Silviase/jgov_v1.4_full_ann huggingface dataset:
## I ntroduction

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.
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
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
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-experimental methods.

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
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
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
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
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
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
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:

$$ T_{i j}=A_{i}+\boldsymbol{\mathbf{X}}_{i j}\boldsymbol{\alpha}+\boldsymbol{\mathbf{S}}_{i j}\boldsymbol{\beta}+e_{i j}, $$

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 $ T_{ij} $ is student achievement measured for twin

i (i = 1, 2) in family j, and $ X_{ij} $ is a vector of individual characteristics that are not constant

over time. The key independent variables of interest, $ S_{ij} $ , represent a vector of school

inputs, including school resources, teacher quality and peer effect. $ A_{i} $ is unobserved

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

common within twin pairs and are constant over time (therefore, $ A_{1}=A_{2}=A_{j} $ ). $ e_{ij} $ is a
random disturbance with mean zero and constant variance. The mathematical expression is then rewritten for twins as follows:

$$ T_{1j}=A_{j}+\boldsymbol{X}_{1j}\boldsymbol{\alpha}+\boldsymbol{S}_{1j}\boldsymbol{\beta}+e_{1j}, $$

$$ T_{2j}=A_{j}+\boldsymbol{X}_{2j}\boldsymbol{\alpha}+\boldsymbol{S}_{2j}\boldsymbol{\beta}+e_{2j}, $$

We took a first difference of equations (2) and (3) to eliminate the unobserved family endowments shared within twin pairs $ (A_{j}) $ and obtained within-fixed effects estimates of $ \beta $ , 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,
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
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 60 $ ^{3} $ . 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 themselves $ ^{4} $ . The entire
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.
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
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
most relevant for evaluating the effect of school quality. Gender and age were controlled in all models $ ^{5} $ .

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