OSIPP Discussion Paper: DP-2013-E-007

Gender, sibling order, and differences in the quantity and quality ofeducational attainment: Evidence using Japanese twin data
October 3, 2013

Tien Manh Vu†
Ph.D Candidate, Osaka School of International Public Policy

Hisakazu Matsushige
Professor, Osaka School of International Public Policy[Key words] identical twins・gender・sibling order・educational attainment・equality[JEL Codes] I24, I25, J13, J16[Abstract]
Using 1,045 pairs of Japanese monozygotic twins, we examine differences in educational
attainment by considering both the years of schooling (quantity) and the reputation of the lastattended school (quality). We find that a difference in learning performance at 15 years of age is
one of the key factors connected with differences in both quantity and quality of educational
attainment. We also find that when the eldest child in the family is the female twin in the 1950s
and 1960s birth cohorts, she forgoes 0.542 years of schooling over her younger twin sister; but
for the same birth cohorts, when the eldest child in the family is the male twin, he gains someadvantage in the quality of educational attainment over his younger twin brother. However, wefind that as the Japanese economy has developed, any difference between twins disappears in
subsequent birth cohorts, regardless of gender and sibling order.

†Corresponding author: Tien Manh Vu.
Osaka School of International Public Policy, Osaka University,
1-31 Machikaneyama, Toyonaka, Osaka 560-0043 Japan.
Tel/Fax: +81-(0)6-6850-5656. E-mail: t-vu@osipp.osaka-u.ac.jp.
[Acknowledgments] The authors acknowledge the useful suggestions and helpful comments of Charles
Yuji Horioka, Midori Wakabayashi, Wataru Kureishi, and Tsunehiro Ootsuki. The authors also gratefully
acknowledge the precious supports from the members of the research project “The Assessments of the
Quality and the Productivity of Non-marketable Services” (Grant-in-Aid for Scientific Research (A), No.
3243044, Research Representative: Takeshi Hiromatsu,). All remaining errors are ours.

11. Introduction
It is debatable whether there is a connection between gender, sibling birth order,
educational attainment, and changes over time. More specifically, as a result of the son
preference in Japan among the 1920–39 birth cohorts reported by Kureishi and Wakabayashi(2011), it is unclear whether the eldest son of that generation have received some advantage interms of educational attainment. Further, it is unknown whether this difference has changed withthe process of economic development in Japan. We argue that monozygotic twins are the best
way to detect any connection between the sibling order given by parents and differences in
educational attainment. In particular, monozygotic twins help to overcome several confounding
problems with birth timing, genetic differences, and unobservable characteristics (Rosenzweig
and Wolpin, 2000).
The purpose of this study is to examine differences in educational attainment inJapanese monozygotic twins using a specific survey detailing all the life events of the twins.
Compared with previous studies, our research inspects the linkage between the sibling order anddifferences in educational attainment in monozygotic twins. In addition, extant studies often
examine only the quantity of education using either the years of schooling or the highest grade
obtained. Our analysis considers not only the difference in the years of schooling, including
incomplete qualifications, but also the quality of educational attainment using the reputation of
the last attended school.
We first investigate whether a difference in the years in schooling could ever exist for
monozygotic twins. We measure the determinants of the difference in years of schooling using
ordinary least squares (OLS) models with fixed effects to exclude the common time-invariant
factors for the twins. Then, using probit models, we examine the probability of the elder twin
receiving either more years of schooling or a better reputation for the last attended school thandoes the other twin. We separate the data into birth cohort decades and gender to obtain
additional insights.
We obtain three main findings. First, the difference in educational attainment between
twins is significant and substantially contributed to by the difference in learning performance
when the twins are 15 years of age. Second, on average, the eldest in the 1950s and 1960s birthcohorts who is also a female twin completes 0.542 years less schooling than does her twin
sibling. Meanwhile, if he is the eldest, the elder male twin gains some advantage in educational
attainment once the quality of education is also considered. Finally, the apparent inequality in
educational attainment by gender and sibling order in both quantity and quality disappears
completely in subsequent decades.
The remainder of the paper is organized as follows. Section 2 details the arguments
concerning the differences in educational attainment among siblings and between twins. Section
3 introduces the data used and Section 4 describes our empirical methodology. Section 5
presents our findings. Section 6 discusses our results and provides the conclusions.

22. Differences in educational attainment
2.1. Differences in the level of education among people in general and siblings

