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ESRI Discussion Paper Series No.304

Impact of School Quality on Student Achievements:

Evidence from a Twin Survey in Japan

Makiko Nakamuro, Takashi Oshio and Tomohiko Inui

October 2013

Economic and Social Research Institute

Cabinet Office

Tokyo, Japan

The views expressed in “ESRI Discussion Papers” are those of the authors and not those of the Economic and Social Research Institute, the Cabinet Office, or the Government of Japan. (Contact us: https://form.cao.go.jp/esri/en_opinion-0002.html)

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Impact of School Quality on Student Achievements:

Evidence from a Twin Survey in Japan1

Makiko Nakamuro2

Faculty of Policy Management, Keio University

Takashi Oshio

Institute of Economic Research, Hitotsubashi University

&

Tomohiko Inui

College of Economics, Nihon University

JEL: I21, I22

Keywords: education production function, money matters debate, identical twins,

endogeneity

1 We gratefully acknowledge that this research was financially supported by a Grant-in-Aid for Scientific Research (A) titled “The Assessments of the Quality and the Productivity of Non-marketable Services” (Research Representative: Takeshi Hiromatsu, No. 3243044). The authors would like to thank the Ministry of Education, Culture, Sports, Science and Technology (MEXT) for permission to use the micro dataset of the School Basic Survey as well as Ryoji Matsuoka, Hisakazu Matsushige, Toshiki Akiyama and participants at the Behaviormetric Society of Japan for their insightful comments, suggestions, and assistance in relation to the draft of this study. All the remaining errors are ours. 2 Corresponding author: Makiko Nakamuro, Faculty of Policy Management, Keio University.

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Introduction

Since the Coleman Report, a congressional report on educational equality in the

United States, was released in 1966 (Coleman et al. 1966), one of the challenges for

economists in understanding more efficient resource allocation in schools is the rigorous

measurement of the impact of school inputs, which is sometimes called “school quality,”

on student achievements. The reason this Report became controversial is that Coleman

and his colleagues showed that school quality was only weakly associated with student

achievements, after controlling for characteristics of parents and communities. The

conclusion derived from the Report has been widely publicized as “money (= investments

in school quality) does not matter” for education.

Subsequently, an influential analysis conducted by Hanushek (1989, 1997)

summarized a large body of empirical evidence on the relationship between school inputs

and student achievements, by counting the results on each side of the debate. Because

there were more studies that reported an insignificant effect of school expenditures than

those showing a positive effect, Hanushek (1997) concluded his analysis with a very

often cited phrase, “there is not a strong or consistent relationship between student

performance and school resources” (p. 141). In other words, Hanushek supported the

central conclusion of the Coleman Report—school quality does not matter.

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However, many economists have questioned Hanushek’s study. In particular, Hedges

et al. (1994) pointed out that Hanushek’s analysis was designed to give a small

probability of falsely detecting a significant effect of education inputs but not to avoid the

possibility of failing to detect such an effect if in fact it exists. Their meta-analysis, using

the same data as Hanushek, confirmed that school resources yielded general

improvement in student achievements, although publication bias remained a research

concern. Some experimental evaluations of school resources have also shown that school

resources do matter for education (Krueger 1999; Angrist & Lavy 1999; Case & Deaton

1999). More specifically, an assignment to a smaller class appeared to substantially raise

student achievements in Tennessee (Krueger 1999) and in Israel (Angrist & Lavy 1999),

while the same was true for a lower student–teacher ratio in South Africa (Case & Deaton

1999).

Further, Card & Krueger (1996) placed more emphasis on the education production

function, treating earnings as a dependent variable, rather than standardized test scores.

They claimed that test scores are good predictors of what students learned at school but

not their success in the labor market in later life. Their survey concluded that there is a

significant relationship between school resources and earnings. However, fewer studies

have explored whether school quality has a significant impact on both subsequent

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earnings and student achievements, and it remains a paradox that many studies find only a

weak link between earnings and student achievements (e.g., Murane, Willet & Levy

1995).

It is noteworthy that, in many cases, the effect size of school resources is substantially

larger for students from low-income families and for minorities. Taken as a whole, a

consensus has emerged from more recent research that school quality raises student

achievements, particularly for students from low income families, although Hanushek’s

(2003) argument: “commonly used input policies are almost certainly inferior to altered

incentives within schools” (p. 64) must deserve greater attention.

Measuring the effects of school quality rigorously is, however, difficult, owing to data

and methodological limitations. Most prior studies have used the variations in school

inputs across schools, which may create a methodological difficulty in estimating the

impact of those school inputs on student achievements. For example, parents with a

strong motivation for their children’s education are more likely to choose good schools

and are willing to pay for expensive tuition and other mandatory fees. Those parents are

more likely to be involved in parental associations and other political activities to

improve school quality. In such situations, a positive relationship between school

resources and student achievements may be attributable to unobserved parental

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characteristics. It is often difficult to isolate the effects of such unobservable factors from

those of school inputs. In other words, children who attend a school that is rich in

resources are probably more likely to have greater advantages than others before

schooling. The goal of economic research is, thus, to overcome this selection bias and to

answer the causal question of whether investments in school quality really matter.

