Institute for Economic Studies, Keio University
Keio-IES Discussion Paper Series

日本における幼児教育の収益率：全国的拡大期を使った推定値
赤林英夫、田中隆一
2024 年 4 月 12 日

DP2024-011
https://ies.keio.ac.jp/publications/23749/

Institute for Economic Studies, Keio University
2-15-45 Mita, Minato-ku, Tokyo 108-8345, Japan
ies-office@adst.keio.ac.jp
12 April, 2024

日本における幼児教育の収益率：全国的拡大期を使った推定値
赤林英夫、田中隆一
IES Keio DP2024-011
2024 年 4 月 12 日
JEL Classification: I22, I28, H75
キーワード: 内部収益率、就学前教育の全国的拡大、幼稚園、保育所【要旨】
本稿は幼児教育投資の内部収益率の新たな推定値を提示する。日本で1960年から1980年にかけて、幼児教育が全国的に拡大したことを利用し、まず、男性の高校卒業率と大学進学率に対する就学前教育の影響を評価した。次に、全国の賃金統計を利用して期待賃金プロ
ファイルを予測し、政府資料に基づいて就学前教育の社会的費用を考慮した上で、男性の就学前教育に対する社会的収益率を計算した。結果として、就学前教育の社会的収益率は5.7%から8.1%の範囲であった。これは既存研究で推定されたアメリカをはじめとする諸外国での幼児教育の収益率と概ね一致している。

赤林英夫
慶應義塾大学経済学部
東京都港区三田2-15-45
hakab@econ.keio.ac.jp
田中隆一
東京大学社会科学研究所
東京都文京区本郷7-3-1
ryuichi.tanaka@iss.u-tokyo.ac.jp

The Rate of Return to Early Childhood
Education in Japan: Estimates from the
Nationwide Expansion∗
Ryuichi Tanaka‡
University of Tokyo

Hideo Akabayashi†
Keio University

April 12, 2024

Abstract
We present new estimates of the internal rate of return to early childhood education. Utilizing the nationwide expansion of preschool education in Japan between 1960 and 1980, we initially assess the impact
of preschool attendance on high school graduation and college enrollment for men. Subsequently, we compute the social rate of return topreschool attendance for men by drawing upon national wage statistics to project expected wage trajectories and referencing government
documents to account for the social cost of preschools. Our findings
indicate a social rate of return ranging from 5.7% to 8.1%, consistent
with previous estimates in the literature.
JEL classification: I22; I28; H75
Keywords: Internal rate of return, Universal preschool expansion, Kindergarten, Nursery school∗
We are grateful to the editor and anonymous referees for useful and encouraging comments. We benefited from the useful comments from the participants of various seminars
and conferences. This research was financially supported by the JSPS Grant-in-Aid for
Scientific Research (Grant Numbers 24330090, 21H04982 for Akabayashi, 20H05629 for
Tanaka). All remaining errors are solely ours.
†
Faculty of Economics, Keio University, 2-15-45 Mita, Minato-ku, Tokyo 108-8345,
Japan hakab@econ.keio.ac.jp‡
Institute of Social Science, University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 1130033, Japan. ryuichi.tanaka@iss.u-tokyo.ac.jp1

1

Introduction

What are the resources used for the welfare of young children in contemporary societies? What is the return to society of early childhood education?
By addressing these questions, Psacharopoulos (1982) highlighted the importance of comprehending the economic underpinnings of early childhood education and daycare. Over four decades later, these questions remain as pertinent as ever, with a growing body of research emphasizing the critical roleof early childhood interventions like preschooling and Head Start programsin shaping long-term educational and labor market outcomes (e.g., Cunha
et al. (2006); Gertler et al. (2014)). Numerous studies have assessed theimpact of early intervention initiatives such as the Perry Preschool Project
and the Head Start Program on both short-term and long-term outcomes forchildren from disadvantaged backgrounds (e.g., Garces et al. (2002); Barnett(2004); Belfield et al. (2006); Heckman et al. (2010a,b)). Collectively, thesestudies consistently reveal positive effects, including higher wages, increased
educational attainment, and reduced likelihood of criminal involvement laterin life.1
The primary challenge in this body of research lies in accurately estimating the rate of return to early childhood interventions while accounting
for long-term child outcomes. One of the seminal studies in this area is thatof Heckman et al. (2010b), who estimated the rate of return on the Perry
Preschool Program, factoring in social and economic outcomes up to age 40.
Their analysis considered various potential biases and assumptions, including compromised randomization, deadweight loss (DWL) associated with
tax financing, and imputation and extrapolation of missing earnings. Theirfindings indicated a rate of return between 7% and 10%. However, given
the limited scale of the Perry intervention, it remains uncertain whether auniversal intervention policy, such as a statewide expansion of preschool programs, would yield similar effects, especially considering potential general
1
Garces et al. (2002) find that children participating in the Head Start program tend to
exhibit higher wages, an increased likelihood of high school graduation, an elevated probability of college enrollment, and a reduced propensity for engaging in criminal activities
during their early twenties. Barnett (2004) delves into the long-term effects of the Perry
Preschool Project, examining outcomes at age 27, while Belfield et al. (2006) provides
further updates on these effects at age 40. Meanwhile, Heckman et al. (2010a) and Akabayashi and Tanaka (2013) have conducted an in-depth examination of both short-termand long-term program effects, employing rigorous statistical analysis. For comprehensive
overviews of early interventions’ effects, refer to Currie (2001) and Blau and Currie (2006).

2equilibrium impacts.
This study, building upon our previous research (Akabayashi and Tanaka(2013)), provides a fresh estimate of the rate of return to early childhood
education programs. In Japan, children under six years of age can optionallyattend kindergarten or nursery schools for two or more years before elementary school. Leveraging prefecture-level panel data, we initially estimate theimpact of preschool enrollment on high school graduation and college advancement rates among men. These estimates, in conjunction with national
fiscal records, enable us to calculate the rate of return on early childhood
education. This paper represents one of the first attempts to estimate therate of return using Japanese data. 2
To identify causal effects of preschool enrollment, we leverage a national
policy implemented in 1964 that triggered an expansion in kindergarten enrollment rates. In 1963, the central government unveiled a seven-year plan,
known as the First Plan for Promoting Kindergarten Education, to increase
the enrollment rate of two- or three-year kindergartens from 39% to 63.5%.3
Because the enrollment rate in 1964 varied widely across cities and prefectures, this new policy undoubtedly affected local educational authorities indifferent magnitudes. The differential enrollment rates in 1964 across cities
and prefectures, combined with Japan’s unique dual preschool system where
full-time nursery schools also accommodate kindergarten-age children withworking mothers, provide a quasi-experimental setting for our analysis oflater expansion of preschool enrollment.
We estimate the effects of preschool enrollment expansion on high school
graduation and college advancement rates, using the rapid expansion ofpreschool enrollment driven by exogenous policy changes. However, measures of preschool education at the prefecture-level may still be endogenous
due to factors like differential availability. To address this potential issue, weestimate the model by both simple ordinary least squares (OLS) and an instrumental variable (IV) method with the number of Buddhist and Christianinstitutions per capita and the gap between the policy target (i.e., 63.5%)
and the kindergarten enrollment rate at age 2 serving as instruments as in
Akabayashi and Tanaka (2013). Estimates with the instrument reveal that2
In this paper, we focus only on men because it is difficult to estimate the long-termmonetary benefits of preschool education for women whose labor market attachment wasmuch lower than men during the period we studied.
3This policy was followed by the Second Plan in 1971.

