Discussion Paper Series
CDP 15/20
Month-of-Birth Effects on Skills and Skill Formation

Shintaro Yamaguchi, Hirotake Ito and Makiko Nakamuro

Centre for Research and Analysis of Migration
Department of Economics, University College London
Drayton House, 30 Gordon Street, London WC1H 0AXw w w .c re a m -m i grati on. or gMonth-of-Birth Effects on Skills andSkill Formation∗
Shintaro Yamaguchi†

Hirotake Ito‡

Makiko Nakamuro§

July 11, 2020

Abstract
We estimate month-of-birth effects on cognitive and noncognitive skills,
as well as factors relevant to skill formation. Our estimates indicate thatyounger students in a given grade cohort have lower cognitive and noncognitive skills. To shed light on the underlying mechanisms, we also examine
children’s time use and interpersonal relationships. We find evidence of compensatory investment in cognitive skills by younger children, however, they
are also more likely to have poor-quality relationships with their classmates
and teachers, which may harm the long-term development of noncognitive
skills. Our findings on skill formation have implications for efficiency andequity.

Keywords: relative age effect, cognitive skill, non-cognitive skill
JEL Codes: I210, I240, J24∗
We thank an anonymous province for providing data and research fund. We gratefully acknowledge financial support from MEXT KAKENHI (16H02022) and JSPS KAKENHI (20H01510) andadministrative support for Research Institute of Economy, Trade and Industry. We thank seminar participants at various universities and conferences for their comments and especially Daiji
Kawaguchi and Michihito Ando for their invaluable comments on our draft.
†
Corresponding author. Faculty of Economics, the University of Tokyo. Hongo 7-3-1, Bunkyoku, Tokyo 113-0033, Japan. Email: syamaguchi@e.u-tokyo.ac.jp.
‡
Graduate School of Media and Governance, Keio University, 5322 Endo, Fujisawa-shi, Kanagawa 252-0882 Japan, Email: itouhrtk@keio.ac.jp.
§ Graduate School of Media and Governance, Keio University, 5322 Endo, Fujisawa-shi, Kanagawa 252-0882 Japan, Email: makikon@sfc.keio.ac.jp.

11Introduction

Month of birth affects one’s well-being in not only school, but also in adulthoodas it influences relative age in a grade cohort. Many papers have documented that
relatively younger students in a given cohort perform more poorly in academic tests(Bedard and Dhuey (2006)), playing sports (Helsen et al. (2005)), and other school
activities (Dhuey and Lipscomb (2008)). In addition, some studies have reported
that month of birth affects long-term outcomes including university enrollment (Bedard and Dhuey (2006)), labor earnings (Kawaguchi (2011)), and top management(Du et al. (2012)) and political positions (Muller and Page (2016)).
While the negative consequences of being among the youngest in class havebeen well documented, what remains less known is how children and their parents respond to the negative birth month shocks beyond delaying school enrollment(known as redshirting). Do parents of younger students in a cohort make a compensatory investment? How do younger students use their time outside of school?
Teachers and peer students can also influence the skill formation of younger students. Do teachers provide younger students with the required support? Do school
peers help younger students or bully them instead? These questions are important for understanding children’s skill formation and how skill gaps by birth monthchange over time.
The objectives of this paper are to estimate the effects of month of birth on
cognitive and noncognitive skills as well as high school quality, and to examine
factors that are likely to influence children’s skill formation. More specifically, we
estimate the month-of-birth gaps in time use, prep school attendance, participationin sports, and the quality of relationships with teachers and peer students.
Our analysis is based on longitudinal data of all fourth to ninth graders in publicschools in an anonymous province1 in Japan in 2015-2018. The data include overone million student-year observations. The timing of school enrollment in Japanis strictly determined by birth date, which differs from many countries, such as theUSA, but is similar to institutions in England, Norway, and Iceland. Because red1 The actual administrative term used by the government is prefecture , but we use province as it

is more intuitive for most readers.

2shirting is virtually impossible in Japan, only the month of birth determines whether
a student is one of the youngest in his/her grade cohort. We estimate the monthof-birth effects by assuming that the month of birth is exogenous. We validate thisassumption by showing that family socioeconomic status (SES) and birth outcomessuch as birth weight are largely uncorrelated with the month of birth.
Our estimation results indicate that the month of birth affects both cognitive andnoncognitive skills, and hence, the quality of high school. The youngest students
in grade four have test scores about 0.35 standard deviations lower than those of
the oldest students; however, these gaps in test score decrease to 0.13-0.18 by gradenine. Findings for noncognitive skills such as conscientiousness are particularly interesting. Consistent with existing psychological studies on adolescence, noncognitive skills tend to decrease with grade in our data. However, the youngest students
in a given grade have noncognitive skills about 0.1 standard deviations lower thanthe oldest students in the same grade. Because noncognitive skills do not improvewith grade, the gap between the oldest and youngest students in the same grade suggests that relative age influences the formation of noncognitive skills. In addition,
this month-of-birth gap in noncognitive skills remains nearly constant over time,
which is in contrast with the dynamics of the cognitive skill gap.
We then examine how younger students and their parents respond to their disadvantages, and find that the youngest students in the ninth grade work 0.3 more
hours per week outside of school than the oldest students. They also read formore hours and are more likely to attend a prep school. Overall, younger students and their parents make compensatory investment in cognitive skills; however,they spend 0.2 fewer hours per week on sports, arts, and music, which may lead tolower noncognitive skills, in light of the findings of psychological studies (Ishihara
et al. (2018); Moreno et al. (2011)). Another important factor that may influencechildren’s noncognitive skills is interpersonal relationships. Our estimates indicatethat the youngest students suffer about 0.06 standard deviations poorer relationships
with their teachers and 0.09-0.12 standard deviations poorer relationships with theirschool peers, which may lead to lower noncognitive skills for younger children(Leary and Baumeister (2000); Reitz et al. (2016); Wu et al. (2018)). Poor interpersonal relationships may lead to persistent month-of-birth effects on noncognitive3

skills.
Our finding regarding compensatory skill investment has implications for efficiency and equity. Younger children and their parents seem to decide on skill
investment through comparisons with exactly the same grade cohort; however, they
will be compared with more diverse individuals when they enter the labor market.
Compensatory skill investment may be individually optimal given their environment, but it may be suboptimal in the long run. The existing school cutoff date may
distort students’ and their parents’ skill investment behavior. Our finding is also
relevant for equity. Even though month-of-birth gaps may disappear in the longrun, the cost of catching up with peers may be paid by younger students and theirparents through their compensatory investment. Policies mitigating month-of-birtheffects could correct the distortion in skill investment and improve equity.
Our results also provide a possible explanation for why month of birth has longterm consequences in some countries. For example, Kawaguchi (2011) finds thatthe youngest men in a grade cohort in Japan earn 4% less income when aged 30-34
years. Although month-of-birth effects on cognitive skills may disappear becauseof maturation and compensatory skill investment, less sports and music experience,as well as poorer interpersonal relationships with teachers and peers, may harm thelong-term development of noncognitive skills, which might account for the lower
earnings seen in adulthood. Although we do not observe noncognitive skills after
grade nine in our data, this hypothesis is consistent with findings by Chetty et al.
(2011), Heckman et al. (2013), and Baker et al. (2019), who find that interventions
during childhood affect noncognitive skills in adulthood.
The rest of the paper is structured as follows. In Section 2, we outline theempirical strategy to identify month-of-birth effects and validate our identifying
assumptions. In Section 3, we describe our data. In Section 4, we estimate monthof-birth effects on skills and high school quality. In Section 5, we estimate monthof-birth gaps in time use and interpersonal relationships. In Section 6, we discuss
the results in the context of the literature. We conclude in Section 7. The appendix
includes an extended literature review, details of the data, and additional analysis.

4

2

Empirical Strategy

2.1

Econometric Model

The school year in Japan runs from April to March. Children who have their sixth
birthday on or before April 2 enter the first grade of elementary school of thatyear, which implies that the youngest children in a given grade were born in Marchand the oldest in April.2 This law on school entry is strictly enforced, and hence,delaying school entry or redshirting is exceptionally rare. Because grade retention
is also extremely uncommon, the timing of school entry and grade promotion in
Japan is therefore completely determined by birth date. These rules are not quite
common among OECD countries; however, England, Iceland, and Norway also
have a school entry rule similar to that in Japan (Bedard and Dhuey, 2006).
We estimate gaps between the oldest and youngest students within the same
grade in several outcomes using the following econometric model, which is estimated separately for each grade,Yit = β0 + β1 AGEit + β2 AGEit2 + β3 Xit + εit ,(1)

where Yit is an outcome for student i in year t, AGEit is age at test measured inmonths, Xit is a vector of control variables, including gender, number of books athome as a proxy for SES, year, and school fixed effects, and εit is an error term.
The variable AGEit is normalized so that the youngest students in a given grade
have the value of 0 while the oldest students have the value of 11. Using the estimated model, we then calculate the estimated gap between the oldest and youngest
students, which is given byc = β̂1 · 11 + β̂2 · 112 .
∆Y(2)

Crawford et al. (2014) argue that the gap is influenced by three factors. First, it
2 Strictly speaking, students born on April 1 are the youngest and those born on April 2 are theoldest. Our data include the month of birth, not the exact date of birth, and hence, we refer to Aprilborn students as the oldest. It is true that this group includes those born on April 1, who are actually
the youngest, but we believe this classification error is negligible.

5captures the absolute age or age-at-test effect. Because all of our outcomes aremeasured in April, students born in different months take tests at different absoluteages. Second, this parameter also captures the effect of age at school entry. Learning at school may not be effective if children are too young to study at school, andhence, age at school entry may have consequences for student outcomes at an older
age. These two factors cannot be disentangled because age at test is the sum of theschool starting age and the length of schooling (i.e., perfect collinearity). The third
factor is relative age. In theory, we can identify a relative-age effect separately fromthe effects of absolute and school starting age. In practice, they are highly correlated, and hence, we do not attempt to identify the relative age effect separately.
The identifying assumption is conditional mean independence, i.e., E(εit |Ageit , Xit ) =E(εit |Xit ). One might be concerned that parents that are enthusiastic for education
may time the birth to give their child the advantage of being among the oldest in
his/her grade, which leads to endogeneity bias. We discuss this issue in the following subsection.

2.2

Assessing Exogeneity of Month of BirthOur main data set is drawn from provincial skill assessments in an anonymous
province near Tokyo, and covers nearly all students from grades four to nine inpublic schools for 2015-2018. The number of student-year observations is overone million. Although the data are rich in terms of coverage of students, the main
limitation is that they include very few family/parental background variables, which
prevents us from assessing exogeneity of month of birth directly. To overcome thislimitation, we conduct two sets of exercises. In the first set, vital statistics thatrecord month of birth, parental characteristics, and birth outcomes for all births in
the province for the birth cohort in the main data set, i.e., all births from April 2000
to March 2011. In the second set, we use administrative data from a municipality
in the province for 2015 and 2018. These data include information on whether afamily is on welfare, receives an education subsidy as a low-income family, and/or
is a single-parent household.
Figure 1 presents the birth month shares after adjustment for unequal number6

of days in each month using vital statistics. Specifically, we divide the unadjusted
relative frequency by the number of days and then multiply by 30. If all births arecompletely random, the share is 8.3% for each month. Thanks to a large numberof observations, the share is statistically significantly different from 8.3% for somemonths, but the magnitudes of the differences are small. The biggest deviation fromthe average is found for September, with a share of 8.7%. Importantly, the gaps inthe shares of March-born (youngest) children and April-born (oldest) children aretiny (7.9% and 8.0%, respectively).

Figure 1: Relative Frequency of Birth Month (Adjusted for Number of Days)
Source: Vital Statistics from April 2000 to March 2011.
Note: The relative frequency of birth months is shown. We adjust for differences in the number ofdays across months as follows; we divide the unadjusted relative frequency by the number of daysin the month and then multiply this by 30. The horizontal bar is drawn at 8.3%, which is therelative frequency of a birth month under the assumption that births are completely random.
April-born children are the oldest, while March-born children are the youngest in a given grade.