There are several studies that have dealt with the potential bias. Some of the most

sophisticated studies to address selection bias are the experimental evaluations conducted

by Krueger (1999), Angrist & Lavy (1999) and Case & Deaton (1999), as mentioned

above. Krueger’s study was designed as a randomized experiment, while the

quasi-experimental studies of Angrist & Lavy (1999) and Case & Deaton (1999) took

advantage of the situation in which assignments to school inputs were randomly

determined by chance. Causation is best established using randomized or quasi

experiments, but this rarely happens especially in Japan.

In addition to those experimental studies, some recent research has used samples of

siblings (Altonji & Dunn 1996; Lindahl & Regner 2005) and twins (Behrman et al. 1996),

which can be regarded to some extent as natural experiments. These studies attempted to

compare the difference in educational experiences between siblings or twins and to

control the common unobserved family endowments and/or genetic makeups. Behrman

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et al. (1996) and Lindahl & Regner (2005) used college data, while Altonji & Dunn

(1996) focused on secondary school. There is a large body of research that compares

siblings or twins in different years of education (e.g., Ashenfelter & Krueger, 1994), but

there is little research that examines the effect of school quality.

In sharp contrast to the abundant and long-standing debate on whether school quality

matters in the United States and other countries, evidence from Japan is relatively scarce,

as surveyed by Oshio & Senoh (2007). This is unfortunate, given that the education

system in Japan is different from that in western countries. In fact, the Japanese system

has been often characterized by a so-called “examination hell” (Ono 2004, p. 597), in

which students and parents have focused more on test-taking techniques to pass the

competitive entrance examinations at all levels of education, rather than to accumulate

the knowledge and skills that are highly rewarded by the labor market. Anecdotal

evidence shows that the age of first starting preparation for the entrance examinations has

recently decreased.

Japanese parents tend to allocate a large part of their educational expenditures to

extra-curricular “shadow education,” such as cramming schools or private tutoring to

prepare for the upcoming entrance examinations. Surprisingly, 50% of educational

expenditures in households with a child (or children) going to public junior high school

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are spent on shadow education (Table 1). This pattern has remained unchanged for many

years, even during the period in which the Japanese economy was significantly suffering

from the global economic downturn in 2009 or the Great East Japan Earth Quake in 2011.

The dependence on private spending on education in Japan is also confirmed by

international comparison: the OECD publication Education at a Glance 2013 showed

that public spending on education in Japan was 3.6% of GDP in 2010, the lowest among

the OECD countries (which averaged 5.4%). Spending on education in Japan is largely

financed by household spending and the proportion of private funding is the third highest

among OECD countries, following Chile and South Korea. Thus, an interesting and open

question is whether school inputs are systematically associated with student

achievements in Japan, where the access to educational opportunities outside formal

education depends heavily on parental financial capabilities.

In one of preceding studies using Japanese data, Oshio et al. (2010) estimated the

education production function by using the data of combined high schools located in

metropolitan areas of Japan, controlling for prior attainment measured by the deviation

values of schools (hensachi in Japanese) at the time of enrollment. They found that

educational outcome, measured by university admission success, was largely determined

by the prior attainment and total amount of school hours. However, controversy exists in

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the extent of aggregation involved in measuring school inputs. In particular, Hanushek,

Rivkin & Taylor (1996) suggested that studies that measure school inputs at higher levels

of aggregation, such as schools or school districts, are more likely to overstate the

positive effect of school inputs. Their research suggested that the probability of finding

positive and significant results for school influence also increases as level of aggregation

increases.

Another research was conducted by Shinozaki (2008), who used the micro data

provided by the local government of Chiba Prefecture, although his targeted students

were primary and junior high school students. He concluded that the standardized test

scores of sixth and ninth grade students were significantly affected by school inputs, such

as the class size and the average age of teachers, after controlling for students’ and

community characteristics. However, some crucial variables that represent students’

socioeconomic factors were missing, or were used only for aggregation at the school

level, leaving selection bias not fully controlled for.

These facts bring us to the main question of interest: after controlling for potential

biases, does school quality really matter in Japan, where family environments may play a

more important role in determining children’s education than in other counterpart

countries? Nakamuro & Inui (2013) examined the effect of college quality using the same

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dataset as this study. Their empirical analysis provided no strong evidence of positive

returns to college quality in Japan. The question that we address in this study is whether

the same is true for a lower level of education. The main objective of this study was, thus,

to measure the causal effect of school quality in Japan. To overcome the selection bias in

question, unique twin data were used, which the authors collected through a web-based

survey. Using a sample of twins enabled us to reduce the possibility of omitted variable

bias arising from unobserved heterogeneity that influences the incentives for educational

investments. In addition, the dataset used in this study contains detailed information

about respondents’ educational history, such as the names of the institution and

department from which a respondent actually graduated. We were, thus, able to match the

names of institutions with information on school characteristics retrieved from the

official statistics.

Our study was expected to make three primary contributions to the previous literature.

