foreign language) 15,426 3.35 3.38 3.42
3.22 −0.04 [0.032]
Main5
16.68 −0.37 [0.124]***
15,426 17.38 17.48 17.85
All
3.41 −0.09 [0.028]***15,426 3.54 3.55 3.65
Non-cognitive skills
Altruism
3.40
0.13 [0.020]***15,426 3.52 3.56 3.43
Trust
3.03
0.00 [0.021]
15,426 3.07 3.08 3.08
Positive reciprocity
3.62
0.13 [0.014]***
15,426 3.78 3.84 3.71
Negative reciprocity
2.71
0.00 [0.020]
15,426 2.69 2.68 2.69
Cooperation outcome
3.34 −0.01 [0.019]
15,426 3.42 3.44 3.45
Cooperation satisfaction
3.39
0.09 [0.022]***
15,426 3.48 3.51 3.42
Note: Standard errors of the test of mean difference between “Yes" and " No” are reported
in bracket. ***, **, and * indicate statistical significance at levels of 1%, 5%, and 10%
respectively.

41

Table 3. Annual Income and Financial Assets
Dependent variable

Log of annual income
Male
FemaleLog of financial assets
Whole
Not livingwith parents

(1)
(2)
(3)
(4)
Group work
−0.089** −0.135**−0.144*** −0.157**[0.040][0.066][0.055][0.061]
Number of observations
6,552
5,243
9,704
7,661
Adjusted R-squared
0.165
0.114
0.165
0.195
Note: For columns (1), (3), and (4), the estimates are calculated by OLS with other controls.
Column (2) is calculated by the control function approach. The exclusion variable is a dummy
variable that selects one if the respondent’s mother worked at the respondent’s age of twelve andzero if otherwise. The control variables are individual characteristics (birth cohort dummies, birth
month dummies, dummy for “do not remember”), family characteristics (parents’ education
dummies, dummies for number of books at home, dummies for living with grandparents at the ageof 12, and dummies for number of siblings at the age of 15), school characteristics (dummies forclass size), community characteristics (city/ward/county dummies at the age of 12), and teacher
characteristics (dummies for classroom disorder, bullying interventions, and corporal punishment).
We include the gender dummy in columns (3) and (4). We adjust the sampling weight to ensurethat our observations are proportional to the overall Japanese population distribution. Numbers inbrackets are Huber-White robust standard errors. ***, **, and * indicate statistical significance atlevels of 1%, 5%, and 10% respectively.

42

Table 4. Why Does the Experience of Group Work Negatively Associate with Annual Income?
(1)
Panel A. MaleGroup work
Dummies for education
Dummies for years working for present company
Dummy for remaining in hometown
Fraction of the coefficient of GW explained
Number of observations
Adjusted R-squared

(2)

(3)

−0.080** −0.066* −0.055*[0.037] [0.035] [0.033]
✓
✓

5,895
0.174

Panel B. Female
Group work

17.5%
5,895
0.221

31.3%
5,895
0.291

(4)

(5)

−0.065*[0.037]−0.029[0.031]
✓
✓
✓
63.8%
5,895
0.351

✓
18.8%
5,895
0.193

−0.154** −0.126* −0.124* −0.150** −0.098[0.070] [0.070] [0.066] [0.070] [0.066]✓✓
Dummies for education✓✓
Dummies for years working for present company✓✓
Dummy for remaining in hometown
Fraction of the coefficient of GW explained
18.2%
19.5%
2.6%
37.0%
Number of observations
4,282
4,282
4,282
4,282
4,282
Adjusted R-squared
0.137
0.160
0.275
0.140
0.294
Note: Dependent variables are logs of annual income in each column. Panel A is calculated by
OLS with other controls. Panel B is calculated by the control function approach with other controls.
The control variables are individual characteristics (birth cohort dummies, birth month dummies,
dummy for “do not remember”), family characteristics (parents’ education dummies, dummies fornumber of books at home, dummies for living with grandparents at the age of 12, and dummies
for number of siblings at the age of 15), school characteristics (dummies for class size), community
characteristics (city/ward/county dummies at the age of 12), and teacher characteristics (dummies
for classroom disorder, bullying interventions, and corporal punishment). We adjust the samplingweight to ensure that our observations are proportional to the overall Japanese population
distribution. Numbers in brackets are Huber-White robust standard errors. ***, **, and * indicate
statistical significance at levels of 1%, 5%, and 10% respectively.