We next examine the association between birth month and parental characteristics as well as birth outcomes. We regress parental characteristics and birth outcomes on age, age squared, and birth-year dummies using equation (1) and estimate
the gaps between March- and April-born children using equation (2). Because wetest multiple hypotheses simultaneously, we adjust the p-values throughout the paper using the method proposed by Benjamini and Hochberg (1995), which controls
for a false discovery rate for independent hypotheses and provides more power than7the Bonferroni correction. Table 1 reports the results with adjusted p-values for
15 hypotheses. The first set of outcomes is parents’ characteristics, such as age,marital status, and household occupation. The average age of mothers is 29.941
years and that of fathers is 32.053 years. Mothers and fathers of April-born (oldest)children are 0.107 and 0.125 years younger, respectively, than those of March-born(youngest) children. Although the vital statistics do not record parents’ education,
we expect that parents’ age and education are positively correlated because Census and other statistics indicate that educated women tend to have their first childat an older age. One might expect that educated mothers try to time birth so that
their child is born in April instead of March, but this is not the case. We find no
significant gap by birth month in mother’s marital status.
Occupation of household head, which is usually father if present, is classified
into one of seven categories: farmer, self-employment, regular employment at asmall firm, regular employment at a large firm, non-regular employment, none, andunknown. We say a firm is small if it hires fewer than 100 employees and large if it
hires 100 employees or more. Large employers tend to pay higher wages and offer
generous fringe benefits, although healthcare is universal in Japan. Compared withregular employment, non-regular employment is lower paid and unstable. We findno statistically significant gap in the fractions of farmers, self-employment, regular
employment at a small firm, regular employment at a large firm, and unknown.
More nonregular employment is observed among families of April-born (oldest)
than among March-born (youngest) children, which is consistent with the fact thatparents of April-born are younger. We also find that joblessness is slightly lessprevalent among families of April-born than March-born children.
We also examine children’s health at birth. Currie and Schwandt (2013) find that
not only parental characteristics, but also seasonal environmental factors, such as
flu prevalence, influence birth outcomes and may eventually lead to poor child development. Motivated by their findings, we examine the association of birth monthwith birth weight, gestation, and other birth outcomes. The regression results indicate no association between birth month and these infant health measures, being
singleton, and gender; however, April-born (oldest) children are about one percentage point less likely to be a first child (mean, 49.2%). According to the literature,8

Table 1: Gaps in Parental Characteristics and Birth Outcomes Between April- andMarch-born Children
Dependent variables
Parental characteristics
Mother’s age

Mean

Estimates Adjusted p-values

29.9410.000

Father’s age

32.053

Unmarried (%)

1.910-0.107(0.021)-0.125(0.023)-0.059(0.072)

Occupation of household head
Farmer (%)

0.690

0.953

Self-employed (%)

7.185

Regular employment (small firm) (%)

34.691

Regular employment (large firm) (%)

41.608

Non-regular employment (%)

8.749

No job (%)

1.723

Unknown (%)

5.354-0.002(0.039)-0.186(0.106)-0.033(0.238)-0.204(0.179)0.499(0.121)-0.155(0.050)0.081(0.192)Birth outcomes
Birth weight (gram)

3016.116

Gestational age (week)

38.897Singleton (%)

97.951First child (%)

49.229Girl (%)

48.710No. obs.

5699832.383(1.909)-0.002(0.009)-0.076(0.082)-1.007(0.259)-0.312(0.239)

0.000
0.561

0.199
0.953
0.425
0.000
0.005
0.840

0.398
0.913
0.528
0.000
0.398

Source: Vital Statistics from April 2000 to March 2011.
Note: We regress parental characteristics and birth outcomes on age, age squared, and birth-year
dummies using equation (1) and estimate the gaps between March- and April-born children using
equation (2). Occupation of household head (father if present) is reported. We say a firm is small if
it hires 1-99 employees and large if it hires 100 employees or more. Standard errors are inparentheses. Because we test 15 hypotheses simultaneously, we adjust the p-values for multiple
testing using the method proposed by Benjamini and Hochberg (1995), which controls for a false
discovery rate.

9

first-born children tend to perform better than their younger siblings. Although themagnitude is small, this first-born advantage is likely to bias downward, instead of
upward, our estimated month-of-birth effects, if any.
Our second set of exercises use administrative data from a municipality in the
anonymous province that conducted the skill assessment (our main data). These
data include whether the family is on welfare, receives an education subsidy for
low-income households, and is a single-parent household for 11,942 students in2015 and 2018. We regress these family SES variables on age, age-squared, andgrade dummies and then calculate the gap between April- and March-born students.
The p-values are adjusted using the method proposed by Benjamini and Hochberg(1995) as there are three hypotheses. The results are reported in Table 2. We do
not find any significant correlation between birth month and these family SES measures. To determine the extent to which we can generalize these results for themunicipality to the whole province, we compare key variables between the two in
Appendix B.1. Most importantly, little statistical and economic difference is seen
between the two in terms of average family SES, which is proxied by the numberof books in our main data set.
Table 2: Gaps in Family Background Between April- and March-born Students
Mean Estimates Adjusted p-values
Education subsidy (%) 14.797
0.041
0.973
(1.214)Single parent (%)
5.393
1.030
0.470
(0.727)
Welfare (%)
0.729
0.217
0.617
(0.265)
No. obs.
11942
Source: Administrative data from a municipality in the anonymous province that conducted theskill assessment.
Note: We regress the family SES measures on age, age squared, and birth-year dummies using
equation (1) and estimate the gaps between March- and April-born children using equation (2).
Standard errors are in parentheses. We adjust the p-values for multiple testing using the method
proposed by Benjamini and Hochberg (1995), which controls for a false discovery rate, as there arethree hypotheses.

10Overall, we do not find clear evidence that the oldest children in a grade cohort
have more advantages from parental characteristics and birth outcomes than theyoungest children. A few statistically significant differences are observed by birth
month, but these are mostly because we have a large number of observations, andthe magnitude does not seem economically large enough to change our substantive
findings in this paper. We admit that the oldest children may be at a slight disadvantage, if any, which implies that our estimates for month-of-birth effects may bebiased downward if this is the case.
A possible explanation for why month of birth may be uncorrelated with parental
SES is that parents have a limited ability to time the birth. It is true that delaying
birth for a few days by cesarean section is feasible (Shigeoka (2016)), but changing
the birth date by more than a few days may not be straightforward. Parents mightwant to time the conception so that their child is born in April, but waiting for theright time to have an April-born baby can be costly. Given that the average childbearing age is high, at 29.941 years, postponing conception can lower the chanceof pregnancy.

3

Data

3.1

Overview

Our main data set is derived from standardized achievement tests and accompanied
questionnaires that measure noncognitive skills and ask about student life in andoutside the school. The tests have been administered by the school board in ananonymous province in a suburb of Tokyo every April since 2015, which is the
beginning of the academic year. Virtually all students in grades four through nine inpublic schools in the province take this test and answer the questionnaire, although
one municipality did not participate in the skill assessment. Only 2-3% of them did
not take the exam for reasons such as illness. 3 In total, 708 elementary and 356
junior high schools in 62 municipalities are included in the data set. Overall, 99.3%
3 This participation rate is comparable to that for National Assessments of Academic Ability

administered by the Ministry of Education, Culture, Sports, Science and Technology (MEXT).

11

of the elementary schools and 93.0% of the junior high schools are public in theprovince; only a few are private institutions.
Table 3 reports the descriptive statistics. The data cover the period 2015-2018
and include about 1.1 million student-year observations. For each grade, we haveabout 180,000 observations when pooled across years. From one year to the next,
about 2% of students exit from the data set because they move out of the province orenter private schools. The attrition rates are 2.25%, 1.96%, 7.88%, and 2.81% for
the transitions from grades four to five, five to six, six to seven, and seven to eight,
respectively. The attrition rate is notably high when some students enter selective
private junior high schools from grade seven. Because these students are likely tohave higher cognitive and noncognitive skills, the results for primary school students (grades from four to six) and those for junior high school (grades from sevento nine) may not be fully comparable. However, we do not expect an attrition bias
for other grades, and hence, the results are comparable across grades within primary and junior high school. In fact, our results do not change when we restrict oursample to individuals who participate in the skill assessment in all years from 2015
to 2018.
Although we have little information on family background, we use the numberof books at home as a proxy for family SES because questions about parental income or educational background would have resulted in a low response rate and/or
substantial measurement error. It is true that this is potentially an outcome influenced by educational achievement, but it is commonly used in sociology and education research.4 As reported in Table 3, 10.896% of students have 0-10 books athome in the period 2016-8, but this variable is unavailable for 2015.

4 TIMSS (Woessmann, 2008) and PISA (Ammermueller, 2005) also use the number of books athome as a measure of family SES. Kawaguchi (2016) shows that the number of books at home iscorrelated with parental income and educational background in Japan.

12

Table 3: Descriptive Statistics
Count
All
Grade 4
Grade 5
Grade 6
Grade 7
Grade 8Grade 91096062
179820182261
186091
179281
182818185791

Female (%)
0-10 books at home (%)

49.65810.896
Mean

Std. Dev.

Math
Japanese
English

0.192
0.233
0.461

1.344
1.666
1.345

Conscientiousness
Self-control
Self-efficacy

3.734
2.649
2.247

0.628
0.438
0.490

Weekly hours of studying outside school
Weekly hours of reading
Prep school participation rate
Weekly hours of playing outside and sports
Weekly hours of arts, music, and sports

9.639
4.153
0.584
3.989
1.818

6.404
3.786
0.493
3.028
1.842

With teachers
With peers

2.430
2.410

0.600
0.740

Demographics

Cognitive skills

13

Noncognitive skills

Time use & activities

Interpersonal relationships

Source: Provincial Skill Assessment 2015-2018.
Note: Noncognitive skills, number of books at home, relationship with peers are available for 2016-2018. Relationship with teachers is available
for 2017-2018. Weekly hours of reading, hours of playing outside and sports, and hours of arts, music, and sports are available only for 2015.
All other variables are available for 2015-2018.

3.2

Variable Definitions

3.2.1

Cognitive Skills

All students in our data set took math and Japanese tests, and the eighth and ninth
graders also took an English test for 2015-2018. Employing item response theory(IRT; see Embretson and Reise (2000) for details), the tests are designed to enable
comparisons of scores across different grades and test dates. IRT is implemented
by including a few identical questions for anchoring in tests for different grades andyears. This methodology is also used by the Programme for International Student
Assessment (PISA) and TIMSS. In our data, test scores are normalized using themean and standard deviation of the sixth graders for math and Japanese and thoseof the eighth graders for English in 2015. Consequently, the mean and standard
deviation for the pooled sample reported in Table 3 are different from zero and one.
3.2.2

Noncognitive Skills

In a 40-minute questionnaire following the achievement test, the provincial schoolboard has collected information on students’ noncognitive skills, including conscientiousness, self-control, and self-efficacy, since 2016. To reduce the students’ test
burden, they measure one of the three noncognitive skills for each birth cohort. Figure 2 illustrates the survey structure. Consider the cohort who was in grade four in2016 (born in 2006/2007). The school board measured their self-control from 2016
to 2018, but not other noncognitive skills. Similarly, the cohort who was in grade
five in 2016 (born in 2005/2006) answered questions to measure their self-efficacy
only. This survey structure is intended to measure the time evolution of noncognitive skills. As such, each of the noncognitive skills is measured by an identical setof questions across grades and survey years. The questions for noncognitive skills
were translated into English, and are listed in Table 9 in the appendix.
Conscientiousness is defined as “the propensity to follow socially prescribed
norms for impulse control, to be goal directed, to plan, and to be able to delay
gratification" (Roberts et al., 2009) and is known as the most robust predictor ofacademic achievement among the Big Five personality traits.5 Students answered
5 For example, Poropat (2009) meta-analysis on the relation among the Big Five personality traits

14

Figure 2: The Survey Structure for Noncognitive Skills
Note: The figure illustrates the structure of the survey for noncognitive skills. Only one
noncognitive skill is measured for each birth cohort. Consider the cohort who was grade four in2016 (born in 2006/2007). The school board measured their self-control from 2016 to 2018, but notother noncognitive skills.

13 questions about conscientiousness on a five-point Likert scale.
Self-control is a psychological state defined as “the tendency to regulate impulses and resist immediately rewarding temptations in the service of long-termgoals" (Duckworth et al., 2013). Moffitt et al. (2011) show that childhood selfcontrol predicts physical health, substance dependence, personal finances, and criminal offences in adulthood. Our measure of self-control is based on students’ answers to eight questions developed by Tsukayama et al. (2013). These questions
are designed specifically for academic and school contexts.
Self-efficacy is defined as “people’s judgments of their capabilities to organize
and execute courses of action required to attain designated types of performances(Bandura, 1986). Previous studies (Pintrich et al., 1991; Pajares and Miller, 1994;
Pajares and Kranzler, 1995; Pajares and Graham, 1999) find that self-efficacy isstrongly correlated with academic test scores, particularly with math.
For each of the three noncognitive skills, we sum all the relevant responses after
correcting reverse-coded items. Table 3 reports the mean and standard deviation of
reveals that the both raw and partial correlations between conscientiousness and GPA are almost as
large as those between GPA and IQ. Barbaranelli et al. (2003) examine the Big Five using factor
analysis for adulthood to childhood to measure the personality ratings of children. They carefully
investigate whether the children’s self-reporting reliably replicates parental and teacher descriptionsof children’s personality and validate their scale as useful for measuring the Big Five personality
traits through self-reporting by children aged 9-13 years.