3

a one percentage point increase in kindergarten attendance rate generates
roughly a 0.3 percentage point increase in high school graduation rates anda 0.1 percentage point increase in college enrollment rates. A one percentage
point increase in nursery school attendance rate generates a roughly 0.9 percentage point increase in high school graduation rates and a 0.3 percentage
point increase in college enrollment rates.
For the calculation of the internal rate of return, we firstly construct the
per child fiscal cost of kindergarten and nursery school attendance using government fiscal records from the 1950s to 1970s. We also use national averageage-earnings and employment rate data to estimate the pecuniary benefitof preschool enrollment, factoring in the deadweight loss due to income tax
financing (Heckman et al. (2010b), and Bessho and Hayashi (2013)). Then,
we calculate the internal rates of return for kindergarten and nursery school
attendance separately. Our results indicate that, even with an extensive sensitivity analysis, the rate of return for preschool attendance falls between
5.7% and 8.1%.
This paper is related to the strand of literature on the effect of earlychildhood intervention on adult outcomes. Extensive research has been
conducted on the short-term effects of early intervention on children fromdisadvantaged households (e.g., Currie and Thomas (1995); Currie (2001))
and its long-term effects by Garces et al. (2002) and Ludwig and Miller(2007). Among them, Garcia et al. (2020) has quantitatively assessed andaggregated the multifaceted lifetime benefits of a high-quality early childhood program with outcomes measured through midlife, concluding thatthe internal rate of return stands at 13.7%, accompanied by a consequential
benefit-to-cost ratio of 7.3. It is worth noting that these investigations haveprimarily focused on the effects of early intervention on children from disadvantaged backgrounds. In contrast, the present study distinguishes itself byexamining the impact of a universal intervention on the entire population.
Several studies investigate the effects of universal early intervention on
the education of children in the entire population (Gormley and Gayer(2005); Baker et al. (2008); Berlinski et al. (2009)), focusing on short-term
outcomes rather than long-term effects. However, inferring long-term policy implications solely from short-term outcomes is a complex endeavor,
given that numerous previous studies have illustrated the transitory nature of the cognitive benefits associated with preschool attendance (Duncan

4and Magnuson (2013)). Furthermore, research indicates that non-cognitive
development may hold more significance in early childhood development,
particularly in the age range of 3 to 5 (Heckman et al. (2010a,b)).
There are several papers estimating the impact of preschool attendanceon a multitude of outcomes and the rate of return linked to universal preschool
education. Arteaga et al. (2014) explores the effects of preschool duration on
adult outcomes, such as special education enrollment and criminal behavior.
Cascio and Schanzenbach (2013) escrutinize the effects of the introductionof universal preschool programs in Georgia and Oklahoma since the 1990s,
identifying a positive impact on children’s test performance lasting until
the eighth grade, predominantly among lower-income families. Notably, vanHuizen et al. (2019) shares a methodological affinity with our study, as they
endeavor to synthesize diverse data sources to ascertain benefits and costs,
leveraging a policy reform in Spain as a source of causal inference. It is pertinent to note that while our dataset does not consist of individual-level data,
it provides a more comprehensive and systematic perspective, encompassing
wage data spanning the entire work history of the population influenced by
the policy.
The present research contributes to the expanding body of literaturethat assesses the impact of early childhood education on diverse outcomes,
particularly in the context of Japan and other Asian nations. Kawarazaki(2022) delves into the examination of a universal early childhood education
program, offering a comprehensive analysis of its long-term effects and theunderlying mechanisms at play. This study illuminates a positive correlation
between the program and income levels, persisting until age 50. In parallel,
Ando et al. (2022) investigate the implications of early childhood education
on risky behaviors during adolescence. Their findings highlight the substantial benefits of early childhood education expansion, particularly in termsof diminished rates of juvenile violent arrests and reduced teenage pregnancy rates. Furthermore, Akabayashi et al. (2023) contribute by estimating the causal effects of education-oriented versus care-oriented preschools onchild development. Leveraging data collected from school-age children in the2010s, this study finds significant enhancements in mathematical and linguistic achievement among children who attended education-oriented preschools,
with these improvements manifesting into early adolescence. Notably, they
reveal that the children who are least likely to enroll in education-oriented5preschools stand to gain the most from such programs. In the Chinese context, Yang (2021) investigates the long-term consequences of early childhood
education and care attendance in China, focusing on adults over the age of
18. This study establishes a positive relationship between early childhood
education and care experiences and enhanced cognitive and non-cognitive
skills in adulthood. Collectively, these studies underscore the importance ofearly childhood education in influencing a spectrum of outcome variables.
Our research serves to augment the body of evidence emphasizing the critical role of early childhood education, examining the causal effects of earlychildhood education on later educational achievement, and providing estimates of internal rates of return by analyzing the universal expansion ofpreschools in Japan during the years 1960-1980.
The remainder of this paper is structured as follows: Section 2 provides
a brief overview of Japan’s preschool education system. Section 3 describes
the econometric model and the identification strategy. Section 4 details thedata used, and Section 5 reports the estimates of the effects of preschoolingon educational attainment. Section 6 presents the empirical results of therate of return to preschool education, and finally, Section 7 offers concluding
remarks.

2

Early childhood education in Japan

This section provides a concise overview of the institutional background of
the Japanese early childhood education system and details the 1964 national
policy implementation aimed at boosting kindergarten enrollment rates.

2.1

Preschools in JapanAll Japanese children aged six years, as of April 1st, are required to commence their education in elementary schools. These elementary schools follow a six-year curriculum, after which children must continue their education in junior high school for three years. Prior to commencing the nine-year
compulsory education, however, many children attend preschools.
In Japan, the two primary options for preschool education are two- or
three-year part-time kindergartens and full-time nursery schools, each historically designed with distinct purposes and characteristics. On one hand,kindergartens were originally established to facilitate the holistic develop6

ment of preschool children, nurturing their cognitive and physical abilities
by providing a conducive educational environment for 3- to 5-year-olds, typically offering one- to three-year courses. Presently, the Ministry of Education, Culture, Sports, Science, and Technology (MEXT) prescribes andoversees the curriculum of kindergartens, with kindergarten teachers being
required to obtain certification from the Ministry.
On the other hand, nursery schools initially served as childcare providers
for children whose parents (or legal guardians) were unable to provide caredue to work commitments or other reasons. The Ministry of Health, Labour,and Welfare defines the guidelines for nursery schools, and those responsible for childcare must obtain certification as “childcare personnel.” Nurseryschools, in addition to childcare, also provide educational services for children aged five and below.
Notwithstanding their divergent origins and administrative frameworks,
these two types of institutions share numerous overlapping roles, especiallywith regard to providing education to preschool-aged children. Consequently,
many individuals consider these two types of schools to be roughly equivalent.4
Japan encompasses both local public and private kindergartens and nursery schools. Several private kindergartens and nursery schools were initiallyestablished and operated by religious organizations, including Buddhism andChristianity. According to data from The Ministry of Education (1979), private kindergartens constituted 64% of preschools in 1969, with 27% of themhaving been established by religious organizations.

2.2

Post-war expansion of early childhood education

Japan has undergone a substantial and rapid expansion in preschool enrollment since the early 1950s. The overall preschool attendance rate, encompassing both kindergartens and nursery schools, has consistently exceeded
90% since 1970, a remarkable increase from the approximately 40% rate
recorded in 1954. As depicted in Figure 1, the average kindergarten enrollment rate surged from 21.8% in 1954 to 64% in 1975.
4

In fact, the Japanese government is currently in the process of amalgamating twopreschool education institutions into a single entity. The MEXT is actively promoting
the adoption of a system established in Fiscal Year 2006 known as the “Center for Early
Childhood Education and Care.” This system offers integrated education and childcare
services, along with an Action Program for the Promotion of Pre-School Education.

7

Figure 1: Preschool enrollment trend: 1953-1988

Significant regional disparities in preschool enrollment rates were evident in the 1950s, as illustrated in Figure 2. Notably, the lowest and highest
overall enrollment rates in 1954 were 8.8% in Aomori Prefecture and 80.1%
in Kagawa Prefecture, respectively. For instance, in 1954, Ibaraki and Tottori Prefectures exhibited nearly identical kindergarten enrollment rates of
7%. However, by 1975, Ibaraki’s enrollment rate had reached 64%, whereas
Tottori’s rate had remained at 35% (see Figure 3).
Throughout the 1960s and 1970s, both kindergarten and full-time nursery school enrollment rates experienced rapid growth. This surge in kindergarten enrollment was partially attributed to a universal policy intervention
by the Japanese government, initiated in the 1960s. In 1964, the central
government introduced the First Plan for Promoting Kindergarten Education, a seven-year initiative aimed at increasing the enrollment rate of twoor three-year kindergartens in cities with populations exceeding 10,000. The
goal was to elevate this rate from 39% to 63.5% by establishing at least one
kindergarten in every such city. To support this endeavor, the Japanese government significantly augmented the budget allocation for preschools, witha special focus on kindergartens. Figure 4 provides a graphical representation of the logarithmic ratios of kindergarten and nursery school subsidies
to the national GDP between 1955 and 1990. This data clearly reveals the
expansion of both subsidies, particularly for kindergartens, during the 1960s
and 1970s.
We seek firstly to assess the impact of preschool attendance on children’s educational achievements. To achieve this, we leverage the substan-8

Figure 2: Kindergarten and nursery school enrollment in 1954

Figure 3: Kindergarten and nursery school enrollment in 1975

9

Figure 4: Fiscal expansion: 1955-1990

tial cross-prefectural variation in preschool enrollment growth, potentiallystemming from national government initiatives supported by increased financial resources. Figures 5 and 6 depict the growth in kindergarten andnursery school enrollment rates from 1954 to 1975 for each prefecture, respectively. Notably, the significant variation in kindergarten enrollment rate
growth resulting from the 1964 government intervention is evident acrossprefectures, creating varying pressures on local educational authorities (asdepicted in Figure 5). Japan’s dual preschool system, accommodating bothkindergarten-age children and those with working mothers, further adds tothe policy divergence across prefectures (as indicated in Figure 6).
Leveraging these unique institutional dynamics within the early childhood education system in Japan, we aim to identify the impact of preschool
attendance on children’s educational outcomes and, consequently, calculate
their rates of return.