43

Table 5. Other Outcomes Related to Annual Income
Dependent variables
Panel A. Male
Years of schoolingYears working for present job
Dummy for remaining in hometown
Public employee dummy
Self-employed dummy
Company executive dummy

Coefficient

Robust S.E.

Num. obs.

Adj-R2Mean−0.191−1.0820.077−0.0700.051
0.020[0.129][0.467]**[0.027]***[0.017]***[0.015]***[0.009]**

5,821
5,895
5,895
5,895
5,895
5,895

0.219
0.375
0.160
0.086
0.102
0.068

14.71
14.68
0.44
0.14
0.11
0.05

Panel B. Female
Years of schoolingYears working for present job−0.480 [0.136]***
4,222
0.299 13.84−0.531 [0.585]
4,282
0.211
9.09
Dummy for remaining in hometown−0.046 [0.039]
4,282
0.183
0.58
Public employee dummy−0.026 [0.016]
4,282
0.114
0.08
Self-employed dummy
0.034 [0.026]
4,282
0.120
0.12
Company executive dummy
0.000 [0.009]
4,282
0.026
0.02
Note: The table reports the estimates from separate regressions in each outcome. Panel A is
calculated by OLS with other controls. Panel B is calculated by the control function approach withother controls except for years of schooling. The control variables are individual characteristics(birth cohort dummies, birth month dummies, dummy for “do not remember”), family
characteristics (parents’ education dummies, dummies for number of books at home, dummies forliving with grandparents at the age of 12, and dummies for number of siblings at the age of 15),
school characteristics (dummies for class size), community characteristics (city/ward/county
dummies at the age of 12), and teacher characteristics (dummies for classroom disorder, bullying
interventions, and corporal punishment). We adjust the sampling weight to ensure that out ourobservations are proportional to the overall Japanese population distribution. Numbers in brackets
are Huber-White robust standard errors. ***, **, and * indicate statistical significance at levels of
1%, 5%, and 10% respectively.

44

Table 6. Well-being and Satisfaction
Dependent variables
Coefficient Robust S.E. Num. obs. Adj-R2
Well-being−0.017 [0.035]
15,426
0.106
Satisfaction with
Whole life
−0.049 [0.035]
15,426
0.084
Present economic status−0.120 [0.036]***
15,426
0.072
Relationship with family
0.071 [0.036]**
14,4940.074
Relationship with spouse
0.128 [0.041]***
11,101
0.096
Relationship with friends
0.032 [0.035]
15,4260.087
Living area
0.019 [0.034]
15,426
0.072
Leisure life
0.015 [0.034]
15,4260.067
Work−0.042 [0.039]
13,3780.064
Note: The table reports OLS estimates from separate regressions of well-being or satisfaction ongroup work. The control variables are individual characteristics (birth cohort dummies, birth
month dummies, gender dummy, dummy for “do not remember”), family characteristics (parents’
education dummies, dummies for number of books at home, dummies for living with grandparents
at the age of 12, and dummies for number of siblings at the age of 15), school characteristics(dummies for class size), community characteristics (city/ward/county dummies at the age of 12),and teacher characteristics (dummies for classroom disorder, bullying interventions, and corporalpunishment). We include the gender dummy in columns (3) and (4). We adjust the sampling weight
to ensure that out our observations are proportional to the overall Japanese population distribution.
Numbers in brackets are Huber-White robust standard errors. ***, **, and * indicate statisticalsignificance at levels of 1%, 5%, and 10% respectively.