To the best of our knowledge, the arguments on why siblings sometimes have different
levels of education focus on differences in individual cognitive abilities, schooling choices,
investment in education, and differences in the family background over time. Of these, the mostcommon explanation is differences in cognitive ability, which is largely assumed to have asubstantial genetic influence. There is, of course, a transfer of human genetic code from parents
to their children based on the mechanics of heredity, but in a huge variety of combinations.
Thus, the intergenerational transmission of cognitive ability can come from both the paternal
and maternal sides (Pronzato, 2012).
Recent research often uses weight at birth as a proxy for the differences. The reasons
are twofold. First, the heavier a child is at birth, the better its physical condition. Consequently, aphysical condition advantage increases the probability of learning at a higher level. Second, theweight at birth is also reflective of nutrient intake and the level of care from the parents to thechild (Behrman and Rosenzweig, 2004; Royer, 2009; Rosenzweig and Zhang, 2012). However,Royer (2009) finds that the marginal benefit of birth weight in terms of education is most robust
for birth weights of 2,500 grams and higher.
Another reason for differences in education is choices and/or investment. Whenchildren are young, the parents very likely influence the decision on choosing a school as a formof investment (Becker, 1991). However, when the child grows to be an adult or is at least
recognized as an adult by law, the decision on schooling now becomes the choice of theindividual. Tastes or the desire for schooling also influence choice. Even within the same family,
tastes can vary among individuals. In addition, decisions by parents influence any differences inearly life. As this affects subsequent choices, it becomes difficult to distinguish between choice(by the child) and investment (by the parents).
The endogeneity of income is a related problem that arises when considering children’s
outcomes. For example, parental job loss or promotion would influence both household income
and parental behaviors. Thus, it is difficult to distinguish between the effects of a variation inincome and variations in other unmeasured household conditions (Dahl and Lochner, 2012).
Furthermore, it is also difficult to separate differences in endowment from differences ininvestment (Behrman, Rosenzweig and Taubman, 1994). Griliches (1979), for example, argues
that parents compensate for any inequality in innate endowment by correspondingly funding
additional investment in human capital and/or adjusting the level of bequests.
Similarly, Becker (1991) asserts that parents invest more in education for a higherability child. In turn, the initially better-endowed child voluntarily transfers resources to the lessendowed child when they reach adulthood. In addition, suppose that parents are free to leave any
volume of bequest to their children, such that the optimal school for the child will not depend on
parental wealth. The child, as well as its parents, then optimizes the final level of schoolingbasing on its costs and benefits, perhaps even their lifetime value of schooling. However, such
optimization is not always perfect, as individuals may under- or overestimate their lifetime
earnings (Cunha and Heckman, 2007).
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The third major factor in determining differences in education is family background.
Regardless of birth order, the wealth of the family can fluctuate over time. If they have just one
child, it is obvious that the parents can devote all their resources to that child. However, if there
is more than one child, they may have to favor one child over another (Behrman and Taubman,
1986; de Haan, 2010). Even if the parents do not have budgetary constraints, the family
background also changes over time. Research in biology and human development indicates thatlater born children are more likely to have birth defects and physical disadvantages (Behrman
and Taubman, 1986). In addition, the level of schooling may correlate with family wealth(income), given that wealth (income) also depends on parental abilities and human capital.
In contrast, in imperfect credit markets with limited borrowing opportunities, poor
parents may have to choose between their own consumption and schooling for their children(Dahl and Lochner, 2012). Thus, family income influences schooling choice. For example,
Becker (1991) argues that among poor families, the marginal rate of return is higher for betterendowed children, whereas marginal utilities are higher for less-endowed children. Thus, any
conclusion about what type of child poor families will invest more in is ambiguous, given thatpoor parents only invest in the human capital of their children. However, all earnings advantages
and ancestor disadvantages disappear in three generations (Becker and Tomes, 1986). In
addition, Becker (1991) argues and Haan (2010) concludes that family size could affect average
investment in child education.
Moreover, abundant evidence exists of the impact of school inputs on an individual’s
school attainment (Krueger, 1999). In the economics of education literature, these school inputs
are generally the curricula, school organization (such as class size, facilities, and administrative
factors), teacher background (education level, experience, and gender), community factors(average expenditure), and school reputation. However, following Hanushek (2003), it isdebatable whether these factors exert any statistically significant influence on individual
educational attainment.
2.2. Twins and differences in educational attainment

Twins are the offspring of the same pregnancy and can be either dizygotic (DZ) or
monozygotic (MZ) twins. DZ twins are the result of two different sperm, whereas MZ twins are
the product of a single ovum dividing into two following fertilizations (Squires, 1943). Thus,
MZ twins are referred to as identical twins. Parisi et al. (1983) found that twinning is inheritablethrough the maternal line. Thus, twins are special cases, with MZ twinning occurring at a
relatively constant rate of 3.5 to 5 in every thousand births, regardless of race, with DZ twinning
in Japan taking place at a rate as low as two births per thousand (Carter, 1970).
Data on twins have some advantage over household data on singletons. First, we candismiss entirely the impact of natural birth order and differences in family background. In
addition, MZ twins possess the same genetic constitution and are always of the same sex(Squires, 1943). Therefore, any difference in endowment in later life would be solely because ofparental discrimination or other environmental factors. During development, there is evidence ofdifferences in genetic aspects between identical twins. Fraga et al. (2005) find that older
monozygotic twins exhibit significant differences in the overall content and genomic
4

distribution of 5-methylcytosine DNA and histone acetylation, affecting their gene-expression
portrait.
However, given that monozygotic twins have the same genetic endowment, theliterature has proposed several reasons for any difference in school attainment. Using therecords of twins separated at least ten years after infancy, Squires (1943) argues that birth
injuries and the nurturing environment can lead to differences in educational attainment between
identical twins, even though genetic constitution is a major factor in characteristics such as
intelligence. In addition, noneconomic reasons could result in different nongenetic abilities. For
instance, the first thing that parents must do differently is to give different first names to theirtwins (Ashenfelter and Rouse, 1998).
Ashenfelter and Rouse (1998) provide five main reasons for differences in educational
attainment between identical twins. The first is measurement error, in that DZ twins could be
mistaken for MZ twins. The family background of twins brought up differently could also
contribute to differences. Environmental influences in the womb could also provide anadvantage or disadvantage to one of the twins. For example, birth weights can differ between
twins, and these correlate with differences in postnatal cognition (Segal, 2012), and could
ultimately result in differences in educational attainment. Ashenfelter and Rouse (1998) also
argue that decisions on marriage or employment could be another reason for differences. Lastly,
random deviation from optimal education may also be a reason.
In addition, Isacsson (1999) tests whether differences in educational attainment are
randomly determined by dividing the twin data for children aged 14–20 years into MZ twins and
DZ twins, and separating males and females. The control variables are mainly height and weightof the individuals. Isacsson (1999) acknowledges that there would not be strong evidence
against the assumption that differences in years of schooling are purely random, at least for MZ
twins. However, we surmise that this test may underestimate educational differences, as the age
for the test ranges between 14 and 20 years. This perhaps provides an insufficient number ofobservations for children who have completed their educational attainments, especially as theSwedish twin data in Isacsson (1999) suggest average years of schooling of 11.36–11.54 years.
As found by Kureishi and Wakabayashi (2011), the Japanese used to have a son
preference, at least for the 1920–39 birth cohorts. We predict that the parents of the individuals
with 1950s or 1960s birth cohorts are likely to be this 1920–39 generation. Thus, wehypothesize that the 1950s and 1960s birth cohorts are more likely to face some discriminationin educational attainment by gender and sibling order. Therefore, our study tests this hypothesis.
In addition, to the best of our knowledge, differences in educational attainment are mostoften measured by years of schooling (quantity), but not by the reputation of the last attended
school (quality). Ashenfelter and Rouse (1998), using interview data, find that parents revealthat it is extremely difficult to treat identical twins in any way other than identically. Using alarge sample of Swedish twins born in the period 1886–1967, Isacsson (1999) also finds little
evidence for the hypothesis that differences in the years of schooling are significantly different
from zero. However, we argue that we should include the quality of the last attended school
when investigating any difference. We examine quality using the school’s reputation, as
measured by its deviation from the mean ranking of all high schools (universities), whenever the5years of schooling are identical for both twins. All other things being equal, in Japan, the higher
the school ranking, the more difficult it is to gain entry, and therefore only students that are morecompetitive and/or a better investment will succeed in the required entrance exams.

