How Does Early Childcare Enrollment AffectChildren, Parents, and Their Interactions?

ISS Discussion Paper Series F-184
University of Tokyo
Shintaro Yamaguchi, Yukiko Asai and Ryo KambayashiHow Does Early Childcare Enrollment AffectChildren, Parents, and Their Interactions?*
Shintaro Yamaguchi†

Yukiko Asai‡

Ryo Kambayashi§

March 14, 2017

Abstract
We estimate the effects of childcare enrollment on child outcomes by exploiting a staggered childcare expansion across regions in Japan. We findthat childcare improves language development among boys and reduces aggression and the symptoms of ADHD among the children of low-education
mothers. Estimates show that the improved child behavior is strongly associated with better parenting quality and maternal wellbeing. Evidence alsosuggests that promoting positive parenting practices is an important elementof an effective childcare program. Our estimates for marginal treatment effects indicate that children who would benefit most from childcare are lesslikely to attend, implying inefficient allocation.

* The authors would like to thank Michihito Ando and seminar participants at the University ofCalifornia, Berkeley, the University of Toronto, and the University of British Columbia for theirhelpful comments. All remaining errors are our own. Yamaguchi gratefully acknowledges financial
support from the Murata Science Foundation. Asai appreciates a Grant-in-Aid for Young Scientists(B) 15K17071 and Kambayashi is supported by a Japan Society for the Promotion of Science KAKENHI Grant No. 16H02020. The data in the Longitudinal Survey of Newborns in the 21st Century
were generously provided by the Japanese Ministry of Health, Labour and Welfare.
† Department of Economics, McMaster University, 1280 Main St. W., Hamilton, ON. Canada
L8S 4M4. Email: yamtaro@mcmaster.ca.
‡ Institute of Social Science, the University of Tokyo. Hongo 7-3-1, Bunkyo, Tokyo, Japan.
Email: y_asai@iss.u-tokyo.ac.jp.
§ Institute of Economic Research, Hitotsubashi University. Naka 2-1, Kunitachi, Tokyo, Japan.
Email: kambayas@ier.hit-u.ac.jp.

11Introduction

Policymakers and experts consider early childhood education to be one of the mostpromising social programs. In 2002, the Barcelona European Council set objectives
to improve the availability of high-quality and affordable childcare in the European
Union. Similarly, in his 2013 State of the Union address, US President Obamacalled on Congress to extend access to high-quality preschool to all children.
Advocates of early childhood education often cite the success of model programs, such as the Perry Preschool Program in the US, targeted at economicallydisadvantaged children. Evidence from these programs shows that program participation improves not only the cognitive skills but also the socioemotional skillsof children. In fact, further examination reveals that the effects on cognitive skills
quickly fade, but those on socioemotional skills can persist over many years. Indeed, Heckman, Pinto, and Savelyev (2013) conclude that the program leads to lesscrime, lower receipt of welfare, and greater voter turnout through improving socioemotional skills. Overall, their findings suggest that the preschool program has
positive externalities and thereby benefits the whole of society, which helps justify
the policy intervention.
While the success of model programs is appealing, it is not immediately obvious whether the results are generalizable to a large-scale universal childcare program. For instance, childcare programs may not benefit children from middle- andhigh-income families because they generally have better home environments and/or
access to alternative high-quality childcare. In addition, maintaining the quality ofchildcare becomes challenging as the program scale increases.
To address this issue, an increasing number of studies estimate the effects of auniversal childcare program on cognitive test scores. However, only a few studies
consider the effects on socioemotional skills. Baker, Gruber, and Milligan (2008)
find that a universal childcare program in Quebec actually harms children’s socioemotional skills. In a subsequent study, Baker, Gruber, and Milligan (2015) showthat these negative effects on socioemotional skills persist and can lead to poorer
health, lower life satisfaction, and higher crime rates later in life. However, while
their analysis is compelling, such negative effects are not found in other countries.