45

Table 7. Subjective Academic Achievement in Junior High School
Dependent variables Coefficient Robust S.E. Num. obs. Adj-R2
Main5−0.076 [0.031]**
15,4260.245
Language−0.012 [0.032]
15,4260.248
Social studies−0.043 [0.032]
15,4260.229
Mathematics−0.095 [0.031]***
15,426
0.197
Science−0.105 [0.033]***
15,426
0.213
English−0.059 [0.033]*
15,4260.204
All−0.080 [0.031]**
15,4260.229
Note: The table reports OLS estimates from separate regressions of the well-being or satisfaction
on group work. The control variables are individual characteristics (birth cohort dummies, birth
month dummies, gender dummy, dummy for “do not remember”), family characteristics (parents’
education dummies, dummies for number of books at home, dummies for living with grandparents
at the age of 12, and dummies for number of siblings at the age of 15), school characteristics(dummies for class size), community characteristics (city/ward/county dummies at the age of 12),and teacher characteristics (dummies for classroom disorder, bullying interventions, and corporalpunishment). We include the gender dummy in columns (3) and (4). We adjust the sampling weight
to ensure that out our observations are proportional to the overall Japanese population distribution.
Numbers in brackets are Huber-White robust standard errors. ***, **, and * indicate statisticalsignificance at levels of 1%, 5%, and 10% respectively.

46

Table 8. The Distributional Associations between Group Work and Academic Achievement
Percentile
Dependent variables
10
25
50
75
90
Main5
−0.175**−0.109***−0.062*−0.022−0.026[0.074][0.040][0.036][0.040][0.053]
Language−0.093−0.015−0.0020.000
0.033[0.056][0.045][0.035][0.045][0.045]Social studies−0.150**−0.083**−0.0300.025
0.015[0.059][0.037][0.033][0.044][0.045]Mathematics−0.188***−0.188***−0.082**
0.023
0.022
[0.070][0.047][0.034][0.035][0.037]
Science−0.245***−0.165***−0.091**−0.0300.015[0.053][0.039][0.041][0.039][0.048]English−0.109*−0.094**−0.070*−0.0170.015[0.066][0.048][0.039][0.037][0.042]All−0.244***−0.155***−0.061*
0.013−0.003[0.059][0.054][0.032][0.048][0.045]
Note: The table reports quantile regression estimates from separate regressions of academic gradeson group work. The number of observations in each regression is 15,426. The dependent variables
are the residuals of regressing each academic achievement variable on the independent variables
in Table 6 without a group work variable and the dummy for “do not remember” by OLS. We
adjusted the sampling weight to make our observations proportional to the overall Japanese
population distribution. Numbers in parentheses are Huber-White robust standard errors.

47

Table 9. Non-Cognitive Skills
Dependent variables
Coef. Robust S.E. Obs. Adj-R2
Altruism
0.154 [0.040]*** 15,426
0.161
Trust
0.027 [0.035]
15,4260.160
Positive reciprocity
0.214 [0.040]*** 15,426
0.189
Negative reciprocity
0.009 [0.035]
15,4260.159
Cooperation outcome
0.019 [0.037]
15,4260.140
Cooperation satisfaction 0.123 [0.036]*** 15,426
0.150
Note: The table reports OLS estimates from separate regressions of the non-cognitive skills ongroup work. The control variables are individual characteristics (birth cohort dummies, birth
month dummies, gender dummy, dummy for “do not remember”), family characteristics (parents’
education dummies, dummies for number of books at home, dummies for living with grandparents
at the age of 12, and dummies for number of siblings at the age of 15), school characteristics(dummies for class size), community characteristics (city/ward/county dummies at the age of 12),and teacher characteristics (dummies for classroom disorder, bullying interventions, and corporalpunishment). We include the gender dummy in columns (3) and (4). We adjust the sampling weight
to ensure that out our observations are proportional to the overall Japanese population distribution.
Numbers in brackets are Huber-White robust standard errors. ***, **, and * indicate statisticalsignificance at levels of 1%, 5%, and 10% respectively.

