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

Education-Oriented and Care-Oriented Preschools:
Implications on Child Development
Hideo Akabayashi, TIm Ruberg, Chizuru Shikishima, Jun Yamashita,
27 February, 2023
DP2023-009
https://ies.keio.ac.jp/en/publications/22477/

Institute for Economic Studies, Keio University
2-15-45 Mita, Minato-ku, Tokyo 108-8345, Japan
ies-office@adst.keio.ac.jp
27 February, 2023

Education-Oriented and Care-Oriented Preschools: Implications on Child Development
Hideo Akabayashi, TIm Ruberg, Chizuru Shikishima, Jun Yamashita
Keio-IES DP2023-009
27 February, 2023
JEL Classification: C26, H75, I26, J13
Keywords: Early childhood education and care, Child development, IV methods, Marginal
treatment effect
Abstract
This paper estimates the causal effect of education-oriented vs. care-oriented preschools
on child development. We use a unique quasi-experiment from Japan that exploits plausibly
exogenous regional and temporal variation in the relative availability of different preschools.
We find that attendance at an education-oriented preschool is associated with significant
improvements in mathematical and linguistic achievement that manifest later inadolescence. Positive effects can also be found for socioemotional measures. Ascending
marginal treatment effect (MTE) curves suggest an inverse selection pattern: children thatare least likely to enroll in the education-oriented preschool gain the most from it. This
heterogeneity is mainly due to specific features of education-oriented preschools (i.e.,
educational orientation, shorter operating hours, and peer effects), while gains fromenrollment in care-oriented preschools appear more homogeneous.
Hideo Akabayashi
Faculty of Economics, Keio University
2-15-45, Mita, Minato-ku Tokyo 108-8345, Japan
hakab@econ.keio.ac.jp
TIm Ruberg
Department of Economics, University of Hohenheim
Schloss Hohenheim, 70593 Stuttgart, Germany
tim.ruberg@uni-hohenheim.de
Chizuru Shikishima
Department of Psychology, Teikyo University
359 Otsuka, Hachioji-city, Tokyo, 192-0395, Japanshiki@main.teikyo-u.ac.jp
Jun Yamashita
Faculty of Integrated Arts and Social Sciences, Japan Women’s University
2-8-1, Mejirodai, Bunkyo-ku, Tokyo 112-8681, Japanyamashitaj@fc.jwu.ac.jp

Acknowledgement : We used the micro data of the Keio Household Panel Survey and Japan
Household Panel Survey (KHPS/JHPS) and the Japan Child Panel Survey (JCPS) obtained fromthe Panel Data Research Center at Keio University. We would like to thank Thomas
Cornelissen, Rita Ginja, Lester Lusher, Christopher Rauh, Katja Kaufmann, Ulf Z¨olitz, Philipp
Lergetporer, Aderonke Osikominu, Hikaru Kawarazaki, Philipp Kugler, and participants at the
VfS Annual Meeting, the Summer School “The Development of Cognitive and Non-Cognitive
Skills in Childhood and Adolescence,” the ECINEQ Meeting, the Asian Meeting of the
Econometric Society, the IAAE Annual Conference, and seminar participants in Hohenheim,
T¨ubingen, Freiburg, Friedrichshafen, Konstanz, and at Keio University for valuable
discussions and comments.

Education-Oriented and Care-Oriented Preschools
Implications on Child Development∗
Hideo Akabayashi †Tim Ruberg ‡Chizuru Shikishima §Jun Yamashita ¶February 27, 2023

Abstract
This paper estimates the causal effect of education-oriented vs. care-oriented preschools on child development. We use a unique quasi-experiment from Japan that
exploits plausibly exogenous regional and temporal variation in the relative availability of different preschools. We find that attendance at an education-oriented
preschool is associated with significant improvements in mathematical and linguistic achievement that manifest later in adolescence. Positive effects can also befound for socioemotional measures. Ascending marginal treatment effect (MTE)
curves suggest an inverse selection pattern: children that are least likely to enroll
in the education-oriented preschool gain the most from it. This heterogeneity ismainly due to specific features of education-oriented preschools (i.e., educational
orientation, shorter operating hours, and peer effects), while gains from enrollment
in care-oriented preschools appear more homogeneous.
JEL Classification: C26, H75, I26, J13
Keywords: Early childhood education and care, Child development, IV methods,
Marginal treatment effect∗This research was financially supported by the Grant-in-Aid for Scientific Research (16H06323,
17H06086, 21H04982). We used the micro data of the Keio Household Panel Survey and Japan Household Panel Survey (KHPS/JHPS) and the Japan Child Panel Survey (JCPS) obtained from the Panel
Data Research Center at Keio University. We would like to thank Thomas Cornelissen, Rita Ginja,
Lester Lusher, Christopher Rauh, Katja Kaufmann, Ulf Zölitz, Philipp Lergetporer, Aderonke Osikominu,
Hikaru Kawarazaki, Philipp Kugler, and participants at the VfS Annual Meeting, the Summer School“The Development of Cognitive and Non-Cognitive Skills in Childhood and Adolescence,” the ECINEQ
Meeting, the Asian Meeting of the Econometric Society, the IAAE Annual Conference, and seminar
participants in Hohenheim, Tübingen, Freiburg, Friedrichshafen, Konstanz, and at Keio University forvaluable discussions and comments. The views expressed in this paper are those of the authors and do
not necessarily reflect the positions or policies of the organizations mentioned above. Any remaining
errors are our own. Declarations of interest: none.
†
Corresponding author: Hideo Akabayashi, Keio University, Faculty of Economics, 2-15-45, Mita,
Minato-ku Tokyo 108-8345, Japan; Email: hakab@econ.keio.ac.jp.
‡
Tim Ruberg, University of Hohenheim, Department of Economics, Schloss Hohenheim, 70593
Stuttgart, Germany; Email: tim.ruberg@uni-hohenheim.de.
§
Chizuru Shikishima, Teikyo University, Department of Psychology, 359 Otsuka, Hachioji-city, Tokyo
192-0395, Japan; Email: shiki@main.teikyo-u.ac.jp.
¶
Jun Yamashita, Japan Women’s University, Faculty of Integrated Arts and Social Sciences, 2-8-1,
Mejirodai, Bunkyo-ku, Tokyo 112-8681, Japan; Email: yamashitaj@fc.jwu.ac.jp.

1

Introduction

It is well known that experiences in early childhood are critical to children’s cognitive
and noncognitive development (Carneiro and Heckman 2003; Cunha et al. 2006; Knudsen
et al. 2006). A large body of research has shown that both targeted and universal earlychildhood education and care (ECEC) programs have positive effects on a wide rangeof child outcomes.1 In recent years, the focus of policy makers has shifted from simply
improving access to ECEC to the importance of quality aspects of ECEC institutions. As
a result, OECD countries such as the United States or Germany have adopted policies(i.e., the Zero to Five Plan and the Gute-KiTa Gesetz (Act on good early childhood
education and care)) that explicitly address quality issues such as better-qualified staff
and better-equipped facilities.
The positive impact of ECEC found in previous studies is often explained by thefact that disadvantaged children would experience worse care if they were not enrolled insuch an institution (Cascio and Schanzenbach 2013; Cornelissen et al. 2018). Similarly,
the most advantaged children show no or even negative treatment effects, because theywould receive high-quality child care even without ECEC (Felfe et al. 2015). Therefore,
it is important for policy makers to understand which aspects of ECEC institutions andcounterfactual care arrangements are most effective in promoting child development inorder to design appropriate policies.
This paper exploits a unique feature of the Japanese preschool system to estimate thecausal effect of an education-oriented against a care-oriented preschool on children’s cognitive and socioemotional development in adolescence. Before children go to elementary
school, almost all of them attend either an education-oriented or a care-oriented preschool.
In contrast to most studies examining the effectiveness of targeted and universal ECEC
programs, we can compare two high-quality preschool institutions and therefore know exactly the features of the counterfactual preschool setting.2 Education-oriented preschools
are half-day facilities that provide about four hours educational instruction per day, whilecare-oriented preschools are full-day facilities that provide care for parents who are unable
to provide care for their child for work or other reasons. Evaluating treatment effects ofthis differential preschool enrollment allows us to draw more precise conclusions about
the specific features of ECEC that are responsible for developmental gains.
Because OLS estimates are likely to be biased due to selective enrollment, we exploit
1

Targeted programs in the United States are Head Start (Currie and Thomas 1995; Garces et al.
2002; Ludwig and Miller 2007; Deming 2009; Carneiro and Ginja 2014), the High/Scope Perry Preschool
program (Heckman et al. 2010b; 2013), and the Abecedarian project (Barnett and Masse 2007; Campbell
et al. 2014). Similarly, universal preschool programs in Uruguay, Argentina, Norway, Denmark, Germany,and Japan have been studied by Berlinski et al. (2008; 2009), Havnes and Mogstad (2011; 2015), Gupta
and Simonsen (2016), Cornelissen et al. (2018), Rossin-Slater and Wüst (2020), and Kawarazaki (2022).
2
Some notable exceptions are Bernal and Keane (2011), Gupta and Simonsen (2010; 2016), Feller
et al. (2016), and Danzer et al. (2020).

1

plausibly exogenous regional and temporal variation in the availability of preschools to
identify the treatment effect of attending an education-oriented against a care-oriented
preschool using instrumental variable (IV) methods. In particular, treatment effects onmathematical and linguistic ability scores, as well as on the socioemotional measure of behavioral problems SDQ, derived from the Strengths and Difficulties Questionnaire (Goodman 1997), and a quality of life (QOL) measure, based on the KINDLR questionnaire(Ravens-Sieberer et al. 2006), will be examined using unique Japanese survey data. By
further estimating the marginal treatment effect (MTE), we are able to identify the subpopulation that benefits the most from enrollment in an education-oriented preschool,
allowing us to better understand the mechanisms through which these developmental
gains operate.
To identify the causal effect, we instrument the preschool decision with the relative
availability of preschool slots in an IV approach.3 We ensure conditional exogeneity of the
instrument by controlling for an extensive set of child, parental, and municipality characteristics, as well as regional fixed effects, cohort fixed effects, and region-specific linear
time trends. We also discuss in detail potential threats to identification and show thatthe historical development of preschools in Japan is independent of the regional distribution of skills and human capital (Cameron and Taber 2004), selective migration between
municipalities is not a threat in our context (Kawarazaki 2022), there are no unobserved
municipality characteristics that could affect our instrument, internal migration of parents and demand for child care does not drive the (relative) supply (Kawarazaki 2022),
and there are no compositional changes in enrollment patterns over time. This extensive
discussion ensures that the relative availability of preschool slots is a valid instrument forthe preschool decision.
Our results show that enrollment in an education-oriented instead of a care-oriented
preschool is associated with strong developmental gains in linguistic achievement. Theseestimates are robust to the exclusion of ability proxies (i.e., parental education and income), the use of a stricter treatment definition, and the use of an adjusted version of the
instrument that is less susceptible to potential demand-side influences. Implementation of
permutation tests following Bertrand et al. (2004) for the reduced-form estimates further
supports the finding of significantly positive effects. In assessing treatment effects at different ages, we find that cognitive gains (i.e., mathematical and linguistic) materialize at
a later stage of compulsory schooling (aged 12 to 15), which seems surprising in the lightof previous research. In particular, Currie and Thomas (1995) show that the cognitive
benefits from Head Start fade early, rationalizing this finding with the worse environment
which treated children would return after completing the program (see also Currie andThomas 2000). Although this explanation makes sense in the context of Head Start,schools in Japan are more homogeneous and of similar quality. This implies that disad3

See Loeb et al. (2007), Berlinski et al. (2008; 2009), and Cornelissen et al. (2018) for a similar strategy.

2

vantaged children who enrolled in an education-oriented preschool face the same schooland neighborhood conditions as their advantaged peers. Within this equal environment,
positive effects from early investments lead to long-lasting improvements, as suggested by
the theory of dynamic complementarities (Cunha and Heckman 2007).
In the next step, we extend our analysis by estimating MTE curves (Björklund andMoffitt 1987; Heckman and Vytlacil 1999; 2001; 2005; 2007) to allow the treatment effects
to vary with unobserved characteristics that are responsible for children being less likely
to attend an education-oriented preschool (i.e., distaste for treatment, Cornelissen et al.
2016). These MTE curves show an increasing pattern, suggesting substantial heterogeneity: children who are least likely to attend an education-oriented preschool benefit themost from it. By decomposing this inverse selection pattern into the unobserved partsof the outcomes when untreated and treated, respectively (Brinch et al. 2017), we findthat all children benefit similarly from attending a care-oriented preschool with respect tolinguistic ability and the QOL score, while returns to enrollment in an education-oriented
preschool are positive only for high-resistance children. This finding suggests that characteristics of education-oriented rather than care-oriented preschools explain the results. In
an extensive discussion, we then argue that the educational orientation, shorter operating
hours and thus a reduction in maternal labor supply which allow for more interaction
with the mother, and stronger peers are the most likely channels.
Our study contributes to the literature in several ways. First, previous literature has
examined quality aspects of schools that are conducive to children’s development, such as
smaller class sizes (Angrist and Lavy 1999), stronger peers (Hanushek et al. 2003; Bifulco
et al. 2011; Sacerdote 2011; Burke and Sass 2013), and better-qualified teachers (Rockoff
2004; Rivkin et al. 2005; Kane et al. 2011). In contrast, studies on the effectiveness ofsuch aspects in preschools are still scarce. Evidence from project STAR suggests that
smaller class sizes in preschool have a positive short-term impact on test scores, but also
improve long-term outcomes such as the likelihood of college attendance, while teacher
characteristics seem to play a minor role (Krueger 1999; Chetty et al. 2011). Usinga reform in the German child care system, Felfe and Zierow (2018) find that a switchfrom half-day to full-day care is associated with negative effects on socioemotional wellbeing. In contrast, attending preschool together with high-ability peers seem to havepositive spillover effects (Henry and Rickman 2007; Neidell and Waldfogel 2010). In
addition, Claessens et al. (2014) find that advanced content covered in preschool benefits
all children’s school readiness.4 Although we cannot quantify the contribution of these
aspects to our treatment effects, our analysis confirms that previously studied quality
aspects of preschools also play a large role in our context. The institutional setting inwhich we know all characteristics of the treatment but also the control institution allows
us to interpret our treatment effects as results stemming from differences between the two
4

See also the discussion in Duncan and Magnuson (2013).

3

preschools, suggesting that educational orientation, shorter operating hours, and peer
effects explain our results.
Second, we add to the literature by estimating longer-term effects of universal preschools for children aged 6 to 15. While many studies assess short-term outcomes prior toschool enrollment (e.g., school readiness, Cornelissen et al. 2018), there is limited evidence
on the durability of such effects. Berlinski et al. (2008) estimate the effect of preschool
attendance on the educational attainment of children aged 15 in Uruguay. Felfe et al.
(2015) estimate the impact of an expansion of high-quality child care in Spain on reading
achievement and grade retention among children aged 15. Similarly, Gupta and Simonsen(2016) find positive effects of center-based child care on the linguistic achievement forchildren aged 15 to 16. In contrast, Kuehnle and Oberfichtner (2020) do not find any
effect of earlier enrollment in day care for children aged 15 in Germany, and Fort et al.
(2020) even find negative effects of day care in Italy on a measure of intelligence andpersonality traits for children aged 8 to 14.5 We add to these studies by estimating theimpact for different age groups to obtain a complete picture of the evolution of gains inadolescence. Since this period is critical for acquiring the skills needed to succeed in thelabor market, it is important to evaluate the effectiveness of earlier investments not onlyin the short run but also in the long run.6
Third, to our knowledge, we are the first to examine differential effects of the twopreschool institutions in Japan on child development. In a recent paper, Yamaguchi et al.
(2018b) find positive effects on behavioral aspects of enrollment in care-oriented preschools
at early ages compared to home care. Because these outcomes are measured at age 3.5
and enrollment in education-oriented preschools is generally limited to children aged 3
to 5, differential effects between education-oriented and care-oriented preschools are notconsidered. In addition, Kawarazaki (2022) examines the long-term effects of attending
preschool at age 4 and shows that the positive effects on future earnings are mainly due
to an increase in educational attainment. We complement these studies by estimating thedifferential effects of preschool institutions on cognitive and socioemotional development inadolescence. We provide a more detailed picture of the features of the Japanese preschool
system that contribute to a child’s development in recent years, which complements theshort-term analysis of Yamaguchi et al. (2018b) and the long-term analysis of Kawarazaki(2022).
The remainder of this paper proceeds as follows. In Section 2, we describe Japan’spreschool system in more detail and explain the institutional background of educationoriented and care-oriented preschools. In Section 3, we describe the data sources used
for the empirical analysis. In particular, the cognitive and socioemotional measures are
5

Studies assessing educational attainment and labor market outcomes of adults are Havnes andMogstad (2011; 2015) for Norway, Rossin-Slater and Wüst (2020) for Denmark, and Kawarazaki (2022)
for Japan.
6
By the age of 15, Japanese children take high school entrance exams to enroll in better-ranked schools.

4presented, and the instrumental variable as well as the control variables are described.
Descriptive statistics are also provided. In Section 4, we introduce our estimation strategy
and discuss the identifying assumptions. In Section 5, we present the results from our
first-stage Probit regression, OLS and 2SLS regressions, and MTE estimations. In Section
6, we discuss possible channels and mechanisms that could explain our results. Finally,
Section 7 concludes.