3. Data
The data we use are from two Web-based surveys conducted in February–March 2012
and August 2012 by Rakuten Research. Rakuten is a Japanese electronic commerce and Internetcompany located in Tokyo, Japan. In February 2012, Rakuten administered a questionnaire to allits users, approximately 75 million users in total, of which 11.72 million users made at least onepurchase on Rakuten Ichiba in each quarter in 2011. The incentive for users to join the survey
was points awarded for completing the questionnaire. One point is equal to one Japanese yen.
The survey first included six questions on family and siblings. The last question was about
whether the respondent was a twin. The Web-based survey design then directed the respondents
with a twin sibling to a survey intended for twins. All other respondents completed a separate
questionnaire. All respondents received the same award points irrespective of whether they hada twin sibling. The design of the questionnaire was similar to the Princeton Twins Survey andthe Employment Status Survey conducted by the Japanese Ministry of Internal Affairs andCommunication.
Our data contain nonstudent respondents aged 20–60 years with a twin sibling. The
sample size is 2,360 pairs of twins, comprising 1,371 pairs of MZ twins, 882 pairs of DZ twins,
and 107 pairs of twins who self-identified as neither MZ nor DZ. To the best of our knowledge,
this is the largest sample of Japanese twins ever examined. We found that some pairs withdifferent genders identified themselves as MZ twins, and included these as DZ twins in the data.
We used the follow-up questionnaire in August 2012 to verify the MZ twins. Based on thenational population statistics, our sample comprises approximately 0.57 percent of the Japanese
population of twins aged 20–60 years that are not students1. We employ only data relating to MZ
twins in all our estimations. The actual number of observations with all the necessary
information is 1,045, consisting of 545 pairs of male MZ twins and 500 pairs of female MZ
twins. Table 1 provides selected descriptive statistics.
[INSERT TABLE 1 HERE]
The survey contains some specific features that can overcome the limitations ofprevious studies concerning educational attainment by twins. Apart from self-reported total
years in school, we also identify and record years repeated in general education, years atuniversity, and years of uncompleted school (dropouts). Therefore, our measure of years inschool is more precise than indicators obtained by converting the highest diploma/degree
achieved to years. Given that the Web survey allowed respondents to fill in the name of the lastschool attended, we combine this information with the high school and university ranking. We
obtained the information on the reputational ranking of each high school and college from
Kanjuku, a major provider of after-school teaching programs for elementary, junior high, andsenior high school students in Japan. We further enriched the data with measures of learning

1

Using the 2013 Japanese Statistical Yearbook.

6

performance at school relative to classmates and family wealth when the twins were 15 years
old. We include Japanese gross domestic product (GDP) per capita at 1990 constant prices fromthe 1968 System of National Accounts (68SNA)2 compiled by the Japan Statistics Bureau in the
Ministry of Internal Affairs and Communications, and we specify real GDP per capita to proxyfor societal wealth for the different birth cohorts.
Given that the survey respondents are all Internet users and concentrated in large cities,
we acknowledge concerns that our sample is not fully random. To reflect this concern, we
compare the distribution of the sample with that of the national population. In our sample, there
are relatively more twins from Japan’s three largest cities, namely Tokyo, Yokohama, andOsaka. The proportion of respondents in our sample aged 40–50 years is also higher than that of
the national population. However, we prefer to use the data as they are rather than adjusting theweight for each individual. This is because in our analysis, we investigate past experience, and it
is very likely that twins live and work in different regions/prefectures than their original
hometown. Moreover, unfortunately we do not have information about the hometowns of the
respondents. Therefore, weighting the current place of residence may only worsen the selection
problem.

4. Empirical models and econometric specification
4.1. Empirical models

Our main hypothesis is whether the difference in educational attainment between twins
is significant using information on both years of schooling completed and the reputation of thelast attended school. We employ fixed effects using ordinary least squares (OLS) and probit
models in our analysis. Without loss of generality, we assume that educational attainment is afunction of the individuals’ observable characteristics (𝑍𝑖 ), family background (𝑋𝑖 ), and otherunobservable characteristics for both individuals (𝜇𝑖 ). We assume that 𝜇𝑖 is uncorrelated with 𝑋𝑖
and 𝑍𝑖 in a pair of twins. For example, 𝜇𝑖 can be the genetic endowment of MZ twins. Thus,
the educational attainment functions for the elder twin3 (𝑒𝑑𝑢𝑐1𝑖 ) and the younger twin (𝑒𝑑𝑢𝑐2𝑖 )are
𝑒𝑑𝑢𝑐1𝑖 = 𝛼𝑋𝑖 + 𝛼1 𝑋𝑖 + 𝛽1 𝑍1𝑖 + 𝜇𝑖 + 𝜀1𝑖 ,(1)

𝑒𝑑𝑢𝑐2𝑖 = 𝛼𝑋𝑖 + 𝛼2 𝑋𝑖 + 𝛽1 𝑍2𝑖 + 𝜇𝑖 + 𝜀2𝑖 .