15the raw scores for each noncognitive skill for the pooled sample.
3.2.3

Human Capital Investment and Interpersonal Relationships

The questionnaire also asks about students’ time use, prep school participation, andquality of relationships with teachers and peers. Time use and prep school participation are direct measures of human capital. The quality of interpersonal relationships is also relevant for human capital formation because teachers and peers caninfluence a student’s behavior and skills.
We construct weekly hours of study outside of school for years 2015-2018 bycombining average daily hours of study on weekdays and on weekends. The original response is provided as an interval, but we construct a point estimate by taking
the midpoint of each interval and assuming 21 × 1.25 hours if students responded
that they study 21 hours or more per week. Only in 2015, weekly hours are measured for reading, playing outside and sports, and organized out-of-school activities,
including sports, music, and arts. Again, interval responses are converted into point
estimates.
We also use whether a student attends a prep school as a measure of educational
input for 2015-2018. Attending a prep school is common in Japan and other East
Asian countries. Students attend a prep school to improve their academic skills
and prepare for school entrance exams. While only about 5% of sixth graders takeentrance exams for private junior high schools, nearly all ninth graders take entrance exams for high schools.6 The average annual expenditure on prep schools in2016 was 218,000 Japanese yen (≈ 2,180 USD) for public elementary students, and
301,200 yen (≈ 3,012 USD) for public junior high school students (MEXT, 2017).
The means and standard deviations of these variables are reported in Table 3.
Students study 9.639 hours per week outside of school and read 4.153 hours per
week. Attendance at prep school is common, with 58.4% of students enrolled.
Students play outside and/or sports for 3.989 hours per week and participate inorganized arts, music, and sports activities outside of school for 1.818 hours per
week.
6 The high school enrollment rate was 98.8% in 2018.

16

To measure the quality of relationships with teachers, we use three questions
asked in 2017-2018: “Did your teacher(s) assist you adequately?", “Did your teacher(s)recognize your strengths?", and “Did your teacher(s) help you understand content
in class that you found confusing?". To measure the quality of relationships withpeer students, we use the question “Did your friend(s) recognize your strengths?"
asked in 2016-2018. Students responded to these questions on a four-point scale.
The means and standard deviations of these variables are reported in Table 3.

4

Month-of-Birth Effects on Skills and High School
Quality

4.1

Cognitive Skills

We begin by examining graphically how average test scores change with age. Figure
3 shows the age-profiles for math, Japanese, and English test scores. We note two
important features. First, average test scores increase with age at a decreasing rate.
In a given grade, older students achieve higher test scores than do younger students,
but the gap between younger and older students is smaller for upper-grade students.
Second, mean test scores rise discontinuously at the grade cutoff for some grades.
The jump between eighth and ninth grades is particularly large, presumably because
ninth graders work much harder to get ready for entrance exams for high school.
In the following, the focus is on the first feature or within-grade skill differences,while the second feature of the data is extensively analyzed in Appendix C.4.
We regress test scores on age and estimate the difference between the oldest
and youngest students (i.e., the average of the oldest minus that of the youngest) for
each grade. Note that the test scores in the regression analysis are normalized foreach grade, which facilitates a comparison of estimates with those in other studies.
Column (1) of Table 4 shows the estimated gaps in the mean math test scores between the oldest and youngest students in a given grade, using a quadratic function
with no control variables except for age (see equations (1) and (2)). The gap is 0.350
standard deviations for grade four, but decreases as students move through higher
grades. The gap is 0.125 standard deviations for grade nine, and is statistically sig17

Math

1.5

Grade 4
Grade 5
Grade 6
Grade 7
Grade 8Grade 9

1.0

Score

0.5
0.0
0.5
1.0
1.5

110

120

130

1.5

140

150

Age (month)
Japanese

160

170

180

Grade 4
Grade 5
Grade 6
Grade 7
Grade 8Grade 9

1.0

Score

0.5
0.0
0.5
1.0
1.5

110

120

130

1.5

140

150

Age (month)English

160

170

180

Grade 8Grade 9

1.0

Score

0.5
0.0
0.5
1.0
1.5

110

120

130

140

150

Age (month)

160

170

180

Figure 3: Mean Test Scores by Age
Source: Provincial Skill Assessment 2015-2018.
Note: Math and Japanese test scores are normalized so that the mean and standard deviation are 0
and 1 for sixth graders in 2015. English test scores are normalized similarly for eighth graders in
2015.

18

Table 4: Month-of-Birth Gaps in Test Scores
Grade 4
Grade 5
Grade 6
Grade 7
Grade 8Grade 9Control variables
Excl. Mar & Apr

Math
(1)
0.350
(0.008)0.297(0.008)0.248(0.008)0.228(0.008)0.178(0.007)0.125(0.008)

Math
(2)
0.343
(0.009)0.290(0.009)0.244(0.009)0.225(0.009)0.183(0.008)0.131(0.008)X

Math Japanese
(3)
(4)
0.350
0.355
(0.011) (0.008)
0.293
0.319
(0.011) (0.008)
0.254
0.275
(0.011) (0.008)
0.224
0.245
(0.011) (0.008)
0.186
0.206
(0.011) (0.007)
0.143
0.173
(0.011) (0.008)
X
X

Japanese(5)
0.346(0.008)0.320(0.009)0.276(0.008)0.242(0.008)0.215(0.008)0.181(0.008)
X

Japanese English English English
(6)
(7)
(8)
(9)
0.357
(0.011)0.321(0.011)0.278(0.011)0.248(0.011)
0.227
0.141
0.153
0.158
(0.011) (0.007) (0.008) (0.011)
0.181
0.103
0.112
0.119
(0.010) (0.008) (0.008) (0.010)
X
X
X
X
X

Note: The gaps between the oldest and youngest students are based on equations (1) and (2). Test
scores in the regression analysis are normalized for each grade. The control variables include
school fixed effects, survey year fixed effects, gender, and number of books at home. Standard
errors are in parentheses. All estimates in this table are statistically significant at the 1% level whenp-values are adjusted for multiple testing, using a method that controls for a false discovery rate
(see Benjamini and Hochberg (1995)). We have 14 (6 + 6 + 2) hypotheses for each econometricspecification.

nificant and smaller than that for grade four. Column (2) presents the estimated gaps
when test year and school fixed effects, as well as students’ gender and number ofbooks at home, are controlled. The estimates are essentially the same as those incolumn (1). This is not surprising given that the month of birth is not correlated withmost observed characteristics. Column (3) reports the estimates when March- andApril-born students are excluded from the regression sample, which is motivatedby the concern that parents may manipulate the timing of birth so that their children
can take advantage of being the oldest in the class, rather than the youngest. The
control variables are the same as those in column (2). The estimates are essentiallythe same as those in columns (1) and (2). Note that all estimates in Table 4 arestatistically significant at the 1% level when p-values are adjusted by a method that
controls for a false discovery rate (see Benjamini and Hochberg (1995)), as we test
14 (= 6 + 6 + 2) hypotheses simultaneously.

19

The estimated gaps in Japanese test scores are similar to those for math testscores. The gaps are 0.355 standard deviations for grade four, decreasing withgrade, and 0.173 for grade nine. The estimates are robust across different specifications. The gaps in English test scores also decrease with grade, but are smaller thanthose in math and Japanese test scores. Given that English classes begin from grade
seven, the smaller age gap in English test scores might imply that age effects are
cumulative; by contrast, it could be because of the different nature of the subjects.

4.2

Noncognitive Skills

We plot the means of noncognitive skills by age in Figure 4. Note that students
in different grades and survey years respond to identical questions that measurenoncognitive skills, and hence, no gaps are driven by the questions themselves. The
striking feature is that older students within the same grade have better noncognitive
skills, despite the fact that noncognitive skills do not monotonically increase withgrade. Indeed, noncognitive skills tend to decrease with grade, which contrasts withcognitive skills, which grow monotonically with grade.
Negative trends in psychological maturity during adolescence have been reported in previous psychological studies. For example, using a large-scale crosssectional sample from English-speaking countries across the world, Soto et al.
(2011) find that conscientiousness and other desirable psychological traits decrease
when individuals transit from late childhood (age 10-12 years) into adolescence(age 13-17 years). Robins et al. (2002) find a similar negative trend for self-esteem.
Denissen et al. (2013) provide a theoretical explanation in development psychology for these negative trends. On one hand, as children grow, their parents andteachers increasingly expect responsible behavior, which is referred to as a shift in
the reference value. On the other hand, children change corresponding behavior
in a desirable direction by internalizing new values and developing a behavioral
repertoire to accommodate them. During late childhood and early adolescence, reference value shifts faster than children can change their behavior, which results indeclining perceived maturity.
To focus on the gaps within grades, we normalize the noncognitive skills for20Conscientiousness

3.90

Grade 6
Grade 7
Grade 8Grade 9

3.85
3.80

Score

3.75
3.70
3.65
3.60
3.55
110

120

130

140

150

Age (month)Self-control

160

170

180

Grade 4
Grade 5
Grade 6
Grade 7
Grade 8Grade 9

2.725
2.700

Score

2.675
2.650
2.625
2.600
2.575
110

120

130

140

150

Age (month)Self-efficacy

2.5

160

170

180

Grade 5
Grade 6
Grade 7
Grade 8Grade 9

Score

2.4

2.3

2.2

2.1

110

120

130

140

150

Age (month)

160

170

180

Figure 4: Noncognitive Skills by Age
Source: Provincial Skill Assessment 2016-2018.
Note: Raw scores are reported.

21

Table 5: Month-of-Birth Gaps in Noncognitive Skills by Grade
Conscientiousness(1)
Estimates
Adj. p-val.
Grade 4
Grade 5
Grade 6
Grade 7
Grade 8Grade 9Control variables

0.098(0.015)0.079(0.015)0.073(0.015)0.088(0.014)
X

0.000
0.000
0.000
0.000

Self-control(2)
Estimates Adj. p-val.
0.092
0.000
(0.015)0.090
0.000(0.015)0.078
0.000(0.015)0.074
0.000(0.014)0.074
0.000(0.014)0.079
0.000(0.014)X

Self-efficacy(3)
Estimates
Adj. p-val.

0.140(0.016)0.126(0.015)0.110(0.015)0.130(0.015)0.144(0.015)
X

0.000
0.000
0.000
0.000
0.000

Note: The gaps between the oldest and youngest students in a given grade are reported. In the
regression analysis, noncognitive skill measures are normalized so that the mean is 0 and standard
deviation is 1 for each grade. The control variables include school fixed effects, survey year fixed
effects, gender, and number of books at home. Standard errors are in parentheses. We adjust
p-values for multiple testing of 15 hypotheses, using a method that controls for a false discovery
rate (see Benjamini and Hochberg (1995)).

each grade and then regress the normalized measures on age and other control variables. The results are presented in Table 5. Remember that we measure only one
noncognitive skill for each birth cohort, and hence, not all noncognitive skills aremeasured for some grades. Column (1) shows the estimated gaps in conscientiousness between the oldest and youngest students. The estimate for grade six implies
that the oldest students have 0.098 standard deviations better conscientiousness than
the youngest students. The estimated gaps remain about the same in grades seventhrough nine. Column (2) presents the estimates for self-control. The estimatedgaps are about 0.074-0.092 and are nearly constant across grades. Column (3)reports the estimates for self-efficacy. The estimates are 0.110-0.144 and do notchange with grade. Note that the estimates are precisely estimated and the insignificant differences in the estimates across grades are not driven by large standard
errors.
The month-of-birth effects show different patterns on noncognitive than on cog22

nitive skills. First, the effect size for noncognitive skills is about one-tenth of thestandard deviations, which is smaller than the effect size for cognitive skills. Second, the month-of-birth effect on noncognitive skills remains nearly constant acrossgrades, which is in contrast with the month-of-birth effect on cognitive skills, which
decreases with grade.