2

Background and Institutional Setting

Preschools in Japan are divided into half-day kindergartens (youchien) and full-day nursery schools (hoikuen), which are distinguished by different purposes and characteristics.7
Originally, kindergartens were intended to promote the mental and physical developmentof children by providing a sound educational environment. The Ministry of Education,
Culture, Sports, Science, and Technology (MEXT) sets the curriculum and teachers mustbe certified by the ministry.8 The curriculum is broadly defined in the Course of Study
for Kindergarten guideline, but usually consists of about four hours per day, covering
topics such as reading Hiragana characters, writing, recognizing numbers and geometry,playing music together, and drawing. Therefore, we refer to kindergartens in this paper
as education-oriented preschools. Nursery schools, on the other hand, were originally designed to care for children whose parents (or equivalents) were unable to provide care forwork or other reasons. The Ministry of Health, Labour, and Welfare sets guidelines fornursery schools, and these providers must be certified as “child care providers” by theministry.9 Therefore, we refer to nursery schools in this paper as care-oriented preschools.
Children aged 3 to 5 can attend an education-oriented preschool while children aged 0 to
5 can attend a care-oriented preschool (Abumiya 2011).10
Although both institutions are overseen by different ministries and teachers/caretakers
receive the certificate from the respective ministry, both institutions must meet strict
requirements and are therefore considered to be of similar high quality (e.g., Kawarazaki2022). This also implies similarly low monthly tuition rates ranging from US$ 290 to
US$ 425 for care-oriented preschools in 2009 and around US$ 452 for education-oriented
7

Although most kindergartens do not operate on a full-day basis, some facilities have expanded theirservices to working mothers in order to maintain the number of children enrolled, also offering childcare until the evening (Ishikida 2005; Imoto 2007). For a detailed discussion of the differences between
kindergartens and nursery schools, see Ben-Ari (2005) and Hegde et al. (2014).
8
The educational standard for the kindergarten curriculum and teachers was originally established in1900 in the Elementary School Order and further specified in 1926 in the Kindergarten Order.
9
The minimum standard for nursery schools and child care providers was first set in 1947 in the Child
Welfare Act, which established that protection and nurture of children who lack sufficient care is the
responsibility of the government.
10
In 2002, some districts began allowing children as young as 2 to attend an education-oriented preschool
as well (Ishikida 2005). Imoto (2007) provides an excellent overview of the origins and development of
the Japanese preschool system.

5preschools in 2010 (Ministry of Health, Labour and Wealth 2009; Ministry of Education,
Culture, Sports, Science and Technology 2010).11
Although parents would in principle have the option to educate their children at home,most families choose to enroll them in one of these two preschools.12 Graph 1 presents
the enrollment rate of children in both preschools over time. Figure (a) shows that inrecent years more than 90% of four and five-year-old children are enrolled in a preschool,
although the share is lower for three-year-olds. This figure has been largely constant for
four- and five-year-old children over the past 30 years, although it has increased significantly for three-year-old children, highlighting the improved supply. Figure (b) showsthat education-oriented preschools are the predominant choice among four- and five-yearold children, although three-year-old children are evenly distributed between educationoriented and care-oriented preschools. In recent years, however, more children have beenenrolled in care-oriented preschools, resulting in a narrowing of the gap in enrollment
rates between the two alternatives. This is not surprising given the steady increase inmaternal labor force participation, which has led to an increasing demand for full-timepreschools (Imoto 2007). Figure A1 in the appendix shows this trend graphically.
In principle, the decision-making process of parents consists of two steps. First, parents
decide whether or not to enroll their child in a preschool. Second, they choose between acare-oriented and an education-oriented preschool, depending on availability and preferences. In reality, however, almost all children enroll in one of the two institutions, so thesmall group of children who do not enroll in any preschool is negligible for our analysis
and we can focus exclusively on the second-step decision (see Figure 1).
Traditionally, care-oriented and education-oriented preschools served different purposes, leading to selective enrollment based on status and perception. The targeting ofcare-oriented preschools to children who lack sufficient care led to the perception that thesepreschools were less prestigious than education-oriented preschools (Ben-Ari 2005). As aconsequence, children from working-class families would go to a care-oriented preschool
while upper-class families who value education more would send their children to an
education-oriented preschool. Although in recent years these traditional differences arediminishing, they are still present today. Apart from these preference differences, lowincome families, where both parents need to work full time, would in practice send theirchildren to a care-oriented preschool early because they need to go back to work to earnsufficient income, while high-income families can wait until the child is old enough to send
11

Tuition rates vary depending on whether the facility is private and on the location.
In fact, only ten children in our data set were never enrolled in any preschool. Moreover, Kachi et al.
(2020) show that only 9% of children aged 3, 3% of children aged 4, and 2% of children aged 5 were neverenrolled in any preschool.
12

6

1
.8

1

Enrollment rate
.4
.6

.8

.2

Enrollmnent rate
.4
.6

0

.2

1985

1990

1995

2000

2005

2010

2015

0

1980
1980

1985

1990
3-year old

1995

2000
4-year old

2005

2010

Kindergarten 3-year old
Kindergarten 5-year old
Nursery school 4-year old

2015

5-year old(a) Preschools in General, 1980–2017

Kindergarten 4-year old
Nursery school 3-year old
Nursery school 5-year old(b) Kindergartens and Nursery Schools, 1980–2015

Figure 1
Enrollment in Preschools Over Time
Source: Authors’ calculations using data from Statistics Bureau, Ministry of Internal
Affairs and Communications (2020), Ministry of Education, Culture, Sports and Technology (MEXT) (2020), and OECD (2020). Note: Figure (a) shows enrollment rates
in either kindergartens (i.e., education-oriented preschools) or nursery schools (i.e.,
care-oriented preschools). No data is available for 2008 and 2009. Figure (b) showsenrollment rates at different ages by preschool type. No data is available for nurseryschool enrollment rates for 2008 to 2015.

him or her to an education-oriented preschool.13 The descriptive evidence from Figure 2
shows this choice pattern clearly, in which wealthier and better-educated families tend toprefer the education-oriented over the care-oriented preschool.

3

Data

3.1

JCPS and KHPS/JHPS

We use data from the Japanese Child Panel Survey (JCPS), Keio Household Panel Survey(KHPS), and Japan Household Panel Survey (JHPS). The JCPS is a nationally representative longitudinal parent-child survey of children enrolled in elementary schools (grades
1 to 6, aged 6 to 12) and lower secondary schools (grades 7 to 9, aged 12 to 15) and was
designed as a complementary survey to the KHPS/JHPS, two comprehensive household
surveys initiated in 2004 (KHPS) and 2009 (JHPS).14 The JCPS consists of a child questionnaire that captures a child’s cognitive ability by achievement tests for math, Japanese,13
In principle, parents can decide to switch between the two institutions. However, because thisprocess involves switching costs and parents might have to wait again for vacancies at another facility,
they typically leave their child in the facility where he or she was first enrolled.
14
Households with children in the JHPS 2010/2012 were invited to participate in the JCPS 2010/2012,
while households with children in the KHPS 2011/2013 were invited to participate in the JCPS 2011/2013.
In 2014, 2016, and 2018, households from both surveys were participating in the JCPS. For details, see
Akabayashi et al. (2016).

7

.5
Share of parents with a college degree
.1
.2
.3
.4

1

p-value: 0.000

p-value: 0.046

5

5.5

6
6.5
Log household income
Nursery school

7

0

0

.2

.4

Density.6.8

K-S-Test (p-value): 0.000

7.5

Nursery school Kindergarten

Kindergarten

Nursery school Kindergarten
Father(a) Distribution of Household Income

Mother(b) Differences in Parental Education

Figure 2
Household Differences in Preschool Choices
Source: Authors’ calculations using data from JCPS and KHPS/JHPS. Note: Figure(a) shows kernel density estimates for the log household income using a bandwidth of
0.1 and an Epanechnikov kernel for households with children enrolled in a nursery school(i.e., care-oriented preschool) and a kindergarten (i.e., education-oriented preschool),respectively, together with the p-value from a Kolmogorov-Smirnov test of equality of
distributions (Kolmogorov 1933; Smirnov 1933). Figure (b) shows the share of fathers
and mothers having a college degree, respectively, separately for households with children enrolled in a nursery school or a kindergarten together with p-values from t-testsof mean equality.

and reasoning, as well as a parent questionnaire that collects information about the schooland home environment, educational expenditures, and social environment. Informationon the socioeconomic background, residence, and parental education are included in the
KHPS/JHPS. Our final sample consists of seven waves of the JCPS from 2010, 2011,2012, 2013, 2014, 2016, and 2018, together with additional household information fromthe KHPS/JHPS. In addition, we use regional information at the municipality level fromthe Statistics Bureau, Ministry of Internal Affairs and Communications (2020) to construct our instrument and some control variables (see Section 3.2).

3.2

Variables

Outcome Variables
Our goal is to estimate the causal effect of differential preschool types on cognitive andsocioemotional outcomes during the 9 years of compulsory education (i.e., adolescence).
As measures of cognitive abilities, we use information from a mathematical and Japaneseability test in the JCPS. The Japanese assessment consists of vocabulary as well as reading
and writing of Kanji characters. The math assessment consists of calculations and word8

problems concerning numbers and the manipulation of figures.15 Because every item of
the tests is vertically equated across grades using item response theory, we estimate theindividual latent math and Japanese theta scores from all participants (see Yamaguchi
et al. 2019). These cognitive scores are standardized by grade with a mean of zero and aunity standard deviation.
As socioemotional measure, we build on the scores for behavioral difficulties basedon the parents’ responses to the Strengths and Difficulties Questionnaire (SDQ) (Goodman 1997).16 The SDQ is a questionnaire for parents to evaluate a child’s strengths anddifficulties along five subscales, namely, conduct problems, emotional symptoms, hyperactivity, peer relationships, and prosocial behavior. We use the parent report version of the
SDQ, that was first provided in the 2011 wave of the JCPS.17 Each of these subscales consists of five items with 3-point Likert scales. Our socioemotional measure, which we willcall SDQ throughout the paper, is derived from the sum of the subscales conduct problems, emotional symptoms, hyperactivity, and peer relationships, and is standardized bygrade such that it has a mean of zero and a unity standard deviation.18 We rescaled theSDQ score such that higher values imply less behavioral problems and therefore a bettersocioemotional development.
We also use a quality of life (QOL) measure obtained from the child questionnaire forchildren enrolled in the third grade and above. Two editions of the KINDLR (RavensSieberer et al. 2006)19 were used, namely, the elementary school children’s edition andthe junior high school children’s edition (Matsuzaki et al. 2007). KINDLR measures QOL
based on six subscales, namely, physical health, emotional well-being, self-esteem, family,
friends, and school. It is a 24-item Likert scale that measures each of these areas throughfour items, which was included in the child’s questionnaire. The total score represents achild’s general QOL score, which is standardized by grade such that it has a mean of zeroand a unity standard deviation. The SDQ as well as the QOL serve as measures of thesocioemotional development in our empirical analysis.
Although we observe outcomes for the same children at different grade levels (seeFigure B2 in the appendix), the decision to attend a preschool is made only once. When
we use the pooled sample for estimation, the same child is used more than once in the first15

The reliability and validity of the ability tests in the JCPS were verified by Shikishima et al. (2013).
This measure of socioemotional skills was also used by Gupta and Simonsen (2010) to evaluate theintroduction of universal kindergarten in Denmark. This measure is often labeled noncognitive ability.
However, because the SDQ is a behavioral screening questionnaire that allows cognitive skills to affect achild’s behavior, we refer to this skill as socioemotional skill.
17
There are also versions of the SDQ for self-completion (Goodman et al. 1998), adding an impact
supplement (Goodman 1999), and using only a three-subscale division (Goodman et al. 2010).
18
The reliability and validity of this scale was confirmed by Stone et al. (2010). In addition, Matsuishi
et al. (2008) show that the Japanese version of the SDQ questionnaire is approximately as reliable as theoriginal English one.
19
KINDLR is a health-related QOL scale for children developed by Bullinger (1994) and Ravens-Sieberer
and Bullinger (1998).
16

9

stage. Therefore, using the panel structure of the data (i.e., using the pooled sample) does
not add additional information to the first-stage estimation. Instead, we take individualspecific averages for each outcome variable for children who were surveyed more than onceat different time points, providing us with a cross-sectional data set.
Treatment Variable
Our binary treatment variable will take on the value 0 if a child attended a care-oriented
preschool and 1 if a child attended an education-oriented preschool.20 Only a few children
attended both preschool alternatives for some time. These children are categorized ashaving attended a care-oriented preschool if the years spent in this institution exceed
the years spent in an education-oriented preschool, but they are categorized as having
attended the latter if the years spent in an education-oriented preschool are at least as
many as the years spent in a care-oriented preschool. Robustness of our results withrespect to a stricter treatment definition is evaluated in the appendix in Tables H8 to
H11.
Because of institutional differences, the preschool decision directly affects the age ofenrollment and thus the length of attendance. Figure 3 shows that, on average, children
who attend a care-oriented preschool enroll at an earlier age than children that attend an
education-oriented preschool. The implication of this difference for the interpretation of
our results will be discussed in Section 5.2.
Instrumental Variable
Our identification strategy requires an instrumental variable that affects the preschool
decision but is exogenous to our cognitive and socioemotional measures after including
control variables. In particular, we exploit regional and temporal variation in the availability of care-oriented and education-oriented preschool slots across Japan as arguably
exogenous variation (Akabayashi and Tanaka 2013).
We use information on children’s enrollment in either institution at the time of achild’s birth t in municipality c as measure of the local availability of these preschool
alternatives.21 Using municipalities as the level of aggregation allows us to consider finerdifferences between geographic units than in previous studies.22 Information at the mu20

We exclude from our analysis children who only experienced home care and children who attended achild care center, because the distinction between education and child care is less clear for this relatively
new institution. This applies to only ten children (0.60%) in our sample.
21
It is reasonable to assume that enrollment corresponds to actual availability, because in most regions
there is a high demand for early child care slots and parents apply for these places months in advance,
although not every child receives a place.
22
Japan is divided into 1,719 municipalities and Tokyo’s 23 special wards, resulting in 1,741 regional
units. Previous studies of Yamaguchi et al. (2018b) and Kawarazaki (2022) use variation at the prefectural level for identification (i.e., 47 prefectures). Because of the stratification of the survey, only 337
municipalities are covered in the final data set.

10

.6
.4
Density
.2
0

0

1

2
3
Age of enrollment
Nursery school

4

5

Kindergarten

Figure 3
Distribution of Age of Enrollment
Source: Authors’ calculations using data from JCPS and KHPS/JHPS. Note: Thisgraph shows the distribution of age of enrollment for children attending a nursery school(i.e., care-oriented preschool) and a kindergarten (i.e., education-oriented preschool),respectively. The dashed lines show the corresponding sample means.

nicipality level is more likely to affect parents’ preschool decision than larger aggregatesbecause changes in the availability of preschool slots are only relevant if they are close tohome. We then construct our instrument as the number of education-oriented preschool
slots relative to the number of all preschool slots in a municipality at the time of a child’sbirth, that is,
Zct =# Education-oriented preschool slotsct.
# All preschool slotsct(1)

Although this share increased in some prefectures (e.g., Kōchi), it decreased in mostothers (e.g., Tokyo). Figure (a) in Figure 4 depicts this temporal development acrossJapan from 1980 to 2016, while Figure (b) depicts the regional variation in our instrument
Z for 2006, the year in which a large share of children in our sample started preschool education. Figure (a) shows a declining trend in the number of education-oriented preschool
slots over time, although the number of care-oriented preschool slots has been steadily
increasing since the mid-1990s. This reflects the trend that preferences are shifting infavor of full-day preschools. Figure (b) provides some suggestive evidence that in 2006,
regions with a higher population density, such as the large cities Sapporo, Tokyo, and Osaka, have a higher share of education-oriented preschool slots than rural areas, depictedby the darker in contrast to the lighter color.
11

2.5

Children (in million)

2.25

2

1.75

1.5

1.25
1980

1985

1990

1995

2000

2005

2010

Children in nursery schools
Children in kindergartens(a) Temporal Variation(b) Regional Variation

Figure 4
Regional and Temporal Variation in Preschool Slots
Source: Authors’ calculations using data from Statistics Bureau, Ministry of Internal Affairs and Communications (2020). Note: Figure (a) shows the development of
the number of children enrolled in kindergartens (i.e., education-oriented preschools)
and nursery schools (i.e., care-oriented preschools), respectively. Figure (b) shows
the regional distribution of children enrolled in kindergartens (i.e., education-oriented
preschools) relative to the total number of preschool children in 2006. The year 2006
was chosen because the largest share of children in our sample started their preschool
education in 2006.

Control Variables
In addition to the control variables necessary to maintain the conditional independence
assumption of our IV strategy, we control for several variables that are likely to affect thepreschool decision or our outcome variables. We control for child characteristics such as
the gender, a dummy for being born in the first quarter of a year,23 and the number ofsiblings. In addition, we control for parental characteristics such as the mother’s age atbirth of the child, dummies for each parent having graduated from a college, dummies forhousehold income quartiles, and a dummy for the grandfather’s college education. We
also include characteristics on the municipality level such as the child per kindergarten
ratio, the child per nursery school ratio, the population density, the female employment
rate,24 the local unemployment rate, and the per capita income.25
Our preferred specification includes fixed effects for prefectures to control for institu23

Children born in the first quarter of a year must go to school on 1st April when turning 6 years old
and are therefore relatively younger than their classmates.
24
Our measure of the female employment rate is the share of employed women in the total female
working population.
25
Cameron and Heckman (1998; 2001) and Cameron and Taber (2004) point out the importance ofcontrolling for local labor market conditions in models with schooling decisions.

12

tional and structural differences between administratively divided regions as well as thebirth year of the child. Furthermore, Mazumder (2008) and Stephens and Yang (2014)
emphasize the importance of controlling for regional time trends to estimate causal effects
of educational choices (i.e., when the model is based on regional variation in schoolinglaws). They show that many results are very sensitive to this common trend assumption.
Therefore, we also include prefecture-specific linear time trends in our model to allow forsystematic changes in prefectures over time.