(2)where the error terms 𝜀1𝑖 and 𝜀2𝑖 are N(0, 𝜎 2 ). However, unlike previous studies, we assume
that 𝛼1 differs from 𝛼2 , as parents may generally discriminate by sibling order and gender. We
then test the hypothesis 𝛼1 − 𝛼2 = 0 using appropriate tests. Therefore, the fixed effects (firstdifference) between twins is𝑑𝑒𝑑𝑢𝑐𝑖 = 𝑒𝑑𝑢𝑐1𝑖 − 𝑒𝑑𝑢𝑐2𝑖 = (𝛼1 − 𝛼2 ). 𝑋𝑖 + 𝛽1 (𝑍1𝑖 − 𝑍2𝑖 ) + (𝜀1𝑖 − 𝜀1𝑖 ),(3)where all common characteristics for a pair of twins, including unobservable characteristics, 𝜇𝑖 ,2 GDP using the 68SNA method is available for 1955–98. We convert the GDP data for 1999–2007
using 92SNA from the same source.
3
We designate the appointed elder sibling as the elder twin and the other twin as the younger twin, eventhough they are of the same age. Deciding upon the eldest child is common and typical among Japanese families.
The survey recorded this information for all individuals.

7are excluded. Equation (3) is then an unbiased estimation. This is the advantage of conducting anatural experiment using data on twins.
In order to integrate information on the difference in school reputation, we employ
probit models. We assume that the elder twin obtains a relative advantage, and investigate theconnections with this event. We distinguish three cases, namely the elder twin has more years ofschooling, the elder twin attends a higher-ranking college given that both twins have somecollege attendance, and the elder twin attends a higher-ranking high school given that both twinsceased education after graduating from high school. With school ranking, we construct thedependent variable ℎ𝑖𝑔ℎ𝑒𝑟 as follows:
1, 𝑖𝑓 𝑒𝑑𝑢𝑐1𝑖 − 𝑒𝑑𝑢𝑐2𝑖 > 0 𝑜𝑟 𝑖𝑓 𝑟𝑎𝑛𝑘𝑖𝑛𝑔1𝑖 > 𝑟𝑎𝑛𝑘𝑖𝑛𝑔2𝑖 |𝑒𝑑𝑢𝑐1𝑖 = 𝑒𝑑𝑢𝑐2𝑖ℎ𝑖𝑔ℎ𝑒𝑟𝑖 = {,0, 𝑖𝑓 𝑜𝑡ℎ𝑒𝑟𝑤𝑖𝑠𝑒(4)

where 𝑟𝑎𝑛𝑘𝑖𝑛𝑔1𝑖 and 𝑟𝑎𝑛𝑘𝑖𝑛𝑔2𝑖 are the deviations of the school ranking from the population
mean on a 0–100 scale. Only when twins have the same years of schooling do we apply either
the high school ranking or university ranking based on the last school attended.
4.2. Specifications

We construct three groups of explanatory variables. The first group, (𝑑𝑍𝑖 = 𝑍1𝑖 − 𝑍2𝑖 ),consists of the weight differences between a pair of twins at birth (𝑑𝑏𝑖𝑟𝑡ℎ_𝑤𝑒𝑖𝑔ℎ𝑡 ) and differencesin learning performance when the twins were 15 years old (𝑑𝑙𝑒𝑎𝑟𝑛𝑖𝑛𝑔_15). The second group ofvariables controls for differences across families. These are the number of siblings other than thetwins (𝑛𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑠𝑖𝑏𝑙𝑖𝑛𝑔𝑠 ), the maternal age of the mother when the twins were born(𝑚𝑎𝑡𝑒𝑟𝑛𝑎𝑙 𝑎𝑔𝑒), and the number of years the twins were brought up together in the same family(𝑡𝑜𝑔𝑒𝑡ℎ𝑒𝑟_𝑎𝑔𝑒). We also include information from when the twins were 15 years of age,
including relative family wealth (𝑓𝑎𝑚𝑖𝑙𝑦𝑤𝑒𝑎𝑙𝑡ℎ_15) and real GDP per capita at 1990 constantprices (𝑟𝑒𝑎𝑙_𝐺𝐷𝑃𝑝𝑐_15). We also add an interaction term between 𝑟𝑒𝑎𝑙_𝐺𝐷𝑃𝑝𝑐_15 and𝑑𝑙𝑒𝑎𝑟𝑛𝑖𝑛𝑔_15 to examine the effect of wealth and learning performance on educational
attainment. The final group of explanatory variables controls for gender and sibling order. Whenthe data is divided into three birth cohorts, we use 𝐵𝐵𝑒𝑙𝑑𝑒𝑠𝑡 (𝐺𝐺𝑒𝑙𝑑𝑒𝑠𝑡 ) to indicate MZ male(female) twins who is also the eldest child of the family. We combine gender, eldest child status,and birth cohort in all other cases. Table 1 explains the combinations. For example, we use
𝐵𝐵𝑒𝑙𝑑𝑒𝑠𝑡70 to designate if the twins are males from the 1970s birth cohort with no elder
siblings. The reference dummy is 𝐺𝐺𝑛𝑜𝑡𝑒𝑙𝑑𝑒𝑠𝑡560 and 𝐵𝐵𝑛𝑜𝑡𝑒𝑙𝑑𝑒𝑠𝑡560 if only MZ BB twins are
considered. Finally, as males and females potentially have different educational attainments bygeneration, we divide the data into several different scenarios by separating the data by gender,
and then both by gender and across three different birth cohorts.