4.3

Additional Analysis

In this subsection, we briefly present the main findings from a few additional setsof analysis. They are extensively discussed in Appendix C. In Section C.2, we estimate month-of-birth effects on high school quality using administrative data fromthe municipality in the province. We find that the oldest students enter better-quality
high school than the youngest students in the same class. In Section C.3, we examine heterogeneity in the month-of-birth effects by gender and family SES, but
find no evidence for heterogeneity in these respects. In addition, using quantile regressions, we estimate month-of-birth effects across different quantiles in an ability
distribution. We find that the effects are larger in the right tail of the distribution,
but the magnitude is modest.

5

Month-of-Birth Effects on Human Capital Investment and Interpersonal Relationships

We have shown that month of birth affects cognitive and noncognitive skills as wellas high school quality significantly. In this section, we examine children’s time use
and activities outside of school to clarify whether and how the youngest students
and their parents make compensatory investments. We also examine children’s interpersonal relationships with their teachers and peers because these are likely to
influence human capital formation both directly and indirectly.

23

5.1

Human Capital Investment

The top of Figure 5 shows the average weekly hours of study outside of school.
While hours of study largely increase with grade, younger students in a given gradestudy longer hours, which suggests that relative age affects hours of study outsideof school. A decline in hours of study is observed from grades six to seven, whichmight be explained by a composition change, as some students exit the survey from
grade seven to enter private junior high school. The middle and bottom of Figure
5 show the average weekly hours of reading and enrollment rate for a prep school;
both present patterns similar to hours of studying. Namely, they largely increase
with grade, but younger students in a given grade are more likely to enroll in a prep
school and read more.
The top panel of Figure 6 presents average hours per week playing sports and/or
outside. Students in grades eight and nine play sports and/or outside more thanelementary schoolers because school-based sports teams are more common in junior high school. According to the National Survey on Sports and Physical Activities 2018 by the Japan Sports Agency, the participation rate in school-based sports
teams is 67.5% among junior high school students, compared with 24.1% among
elementary school students. Although hours playing sports and/or outside change
nonmonotonically with grade, younger students in a given grade do so for fewer
hours than older students in any grade. The bottom panel of Figure 6 shows theaverage hours for organized art, music, and sports activities outside of school. Unfortunately, we are unable to disentangle hours for sports from those for art andmusic activities. The hours for these activities decrease with grade because students shift to prep school. Within each grade, younger students spend fewer hours
on organized art, music, and sports outside of school.

24

11.00Weekly hours of studying outside school
Grade 4
Grade 5
Grade 6
Grade 7
Grade 8Grade 9

10.75
10.50

Hours

10.25
10.00
9.75
9.50
9.25
9.00
110

120

130

140

150

160

Age (month)Weekly hours of reading

170

180

Grade 4
Grade 5
Grade 6
Grade 7
Grade 8Grade 9

4.4

Hours

4.3
4.2
4.1
4.0
3.9
110

0.725

120

130

140

150

160

170

Age (month)
Prep school participation rate

180

Grade 4
Grade 5
Grade 6
Grade 7
Grade 8Grade 9

0.700
0.675

Rate

0.650
0.625
0.600
0.575
0.550
0.525
110

120

130

140

150

Age (month)

160

170

180

Figure 5: Human Capital Investment Outside of School (1)Source: Provincial Skill Assessment 2015-2018.
Note: Hours of studying and prep school participation rates are available for 2015-2018. Hours ofreading are available for 2015 only.

25

Weekly hours of playing outside and sports
Grade 4
Grade 5
Grade 6
Grade 7
Grade 8Grade 9

5.0

Hours

4.5

4.0

3.5

3.0
110

120

130

140

150

160

170

180

Age (month)Weekly hours of arts, music, and sports

Grade 4
Grade 5
Grade 6
Grade 7
Grade 8Grade 9

2.4
2.2

Hours

2.0
1.8
1.6
1.4
1.2
1.0
110

120

130

140

150

Age (month)

160

170

180

Figure 6: Human Capital Investment Outside of School (2)Source: Provincial Skill Assessment 2015.
Note: Hours of playing sports and/or outside and hours of art, music, and sports are available for2015 only.

26

Table 6: Month-of-Birth Effects on Human Capital Investment Outside of School

Grade 4

Estimates
Adj. p-val.

Grade 5

Estimates
Adj. p-val.

Grade 6

Estimates
Adj. p-val.

27

Grade 7

Estimates
Adj. p-val.

Grade 8Estimates
Adj. p-val.

Grade 9Estimates
Adj. p-val.

Gender & school FE & year FE
No. of books

Studying(1)

Reading(2)

Prep school Play outside/sports(3)(4)

Arts, music, and sports(5)-0.361(0.061)0.000-0.179(0.069)0.012-0.039(0.004)
0.000

0.517
(0.045)
0.000

0.077
(0.028)0.007-0.145(0.060)0.018-0.047(0.065)0.470-0.032(0.004)
0.000

0.459
(0.045)
0.000

0.140
(0.027)0.000-0.048(0.056)0.402-0.140(0.065)0.034-0.028(0.004)
0.000

0.307
(0.043)
0.000

0.192
(0.026)0.000-0.241(0.050)0.000-0.114(0.061)
0.066-0.027(0.004)
0.000

0.296
(0.043)
0.000

0.153
(0.026)0.000-0.402(0.051)0.000-0.250(0.072)0.001-0.028(0.004)
0.000

0.441
(0.041)
0.000

0.085
(0.025)0.001-0.295(0.057)0.000
X
X-0.224(0.074)0.003
X-0.022(0.004)
0.000
X
X

0.285
(0.046)
0.000
X

0.164
(0.025)0.000
X

Source: Provincial Skill Assessment 2015-2018.
Note: Gaps between the oldest and youngest students are reported using equations (1) and (2). The variables Studying and Prep School areavailable for 2015-2018, while other dependent variables are available only for 2015. Standard errors are in parentheses. We adjust the p-values
for multiple testing of 30 hypotheses, using a method that controls for a false discovery rate (see Benjamini and Hochberg (1995)).

We now turn to a regression analysis in which the variables for human capital
investment are regressed on age, age squared, gender, and school and year fixed
effects. The number of books at home is also controlled, if available. Table 6 reports
the estimated gaps between the oldest and youngest students based on the regression
model (see Equations 1 and 2) with p-values adjusted for multiple testing. Column(1) shows the results for hours of studying outside the home. The estimates arenegative and statistically significant, except for grade six. Among ninth graders,
the youngest students study 0.295 hours per week longer than the oldest students.
We also find that the youngest students read at most 0.250 more hours and are 3.9
percentage points more likely to go to prep school than the oldest students in thesame grade (see columns 2 and 3). By contrast, the youngest students spend at most
0.517 fewer hours per week playing sports and/or outside and 0.192 fewer hours per
week on art, music, and sports activities outside of school.

5.2

Interpersonal Relationships

Figure 7 shows self-assessed relationships with teachers (top) and peers (bottom).
Exactly the same questions are used across grades. The striking feature is that older
students have better relationships with teachers and peers, even though the relationships with teachers worsen with grade and those with peers change nonmonotonically with grade. Again, the positive slopes within grade suggest that relative age
influences relationships with teachers and peers.
Table 7 presents the estimated gap between the oldest and youngest students ina given grade, based on the regression model (see equations 1 and 2). In the regression, we normalize the outcome variables for each grade to facilitate interpretation.
We find that younger students suffer 0.052-0.089 standard deviations poorer relationships with teachers and 0.096-0.127 standard deviations poorer relationships
with peers. All of the estimates are statistically significant when the p-values areadjusted for multiple testing.

28

Relationship with teachers
Grade 4
Grade 5
Grade 6
Grade 7
Grade 8Grade 9

2.50

Score

2.45

2.40

2.35

2.30
110

120

130

140

150

160

Age (month)
Relationship with peers

170

180

2.55

Grade 4
Grade 5
Grade 6
Grade 7
Grade 8Grade 9

Score

2.50

2.45

2.40

2.35

2.30
110

120

130

140

150

Age (month)

160

170

180

Figure 7: Quality of Relationships with Teachers and Peers
Source: Provincial Skill Assessment 2016-2018.
Note: Raw scores are reported. Relationship with teachers is available for 2017-2018, while thatfor peers is available for 2016-2018.

29

Table 7: Month-of-Birth Effects of Relationships with Teachers and Peers
Teachers Peers(1)(2)
Grade 4

Estimates
Adj. p-val.

Grade 5

Estimates
Adj. p-val.

Grade 6

Estimates
Adj. p-val.

Grade 7

Estimates
Adj. p-val.

Grade 8Estimates
Adj. p-val.

Grade 9Estimates
Adj. p-val.

Control variables

0.089(0.008)
0.000

0.122
(0.009)
0.000

0.059
(0.008)
0.000

0.110
(0.008)
0.000

0.052
(0.008)
0.000

0.127
(0.008)
0.000

0.064
(0.009)
0.000

0.117
(0.009)
0.000

0.078
(0.009)
0.000

0.100
(0.009)
0.000

0.068
(0.008)
0.000
X

0.096
(0.009)0.000
X

Source: Provincial Skill Assessment 2016-2018.
Note: The gaps in the quality of interpersonal relationships between April- and March-bornstudents are reported. The dependent variables are normalized for each grade to have a zero mean
and a standard deviation of one. Control variables include school fixed effects, survey year fixed
effects, gender, and number of books at home. Standard errors are in parentheses. The p-values areadjusted for multiple testing of 12 hypotheses, using a method that controls for a false discovery
rate (see Benjamini and Hochberg (1995)).

30

6Discussion

Our estimates indicate that the youngest students in grade four achieve 0.35 standard deviations lower test scores than the oldest students; however, by grade nine,
this gap diminishes to 0.13-0.18 standard deviations. The magnitude of monthof-birth effects is similar to the previous estimates across different countries. The
most comparable study to the current paper is Bedard and Dhuey (2006), who use
TIMSS, which includes data from Japan. They find that the impact of month-ofbirth is 0.25-0.31 standard deviations for grade four and 0.23 standard deviations
for grade eight among Japanese students.
We also find that month-of-birth effects on noncognitive skills are significant,and the gap between the oldest and youngest students in the same grade is about
0.1 standard deviations. Interestingly, month-of-birth effects on noncognitive skills
remain constant from grade four to nine. Only a few previous papers have estimatedmonth-of-birth effects on noncognitive skills. These papers suggest that month-ofbirth influences many, if not all, types of noncognitive skills.7 Among them, only
Datar and Gottfried (2015) and Peña and Duckworth (2018) estimate month-ofbirth effects on noncognitive skills for different grades. Datar and Gottfried (2015)
find that the effect is significant for elementary school students, but not for thosein grade eight in the USA. By contrast, Peña and Duckworth (2018) find that theeffects on grit do not vary between ninth and twelfth graders (0.122 vs. 0.145
standard deviations, respectively) in Mexico. We add to this literature using datathat cover a broader range of noncognitive skills for a longer period of time than inprevious studies.
We depart previous contributions by examining human capital investment andinterpersonal relationships, both of which are relevant for children’s skill formation. We find evidence of compensatory investment by the youngest children andtheir parents. Namely, the youngest students study and read more hours and are
more likely to go to a prep school than are the oldest students in the same grade.
Although our data do not allow us to test whether these inputs improve test scores,
7 See Crawford et al. (2014), Datar and Gottfried (2015), Lubotsky and Kaestner (2016), Pageet al. (2017), and Peña and Duckworth (2018)31

we find hours of study outside of school and prep school attendance to be positively
correlated with test scores after controlling for student fixed effects (see Appendix
C.5). Moreover, Stinebrickner and Stinebrickner (2008) find causal evidence thathours of studying improve the GPA of first year students at a US college. In arandomized controlled trial (RCT), Akabayashi et al. (2018) find that after-school
education programs improve Japanese students’ test scores.
Regarding noncognitive skills, we suspect that the youngest students’ low participation at sports, music, and art activities and poor-quality relationships withpeers and teachers exacerbate their disadvantage in noncognitive skills development relative to the oldest students. Psychologists argue that sports, music, and art
improve noncognitive skills. For example, Ishihara et al. (2018) find a strong association between sports experience and noncognitive skills. Moreno et al. (2011)
find causal evidence in an RCT that music training enhances noncognitive skills.
In addition, psychologists find evidence that poor interpersonal relationships negatively affect noncognitive skills.8 In a recent study, Wu et al. (2018) find strong
association between teacher-child relation and children’s social skills. Reitz et al.
(2016) find that popularity among peers predicts self-esteem, but the opposite is nottrue, which provides evidence in support of sociometer theory,9 which argues that
self-esteem reflects an individual’s perceptions of how they are viewed by others,
mediated by self-perceived popularity. The current paper also finds that personal
relationships are positively correlated with noncognitive skills after controlling forstudent fixed effects (see Appendix C.5), although this does not necessarily imply
causal effects. The observed patterns of time use and interpersonal relationships
across children with different birth months may explain why month-of-birth gaps
in noncognitive skills do not disappear as students grow.
Our findings on time use and prep school attendance suggest that skill investment is distorted by the existing school cutoff. They imply that parents decide
8 Davis (2003) synthesizes several psychological studies and finds that both attachment and motivation theories predict that teacher-child relationships improve children’s social and cognitive outcomes.
9 Leary and Baumeister (2000) develop the sociometer theory, which argues that self-esteem
reflects an individual’s perceptions of how they are viewed by others, mediated by self-perceived
popularity.