3

The econometric modelThis section provides a detailed exploration of the econometric model andthe identification strategy employed in our analysis. To evaluate the impact
of preschool attendance on high school graduation and college advancement

10Figure 5: Kindergarten enrollment from 1954 to 1975

Figure 6: Nursery school enrollment from 1954 to 197511

rates, we estimate the following model:
yit = β0 + β1 kindit−12 + β2 nurseit−12 + Xit γ + ci + dt + εit ,(1)

where yit is either the high school graduation rate in year t or the college
advancement rate in year t for prefecture i; kindit−12 and nurseit−12 areenrollment rates in kindergartens and nursery schools in year t − 12 forprefecture i as the preschool attendance rates at age five were measured 12
years prior to the outcome measures; Xit are controls such as female ratio;
ci and dt are prefecture and year dummies, respectively; and εit is an errorterm.
The estimation of this equation via the two-way fixed effect model serves
as the basis for our results. We employ prefecture fixed effects in the regression equation to account for time-invariant, prefecture-specific unobserved
heterogeneity. This serves to partially, if not entirely, mitigate the potential
endogeneity resulting from such unobserved heterogeneity.
Since we utilize prefectural statistics, rather than individual-level data,
to estimate the impact of kindergarten and nursery enrollment rates onschool outcomes 12 years later, aligning data from various sources by year
and prefecture necessitates several assumptions for the validity of our approach. First, there is no delayed or early enrollment in schools. Second,
there is no grade repetition. Third, the enrollment levels in kindergarten
and nursery at age 5 reflect the enrollment levels of younger children ineach prefecture. Fourth, inter-prefectural migration during the study period is negligible. In Appendix C, we discuss in detail how our approachremains reasonably valid, considering the institutional settings of Japan andadditional evidence.
To further strengthen our findings and assess the robustness of the results, we provide estimates through instrumental variable methods. Initially,
we utilize the generalized method of moments (GMM), followed by the application of GMM with HAC (heterogeneity and autocorrelation consistent).
This analysis aims to ascertain whether our results withstand the potential
presence of endogeneity in the past enrollment rates of kindergartens andnursery schools.
As for the supply factor, we choose the number of Buddhist and Christian institutions within the population as instruments for the enrollment

12

rates in kindergartens and nursery schools. As previously discussed in 1
and 2.1, these variables possess a historical determination and can serve as
instruments for the enrollment rates in preschools. This is due to the factthat many religious groups in Japan, including Buddhism and Christianity,
have established private kindergartens and nursery schools. In addition to
the religious institutions, we use the gap between the policy target (i.e.,
63.5%) and the total kindergarten enrollment ratio at t-3 as an instrument
for the male kindergarten enrollment rate in year t. The rationale for using
the gap at t-3 as an instrument is to provide a political incentive to increasekindergarten enrollment up to the policy goal. If enrollment in age 5 classes
increases only after the approval, construction, and recruitment process toestablish new kindergartens, it takes at least three years. Specifically, whattends to happen in terms of policy is a case where capacity is increased
from the lowest 3-year-old classes rather than a sudden increase in children
entering kindergarten at age 5. In that case, it would take two years for thecapacity expansion to reach the 5-year-old class we use for the measurementof the enrollment rate because the capacity improvement proposal/decisionand room expansion would be completed at least one year before the 3-yearold capacity increase, there would still be a 3-year lag. The gap is set to 0
if the kindergarten enrollment ratio is above 63.5 percent.

4

Data

4.1

Preschool attendance and educational achievement

In conducting our estimation, our data set is meticulously constructed froma variety of information sources. The principal source for enrollment rates
is the School Basic Survey, spanning from 1951 to 1999, as provided by the
Ministry of Education, Culture, Sports, Science, and Technology (MEXT).
For nursery school enrollment rates, we draw upon reports published by the
Ministry of Health, Labor, and Welfare (MHLW).
The School Basic Survey represents an annual census survey administered by the MEXT, designed to compile fundamental information concerning educational institutions of all levels. This includes data on the numberof students and teachers, students’ post-graduation courses, and the current
financial resources and assets of schools. At the prefecture-level, panel data
are publicly accessible across several volumes of these surveys.
13

To elucidate our approach, we will now outline the construction of variables utilized in our analysis. We identify two variables as indicators ofeducational outcomes: the High School Graduation Rate and the 4-Year
College Advancement Rate. The High School Graduation Rate is definedas the gross high school graduation rate, which reflects the ratio of highschool graduates to the number of students in the 9th grade, the final yearof compulsory schooling, occurring three years prior. The 4-Year College
Advancement Rate is derived from data on how many high school graduates
in a given prefecture transitioned to a 4-year college. This rate is calculated
as the ratio of college students among high school graduates to the numberof students in the 9th grade, also measured three years prior.
For the kindergarten enrollment rate at age five, we ascertain it throughthe ratio of the number of five-year-old pupils attending kindergartens in thepreceding year to the number of students in the first grade of elementary
schools. Since elementary school attendance is compulsory, the number offirst-grade students theoretically represents the maximum number of fiveyear-olds enrolled in kindergartens in the preceding year.
Regarding the nursery school enrollment rate at age five, data is sourced
from the 1953-1977 Case Reports of the Welfare Administration (CRWA:
Fukushi Gyosei Houkoku Rei) and the 1977-1992 Survey of Social Welfare Facilities (SSWF: Shakai Fukushi Shisetsu Chosa Houkoku), both provided by the MHLW.5 The two data sets have complementary strengths
and weaknesses. These two datasets possess complementary strengths andweaknesses. The CRWA records nursery school enrollment on March 1st,
segmented into four “administrative age” categories (defined as age on April1), while the SSWF records nursery school enrollment on October 1st, basedon the real (biological) age as of October 1st. Although the SSWF offersa detailed breakdown of enrollment by age category, it measures ages as of
October 1st rather than April 1st. Nearly half of the five-year-old enrollment in the SSWF should be attributed to “administrative age 4” based on
the CRWA records.
To approximate the nursery school enrollment rate relative to the April
1st elementary school enrollment for years preceding 1977, which are omitted
5

The nursery school data are unavailable for the years 1963-1965. To address thisgap in our analysis, we applied linear interpolation to estimate the missing data. In arobustness check, we repeated the same estimations using data from 1964 onward, andfound that these results closely mirrored our initial findings.