5. Results
5.1. Nonparametric evidence of differences between twins in years of schooling

We conduct one-sample 𝑡-tests to examine the difference in education attainment asmeasured by years of schooling in our sample. As shown in Table 2, we accept that thedifference in years of schooling between MZ twins is zero with a 95 percent confidence level.
However, similar to Isacsson (1999), we find that the probability of being correct if choosing the8alternative null hypothesis (elder twin has more years in school) is 18.49 percent among MZ
twins.
[INSERT TABLE 2 HERE]We also examine whether educational attainment as measured by years of schoolingchanges by sibling order and gender over time. We elaborate using visual evidence from the dataand illustrate the results in Graph 1. As shown, MZ GG twins in the 1950s and 1960s birthcohorts tend to have a greater variance in years of schooling than MZ BB twins of the samebirth cohorts. Further, the quintile distributions of MZ BB twins exhibit less visible differences
within each birth cohort, while those for MZ GG twins change noticeably over time. Clearly,
MZ elder female twins tend to have fewer years of schooling when compared with their twin
siblings in the 1950s and 1960s birth cohorts.
[INSERT GRAPH 1 HERE]
5.2. Differences between twins
5.2.1. Evidence from the OLS model with fixed effectAs shown in Table 3, the difference in attitude toward gender and the eldest child bybirth cohort is minimal in explaining the difference in total years of schooling between the elder
and younger MZ BB twins, as shown by the results of the Wald tests. However, attitudes have asignificant nexus (5 percent error) with the differences in the total years of schooling between
the elder and younger MZ GG twins. More specifically, the eldest female of the family who isalso a twin born in the 1950s or 1960s would have approximately 0.542 years less schooling on
average than her twin sister. This result is consistent with the evidence presented in Graph 1.
[INSERT TABLE 3 HERE]
5.2.2. Evidence from probit models including the difference in the reputation of the last attended school

When school rank is considered, the priority given to the elder MZ BB twin is likely toconnect with the fact that he is the eldest child of the family, as evidenced in column 2 of Table
4. The priority is at least a better reputation of the last attended school. This difference is not
identifiable when using only the years of schooling, as in Ashenfelter and Rouse (1998) andIsacsson (1999).
[INSERT TABLE 4 HERE]
We further examine the data by dividing the sample into three different birth cohorts.
The estimated coefficient for the eldest child in column 2 of Table 5 indicates that for the 1950s
and 1960s birth cohorts, there is better educational attainment by the elder twin. This result
complements the findings in Kureishi and Wakabayashi (2011) on son preference in Japan. The
eldest son appears to have some advantage over his siblings, even his twin brother of the sameage, and the difference is more complicated than that suggested merely by years of schooling.
[INSERT TABLE 5 HERE]
5.3. The disappearance of differences by gender and sibling order in recent decades

The evidence shows that the differences in educational attainment by gender and sibling9

order evident in the 1950s and 1960s birth cohorts largely evaporate in subsequent decades. First,
the distributions of MZ BB and GG twins are much more alike among 1980s and 1990s birthcohorts, as shown in Graph 1. The distributions of the MZ elder twins and younger twins converge
after the 1950s and 1960s birth cohorts. Second, as mentioned, the interaction between 𝑟𝑔𝑑𝑝𝑝𝑐 and𝑑𝑙𝑒𝑎𝑟𝑛𝑖𝑛𝑔_15 in column 5 of Table 3 shows an improvement in equality for MZ BB twins. Similarly,
as shown by the estimated coefficient for the interaction term (real_GDPpc_15 ∗ dlearning_15) incolumn 6 of Table 4, higher real GDP per capita is associated with the elder MZ GG twin having
better educational attainment, given the difference in learning performance at age 15. Thus, unlike
the situation where the elder MZ GG twin would surrender some years of schooling in the past, thisindicates that the elder MZ GG twin is better off with increases in wealth. Third, we witness that theelder male twin may have some advantage because he is the eldest child in the family, as shown incolumn 2 of Table 5. However, this advantage vanishes during the following decades. As shown in
Table 5, the coefficients of eldest child in column 5 and 8 are statistically insignificant among birthcohorts in the 1970s, 1980s, and 1990s. Thus, the result supports the prediction in Vu (2013) that
discrimination among children by gender due to son preference would soon disappear as a result of
economic development.
5.4. Differences in learning performance at age 15 and educational attainment

Through the OLS model with fixed effects and the probit models considering both thedifferences in years of schooling and the reputation of the last attended school, we find that𝑑𝑙𝑒𝑎𝑟𝑛𝑖𝑛𝑔_15 is the more influential factor in explaining the differences in educational
attainment between MZ twins. In all of our estimations, the sign of 𝑑𝑙𝑒𝑎𝑟𝑛𝑖𝑛𝑔_15 is always
positive when statistically significant, implying that better learning at age 15 leads to higher
educational attainment. Interestingly, this challenges the conventional hypothesis thatmonozygotic twins are indeed strictly identical. In fact, fifteen years after their births, there are
significant differences between twins in terms of their ability to learn. This finding suggests
further study to investigate the development of variances between twins throughout their
lifetimes.