32

on skill investment by comparing their children with exactly the same grade cohort; however, their children will be compared with more diverse individuals once
they are out of school. Compensatory skill investment is an individually optimal
strategy in the short run, but may be suboptimal from a long-term and/or societal
perspective. Note that the same argument applies to older students because they
may underinvest in cognitive skills. Hence, correcting the distortion may improve
the aggregate human capital stock and benefit all students in the long run. While
this issue has not been raised in the relative age literature, to the best of our knowledge, Kinsler and Pavan (forthcoming) also show evidence that parental investment
is distorted by local information.10
Our results indicate that month of birth has negative consequences in the shortrun. First, the youngest students suffer poor relationships with teachers and peers,which is a serious concern. Second, the evidence for compensatory investment implies that younger students and their parents pay the cost to fill the month-of-birthgaps, although maturation may also be an important factor for convergence. Our
results suggest that, even if part of the month-of-birth gaps disappear by adulthood,month of birth hurts the welfare of younger children in a given grade cohort. Indeed, Matsubayashi and Ueda (2015) find that month of birth affects the suicide
rate among young Japanese aged 15-25 years.
Furthermore, these findings may account for the month-of-birth effects on adulthood outcomes reported in some countries. Specifically, noncognitive skill gaps
may be one of key driving forces behind the month-of-birth effects on earnings at
age 30-34 years reported by Kawaguchi (2011) using data from Japanese Labor
Force Survey. Noncognitive skills not only help one acquire cognitive skills, butalso have independent effects on labor market outcomes (Heckman et al., 2006). If
month-of-birth effects on noncognitive skills remain in adulthood, they are likely totranslate into an earnings gap. Of course, this is only a hypothesis, because our data
do not follow individuals after grade nine11 ; however it is consistent with Dhuey and
10 They find that parents form a belief about their child’s skill by comparing the child with thosein the same school, rather than those of the same age.
11 The Preference Parameters Study of Osaka University measures noncognitive skills for Japanese
adults, but the sample size is only about 2000, which is too small to detect the small gap in noncognitive skills found in this paper.

33

Lipscomb (2008), who find that relatively older students take leadership activities
in high school. In addition, Chetty et al. (2011), Heckman et al. (2013), and Baker
et al. (2019) find that noncognitive skills formed in childhood influence outcomesin adulthood. It is true that month-of-birth effects on cognitive skills may also bepreserved into adulthood through a high school entrance exam (see Appendix C.2)
and eventually influence earnings in adulthood, but our findings offer an alternative
explanation for the persistent effect of month-of-birth.

7

Conclusion

We estimated month-of-birth effects on cognitive skills, noncognitive skills, andhigh school quality using a large longitudinal data set for students in grades four
through nine. We find statistically and economically significant effects of monthof-birth on all of these outcomes. While the effects on cognitive skills diminish
over time, those on noncognitive skills remain constant throughout elementary andjunior high school.
We explored children’s time use and interpersonal relationships, both of which
are relevant for their skill formation. On one hand, we find evidence of compensatory investment in cognitive skills by younger students in a given cohort. Younger
students spend more hours studying and reading and are more likely to attend private prep schools. On the other hand, they are less likely to participate in sports,
music, and arts, and have poorer relationships with teachers and peers, all of which
may harm the long-term development of noncognitive skills. This finding offers analternative explanation for why month of birth affects adulthood outcomes in somecountries, including Japan.
Our findings raise concern about efficiency and equity. Different time use andprep school attendance by birth month suggest that skill investment may be distorted for not only younger, but also older students from a long-term perspective.
We also find that younger students suffer the disadvantage of poor-quality interpersonal relationships, which is caused by the arbitrarily determined school cutoff
date. Policies that mitigate month-of-birth effects could increase the aggregate human capital stock and improve individuals’ welfare. For example, age-adjusted
34

evaluation may be a feasible option, and is particularly relevant for high school anduniversity entrance exams.
Our findings also have implications for the current policy debate regardingwhether to change the school starting date in Japan from April to September. Dueto the COVID-19 pandemic, the Japanese government closed schools from March
to May 2020. Some politicians argue for deferring the school starting date from
April to September commencing in 2021 to make up for the lost school days. Oneof the possible consequences is an increase in cohort size, because children born between April 2014 and August 2015 would start school together. This would increase
the maximum age gap from 11 to 17 months and exacerbate the disadvantages ofyounger students in the cohort. If they change the school starting date, the government should minimize the growth of cohort size and provide necessary assistanceto younger students.
There are at least two limitations of this paper. First, our data do not include
skills and other outcomes in adulthood. Second, our data do not allow us to estimate
the causal effects of time use and interpersonal relationships on skills because weare unable to find exogenous variations in these factors, although other papers haveestablished causality for some. These issues should be addressed in future research.

Bibliography
Akabayashi, Hideo, Hiroko Araki, and Ryuichi Tanaka. 2018. Effects of After-School
Education Vouchers on Children’s Academic and Behavioral Outcomes: Evidence froma Randomized Experiment. Tech. Rep. 48, University of Tokyo.
Attar, Itay and Danny Cohen-Zada. 2018. The effect of school entrance age on educational outcomes: Evidence using multiple cutoff dates and exact date of birth. Journal of
Economic Behavior & Organization 153:38–57.
Baker, Michael, Jonathan Gruber, and Kevin Milligan. 2019. The long-run impacts of auniversal child care program. American Economic Journal: Economic Policy 11 (3):1–
26.

35

Bandura, Albert. 1986. The explanatory and predictive scope of self-efficacy theory. Journalof social and clinical psychology 4 (3):359–373.
Barbaranelli, Claudio, Gian Vittorio Caprara, Annarita Rabasca, and Concetta Pastorelli.
2003. A questionnaire for measuring the big five in late childhood. Personality andIndividual Differences 34 (4):645–664.
Bedard, Kelly and Elizabeth Dhuey. 2006. The persistence of early childhood maturity:
International evidence of long-run age effects. The Quarterly Journal of Economics
121 (4):1437–1472.
Benjamini, Yoav and Yosef Hochberg. 1995. Controlling the false discovery rate: a practicaland powerful approach to multiple testing. Journal of the Royal statistical society: series
B (Methodological) 57 (1):289–300.
Black, Sandra E, Paul J Devereux, and Kjell G Salvanes. 2011. Too young to leave the nest?
the effects of school starting age. The Review of Economics and Statistics 93 (2):455–
467.
Chetty, Raj, John N. Friedman, Nathaniel Hilger, Emmanuel Saez, Diane Whitmore
Schanzenbach, and Danny Yagan. 2011. How does your kindergarten classroom affect your earnings? Evidence from project star.

Quarterly Journal of Economics

126 (4):1593–1660.
Crawford, Claire, Lorraine Dearden, and Ellen Greaves. 2014. The drivers of month-ofbirth differences in children’s cognitive and non-cognitive skills. Journal of the Royal
Statistical Society: Series A (Statistics in Society) 177 (4):829–860.
Currie, Janet and Hannes Schwandt. 2013. Within-mother analysis of seasonal patterns inhealth at birth. Proceedings of the National Academy of Sciences 110 (30):12265–12270.
Datar, Ashlesha and Michael A Gottfried. 2015. School entry age and children‚Äôs socialbehavioral skills: Evidence from a national longitudinal study of us kindergartners.
Educational evaluation and policy analysis 37 (3):333–353.
Davis, Heather A. 2003. Conceptualizing the role and influence of student-teacher relationships on children’s social and cognitive development. Educational psychologist
38 (4):207–234.

36

Dawid, Herbert and Gerd Muehlheusser. 2015. Repeated selection with heterogeneous individuals and relative age effects. Journal of Economic Behavior & Organization 116:387–
406.
Denissen, Jaap J.A., Marcel A.G. van Aken, Lars Penke, and Dustin Wood. 2013. SelfRegulation Underlies Temperament and Personality: An Integrative Developmental
Framework. Child Development Perspectives 7 (4):255–260.
Dhuey, Elizabeth and Stephen Lipscomb. 2008. What makes a leader? relative age and highschool leadership. Economics of Education Review 27 (2):173–183.
Dobkin, Carlos and Fernando Ferreira. 2010. Do school entry laws affect educational attainment and labor market outcomes? Economics of education review 29 (1):40–54.
Du, Qianqian, Huasheng Gao, and Maurice D Levi. 2012. The relative-age effect and career
success: Evidence from corporate ceos. Economics Letters 117 (3):660–662.
Duckworth, Angela L, Eli Tsukayama, and Teri A Kirby. 2013. Is it really self-control?
examining the predictive power of the delay of gratification task. Personality and Social
Psychology Bulletin 39 (7):843–855.
Dustmann, Christian, Patrick A Puhani, and Uta Schönberg. 2017. The long-term effects ofearly track choice. The Economic Journal 127 (603):1348–1380.
Embretson, Susan E and Steven Paul Reise. 2000. Item response theory for psychologists.
maheah.
Fredriksson, Peter and Björn Öckert. 2014. Life-cycle effects of age at school start. The
Economic Journal 124 (579):977–1004.
Grenet, Julien. 2009. Academic performance, educational trajectories and the persistenceof date of birth effects. evidence from france. Unpublished manuscript .
Heckman, James, Rodrigo Pinto, and Peter Savelyev. 2013. Understanding the mechanisms
through which an influential early childhood program boosted adult outcomes. American
Economic Review 103 (6):2052–86.
Heckman, James J., Jora Stixrud, and Sergio Urzua. 2006. The Effects of Cognitive andNoncognitive Abilities on Labor Market Outcomes and Social Behavior. National Bureau
of Economic Research 24 (3):1689–1699.

37

Helsen, Werner F., Jan Van Winckel, and A. Mark Williams. 2005. The relative age effectin youth soccer across Europe. Journal of Sports Sciences 23 (6):629–636.
Ishihara, Toru, Shigemi Sugasawa, Yusuke Matsuda, and Masao Mizuno. 2018. Relationship between sports experience and executive function in 6-12-year-old children: independence from physical fitness and moderation by gender. Developmental Science
21 (3):1–13.
Kawaguchi, Daiji. 2011. Actual age at school entry, educational outcomes, and earnings.
Journal of the Japanese and International Economies 25 (2):64–80.
———. 2016.

Fewer school days, more inequality.

Journal of the Japanese andInternational Economies 39:35–52.
Kinsler, Josh and Ronni Pavan. forthcoming. Local distortions in parental beliefs over childskill. Journal of Political Economy .
Larsen, Erling Røed and Ingeborg F Solli. 2017. Born to run behind? persisting birth montheffects on earnings. Labour Economics 46:200–210.
Leary, Mark R and Roy F Baumeister. 2000. The nature and function of self-esteem: Sociometer theory. In Advances in experimental social psychology, vol. 32. Elsevier, 1–62.
Lubotsky, Darren and Robert Kaestner. 2016. Doskills beget skills’? evidence on the effectof kindergarten entrance age on the evolution of cognitive and non-cognitive skill gaps
in childhood. Economics of Education Review 53:194–206.
Matsubayashi, Tetsuya and Michiko Ueda. 2015. Relative Age in School and Suicide
among Young Individuals in Japan: A Regression Discontinuity Approach. PLOS ONE
10 (8):e0135349.
Matta, Rafael, Rafael P Ribas, Breno Sampaio, and Gustavo R Sampaio. 2016. The effectof age at school entry on college admission and earnings: a regression-discontinuity
approach. IZA Journal of Labor Economics 5 (1):9.
McEwan, Patrick J and Joseph S Shapiro. 2008. The benefits of delayed primary school
enrollment discontinuity estimates using exact birth dates. Journal of human Resources
43 (1):1–29.