To the best of our knowledge, this may be the first study in Japan to measure the causal

relationship between school quality and student achievements, holding the differences in

ability and family endowments constant. Second, we basically followed the protocol of

Behrman et al. (1996), who used college data, but focused on high school quality, which

has been little investigated in the previous literature. Third, we examined the effect of

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school quality on both academic achievements and earnings, and looked at how the

effects differ according to the output. The results should have meaningful implications for

public policy in Japan. If there was a causal relationship between a certain set of school

inputs and student achievements or earnings, the policy prescription would be obvious:

the government should increase school resources. However, if there was no relationship,

the government should reconsider the current resource allocation in education.

The conclusion drawn in this study, from empirical analysis based on twin data

combined with official data regarding school characteristics, suggests that school inputs

at the high school level are not associated with student achievements, but are associated

with earnings measured in later life. Therefore, our answer to the specific causal question,

does school quality matter in Japan, is “no” for academic achievements, but “yes” for

labor market outcomes. In other words, unobserved family components may play a

crucial role in determining academic achievements.

The remainder of this study is organized as follows: the next section introduces the

empirical models to be estimated; the third sections introduce the data used and variables

defined for empirical analyses; the fourth section presents the empirical results; and the

final section provides conclusions.

Empirical Models

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We employ the production function of education, which has been conventionally used

in the literature to explore input-output relationships in school. The common school

inputs are classified into school resources, teacher quality, peer effect, and parental

socioeconomic status, while the outcome is often measured by student achievements. The

specification of the educational production function varies among studies, but most

studies share the features described by the following equation. Consider the following

education production function outlined for individual i from family j:

, (1)

If we estimate equation (1) using the conventional Ordinary Least Squares (OLS), the

results may be affected by the problem of omitted variable bias, such as unobserved

differences in native abilities and family backgrounds, which makes the OLS estimates

biased and inconsistent. To deal with this potential bias, we applied a twins setting to the

above education production function, where Tij is student achievement measured for twin

i (i = 1, 2) in family j, and Xij is a vector of individual characteristics that are not constant

over time. The key independent variables of interest, Sij, represent a vector of school

inputs, including school resources, teacher quality and peer effect. Ai is unobserved

family endowments, including family environments and genetic makeup, which are

common within twin pairs and are constant over time (therefore, A1 = A2 = Aj). eij is a

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random disturbance with mean zero and constant variance. The mathematical expression

is then rewritten for twins as follows:

, (2)

, (3)

We took a first difference of equations (2) and (3) to eliminate the unobserved family

endowments shared within twin pairs (Aj) and obtained within-fixed effects estimates of

β, holding other factors constant.

Data

We combined three datasets in estimating the education production function. The first

dataset consisted of twins’ data collected through a web-based survey (see Nakamuro &

Inui (2012) and Nakamuro, et al. (2013) for more detailed information about the survey).

This survey was designed to gather more than 4,700 twins from over 2,300 households. In

addition to a wide range of socioeconomic information that this dataset conveyed, one

noteworthy feature of this survey was that it included the name of the institution and

department from which a respondent actually graduated. We were, thus, able to match the

names of institutions with information about high school characteristics provided in other

statistics.

The data that were matched with the twin survey were the School Basic Survey,

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Kawai juku’s Ranking of Deviation Values and Kan juku’s Deviation Values in Junior

High and High School Nationwide. The School Basic Survey, which is released by

Ministry of Education, Culture, Sports, Science and Technology (MEXT) in Japan,

contains time-series data on key school characteristics, starting in 1948. The other data

were a series of deviation values (hensachi in Japanese), a popular indicator in Japan of

selectivity or relative competitiveness to gain admission to each educational institution.

The interpretation of the deviation value is that the larger the value, the more selective or

the more competitive the institution. We retrieved two sets of information from

Kawai-juku and Kan-juku, both of which are among the largest-scale cramming schools

in Japan: while Kawai-juku is geared for high school students preparing for

college/university entrance examinations, Kan-juku is geared for junior high school

students. The dataset collected by Kawai-juku is called Kawai-juku’s Ranking of

Deviation Values and is released every year on its website (see at

http://www.keinet.ne.jp/rank/index.html). This was our measure of the educational

output: the idea behind this variable is similar to that of Oshio et al. (2010). The other data

for deviation values, collected by Kan-juku is called Kan-juku’s Deviation Values in

Junior High and High School Nationwide and has been released every year since 2004.

This was expected to be an important control variable that represents the prior attainment

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before the student enrolled in high school.