14

in the CRWA data, we construct a proxy measure of “administrative age”
5 enrollment by utilizing the SSWF “real age” total enrollment for the age
category of 4 and 5. While no method is perfect given the limited data,
we simply apply weights for each age group derived from the average “realage” distribution in the SSWF. In periods of increasing enrollment, ourestimates of five-year-olds’ enrollment prior to 1977 may exhibit an upward
bias. However, we assume that this type of measurement error becomes
negligible after accounting for year-specific effects.
Incorporating control variables, we include real gross domestic expenditure (GDE) per capita and the male-to-female ratio. Real GDE data
is obtained from relevant volumes of the Cabinet Office’s Annual Reporton Prefectural Accounts, and it is divided by the prefectural population,
sourced from the MHLW’s Vital Statistics of Japan. The male-to-female
ratio is calculated as the proportion of girls in the first grade of elementary
schools, as reported in the School Basic Survey.
Our econometric analysis takes into consideration the potential endogeneity of kindergarten and nursery school enrollment rates. As in instrument, we employ the number of Buddhist and Christian institutions in addition to the gap between the policy target (i.e., 63.5%) and 3-year laggedkindergarten enrollment rate. Information on the number of Buddhist andChristian institutions by prefecture is extracted from the relevant volumesof the Agency of Cultural Affairs’ Annual Report on Religion, with data
unavailable between 1963 and 1967.6
Table 1 presents the descriptive statistics. The dataset for kindergarten
and nursery school attendance rates, which we employ in our analysis, spansfrom 1957 to 1987. This timeframe allows us to relate educational outcomesto measures of preschool attendance twelve years earlier, both in the context
of the high school graduation rate equation and the college advancement rateequations.
On average, the attendance rate for kindergartens hovers around 51%,
while that for nursery schools is approximately 32%. Notably, a substantial
degree of variability is evident in these attendance rates across both time
6

The Annual Report on Religion is published by the Agency of Cultural Affairs. The
data in this report is derived from the results of the Survey of Religion, which is anannual census survey conducted by the MEXT. The primary objective of this survey is togather fundamental information, including the number of religious groups, the workforce
associated with these groups, and the count of believers.

15

Table 1: Descriptive statistics

Variable
Mean
Outcomes
High School Graduation Rate (male)
0.872
4-Year College Advancement Rate (male)
0.230
Preschool
Kindergarten Attendance Rate at 5 (male)
0.507
Nursery School Attendance Rate at 5
0.317
Controls
Female Ratio
0.510
Instruments
Religious Corporations at 5 (per 100)
0.093
Religious Corporations at 4 (per 100)
0.093
Gap betw’n target and 3-year lagged kind.rate 0.186
Number of observations

Std. Dev.

Min

Max

0.091
0.068

0.532
0.087

1.112
0.432

0.210
0.170

0.051
0.001

0.961
0.980

0.007

0.475

0.524

0.058
0.058
0.185
1,164

0.007
0.007
0.000

0.361
0.361
0.603

Note: For the outcomes and controls, the averages of prefecture-level observations arefrom 1966 to 1999 for High School Graduation Rate and from 1969 to 1999 for College
Advancement Rate. For the preschool variables and the instruments, the numbers areaverages of the lagged variables based on the corresponding age. Religious facilities are
the sum of corporations of the Christian and the Buddhism. Outcome and preschool
variables are for boy except for nursery school attendance rate and the Gap between
policy target (i.e., 63.5%) and kindergarten rate at age 2.

periods and regions. For instance, the minimum nursery school attendance
rate registers at a mere 0.1%, while the highest reaches an impressive 98%.
Kindergartens exhibit a standard deviation in their attendance rate of 0.21,
which is notably higher than the corresponding figure for nursery schools at
0.17.
The average high school graduation rate stands at 87%. Nevertheless,
we observe significant variations even within this cohort of students withrelatively high advancement rates. The lowest high school advancement rate
records at 53%, while the highest reaches 111%.7 Meanwhile, the averagecollege advancement rate to four-year institutions hovers around 24%.

4.2

Fiscal costs of preschools and data for earnings

In this subsection, we elucidate the data used to estimate the rate of returns
to preschool education. Our cost and benefit calculations for preschooling draw from a range of information sources. It’s important to note that
7

The maximum number exceeds 100% partly since this is the gross graduation ratethat ignores the grade repetition, and there were some high schools which had 4 year
programs.

16these sources provide aggregate data at the national level, as data at theprefecture-level is not available to calculate costs and benefits.
Our quantification of monetary benefits relies on two national datasets.
Firstly, we utilize data from the Basic Survey of Wage Structure (BSWS)
that covers the national average wage of employees, encompassing both regular monthly salaries and annual bonuses, segmented by educational attainment and 5-year age groups.8 Secondly, we calculate the probability of
employment by age and educational achievement, employing data from the1970, 1980, 1990, 2000, 2010, and 2020 Population Census. In cases wheredata is missing for certain years within the Population Census, we employ
simple linear interpolation to estimate these values. These two datasets are
instrumental in calculating the expected returns associated with high school
and college graduation.
However, it is imperative to acknowledge a limitation in our calculation
process. Specifically, we are forced to disregard the wages of non-regular
workers and set them to zero. It’s worth noting that not all employees holdregular positions, particularly among those with lower educational attainment. Our data sources, including the Population Census, either do notencompass non-regular workers or fail to differentiate them. The directionof any bias arising from this limitation remains unclear, although it may bepositive if our assumption leads to an underestimation of the wages for highschool dropouts.9
To compute the rates of return, we focus on two distinct cohorts. Cohort
1 comprises individuals who graduated from junior high school in 1971 andpotentially attended kindergarten or nursery school in 1961 at the age of 5.
8
The BSWS is an annual survey that targets regular workers employed by businesses
with more than nine employees. The age categories in the published tables are “under
17,” “18-19,” “20-24,” “25-29,” ...“Over 65.”
9
One may also think that the short-run benefits of childcare use should include its
impact on parental labor supply. While there is a large empirical literature on the relationship between childcare availability and female labor force participation, the evidenceis mixed. In Japan, for example, Asai et al. (2015) found, using data from 1990 to 2010
and employing a prefecture fixed effect model, the variation in the availability of full-timechildcare, which corresponds to nursery school in our study, was not significantly associated with the labor supply of mothers with children under 6. Their findings suggest thepresence of crowding out of informal childcare by other family members due to formalcare. During the 1960s-70s, informal care was more prevalent than today, suggesting thatthe impact on employment would have been even smaller. Additionally, as kindergarten
programs were typically half-day, the effect on employment was likely to be further minimized. Considering these factors, we have chosen not to incorporate the potential effects
on parental labor supply into the benefits of childcare.

17

Cohort 2, on the other hand, consists of individuals who are 5 years younger
than those in Cohort 1. The choice of these two cohorts for our rate of
return calculations is a compromise stemming from the lack of wage data
beyond age 60 for younger cohorts and the absence of preschool cost datafor older cohorts.

18

19

Cohort 2
23,7203,985
1.60
26,21837,6510.74
29,197
46,581
14,225,967
0.8
313,982
0.5
18,410,423
847,595
21,721
10,86032,581
1.92
62,457

Cohort 1
11,3011,541
1.38
12,41917,6690.70
14,005
19,306
4,865,265
0.8
53,635
0.5
6,188,851
752,293
8,227
4,113
12,3401.92
23,656Social Security Statistics*
Social Security Statistics*
The Ministry of Health and Welfare (1978)*The Ministry of Health and Welfare (1978)*(A)/(B) + (C)/(D)
Values for 1971 cohort is an estimate
Report on Social Welfare Admin. and Services*Zenhoren (1976)*(F) + (G)National Budget, Min. of Health and Welfare*(H) x (I)Annual Report of Education Budget*
Annual Report of Education Budget*
School Basic Survey(A)+(B)/(C)Survey of Local Educational Expenditure
School Basic Survey
Weighted average of (D) and (E)(G) x (C)

Data Source

Note: Cohort 1 (2) is the birth cohort with age 5 in 1961 (1966). Fiscal year for preschool budget is 1961 (1966) for Cohort 1 (2). Annual Reportof Education Budget: ”Annual Report of Education Budget of National and Local Governments.” Social Security Statistics: ”National Budget,
Annual Report on Social Security Statistics.” The data since 1965 for National government subsidy for nursery school facility and the grant rate ofnursery school facilities are based on Ministry of Health and Welfare (1978), and the values prior to 1965 are assumed as 10 of the total amount forchild welfare facilities. In the calculation of total subsidy for nursery school per child, additional subsidy from local government is ignored. Nurseryschool tuition is assumed as 50% of the subsidy running cost subsidy based on the value in 1976, which was 53% based on Zenhoren (1976). Average
enrollment duration is calculated based on the statistics from National Budget and The Ministry of Health and Welfare (1978), The Ministry of
Health and Welfare (1988), and The Ministry of Health and Welfare (1998). The oldest available statistics of the average enrollment duration for
nursery schools is 1966 and use the same value for 1961. For the total subsidy for nursery school per child, we ignored additional subsidy from local
government.