48

Table 10. Non-Cognitive Skills Split by Academic Achievement
Panel A. Main5 is less than p25 (Obs. = 6,022)
Altruism
Trust
Positive reciprocity
Negative reciprocity
Cooperation outcome
Cooperation satisfaction
Panel B. Main5 is p25−p50 (Obs. = 2,074)
Altruism
Trust

Coef.

Robust S.E.

0.167
0.018
0.324
−0.0440.072
0.068[0.070]**[0.056][0.070]***[0.054][0.062][0.065]0.292 [0.108]***−0.117 [0.116]

Positive reciprocity
0.238 [0.132]*
Negative reciprocity
0.005 [0.122]
Cooperation outcome−0.026 [0.111]
Cooperation satisfaction
0.053 [0.127]
Panel C. Main5 is p50−p75 (Obs. = 3,959)
Altruism
0.099 [0.085]
Trust
0.018 [0.076]
Positive reciprocity
0.044 [0.089]
Negative reciprocity
0.115 [0.078]
Cooperation outcome−0.076 [0.084]
Cooperation satisfaction
0.058 [0.079]
Panel D. Main5 is above p75 (Obs. = 3,371)
Altruism−0.053 [0.080]
Trust
0.134 [0.086]
Positive reciprocity
0.169 [0.084]**
Negative reciprocity
0.045 [0.078]
Cooperation outcome
0.001 [0.085]
Cooperation satisfaction
0.173 [0.078]**
Note: The table reports OLS estimates from separate regressions of the non-cognitive skills ongroup work. The control variables are individual characteristics (birth cohort dummies, birth
month dummies, gender dummy, dummy for “do not remember”), family characteristics (parents’
education dummies, dummies for number of books at home, dummies for living with grandparents
at the age of 12, and dummies for number of siblings at the age of 15), school characteristics(dummies for class size), community characteristics (city/ward/county dummies at the age of 12),and teacher characteristics (dummies for classroom disorder, bullying interventions, and corporalpunishment). We include the gender dummy in columns (3) and (4). We adjust the sampling weight
to ensure that out our observations are proportional to the overall Japanese population distribution.
Numbers in brackets are Huber-White robust standard errors. ***, **, and * indicate statistical49

significance at levels of 1%, 5%, and 10% respectively. The number of observations for quartile
subsamples are 6,022 (1st quartile), 2,074 (2nd quartile), 3,959 (3rd quartile), and 3,371 (4thquartile). This is because the distribution of main5 has some spikes. The distribution is provided
in Figure B of Appendix B.

50

Appendix A: Wording of Questionnaire
Q1. This item gauges part of your experience at elementary school. Please respond to eachstatement.
・ There was a task in which students worked together as a group. (1. Yes, 2. No, 3. I do notremember)・ Sometimes, there was no classroom lesson due to a teachers’ strike. (1. Yes, 2. No, 3. I do notremember)・ There was classroom disorder. (1. Yes, 2. No, 3. I do not remember)・ The teacher intervened when bullying occurred. (1. Yes, 2. No, 3. I do not remember)・ The teacher did not use corporal punishment. (1. Yes, 2. No, 3. I do not remember)
Q2. How many students in your class were there when you were in 6th grade at elementary
school?
1. 1−5 persons
2. 6−10 persons
3. 11−15 persons
4. 16−20 persons
5. 21−25 persons
6. 26−30 persons
7. 31−35 persons
8. 36−40 persons
9. 41−45 persons
10. 46−50 persons
11. more than 51 persons

Q3. How many books were there in your house during your childhood (comics and magazines
excluded)?
1. More than 100
2. 50 to less than 99
3. 10 to less than 49
4. 1 to less than 9
5. zero6. I do not remember

Q4. How satisfied are you with each of the following?
SatisfiedUnsatisfied

Your whole life

○

○

○

○

○

The place you live in

○

○

○

○

○

Your leisure activities

○

○

○

○

○

51

The current financial situationof your household
Relationships with your friends

○

○

○

○

○

○

○

○

○

○

Work

○

○

○

○

○

○

Do not work

Relationship with your spouse
Relationship with your family
member(s) except for your
spouse