2

For example, Datta Gupta and Simonsen (2010) evaluate the universal childcare
program in Denmark and identify no significant effects on noncognitive child outcomes. Likewise, Berlinski, Galiani, and Gertler (2009) and Felfe and Lalive (2015)
estimate the effects on children’s behavioral outcomes in Argentina and Germany,
respectively, and find childcare enrollment improves children’s behavior. Accordingly, the existing evidence concerning the effect of childcare on socioemotional
skills is mixed. Furthermore, it is not entirely clear exactly how childcare enrollment affects children’s socioemotional skills.
The objective of this paper is to estimate the effects of a large-scale nontargetedchildcare program on children’s cognitive and socioemotional skills, using Japanesedata from the Longitudinal Survey of Newborns in the 21st Century (LSN21). We
depart from previous work in this area by shedding light on the underlying mechanisms through which childcare influences children. Specifically, we examine how
childcare enrollment changes the quality of parenting, the amount of child-related
expenses outside of childcare, parents’ knowledge of good parenting practices, andparental wellbeing, all of which may eventually also affect the children.
Another key feature of this paper is that we estimate richer treatment effectheterogeneity than most previous studies by adopting the marginal treatment effect(MTE) framework (see Björklund and Moffitt (1987) and Heckman and Vytlacil(2005)). The MTE is a treatment effect varying by the unobserved propensity forchildcare use, and this framework enables us to identify who would benefit mostfrom childcare and how likely they are to use it. Through counterfactual simulation,
we assess how children with different treatment effects are expected to be graduallyinduced into treatment as childcare reform progresses.
In the early 2000s, the national government initiated childcare reforms to increase female labor force participation and fertility rates. Although the national
government covers half of the program cost, local governments are responsible forthe rollout of the program. Depending on local governments’ financial conditions
and policy priorities, the pace of childcare expansion varies significantly. This has
led to differences between regions across Japan, a feature we exploit for identification of the causal effects.
We estimate the treatment effects of childcare enrollment on various outcomes3

for children and their parents using instrumental variable (IV) regression, specifying childcare slots per child in a given region as an instrument. Because wecontrol for year and region fixed effects in the estimation, the identifying variationis the regional variation in the rate of childcare expansion, which is similar to thedifference-in-differences approach. The estimates show that childcare enrollment
has significant positive effects on language development. These treatment effects
are stronger for boys, particularly low-birthweight boys. Because the language development of boys is slower than that of girls if not enrolled, childcare enrollment
addresses the gap between them.
The estimates consistently indicate that childcare enrollment reduces aggression and the symptoms of attention deficit hyperactivity disorder (ADHD) amongchildren of low-education mothers relative to those of university-educated mothers. We also find childcare enrollment tends to improve the behavior of boys andlow-birthweight children, but the estimates are sometimes noisy. These children
demonstrate greater aggression and ADHD symptoms if they are not enrolled inchildcare, and hence, childcare enrollment reduces the gaps in behavioral outcomesamong children.
To shed light on the mechanisms through which childcare influences children,
we also examine how their home environment changes in response to childcare
enrollment. The estimates show that childcare enrollment improves monetary investment in children, parenting quality, and the wellbeing of low-education mothers. Further analysis suggests that a more positive home environment for children
is brought about by informing low-education mothers about better parenting practices, although we cannot exclude the possibility that better behavior by children
also improves their mothers’ parenting practices. Given the strong and consistentassociation between child behavior and the home environment over a wide array ofvariables, our analysis suggests that a childcare program should educate not only
children but also their parents, so as to promote their positive involvement.
Finally, our estimates for the MTE indicate that while there are children who
would particularly benefit from childcare enrollment, their mothers are less likely
to use childcare. It is interesting to consider the characteristics of these mothers.
Note that maternal employment is effectively a prerequisite for childcare access,4and local governments will give higher priority to fulltime working mothers. Hence,
the mothers who are less likely to use childcare tend to have weaker labor market
attachment and lower labor market skills. Our counterfactual simulations indicatethat while these mothers will eventually use childcare if its supply is sufficiently
large, their responses to childcare reform are slow. Therefore, our analysis suggeststhat increasing the supply of childcare may not be enough, in itself, to improvechildcare participation, and that other policy measures may also be necessary tobring the children of these mothers into formal childcare.
The remainder of the paper is structured as follows. Section 2 reviews the literature. Section 3 describes the institutional background and Section 4 examines ourhousehold data. Section 5 outlines the econometric methods employed, including
the MTE framework. Section 6 presents the estimation results for the IV regression
and Section 7 those for the MTE. Section 8 concludes.