38

Moffitt, Terrie E, Louise Arseneault, Daniel Belsky, Nigel Dickson, Robert J Hancox,
HonaLee Harrington, Renate Houts, Richie Poulton, Brent W Roberts, Stephen Ross
et al. 2011. A gradient of childhood self-control predicts health, wealth, and public
safety. Proceedings of the National Academy of Sciences 108 (7):2693–2698.
Moreno, Sylvain, Ellen Bialystok, Raluca Barac, E. Glenn Schellenberg, Nicholas J.
Cepeda, and Tom Chau. 2011. Short-term music training enhances verbal intelligenceand executive function. Psychological Science 22 (11):1425–1433.
Mühlenweg, Andrea M and Patrick A Puhani. 2010. The evolution of the school-entry age
effect in a school tracking system. Journal of human Resources 45 (2):407–438.
Muller, Daniel and Lionel Page. 2016. Born leaders: political selection and the relative age
effect in the us congress: D. muller and l. page. Journal of the Royal Statistical Society:
Series A (Statistics in Society) 179 (3):809–829.
Nam, Kigon. 2014. Until when does the effect of age on academic achievement persist?
evidence from korean data. Economics of Education Review 40:106–122.
Page, Lionel, Dipanwita Sarkar, and Juliana Silva-Goncalves. 2017. The older the bolder:
Does relative age among peers influence children‚Äôs preference for competition?
Journal of Economic Psychology 63:43–81.
Pajares, Frank and Laura Graham. 1999. Self-efficacy, motivation constructs, and mathematics performance of entering middle school students. Contemporary educational
psychology 24 (2):124–139.
Pajares, Frank and John Kranzler. 1995. Self-efficacy beliefs and general mental ability inmathematical problem-solving. Contemporary educational psychology 20 (4):426–443.
Pajares, Frank and M David Miller. 1994. Role of self-efficacy and self-concept beliefs
in mathematical problem solving: A path analysis. Journal of educational psychology
86 (2):193.
Peña, Pablo A. 2017. Creating winners and losers: Date of birth, relative age in school, andoutcomes in childhood and adulthood. Economics of Education Review 56:152–176.

39

Peña, Pablo A and Angela L Duckworth. 2018. The effects of relative and absolute age
in the measurement of grit from 9th to 12th grade. Economics of Education Review
66:183–190.
Pintrich, Paul R et al. 1991. A manual for the use of the motivated strategies for learningquestionnaire (mslq). ERIC.
Poropat, Arthur E. 2009. A meta-analysis of the five-factor model of personality and academic performance. Psychological bulletin 135 (2):322.
Puhani, Patrick A. and Andrea M. Weber. 2007. Does the early bird catch the worm?
Empirical Economics 32 (2-3):359–386.
Reitz, Anne K., Frosso Motti-Stefanidi, and Jens B. Asendorpf. 2016. Me, us, and them:
Testing sociometer theory in a socially diverse real-life context. Journal of Personality
and Social Psychology 110 (6):908–920.
Roberts, B. W., J. J. Jackson, J. V. Fayard, G. Edmonds, and J. Meints. 2009. Conscientiousness. In Handbook of individual differences in social behavior, ed. M. R. Leary &R. H. Hoyle. The Guilford Press, 369–381.
Robins, Richard W., Kali H. Trzesniewski, Jessica L. Tracy, Samuel D. Gosling, and Jeff
Potter. 2002. Global self-esteem across the life span. Psychology and Aging 17 (3):423–
434.
Shigeoka, Hitoshi. 2016. School entry rule and the timing of births. Tech. rep., Simon
Fraser University.
Soto, Christopher J., Oliver P. John, Samuel D. Gosling, and Jeff Potter. 2011. Age Differences in Personality Traits From 10 to 65: Big Five Domains and Facets in a Large
Cross-Sectional Sample. Journal of Personality and Social Psychology 100 (2):330–348.
Stinebrickner, Ralph and Todd R. Stinebrickner. 2008. The causal effect of studying on
academic performance. B.E. Journal of Economic Analysis and Policy 8 (1).
Tsukayama, Eli, Angela Lee Duckworth, and Betty Kim. 2013. Domain-specific impulsivity in school-age children. Developmental Science 16 (6):879–893.

40

Tukiainen, Janne, Tuomas Takalo, and Topi Hulkkonen. 2017. Gender specific relative
age effects in politics and football. Working Papers 94, VATT Institute for Economic
Research.
Wu, Zhongling, Bi Ying Hu, Xitao Fan, Xiao Zhang, and Juan Zhang. 2018. The associations between social skills and teacher-child relationships: A longitudinal study among
Chinese preschool children. Children and Youth Services Review 88 (March):582–590.
Zweimüller, Martina. 2013.

The effects of school entry laws on educational attain-

ment and starting wages in an early tracking system.

Annals of Economics andStatistics/ANNALES D’ÉCONOMIE ET DE STATISTIQUE :141–169.

41

For Online Publication
A

Extended Literature Review

This paper contributes to a large literature on the effects of month of birth. It has
been widely documented that the younger children in a grade cohort perform morepoorly in academic tests; examples include, but are not limited to, Bedard andDhuey (2006), Puhani and Weber (2007), McEwan and Shapiro (2008), Mühlenweg and Puhani (2010), Crawford et al. (2014), Nam (2014), Matta et al. (2016),
Peña (2017), and Attar and Cohen-Zada (2018). For example, Bedard and Dhuey(2006) find that the youngest students of each cohort score 4-12 percentiles lower
than the oldest students in grade four and 2-9 percentiles lower in grade eight, using Trends in International Mathematics and Science Study (TIMSS), which covers
19 Organisation for Economic Co-operation and Development (OECD) countries.
The month-of-birth gaps in test scores seem to be largely explained by absolute age
effects because the gaps diminish with grade. In fact, Black et al. (2011) find nomonth-of-birth effects on IQ tests measured around the 18th birthday. Nevertheless,little remains known about why month-of-birth effects decrease with absolute age.
Maturation is obviously an important reason, but the youngest students’ compensatory behavior can also explain the catching-up process, which has been paid scantattention in the literature.
Evidence for month-of-birth effects on noncognitive skills is mixed and more
scarce than that for cognitive skills. Crawford et al. (2014) find that the youngest
students value their own scholastic competence less than other students, but observe
no month-of-birth effects on self-esteem and locus of control among UK students
at age 8 years. Datar and Gottfried (2015) and Lubotsky and Kaestner (2016) findthat the youngest students have weaker social-behavioral outcomes during kindergarten and elementary school in the USA, but the negative effect on young students
disappears before high school. Page et al. (2017) examine Australian students andfind that the youngest male students dislike competition more than do the oldest
students, but observe no month-of-birth effects on risk attitude or self-confidence.
Peña and Duckworth (2018) show that the youngest students in grades six and nine42

in Mexico have less grit. Overall, month-of-birth effects are significant at least
for some dimensions of noncognitive skills, and the effect tends to be stronger for
lower-grade students. We contribute to the literature as our large panel data set with
one million observations covers a broader range of noncognitive skills for a longer
period than previous studies.
Several papers estimate month-of-birth effects on educational attainment andlabor market outcomes, but the evidence is mixed. Black et al. (2011) and Larsen
and Solli (2017) find no month-of-birth effects on prime-age earnings in Norway.
Dobkin and Ferreira (2010) report similar results for California and Texas in theUSA. Nam (2014) and Dustmann et al. (2017) find no month-of-birth effects on
educational attainment or earnings in Korea and Germany, respectively.
Many other papers report significant month-of-birth effects on educational attainment and labor market outcomes. Most importantly for this paper, Kawaguchi(2011) finds that the youngest individuals in grade cohorts received 0.13 fewer yearsof education and earn about 4% less income at ages 30-34 years in Japan. Bedardand Dhuey (2006), Grenet (2009), Zweimüller (2013), Matta et al. (2016), and Peña
(2017) find significant month-of-birth effects on university attainment and/or earnings in Canada and the USA, France, Austria, Brazil, and Mexico, respectively. It isalso worth mentioning that Fredriksson and Öckert (2014) report significant monthof-birth effects on educational attainment and earnings of women and individuals
with poorly-educated parents in Sweden. Lastly, month of birth also affects the
chance of occupying highly competitive positions, including the CEOs of US S&P
500 companies (Du et al., 2012) and top political positions in the USA (Muller andPage, 2016) and Finland (Tukiainen et al., 2017).
While numerous papers document month-of-birth gaps in short- and long-run
outcomes, little remains known about factors that mitigate or exacerbate these gaps.
One of the most common explanations for why month-of-birth effects are persistentis early school tracking. The youngest children in a grade cohort are less likely to
be selected for a higher quality academic program because they perform worse thantheir older peers, which exacerbates the gap between the oldest and youngest students. Dawid and Muehlheusser (2015) theoretically show that early school tracking can result in an erroneous selection. Fredriksson and Öckert (2014) estimate
43

the effect of tracking using a Swedish compulsory school reform as a natural experiment that changed a selective system with early tracking to a comprehensive
system. They find that the month-of-birth effects are more pronounced by earlytracking. Our contribution to the literature is to offer an alternative explanation
for why month-of-birth effects persist by showing how parents, teachers, and peers
influence the skill formation of children.

B

Details of The Data

B.1

Supplementary Administrative Data

An anonymous municipality in the province provided administrative data on family
SES and the names of high schools that students applied for and entered after gradenine. To examine the external validity of our analysis relying on the administrative
data from the municipality, we compare the municipality and province in terms ofkey variables in our main data set. Table 8 summarizes the differences between thetwo for ninth graders. We omit the results for students in other grades because they
do not differ substantially from those for grade nine. Cognitive and noncognitive
skills as well as interpersonal relationships measures are normalized to have a meanof zero and a standard deviation of one for the whole province for the ninth graders.
Students in the municipality have about 0.2 standard deviations higher test scoresthan the provincial average, but lower self-control skills and poorer interpersonal
relationships. In terms of the number of books at home, which is a proxy for family SES, the municipality is not significantly different from the provincial average.
While the prep school attendance rate is slightly higher than the average, we findvery little difference in time use.

44

Table 8: Difference Between the Province and Municipality

45

Subject
Japanese
MathEnglishSelf-control
Self-efficacy
Conscientiousness
Relative age
Girl# of books: 0 ~ 10
Weekly hours of studying outside school
Prep school
Weekly hours of reading
Weekly hours of arts, music, and sports
Weekly hours of playing outside and sports
Peers
Teachers

The province The municipality Difference
(A)
(B)
(A-B)
Adjusted p-values
0.000
0.187
-0.187
0.000
0.000
0.223
-0.223
0.000
0.000
0.228
-0.2280.000
0.000-0.298
0.298
0.000
0.000
-0.029
0.029
0.446
0.000
-0.053
0.053
0.129
5.563
5.535
0.029
0.644
0.492
0.487
0.005
0.633
0.135
0.124
0.011
0.094
10.62410.879-0.255
0.062
0.699
0.724
-0.024
0.007
4.060
3.843
0.216
0.220
1.128
1.115
0.013
0.840
4.848
4.792
0.055
0.637
0.000
-0.054
0.054
0.002
0.000
-0.1900.190
0.000

Source: Provincial Skill Assessment 2015-2018.
Note: We compare the means of the key variables for the ninth grade students in the Skill Assessment between the municipality that provided
administrative data and the province that conducted the Provincial Skill Assessment. Cognitive and noncognitive skills as well as interpersonal
relationships measures are normalized to have a mean of zero and a standard deviation of one for the whole province for the ninth graders. The
p-values are adjusted by a method that controls for a false discovery rate (see Benjamini and Hochberg (1995)) because there are 16 hypotheses.

B.2

Questions Measuring Noncognitive Skills

Table 9 lists questions used to measure noncognitive skills. The original questions
are written in Japanese and translated into English by the authors.
Table 9: Questionnaire on Noncognitive AbilityGroup
Self-control

Question items
I forgot something I needed for school.
I interrupted other people.
I said something rude.
I could not find something because of the mess.
I lost my temper.
I did not remember what someone told me to do.
My mind wandered.
I talked back when upset.
Self-efficacy
I believe I will receive an excellent grade in this class.
I’m certain I can understand the most difficult material presented in the readings for this course.
I’m confident I can understand the basic concepts taught in this course.
I’m confident I can understand the most complex material presented by the instructor in this course.
I’m confident I can do an excellent job on the assignments and tests in this course.
I expect to do well in this class.
I’m certain I can master the skills being taught in this class.
Considering the difficulty of this course, the teacher, and my skills, I think I will do well in this class.
Conscientiousness I do my job without carelessness and inattention.
I work hard and with pleasure.
I engage myself in the things I do.
During class time, I concentrate on the tasks given to me.
When I finish my homework, I check it many times to see if I did it correctly.
I respect the rules and help maintain order.
If I make an appointment, I keep it.
My room is in order.
When I start to do something I have to finish it at all costs.
I like to keep all my school things in good order.
I play only when I have finished my homework.
It is unlikely that my attention will be diverted.
I take care of my responsibilities.