3.3

Descriptive Statistics

Table 1 provides descriptive statistics of the cognitive and socioemotional ability measures,the instrument, and covariates for children who attended a care-oriented and an educationoriented preschool, respectively.
These descriptive statistics suggest that, on average, children who attended an educationoriented preschool have significantly higher mathematical and linguistic ability and alsoscore higher on the SDQ score. However, no significant mean difference can be found
for the QOL score. As expected, children who attend an education-oriented preschool
instead of a care-oriented preschool are born in municipalities with a significantly higher
share of education-oriented preschool slots among all preschool slots. This can be taken
as first evidence for the relevance of our instrument.26 Although there are no significant
differences in gender or quarter of birth between education-oriented and care-oriented
preschool children, the former have slightly more siblings on average. In terms of household characteristics, we can confirm our observations from Figure 2. Children who attend an education-oriented instead of a care-oriented preschool are more likely to comefrom households with high-educated parents and with higher incomes on average. These
children also appear to have better-educated grandfathers. Finally, there are significant
differences with respect to the child per kindergarten ratio, the population density, theunemployment rate, and the per capita income. Education-oriented preschool children
come, on average, from more densely populated areas, municipalities with a higher childper kindergarten ratio, and municipalities with a better economic performance. This observation is in line with the fact that larger (and wealthier) cities such as Sapporo, Tokyo,
and Osaka have a higher share of education-oriented preschool slots than rural areas (seeFigure 4).
26

Throughout the paper, we will use the terms “relative preschool enrollment” and “relative preschool
availability” interchangeably.

13

Table 1
Summary Statistics
Care orientation

Educational orientation

(1)
Obs.

(2)
Mean

(3)
SD

(4)
Obs.

(5)
Mean

(6)
SD

(7)
p > |t|

552
552
491
417

−0.173−0.160−0.116−0.018

0.95
0.97
0.96
0.95

1128
1128
1023
924

0.048
0.022
−0.010−0.027

0.87
0.87
0.95
0.88

0.000
0.000
0.040
0.860

552

0.451

0.16

1128

0.551

0.13

0.000

552
552
552

0.500
0.241
2.022

0.50
0.43
0.89

1128
1128
1128

0.460
0.229
2.177

0.50
0.42
0.79

0.120
0.580
0.000

552
552
552
552
552
552
552
552

30.344
0.155
0.302
0.257
0.315
0.236
0.192
0.176

5.13
0.36
0.46
0.44
0.47
0.42
0.39
0.38

1128
1128
1128
1128
1128
1128
1128
1128

30.412
0.195
0.461
0.152
0.336
0.338
0.174
0.216

4.29
0.40
0.50
0.36
0.47
0.47
0.38
0.41

0.780
0.050
0.000
0.000
0.390
0.000
0.360
0.060

552
552
552
552
552
552

145.360
98.86437.8780.953
0.055
1461.618

59.75
23.68
45.56
0.01
0.01
293.55

1128
1128
1128
1128
1128
1128

160.076
98.28750.2180.952
0.054
1577.394

56.73
20.06
46.66
0.01
0.01
323.10

0.000
0.600
0.000
0.450
0.040
0.000

Panel A: Outcome variables
Math score
Japanese score
SDQ score
QOL score
Panel B: Instrumental variable
Relative preschool enrollment
Panel C: Child characteristics= 1 if female= 1 if born in first quarter# siblings
Panel D: Household characteristics
Mother age at birth= 1 if mother has college= 1 if father has college= 1 if HH in 1st income quartile= 1 if HH in 2nd income quartile= 1 if HH in 3rd income quartile= 1 if HH in 4th income quartile= 1 if grandfather has college
Panel E: Regional characteristics# children per kindergarten# children per nursery school
Inhabitants per haFemale empl. rate
Unemployment rate
Per capita income in 1,000 Yen

Source: Authors’ calculations using data from JCPS, KHPS/JHPS, and Statistics Bureau, Ministry ofInternal Affairs and Communications (2020). Note: This table presents summary statistics for variables forchildren who attended a care-oriented and an education-oriented preschool, respectively. The last column
shows p-values from a t-test of mean equality.

14

4

Identification Strategy

4.1

Ordinary Least Squares Regression

For the empirical analysis, we first estimate a standard OLS regression assuming exogeneity of the treatment decision conditional on the inclusion of control variables as abenchmark. In particular, we estimate the following equation,
Yic = α + βDic + Xic′ λ + γp + δt + ζp t + ϵic ,(2)where α is a constant and Yic corresponds to either the math score, Japanese score,
SDQ score, or QOL score for child i who was born in municipality c. Treatment status
is captured by Dic and equals one if children attended an education-oriented preschooland zero if they attended a care-oriented preschool. If the OLS assumptions are met, βcorresponds to the causal effect of attending an education-oriented preschool compared
to a care-oriented preschool. Control variables are summarized by the vector Xic , while λis the corresponding coefficient vector. γp are prefecture fixed effects, δt are cohort fixed
effects, and ζp are prefecture-specific linear time trends. Finally, ϵic is an error term.

4.2

Instrumental Variable Regression

If parents choose a preschool alternative based on individual-specific and municipalityspecific unobserved characteristics which in turn may be correlated with a child’s cognitiveand socioemotional development, this selection pattern would lead to inconsistent estimates of β in Equation (2) from OLS. To circumvent this problem, we follow an IV
strategy.
The identification strategy exploits regional and temporal variation in the supply of
educational opportunities, which follows a long tradition in economic research.27 We
assume that the relative availability of preschool slots Z in a municipality significantly
affects the parental decision about which preschool their child should attend. If there
are only few children enrolled in education-oriented preschools in municipality c compared to care-oriented preschools, parents are more inclined to choose the latter because
children in the neighborhood are also enrolled in that preschool. Also, because there
are relatively few education-oriented preschools in this municipality, the commuting distance to a care-oriented preschool is relatively short. The same argument applies to a
relatively higher share of children enrolled in education-oriented preschools compared to27

Since Duflo (2001), countless papers have used variation in supply-side factors to instrument educational decisions within an IV framework. In the context of a child’s cognitive and socioemotional
development, Loeb et al. (2007), Berlinski et al. (2008; 2009), and Cornelissen et al. (2018) followed thisstrategy. Yamaguchi et al. (2018b) and Kawarazaki (2022) used an instrument similar to ours at theprefectural level to explain the preschool decision in Japan.

15

care-oriented preschools. Following this argument, we can interpret the instrument as a
cost-shifter leaving potential outcomes unchanged. This reasoning leads to the expectation of a positive correlation between Z and D. The empirical results in Section 5.1 showthat the coefficients have the correct sign and that F -statistics are significantly larger
than the rule-of-thumb F -statistic of 10 (Staiger and Stock 1997).
Our IV estimation strategy yields consistent estimates only if the instrument is valid.
In addition to being relevant, it must be conditionally exogenous to a child’s cognitiveand socioemotional development measured by our outcome variables. In particular, after
controlling for confounding factors that are associated with the preschool decision, variation in the local availability of preschool slots must be independent of unobserved factorsthat affect the outcome variables.
A concern raised by Cameron and Taber (2004) in the context of estimating returns
to education is that the geographic distribution of educational facilities may coincide withthe distribution of cognitive abilities and human capital. Historically, the kindergarten
movement in the late 1800s was strongly influenced by the ideas of Friedrich Fröbel andthe Christian missionaries who brought these ideas to Japan (Nishida 2015). Therefore,
many kindergartens (i.e., education-oriented preschools) were established by Christian
missionaries and organizations. Although nowadays more pragmatic aspects, such as
the local demand, are taken into account when opening preschools, we can evaluate thehistorical relationship between Christian settlement, the opening of kindergartens, andthe distribution of abilities using data from the Japan Imperial Ministry of Education(1902), Ministry of War (1917), Bassino et al. (2009), and Oda (2017).28 Figure C3 in
the appendix shows a positive relationship between the number of kindergarten teachers
as a proxy for the number of preschool facilities and the number of Christian facilities in
1900, supporting the previous argument that the opening of kindergartens was related toChristian settlement. However, this geographic pattern was unrelated to the distributionof abilities measured by social, mathematical, and linguistic test scores from 1917. We
find a similar pattern using the GDP per capita in 1909 provided by Bassino et al. (2009)as a potential confounder for Christian settlement and the development of human capitalin a region. Although the GDP per capita was positively correlated with the number ofkindergarten teachers, there is no effect on test scores, relaxing the concern that Christiansettlement, even if it was not random, is itself, as well as factors that may underlie it,
independent of the regional distribution of cognitive abilities. This evidence supports ouridentification strategy in the sense that, at least historically, the regional distribution ofkindergartens is not related to the regional distribution of cognitive abilities and human28

The establishment of nursery schools (i.e., care-oriented preschools) began slightly later (i.e., around
1900, Imoto 2007) and was rather driven by the local demand for child care.

16

capital.29
Another potential threat is that highly-educated parents might move to municipalities
with a better supply of education-oriented preschool slots because of their own educational
orientation, while at the same time their children might perform better in school. This
would lead to an upward bias of our IV estimates if we cannot control for the ability ofparents. However, the rich information provided by the KHPS/JHPS allows us to control
directly not only for the parents’ educational attainment and their economic success, butalso to control for the educational attainment of the grandfather, thereby capturing abilityof two generations. Moreover, internal migration between municipalities is generally low,30
and in Table D1 in the appendix we show that parental characteristics (i.e., ability proxies)
are unrelated to the instrument, supporting the assumption that better-educated parents
do not move to municipalities with more education-oriented preschools. This exogeneityof the instrument with respect to household characteristics is also confirmed by Table
H7 in the appendix, which reports results from 2SLS regressions excluding these ability
proxies. They show that the estimated treatment effects are virtually unchanged fromour main specification.
Although it is likely that we can control for most confounding child and parent characteristics by using the rich information from the JCPS and KHPS/JHPS, there may bemunicipality characteristics that are correlated with the relative availability of preschool
slots and a child’s cognitive and socioemotional development. Ideally, one would control
for municipality fixed effects to account for differences in initial conditions. Because wehave only a few observations in each municipality, this strategy would remove too muchvariation in the instrument. Therefore, we add further control variables to capture theeconomic development of municipalities over time and to account for initial differences at
a higher regional level. First, preferences for investments into one preschool alternative
might depend on the financial resources of a municipality, which are likely to be correlatedwith a child’s academic achievement. Therefore, we control for the per capita income ina municipality. Second, one of the main drivers behind the expanding supply of careoriented preschool slots (see Figure 4) is the steady increase in female labor supply inrecent years (see Figure A1 in the appendix). Therefore, we add a control for the local
female employment rate. Third, prefectures may simply differ in their preschool preferences and education policies, which affects a child’s outcomes through the provision ofregionally varying financial resources. Therefore, we control for prefecture fixed effects
and allow for differential trends across prefectures.
29

When we regress our instrument on the social, mathematical, and linguistic test scores at the prefectural level (results available upon request), we cannot find any relationship, further relaxing the concernof Cameron and Taber (2004).
30
Based on data from Statistics Bureau, Ministry of Internal Affairs and Communications (2020), we
find that only about 4% of the total population moved from one municipality to another in any given
year between 1996 and 2017. Yamaguchi et al. (2018a;b) also show that selective migration is a minor
threat when estimating the effect of child care enrollment on child and parental outcomes in Japan.

17

Even if parents do not move based on their own education and financial resources, they
could still be attracted by potentially unobserved municipality characteristics that affectthe local availability of preschool slots. To investigate this potential threat, we use regional
information from Statistics Bureau, Ministry of Internal Affairs and Communications(2020) to estimate a flexible panel fixed effects model of Z on municipality characteristics
that are potentially correlated with the parents’ location decision. These characteristics
consist of the set of variables that are used as covariates in both stages in the main
analysis (see Section 3.2), as well as other municipality characteristics that correspond to
the unobservables we are testing for. Table D2 in the appendix shows that municipality
characteristics that are excluded from the main analysis but are potentially correlatedwith the parents’ location decision are unrelated to the relative preschool availability.
Another potential threat to identification is that parental preferences could influence
the local supply of preschools through lobbying, for example, leading to the conclusion that
our instrument would be endogenous to demand-side factors.31 Although this potential
threat is indirectly rejected by Table D1, we additionally check the robustness of ourmain results to an alternative instrument that is less prone to short-time fluctuations indemand-side factors. In particular, instead of using the number of preschool slots, we
use the number of preschools to create the alternative instrument Zct,alt , which is definedas the number of education-oriented preschools in municipality c at the time of a child’sbirth t divided by the total number of preschool facilities, that is,
Zct,alt =# Education-oriented preschoolsct,alt.
# All preschoolsct,alt(3)

The idea behind this approach is that the number of facilities cannot be easily adjusted
because establishing a new facility requires time and is costly. Tables H8 to H11 in the
appendix show that our main results hardly change when using this instrument instead,further supporting our identification strategy.
Finally, even after controlling for prefecture-specific linear time trends, compositionalchanges in enrollment patterns over time could bias our results. In particular, it is possiblethat more and more children from advantaged backgrounds will attend an educationoriented instead of a care-oriented preschool. We can test this threat by examining
whether the correlation between the instrument and individual as well as parental characteristics is stable across birth cohorts. Therefore, Table D3 in the appendix presents
results of regressions of individual and parental characteristics on interactions of the instrument with birth cohorts. We do not find evidence of compositional changes over time.
There is little, if any, change in mothers’ education.
Another important assumption that is often omitted from the discussion of IV strate31

Kawarazaki (2022), however, shows that demand for child care has not affected supply in Japan.

18

gies, but is important for the interpretation of 2SLS estimates as local average treatment
effects (LATEs), is monotonicity in the spirit of Imbens and Angrist (1994). This assumption implies that an improvement in the local availability of education-oriented preschool
slots at each Z must increase the probability of sending a child to this institution forsome children, while not reducing this probability for any child. If we follow the reasoning from before and interpret the relative availability of preschool slots as cost-shifter, it
is reasonable to assume that monotonicity holds in our context. In Appendix E, we also
provide evidence of monotonicity by presenting first-stage estimates for several subsamples showing that the effect is always strongly positive. Furthermore, we use partitions of
Z to allow for a flexible first stage showing that more and more children are shifted intoa kindergarten at different levels of Z.

4.3

Marginal Treatment EffectUsing a continuous instrument to identify the treatment effect in an IV strategy complicates the interpretation of this estimate as LATE. This effect is representative forcompliers changing treatment status for changes at different values of the instrument,
and the overall effect is obtained by attaching different weights to complier-specific treatment effects (Cornelissen et al. 2016). Because this treatment effect hides interesting
patterns by aggregating treatment effects for different groups of compliers, we use ourcontinuous instrument to estimate the marginal treatment effect (MTE, Björklund andMoffitt 1987; Heckman and Vytlacil 1999; 2001; 2005; 2007), which is the treatment effectfor individuals at different margins of treatment take up. This allows us to estimate a
flexible relationship between preschool education and children’s cognitive and socioemotional development, identifying the subgroup that gains the most from enrolling in an
education-oriented preschool.
Parents choose to enroll their children in a care-oriented preschool (D = 0) or an
education-oriented preschool (D = 1). The cognitive or socioemotional outcome duringadolescence is Y0 if the child attended a care-oriented preschool and Y1 if the child attended
an education-oriented preschool. These potential outcomes can be written as the sum ofa function of the observed characteristics Xλj and unobserved characteristics Uj , withj = {0, 1}:32
Yj = Xλj + Uj

with E [Uj | X] = 0,

j = {0, 1}.

(4)

Because we only observe children in one treatment state but not the other at the sametime, we write the equation of the observed outcome Y based on the switching regression
32

For clarity, we omit indices and assume that X also includes prefecture fixed effects, cohort fixed
effects, and prefecture-specific linear time trends.

19model of Quandt (1972) as
Y = (1 − D)Y0 + DY1= Y0 + D(Y1 − Y0 )(5)

= Xλ0 + D [X(λ1 − λ0 ) + U1 − U0 ] + U0 ,where the last line follows from replacing Y1 and Y0 with Equation (4). Treatment effectheterogeneity in Equation (5) arises either from different valuations of observed characteristics, λ1 ̸= λ0 , or differences in unobserved characteristics, U1 ̸= U0 , the latter being
called unobserved gain from treatment.
The latent net benefit of treatment D∗ is defined asD∗ = Z̃βd − V,(6)where Z̃ ≡ (X, Z) implies that there is at least one excluded instrument Z, and V denotes
unobserved characteristics that reduce the likelihood of choosing an education-oriented
instead of a care-oriented preschool, often called resistance or distaste for treatment (e.g.,Cornelissen et al. 2016).
Parents choose an education-oriented preschool if the net benefit from observed characteristics exceeds the unobserved resistance or distaste for it:0 if Z̃β < VdD=
1 if Z̃β ≥ V.

(7)d

By applying the cumulative distribution function (cdf) of V to both sides of Equation(7), the left-hand side becomes FV (Z̃βd ) ≡ P (Z̃), the propensity score,33 and the righthand side becomes FV (V ) ≡ UD , the quantiles of unobserved resistance or distaste fortreatment. This model assumes that Z̃ is statistically independent of (U0 , U1 , V ) given
X.34
The MTE is the average gain from treatment for children who are indifferent between
care-oriented and education-oriented preschools at different UD for fixed X:
MTE(X, UD ) = E [Y1 − Y0 | X, UD ]= X(λ1 − λ0 ) + E [U1 − U0 | UD ] .
33(8)The propensity score can be estimated using standard binary choice models such as Probit and Logit
regressions.
34
Vytlacil (2002) shows that additive separability between Z̃ and V implies monotonicity becausechanges in the instrument Z and hence in the propensity score P (Z̃) shift individuals either into or outof treatment, but never both simultaneously.

20

Therefore, treatment effect heterogeneity arising from X affects the intercept of the MTE
curve, while its slope depends on heterogeneity in U1 − U0 . Heckman and Vytlacil(2007)ihshow that the MTE can be recovered by taking the first derivative of E Y | X, P (Z̃)
with respect to the propensity score:∂E [Y | X, p]∂p∂ [Xλ0 + X(λ1 − λ0 )p + Π(p)]
=
∂p
∂Π(p)
,
= X(λ1 − λ0 ) +
{z
}
|
∂p
| {z }
observed

MTE(X, UD ) =(9)

unobserved

where p is the estimated propensity score and Π(p) is a flexible function of it.
In our main specification, we model Π(p) as a polynomial of p of degree K = 2,35 where
p is estimated using a Probit regression. We also test sensitivity of our results by allowing
a more flexible shape of the MTE curve. In particular, we estimate the MTE using apolynomial of degree 3 and 4 and by following the semiparametric approach described indetail in Carneiro et al. (2011).