Our twin survey was implemented during the fiscal year 2010. This survey contains

one of the largest twin dataset in Japan with a wide variety of socio-demographic

characteristics. More specifically, we conducted the survey through Rakuten Research,

which is affiliated with Rakuten, a major Internet shopping site (similar to Amazon.com

or eBay, for example), and monitors over 2.2 million people. To analyze the effect of

education on earnings, our sample targeted twins who were non-students between the

ages of 20 and 603. Through this web-based survey, one member of a twin pair was

responsible for reporting at the same time on him/herself and his/her twin sibling. In the

analysis, information on respondents’ twin siblings retrieved from respondents was

treated as if it were directly provided by such twin siblings themselves4. The entire

3 Once the monitor(s) filled out the questionnaires, they were given a certain amount of cash-equivalent “points” that could be spent on Rakuten online shopping. To exclude “fake” twins, who pretend to be twins to collect the cash-equivalent points, we carefully developed the following data collection strategy. First, we did not inform respondents that the purpose of our survey was to collect data from twins. Furthermore, we started with five questions on family and siblings that were not related to twin status and then, in the sixth question, for the first time, asked whether or not a respondent was a twin. If the respondent answered “No” in this question, s/he would be automatically excluded from the survey. We discovered 23 twin pairs, both members of which were included in this survey, then thoroughly checked the responses of both twins, and eliminated one of the twins randomly from our sample. 4 One may question whether, in our survey, there may exist substantial measurement errors in self-reported outcomes by one of the twin pairs, rather than both. It is important to note that we had 23 twin pairs for which both members were included in this survey. When we checked their responses, we found out that their responses as reported by each other were accurate: the correlations between self-reported and cross-reported birth weight was 91.2%, and other outcomes also showed correlations of over 90%. Furthermore, we checked whether there existed significant differences between responses on him/herself and on his/her twin siblings; for example, one might ask whether respondents tended to pretend that their earnings or education were higher than those of their twin siblings. However, according to the results drawn from two sample t-tests for difference of the means, there was no difference between them. As a further robustness check, we included a respondent dummy in all

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sample in this survey was 2,360 complete pairs of twins (4,720 individuals) including

1,371 pairs (2,742 individuals) of identical (monzygotic: MZ) twins and 882 pairs (1,764

individuals) of non-identical (dizygotic: DZ) twins.

In this study, we restricted the sample to twins who graduated from high schools and

went to college. The sample used for estimation was 1,082 twins (541 complete pairs), of

whom 722 (361 complete pairs) were MZ twins and 360 (180 complete pairs) were DZ

twins. Of those twins, only 468 (234 complete pairs) twins went to different high schools,

of whom 272 (136 complete pairs) were MZ twins and 196 (98 complete pairs) were DZ

twins. Based on the prior research findings on school quality effects, we selected the high

school characteristics from the School Basic Survey, which included information on

approximately 5,300 high schools and combined high schools nationwide in Japan.

We used the following information on school characteristics: (1) type of high school

(1 = public); (2) the total number of students enrolled; (3) the ratio of students to

administrative staff; (4) the proportion of students who work after graduation rather than

go to college; (5) the ratio of students to teachers; (6) the percentage of teachers with

long-term absence;. Along with previous literature, (1), (2), (3), and (4) were classified as

school characteristics and resources, (5) and (6) as teacher qualities.

specifications, but the dummies were statistically insignificant.

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Concerning individual characteristics, we considered gender (female = 1), age (30–

39, 40–49, 50–59), academic achievement at the time of entering the high school as

measured by the deviation value of the school, whether or not the student was involved in

sports as a part of after-school club activities at least once during the high school days,

and whether or not the student went to cram schools/prep schools to enhance academic

achievements for at least three months during the high school days.

The largest drawback of the data used in this study is that only recent information on

school quality was available in the School Basic Survey and the deviation values.

Although a majority of twins in our sample attended high schools in the mid-1990s, the

access to data on earlier high school characteristics was limited. We, thus, took the mean

value between 2004 through 2010 for the School Basic Survey and between 2004 and

2010 for the deviation values, assuming that school characteristics were stable over years.

Indeed, the correlation for major school characteristics between 2004 and 2010 is above

90% for approximately 5,300 institutions. Another drawback of these data is that

information on some unobserved but important school characteristics was not available,

such as differences in peers’ achievements, teaching skills, and the principal’s leadership.

However, these unobserved school characteristics may be partly controlled by one of the

control variables, the deviation value of high school: because this deviation value was

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uniquely assigned to each school, which is also regard as school quality as well as the

proxy for the prior attainment before entering high school.

The descriptive statistics summarized in Table 2 show that the average deviation

value at high school was 59.25, somewhat higher than average (50). The average

placement rate was 5%, indicating that a majority of peers went to college. The average

student–teacher ratio was 39, while only 1% of teachers were on long-term absence.

Thirty-four percent of students engaged in sports as a part of after school club activities

and 33% received shadow education. There was no significant difference in key

independent variables between MZ and DZ twins.

Empirical Results

Main results

Our analysis employed both OLS and twin-fixed effect estimates of the education

production function for the entire sample, which included both MZ and DZ twins,

respectively. For each estimate, we considered three models. Model I included school

resources aggregated at the school level, controlling for individual demographic factors

(gender and age). Model II included the prior achievements at enrollment in high school

and individual heterogeneity in experiences at school (access to sports activities and

shadow education). Model III included all the above-mentioned variables and may be the

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most relevant for evaluating the effect of school quality. Gender and age were controlled

in all models5.

Table 3-1 summarizes the results of OLS estimates for the entire sample in terms of

the standardized coefficients, along with the heteroskedasticity-robust standard errors.