Variable
Kindergarten
Tuition at private kind. (Yen/year) (A)Entrance fee at private kind. (Yen) (B)Average enrollment duration (Year) (C)Average annual cost for private kind. per child (Yen) (D)Local gov. expenditure for public kind. per child (Yen/year) (E)Private kind. enrollment ratio (F)Average annual cost for kind. per child (Yen) (G)Average cost of kind. per child (Yen)
Nursery School
National gov. subsidy for running cost (1,000 yen) (A)Grant rate of running cost subsidy (B)National government subsidy for facility (1,000 yen) (C)Grant rate of facility (D)Total subsidy (1,000 yen)
Total enrollment
Total subsidy per child. (Yen) (F)Tuition (Yen) (G)
Total cost per child (Yen) (H)Average enrollment duration (Year) (I)
Average cost of nursery school per child (Yen)(1) Kindergarten and Nursery School

Table 2: Description and summary of education cost data

20

Cohort 2
352,737
128,101
68,453
187,5620.31
414.282,325,134
147,8,6
158,724
422,7100.76
1042.54

Cohort 1
156,451
75,570
14,11972,242
0.31
187.12
1,505,191
41,33353,728172,8410.76
538.57National Budget, Suetomi (2010)
National Budget, Suetomi (2010)
Suetomi (2010)
Suetomi (2010)
School Basic Survey
National Budget, Suetomi (2010)

Survey of Local Educational Expenditure, Suetomi (2010)
Survey of Local Educational Expenditure, Suetomi (2010)
Suetomi (2010)
Suetomi (2010)
School Basic Survey
National Budget, Suetomi (2010)

Data Source

Note: Cohort 1 (2) is the birth cohort with age 5 in 1961 (1966). Fiscal year for high school cost is 1971-73 (1976-79) for Cohort 1 (2). Fiscal year
for 4-year college cost is 1974-77 (1979-82) for Cohort 1 (2). The table reports the high school costs for 1971 (1976) fiscal year for Cohort 1 (2) andthe 4-year college costs for 1974 (1979) fiscal year for Cohort 1 (2). In the calculation of the average cost of university education per person, thecost of local public universities is assumed to be the same as national universities.

VariableHigh School
Public expenditure for public high school
Tuition at public high school (Yen)
Public expenditure for private high school (Yen)
Tuition at private high schools (Yen)Private high school enrollment ratio
Average cost of high school education per child (1,000 Yen)
4-year College
Public Expenditure for national universities (Yen)
Average tuition at national university (Yen)
Public expenditure for private universities (Yen)
Average tuition at private university (Yen)Private university enrollment ratio
Average cost of university education per person (1,000 Yen)

(2) High School and 4-year college

Table 2: Description and summary of education cost data-cont.

The measures of education costs, encompassing kindergarten, nurseryschool, high school, and college, are summarized in Table 2, presented separately for Cohorts 1 and 2. For kindergarten costs, we utilize data fromthe Annual Report of Education Budget of National and Local Government(Kuni to Chiho no Bunkyo Yosan), which covers private kindergarten tuition and entrance fees paid by parents, as well as subsidies from the national
government to private kindergartens. It is plausible that local governments
may have also provided subsidies to private kindergartens, but a lack of data
prevents us from explicitly incorporating this in our calculations. For highschool and college attendance, we account for both direct and indirect costs.
Direct costs encompass the per-student high school expenditure and fouryear college expenditure, calculated as the weighted average of both public
and private schools, with enrollment ratios serving as weights. All costs andbenefits are deflated to the 2010 Yen for consistency. Appendix B provides
further details of the data and variables used in the analysis.

5

Effects of preschooling on educational attainments

This section presents the primary estimation results based on Equation (1).
We provide estimates using both Ordinary Least Squares (OLS) and Instrumental Variable (IV) methods. The IV estimations are conducted using theGeneral Method of Moments (GMM) with an optimal weighting matrix. In
the GMM framework, we employ the following variables as excluded instruments for the kindergarten enrollment rate, nursery school enrollment rate,
and real Gross Domestic Expenditure (GDE) per capita for the same year
as the outcome variable: the number of religious corporations (Christianand Buddhist) per 100 members of the population, the one-year lag of thesame variable, real GDE per capita for the same year as the preschoolingvariables, and the kindergarten enrollment rate from the previous year.10
Table 3 reports the coefficient estimates in the regression equation. The
upper panel comprises three columns displaying the results for the highschool graduation rate. The first column presents the results using OLS,
10In OLS, standard errors are robust to heteroskedasticity. In GMM, standard errors areheteroskedasticity- and autocorrelation-consistent (HAC: Newey and West (1987)) using
Bartlett kernel with band-width 3=T1/3 (Li and Racine (2006), p.405).

21

Table 3: Estimation results of the effects of preschools on educational
achievement
OLS
IV(1)
Dependent Variable: High School Graduation
Kindergarten attendance rate
0.241***
0.251***(0.055)(0.046)
Nursery School attendance rate
0.241***
0.971***(0.068)(0.096)
Female ratio
0.649
0.612
(0.623)(0.519)R-squared
0.772
0.602
C-D (or K-P) Statistics for weak IV
77.38
P-value for Overidentification test
0.824
Number of Observations
1,164
Dependent Variable: 4-Year College Advancement
Kindergarten attendance rate
0.057
0.081***(0.035)(0.021)
Nursery School attendance rate
0.062**
0.326***(0.030)(0.045)
Female ratio
0.398
0.338
(0.328)(0.241)R-squared
0.767
0.838
C-D (or K-P) Statistics for weak IV
77.38
P-value for Overidentification test
0.222
Number of Observations
1,164

IV(2)
0.253***(0.065)0.971***(0.126)0.600(0.596)
0.602
37.40
0.804

0.082***(0.030)0.329***(0.064)0.315(0.278)
0.837
37.40
0.200

Note: Year fixed effects are included. IV(1) is the GMM estimate with prefecture fixed
effects. IV(2) is the GMM-HAC estimate with prefecture fixed effects. Robust standard
errors are reported in the parenthesis. *** 1%, ** 5%, * 10%.

the second using GMM with prefecture fixed effects, and the third using
GMM-HAC with prefecture fixed effects. All findings indicate that preschool
expansion positively impacts the high school graduation rate. The coefficientfor the kindergarten attendance rate is 0.241 using OLS, 0.251 and 0.253
using GMM and GMM-HAC with prefecture fixed effects, respectively. All
these coefficients are statistically significantly different from zero. The IV
coefficients suggest that a one-percentage-point increase in the kindergarten
attendance rate results in approximately a 0.3 percentage point increase in
the high school graduation rate.
Similarly, the coefficient for the nursery school attendance rate is 0.241
using OLS, and 0.971 using GMM and GMM-HAC with prefecture fixed effects. Again, all these coefficients are statistically significantly different fromzero. These IV coefficients indicate that a one-percentage-point increase in
the nursery school attendance rate leads to roughly a 0.9 percentage point22

increase in the high school graduation rate.
The lower panel of the table reports results for the four-year college advancement rate. Coefficients for the nursery school attendance rates are
consistently positive and statistically significantly different from zero, regardless of the estimation method. The magnitude of the coefficients varies
between 0.062 and 0.329. The coefficients for the kindergarten attendance
rate are statistically significant under GMM and GMM-HAC with prefecture
fixed effects but not under OLS.
The coefficients obtained using GMM are larger than those using OLS,
indicating that OLS underestimates the effects of preschooling on educational outcomes, possibly due to attenuation bias stemming from measurement errors in the preschooling variables. The larger bias in the estimates of
the coefficients for nursery school attendance rate compared to the kindergarten rate may be attributed, in part, to larger measurement errors in
the construction of nursery school attendance rates. Another contributing
factor could be that the effects of preschooling on the population segment
affected by the expansion of kindergarten and/or nursery schools provided
by religious institutions and/or induced by policy may be larger than on the
average population.
Regarding the validity of instruments in the GMM, Table 3 presents
the F-test results for the excluded instruments in the first-stage regression,indicating a strong rejection of their insignificance across all specifications.
Furthermore, the over-identification test results for instrument exogeneity
consistently support the exogeneity of these instruments. Therefore, we
conclude that our instruments are valid, and our estimates are suitable forcalculating the rate of returns to preschooling in the subsequent section.