5

Results

5.1

First-Stage Selection Equation

In a first step, we estimate a Probit regression to test the strength of the relationshipbetween the relative preschool availability and the preschool choice. Average partial
effects of this Probit regression for different sets of control variables are presented inTable 2. This model is then used to calculate predicted probabilities used to estimate theMTE (see Section 5.3).
The relative availability of education-oriented preschool slots within a municipality
significantly influences parents’ preschool choice. As expected, results show that an increase in this share by 1 percentage point leads to a substantial increase in the probabilityof choosing an education-oriented over a care-oriented preschool by 0.63 to 0.92 percentage points on average. This strong positive relationship is highly significant and leads toa partial χ2 -statistic of 20.11 in our preferred specification (Column (3)).36 Thus, we canconclude that our instrument meets the relevance criterion and any remaining bias in our
35This approach has been used by empirical applications of Cornelissen et al. (2018) and Felfe andLalive (2018), among others.
36
If we run a linear probability model instead and test for weak instruments, we obtain a partial
F -statistic of 19.18 in our preferred specification (Column (3)), which is well above the rule-of-thumb
threshold of 10 (Staiger and Stock 1997).

21

Table 2
First-Stage Probit Regression
Educational vs. care orientation

Relative preschool enrollment

(1)

(2)

(3)

0.923∗∗∗(0.088)0.842∗∗∗(0.108)0.632∗∗∗(0.138)

✓

✓
✓
✓
✓

Control variables
Prefecture fixed effects
Cohort fixed effects
Prefecture-specific linear time trends
χ2 -statp-value

83.6960.000

52.6050.000

20.107
0.000

Observations

1680

1680

1680

Source: Authors’ calculations using data from JCPS, KHPS/JHPS, and Statistics Bureau, Ministry of Internal Affairs and Communications (2020). Note: This
table presents average partial effects from first-stage Probit regressions of enrollment in an education-oriented against a care-oriented preschool on the relative
preschool availability and a set of control variables described in Section 3.2. χ2 tests of significance of the coefficient on the instrument are conducted and theresults are presented at the end of the table. Table F5 in the appendix presents
full estimation results. Robust standard errors clustered at the municipality levelare given in parentheses. ∗ , ∗∗ , and ∗∗∗ denote significance at the 10%-, 5%-, and
1%-level, respectively.

2SLS estimates will be small.
The full estimation results in Table F5 in the appendix also show that children withmore siblings, from households with a more educated father and higher income, and frommunicipalities with a low unemployment rate are more likely to enroll in an educationoriented instead of a care-oriented preschool. These findings are consistent with ourobservations from the descriptive statistics and confirm that parents from different backgrounds have different preferences for their children’s preschool education. Those fromadvantaged backgrounds place more emphasis on the educational orientation and tend tosend their children to this institution rather than to a care-oriented preschool.

5.2

Estimation Results Based on OLS and 2SLSOur main analysis starts with benchmark OLS regressions of our cognitive and socioemotional ability measures on a dummy for attendance at an education-oriented against acare-oriented preschool based on Equation (2) in Columns (1), (3), (5), and (7) of Table
3.
These regressions suggest a positive relationship between educational orientation andthe cognitive development. Children who attended an education-oriented preschool have
22

23
1680

✓
✓
✓
✓
1680

✓
✓
✓
✓

0.324
(0.344)0.187∗∗∗(0.055)

1680

✓
✓
✓
✓

0.193∗∗∗(0.056)

OLS
(3)

1680

✓
✓
✓
✓

0.871∗∗(0.386)2SLS(4)Japanese

1514

✓
✓
✓
✓

0.102
(0.067)

OLS
(5)

1514

✓
✓
✓
✓

0.608
(0.404)

2SLS
(6)

SDQ

1341

✓
✓
✓
✓

−0.005(0.065)

OLS
(7)

1341

✓
✓
✓
✓

0.747
(0.514)

2SLS
(8)

QOL

Source: Authors’ calculations using data from JCPS, KHPS/JHPS, and Statistics Bureau, Ministry of Internal Affairs andCommunications (2020). Note: This table presents results from OLS and 2SLS regressions of the cognitive and socioemotional
ability measures on a dummy for attendance at an education-oriented against a care-oriented preschool and a set of control variables
described in Section 3.2, while instrumenting the preschool decision with our measure of the relative preschool availability. Table
G6 in the appendix presents full estimation results. Robust standard errors clustered at the municipality level are given inparentheses. ∗ , ∗∗ , and ∗∗∗ denote significance at the 10%-, 5%-, and 1%-level, respectively.

Observations

Control variables
Prefecture fixed effects
Cohort fixed effects
Prefecture-specific linear time trends

Educational vs. care orientation

2SLS
(2)

OLS
(1)

Math

Table 3
OLS and 2SLS Regressions

on average 0.19 SD higher scores in math and Japanese than children who attended acare-oriented preschool instead. The coefficients for the regressions of the SDQ score and
the QOL score are 0.10 and −0.01, respectively, indicating little or even no relationshipbetween the educational orientation and the socioemotional development.
As it is likely that these estimates are biased estimates of the true causal effect, inColumns (2), (4), (6), and (8) we present results from 2SLS regressions instrumenting thepreschool decision with the relative preschool availability. The effect of the educational
orientation on the math score almost doubles when estimating a 2SLS instead of an OLS
regression. This coefficient, however, is not statistically significant anymore. Even more,
the effect on the Japanese score rises to 0.87 SD, suggesting significant improvements incognitive development.37 Somewhat smaller and not statistically significant anymore are
the effects of the educational orientation on the SDQ score and the QOL score. Children
who attended an education-oriented instead of a care-oriented preschool have on average
a 0.61 SD higher SDQ score and a 0.75 SD higher QOL score.
These treatment effects seem quite large compared to similar studies evaluating preschool programs in various countries. Considering that children in our sample attend theeducation-oriented preschool for an average of 2.7 years, the annualized returns are 0.12
SD on the math score, 0.32 SD on the Japanese score, 0.23 SD on the SDQ score, and 0.28
SD on the QOL score, respectively. These effect sizes are very similar to, for instance,
annualized returns to universal pre-primary education on test scores in Argentina (0.23
SD, Berlinski et al. 2009).
Although we suspected estimates from OLS to be biased upwards because of positive
selection into treatment, it appears that they understate the true causal effect. While
treatment effects from OLS are also identified by always-takers, treatment effects from
2SLS are identified by compliers that enroll in an education-oriented preschool due to ashift in the instrument. If the returns are heterogeneous such that high-resistance children
have higher returns than low-resistance children, and these high-resistance children receivea larger weight than low-resistance children in the 2SLS estimation, effects from 2SLS arelarger than those from OLS. This explanation is supported by the IV weights obtained
from the MTE curves in the next subsection (see Figure L7 in the appendix), which assign
larger weight to high-resistance children with large treatment effects.38
The full estimation results in Table G6 in the appendix show that girls perform betterin Japanese and show less behavioral problems, that relatively young age is associatedwith negative effects on mathematical and linguistic ability, and that the number ofsiblings has a negative effect on the cognitive development as well as on the QOL score.
Although parental education is positively related to the cognitive development, there is no37

The effect on the Japanese score also survives a correction for multiple hypothesis testing following
Romano and Wolf (2005).
38
We thank an anonymous referee for pointing this out.

24effect on the socioemotional development. Finally, a higher household income significantly
improves the cognitive and socioemotional development of children.
In the discussion of the identification strategy, we emphasized that our results arerobust if a stricter treatment definition is used instead, that is, all children who attended
both types of preschools for a short time period are excluded from the analysis. Columns(4) to (6) in Tables H8 to H11 in the appendix show that the 2SLS estimates remain
virtually unchanged. Furthermore, using the relative number of preschool facilities as
instrument instead of available slots in Columns (7) to (9) in Tables H8 to H11 in the appendix further support our main results, as the 2SLS estimates remain largely unchanged,
although the coefficients increase slightly.
The comparatively large standard errors of our 2SLS estimates may raise concerns
about statistical inference in the small sample. Therefore, as a further sensitivity check,we estimate reduced-form regressions of our outcome variables on relative preschool availability and compare these effects with the distribution of placebo treatment effects basedon the permutation test suggested by Bertrand et al. (2004). It is not possible to perform
this exercise with the fully specified 2SLS regression because random assignment of the
instrument would lead to a weak instrument problem in the first stage and thus to highlybiased estimates in the second stage. Instead, evaluating inference of reduced-form estimates can serve as a check for the existence of positive effects. Given that the assumptionsof a valid instrumental variable hold (see Section 4), positive reduced-form effects suggest improved child development as a result of improved availability of education-oriented
preschools, which operates through enrollment in them. For the permutation test, we
randomly assign birth years and municipalities to children (without replacement) and estimate the placebo treatment effect of the relative preschool availability on the outcomes
for these children. This procedure is repeated 3000 times so that we can obtain an exact
finite sample distribution of hypothetical effects against which we can compare the actual
reduced-form estimates. Figure I5 in the appendix shows the results. We find that our
reduced-form estimates are positive and statistically significant for all outcome variablesat least at the 5% level, suggesting that our positive treatment effects are not due tochance.
Although these results suggest strong improvements in children’s socioemotional development after attending an education-oriented preschool, these developmental gains
could also be driven by gains from cognitive improvements, as these children are exposed
to less stress and pressure due to better performance in math and Japanese. Therefore,
we disentangle the effects on our socioemotional measures by repeating the same 2SLS
regressions for the subscales of the SDQ score in Table J12 and the QOL score in Table
J13 in the appendix. We find that the positive effect on the SDQ score is mainly driven
by improvements in the subscales emotional symptoms and peer relationships, although
the effect on the former is not statistically significant. For the QOL score, however, we25

are not able to find the driving subscale behind the positive effect due to large standard
errors. This suggests that positive effects for this socioemotional measure might be driven
by the improved cognitive ability instead.

5.3

Who Gains from an Education-Oriented Preschool?

To shed more light on the distribution of these positive effects, we will estimate MTE
curves in the next step to allow for heterogeneity in the effect based on the unobserved
resistance to treatment. This allows us to draw conclusions about which children gain
more (or less) from enrolling in an education-oriented instead of a care-oriented preschool.
Estimation of the MTE requires common support of the propensity score over the fullunit interval, that is, treated and untreated observations with predicted probabilities oftreatment for all values between zero and one. Figure 5 shows these predicted probabilities
from a first-stage Probit regression for children who attended a care-oriented preschool inblue and children who attended an education-oriented preschool in orange.
The estimated propensity score is more evenly distributed for children who attended
a care-oriented preschool and has a high probability mass around zero. In contrast,
our model assigns a high predicted probability of treatment to children who attended
an education-oriented preschool. Although we have a remarkable common support overthe interval [0.10, 0.95], there are few observations that fall in the lower range of thepropensity score. Therefore, we restrict the estimation of the MTE to percentile intervalsof the propensity score with at least three treated and untreated observations, whichleaves us with a practical common support of [0.32, 0.93].
In the next step, this propensity score is used to parametrically model Π(p) as a polynomial of degree K = 2 and obtain estimates for the MTE for the math, Japanese, SDQ,
and QOL score, respectively. These MTE curves are presented in Figure 6. Althoughthese curves can be obtained for the full unit interval of the unobserved resistance by
extrapolation based on the parametric assumption, we believe that this strategy only
confuses about the population we are able to draw conclusions about. Furthermore, extreme values at low/high quantiles of the unobserved resistance could bias the MTE curve
due to its parametric shape. Therefore, we focus only on the MTE obtained from thepractical common support defined above. This means that we cannot draw conclusions
about the average treatment parameters in the population and we refer to the average] ATE,]
treatment parameters obtained from the MTE curves presented in Table 4 as ATT,
] to distinguish them from their population counterparts.39
and TUT,The MTE curves for cognitive and socioemotional development show an increasing
pattern, suggesting inverse selection on gains. Children who are least likely to attend an
39
For estimation, we use the Stata code provided by Cornelissen et al. (2018), which is an enhanced
version of the margte command of Brave and Walstrum (2014).

26

.025
.02
Density
.01
.015
.005
0
0

.2

.4
.6
Propensity Score
Care orientation.8

1

Educational orientation

Figure 5
Common Support
Source: Authors’ calculations using data from JCPS, KHPS/JHPS, and Statistics Bureau, Ministry of Internal Affairs and Communications (2020). Note: This graph shows
the distribution of the propensity score summarized in 0.01-intervals. The propensity
score is obtained from a Probit regression of enrollment in an education-oriented against
a care-oriented preschool on the relative preschool availability and a set of control variables described in Section 3.2, prefecture fixed effects, birth year fixed effects, andprefecture-specific linear time trends. The instrument is interacted with all child andhousehold characteristics.

education-oriented preschool based on their unobserved characteristics benefit the mostfrom attending it, while children who have a lower resistance for treatment do not exhibit
any positive gains. These unobserved characteristics most likely correspond to lower
ability or less emphasis parents place on their children’s education (or distaste against
education), even after controlling for socioeconomic background. This implies that theeducational orientation plays a minor role in the cognitive and socioemotional developmentof children who have a higher ability or come from households that place more emphasison education. These children likely know most of the content taught in the educationoriented preschool, or they learn it at home and therefore do not benefit from this typeof institution.40
This selection pattern can be tested statistically under the null hypothesis that theslope of the MTE curve, and hence the first derivative of the unobserved part of Equation(9), is constant. This is the same as testing for no effect heterogeneity based on unobserved
characteristics, or equivalently for no selection on gains. Results from this test for each40

For a similar argument in the context of mathematical instruction in kindergarten, see Engel et al.
(2013).

27

4

4Treatment Effect022Treatment Effect
0

-2

-2
-4
0

.2

.4

.6

UD

MTE

.8

1

0

.2

90% Confidence Interval

.4
MTE

.8

1

90% Confidence Interval(b) Japanese

-1

-2

0

-1

Treatment Effect01Treatment Effect
1
2

2

3

3

(a) Math

.6

UD

0

.2

.4
MTE

.6

UD

.8

1

0

90% Confidence Interval

.2

.4
MTE

(c) SDQ

.6

UD

.8

1

90% Confidence Interval(d) QOL

Figure 6
MTE Curves
Source: Authors’ calculations using data from JCPS, KHPS/JHPS, and Statistics Bureau, Ministry of Internal Affairs and Communications (2020). Note: These graphs
show MTE curves from a parametric MTE specification with K = 2 and their 90%
confidence intervals. Covariates are held constant at their means. Confidence intervals
are obtained from a municipality level clustered bootstrap with 299 replications.

outcome variable are presented in Table 4. In fact, we can show that for the math andJapanese score an inverse selection pattern can be statistically detected, that is, p-values
below conventional critical values allow us to reject the null hypothesis of no selection on
gains.
Our parametric specification of the MTE using a polynomial of degree K = 2 is acommon choice in applied work (e.g., Cornelissen et al. 2018). However, alternative specifications are also possible, and one should test the robustness of this choice with respect
to more flexible relationships. Therefore, we reestimate the parametric MTE specification
using polynomials of degree 3 and 4, respectively, as well as a semiparametric specification(e.g., Carneiro et al. 2011), and compare these curves to our main specification in Figure
K6 in the appendix. Allowing a more flexible relationship, the shape of the MTE curves
28

for the math and QOL score hardly changes. Most remarkably, the semiparametric curve
resembles the parametric curve for the math score, while for the QOL score it is very
similar but somewhat steeper. More flexible parametric specifications for the Japanesescore suggest a stronger increase in the treatment effect for higher values of UD . However, the semiparametric curve is flatter, suggesting more moderate treatment effects forhigher values of UD . Our main specification lies in between those two extreme shapes, butcovers the increasing pattern of the other parametric specifications. Therefore, we believethat our preferred specification is a good and reliable choice for the Japanese score. In
contrast, different specifications for the SDQ score suggest different shapes and a flat orinverse U-shape rather than an increasing curve. Thus, we should be careful in interpreting the inverse selection pattern in the case of the SDQ score, even though the different
specifications still confirm strong positive effects.
In estimating average treatment parameters, we can draw conclusions about clearlyspecified subpopulations and learn about their behavior, which is not possible if we only
] ATE,]
rely on simple 2SLS estimates. Therefore, Table 4 presents estimates of the ATT,
] of attending an education-oriented instead of a care-oriented preschool on chiland TUT
dren’s cognitive and socioemotional development.41 The inverse selection pattern suggests that children randomly drawn from the population of education-oriented preschool] do not gain at all from it with respect to the math, Japanese, and QOL
attendees (ATTs)score. A similar, albeit more positive, picture arises for children randomly drawn from] suggest slightly positive returns to the educational orithe overall population. The ATEs
entation with respect to the math score, while they are close to zero for the Japanese and
QOL score. Interestingly, those children who attended a care-oriented preschool would]
benefit the most if they had attended an education-oriented preschool instead. The TUTs
suggest gains of around 1.58 SD for the math score and strong but not statistically significant gains for the Japanese and QOL score. Only with respect to the SDQ score all
children would gain in a similar way, regardless of whether they are from the populationof education-oriented preschool attendees or care-oriented preschool attendees.

5.4

Which Preschool Drives Heterogeneity?

In this subsection, we use the approach suggested by Brinch et al. (2017) to split up
heterogeneity of the MTE (E(U1 −U0 | UD = ud )) in the unobserved parts of the outcomeswhen untreated (E(U0 | UD = ud )) and when treated (E(U1 | UD = ud )). This allows us toshed further light on the inverse selection pattern (i.e., why children with a high resistancefor treatment gain while those with a low resistance do not). Therefore, in Figure 7, we
41] and TUTs
] from the MTE
Figure L7 in the appendix shows the weights used to calculate the ATTs]
curves. The ATE gives equal weights to all values of the MTE curve, and therefore these weights are notpresented.