6. Discussion and conclusion
This paper examines the differences in educational attainment between MZ twins byconsidering both the total years of schooling (quantity) and the reputation of the last attended
school (quality). We use OLS fixed effects analysis for the former and probit models for both theformer and the latter to investigate the differences in educational attainment. We find that thedifference in learning performance at 15 years of age contributes significantly to the differencesin educational attainment in terms of both quantity and quality. In addition, differences in yearsof schooling between MZ BB twins generally have minimal connection with gender, siblingorder, and birth cohort. However, once we consider the reputation of the last attended school, as
well as being the eldest son of the family, the MZ BB elder twin in the 1950s and 1960s birthcohorts exhibits higher educational attainment than his twin sibling. In contrast, we find the MZ
female elder twin in the same birth cohort has fewer years of schooling than her twin sibling.
Nevertheless, greater equality in educational attainment is in evidence in birth cohorts in recent
decades. As a rule, the gap between twins, by both gender and sibling order, has disappeared as
10the Japanese economy has developed.
Although we do not have any direct evidence to explain the reason for the fewer yearsof schooling for the eldest MZ female twin in the 1950s and 1960s birth cohorts, we would
argue that the notion of a role model is one possible explanation. In Japan, the elder sibling(sempai) assists the younger sibling (kohai), a custom that prevails in contemporary Japanesesociety. This does not conflict with the case where the eldest son of the same birth cohort ismore likely to have a greater advantage than his twin. Put differently, the eldest son could reflect
the lineage of the whole family, which would be more important than the relationship between
twins.
We acknowledge that the difference we have found between male twins where one isthe eldest child in the family provides only weak evidence, as the estimated coefficients are
statistically significant at only the 90 percent level. In addition, we included an additionaldummy identifying the case where the twins have no older brothers but do have one or more
older sisters. In practice, Japanese families appoint the elder male twin as the eldest son eventhough he may not be the eldest child. However, the marginal benefit of including additionaldummy variables to reflect this would be statistically small. The likelihood ratio test that we cannest the adjusted models in the original models can be accepted. The added dummy is also
statistically insignificant. We predict that birth order then has a stronger negative relationshipwith the difference, given that the difference is statistically sensitive to change. Thus, we
exclude this dummy variable from our analysis.
There are possible concerns about sample selection in our data. More specifically, given
the nature of the survey, we may have inadvertently sampled only the computer literate. This
may account for educated older monozygotic twins being overrepresented in the sample.
However, among the 1950s and 1960s birth cohorts, the number of direct respondents is 88 elder
female twins and 60 younger female twins, while the results show that the MZ GG elder twin ismore likely to receive fewer years of schooling. Similarly, although the number of direct
respondents includes 140 elder male twins and 78 younger male twins in the same birth cohorts,
there is approximately 15 mean years of schooling for both the elder and younger twins4. Thus,
there should be no problem of bias selection based on computer literacy. Nevertheless, we
acknowledge that there could be bias as reflected in online shopping habits.
Another concern is whether we can transfer these findings to singletons within a family,
given that twinning is obviously a special case. We acknowledge two other limitations of ourresearch. First, we do not have singleton siblings with which to compare, perhaps using a similar
method to that used by Behrman et al. (1994), to enable us to conclude whether the inference isvalid for all kinds of siblings. Second, in our estimations, we omit the fact that it is moredifficult, and possibly costly (Behrman et al., 1994), for parents to differentiate between twins
than between singletons. Thus, the differences we have found may underestimate the reality
among singletons. We suggest the need for further research to test a similar hypothesis fordifferences in educational attainment in both quantity and quality among singletons, perhaps
using household fixed effects.
4

The 95 percent confidence intervals for years of schooling are 15.21–15.8 and 15.01–15.6 for the elder
and younger twin cohorts, respectively.

11

As a final point, the disappearance of the difference in educational attainments bygender and sibling order in the 1970s birth cohort and beyond implies that equality is inevidence among Japanese twins. Our estimations also suggest that monozygotic twins are not
identical in educational attainment, with learning performance at age 15, sibling order, andgender being potential candidate explanations.

References
Ashenfelter, O., & Rouse, C. (1998). Income, schooling, and ability: evidence from a new
sample of identical twins. The Quarterly Journal of Economics, 113(1), 253–284.
Becker, G. S. (1991). A treatise on the family. Cambridge, Mass., Harvard University Press.
Becker, G. S., & Tomes, N. (1986). Human capital and the rise and fall of families. Journal ofLabor Economics, 4(3), S1–39.
Behrman, J. R., & Rosenzweig, M. R. (2004). Returns to birth weight. Review of Economics andStatistics, 86(2), 586–601.
Behrman, J. R., Rosenzweig, M. R., & Taubman, P. (1994). Endowments and the allocation ofschooling in the family and in the marriage market—the twins experiment. Journal of
Political Economy, 102(6), 1131–1174.
Behrman, J. R., & Taubman, P. (1986). Birth-order, schooling, and earnings. Journal of Labor
Economics, 4(3), S121–S145.
Carter, C. O. (1970). Twin births. Nature, 226, 1271–1271.
Cunha, F., & Heckman, J. J. (2007). Identifying and estimating the distributions of ex post andex ante returns to schooling. Labour Economics, 14(6), 870–893.
Dahl, G. B., & Lochner, L. (2012). The impact of family income on child achievement:
evidence from the earned income tax credit. American Economic Review, 102(5), 1927–
1956.
de Haan, M. (2010). Birth order, family size and educational attainment. Economics of
Education Review, 29(4), 576–588.
Fraga, M. F., Ballestar, E., Paz, M. F., Ropero, S., Setien, F., Ballestart, M. L., et al. (2005).
Epigenetic differences arise during the lifetime of monozygotic twins. Proceedings of
the National Academy of Sciences of the United States of America, 102(30), 10604–
10609.
Griliches, Z. (1979). Sibling models and data in economics: beginnings of a survey. Journal of
Political Economy, 87(5), S37–64.
Hanushek, E. A. (2003). The failure of input-based schooling policies. The Economic Journal,
113, F64–F98.
Isacsson, G. (1999). Estimates of the return to schooling in Sweden from a large sample oftwins. Labour Economics, 6(4), 471–489.
Krueger, A. B. (1999). Experimental estimates of education production functions. The Quarterly
Journal of Economics, 114(2), 497–532.
Kureishi, W., & Wakabayashi, M. (2011). Son preference in Japan. Journal of Population
Economics, 24(3), 873–893.
Parisi, P., Gatti, M., Prinzi, G., & Caperna, G. (1983). Familial incidence of twinning. Nature,12304(5927), 626–628.
Pronzato, C. (2012). An examination of paternal and maternal intergenerational transmission ofschooling. Journal of Population Economics, 25(2), 591–608.
Rosenzweig, M. R., & Wolpin, K. I. (2000). Natural “natural experiments” in economics.
Journal of Economic Literature, 48(4), 827–847.
Rosenzweig, M. R., & Zhang, J. (2012). Economic growth, comparative advantage, and genderdifferences in schooling outcomes: evidence from the birth weight differences ofChinese twins. Yale University Discussion Paper.
Royer, H. (2009). Separated at girth: US twin estimates of the effects of birth weight. Applied
Economics, 1(1), 49–85.
Segal, N. L. (2012). Twins, birth weight, cognition, and handedness. Proceedings of the
National Academy of Sciences of the United States of America, 109(48), E3293–E3293.
Squires, J. C. (1943). Studies of twins in the social sciences. Nature, 152, 437–440.
Vu, T. M. (2013). One male offspring preference: evidence from Vietnam using a splitpopulation model. Review of Economics of the Household. In press. DOI:
10.1007/s11150–013–9183–z.