6

Estimation of the rate of returns to preschool
education

In this section, we present the calculated internal rates of return for the two
types of preschool education in Japan, namely, kindergartens and nurseryschools. The internal rate of return (hereafter, IRR) allows us to compare
the efficiency of alternative educational investments in the same measurement unit. For each Cohort 1 and 2, we construct a national average lifecycle earnings profile up to age 65 for 4-year college graduates, high school
23

Table 4: Internal rates of return to preschool attendance: Benchmark estimates

Benchmark
OLS
Cohort 1 (Age 5 in 1961)
Confidence Interval

IRR of Kindergarten

IRR of Nursery School

7.27%[5.82%, 7.71%]

7.40%[6.15%, 7.75%]

Cohort 2 (Age 5 in 1966)
Confidence Interval

6.70%[4.67%, 7.37%]

6.65%[4.78%, 7.26%]

FE-IV/HAC
Cohort 1 (Age 5 in 1961)
Confidence Interval

7.46%[6.98%, 7.70%]

8.07%[7.98%, 8.14%]

Cohort 2 (Age 5 in 1966)
Confidence Interval

6.98%[6.28%, 7.36%]

7.91%[7.73%, 8.04%]

graduates, and individuals who completed only junior high school, using thedata described in Section 4.2.11 We also calculate the costs of education,
incorporating forgone earnings and direct costs associated with each level of
education. We then determine the discount rate that equates the present
values of the benefits and costs for each cohort and preschool education type.
For the education effect parameter, we rely on the estimates obtained using
FE/GMM-HAC, as we consider them the most reliable, although we also
use the other estimates as robustness checks.
Table 4 presents the estimates of the rate of returns to kindergarten andnursery schools for Cohorts 1 and 2, based on the estimates using OLS and
FE/GMM-HAC from the previous section. We compute 95% confidence
intervals using the lower values of the 95% confidence intervals for the coefficient estimates on high school graduation and 4-year college attendancefrom Table 3. The results indicate that the point estimates of the IRR fall
between 6.6% and 8.1% under FE/GMM-HAC. Notably, the IRR for nurseryschool is generally higher than the IRR for kindergarten, consistent with thecoefficient estimates in Table 3. This finding is striking as it suggests that
nursery school is more productive than kindergarten in terms of education
11

Here, we assume that using the 4-year college advancement rate for its graduation rate
does not significantly bias our estimates, although it is challenging to assess the potential
bias due to the lack of official data on college dropout rates during the 1970-1980s.

24

Table 5: Sensitivity analyses of rates of return to preschool attendance(1) With deadweight loss (10%)
BenchmarkIRR of Kindergarten

IRR of Nursery School

Cohort 1 (Age 5 in 1961)
Confidence Interval

7.40%[6.89%, 7.65%]

8.05%[7.95%, 8.12%]

Cohort 2 (Age 5 in 1966)
Confidence Interval

6.88%[6.15%, 7.27%]

7.87%[7.68%, 8.00%](2) With deadweight loss (10%) and add. costs for local government (20%)
BenchmarkIRR of Kindergarten

IRR of Nursery School

Cohort 1 (Age 5 in 1961)
Confidence Interval

7.26%[6.71%, 7.54%]

8.01%[7.90%, 8.08%]

Cohort 2 (Age 5 in 1966)
Confidence Interval

6.67%[5.90%, 7.09%]

7.77%[7.57%, 7.91%]

production, even after accounting for the endogeneity of the choice of earlychildhood education and the average years of enrollment. Even considering
the wide confidence intervals, which are not necessarily balanced due to thenonlinearity involved in the computation, the values of IRRs are notably
high when compared with the average returns on physical assets in Japanover the past 30 years. Moreover, the IRRs based on the FE/GMM-HAC
estimates tend to be higher than those based on the OLS estimates due tothe possible presence of measurement error bias in the OLS estimates, as
discussed in section 5.
Next we conduct several sensitivity analyses. First, we consider theeffect of potential deadweight loss (DWL) due to financing preschool education through income tax. We assume that the DWL is 10% of the total
costs for preschool education based on Bessho and Hayashi (2013).12 Then,
we additionally consider the possibility of underestimating the social costsof preschool education, as discussed in Section 4.2, especially because thehistorical government documents lack information about the additional sub12

We are grateful to the suggestion from Shun-ichiro Bessho on the recommended valueof DWL.

25

sidy from municipality offices to private and public nursery schools. This
underestimation may arise from a lack of historical government documents,
as well as potential general measurement errors associated with estimating
the direct costs of education. Table 5 presents the results of sensitivity
analysis considering these two possibilities.
Table 5 (1) displays the results under the assumption that the social
costs of preschool education are 10% higher due to the DWL. These results reveal that the effects of DWL are very small, causing only a 0.04 to
0.10% point reduction in IRRs. Table 5 (2) shows the results under the
additional assumption that the total social costs of preschool education are
20% higher. Under this assumption, the IRRs become 0.06 to 0.31% point
lower than the original estimates in Table 4, with the lowest IRR within the
95% confidence interval at 5.90% for kindergarten attendance for Cohort 2.
However, all point estimates remain above 6%, significantly higher than the
average return on physical assets in Japan.13

7

Concluding remarks

This research presents a novel assessment of the rate of return associatedwith early childhood education, drawing from a comprehensive analysis of
the nationwide expansion of preschool education in Japan during the period
from 1960 to 1980. By utilizing panel data at the prefecture-level, we initiallyinvestigate the impact of the widespread increase in preschool enrollment on
two key indicators of educational attainment: high school graduation rates
and the percentage of students advancing to 4-year colleges. Subsequently,
we employ national wage statistics and government records on preschool
costs to calculate the social rate of return to preschool attendance. Our
13
For a more comprehensive robustness check, we estimated Model 1, allowing for theincorporation of sample weights based on preschool enrollment at age 5, as well as clustering of errors at the prefecture-level. When introducing the weights, the estimated IRRs
ranged from 7.11% to 8.17%. Furthermore, with the additional introduction of clustering,
the point estimates ranged from 7.09% to 8.15%, with statistical significance for kindergarten. We also considered the potential presence of severe aggregation bias due to thenature of our prefecture-level dataset. According to Hanushek et al. (1996) and Fertig andWright (2005), aggregation bias in the estimation of the education production function
is likely to overestimate the effect of schooling. We assumed that a substantial positive
aggregation bias existed in the first stage estimation and that the true coefficients basedon individual data would be half of our estimated values. Even under this assumption, thecalculated IRRs based on the point estimates (as the standard error would also changewith individual data) still ranged between 5.71% and 7.88%.

26

findings reveal a range of 5.7% to 8.1% for the social rate of return.
Notably, our estimated values for the Internal Rate of Return (IRR) to
preschool education in Japan closely align with those put forth by Heckman et al. (2010b). As highlighted by Duncan and Magnuson (2013), it isplausible that the returns on early childhood education have seen a gradualdecline over the years. The resemblance between our estimates and thosepresented in Heckman et al. (2010b) can be seen as generally consistent
with this prevailing trend. Furthermore, there are potentially two key factors contributing to the larger rate of return to nursery school comparedto kindergarten. These factors include the extended duration of nurseryschool programs and their full-time nature. Our analysis, comparing the
IRRs of nursery school and kindergarten, reaffirms the effectiveness of theformer even when considering the comprehensive program costs. We believethat further research in this direction is pivotal to elucidating the underlying mechanisms that generate these different returns in early childhood
education.
While our rate of return estimates demonstrate robustness through abattery of sensitivity analyses, our study is subject to several limitations.
We were unable to estimate the non-pecuniary benefits of early childhood
education, such as the reduction in criminal activities noted by Heckman
et al. (2010b). Despite Japan’s notably lower homicide rate compared to
the United States (0.2 in 100,000 people in Japan and 5.8 in the U.S. in
2019 based on OECD Statistics), making the impact of crime reduction in
the Japanese context seemingly negligible, exploring the influence of earlyeducation on non-monetary benefits remains an essential avenue for future
research. Additionally, although we exclusively focused on the rate of returnfor men due to the challenges associated with estimating long-term monetary benefits for women with weaker labor market attachment in Japan,
investigating the internal rate of return for women is a significant and necessary area for further exploration. Lastly, Japan has notable social benefits
tied to poverty prevention and welfare reduction, leaving the substantialconsideration of social benefits for future investigations.