29

Table 4
Average Treatment Parameter

]
ATT
]
ATE
]
TUT

p-value for test of heterogeneity

MathJapanese

SDQ

QOL

(1)

(2)

(3)

(4)

−0.214(0.482)0.507(0.431)
1.582∗∗∗(0.537)−0.376(0.482)0.152(0.437)0.935(0.594)

1.007∗∗(0.442)
1.184∗∗∗(0.414)
1.461∗∗∗(0.560)−0.167(0.405)0.239(0.374)0.877(0.575)

0.004

0.079

0.515

0.112

Source: Authors’ calculations using data from JCPS, KHPS/JHPS, and Statistics
Bureau, Ministry of Internal Affairs and Communications (2020). Note: This table
presents estimates of the ATT, ATE, and TUT from a parametric MTE specification
with K = 2. For details of the calculation of these parameters, see Appendix L.
Standard errors are obtained from a municipality level clustered bootstrap with 299
replications and are given in parentheses. ∗ , ∗∗ , and ∗∗∗ denote significance at the
10%-, 5%-, and 1%-level, respectively.

plot the unobserved parts of the outcomes when untreated and when treated, respectively,
against the unobserved resistance for treatment.
For math, the curve in the untreated state is slightly falling, while it is almost flat
for the Japanese and the QOL score. This implies that high-resistance children have
only slightly worse math development than low-resistance children from attending a careoriented preschool, while there are almost no differences for the linguistic ability and the
QOL score.42 This emphasizes the high-quality care that applies equally to all children
enrolled in a care-oriented preschool. In contrast, for all outcome variables, the curve inthe treated state is strongly increasing, implying that especially high-resistance children
benefit from attending an education-oriented preschool. These differences can explain the
inverse selection pattern and hence the heterogeneity in returns. Although children can
expect essentially the same returns from attending a care-oriented preschool, regardless ofwhether they are more or less likely to enroll, attending an education-oriented preschoolleads to strong positive developmental gains only for high-resistance children. Thoseare the children whose unobserved characteristics, such as lower ability, make them lesslikely to enroll in the education-oriented preschool. These children lack behind theirhigher-ability peers when they start preschool, and therefore can catch up by attending
an education-oriented preschool. This finding suggests that specific aspects of educationoriented preschools rather than aspects of care-oriented preschools are responsible for the
positive treatment effect of high-resistance children. Therefore, it is very likely that the42

Because of the instability of the corresponding MTE curve (see Figure K6), we should not overinterpret the falling curve for the SDQ score.

30

Counterfactual Outcome (Unobserved Part)
-.2
0
.2
.4
.6

Counterfactual Outcome (Unobserved Part)
-.2
0
.2
.4
0

.2

.4

UD

.6

Untreated

.8

1

0

.2

Treated

.4

UD

.6

Untreated

1

.8

1

Treated(b) JapaneseCounterfactual Outcome (Unobserved Part)
-.2
0
.2
.4

Counterfactual Outcome (Unobserved Part)
-.4
-.2
0
.2
.4

(a) Math

.8

0

.2

.4

UD

Untreated

.6

.8

1

0

Treated

.2

.4

UD

Untreated(c) SDQ.6
Treated(d) QOL

Figure 7
Counterfactual Outcomes by Treatment Status
Source: Authors’ calculations using data from JCPS, KHPS/JHPS, and Statistics Bureau, Ministry of Internal Affairs and Communications (2020). Note: This graph shows
the unobserved part of the cognitive and socioemotional ability measures separately fortreated and untreated children, based on the approach suggested by Brinch et al. (2017).

educational orientation as one of the main features of kindergartens is responsible forthese gains.43

5.5

Short-Term vs. Long-Term Effects

One aspect that is widely discussed in the context of early childhood education is the
durability of positive effects. In a seminal work, Currie and Thomas (1995) show thatcognitive gains from Head Start fade early (see also Hill et al. 2015). In contrast, Heckman et al. (2010b) and Carneiro and Ginja (2014) find that noncognitive gains remain
43

In Section 6, however, we discuss possible features other than the educational orientation that could
explain parts of the treatment effect.

31

remarkably stable into adolescence.44 The question of whether or not effects fade has very
different implications for the long-term evaluation of early childhood interventions.
The unique structure of the JCPS allows us to assess the evolution of effects over timeto test the hypotheses of declining cognitive and stable socioemotional gains in the context
of two high-quality preschool institutions. In particular, we repeat the IV strategy withsamples for children in the first three years of elementary school (aged 6 to 9), in thelast three years of elementary school (aged 9 to 12), and in lower secondary school (aged
12 to 15). This allows us to evaluate effect heterogeneity over the course of compulsory
schooling in Japan. Results are presented in Figure 8 (and Table M14 in the appendix).
The results are surprising in the light of previous research. Attending an educationoriented preschool before elementary school has almost no effect on mathematical and linguistic development in the first three years of elementary school, while cognitive scores increase strongly thereafter. Effects range from −0.17 to 1.11 SD on mathematical achievement and from −0.25 to 1.06 SD on linguistic achievement. These results demonstratethat education-oriented preschools have an increasingly positive impact on children’s cognitive development over time. In contrast, the findings on the evolution of effects onsocioemotional outcomes are mostly in line with previous research. It appears that thepositive effects on our socioemotional measures are mostly stable over time, at most decreasing slightly at the end of the observation period.
One potential explanation for increasing positive effects on cognitive development isselective panel attrition. This problem could arise if children attending an educationoriented preschool and performing poorly in the early grades dropped out of the survey
in later waves, leading to an upward bias over time.45 We can test for this by estimating a Probit regression of a dummy variable indicating whether children in the lower
grades at survey wave s would drop out at survey wave s + 1 on the cognitive achievement in those lower grades as well as child and parental characteristics. Results of thisexercise can be found in Table N15 in the appendix. We find no evidence of high- or
low-achieving children to be more or less likely to drop out of the survey in either the
sample of care-oriented preschool children or the sample of education-oriented preschool
children, providing evidence against the selective panel attrition explanation.
Another explanation is rooted in the differences between the Japanese and the U.S.
school system. Diminishing positive cognitive effects in the United States were explainedby the worse environment to which treated children would return after completing the
program (e.g., Currie and Thomas 2000). In Japan, on the other hand, schools are morehomogeneous and have similar quality across the country. Therefore, disadvantaged children who attended an education-oriented preschool will experience the same school and44

Similar results were found from the Perry Preschool program (e.g., Schweinhart et al. 2005; Heckman
et al. 2010a; Conti et al. 2016).
45
In general, the same pattern could arise if high-performing children attending a care-oriented
preschool dropped out over time. However, this is unlikely to happen.

32

-1

Treatment effect (in std. dev.)
0
1

2

2
Treatment effect (in std. dev.)
0
1-1
Lower elem. school

Higher elem. school

Lower sec. school

Lower elem. school

Lower sec. school(b) Japanese

-.5

-.5

0

0

Treatment effect (in std. dev.)
.5
1
1.5

Treatment effect (in std. dev.)
.5
1
1.5

2

2

(a) Math

Higher elem. school

Lower elem. school

Higher elem. school

Lower sec. school

Lower elem. school(c) SDQ

Higher elem. school

Lower sec. school(d) QOL

Figure 8
Effect of Preschool Types Over Time
Source: Authors’ calculations using data from JCPS, KHPS/JHPS, and Statistics Bureau, Ministry of Internal Affairs and Communications (2020). Note: This graph shows
results from 2SLS regressions of the cognitive and socioemotional ability measures on adummy for attendance at an education-oriented against a care-oriented preschool anda set of control variables described in Section 3.2, while instrumenting the preschool
decision with our measure of the relative preschool availability. These regressions are
conducted separately for children in the first three years of elementary school, the last
three years of elementary school, and lower secondary school. There are not enoughobservations for the QOL score at lower elementary school. Table M14 in the appendix
presents full estimation results together with F -statistics from tests of weak instrumentsin the first stage. The whiskers indicate 90% confidence intervals based on robust standard errors clustered at the municipality level.

neighborhood environment as advantaged children. Within this rather equal environment, the early investment into human capital can bear fruits and lead to longer-lasting
improvements in the cognitive development, as suggested by the theory of dynamic complementarities (Cunha and Heckman 2007).

33

6

Educational Orientation or Other Channels?

Although we cannot directly test the conjecture that the educational orientation and thestructured curriculum of education-oriented preschools are the main drivers behind thetreatment effect, our results point to this interpretation. In particular, in Section 5.4 weconcluded that specific aspects of education-oriented preschools, such as the structured
curriculum, are responsible for the positive treatment effect, rather than aspects of careoriented preschools. Studies in the United States argue in a similar way, although theycannot identify the characteristics of the counterfactual care that children would experience in absence of the targeted programs.46 In this section we discuss other potential
channels and mechanisms through which our treatment effect might also operate.
A key difference between education-oriented and care-oriented preschools are operating
hours (full-day vs. half-day). While mothers of care-oriented preschoolers are able to work
full-time, mothers of education-oriented preschoolers often work part-time at best. A large
literature shows that the use of child care instead of informal care alternatives leads toan increase in maternal labor supply and reduces child-mother interaction (e.g., Gelbach
2002; Baker et al. 2008; Lefebvre and Merrigan 2008; Nollenberger and Rodrı́guez-Planas
2015; Yamaguchi et al. 2018a;b). Thus, the positive effect of educational orientation
could be explained by the additional interaction with the mother in the afternoon, whichcare-oriented preschoolers do not have.47 Using data from KHPS/JHPS and estimating
difference-in-differences regressions of parental labor supply on the relative availability ofpreschool slots, we find a strong negative effect of the relative availability of educationoriented preschool slots on maternal labor supply, while paternal labor supply is largely
unaffected (see Figure O8 in the appendix).48 Similarly, there is a strong positive effect ontime spent at home for child care. Both results suggest that the interaction with mothers
may partially explain the treatment effect.
One could further argue that enrolling children in different preschool alternatives could
have an impact on parental behavior, leading to potential multiplier effects. For example,
Gelber and Isen (2013) find that Head Start led to greater parental engagement in termsof time spent reading, math activities, and days spent with the child (for Japan, see Yamaguchi et al. 2018b). If attending an education-focused preschool also improves parental
behavior, this, along with afternoon interaction, could enhance the positive impact on the46

For example, Barnett and Masse (2007) argue that the Abecedarian project’s returns are higher than
Perry’s because of the additional hours of instruction. Similarly, Schweinhart (2007) expresses the opinion
that the HighScope curriculum is the main reason for Perry’s success (see also Heckman et al. 2010b;
Conti et al. 2016). See also the discussion in Duncan and Magnuson (2013).
47
Although we cannot test this, parents might also use the afternoons to send their children to extracurricular activities that have a positive impact. This means that the positive effects cannot necessarily be
explained by the interaction with parents alone.
48
Because we do not observe preschool enrollment for these children, our estimates are intention-to-treat(ITT) estimates.

34

child’s cognitive and socioemotional development. Although we cannot test this hypothesis directly, we follow the same strategy as before and estimate the effect of the relative
preschool availability on education expenditures and total expenditures (see Figure O8
in the appendix) as proxies for behavioral change. We cannot find clear evidence for achange in spending behavior, relativizing the behavioral channel.
Another possible channel through which education-oriented preschools could lead todevelopmental gains is through interaction with stronger peers. Henry and Rickman(2007) find positive spillover effects of peers in Head Start on children’s cognitive development (see also Neidell and Waldfogel 2010). Throughout the paper we have seen thatchildren from more advantaged backgrounds are more likely to be enrolled in educationoriented preschools. Therefore, it is possible that children attending this preschool aresimply learning from each other and that the positive effect is ultimately due to interaction with stronger peers. This could explain why children who are less likely to attend an
education-oriented preschool and are therefore of lower ability or come from households
that place less value on education gain more from it than their peers who are more likely
to attend it (see Figure 6). This explanation is also supported by the strong positiveeffect on peer relationships (see Table J12).
Finally, because the two institutions are supervised by different ministries and teachers/caretakers receive the certificate from the respective ministry, it is possible that teachers in education-oriented preschools are better qualified than caretakers in care-oriented
preschools, suggesting that the treatment effect is due to this difference in institutional
quality. For example, the results of Guarino et al. (2006) show that teacher qualifications
and instructional practices play an important role in the effectiveness of kindergartenson the cognitive development of children in the United States.49 Although we cannot
empirically test this hypothesis given our data, both teachers and caretakers are generally
well-trained and therefore of similar high quality (e.g., Kawarazaki 2022), and possible
differences between the two groups contribute only slightly to the treatment effect.

7

Conclusion

This paper uses a unique feature of the Japanese preschool system to estimate the causal
effect of an education-oriented against a care-oriented preschool on children’s cognitiveand socioemotional development in adolescence. Relying on plausibly exogenous regional
and temporal variation in the relative availability of preschool slots, we estimate 2SLS
regressions of a measure of mathematical and a linguistic ability, the socioemotional measure of behavioral problems SDQ, and a quality of life measure on children’s preschool
attendance.
49

In contrast, Palardy and Rumberger (2008) show that teacher qualifications play a minor role compared to instructional practices in first grade of elementary school.

35

Our main findings suggest strong developmental gains, particularly in linguistic ability,
which materialize at a later stage of adolescence. Although previous research has shownthat cognitive gains from targeted programs such as Head Start fade soon after the program ends (Currie and Thomas 1995), we can explain our results with the homogeneous
high-quality schooling across Japan, as opposed to the more heterogeneous environmentsin the United States, where disadvantaged but treated children experience the same environment as their advantaged but nontreated peers. This finding is consistent with thetheory of dynamic complementarities (Cunha and Heckman 2007) and underscores theimportance of the post-treatment period for the effectiveness of preschool programs in thelong run.
To further examine heterogeneity of this treatment effect, we estimate the MTE andfind an inverse selection pattern: children who are least likely to attend an educationoriented preschool are those who benefit the most. This finding can most likely be explained by aspects of education-oriented preschools as opposed to care-oriented preschools,
such as the educational instruction. Children with high resistance are less gifted or comefrom households that place less emphasis on education. These children benefit from theadditional instruction that advantaged children can replace with knowledge and skills
acquired in their home environment. Nevertheless, shorter hours of operation leading toa reduction in maternal labor supply and allowing more interaction with the mother, as
well as interaction with stronger peers, could serve as further explanations. Thus, wewelcome future research on the channels and mechanisms through which our estimatedtreatment effect might operate to gain a deeper understanding of the forces behind thesedevelopmental gains.

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46

.41

Female employment rate
.42
.43
.44

.45

Female Employment Rate

.4

A

1990

1995

2000

2005

2010

2015

2020

Figure A1
Female Employment Rate Over Time
Source: Authors’ calculations using data from The World Bank (2022). Note: Thisgraph shows the female employment rate over time for Japan, calculated as the shareof working women among all women aged 15 or above.

47

300

Observations
350

400

450

Distribution of Outcome Observations Across Grades

250

B

1

2
Math

3

4

5
Grade

Japanese

6

7
SDQ

8

9
QOL

Figure B2Distribution of Outcome Observations Across Grades
Source: Authors’ calculations using data from JCPS and KHPS/JHPS. Note: Thisgraph shows the number of observations for each outcome variable across grades. One
child might be measured several times at different grades. These observations are used
to obtain the individual-averaged outcome variables used in the main analysis.

48

49

C

Historical Relationship Between Christianity andKindergarten teacher per capita, 1900 (in mio)
20
40
60
80
100

Kindergarten teacher per capita, 1900 (in mio)
20
40
60
80
100

Kindergartens

00Linear fit: .309 (.167)

Linear fit: .558 (.082)

0

20
40
60
Christian facilities per capita, 1900 (in mio.)

80

50

200

60

80

(b) Kindergarten teachers and GDP

60(a) Kindergarten teachers and Christianity

100
150
GDP per capita, 1909 (in Yen)

Linear fit: -.023 (.051)

Linear fit: -.056 (.055)

40

20

20

30

30

Social test score, 1917
40
50

Math test score, 1917
40
50

Linguistic test score, 1917
50
60
70

Linear fit: -.051 (.054)

0

20
40
60
Christian facilities per capita, 1900 (in mio.)

80

0

20
40
60
Christian facilities per capita, 1900 (in mio.)

80

0

20
40
60
Christian facilities per capita, 1900 (in mio.)

80(c) Social score and Christianity (d) Math score and Christianity (e) Linguistic score and Chris-

80

60

60

tianityLinear fit: .02 (.035)

Linear fit: -.001 (.037)

40

20

20

30

30

Social test score, 1917
40
50

Math test score, 1917
40
50

Linguistic test score, 1917
50
60
70

Linear fit: -.003 (.037)

50

100
150
GDP per capita, 1909 (in Yen)(f) Social score and GDP

200

50

100
150
GDP per capita, 1909 (in Yen)(g) Math score and GDP

200

50

100
150
GDP per capita, 1909 (in Yen)(h) Linguistic score and GDP

Figure C3
Christian Facilities, GDP, Kindergartens, and Test Scores
Source: Authors’ calculations using data from Imperial Bureau of Statistics of Japan(1898; 1903), Japan Imperial Ministry of Education (1902), Ministry of War (1917),
Bassino et al. (2009), and Oda (2017). Note: These graphs show the relationshipbetween outcomes described below and the number of Christian facilities per capita ina prefecture in 1900 or the GDP per capita in 1909. Figures (a) and (b) use the numberof kindergarten teachers per capita on the ordinate. Figures (c) and (f) use the social
test score on the ordinate, Figure (d) and (g) use the math test score on the ordinate,
and Figure (e) and (h) use the linguistic test score on the ordinate. Each figure presents
a linear fit, where the estimated coefficient is presented in the upper right corner with
its standard error given in parentheses.