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10

15

20

Graph 1
Quintile distributions of educational attainment by sex, sibling order, and birth cohorts among
MZ twins

1950-19601970s

1980-1990

MZ BB twins

1950-19601970s

1980-1990

MZ GG twins
Elder

Younger

14

Table 1
Descriptive statistics.
VariablededuchigherBBeldest
GGeldest
BBeldest560
BBeldest70
BBeldest890
BBnoteldest560
BBnoteldest70

Description
Obs.
Difference in years of schooling between the elder and younger twin
1,045
=1 if the elder twin has more years of schooling or better reputation for the last attended school 1,045
MZ male–male twins who do not have elder siblings
1045
MZ female–female twins who do not have elder siblings
1045
BB twins who are the eldest children of the family and born in 1950s or 1960s
1,045
BB twins who are the eldest children of the family and born in 1970s
1,045
BB twins who are the eldest children of the family and born in 1980s or 1990s
1,045
BB twins who have elder sibling and born in 1950s or 1960s
1,045
BB twins who have elder sibling and born in 1970s
1,045

Mean
0.042
0.553
0.260
0.234
0.105
0.096
0.059
0.103
0.101

Std. Dev. Min.
1.517
–9
0.497
0
0.439
0
0.424
0
0.307
0
0.294
0
0.236
0
0.305
0
0.302
0

Max.
9
1
1
1
1
1
1
1
1

BBnoteldest890
GGeldest560
GGeldest70
GGeldest890
GGnoteldest560
GGnoteldest70
GGnoteldest890

BB twins who have elder sibling and born in 1980s or 1990s
GG twins who are the eldest children of the family and born in 1950s or 1960s
GG twins who are the eldest children of the family and born in 1970s
GG twins who are the eldest children of the family and born in 1980s or 1990s
GG twins who have elder sibling and born in 1950s or 1960s
GG twins who have elder sibling and born in 1970s
GG twins who have elder sibling and born in 1980s or 1990s

1,045
1,045
1,045
1,045
1,045
1,045
1,045

0.056
0.066
0.098
0.071
0.076
0.102
0.066

0.231
0.248
0.297
0.257
0.264
0.303
0.248

0
0
0
0
0
0
0

1
1
1
1
1
1
1

1,045
1,045
1,045
1,045
1,045
1,045
1,045
1,045

1.099
16.0150.294
309.363
27.50241.8740.063
19.8561.071
10.6530.456
70.8654.079
292.1810.814
264.225

0
8
0
40
0
1
134.18 425.96
1848–3001 1500
–4
4–1639.55 1548.948

number of siblings
Number of siblings other than the twins
together_age
Total years the twins were raised together
familywealth_15
Family wealth when twins were 15 years old (=1 if wealth was average or above)
real_GDPpc_15
Real GDP per capita of Japan when twins were 15 years old (68SNA)
maternal age
Maternal age of the mother when twins were borndbirth_weight
Difference in weight at birth between the elder and younger twin
dlearning_15
Difference in learning performance when twins were 15 years old
real_GDPpc_15 * dlearning_15 Interaction term between real_GDPpc_15 and dlearning_15

15

Table 2
Tests for differences in educational attainment between twins by years in school.
H0: The years of schooling are the same for both twins
MZ twins
Pr(T<t)0.8151
Pr(T>t)0.1849Pr(|T|>|t|)0.3699N (pairs of twins)
1,045

MZ BB twins
0.97790.0221
0.0442545

MZ GG twins
0.2111
0.78890.4222500

16

Table 3
OLS with fixed effects estimates of differences in years of schooling between MZ twins.
Twins sample

Pooled MZ(1)deduc
0.1045(0.1954)0.4038(0.2876)0.1004(0.3504)
0.2629(0.2552)
0.5400(0.3504)
0.1075(0.2043)–0.4444**(0.2131)0.1846(0.3022)0.5416(0.3317)0.0185(0.2820)0.3404(0.3399)–0.0167(0.0530)
0.0060(0.0047)–0.0779(0.0944)–0.0019(0.0017)–0.0056(0.0106)–0.0002(0.0002)0.4960***(0.0733)

MZ BB
(2)
deduc
0.0346(0.2170)0.2451(0.3243)–0.1240(0.4111)0.0273(0.2825)
0.2299(0.4030)

MZ GG
(3)
deduc

Pooled MZ(4)
VARIABLES
deduc
0.0825
BBeldest560(0.1940)0.3935
BBeldest70(0.2881)0.0914
BBeldest890(0.3501)
0.2498BBnoteldest70(0.2564)0.5154
BBnoteldest890(0.3523)0.0851
BBnoteldest560(0.2047)–0.5463** –0.4521**
GGeldest560(0.2377)(0.2148)
0.19590.1720GGeldest70(0.3837)(0.3029)0.63700.5294
GGeldest890(0.4499)(0.3329)0.12970.0003
GGnoteldest70(0.3502)(0.2834)0.53050.3244
GGnoteldest890(0.4734)(0.3412)
number of siblings
0.0475–0.1041–0.0137(0.0631)(0.0962)(0.0529)
together_age
0.00110.01050.0061(0.0062)(0.0069)(0.0047)
familywealth_15–0.1808
0.0387–0.0741(0.1192)(0.1474)(0.0944)
real_GDPpc_15–0.0012–0.0029–0.0018(0.0023)(0.0026)(0.0018)
maternal age–0.0101
0.0005–0.0052(0.0143)(0.0162)(0.0106)
dbirth_weight
0.0001–0.0004*–0.0002(0.0002)(0.0002)(0.0002)
dlearning_15
0.5168*** 0.4648*** 0.8444***(0.0892)(0.1222)(0.2951)
real_GDPpc_15 * dlearning_ 15–0.0011(0.0009)
Constant
0.53390.63890.63030.4997(0.5666)(0.7744)(0.8515)(0.5720)
N (pairs of twins)
1,045
545
500
1,045
R-squared
0.092
0.094
0.095
0.093
Wald test with H0: All dummies combining gender, eldest and birth cohort are zeroF-statistic
1.80
0.78
2.24
1.73
Prob>F0.04930.56790.049
0.0627
Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1.