2

Related LiteratureThis paper contributes to the literature on early childhood education. Studies inArgentina (Berlinski et al. (2009)), Germany (Cornelissen, Dustmann, Raute, and
Schönberg (2015) and Felfe and Lalive (2015)), Norway (Havnes and Mogstad(2011, 2015) and Drange and Havnes (2015)), (Felfe, Nollenberger, and RodríguezPlanas (2015)), and the US (Cascio and Schanzenbach (2013)) all point to gains
in test scores from participation in early childhood education, and some identify
stronger effects for disadvantaged children. Although the estimates are not directlycomparable across studies, the test score effects appear to diminish with age. For
example, in a US study, Cascio and Schanzenbach (2013) evaluate the effects of
pre-K programs in Georgia and Oklahoma on test scores up until the eighth gradeand find that any gains gradually decline.
Evidence for the effects on socioemotional skills is less extensive and the implications mixed. Baker et al. (2008) examine Quebec’s universal childcare programs
and find children are made worse in terms of aggression, motor and social skills,
and health. Kottelenberg and Lehrer (2013) confirm that this negative finding isrobust to the choice of statistical method and the cohort studied. Berlinski et al.
5(2009) estimate the effects of a universal program in Argentina and find positiveeffects on student self-control in the third grade, while Datta Gupta and Simonsen(2010) find that enrollment at 3 years of age in the universal childcare program in
Denmark does not affect noncognitive outcomes at 7 years, regardless of the child’s
gender and her or his mother’s education. Lastly, Felfe and Lalive (2015) examine
the effect of childcare enrollment in Germany before 3 years of age on development
at 6 years and conclude that enrollment improves the socioemotional developmentof children of less-educated mothers.
Evidence on the long-term effects of universal childcare programs is also scarceand mixed. Havnes and Mogstad (2011) find that childcare enrollment increases
educational attainment and labor market participation and reduces welfare dependency in Norway. Estimates from this study, and later Havnes and Mogstad (2015),
show that the Norwegian universal childcare program most benefits children fromdisadvantaged families, but does not have any positive impact on middle- and upperclass children. However, while their estimates are credible, they do not analyze howthese positive outcomes are associated with the cognitive and socioemotional skills
developed in formal childcare. Baker et al. (2015) consider the long-term consequences of the universal childcare program in Quebec, confirming their earlier
finding of a negative impact on children’s socioemotional skills. The results indicate that cohorts with increased childcare access subsequently have poorer health,lower life satisfaction, and higher crime rates later in life. Their analysis and evidence from targeted programs in the US (see Heckman et al. (2013)) show that it is
the socioemotional skills that play a central role in long-term success in life.
While a number of studies estimate the efficacy of childcare programs, only
a few examine the mechanisms through which childcare enrollment impacts uponchildren. Baker et al. (2008) find that the childcare program in Quebec leads tomore hostile and less consistent parenting and lower-quality parental relationships.
By contrast, Gelber and Isen (2013) analyze US data from the Head Start Impact
Study and find that Head Start causes a substantial increase in parental involvementwith their children. Herbst and Tekin (2010, 2014) analyze the effects of the Child
Care and Development Fund, although this is not a childcare program but rathera childcare subsidy in the US. They find that receipt of the subsidy leads to lower
6child test scores, poorer child behavior, worse maternal health, and lower-quality
interactions between parents and their children. Although the nature of the studied
programs varies, all of these studies consistently indicate a strong association between child behavior, parenting quality, and maternal wellbeing. However, while
the evidence suggests an important role for positive parenting practices, it is notentirely clear how childcare enrollment affects parenting quality.
Finally, most extant studies account for heterogeneity in treatment effects only
using the observed characteristics. Cornelissen et al. (2015) and Felfe and Lalive(2015) are exceptions and estimate the MTE that varies by the unobserved characteristics of German children. They find that while the average effects are insignificant, there are children who benefit from childcare enrollment. Cornelissen et al.
(2015) find that many of these children are from disadvantaged and/or immigrantfamilies and less likely to attend childcare. While our results echo these findings,
we go a step further to uncover the underlying mechanisms by examining parental
outcomes such as parenting quality.