50

200

D

Instrument Validity – Placebo Analyses
Table D1
Potential Relationsip between Parental Characteristics andRelative Preschool Availability
Relative preschool availability

Mother age at birth= 1 if mother has college= 1 if father has college= 1 if HH in 2nd income quartile= 1 if HH in 3rd income quartile= 1 if HH in 4th income quartile= 1 if grandfather has college

(1)

(2)

(3)

−0.002∗∗(0.001)0.007(0.012)0.023∗∗(0.010)0.019(0.013)0.034∗∗(0.014)0.027(0.019)−0.001(0.013)−0.001(0.001)
0.003(0.009)0.003(0.008)0.001(0.012)0.007(0.011)−0.011(0.017)−0.004(0.011)−0.001(0.001)
0.009(0.008)−0.000(0.006)0.003(0.008)0.013(0.008)−0.008(0.011)−0.002(0.007)

✓

✓
✓
✓
✓

Further municipality characteristics
Prefecture fixed effects
Cohort fixed effects
Prefecture-pecific linear time trends
F -stat of test of joint significancep-value of test of joint significance

2.791
0.008

0.988
0.440

1.920
0.066

Observations

1680

1680

1680

Source: Authors’ calculations using data from JCPS, KHPS/JHPS,
and Statistics Bureau, Ministry of Internal Affairs and Communications(2020). Note: This table presents results from regressions of the relative
preschool availability on household characteristics. Further municipality
characteristics are described in Section 3.2. F -tests for joint significanceof the presented coefficients are conducted and the results are presented
at the end of the table. Robust standard errors clustered at the municipality level are given in parentheses. ∗ , ∗∗ , and ∗∗∗ denote significanceat the 10%-, 5%-, and 1%-level, respectively.

51

Table D2
Potential Determinants of Preschool Availability
Relative preschool availability
(1)
∗∗∗

(2)
∗∗∗

(3)

2.924
(0.347)0.004(0.015)−0.024(0.017)0.024(0.033)0.990∗∗∗(0.206)−0.000(0.000)1.374(0.508)−0.010(0.026)−0.106∗∗∗(0.034)−0.008(0.029)0.449∗∗∗(0.142)−0.000(0.000)−0.392(0.594)−0.027(0.024)−0.076∗(0.046)−0.013(0.029)0.110(0.146)0.000(0.000)

✓

✓
✓

✓
✓
✓
✓

F -stat of test of joint significancep-value of test of joint significance35.636
0.000

7.539
0.000

1.094
0.364

Observations

15240

12416

12416

Share of children
Per capita educ. expendit. in 1,000 Yen
Per capita local taxes in 1,000 Yen
Per capita health expendit. in 1,000 Yen
Per capita crimes
Industrial area in ha

Further municipality characteristics
Municipality fixed effects
Year fixed effects
Prefecture-pecific linear time trends

Source: Authors’ calculations using data from JCPS, KHPS/JHPS, andStatistics Bureau, Ministry of Internal Affairs and Communications (2020).
Note: This table presents results from panel fixed effects models of the relative
preschool availability in a municipality on various municipality characteristics.
Further municipality characteristics are described in Section 3.2. Because somevariables are not available in all years, these regressions are based on data from2000 to 2008, covering the birth year of around 60% of the children in our sample. F -tests for joint significance of the presented coefficients are conducted
and the results are presented at the end of the table. Robust standard errors
clustered at the municipality level are given in parentheses. ∗ , ∗∗ , and ∗∗∗denote significance at the 10%-, 5%-, and 1%-level, respectively.

52

53
1680

Observations

1680

0.784
0.712

✓
✓
✓
✓

−0.939(0.575)0.305(0.363)0.239(0.335)−0.063(0.341)−0.142(0.330)0.033(0.317)−0.362(0.304)−0.323(0.290)−0.050(0.295)−0.207(0.335)−0.316(0.408)−0.207(0.396)−0.194(0.390)0.174(0.478)−0.015(0.516)
1.229∗(0.708)−0.273(0.586)

(2)

1680

2.207
0.004

✓

✓

0.426
(0.374)−0.349∗(0.207)0.140(0.194)−0.233(0.232)0.046(0.179)−0.471∗(0.242)0.240(0.213)0.043(0.258)−0.185(0.139)0.124(0.172)−0.602∗∗(0.270)0.460∗(0.238)0.279(0.214)0.901∗∗(0.368)0.743∗∗(0.317)0.367(0.549)0.301(0.540)

(3)

1680

1.692
0.043

✓
✓
✓
✓

0.550
(0.480)−0.252(0.283)0.249(0.248)−0.122(0.259)0.074(0.232)−0.213(0.268)0.296(0.253)0.102(0.272)−0.059(0.186)0.266(0.220)−0.559∗(0.295)0.522(0.318)0.425(0.286)1.023∗∗(0.442)0.707(0.449)1.179(0.848)0.483(0.503)(4)= 1 if mother has college

1680

2.282
0.003

✓

1680

1.440
0.115

✓
✓
✓
✓

0.866∗(0.517)0.106(0.462)0.284(0.374)−0.844∗∗(0.327)−0.087(0.324)0.023(0.351)−0.166(0.327)0.396(0.336)−0.219(0.275)0.086(0.279)−0.440(0.369)0.059(0.381)0.539∗(0.323)0.252(0.520)0.228(0.526)0.057(0.695)0.798(0.489)0.882∗∗(0.428)−0.138(0.404)0.052(0.352)−0.962∗∗∗(0.271)−0.209(0.296)−0.158(0.305)−0.163(0.293)0.463(0.285)−0.208(0.240)0.230(0.239)−0.287(0.343)0.334(0.312)0.781∗∗∗(0.253)0.646(0.464)0.325(0.417)−0.432(0.634)0.804(0.567)
✓

(6)

(5)

= 1 if father has college

1680

0.774
0.724

✓

✓

0.703
(0.578)−0.149(0.309)0.152(0.385)−0.375(0.277)−0.505∗(0.299)0.182(0.330)−0.267(0.296)0.022(0.292)
0.198(0.252)−0.019(0.282)0.562(0.342)0.053(0.361)0.163(0.315)0.138(0.498)0.014(0.442)0.204(0.626)−0.281(0.477)

(7)

1680

0.716
0.787

✓
✓
✓
✓

0.576
(0.612)−0.033(0.390)0.123(0.428)−0.267(0.307)−0.404(0.359)0.421(0.366)−0.197(0.331)0.154(0.347)0.349(0.294)0.068(0.335)0.689∗(0.378)0.030(0.394)0.276(0.395)0.146(0.583)0.435(0.544)0.309(0.714)0.266(0.609)(8)= 1 if HH in 3rd
or 4th income quartile

Source: Authors’ calculations using data from JCPS, KHPS/JHPS, and Statistics Bureau, Ministry of Internal Affairs and Communications (2020). Note: This
table presents results from linear probability models of individual and parental characteristics on the instrument interacted with birth cohorts. Further municipality
characteristics are described in Section 3.2. χ2 -tests for joint significance of the presented coefficients are conducted and the results are presented at the end of
the table. Robust standard errors clustered at the municipality level are given in parentheses. ∗ , ∗∗ , and ∗∗∗ denote significance at the 10%-, 5%-, and 1%-level,
respectively.

0.596
0.895

✓

✓

−0.713(0.498)0.306(0.325)0.324(0.317)0.019(0.305)−0.039(0.302)0.074(0.304)−0.266(0.297)−0.238(0.268)0.096(0.262)0.070(0.289)−0.151(0.363)−0.019(0.379)−0.219(0.361)0.304(0.493)0.080(0.446)0.914(0.589)−0.151(0.507)

χ2 -stat of test of joint significancep-value of test of joint significanceFurther municipality characteristics
Prefecture fixed effects
Cohort fixed effects
Prefecture-specific linear time trends

Z × 2011

Z × 2010

Z × 2009

Z × 2008

Z × 2007

Z × 2006

Z × 2005

Z × 2004

Z × 2003

Z × 2002

Z × 2001

Z × 2000

Z × 1999

Z × 1998

Z × 1997

Z × 1996

Z × 1995(1)= 1 if female

Table D3
Test for Unobserved Compositional Changes

1

Density of Z

2

Cond. probability of treatment
.4
.6

.8

3

Instrument Monotonicity

0

.2

E

0

.2

.4
Bin indicators of Z

Treatment probability.6.8
Density of Z

Figure E4
Probability of Enrollment in an Education-Oriented Preschool
Source: Authors’ calculations using data from JCPS, KHPS/JHPS, and Statistics Bureau, Ministry of Internal Affairs and Communications (2020). Note: This graph shows
the predicted probability of education-oriented preschool enrollment conditional on controlling for a set of control variables described in Section 3.2 for different partitions of
the relative preschool availability. Partitions are based on the 20th, 40th, 60th, and80th percentile. It also provides a kernel density estimate of the distribution of therelative preschool availability.

54

55
885

Observations

795

✓
✓
✓
✓
391

✓
✓
✓
✓

0.634∗∗(0.321)

1st quarter
(3)

1289

✓
✓
✓
✓

0.635∗∗∗(0.174)

2nd–4th quarter
(4)

867

✓
✓
✓
✓

0.848∗∗∗(0.240)

Low income
(5)

813

✓
✓
✓
✓

0.677∗∗∗(0.196)

High income
(6)

915

✓
✓
✓
✓

0.610∗∗∗(0.204)

No college
(7)

755

✓
✓
✓
✓

0.810∗∗∗(0.272)

Some college(8)

Source: Authors’ calculations using data from JCPS, KHPS/JHPS, and Statistics Bureau, Ministry of Internal Affairs and Communications (2020).
Note: This table shows results from first-stage regressions of enrollment in an education-oriented against a care-oriented preschool on the relative
preschool availability and a set of control variables described in Section 3.2. Column (1) runs this regression only for males, Column (2) only forfemales, Column (3) only for children born in the first quarter, Column (4) only for children born in the second to fourth quarter, Column (5) only forchildren from low-income households, Column (6) only for children from high-income households, Column (7) only for children from parents without acollege degree, and Column (8) only for children from parents with at least one college degree. Robust standard errors clustered at the municipality
level are given in parentheses. ∗ , ∗∗ , and ∗∗∗ denote significance at the 10%-, 5%-, and 1%-level, respectively.

✓
✓
✓
✓

0.915∗∗∗(0.219)0.464∗∗(0.203)Control variables
Prefecture fixed effects
Cohort fixed effects
Prefecture-pecific linear time trends

Relative preschool enrollment

Female
(2)

Male
(1)

Educational vs. care orientation

Table E4First-Stage Estimates for Subsamples

56

F

Full Estimation Results – First-Stage
Table F5First-Stage Probit Regression – Full Estimation Results
Educational vs. care orientation(1)
Relative preschool enrollment

∗∗∗

0.923
(0.088)= 1 if female= 1 if born in first quarter# siblings
Mother age at birth= 1 if mother has college= 1 if father has college= 1 if HH in 2nd income quartile= 1 if HH in 3rd income quartile= 1 if HH in 4th income quartile= 1 if grandfather has college# children per kindergarten# children per nursery school
Inhabitants per haFemale empl. rate
Unemployment rate
Per capita income in 1,000 Yen

(2)
∗∗∗

0.842
(0.108)−0.030(0.020)−0.008(0.027)0.049∗∗(0.020)−0.000(0.003)−0.009(0.036)0.119∗∗∗(0.032)0.103∗∗∗(0.038)0.121∗∗∗(0.042)0.008(0.049)0.023(0.035)−0.000(0.000)−0.000(0.001)0.000(0.000)−5.338(3.824)−4.700(3.179)−0.000(0.000)Prefecture fixed effects
Cohort fixed effects
Prefecture-pecific linear time trends(3)
0.632∗∗∗(0.138)−0.028(0.019)−0.017(0.026)
0.048∗∗∗(0.018)0.000(0.003)0.020(0.034)0.103∗∗∗(0.030)0.092∗∗(0.037)0.105∗∗∗(0.040)−0.012(0.046)0.021(0.034)−0.000(0.000)−0.001(0.001)0.000(0.000)−4.283(5.207)−8.417∗(4.387)−0.000(0.000)✓✓✓χ2 -statp-value

83.6960.000

52.6050.000

20.107
0.000

Observations

1680

1680

1680

Source: Authors’ calculations using data from JCPS, KHPS/JHPS,
and Statistics Bureau, Ministry of Internal Affairs and Communications(2020). Note: This table presents average partial effects from first-stage
Probit regressions of enrollment in an education-oriented against a careoriented preschool on the relative preschool availability and a set of control
variables described in Section 3.2. χ2 -tests of significance of the coefficienton the instrument are conducted and the results are presented at the endof the table. Robust standard errors clustered at the municipality levelare given in parentheses. ∗ , ∗∗ , and57∗∗∗ denote significance at the 10%-,5%-, and 1%-level, respectively.

G

Full Estimation Results – OLS and 2SLS

58

59
1680

✓
✓
✓

1680

✓
✓
✓

0.324
(0.344)−0.062(0.044)−0.140∗∗(0.058)−0.131∗∗∗(0.036)0.018∗∗∗(0.006)
0.242∗∗∗(0.062)
0.236∗∗∗(0.063)0.162∗∗(0.075)0.163∗(0.087)0.402∗∗∗(0.083)0.011(0.058)0.000(0.001)0.001(0.002)−0.000(0.001)
2.200(9.241)
2.687(7.962)−0.000(0.000)−3.060(9.255)0.187∗∗∗(0.055)−0.066(0.045)−0.142∗∗(0.059)−0.124∗∗∗(0.035)0.018∗∗∗(0.006)0.246∗∗∗(0.062)0.250∗∗∗(0.054)0.175∗∗(0.071)0.180∗∗(0.080)0.400∗∗∗(0.085)0.014(0.059)0.000(0.001)0.001(0.002)0.000(0.001)
1.208(9.572)
1.223(7.836)−0.000(0.000)−1.995(9.553)

1680

✓
✓
✓

0.193∗∗∗(0.056)0.126∗∗∗(0.042)−0.122∗∗(0.051)−0.157∗∗∗(0.034)0.021∗∗∗(0.006)0.240∗∗∗(0.071)0.213∗∗∗(0.060)0.080(0.077)0.018(0.081)0.250∗∗∗(0.096)0.037(0.067)−0.000(0.001)
0.002(0.002)0.001(0.001)−2.770(10.438)−0.603(8.259)−0.000(0.000)1.961(10.425)

OLS
(3)

1680

✓
✓
✓

0.871∗∗(0.386)0.146∗∗∗(0.045)−0.112∗∗(0.053)−0.190∗∗∗(0.038)0.021∗∗∗(0.006)0.217∗∗∗(0.074)0.147∗∗(0.073)0.014(0.091)−0.064(0.095)0.257∗∗∗(0.099)0.023(0.070)−0.000(0.001)
0.003(0.002)0.001(0.001)
2.164(11.345)6.687(9.642)−0.000(0.000)−3.335(11.381)

2SLS(4)Japanese

1514

✓
✓
✓

0.102
(0.067)0.261∗∗∗(0.050)−0.080(0.058)0.047(0.045)0.016∗∗(0.007)0.068(0.080)0.046(0.072)
0.236∗∗∗(0.085)0.160∗(0.092)0.373∗∗∗(0.108)−0.076(0.068)−0.000(0.001)0.006∗∗∗(0.002)−0.001(0.001)13.438(10.882)
10.727(9.035)0.000(0.000)−15.288(10.931)

OLS
(5)

2SLS
(6)

1514

✓
✓
✓

0.608
(0.404)0.273∗∗∗(0.049)−0.067(0.058)0.024(0.044)0.018∗∗∗(0.006)0.045(0.081)0.001(0.076)0.188∗∗(0.091)
0.099(0.101)0.380∗∗∗(0.108)−0.087(0.070)−0.000(0.001)0.007∗∗∗(0.002)−0.001(0.001)18.926(11.801)
17.234(10.532)0.000(0.000)−21.336∗(12.003)

SDQ

1341

✓
✓
✓

−0.005(0.065)0.043(0.055)−0.086(0.065)−0.082∗∗(0.036)−0.001(0.006)−0.011(0.077)0.074(0.063)0.033(0.079)0.080(0.081)0.228∗∗(0.095)0.020(0.088)−0.001(0.001)−0.001(0.002)−0.002(0.001)−19.079∗∗(9.577)−12.232(7.839)0.000(0.000)18.720∗(9.577)

OLS
(7)

2SLS
(8)

1341

✓
✓
✓

0.747
(0.514)0.060(0.055)−0.052(0.070)−0.117∗∗(0.048)0.000(0.006)−0.059(0.087)0.006(0.083)−0.014(0.083)−0.005(0.098)0.250∗∗(0.105)0.000(0.091)−0.001(0.001)0.001(0.002)−0.002∗(0.001)−12.105(10.487)−3.648(10.193)−0.000(0.000)11.245(10.709)

QOL

Source: Authors’ calculations using data from JCPS, KHPS/JHPS, and Statistics Bureau, Ministry of Internal Affairs and Communications (2020). Note: This table
presents results from OLS and 2SLS regressions of the cognitive and socioemotional ability measures on a dummy for attendance at an education-oriented against acare-oriented preschool and a set of control variables described in Section 3.2, while instrumenting the preschool decision with our measure of the relative preschool
availability. Robust standard errors clustered at the municipality level are given in parentheses. ∗ , ∗∗ , and ∗∗∗ denote significance at the 10%-, 5%-, and 1%-level,
respectively.