B.3

Correlations Between Cognitive and Noncognitive Skills

Table 10 reports the correlation coefficients between test scores and noncognitive
skills. All three types of noncognitive skills are positively and significantly correlated with test scores in grades six and nine. The correlation with conscientiousness
is about 0.15-0.20. The correlation with self-control is slightly weaker, at 0.10-0.20,
and decreases with grade. The correlation with self-efficacy is much stronger thanthat for the other two noncognitive skills and becomes increasingly strong with46

grade. The correlation coefficients are in the range of 0.30-0.50. Of course, correlation by no means indicates causation, but does demonstrate that it is useful in
predicting cognitive skills.
Table 10: Correlation Coefficients Between Cognitive and Noncognitive Skills
Conscientiousness Self-control Self-efficacy
(1)
(1)
(2)
Grade 6 Math
0.171
0.140
0.375
(0.005)(0.005)(0.004)
Japanese
0.181
0.139
0.260
(0.005)(0.005)(0.005)
Grade 9 Math
0.172
0.103
0.478
(0.005)(0.005)(0.004)
Japanese
0.152
0.104
0.372
(0.005)(0.005)(0.004)
English
0.196
0.098
0.450
(0.005)(0.005)(0.004)
Source: Provincial Skill Assessment 2016-2018. Standard errors are in parentheses.

C

Additional Analysis

C.1

Month-of-Birth Gaps in Skill Growth

A potential problem with the regression analysis is that the composition of students differs across grades because some students move in or out of the province
or switch between private and public schools. While the annual attrition rate is low(see Section 3.1), some of the observed convergence in the test score gaps might bedriven by changes in the composition of the individuals in the tests. To address thisconcern, taking advantage of the panel structure of the data, we regress the growth(i.e., 1-year difference) as opposed to the level of test scores on age and other control variables. Table 11 shows the estimated gap between the oldest and youngest
students (see Equation 2). The growth in test scores is faster for the youngest thanfor the oldest children in most grades, which implies that month-of-birth gaps in47the test scores diminish over time. Note that all estimates in columns (1)-(3) of
Table 11 are statistically significant at the 1% level when p-values are adjusted for
multiple testing of 11 hypotheses.
We again address biases arising from possible changes of student composition.
We regress the growth of noncognitive skills on age and other control variables.
Table 11 indicates that there is no significant gap in the growth of noncognitive
skills in all grades, which implies that month-of-birth gaps in noncognitive skills
do not converge over time. Again, these estimates are precisely estimated.

48

49

Table 11: Month-of-Birth Gaps in the Growth of Test Scores and Noncognitive Skills
Japanese Math English Conscientiousness Self-control Self-efficacy
(1)
(2)
(3)
(4)
(5)
(6)
G4 to G5
-0.097-0.080-0.010(0.009) (0.009)(0.015)G5 to G6
-0.114-0.055-0.009-0.014(0.009) (0.009)(0.014)(0.015)G6 to G7
-0.063-0.016-0.011-0.010(0.009) (0.009)(0.015)(0.015)G7 to G8
-0.095-0.085-0.006-0.003(0.010) (0.008)(0.015)(0.015)G8 to G9-0.010-0.062 -0.052
0.008
0.020
(0.008) (0.009) (0.008)(0.013)(0.015)Control variables
X
X
X
X
X
X
Note: The gaps in 1-year growth of an outcome between the oldest and youngest students are reported. Control variables include school fixed
effects, survey year fixed effects, gender, and number of books at home. Standard errors are in parentheses. All estimates in columns (1)-(3) are
statistically significant at the 1% level when p-values are adjusted for multiple testing of 11 hypotheses using a method that controls for a false
discovery rate (see Benjamini and Hochberg (1995)). None of the estimates in columns (4)-(6) are statistically significant at any conventional
level when p-values are adjusted.

C.2

Month-of-Birth Effects on High School Quality

One might argue that students may not take the academic tests and questionnaire
for noncognitive skills seriously because there is no incentive to do so. To address
this concern, we estimate month-of-birth effects on a high-stake academic outcome,which is the quality of high school that students enter after grade nine. High school
education is not compulsory in Japan, but 98% of students enter high school immediately after the ninth grade. Students can apply for any public high school in
the province and any private high school in/outside the province. Oversubscribedschools select students based on a written examination and grade point average.
Very few schools interview candidates. The quality of high school in Japan is often measured by academic test scores, which are perceived as a requirement for
entrance.
A municipality in an anonymous province provided us with information on thehigh school their students entered after grade nine. We link these data with themain skill assessment data and the quality of high school published by a private
firm.12 The measured quality was originally scaled to a mean of 50 and a standard
deviation of 10. The sample size is 937, as the data cover only one municipality inthe province.
Figure 8 shows clearly that high school quality increases with month of birth.
We also regress high school quality on age and other control variables using equation 1. Table 12 presents the estimated gap in high school quality between theoldest and youngest students among the ninth graders (see equation 2). Without thecontrol variables, the oldest students enter 4.653 points higher quality high schools
than the youngest students in the same class (see column 1). Controlling for school
fixed effects, family SES, and gender does not change the estimate (see column 2).
Additionally controlling for math, Japanese, and English test scores conducted at
the beginning of the ninth grade reduces the gap substantially, but the remaining
gap is still sizable and statistically significant (see column 3). The estimate implies
12 The data were downloaded from minkou.jp in October 2018. A marketing research company

claims that this website is the most viewed among those providing information on schools in Japan.
Other firms also publish high school quality data, but they are similar to each other. We also confirm
that teachers, students, and their parents often referred to the website during informal interviews.

50

High school qualitythat about two-thirds of the month-of-birth gaps in high school quality are explainedby cognitive skills. Finally, we control for self-efficacy as a noncognitive skill in
addition to all other control variables, but the estimate in column 4 does not change
relative to that without controlling self-efficacy. We also note that the results do notchange when we use the quality of high school for which students applied insteadof the high school they entered because they match most of the cases.
To put the estimates in context, we also report estimated test score gaps for the
ninth graders in this municipality. The estimates indicate that the oldest students
have 0.381, 0.329, and 0.253 standard deviations higher scores than the youngest
students in math, Japanese, and English, respectively. These gaps are about twiceas large as those for the whole province (see Table 4), which suggests that the highschool quality gap may also be substantially greater than that for other municipalities in the province.

54
53
52
51
50
49
3 2 1 12 11 10 9 8 7 6 5 4Age (month of birth)

Figure 8: Average High School Quality by Month of Birth
Source: Administrative data in 2017 from a municipality in the province.
Note: High school quality is normalized so that the mean is 50 and the standard deviation is 10.
The number of observations is 937.

51

Table 12: Month-of-Birth Effects on High School Quality
(1)
(2)
(3)
(4)
(5)
(6)
Dependent var.
High school quality
Math JapaneseMonth of birth effect
4.653
4.521
1.422
1.327
0.381
0.329
(1.110) (1.040) (0.699) (0.688) (0.099) (0.099)
School FE, SES, gender
X
X
X
X
X
Test scores
X
X
Self-efficacy
X
No. of obs.
937
931
930
916
961
961

(7)
English0.253(0.102)
X

961

Source: Administrative data from a municipality in the province that conducted the skill assessment.
Note: The estimated differences between the oldest (April-born) and youngest (March-born) students in the same grade are reported. Highschool quality is normalized so that the mean is 50 and the standard deviation is 10. The standard errors are in parentheses.

52

C.3

Heterogeneity of Month-of-Birth Effects

C.3.1

By Gender and SES

The literature shows that boys mature more slowly than girls and are more susceptible to negative family and school environments, which suggests that month-of-birtheffects may be stronger for boys than for girls. Similarly, children from low-SES
families may be more susceptible to month-of-birth effects because they have fewer
resources to fund compensatory behavior. Although some papers find that monthof-birth effects tend to be stronger for boys and/or low-SES children, the overall
evidence is mixed. 13 We provide an additional piece of evidence about the existence of heterogeneous effects by gender and SES.
Table 13 reports the estimated girl vs. boy gaps in the month-of-birth effects,
i.e., the gap between ∆Y for girls and that for boys (see equation (2)). None of theestimates are statistically significant when we adjust p-values for multiple testingwith 29 (6 + 6 + 2 + 4 + 6 + 5) hypotheses. The results do not change when we poolobservations across grades as long as the p-values are adjusted for six hypotheses.
We conclude that there is no evidence in our data of gender heterogeneity in monthof-birth effects on cognitive and noncognitive skills.
Table 14 reports the estimated gap in month-of-birth effects ∆Y between lowSES and other students. Our proxy for being a low-SES student is having 0-10
books at home. Again, none of the estimates are statistically significant when pvalues are adjusted for multiple testing. When we pool observations across grades,
only the estimated effect on math is statistically significant, even if the p-values areadjusted. The estimate implies that the month-of-birth effect for low-SES students
is 0.033 standard deviations smaller than that for other students.
Table 15 shows estimates using an alternative measure of low SES. Here, we use
administrative data from the municipality in the province and define low SES bywhether the student’s family receives an education subsidy for low-income house13 Puhani and Weber (2007), McEwan and Shapiro (2008), and Matta et al. (2016) find stronger

month-of-birth effects for boys than for girls, while Peña (2017) and Attar and Cohen-Zada (2018)
find that the gender gap is insignificant. Only a few papers examine heterogeneity by family SES.
Matta et al. (2016) find that the effects are stronger for low-SES children, while Attar and CohenZada (2018) find no heterogeneity by family SES.

53

holds. We find that the month-of-birth effects on self-efficacy is significantly stronger
for low-income households in this subsample. We find no evidence for heterogeneous treatment effects on other outcomes.
Table 13: Heterogeneous Month-of-Birth Effects by Gender
Math Japanese English Conscientiousness Self-control
(1)
(2)
(3)
(4)
(5)
Grade 4 -0.008
0.011-0.017(0.017) (0.016)(0.030)
Grade 5
0.005
0.013
0.007
(0.016) (0.016)(0.031)
Grade 6
0.008
0.008
0.040
0.004
(0.017) (0.017)(0.030)(0.030)
Grade 70.004
-0.0180.059-0.007(0.017) (0.018)(0.027)(0.030)
Grade 8 -0.003-0.008-0.0020.011-0.005(0.017) (0.019) (0.017)(0.029)(0.031)
Grade 9 -0.002-0.004-0.011-0.041-0.020(0.017) (0.017) (0.017)(0.030)(0.031)
All
0.000
0.001
-0.0060.015-0.006(0.009) (0.009) (0.015)(0.016)(0.018)Controls
X
X
X
X
X

Self-efficacy
(6)

0.051
(0.029)0.028(0.030)0.045(0.031)-0.003(0.028)0.031(0.028)0.030(0.016)X

Source: Provincial Skill Assessment 2015-2018.
Note: The gaps in month-of-birth effects (based on equation 2) between boys and girls arereported. The control variables include school fixed effects, survey year fixed effects, gender, andnumber of books at home. Standard errors are in parentheses. None of the estimates are statisticallysignificant when p-values are adjusted for multiple testing, using a method that controls for a false
discovery rate (see Benjamini and Hochberg (1995)).

C.3.2

Quantile Regression

Du et al. (2012), Muller and Page (2016), and Tukiainen et al. (2017) find thatmonth of birth affects the probability of occupying top management and political
positions, which suggests that month-of-birth effects might be stronger at the righttail of an ability distribution. To address this issue, we estimate month-of-birtheffects on test scores using quantile regressions. Unfortunately, quantile regression
54

Table 14: Heterogeneous Month-of-Birth Effects by SES (No. of Books at Home)
Math Japanese English Conscientiousness Self-control Self-efficacy
(1)
(2)
(3)
(4)
(5)
(6)
Grade 4 -0.029-0.001
0.061(0.023) (0.026)(0.053)
Grade 5 -0.005-0.044-0.046-0.033(0.030) (0.030)(0.054)(0.057)
Grade 6 -0.023
0.008-0.0970.082-0.050(0.030) (0.030)(0.063)(0.059)(0.060)
Grade 7 -0.036-0.025-0.060-0.010-0.059(0.028) (0.026)(0.049)(0.051)(0.049)Grade 8 -0.011
0.001-0.0140.107
0.008-0.067(0.027) (0.026) (0.026)(0.053)(0.047)(0.045)
Grade 9 -0.060-0.052-0.0480.041-0.034-0.023(0.027) (0.026) (0.024)(0.045)(0.049)(0.044)All-0.035-0.026-0.0320.001
0.006-0.046(0.013) (0.013) (0.020)(0.027)(0.024)(0.025)Controls
X
X
X
X
X
X
Source: Provincial Skill Assessment 2015-2018.
Note: The gaps in month-of-birth effects (based on equation 2) between low-SES students andothers are reported. We define low-SES students as those who have 0-10 books at home. The
control variables include school fixed effects, survey year fixed effects, gender, and number ofbooks at home. Standard errors are in parentheses. All but one of the estimates are statisticallysignificant when p-values are adjusted for multiple testing, using a method that controls for a false
discovery rate (see Benjamini and Hochberg (1995)). The exception is the estimate for math whenobservations are pooled across grades (labeled as All).