27

Appendix A

First stage results

Table A. Estimation results of the first-stage regression

Variables
Religious Corporations at 5
Religious Corporations at 4
Gap between target and 3-year lagged kind. rate
Female Ratio

Kindergarten

Nursery School-0.142(0.325)-0.340(0.324)-0.846***(0.021)0.371*(0.224)0.880(0.711)
1.174(0.723)0.065(0.040)0.277(0.505)

Number of Observations

1,164

Note: The estimates with prefecture fixed effects are reported. Year fixed effects areincluded. Standard errors robust to heteroskedasticity and autocorrelation with Bartlett
kernel (bandwidth= 3) are reported in the parenthesis. *** 1%, ** 5%, * 10%.

Appendix B

Details of IRR calculation

B.1

Cost calculation

B.1.1

Social cost of preschool

Due to data limitations, we calculated the costs of nursery schools and private kindergartens from the revenue side, while the costs of public kindergartens were calculated from the expenditure side.
Average annual cost per child in kindergarten(1) To obtain the annual total cost per child for private kindergartens,
we summed the average tuition fees for private kindergartens in 1961, as reported in the “National and Local Education Budgets Each Year” from the
Ministry of Education, Culture, Sports, Science, and Technology (MEXT).
We also included the national and local subsidies per child for private kindergartens from the same source. The initial registration fee (Nyuenryo) was
divided by 2 to represent the one-year cost, as most kindergartens offered a
two-year program, and this cost was factored into the total annual cost.
(2) For public kindergartens, we calculated the annual per child expenditure using the “Local Education Expenditure Survey” provided by MEXT.
(3) Finally, the average cost of kindergarten was computed as a weighted
28

average of the costs for private and public kindergartens, based on the ratioof children enrolled in each type of kindergarten.14 This was multipliedby the average years of kindergarten enrollment, which we estimated fromenrollment statistics, to obtain the total average social cost of kindergarten
enrollment.
Average annual cost per child in nursery school(1) Due to the lack of data, to estimate the total number of children
enrolled in nursery schools for each year before 1966, we divided the number of 5-year-olds enrolled in nursery schools for each year by the ratio of
5-year-olds in nursery schools in 1966. The cost of establishing nurseryschools was obtained from government statistics, specifically “Social Welfare Facility Equipment Expenses” from the Ministry of Health, Labor, andWelfare (MHLW) for the year 1966 and onwards. For years prior to 1966,
we assumed that 10% of the total expenses for “Child Welfare” from ”Social Welfare Facility Equipment Expenses” was allocated to nursery schools
based on the ratio in 1966.
(2) The government administrative cost per child in nursery schools,
referred to as “Sochihi,” was calculated using government statistics.
(3) The fees paid by parents at nursery schools were estimated to be 50%of the total governmental cost, based on a typical municipality. The totalsocial costs for nursery schools were derived as the sum of governmental costs
and parental fees. However, it’s important to note that this estimation does
not account for local government subsidies for childcare fees, among otherfactors.15 We then multiplied the average years of nursery school enrollment,
estimated from government statistics in 1966 (the earliest available record),by the annual cost to determine the total average social cost of nurseryschool enrollment.
14

Tuition fees for public kindergartens are disregarded as being reflected in municipal
spending. However, subsidies to private kindergartens provided by the municipality are
not taken into account.
15
Essentially, it is difficult to assume that both kindergarten and preschool children
attend school only at age 5, and it is necessary to reflect the ratio of children attending
school at ages 3-4 and the cost of childcare in 1960-1959. However, due to data limitations,this is difficult to do, so data from 1961 is used as an approximation.

29

B.1.2Social cost of high school and 4-year college

Education expenditures for high school and college were sourced from Table
2-4 (pages 87-92) in Suetomi (1998). We made the assumption that childrenof Cohort 1, who were 5 years old in 1961 and graduated from junior highschool in 1971, attended high school and 4-year college during the periodsof 1971-73 and 1974-1977, respectively. We applied a similar assumption tochildren of Cohort 2, who were 5 years old in 1966.
To calculate the social cost per student per year for high school and 4year college, we used the public education costs provided by Suetomi (1998)
and the enrollment figures for national, public, and private schools obtained
from the Basic School Survey. The calculations were as follows:
Social cost per student per year of high school = (nominal cost of publichigh school education + nominal tuition fees) x public and national highschool enrollment rate + (nominal public subsidy for private high schools +nominal tuition fees) x private high school enrollment rate.16
Social cost per student per year of 4-year college = (nominal government
expenditure for national universities + nominal tuition and fees) x public
university enrollment rate + (nominal government subsidy for private universities + nominal tuition and fees) x private university enrollment rate.
Regarding the deadweighted cost of taxation, similar to the approachin Heckman et al. (2010b), we considered the income tax financing costs ofeducation and assumed a deadweight of 10% for Japan, based on findings
from Bessho and Hayashi (2013).

B.2

Calculation of earnings

We utilized data from the ”Basic Survey of Wage Structure” (The Statistics
Bureau of Japan) to compute annual earnings within specific 5-year age categories and educational attainment levels. Initially, we extracted the averagemonthly cash salary of regular workers (including part-time workers before1969) in businesses with 10 or more employees, categorized by education,
gender, and 5-year age groups. To establish annual average earnings, we
multiplied the monthly salary by 12 and then added the “annual bonus andother special salary.” Annual earnings were recorded as zero during ages
16
Although public expenditures for national high schools may be higher than those forpublic high schools, they can be ignored because the number of students enrolled is lessthan 0.3% of that for public high schools.

30corresponding to enrollment in school, specifically from ages 16 to 18 forhigh school graduates and from ages 19 to 22 for 4-year college graduates.
For the employment rate by age group, we sourced data from the 19702020 Census, which tabulated labor force statistics, including employed,
unemployed, and non-labor force individuals, at 10-year intervals based onage and the last level of education completed (junior high school, high school,
2-year college, or 4-year college). Using this dataset, we calculated theemployment rate, defined as the ratio of employed individuals to the total
population, based on the last level of education completed. For years without
survey data, we conducted linear interpolation (for years prior to 1970) andextrapolated missing data by using the same ratio as that observed in the
1970-80 period.
Subsequently, we determined the difference in the average annual compensation multiplied by the employment rate between 4-year college graduates and high school graduates, as well as between high school graduates
and junior high school graduates.17 With this information, coupled with thedirect costs of high school and 4-year college education, we constructed a
net return profile for the cohort of interest.
Figures B.1 and B.2 illustrate the net return profiles (i.e., nominal earnings net tuition) for Cohort 1 (Age 5 in 1961) and Cohort 2 (Age 5 in 1966),
respectively.

B.3

Calculating the rate of return

To determine the discounted sum of present value returns, we deflate thenominal net returns using the consumer price index and discount them byapplying the discount rate, which will be established at a later stage. orthese calculations, we rely on the coefficient estimates obtained from the
GMM-HAC results for high school graduation and college attendance, aspresented in Table 3, specifically in IV(2). These coefficients are instrumental in calculating the rate of return associated with an increased probability
for a child to enroll in kindergarten or nursery school at age 5 through fiscal
expansion.
17

Although we did not consider 2-year college, such as the junior colleges and technicalcolleges, separately from high school graduates, we believe that its effect on the estimatesis negligible since the share of attendants of these 2-year colleges were extremely low for
men.

31

Figure B.1: Net return profile for Cohort 1 (Age 5 in 1961)

Figure B.2: Net return profile for Cohort 2 (Age 5 in 1966)

32

Once the discount factor, denoted as beta, is established, we can proceed
to compute the discounted sum of present value returns. Subsequently, we
determine the value of beta that ensures this sum is equal to zero.