Our primary interest was whether the coefficients for school quality variables were

statistically significant. Model I showed that a lower placement rate and a male student

raise student achievements. Model II confirmed that the deviation value of the high

school, which indicates prior attainment before entering high school, shadow education,

and being male also had effects on student achievements. Finally, Model III showed that

the school quality does not matter, while the coefficients for prior attainment, shadow

education, and gender remained statistically significant. The importance of prior

attainment is consistent with the results of Oshio et al. (2010), who concluded that

success in university admission is largely explained by prior attainment measured by

deviation values.

Table 3-2 shows the results when employing twin-fixed effects (with ages removed

from the explanatory variable list)6. The coefficient for shadow education became

5 The inclusion of many independent variables in the models could potentially cause multicolinearity. However, we confirmed that there was no evidence of severe colinearity among the independent variables according to the Variance Inflation Factors (VIF) we computed. 6 To ensure the good performance of the within-twin estimation of school quality, the with-twin variation in high school quality needs to be substantial. As already explained, 40% of twin-pairs

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insignificant across the models and the deviation value in Model III also became

insignificant. The most important finding to note is that Model III showed that school

quality did not matter after all. As already described, we treated the deviation value for

high schools as a proxy of the prior attainment. However, this variable may in part reflect

school quality, because students who have been accepted by higher-ranking high schools

are more likely to pursue higher education.

We then restricted our sample to only MZ twins (Tables 3-3 and 3-4). MZ twins are

produced in the same pregnancy when a single zygote splits by chance to result in two

separate embryos. The two are, thus, genetically identical, which hence enables us to

hypothesize that a pair of MZ twin share not only the same family and neighborhood

environments but also genetic endowments. The fixed-effect estimate restricted to MZ

twins must be more accurate than the estimate for the entire sample because it can control

unobserved ability that may be determined by genetic makeups.

As seen in Table 3-3, Model I showed that a lower student–teacher ratio and a lower

placement rate were associated with higher student achievements. Model II indicated that

the deviation value and shadow education were the crucial determinants. Model III told a

enrolled different high schools, which may be enough large. To test the robustness of our results, we ran separate regressions using the sample restricted with twin-pairs who attended the different schools and replicated the estimation of Table 3-2, 3- 4, and 3-6. The results are the same with the Table we presented in this paper.

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similar story to Model I and Model II, but the placement rate became insignificant.

Considering the twins-fixed effect estimates in Table 3-4, we found no significant

association of student achievement with any variable—not only school quality but also

prior attainment and individual activity during attendance at high school. Looking at the

coefficients for prior attainment and shadow education, the magnitude of fixed-effect

estimates is smaller than the OLS estimates. This drop in magnitude is consistent with the

idea that more able individuals—as captured by a higher unobserved heterogeneity—are

more likely to go to higher-ranking schools and receive shadow education. Regardless of

whether the deviation value of the high school reflects the school quality, after controlling

for unobserved characteristics, the effect of prior attainment is indistinguishable from

zero.

We then repeated the estimates with the restricted sample of DZ twins. Because DZ

twins are produced when two eggs are fertilized to form two embryos in the uterus at the

same time, DZ twins are not genetically identical. They are, thus, regarded as ordinary

siblings of the same age who are raised by the same parent(s) in the same family

environments. This setting enables us to understand how genetic makeups play a role in

explaining student achievements: if the results for MZ and DZ twins were the same, we

could argue that genetic endowments do not matter.

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Table 3-5 shows the OLS results for DZ twins. Similar to the results for the entire

sample and MZ twins, a lower proportion of students who work after graduation was

associated with higher student achievement. In addition, a higher ratio of students to

teachers raised student achievements, which is a counterintuitive result, but the

coefficient became insignificant when we applied the fixed-effects model (Model III in

Table 3-6). In Models II and III, prior attainment was positively associated with student

achievements. A more important finding was that school quality and individual

characteristics variables were significantly associated with student achievements even in

twins-fixed effect estimates (Table 3-6), unlike in the case of MZ twins. Indeed, a higher

proportion of students who work after graduation was associated with decreased student

achievements in Model III. These results contrast with those for MZ twins reported in

Table 3-4, which showed no significant effect of any school or individual variable on

student achievements.

The difference between the results for MZ and DZ twins indicates that even if the

family environments are the same, different genetic makeups can confound the impact of

school quality as well as that of individual characteristics on student achievement. This,

in turn, underscores the importance of genetic makeup as a determinant of student

achievements, at least at the level of college enrollment, although we should be cautious

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in interpreting these results given the small sample size of the dataset in the current study.

Effects on Labor Market Outcomes

Much previous literature has pointed out that even though the school quality does not

matter for academic achievements, it does matter for longer-run educational outcomes,

such as earnings (see Card & Krueger (1996) for a comprehensive review). To investigate

this point, we ran a separate regression with logarithm of annual earnings as the

dependent variable, instead of academic achievements. Our measure of income is the

natural logarithm of annual before-tax wage earned during the fiscal year 2009. The

response category in the twin survey ranged from 1 (= no income or less than 0.5 million

JPY) through 16 (= more than 15 million JPY). We set the minimum (1 = no income and

less than 0.5 million JPY) to zero and the maximum (16 = more than 15 million JPY) to

15 million JPY. We then took the median value for categories between 2 (= 0.5 million to

0.99 million JPY) and 15 (= 10 million to 14.99 million JPY). In the wage equation, we

included the control variables deemed to affect productivity, and hence, earnings: marital

status (1 = married), hours worked per day, and number of years in current employment.