○

○

○

○

○

○

Do not have spouse

○

○

○

○

○

○

Do not have any family
except for spouseQ5. Overall, how happy would you say you are currently? Using a scale from 0–10 where “10”is “very happy” and “0” is “very unhappy,” how would you rate your current level of happiness?
Very happy
Very Unhappy
10

9

8

7

6

5

4

3

2

1 0

Q6. When you were fifteen years old, where did your grades rank among others in your grade?
In lower
In ratherIn theIn the rather
In higher
lank
lower rank
middlehigher rank
lank
Average of All
○
○
○
○
○
Subjects
Language○○○○○
Mathematics

○

○

○

○

○

Science

○

○

○

○

○

Social Studies

○

○

○

○

○

English

○

○

○

○

○

Q7. How many brothers and sisters did you have when you were 15 years old?
・ Older brothers
・ Older sisters
・ Younger brothers
・ Younger sisters

Q8. Please indicate the highest level of education (or equivalent) completed by you.
1. Completed some grades from elementary school / junior high school
2. Dropped out of high school
3. Completed some grades from high school
4. Junior college – no degree
5. Completed some grades from junior college52

6. College – no degree
7. Graduated from college
8. Masters’ course – no degree
9. Masters’ degree
10. Doctoral course – no degree
11. Doctoral degree
12. I do not like to answer.

Q9. Approximately how much was your annual earned income before taxes in 2014 (including
business income if you are self-employed)?
1. less than 1 million yen
2. 1 million yen to less than 2 million yen
3. 2 million yen to less than 3 million yen
4. 3 million yen to less than 4 million yen
5. 4 million yen to less than 5 million yen
6. 5 million yen to less than 6 million yen
7. 6 million yen to less than 7 million yen
8. 7 million yen to less than 8 million yen
9. 8 million yen to less than 9 million yen
10. 9 million yen to less than 10 million yen
11. 10 million yen to less than 11 million yen
12. 11 million yen to less than 12 million yen
13. 12 million yen to less than 13 million yen
14. 13 million yen to less than 14 million yen
15. more than 14 million yen
16. I do not like to answer

Q10. Approximately how much is the balance of financial assets (savings, stocks, bonds,
insurance, etc.) of your entire household? (If you are a student, please indicate the balance offinancial assets of your parents' entire household.)
1. less than 2.5 million yen
2. 2.5 million yen to less than 5 million yen
3. 5 million yen to less than 7.5 million yen
4. 7.5 million yen to less than 10 million yen
5. 10 million yen to less than 15 million yen
6. 15 million yen to less than 20 million yen
7. 20 million yen to less than 30 million yen
8. 30 million yen to less than 50 million yen
9. 50 million yen to less than 100 million yen
10. more than 100 million yen
11. I do not like to answer/I do not remember53

Q11. Please answer if you work. What is your type of employment?
1. Company employee/Organization staff
2. Government employee
3. Businessman/Director
4. Self-employee
5. Family business employee (in self-employed business)

Q12. Please answer if you work. How many years have you been working for your present
company?
1. Less than a year
2. A year to less than 5 years
3. 5 years to less than 10 years
4. 10 years to less than 20 years
5. 20 years to less than 30 years
6. 30 years to less than 40 years
7. More than 40 years

54Appendix B Figures and Tables
2500

Frequency

2000

1500

1000

500

0
1

2

3

4

5

6

7

8

9 10 11 12 13 14 15 16 17 18 19 20 21

Main5

Figure A. Distribution of Subjective Academic Achievement Averages in Junior High School(Main5)55

Table A. Descriptive Statistics
Male dummy
Father's education
Junior high school
High school
Junior college/technical junior college
Bachelor’s degree
Master’s degree
Doctor of philosophy
Do not want to answer/Do not remember
Mother's education
Junior high school
High school
Junior college/technical junior college
Bachelor’s degree
Master’s degree
Doctor of philosophy
Do not want to answer/Do not remember
Number of books
1−9
10−49
50−99
More than 100
Nothing
Do not remember
Number of siblings
Class size at public elementary school
Teacher characteristics(1) There was classroom chaos
Yes
Do not remember(2) Teacher intervened with bullying
Yes
Do not remember(3) Teacher used corporal punishment
Yes
Do not remember