Appendix C

The validity of using prefecture-leveldata

In this section, we provide detailed justification for our approach using prefectural average data, considering the institutional settings of Japan andevidence from additional sources.
1. Japan’s School Education Law prohibits early school enrollment.
While school authorities may allow parents to delay their child’s enrollment under specific circumstances, such as serious health and developmental
problems, the number of children delaying enrollment has been extremely
low. According to the School Basic Statistics, the number of elementary
school-aged children who delayed enrollment was 14,946 in 1961 and 10,953
in 1966. Relative to the grade 1 enrollment figures for those years, thesedelayed enrollments accounted for only 0.89% in 1961 and 0.70% in 1966.
Therefore, there is minimal risk of potential estimation bias resulting fromdisregarding delayed or early school enrollments.
2. Under Japan’s School Education Law, grade repetition is not permitted (OECD, 2011).
3. We acknowledge that we assumed that the enrollment rate at age 5 isperfectly correlated with the enrollment rates at earlier ages to simplify our
calculations. To justify this assumption, we calculated the correlation between the enrollments at different ages of children across prefectures in 1961
and 1966. The correlation coefficients were generally very high, ranging between 0.912 and 0.977 for ages with a one-year difference and between 0.786
and 0.829 for ages with a two-year difference, supporting our approach. Unfortunately, similar statistics are unavailable for nursery school enrollment
rates.
4. Migration across prefectures was very rare in the 1960s-70s. According
to The National Survey on Migration in 1976 (The Institute of Population
Problems, 1977), among individuals under age 20 in 1976 (equivalent to thecohort aged 5 or younger in 1961), 87% of men attended elementary schoolin their birth prefecture, and 88% attended high school in the prefecture of33

their elementary school. This suggests that nearly 80% of preschool children
attended high school in the same prefecture. Given the low migration rate
before high school and limited availability of information on preschool options in the 1960s-70s, it is unlikely that parents moved between prefectures
based on their educational preferences, and therefore, we believe that disregarding inter-prefectural migration before high school age does not severely
bias our estimates. Kawarazaki (2022) also supports this view.

34References
Akabayashi, H., T. Ruberg, C. Shikishima, and J. Yamashita(2023): “Education-Oriented and Care-Oriented Preschools: Implications
on Child Development,” Labour Economics, 102410.
Akabayashi, H. and R. Tanaka (2013):“Long-Term Effects of

Preschooling on Educational Attainments,” Keio/Kyoto Joint Global
COE Discussion Paper Series 2012-033, Keio/Kyoto Joint Global COE
Program.
Ando, M., H. Mori, and S. Yamaguchi (2022): “Universal Early Childhood Education and Adolescent Risky Behavior,” CReAM Discussion Paper Series 2218, Centre for Research and Analysis of Migration (CReAM),
Department of Economics, University College London.
Arteaga, I., S. Humpage, A. J. Reynolds, and J. A. Temple (2014):“One year of preschool or two: Is it important for adult outcomes?” Economics of Education Review, 40, 221–237.
Asai, Y., R. Kambayashi, and S. Yamaguchi (2015): “Childcare availability, household structure, and maternal employment,” Journal of theJapanese and International Economies, 38, 172–192.
Baker, M., J. Gruber, and K. Milligan (2008): “Universal Child
Care, Maternal Labor Supply, and Family Well-Being,” Journal of Political Economy, 116, 709–745.
Barnett, W. (2004): “Maximizing Returns from Pre-Kindergarten Education,” Tech. rep., Power Point Presentation at Conference on Educationand Economic Development, Federal Reserve Bank of Cleveland, November 19th.
Belfield, C. R., M. Nores, S. Barnett, and L. Schweinhart (2006):“The High/Scope Perry Preschool Program: Cost-Benefit Analysis Using
Data from the Age-40 Followup,” Journal of Human Resources, 41.
Berlinski, S., S. Galiani, and P. Gertler (2009): “The effect of preprimary education on primary school performance,” Journal of Public
Economics, 93, 219–234.
35

Bessho, S. and M. Hayashi (2013): “Estimating the Social Marginal Costof Public Funds,” Public Finance Review, 41, 360–385.
Blau, D. and J. Currie (2006): “Pre-School, Day Care, and After-School
Care: Who’s Minding the Kids?” in Handbook of Economics of Education,
ed. by E. Hanushek and F. Welch, Elsevier, vol. 2, chap. 20, 1163–1278.
Cascio, E. and D. W. Schanzenbach (2013): “The Impacts of Expanding Access to High-Quality Preschool Education,” Brookings Papers on
Economic Activity, 44, 127–192.
Cunha, F., J. J. Heckman, and L. Lochner (2006): “Interpreting the
Evidence on Life Cycle Skill Formation,” in Handbook of the Economicsof Education, ed. by E. Hanushek and F. Welch, Elsevier, vol. 1, chap. 12,
697–812.
Currie, J. (2001): “Early Childhood Education Programs,” Journal of
Economic Perspectives, 15, 213–238.
Currie, J. and D. Thomas (1995): “Does Head Start Make a Difference?”
American Economic Review, 85, 341–364.
Duncan, G. J. and K. Magnuson (2013): “Investing in Preschool Programs,” Journal of Economic Perspectives, 27, 109–132.
Fertig, M. and R. E. Wright (2005): “School quality, educational attainment and aggregation bias,” Economics Letters, 88, 109–114.
Garces, E., D. Thomas, and J. Currie (2002): “Longer-Term Effects
of Head Start,” American Economic Review, 92, 999–1012.
Garcia, J. L., J. J. Heckman, D. E. Leaf, and M. J. Prados (2020):“Quantifying the Life-Cycle Benefits of an Influential Early-Childhood
Program,” Journal of Political Economy, 128, 2502–2541.
Gertler, P., J. Heckman, R. Pinto, A. Zanolini, C. Vermeersch,
S. Walker, S. M. Chang, and S. Grantham-McGregor (2014):“Labor market returns to an early childhood stimulation intervention in
Jamaica,” Science, 344, 998–1001.

36

Gormley, W. T. J. and T. Gayer (2005): “Promoting School Readiness in Oklahoma: An Evaluation of Tulsa’s Pre-K Program,” Journal of
Human Resources, 40.
Hanushek, E. A., S. G. Rivkin, and L. L. Taylor (1996): “Aggregationand the Estimated Effects of School Resources,” The Review of Economics
and Statistics, 78, 611–627.
Heckman, J., S. H. Moon, R. Pinto, P. Savelyev, and A. Yavitz(2010a): “Analyzing social experiments as implemented: A reexaminationof the evidence from the HighScope Perry Preschool Program,” Quantitative Economics, 1, 1–46.
Heckman, J. J., S. H. Moon, R. Pinto, P. A. Savelyev, andA. Yavitz (2010b): “The rate of return to the HighScope Perry Preschool
Program,” Journal of Public Economics, 94, 114–128.
Kawarazaki, H. (2022): “Early childhood education and care: Effects
after half a century and their mechanisms,” Journal of Population Economics, 1–73.
Li, Q. and J. S. Racine (2006): Nonparametric Econometrics: Theory
and Practice, no. 8355 in Economics Books, Princeton University Press.
Ludwig, J. and D. L. Miller (2007): “Does Head Start Improve Children’s Life Chances? Evidence from a Regression Discontinuity Design,”
The Quarterly Journal of Economics, 122, 159–208.
Newey, W. K. and K. D. West (1987): “A Simple, Positive Semidefinite, Heteroskedasticity and Autocorrelation Consistent CovarianceMatrix,” Econometrica, 55, 703–708.
OECD (2011): When students repeat grades or are transferred out of school:
What does it mean for education systems?, PISA in Focus, The Organisation for Economic Co-operation and Development (OECD).
Psacharopoulos, G. (1982): “The economics of early childhood educationand day-care,” International Review of Education, 28, 53–70.
Suetomi, K. (1998): The Political Economy of Education Expenditure(Kyoiku-hi no Seiji Keizaigaku, in Japanese), Keiso Shobo.
37

The Institute of Population Problems (1977): Summary Report of
The National Survey on Migration 1976 (Chiiki Jinko Ido ni Kansuru
Hokokusho, in Japanese), Institute of Population Problems, The Ministryof Health and Welfare.
The Ministry of Education (1979): Hundred-years History of Educationin Kindergartens (Youchien Kyoiku Hyakunen-shi, in Japanese), Hikarino-Kuni Publisher.
The Ministry of Health and Welfare (1978): Thirty Years of Child
Welfare (Jidou Fukushi 30 nen no Ayumi, in Japanese), The Ministry of
Health and Welfare.
——— (1988): Forty Years of Child Welfare (Jidou Fukushi 40 nen no
Ayumi, in Japanese), The Ministry of Health and Welfare.
——— (1998): Fifty Years of Child Welfare (Jidou Fukushi 50 nen no
Ayumi, in Japanese), The Ministry of Health and Welfare.
van Huizen, T., L. Dumhs, and J. Plantenga (2019): “The Costs andBenefits of Investing in Universal Preschool: Evidence From a Spanish
Reform,” Child Development, 90, e386–e406.
Yang, S. (2021): “The Long-Run Effects of Early Childhood Education andCare—An Empirical Analysis Based on the China Family Panel Studies
Survey,” Child Indicators Research, 14, 2021–2044.

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