Observations

Prefecture fixed effects
Cohort fixed effects
Prefecture-specific linear time trends

Constant

Per capita income in 1,000 Yen

Unemployment rate

Female empl. rate

Inhabitants per ha# children per nursery school# children per kindergarten= 1 if grandfather has college= 1 if HH in 4th income quartile= 1 if HH in 3rd income quartile= 1 if HH in 2nd income quartile= 1 if father has college= 1 if mother has college

Mother age at birth# siblings= 1 if born in first quarter= 1 if female

Educational vs. care orientation

2SLS
(2)

OLS
(1)

Math

Table G6
OLS and 2SLS Regressions – Full Estimation Results

H

Robustness Check – 2SLS

60

61
1680

✓
✓
✓

0.000
(0.001)0.001(0.002)0.000(0.001)−0.868(9.184)−2.231(7.856)−0.000(0.000)0.070(9.211)0.325(0.338)−0.065(0.046)−0.135∗∗(0.060)−0.146∗∗∗(0.038)0.028∗∗∗(0.006)

1680

✓
✓
✓

0.324
(0.344)−0.062(0.044)−0.140∗∗(0.058)−0.131∗∗∗(0.036)0.018∗∗∗(0.006)
0.242∗∗∗(0.062)
0.236∗∗∗(0.063)0.162∗∗(0.075)0.163∗(0.087)0.402∗∗∗(0.083)0.011(0.058)0.000(0.001)0.001(0.002)−0.000(0.001)
2.200(9.241)
2.687(7.962)−0.000(0.000)−3.060(9.255)(2)(0.386)0.146∗∗∗(0.045)−0.112∗∗(0.053)−0.190∗∗∗(0.038)0.021∗∗∗(0.006)0.217∗∗∗(0.074)0.147∗∗(0.073)0.014(0.091)−0.064(0.095)0.257∗∗∗(0.099)0.023(0.070)−0.000(0.001)
0.003(0.002)0.001(0.001)
2.164(11.345)6.687(9.642)−0.000(0.000)−3.335(11.381)(0.379)0.143∗∗∗(0.047)−0.103∗∗(0.052)−0.196∗∗∗(0.038)0.028∗∗∗(0.005)

1680

✓
✓
✓

1680

✓
✓
✓

0.871∗∗

0.797∗∗−0.000(0.001)
0.003(0.002)0.001(0.001)−0.676(11.510)2.575(9.848)−0.000(0.000)−0.508(11.569)(4)(3)

Japanese

1514

✓
✓
✓

−0.000(0.001)0.007∗∗∗(0.002)−0.001(0.001)16.659(12.338)
13.665(10.810)0.000(0.000)−18.855(12.547)

0.560(0.401)0.267∗∗∗(0.050)−0.066(0.059)0.020(0.044)0.021∗∗∗(0.006)

(5)

(6)

1514

✓
✓
✓

0.608
(0.404)0.273∗∗∗(0.049)−0.067(0.058)0.024(0.044)0.018∗∗∗(0.006)0.045(0.081)0.001(0.076)0.188∗∗(0.091)
0.099(0.101)0.380∗∗∗(0.108)−0.087(0.070)−0.000(0.001)0.007∗∗∗(0.002)−0.001(0.001)18.926(11.801)
17.234(10.532)0.000(0.000)−21.336∗(12.003)

SDQ

1341

✓
✓
✓

−0.001(0.001)0.000(0.002)−0.002∗(0.001)−13.849(10.341)−6.302(9.955)−0.000(0.000)13.001(10.559)

0.699(0.490)0.062(0.055)−0.057(0.071)−0.117∗∗(0.046)0.004(0.006)

(7)

(8)

1341

✓
✓
✓

0.747
(0.514)0.060(0.055)−0.052(0.070)−0.117∗∗(0.048)0.000(0.006)−0.059(0.087)0.006(0.083)−0.014(0.083)−0.005(0.098)0.250∗∗(0.105)0.000(0.091)−0.001(0.001)0.001(0.002)−0.002∗(0.001)−12.105(10.487)−3.648(10.193)−0.000(0.000)11.245(10.709)

QOL

Source: Authors’ calculations using data from JCPS, KHPS/JHPS, and Statistics Bureau, Ministry of Internal Affairs and Communications (2020). Note: This
table presents results from 2SLS regressions of the cognitive and socioemotional ability measures on a dummy for attendance at an education-oriented against acare-oriented preschool and a set of control variables described in Section 3.2, while instrumenting the preschool decision with our measure of the relative preschool
availability. Columns (1), (3), (5), and (7) exclude household characteristics that are most likely to capture ability of the children. Robust standard errors clusteredat the municipality level are given in parentheses. ∗ , ∗∗ , and ∗∗∗ denote significance at the 10%-, 5%-, and 1%-level, respectively.

Observations

Prefecture fixed effects
Cohort fixed effects
Prefecture-specific linear time trends

Constant

Per capita income in 1,000 Yen

Unemployment rate

Female empl. rate

Inhabitants per ha# children per nursery school# children per kindergarten= 1 if grandfather has college= 1 if HH in 4th income quartile= 1 if HH in 3rd income quartile= 1 if HH in 2nd income quartile= 1 if father has college= 1 if mother has college

Mother age at birth# siblings= 1 if born in first quarter= 1 if female

Educational vs. care orientation

(1)

Math

Table H7
2SLS Estimates with and without Ability Proxies

62
1680

−0.187(0.120)0.241(0.176)

1680

0.328
(0.216)−0.065(0.044)−0.146∗∗∗(0.056)−0.123∗∗∗(0.036)0.017∗∗∗(0.006)0.241∗∗∗(0.056)
0.219∗∗∗(0.059)0.156∗∗(0.076)0.150∗(0.082)0.388∗∗∗(0.085)0.016(0.053)0.000(0.000)0.000(0.001)−0.000(0.001)−1.808(6.341)−4.424(5.152)−0.000∗∗(0.000)1.596(6.366)

(2)

1680

✓
✓
✓

0.324
(0.344)−0.062(0.044)−0.140∗∗(0.058)−0.131∗∗∗(0.036)0.018∗∗∗(0.006)
0.242∗∗∗(0.062)
0.236∗∗∗(0.063)0.162∗∗(0.075)0.163∗(0.087)0.402∗∗∗(0.083)0.011(0.058)0.000(0.001)0.001(0.002)−0.000(0.001)
2.200(9.241)
2.687(7.962)−0.000(0.000)−3.060(9.255)(3)1573−0.156(0.120)0.205(0.175)

(4)

1573

0.279
(0.211)−0.094∗∗(0.045)−0.152∗∗∗(0.057)−0.128∗∗∗(0.037)0.015∗∗(0.006)
0.234∗∗∗(0.057)
0.236∗∗∗(0.059)0.141∗(0.079)0.126(0.084)0.348∗∗∗(0.086)0.014(0.054)−0.000(0.000)0.001(0.001)−0.000(0.001)−3.972(6.767)−5.981(5.386)−0.000∗∗(0.000)
3.868(6.781)

(5)

1573

✓
✓
✓

0.297
(0.330)−0.095∗∗(0.046)−0.145∗∗(0.058)−0.136∗∗∗(0.036)0.014∗∗(0.006)0.230∗∗∗(0.062)0.250∗∗∗(0.061)0.170∗∗(0.079)0.155∗(0.091)0.383∗∗∗(0.083)−0.003(0.059)−0.000(0.001)
0.002(0.002)−0.000(0.001)−1.057(9.444)−0.162(8.100)−0.000(0.000)0.353(9.461)(6)

Stricter treatment definition

1680−0.145(0.144)0.178(0.216)

(7)

1680

0.308
(0.222)−0.066(0.044)−0.147∗∗∗(0.056)−0.122∗∗∗(0.035)0.017∗∗∗(0.006)0.241∗∗∗(0.056)0.221∗∗∗(0.059)0.159∗∗(0.077)0.153∗(0.084)0.388∗∗∗(0.085)0.016(0.053)0.000(0.000)0.000(0.001)−0.000(0.001)−2.072(6.345)−4.611(5.159)−0.000∗∗(0.000)1.864(6.368)

(8)

1680

✓
✓
✓

0.411
(0.403)−0.060(0.044)−0.138∗∗(0.058)−0.135∗∗∗(0.037)0.019∗∗∗(0.006)
0.239∗∗∗(0.063)0.228∗∗∗(0.067)0.153∗(0.079)0.152∗(0.091)0.403∗∗∗(0.083)0.009(0.058)−0.000(0.001)0.001(0.002)−0.000(0.001)
2.833(9.359)3.624(8.177)−0.000(0.000)−3.740(9.389)(9)Facilities as instrument

Source: Authors’ calculations using data from JCPS, KHPS/JHPS, and Statistics Bureau, Ministry of Internal Affairs and Communications (2020). Note: This table presents
results from 2SLS regressions of the math score on a dummy for attendance at an education-oriented against a care-oriented preschool and a set of control variables describedin Section 3.2, while instrumenting the preschool decision with our measure of the relative preschool availability. Columns (1) to (3) correspond to our main specification.
Columns (4) to (6) use a stricter treatment definition to assign children as having attended a kindergarten or a nursery school (i.e., children who attended both preschools fora short period of time are excluded). Columns (7) to (9) use the relative availability of preschool facilities as instrument in the first stage. Robust standard errors clustered at
the municipality level are given in parentheses. ∗ , ∗∗ , and ∗∗∗ denote significance at the 10%-, 5%-, and 1%-level, respectively.

Observations

Prefecture fixed effects
Cohort fixed effects
Prefecture-specific linear time trends

Constant

Per capita income in 1,000 Yen

Unemployment rate

Female empl. rate

Inhabitants per ha# children per nursery school# children per kindergarten= 1 if grandfather has college= 1 if HH in 4th income quartile= 1 if HH in 3rd income quartile= 1 if HH in 2nd income quartile= 1 if father has college= 1 if mother has college

Mother age at birth# siblings= 1 if born in first quarter= 1 if female

Educational vs. care orientation(1)

Main specification

Table H8
2SLS Estimates for the Math Score

63
1680

−0.146(0.128)0.161(0.183)

1680

0.328
(0.233)0.127∗∗∗(0.041)−0.153∗∗∗(0.050)−0.169∗∗∗(0.037)0.020∗∗∗(0.005)0.217∗∗∗(0.066)0.177∗∗∗(0.061)
0.049(0.081)−0.019(0.085)0.254∗∗∗(0.091)0.010(0.063)−0.001(0.001)
0.002(0.001)0.001(0.001)−5.775(7.831)−7.148(6.190)−0.000∗∗(0.000)5.585(7.850)

(2)

1680

✓
✓
✓
1573

−0.102(0.124)0.100(0.177)0.871∗∗(0.386)0.146∗∗∗(0.045)−0.112∗∗(0.053)−0.190∗∗∗(0.038)0.021∗∗∗(0.006)0.217∗∗∗(0.074)0.147∗∗(0.073)0.014(0.091)−0.064(0.095)0.257∗∗∗(0.099)0.023(0.070)−0.000(0.001)
0.003(0.002)0.001(0.001)
2.164(11.345)6.687(9.642)−0.000(0.000)−3.335(11.381)

(4)

(3)

1573

0.264
(0.228)0.105∗∗(0.042)−0.147∗∗∗(0.053)−0.171∗∗∗(0.039)0.020∗∗∗(0.006)
0.239∗∗∗(0.067)0.189∗∗∗(0.060)0.040(0.084)−0.014(0.090)0.222∗∗(0.093)0.013(0.065)−0.001(0.001)
0.002(0.002)0.001(0.001)−6.210(8.146)−7.744(6.454)−0.000∗∗(0.000)6.108(8.162)

(5)

1573

✓
✓
✓

(0.371)0.124∗∗∗(0.045)−0.115∗∗(0.055)−0.197∗∗∗(0.039)0.021∗∗∗(0.006)0.243∗∗∗(0.076)0.173∗∗(0.072)0.016(0.093)−0.064(0.098)0.219∗∗(0.098)0.019(0.074)−0.001(0.001)
0.003(0.002)0.001(0.001)
1.683(11.773)
4.761(9.761)−0.000(0.000)−2.532(11.802)0.801∗∗(6)

Stricter treatment definition

1680−0.188(0.154)0.223(0.225)

(7)

1680

0.298
(0.242)0.126∗∗∗(0.041)−0.153∗∗∗(0.050)−0.168∗∗∗(0.037)0.020∗∗∗(0.005)0.217∗∗∗(0.066)0.181∗∗∗(0.061)
0.053(0.082)−0.015(0.087)0.254∗∗∗(0.091)
0.011(0.063)−0.001(0.001)0.001(0.001)0.001(0.001)−6.177(7.794)−7.432(6.165)−0.000∗∗(0.000)5.994(7.811)

(8)

1680

✓
✓
✓

0.966∗∗(0.454)0.148∗∗∗(0.047)−0.110∗∗(0.054)−0.194∗∗∗(0.041)
0.021∗∗∗(0.006)0.214∗∗∗(0.076)0.137∗(0.077)0.004(0.095)−0.076(0.100)0.258∗∗(0.101)
0.021(0.072)−0.000(0.001)
0.003(0.002)0.001(0.001)
2.854(11.806)
7.705(10.340)−0.000(0.000)−4.076(11.870)(9)

Facilities as instrument

Source: Authors’ calculations using data from JCPS, KHPS/JHPS, and Statistics Bureau, Ministry of Internal Affairs and Communications (2020). Note: This table presents
results from 2SLS regressions of the Japanese score on a dummy for attendance at an education-oriented against a care-oriented preschool and a set of control variables describedin Section 3.2, while instrumenting the preschool decision with our measure of the relative preschool availability. Columns (1) to (3) correspond to our main specification.
Columns (4) to (6) use a stricter treatment definition to assign children as having attended an education-oriented or a care-oriented preschool (i.e., children who attended bothpreschools for a short period of time are excluded). Columns (7) to (9) use the relative availability of preschool facilities as instrument in the first stage. Robust standard
errors clustered at the municipality level are given in parentheses. ∗ , ∗∗ , and ∗∗∗ denote significance at the 10%-, 5%-, and 1%-level, respectively.

Observations

Prefecture fixed effects
Cohort fixed effects
Prefecture-specific linear time trends

Constant

Per capita income in 1,000 Yen

Unemployment rate

Female empl. rate

Inhabitants per ha# children per nursery school# children per kindergarten= 1 if grandfather has college= 1 if HH in 4th income quartile= 1 if HH in 3rd income quartile= 1 if HH in 2nd income quartile= 1 if father has college= 1 if mother has college

Mother age at birth# siblings= 1 if born in first quarter= 1 if female

Educational vs. care orientation(1)

Main specification

Table H92SLS Estimates for the Japanese Score

64
1514

−0.203(0.134)0.235(0.200)

1514

1514

✓
✓
✓

0.608
(0.404)0.273∗∗∗(0.049)−0.067(0.058)0.024(0.044)0.018∗∗∗(0.006)0.045(0.081)0.001(0.076)0.188∗∗(0.091)
0.099(0.101)0.380∗∗∗(0.108)−0.087(0.070)−0.000(0.001)0.007∗∗∗(0.002)−0.001(0.001)18.926(11.801)
17.234(10.532)0.000(0.000)−21.336∗(12.003)

0.433∗(0.255)0.246∗∗∗(0.048)−0.079(0.057)0.018(0.042)0.017∗∗∗(0.006)0.044(0.076)0.013(0.072)0.120(0.083)0.062(0.089)0.282∗∗∗(0.099)−0.061(0.070)−0.001∗(0.001)0.005∗∗∗(0.001)−0.001(0.001)
4.346(8.318)0.830(6.671)0.000(0.000)−5.631(8.398)

(3)

(2)

1414

−0.216∗(0.131)

0.272(0.195)(4)1414(0.249)0.220∗∗∗(0.048)−0.114∗(0.059)−0.006(0.042)0.017∗∗∗(0.006)0.070(0.074)0.002(0.071)0.101(0.087)0.068(0.095)0.267∗∗∗(0.099)−0.058(0.072)−0.001∗(0.001)0.005∗∗∗(0.001)−0.001(0.001)
4.137(8.634)−1.445(6.681)0.000(0.000)−5.221(8.678)0.457∗

(5)

1414

✓
✓
✓

0.603
(0.379)0.234∗∗∗(0.050)−0.108∗(0.059)−0.002(0.045)0.017∗∗∗(0.007)0.072(0.080)−0.011(0.077)0.163∗(0.097)0.089(0.107)0.344∗∗∗(0.109)−0.100(0.075)−0.000(0.001)0.008∗∗∗(0.002)−0.001(0.001)20.733(12.611)17.515(10.790)0.000(0.000)−22.971∗(12.797)(6)

Stricter treatment definition

1514−0.424∗∗∗(0.164)(0.240)0.562∗∗(7)1514(0.271)0.247∗∗∗(0.048)−0.078(0.057)0.015(0.043)0.017∗∗∗(0.006)0.044(0.076)0.008(0.073)0.115(0.083)0.056(0.090)0.283∗∗∗(0.100)−0.062(0.070)−0.001∗(0.001)0.005∗∗∗(0.001)−0.001(0.001)
5.116(8.458)1.367(6.762)0.000(0.000)−6.415(8.542)

0.481∗

(8)

1514

✓
✓
✓

0.779
(0.489)0.277∗∗∗(0.050)−0.062(0.060)0.016(0.047)0.018∗∗∗(0.007)0.038(0.084)−0.015(0.081)0.172∗(0.095)0.078(0.106)
0.383∗∗∗(0.112)−0.091(0.073)−0.000(0.001)0.008∗∗∗(0.002)−0.001(0.001)20.778∗(12.546)
19.428∗(11.453)
0.000(0.000)−23.352∗(12.826)(9)

Facilities as instrument

Source: Authors’ calculations using data from JCPS, KHPS/JHPS, and Statistics Bureau, Ministry of Internal Affairs and Communications (2020). Note: This table
presents results from 2SLS regressions of the SDQ score on a dummy for attendance at an education-oriented against a care-oriented preschool and a set of control variables
described in Section 3.2, while instrumenting the preschool decision with our measure of the relative preschool availability. Columns (1) to (3) correspond to our main
specification. Columns (4) to (6) use a stricter treatment definition to assign children as having attended an education-oriented or a care-oriented preschool (i.e., children
who attended both preschools for a short period of time are excluded). Columns (7) to (9) use the relative availability of preschool facilities as instrument in the firststage. Robust standard errors clustered at the municipality level are given in parentheses. ∗ , ∗∗ , and ∗∗∗ denote significance at the 10%-, 5%-, and 1%-level, respectively.