56

Mean S.D. Min
0.485 0.50 0.00

Max
1.00

0.23
0.36
0.02
0.17
0.01
0.00
0.20

0.42
0.48
0.14
0.38
0.09
0.07
0.40

0.00
0.00
0.00
0.00
0.00
0.00
0.00

1.00
1.00
1.00
1.00
1.00
1.00
1.00

0.22
0.45
0.07
0.05
0.00
0.00
0.20

0.41
0.50
0.26
0.23
0.04
0.03
0.40

0.00
0.00
0.00
0.00
0.00
0.00
0.00

1.00
1.00
1.00
1.00
1.00
1.00
1.00

0.15
0.40
0.12
0.15
0.02
0.15
1.43
35.81

0.36
0.49
0.33
0.36
0.13
0.36
1.08
7.58

0.00 1.00
0.00 1.00
0.00 1.00
0.00 1.00
0.00 1.00
0.00 1.00
0.00 10.00
3.00 53.00

0.19 0.39 0.00
0.08 0.28 0.00

1.00
1.00

0.29 0.45 0.00
0.38 0.48 0.00

1.00
1.00

0.64 0.48 0.00
0.10 0.30 0.00

1.00
1.00

Table B. The Determinants of Group Work Experience
(1)
Coef.
−0.068−0.037−0.126−0.148−0.136−0.137−0.191

Robust S.E.
[0.010]***[0.020]*[0.021]***[0.021]***[0.021]***[0.021]***[0.021]***(2)
Coef. Robust S.E.

Male dummy
Age 30−34 dummy−0.061 [0.025]**
Age 35−39 dummy−0.162 [0.026]***
Age 40−44 dummy−0.186 [0.026]***
Age 45−49 dummy−0.152 [0.025]***
Age 50−54 dummy−0.188 [0.026]***
Age 55−59 dummy−0.212 [0.026]***
Male dummy × Age 25−29 dummy−0.125 [0.031]***
Male dummy × Age 30−34 dummy−0.074 [0.025]***
Male dummy × Age 35−39 dummy−0.052 [0.027]*
Male dummy × Age 40−44 dummy−0.048 [0.027]*
Male dummy × Age 45−49 dummy−0.093 [0.026]***
Male dummy × Age 50−54 dummy−0.023 [0.027]
Male dummy × Age 55−59 dummy−0.082 [0.027]***
City/Ward/County dummies
Yes
Yes
Observations
15,426
15,426
Adjusted R−squared
0.072
0.073
Note: All estimations are implemented by OLS. We adjusted the sampling weight to ensure thatout our observations were proportional to the overall Japanese population distribution. Numbersin brackets are Huber-White robust standard errors. ***, **, and * indicate statistical significanceat levels of 1%, 5%, and 10% respectively.

57

Table C. Inequality of Academic Achievement by Group Work

Panel A. Standard deviation
Main5
LanguageSocial studies
Mathematics
ScienceEnglish (foreign language)All
Panel B. Coefficient of variation
Main5
LanguageSocial studies
Mathematics
ScienceEnglish (foreign language)
All

Group work
Yes − No Do not remember

Yes

No

5.041
1.112
1.165
1.282
1.184
1.287
1.124

4.742
1.067
1.079
1.185
1.115
1.219
1.047

0.299
0.045
0.086
0.097
0.068
0.068
0.077

4.968
1.090
1.115
1.240
1.158
1.248
1.103

0.288
0.304
0.329
0.373
0.342
0.381
0.316

0.266
0.295
0.299
0.330
0.309
0.356
0.287

0.023
0.010
0.029
0.043
0.033
0.025
0.029

0.298
0.316
0.330
0.375
0.347
0.388
0.323

58