The estimation results are summarized in Table 3-7, which employed twins-fixed

effect models. The results of analysis restricted to MZ twins show that the student–

teacher ratio was negatively associated with subsequent earnings in later life, which is

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consistent with the evidence reported in Dearden et al (2002), while the remaining school

quality variables were not statistically significant. However, the results of analysis

restricted to DZ twins show that no school quality variables were statistically significant

at a 5% level. The conclusion drawn from these analyses is that we found evidence of

some effects of the student–teacher ratio. It is consistent with the results demonstrated by

Dearden (2002), which is that the secondary student–teacher ratio has a stronger impact at

an older age. This result lends some supports to findings in the previous literature that

school quality matters for outcomes measured later in life. Furthermore, in this context,

the differences in genetic makeup between twin pairs did not affect the results.

Conclusion

Does school quality really matter in Japan even after controlling for family and

genetic endowments? Based on our empirical analysis, the answer to this specific causal

question is “no” for academic achievements, but “yes” for labor market outcomes. There

has been a growing consensus in the literature that school quality is positively associated

with student achievements. This conventional view, however, began to be questioned

when we realized that, in Japan, parent(s) are playing a more crucial role in financing

children’s educational investments relative to public expenditure.

This study employed a large set of twin data combined with official statistics, the

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School Basic Survey and a series of deviation values and attempted to estimate the effect

of school quality at high schools, holding unobserved family characteristics constant. The

School Basic Survey collected by the Ministry of Education, Culture, Sports, Science and

Technology contains a wide variety of school characteristics, such as student–teacher

ratios and the number of students enrolled. A dependent variable defined as student

achievements was the deviation value of the college/university from which a respondent

graduated, which represents the ranking or relative competitiveness of each educational

institution across the country. We then estimated the effect of school resources controlling

for observed individual demographic factors and unobserved family components.

The results drawn from the sample restricted to identical twins suggested no

significant association of student achievement with any variable—not only school

resources but also prior attainment and individual activity during attendance at high

school. This result suggests that student achievements may be largely determined by

unobserved family environments and genetic makeups. Surprisingly, the result from the

sample restricted to non-identical twins indicated that some school quality variables were

significantly associated with student achievements, unlike the case of identical twins.

This means that different genetic makeups can confound the impact of school quality as

well as that of individual characteristics on student achievement. Moreover, we found

25

evidence of some effects of student–teacher ratio on earnings. This result lends some

support to earlier findings that school quality matters for outcomes measured later in life.

Furthermore, in this context, the differences in genetic makeup between twin pairs did not

affect the results. The messages of this result may be that genetic makeup has some

effects when we consider children’s educational outcomes, although we should be

cautious in interpreting these results given the small sample size of the dataset in the

current study.

26

Table 1: Household Educational Expenditures on Schools and Shadow Education

(Thousand JPY)

Elementary Sch Junior High Sch High Sch Public Private Public Private Public Private

Educational expenditure 304 1,465 460 1,279 393 923 Within School 55 835 132 990 238 685

Outside School 207 584 293 279 156 238 Shadow Education % of total expenditure

86 (28%)

296 (20%)

229 (50%)

181 (14%)

125 (31%)

188 (13%)

(Note) The expenditure on school meals is included in the total educational expenditures for

elementary and junior high schools. (Source) Ministry of Education, Culture, Sports, Science and Technology, “Statistics for

Children’s Educational Expenditures” (2010)

27

Table 2: Descriptive Statistics

Total (541 pairs)

MZ Twins (361 pairs)

DZ Twins (180 pairs)

Mean STDV Mean STDV Mean STDV

Dependent variables Deviation value at college 52.26 9.43 52.55 9.44 51.68 9.37

log(wage) 6.21 0.70 6.21 0.69 6.20 0.71

School quality (1) A type of high school (1=public) 0.71 0.46 0.72 0.45 0.68 0.47

(2) The total number of students enrolled (/100) 9.31 3.31 9.34 3.25 9.25 3.42 (3) The ratio of students to administrative staff (/100) 2.03 2.72 1.96 2.62 2.17 2.90 (4) The proportion of students who find work after graduation 0.05 0.11 0.05 0.11 0.05 0.10 (5) The ratio of students to teachers (/100) 0.39 1.21 0.35 0.90 0.45 1.66 (6) The proportion of teachers with long-term absence 0.01 0.02 0.01 0.02 0.01 0.02

Other individual characteristics Gender (female=1) 0.38 0.49 0.34 0.47 0.47 0.50

Age (if 20-29, reference) 0.15 0.36 0.14 0.35 0.17 0.37 Age (if 30-39) 0.36 0.48 0.35 0.48 0.37 0.48 Age (if 40-49) 0.31 0.46 0.34 0.48 0.24 0.43 Age (if 50-59) 0.18 0.39 0.16 0.37 0.17 0.42 Deviation value at high school 59.25 10.40 59.93 10.26 57.89 10.57 Club activity (1=participated in sports club activities at least once) 0.34 0.47 0.36 0.48 0.30 0.46 Shadow education (1=participated more than three months) 0.33 0.47 0.34 0.47 0.33 0.47