MZ BB
(5)
deduc
0.0385(0.2167)0.2602(0.3230)–0.1129(0.4095)
0.0296(0.2824)0.2042(0.4037)

MZ GG
(6)
deduc

–0.5415**(0.2362)0.1998(0.3899)
0.6398(0.4535)
0.1349(0.3565)
0.5335(0.4782)
0.0556–0.1046(0.0619)(0.0967)0.00130.0105(0.0062)(0.0069)–0.16650.0396(0.1182)(0.1473)–0.0009–0.0029(0.0024)(0.0027)–0.0095
0.0005(0.0143)(0.0163)0.0001–0.0004**(0.0003)(0.0002)1.1295*** 0.3317(0.2881)(0.7095)–0.0020** 0.0004(0.0009)(0.0019)0.50950.6292(0.7813)(0.8514)
545
500
0.100
0.095
0.74
0.59132.27
0.046817

Table 4
Marginal effects using probit estimation for the differences in both years of schooling and thereputation of the last attended school between MZ twins.
Twins sample

Pooled MZ(1)mfx
0.0631(0.0451)0.0403(0.0468)
0.0027(0.0185)
0.0032**(0.0016)–0.0042(0.0351)0.0007***(0.0002)0.0061(0.0040)–0.0000(0.0001)
0.1021***(0.0203)

MZ BB
(2)
mfx
0.0953*(0.0555)

MZ GG
(3)
mfx

Pooled MZ(4)
VARIABLES
mfx
BBeldest
0.0630(0.0451)
GGeldest
0.02400.0401(0.0609)(0.0469)
number of siblings
0.00290.01320.0025(0.0242)(0.0297)(0.0185)
together_age
0.00250.00340.0032**(0.0023)(0.0022)(0.0016)
familywealth_15
0.0355–0.0429–0.0043(0.0484)(0.0519)(0.0352)
real_GDPpc_15
0.0006**0.0006*0.0007***(0.0003)(0.0004)(0.0002)
maternal age
0.00150.0135** 0.0061(0.0054)(0.0061)(0.0040)dbirth_weight
0.0000–0.0001–0.0000(0.0001)(0.0001)(0.0001)
dlearning_15
0.1203*** 0.0831*** 0.0817(0.0288)(0.0288)(0.0947)
real_GDPpc_15 * dlearning_ 15
0.0001(0.0003)
N (pairs of twins)
1,045
545
500
1,045
Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1.

MZ BB
(5)
mfx
0.0993*(0.0556)

MZ GG
(6)
mfx

0.0254(0.0609)0.00520.0117(0.0243) (0.0298)
0.00250.0033(0.0023) (0.0022)
0.0378–0.0410(0.0484) (0.0520)
0.0007** 0.0006*(0.0003) (0.0004)
0.00170.0135**(0.0054) (0.0061)
0.0000–0.0001(0.0001) (0.0001)
0.2734** –0.2120(0.1283) (0.1566)–0.0005 0.0009*(0.0004) (0.0005)
545
500

18

Table 5
Marginal effects using probit estimation for the differences in both years of schooling and the reputation of the last attended school between MZ
twins by birth cohort.
Birth cohort
Sample1950s–1960s
Pooled MZ
MZ BB
MZ GG
(1)
(2)
(3)
VARIABLES
mfx
mfx
mfx
BBeldest
0.09880.1672*(0.0738)(0.0868)
GGeldest–0.0056
0.0155(0.0884)(0.1291)
number of siblings
0.0648*0.05800.1348*(0.0356)(0.0426)(0.0708)
together_age
0.00110.0019–0.0010(0.0031)(0.0041)(0.0050)
familywealth_15–0.1094*–0.0469–0.1436(0.0587)(0.0759)(0.0988)
maternal age
0.00490.00430.0136(0.0064)(0.0082)(0.0109)
dbirth_weight
0.00010.0001–0.0000(0.0001)(0.0001)(0.0001)
dlearning_15
0.0857**0.1544***0.0207(0.0354)(0.0499)(0.0560)
Observations
366
218
148
Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1.

1970s
Pooled MZ(4)mfx
0.0656(0.0736)
0.0369(0.0721)–0.0015(0.0306)
0.0035(0.0025)
0.0480(0.0600)
0.0046(0.0069)–0.0000(0.0001)
0.1252***(0.0362)
415

MZ BB
(5)
mfx
0.0844(0.0935)

0.0183(0.0421)0.0021(0.0037)
0.0390(0.0878)–0.0061(0.0094)0.0000(0.0001)
0.1289***(0.0471)
206

MZ GG
(6)
mfx

0.0102(0.0893)–0.0241(0.0454)0.0053(0.0035)
0.0651(0.0846)
0.0185*(0.0103)–0.0000(0.0001)0.1102*(0.0569)
209

1980s–1990s
Pooled MZ(7)mfx
0.0156(0.0925)
0.0998(0.0857)–0.0525(0.0330)
0.0068**(0.0030)
0.0486(0.0634)
0.0092(0.0080)–0.0001(0.0001)
0.0944***(0.0347)
264

MZ BB
(8)
mfx
0.0035(0.1211)–0.0743(0.0478)0.0044(0.0047)
0.1968**(0.0925)
0.0111(0.0127)–0.0002(0.0002)0.0804(0.0618)
121

MZ GG
(9)
mfx

0.1472(0.1122)–0.0121(0.0486)
0.0089**(0.0038)–0.0948(0.0901)
0.0088(0.0105)–0.0001(0.0002)0.1011**(0.0418)143

19