55

Table 15: Heterogeneous Month-of-Birth Effects by SES (Family Income)Math Japanese English Conscientiousness Self-control
(1)
(2)
(3)
(4)
(5)
All
0.095
0.088
-0.1000.049-0.049(0.069) (0.067) (0.153)(0.081)(0.229)Adjusted p-values 0.376
0.376
0.651
0.651
0.829
No. of obs
11698
11695
2985
1001
2066
Controls
X
X
X
X
X
Source: Provincial Skill Assessment and administrative data from a municipality in 2015 and 2018.
Note: The gaps in month-of-birth effects (based on equation 2) between low-SES students andothers are reported. We define low-SES students by whether their family receives an education
subsidy for low-income households. The control variables include school fixed effects, survey year
fixed effects, gender, and number of books at home. Standard errors are in parentheses.

for noncognitive skills turns out to be uninformative because it takes only a few
distinct values, and hence, many estimates are exactly zero.
Table 16 presents the estimated gaps between the oldest and youngest students
in the same cohort for the 10th, 50th, and 90th percentiles. We pool primary school
students (grades 4-6) and junior high school students (grades 7-9) to reduce noise.
For math and Japanese test scores, month-of-birth effects are more pronounced
toward the right tail of the distribution. While the gaps between percentiles are
statistically significant (see Table 17), the magnitude does not seem very large. For
example, the month-of-birth gaps in math score between the oldest and youngest
junior high school students for the 10th and 90th percentiles are 0.151 and 0.202,
respectively.

C.4

Comparison of Different Grades for Same Age

We also estimate cognitive and noncognitive skill gaps between students born before and after the cutoff date. Because all students take the tests in the same month,
the former or March-born students have been in school a year longer than the latteror April-born students, while both are nearly the same absolute age. The gaps are
estimated using the regression discontinuity design (RDD), and the econometricmodel is given by:
56

Self-efficacy
(6)
0.346
(0.097)
0.002
988
X

Table 16: Month-of-Birth Effects for 10th, 50th, and 90th Percentiles
10th
50th
90th
Japanese G4-G6 0.284
0.297
0.324
(0.007) (0.005) (0.004)
G7-G9 0.202
0.222
0.230
(0.009) (0.006) (0.004)
Math
G4-G6 0.274
0.258
0.352
(0.007) (0.005) (0.010)
G7-G9 0.151
0.164
0.202
(0.007) (0.005) (0.008)
English G4-G6
G7-G9 0.098
0.133
0.046
(0.009) (0.007) (0.005)
Table 17: 10th-50th and 50th-90th Percentile Gaps in Month-of-Birth Effects
10th - 50th
50t - 90th
Japanese G4-G6-0.013-0.027(0.007)(0.010)(-0.033 -0.005) (-0.053 -0.013)
G7-G9-0.020-0.007(0.011)(0.009)(-0.029 0.016) (-0.037 -0.001)
Math
G4-G6
0.016
-0.094(0.008)(0.009)(0.001 0.034) (-0.110 -0.077)
G7-G9-0.013-0.038(0.008)(0.013)(-0.021 0.007) (-0.053 -0.011)
English G7-G9-0.0350.088(0.014)(0.013)(-0.070 -0.017) (0.066 0.117)
Note: Month-of-birth effects are estimated using quantile regression. All models include school
fixed effects, survey year fixed effects, gender, and number of books at home. The standard errors
for the gaps in the estimates are based on nonparametric bootstrap with 100 replications.

57

Yit = γ0 + γ1 Mit + γ2 Mit2 + γ3 Dit + γ4 Dit Mit + γ5 Dit Mit2 + γ6 Xit + νit ,(3)

where Yit is an outcome of student i in year t, Mit represents the distance from thecutoff measured in months, Dit is an indicator that equals one if student i was born
before the cutoff date and zero otherwise, Xit is a vector of control variables that
include gender and year and school fixed effects, and νit is an error term.
The parameter of interest here is γ3 , which reflects three different factors. First,
this parameter captures the effect of 1-year schooling. Students born before thecutoff have been in school a year longer than those born after the cutoff. Second,it captures the effect of school starting age. Students born before the cutoff started
schooling a year earlier. We cannot identify these two effects separately because of
perfect collinearity. Third, the parameter reflects relative age effects. Students born
before the cutoff are the youngest in their grade, while those born after the cutoff
are the oldest in their grade. It is hard to disentangle the relative age effect fromother factors because there is little variation in relative age.
The key identifying assumption for this RDD model is that students’ characteristics other than birth month must be continuous at the school-entry cutoff. This
assumption is violated if parents manipulate children’s birth month, which is thesame as the identifying assumption for the regression model (1).
Figure 9 shows graphically the estimated test score gaps between two adjacentgrades for students of the same age. The estimates, or the coefficients for grade
dummy (γ3 ) in equation (3), are also reported in Table 18. For the math test, thegap between grades four and five is 0.078, the gaps are insignificant for grades fiveto eight, and the gap is 0.233 between grades eight and nine. For the Japanese test,
the grade gaps are small in magnitude except for the gap between grades eight andnine. Finally, for the English test, the gap between grades eight and nine is 0.537,
which is greater than the gaps for the math and Japanese tests.
For all subjects, the gap between grades eight and nine is largest, presumably
because ninth graders work more hours per week and are more likely to go to prep
school to get ready for high school entrance exams. To be specific, 70% of ninth

58

graders go to prep school, compared with 60% of eighth graders. Ninth graders
study 10.6 hours per week outside of school, compared with 9.6 hours for eighth
graders. The gap in the English test is greater than that for the math and Japanese
tests, which reflects the gap in accumulated years of studying the subjects. Eighth
graders have studied English for only a year, which is half the accumulated yearsof studying English for ninth graders. By contrast, eighth graders have studied
math and Japanese for 7 years, which is seven-eighths of the accumulated years ofstudying for ninth graders.
Table 18: Test Score Gaps Between Grades for the Same AgeMath Japanese English
(1)
(2)
(3)
G4 vs. G5 0.078
0.005(0.009) (0.013)
G5 vs. G6 -0.013
0.030(0.009) (0.011)
G6 vs. G7 -0.016
0.076(0.009) (0.010)
G7 vs. G8 0.015
0.033(0.009) (0.009)
G8 vs. G9 0.233
0.189
0.537
(0.009) (0.010) (0.009)
Source: Provincial Skill Assessment 2015-2018.
Note: Test score gaps for students in different grades but of the same age arereported using equation (3). The controls include school fixed effects, survey year
fixed effects, gender, and number of books at home. Standard errors are inparentheses.

59Grade 4 vs Grade 5

Grade 5 vs Grade 6
0.4

Japanese

●●
●

−0.4

●

●●
●●

●

●

●

0.0

●

●

−0.8

●
●●

●

●

●●

●

●

●

●

●

0.50

●

●

●

●

0.00

●

0.00

●

●

●●

120

130

140

0.25

−0.50

●●

●

●●

●

0.4

●●

●
●●

160

170
●

●

0.4

●●

●

●

●●

●

●

1.0

●

●

0.8

●●●

●
●●
●●

●

60

●●

●

●

●●

●●
●
●
●

●

180
●●

●

●
●

●●

0.2
0.0

●

1.2

●

●●

0.6

●

●●

●●

●

●●

●

●

●

●

●

●

170
●

●

●

160

●●

●●●

●

●
●

●

150

0.8

●

●

0.8

●●
●

●

●

1.0

●

●

0.3

●

●

●

150

●
●

●

●

●●

0.5

●●

●

1.2

●

●
●

●

●

●●

−0.25

●

0.7
●

●●

●●

105 110 115 120 125 130

●

●

0.00

●
●●

●

●

140

●

1.4

●

●●

●

−1.00

●

●

●●

●

●
●

●

●

0.9

●

130

●●

●●
●

●

●

●

●
●●

●

●

●●

●●

●

●●

●●
●

●

−0.8

●

−0.25

●

Grade 8 vs Grade 9

●

●●

●

●

−1.6
105 110 115 120 125 130●

Grade 7 vs Grade 8

●

●●

●

●

●

●

0.25

●

●

●
●

Math

●●

●

−0.4

●

−0.75

●

●
●●

●●

●

●

−1.2

Grade 6 vs Grade 7
0.75

●●

●
●

●

●

●

●●

0.6

●

●●

●

●

0.2

−0.50
120

130

140

130

140

150

150

160

170

160

170
●●

English

0.75

●

●

●

180
●●

●

●
●

●

●

0.50
0.25
●●

●●

●

●

●●●

0.00

●

●

●

160

Figure 9: Test Score Gaps Between Grades for the Same Age
Source: Provincial Skill Assessment 2015-2018.

170

180

C.5

Correlation Between Time Use and Interpersonal Relationships

We estimate how time use and interpersonal relationships are associated with cognitive and noncognitive skills. We regress these skills on hours of study, prep school
attendance, relationships with teachers, and relationships with peers along withother control variables, including gender, number of books at home, and schooland year fixed effects. In addition, we also estimate models with student fixed effects. Other time use variables, such as hours of reading, are available only for
2015, and hence, cannot be included in a fixed-effect regression. All models are
estimated separately for elementary and junior high schools and by subject.
The regression results for test scores are summarized in Table 19. We focus on
results with student fixed effects, but OLS results are also reported for reference.
For both math and Japanese and for both elementary and junior high school, all
the educational inputs and interpersonal relationships are positively correlated withtest scores given student fixed effects. Table 19 also shows the results for regressions of noncognitive skills. Hours of study, relationship with teachers, and relationship with peers are positively associated with noncognitive skills, whereas only
prep school attendance is negatively associated with these three noncognitive skills.
These results are at best suggestive and should be carefully interpreted because they
by no means imply causality.

61

Table 19: Regressions of Cognitive and Noncognitive Skills on Inputs and Personal Relationships

Hours of studying
Prep school
Teachers
Peers

62

Control variables
Individual fixed effect

Grade 4-6
Math
(1)
0.025
(0.001)-0.001(0.004)0.012(0.002)0.078(0.002)
X

Hours of studying
Prep school
Teachers
Peers
Control variables
Individual fixed effect

(2)
0.006
(0.001)
0.024(0.005)0.007(0.003)0.008(0.003)X
X

Conscientiousness
(1)
0.038
(0.001)-0.095(0.009)0.106(0.006)0.180(0.006)

Japanese(3)
0.024(0.001)-0.086(0.004)0.025(0.002)0.102(0.002)
X

(2)
0.018
(0.001)-0.020(0.008)0.058(0.004)0.067(0.004)
X
X

(4)
0.004
(0.000)0.021(0.005)0.007(0.002)0.004(0.002)
X
X

Grade 7-9
Math
(5)
0.026
(0.000)0.163(0.006)-0.009(0.002)0.074(0.002)X

Self-control
(3)
0.027
(0.000)-0.078(0.005)0.141(0.002)0.195(0.002)
X

(4)
0.011
(0.000)-0.023(0.006)0.070(0.003)0.067(0.003)
X
X

(6)
0.009
(0.000)0.081(0.005)0.008(0.002)0.006(0.002)
X
X

Japanese(7)
0.023(0.000)-0.039(0.006)0.001(0.002)0.088(0.002)X

Self-efficacy
(5)
0.043
(0.000)0.052(0.005)0.066(0.003)0.190(0.003)
X

(8)
0.005
(0.000)0.004(0.005)0.006(0.003)0.004(0.002)
X
X

(6)
0.018
(0.001)
0.019(0.006)0.051(0.003)0.076(0.003)X
X

Source: Provincial Skill Assessment 2015-2018.
Note: Control variables include gender, number of books at home, and school and year fixed effects. Standard errors are in parentheses.