(Source) Authors’ calculations from School Basic Survey and Kanjuku’s Deviation Values in Junior High and High School Nationwide

28

Table 3-1: Empirical Results (OLS, Entire Sample)

Dependent Variable: Deviation value at College OLS Model I Model II Model III

School quality (1) A type of high school (public=1) (2) The total number of students enrolled (/100) (3) The ratio of students to administrative staff (/100) (4) The proportion of students who work after graduation (5) The ratio of students to teachers (/100) (6) The proportion of teachers with long-term absence Individual Characteristics Gender (female=1) Age (=30-39) Age(=40-49) Age(=50-59) Deviation value at high school Club sports activity Shadow education Constant R-Squared Observations (# of twin pairs)

-0.835 (0.695) 0.108

(0.097) 0.039

(0.103) -15.916***

(2.820) 0.185

(0.282) -16.403 (10.757)

-3.316***

(0.596) 0.91

(0.801) -0.449 (0.848) 0.306

(0.930)

53.699*** (1.501) 0.083 1,082 (541)

-2.664*** (0.555) 0.746

(0.765) -0.911 (0.800) 0.155

(0.892) 0.322*** (0.027) 0.253

(0.548) 2.574*** (0.562)

33.257*** (1.757) 0.182 1.082 (541)

-0.657 (0.632) 0.072

(0.095) -0.058 (0.095) 0.663

(3.585) 0.102

(0.224) -5.299

(10.351)

-2.656*** (0.565) 0.727

(0.762) -0.915 (0.796) 0.147

(0.898) 0.318*** (0.033) 0.325

(0.553) 2.483*** (0.565)

33.424*** (2.426) 0.185 1,082 (541)

(Note) The standard errors in parentheses represent heteroskedasticity-robust standard errors. *** and

** represent 1% and 5% significance level, respectively. (Source) Authors’ calculations

29

Table 3-2: Empirical Results (Twin-Fixed Effects, Entire Sample)

Dependent Variable: Deviation value at College Twin-Fixed Effects Model I Model II Model III

School quality (1) A type of high school (public=1) (2) The total number of students enrolled (/100) (3) The ratio of students to administrative staff (/100)

(4) The proportion of students who work after graduation (5) The ratio of students to teachers (/100) (6) The proportion of teachers with long-term absence Individual Characteristics Gender (female=1) Deviation value at high school Club sports activity Shadow education Constant R-Squared Observations (# of twin pairs)

1.488

(0.885) 0.102

(0.083) -0.189 (0.154) -9.394 (4.239) 0.067

(0.215) 12.46

(11.613)

-4.704*** (1.005)

52.700*** (1.216) 0.077 1,082 (541)

-4.371*** (1.027) 0.136** (0.045) 0.311

(0.731) 0.404

(1.225) 45.605***

(2.893) 0.072 1,082 (541)

1.227

(0.911) 0.081

(0.078) -0.191 (0.151) -5.495 (4.574) 0.026

(0.214) 12.918

(11.524)

-4.364*** (1.036) 0.096

(0.048) 0.183

(0.745) 0.243

(1.264) 46.944***

(3.327) 0.084 1,082 (541)

(Note) The standard errors in parentheses represent heteroskedasticity-robust standard errors and

clustering at the family level. *** and ** represent 1% and 5% significance level, respectively. (Source) Authors’ calculations

30

Table 3-3: Empirical Results (OLS, MZ Twins)


School quality (1) A type of high school (public=1) (2) The total number of students enrolled (3) The ratio of students to administrative staff

(4) The proportion of students who work after graduation (5) The ratio of students to teachers (6) The proportion of teachers with long-term absence Individual Characteristics Gender (female=1) Age (=30-39) Age(=40-49) Age(=50-59) Deviation value at high school Club sports activity Shadow education Constant R-Squared Observations (# of twin pairs)

-2.073 (0.864) 0.106

(0.119) -0.009 (0.131)

-12.958*** (3.430)

-0.769*** (0.218) -25.514 (13.796)

-2.693***

(0.751) 1.798

(1.014) -0.315 (1.079) -0.479 (1.186)

54.761*** (1.865) 0.083 722

(361)

-1.942** (0.691) 2.058

(0.949) 0.116

(0.999) 0.446

(1.135) 0.303*** (0.036) 0.109

(0.675) 2.782*** (0.706)

33.220*** (2.391) 0.162 722

(361)

-1.328 (0.798) 0.101

(0.118) -0.072 (0.123) 2.848

(4.690) -0.627** (0.232) -17.017 (12.563)

-2.028** (0.707) 2.003

(0.943) 0.017

(0.993) 0.138

(1.139) 0.301*** (0.046) 0.187

(0.677) 2.564*** (0.700)

33.984*** (3.350) 0.175 722

(361)