Observations

Prefecture fixed effects
Cohort fixed effects
Prefecture-specific linear time trends

Constant

Per capita income in 1,000 Yen

Unemployment rate

Female empl. rate

Inhabitants per ha# children per nursery school# children per kindergarten= 1 if grandfather has college= 1 if HH in 4th income quartile= 1 if HH in 3rd income quartile= 1 if HH in 2nd income quartile= 1 if father has college= 1 if mother has college

Mother age at birth# siblings= 1 if born in first quarter= 1 if female

Educational vs. care orientation(1)

Main specification

Table H10
2SLS Estimates for the SDQ Score

65
1341

−0.237(0.145)0.308(0.204)

1341

1341

✓
✓
✓

0.747
(0.514)0.060(0.055)−0.052(0.070)−0.117∗∗(0.048)0.000(0.006)−0.059(0.087)0.006(0.083)−0.014(0.083)−0.005(0.098)0.250∗∗(0.105)0.000(0.091)−0.001(0.001)0.001(0.002)−0.002∗(0.001)−12.105(10.487)−3.648(10.193)−0.000(0.000)11.245(10.709)

0.505∗(0.276)0.038(0.053)−0.094(0.066)−0.122∗∗∗(0.040)0.004(0.006)0.005(0.074)0.011(0.071)0.018(0.083)−0.004(0.094)0.211∗∗(0.098)−0.026(0.085)−0.000(0.001)
0.002(0.001)−0.000(0.001)
0.926(7.581)
1.858(6.329)−0.000(0.000)−1.198(7.632)

(3)

(2)

1256

−0.232∗(0.139)0.298(0.192)

(4)

1256

0.397
(0.255)0.012(0.054)−0.098(0.066)−0.124∗∗∗(0.040)0.003(0.006)0.028(0.078)0.009(0.071)
0.015(0.085)0.010(0.097)0.215∗∗(0.098)−0.034(0.088)−0.000(0.001)
0.002(0.001)−0.000(0.001)−0.306(7.830)
1.382(6.486)−0.000(0.000)0.024(7.872)

(5)

1256

✓
✓
✓

0.738
(0.468)0.040(0.056)−0.049(0.070)−0.122∗∗(0.048)−0.001(0.007)−0.052(0.088)0.000(0.082)−0.013(0.085)0.008(0.098)0.273∗∗∗(0.105)−0.022(0.095)−0.001(0.001)
0.002(0.002)−0.002∗(0.001)−11.534(10.929)−1.682(10.230)0.000(0.000)10.497(11.123)(6)

Stricter treatment definition1341−0.322∗(0.180)(0.255)0.432∗(7)1341(0.297)0.037(0.053)−0.095(0.066)−0.121∗∗∗(0.040)0.004(0.006)0.006(0.074)0.012(0.072)0.019(0.082)−0.002(0.095)0.211∗∗(0.098)−0.025(0.086)−0.000(0.001)
0.002(0.001)−0.000(0.001)
0.765(7.645)1.752(6.352)−0.000(0.000)−1.035(7.704)0.494∗

(8)

1341

✓
✓
✓

0.790
(0.613)0.061(0.056)−0.050(0.072)−0.119∗∗(0.051)0.000(0.006)−0.062(0.091)
0.002(0.088)−0.017(0.086)−0.010(0.107)0.252∗∗(0.106)−0.001(0.092)−0.001(0.001)0.001(0.002)−0.002∗(0.001)−11.703(10.961)−3.154(10.917)−0.000(0.000)10.801(11.272)(9)

Facilities as instrument

Source: Authors’ calculations using data from JCPS, KHPS/JHPS, and Statistics Bureau, Ministry of Internal Affairs and Communications (2020). Note: This table presents
results from 2SLS regressions of the QOL score on a dummy for attendance at an education-oriented against a care-oriented preschool and a set of control variables describedin Section 3.2, while instrumenting the preschool decision with our measure of the relative preschool availability. Columns (1) to (3) correspond to our main specification.
Columns (4) to (6) use a stricter treatment definition to assign children as having attended an education-oriented or a care-oriented preschool (i.e., children who attended bothpreschools for a short period of time are excluded). Columns (7) to (9) use the relative availability of preschool facilities as instrument in the first stage. Robust standard
errors clustered at the municipality level are given in parentheses. ∗ , ∗∗ , and ∗∗∗ denote significance at the 10%-, 5%-, and 1%-level, respectively.

Observations

Prefecture fixed effects
Cohort fixed effects
Prefecture-specific linear time trends

Constant

Per capita income in 1,000 Yen

Unemployment rate

Female empl. rate

Inhabitants per ha# children per nursery school# children per kindergarten= 1 if grandfather has college= 1 if HH in 4th income quartile= 1 if HH in 3rd income quartile= 1 if HH in 2nd income quartile= 1 if father has college= 1 if mother has college

Mother age at birth# siblings= 1 if born in first quarter= 1 if female

Educational vs. care orientation(1)

Main specification

Table H11
2SLS Estimates for the QOL Score

I

Permutation Test of Inference – Reduced-Form Es-

4
3
Density
2
1
0

0

1

Density2

3

4

timates

-.3

-.15
0
Estimated coefficient.15

Actual

.3

-.3

0
.15
Estimated coefficient.3

Actual3
Density
2
1
0

0

1

Density234(b) Japanese

4(a) Math

-.15

-.3

-.15
0
.15
Estimated coefficient.3

Actual-.3(c) SDQ

-.15

0
.15
Estimated coefficient.3(d) QOL

Figure I5
Placebo Distributions of Reduced-Form Estimates
Source: Authors’ calculations using data from JCPS, KHPS/JHPS, and Statistics Bureau, Ministry of Internal Affairs and Communications (2020). Note: These graphs
show the distributions of placebo reduced-form regressions and the actual reduced-form
estimates as solid vertical line. First, birth years are randomly assigned to children.
Second, municipalities are randomly assigned to children living in the same municipality (without replacement). Given this random assignment, we run the reduced form of
the outcome variables on the relative preschool availability and a set of control variables described in Section 3.2. This procedure is repeated 3000 times. This approachis based on Bertrand et al. (2004). Dashed lines indicate 95% confidence intervals.

66

ActualJ

Effect on the Subscales of the SDQ and QOL scores
Table J12
Effect on Subscales of the SDQ Score (2SLS)Socioemotional outcomes – SDQ
Conduct
problems(1)

Emotional
symptoms(2)

Hyperactivity

0.158(0.388)Control variables
Prefecture fixed effects
Cohort fixed effects
Prefecture-pecific linear time trends
Observations

Educational vs. care orientation

Prosociality

(3)

Peer
relationships
(4)

0.425
(0.369)−0.003(0.375)1.084∗∗(0.440)−0.114(0.478)

✓
✓
✓
✓

✓
✓
✓
✓

✓
✓
✓
✓

✓
✓
✓
✓

✓
✓
✓
✓

1514

1514

1514

1514

1514

(5)

Source: Authors’ calculations using data from JCPS, KHPS/JHPS, and Statistics Bureau, Ministry of Internal
Affairs and Communications (2020). Note: This table presents results from 2SLS regressions of the subscalesof the SDQ score and prosociality on a dummy for attendance at an education-oriented against a care-oriented
preschool and a set of control variables described in Section 3.2, while instrumenting the preschool decision
with our measure of the relative preschool availability. All subscales are rescaled such that higher values imply
a better socioemotional development. Robust standard errors clustered at the municipality level are given inparentheses. ∗ , ∗∗ , and ∗∗∗ denote significance at the 10%-, 5%-, and 1%-level, respectively.

67

Table J13
Effect on Subscales of the QOL Score (2SLS)Socioemotional outcomes – QOL
Physicalhealth(1)

Emotional
well-being(2)

Selfesteem(3)

FamilyFriends

School

(4)

(5)

(6)

−0.324(0.380)−0.157(0.425)0.371(0.399)0.231(0.384)0.417(0.353)0.205(0.391)

Control variables
Prefecture fixed effects
Cohort fixed effects
Prefecture-pecific linear time trends

✓
✓
✓
✓

✓
✓
✓
✓

✓
✓
✓
✓

✓
✓
✓
✓

✓
✓
✓
✓

✓
✓
✓
✓

Observations

1289

1289

1289

1289

1289

1289

Educational vs. care orientation

Source: Authors’ calculations using data from JCPS, KHPS/JHPS, and Statistics Bureau, Ministry ofInternal Affairs and Communications (2020). Note: This table presents results from 2SLS regressionsof the subscales of the QOL score on a dummy for attendance at an education-oriented against acare-oriented preschool and a set of control variables described in Section 3.2, while instrumenting
the preschool decision with our measure of the relative preschool availability. Robust standard errors
clustered at the municipality level are given in parentheses. ∗ , ∗∗ , and ∗∗∗ denote significance at the
10%-, 5%-, and 1%-level, respectively.

68

Robustness Check – MTE

Treatment Effect
1
2

-2

-1

0

Treatment Effect
0
2

3

4

4

K

0

.2

.4

UD

.6

Parametric, K=2
Parametric, K=4

.8

1

0

.2

Parametric, K=3
Semiparametric

.4

UD

.6

Parametric, K=2
Parametric, K=4

1

Parametric, K=3
Semiparametric

0

-2

-1

.5

Treatment Effect01Treatment Effect
1
1.5

2

3

(b) Japanese2(a) Math

.8

0

.2

.4

UD

Parametric, K=2
Parametric, K=4

.6

.8

1

0

Parametric, K=3
Semiparametric

.2

.4

UD

.6

Parametric, K=2
Parametric, K=4(c) SDQ.8
Parametric, K=3
Semiparametric(d) QOL

Figure K6
MTE Curves for Different Functional Form Assumptions
Source: Authors’ calculations using data from JCPS, KHPS/JHPS, and Statistics Bureau, Ministry of Internal Affairs and Communications (2020). Note: These graphs
show MTE curves from parametric MTE specifications with K = 2, K = 3, and K = 4,
as well as from a semiparametric MTE specification. Covariates are held constant attheir means.

69

1

.03
.02
Weights

Treatment Effect
0
.5

0

.2

.4

UD

.6

MTE
IV (2SLS)ATT Weights

.8

.01
0

0

-2

-1

-1

.01

-.5

.02
Weights

Treatment Effect
0
1

.03

2

1

.04

.04

1.5

MTE Weights
3

L

1

0

.2

IV (MTE)IV Weights
TUT Weights

.4

UD

.6

MTE
IV (2SLS)ATT Weights

1

IV (MTE)IV Weights
TUT Weights

.04
0

.2

.4

UD

.6

MTE
IV (2SLS)ATT Weights

.8

.03
.02
Weights
.01

-.5

1

0

-1

0

.6

.8

.01

.02
Weights

Treatment Effect
0
.5

.03

Treatment Effect
1
1.2
1.4

1

1.6

.04

(b) Japanese
1.5(a) Math.80

IV (MTE)IV Weights
TUT Weights

.2

.4

UD

.6

MTE
IV (2SLS)ATT Weights(c) SDQ.8
IV (MTE)IV Weights
TUT Weights(d) QOL

Figure L7
MTE Weights
Source: Authors’ calculations using data from JCPS, KHPS/JHPS, and Statistics Bureau, Ministry of Internal Affairs and Communications (2020). Note: These graphs
show the weights put on the propensity score to calculate the 2SLS results in Table 3
and the ATT and ATU in Table 4, together with 2SLS estimates as horizontal lines.
MTE curves from a parametric MTE specification with K = 2 are also added.

More formally, the parameters are defined as
ATT(x) = E(Y | X = x, D = 1)
Z 1=
MTE(x, uD )ωAT T (x, uD )duD ,0

ATE(x) = E(Y | X = x)
Z 1=
MTE(x, uD )ωAT E (x, uD )duD ,0

TUT(x) = E(Y | X = x, D = 0)
70

1

Z 1
MTE(x, uD )ωT U T (x, uD )duD ,=0with the weights taken from Heckman et al. (2006) and Carneiro et al. (2011) asZ 1f (p | X = x)dp

ωAT T (x, uD ) =uD

1,
E(P | X = x)

ωAT E (x, uD ) = 1,
Z uDωT U T (x, uD ) =f (p | X = x)dp01.
E((1 − P ) | X = x)71

M

Effect of Preschool Types Over Time – Estimation
Results

72

73
846

Observations

943

11.43

✓
✓
✓
✓

913

14.18
846

15.14

✓
✓
✓
✓

−0.25
(0.51)

1.11∗∗(0.53)
✓
✓
✓
✓

LES
(4)

LSS
(3)

943

11.43

✓
✓
✓
✓

0.97
(0.64)

HES(5)

Japanese

913

14.18

✓
✓
✓
✓

1.06∗∗(0.44)

LSS
(6)

743

10.47

✓
✓
✓
✓

0.42
(0.48)

LES
(7)

847

11.51

✓
✓
✓
✓

1.09∗
(0.56)

HES
(8)

SDQ

812

9.65

✓
✓
✓
✓

0.90
(0.61)

LSS
(9)

LES
(10)

826

9.86

✓
✓
✓
✓

1.17∗
(0.63)

HES
(11)

QOL

807

8.93

✓
✓
✓
✓

0.66
(0.60)LSS(12)Source: Authors’ calculations using data from JCPS, KHPS/JHPS, and Statistics Bureau, Ministry of Internal Affairs and Communications (2020).
Note: This table presents results from 2SLS regressions of the cognitive and socioemotional ability measures on a dummy for attendance at an educationoriented against a care-oriented preschool and a set of control variables described in Section 3.2, while instrumenting the preschool decision with ourmeasure of the relative preschool availability for children in lower elementary school (LES), higher elementary school (HES), and lower secondary school(LSS), respectively. There are not enough observations for the QOL score at lower elementary school. F -tests of weak instruments in the first stage areconducted and the results are presented at the end of the table. Robust standard errors clustered at the municipality level are given in parentheses. ∗ ,∗∗, and ∗∗∗ denote significance at the 10%-, 5%-, and 1%-level, respectively.

15.14

✓
✓
✓
✓

0.23
(0.58)−0.17(0.42)

F -stat

Control variables
Prefecture fixed effects
Cohort fixed effects
Prefecture-specific linear time trends

Educational vs. care orientation

HES
(2)

LES
(1)

Math

Table M14
Effect of Preschool Types Over Time

N

Test for Selective Panel Attrition

74

75
342

Observations

300

4.081
0.130

✓
✓
✓
✓

394

0.350
0.840

✓

265

1.293
0.524

✓
✓
✓
✓

−0.025(0.027)0.020(0.024)

612

2.134
0.344

✓

−0.017(0.015)−0.006(0.016)

(5)

545

2.274
0.321

✓
✓
✓
✓

−0.023(0.015)0.009(0.018)(6)

One panel obs.

685

2.945
0.229

✓

−0.010(0.026)−0.035(0.024)

(7)

504

1.398
0.497

✓
✓
✓
✓

−0.027(0.024)0.001(0.027)(8)

Two panel obs.

Education-oriented preschool sample

Source: Authors’ calculations using data from JCPS, KHPS/JHPS, and Statistics Bureau, Ministry of Internal Affairs and Communications(2020). Note: This table presents average partial effects from Probit regressions of dummy variables indicating panel dropout after one
observation in Columns (1), (2), (5) and (6), or after two observations in Columns (3), (4), (7), and (8) on the math and Japanese score anda set of control variables described in Section 3.2. Regressions were run separately for the nursery school sample in Columns (1) to (4) andthe kindergarten sample in Columns (5) to (8). χ2 -tests for joint significance of the presented coefficients are conducted and the results arepresented at the end of the table. Robust standard errors clustered at the municipality level are given in parentheses. ∗ , ∗∗ , and ∗∗∗ denotesignificance at the 10%-, 5%-, and 1%-level, respectively.

1.071
0.585

✓

−0.016(0.027)0.004(0.025)−0.024(0.024)0.005(0.023)(0.023)−0.008(0.018)−0.041∗χ2 -stat of test of joint significancep-value of test of joint significanceControl variables
Prefecture fixed effects
Cohort fixed effects
Prefecture-specific linear time trends

Japanese score

Math score

(3)

(2)

(1)

(4)

Two panel obs.

One panel obs.

Care-oriented preschool sample

Table N15
Probit Regressions of Panel Dropout after One or Two Observations

O

Effect of Relative Preschool Enrollment on House-.05
Effect of relative preschool enrollment
-.05
0

.05
Effect of relative preschool enrollment
-.1
-.05
0
-4

-3

-2
-1
0
1
2
3
Time relative to year before birth

4

5

6

-5

-3

-2
-1
0
1
2
3
Time relative to year before birth

4

5

6

-5

(b) Full-time empl., father

-4

-3

-2
-1
0
1
2
3
Time relative to year before birth

4

5

6

5

6

(c) Household income

Effect of relative preschool enrollment
0.05

Effect of relative preschool enrollment
0
.5
-.5

-.05

0

Effect of relative preschool enrollment
.5
1

1.5

1

(a) Full-time empl., mother

-4

.1

-5

-.1

-.15

-.15

Effect of relative preschool enrollment
-.1
-.05
0

.05

hold Characteristics

-5

-4

-3

-2
-1
0
1
2
3
Time relative to year before birth45(d) Child care time at home

6

-5

-4

-3

-2
-1
0
1
2
3
Time relative to year before birth45(e) Education expenditures

6

-5

-4

-3

-2
-1
0
1
2
3
Time relative to year before birth

4(f) Total expenditures

Figure O8
Effect of Relative Preschool Enrollment on Household Characteristics
Source: Authors’ calculations using data from KHPS/JHPS and Statistics Bureau,
Ministry of Internal Affairs and Communications (2020). Note: These graphs show
the effect of relative preschool enrollment
based on the
P on household characteristics′
difference-in-differences model Yit = α+ p βp Zcp ×1(m = p)+Xitξ +γc +δt +ϕk +ϵit ,where Yit is (a) a dummy indicating whether the mother is full-time employed (N =5336), (b) a dummy indicating whether the father is full-time employed (N = 5336),(c) the log household income (N = 4492), (d) the weekly hours spent doing child care
at home (N = 4864), (e) log education expenditures (N = 5336), and (f) log total
expenditures (N = 5324). Xit corresponds to household characteristics (i.e., mother’s
age at birth of the child, dummies for each parent having graduated from a college, anda dummy for the grandfather’s college education), γc are municipality fixed effects, δt
are year fixed effects, ϕk are fixed effects for the year of child birth, and ϵit is an error
term. βp is the year-specific effect of the relative preschool enrollment on Yit relative
to the year before child birth (i.e., β0 = 0) presented in the graphs together with 95%
confidence intervals based on robust standard errors clustered at the municipality level.
Effects correspond to responses in Y to a 10 percentage points increase in the relative
preschool enrollment (i.e., more children in education-oriented preschools) in the year
relative to a child’s birth. The sample is restricted to households with children, andthe effects are for the first-born child.

76