31

Table 3-4: Empirical Results (Twin-Fixed Effects, MZ Twins)



(4) The proportion of students who work after graduation (5) The ratio of students to teachers (6) The proportion of teachers with long-term absence Individual Characteristics Deviation value at high school Club sports activity Shadow education Constant R-Squared Observations (# of twin pairs)

0.038

(1.159) 0.068

(0.112) -0.040 (0.191) -4.377 (5.631) -0.178 (0.261) 5.645

(14.353)

52.174***

(1.279) 0.005 722

(361)

0.099 (0.063) -0.303 (0.874) -1.873 (1.873)

47.391*** (3.838) 0.014 722

(361)

-0.233 (1.218) 0.081

(0.105) -0.078 (0.191) -1.117 (5.551) -0.181 (0.284) 5.442

(13.438)

0.096 (0.066) -0.341 (0.877) -1.901 (1.883)

47.142*** (4.293) 0.016 722

(361)



32

Table 3-5: Empirical Results (OLS, DZ Twins)



(4) The proportion of students who work after graduation (5) The ratio of students to teachers (6) The proportion of teachers with long-term absence Individual Characteristics Gender (female=1) Age (=30-39) Age(=40-49) Age(=50-59) Deviation value at high school Club sports activity Shadow education Constant R-Squared Observations (# of twin pairs)

1.194

(1.124) 0.109

(0.166) 0.055

(0.168) -26.067***

(4.208) 0.841*** (0.158) 13.516

(16.871)

-4.501*** (0.976) -0.508 (1.296) -0.548 (1.429) 1.194

(1.502)

52.580*** (2.505) 0.172 360

(180)

-3.858***

(0.919) -1.937 (1.289) -2.748 (1.346) -0.804 (1.457)

0.372*** (0.040) 0.447

(0.947) 1.756

(0.960) 32.807***

(2.582) 0.246 360

(180)

0.659

(1.039) 0.030

(0.172) -0.062 (0.155) -9.000 (4.734)

0.626*** (0.168) 23.995

(15.631)

-3.933*** (0.944) -1.536 (1.291) -2.398 (1.367) -0.307 (1.491)

0.330*** (0.050) 0.165

(0.973) 1.343

(0.974) 34.390***

(3.641) 0.268 360

(180)



33

Table 3-6: Empirical Results (Twin-Fixed Effects, DZ Twins)


School quality (1) A type of high school (public=1) (2) The total number of students enrolled

(3) The ratio of students to administrative staff

(4) The proportion of students who work after graduation (5) The ratio of students to teachers (6) The proportion of teachers with long-term absence Individual Characteristics Gender (female=1) Deviation value at high school Club sports activity Shadow education Constant R-Squared Observations (# of twin pairs)

3.287** (1.203) 0.209

(0.114) -0.449 (0.261)

-15.991*** (4.425) 0.539** (0.181) 25.593

(19.421)

-4.418*** (1.015)

50.755*** (1.699) 0.203 360

(180)

-3.996*** (1.056) 0.170** (0.061) 1.337

(1.226) 2.350

(1.612) 42.524***

(3.924) 0.159 360

(180)

2.821

(1.312) 0.129

(0.129) -0.425 (0.257)

-13.166** (4.782) 0.408

(0.218) 26.427

(19.416)

-4.090*** (1.109) 0.066

(0.074) 0.759

(1.334) 1.812

(1.664) 46.847***

(4.901) 0.212 360

(180)



34

Table 3-7: Empirical Results (Twin-Fixed Effects)

Dependent Variable: log(wage) Twin-Fixed Effects ALL MZ DZ

School quality (1) A type of high school (public=1) (2) The total number of students enrolled

(3) The ratio of students to administrative staff

(4) The proportion of students who work after graduation

(5) The ratio of students to teachers

(6) The proportion of teachers with long-term absence Individual Characteristics Gender (female=1) Deviation value at high school Club sports activity Shadow education Marital status (married=1) Years of the current employment Working hours a day Constant R-Squared Observations (# of twin pairs)

-0.016 (0.061) 0.002

(0.005) 0.000

(0.009) -0.256 (0.231) 0.015

(0.017) 0.132

(0.700)

-0.409*** (0.117) 0.002

(0.002) -0.057 (0.050) -0.012 (0.073) 0.004

(0.047) 0.017*** (0.005)

0.081*** (0.019)

5.258*** (0.264) 0.303 876

(438)

-0.114 (0.065) -0.008 (0.006) -0.002 (0.009) -0.354 (0.253)

-0.032** (0.012) 0.698

(0.736)

0.002 (0.003) -0.126 (0.059) -0.043 (0.086) 0.023

(0.061) 0.012** (0.005) 0.060** (0.020)

5.619*** (0.277) 0.200 596

(298)

0.127

(0.122) 0.019

(0.009) -0.006 (0.025) 0.312

(0.520) 0.045

(0.019) -0.627 (1.823)

-0.313** (0.115) 0.004

(0.004) 0.036

(0.098) -0.094 (0.138) 0.022

(0.088) 0.025

(0.011) 0.110** (0.035)

4.436*** (0.516) 0.451 280

(140)



35

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