3

Institutional Background

3.1

The Childcare System in Japan

Accredited Childcare Centers Some 94 percent of childcare centers in Japansatisfy the national quality standard set by the Child Welfare Act and are accredited by the governor of the province in which they are located1 . In Japan, accredited
childcare centers are subsidized by municipal, provincial, and national governments
so that the average user pays only about 40% of the total cost. The average monthly
fee per child is low at about 28,408 JPY (≈ 284 USD), although this depends onthe child’s age, region, household income, and number of siblings. Because thevast majority of childcare centers are nationally accredited, and our main data set,
LSN21, does not distinguish between accredited and nonaccredited centers, we refer to all nationally accredited childcare centers as childcare centers unless other1 The actual administrative term used by the government is prefecture, but we use province as it

is more intuitive for most readers.

7wise noted2 .
Quality The quality standard for childcare centers is established by the national
government and is uniform across the country. In terms of the qualifications required by caregivers and the child-to-caregiver ratio, Japan provides higher-quality
childcare than most other countries in the OECD. Licensed caregivers have typically completed 2 years of postsecondary education, which is higher than elsewherein the OECD (e.g. Germany, Norway, Sweden, and the Netherlands) where upper
secondary education is required. There are three children per caregiver for children
aged less than 1 year, and six for children aged between 1 and 2 years. These ratios
are lower than in many comparable European countries. For example, the child-tocaregiver ratio in early childhood education is 16.2 in the UK, 12.0 in Denmark, 9.4
in Spain, 9.3 in Austria, 5.0 in Germany, and 4.8 in Sweden.
Program Intensity Most childcare programs are fulltime. Only 10 percent ofenrolled children spend less than 7 hours per day in childcare, and most spend 7
to 10 hours. In addition, the vast majority attend childcare at least 5 days a week,with about 18 percent attending as much as 6 days a week. Only about 9 percent
of children attend between 1 and 4 days a week. Figure 4 in Appendix C provides
detailed statistics.
Eligibility While the childcare program in Japan is not targeted at children from
low-income households, neither is it quite universal. The Child Welfare Act imposes as an eligibility condition that parents and cohabiting adults must be unable
to provide care for the child because of their usual work during the day, disability,
sickness, pregnancy, participation in disaster restoration work, or other reasons approved by the local mayor. In practice, 94.2 percent of parents using a childcare
center satisfy the eligibility condition on the basis of their usual work during theday.
2 The remaining 6 percent are unaccredited childcare centers, which do not receive subsidies

from the national government. However, many unaccredited childcare centers satisfy the quality
standards set by local governments and receive subsidies accordingly.

8Rationing Rule In Japan’s major cities, notably Tokyo, the demand for subsidized childcare often exceeds supply. If this is the case, applications are ranked byneed as assessed by the municipal government. Single parents are given highest
priority and usually assigned a slot. Children from two-parent families are ranked
highest when both parents work fulltime. A lower rank is given when at least one
parent works less than fulltime. For example, the city of Yokohama assigns its highest rank (A) if both parents work at least 20 days per month and 40 hours per week,
but its lowest rank (F) if at least one parent works for 16 days per month and 16–28
hours per week. Note that household income does not affect the rank, although it
does change the fees to be paid.

3.2

Childcare Reform

The demand for subsidized childcare has exceeded supply in many regions becauseof the increase in the female labor supply since the early 1990s. Experts and policymakers believe that a lack of subsidized childcare increases the conflict between
work and family life, and hence is responsible for Japan’s low fertility rate.3 The
Basic Act for Measures to Cope with Society with Declining Birthrate was legislated in 2003, and the national government committed itself to taking legal andfinancial measures to increase the supply of childcare. As a result, between 2000
and 2010, the number of slots in childcare centers increased by about 12 percent,and the number of slots per child increased from 0.27 to 0.34. The rate of expansion
in the number of childcare slots was slower than in other countries,4 partly because
the national government did not compromise its strict quality standards.
Even though the national government provides legal and financial support toexpand the supply of childcare, the provincial and municipal governments are responsible for the rollout of the reform and need to match the funds made available
by the national government. Because financial conditions and the policy priority
placed on childcare vary between local governments, the pace of the rollout has dif3 In 1990 the fertility rate was 1.54, and this had declined to 1.36 by 2000.
4 For example, in Quebec, the total number of childcare slots more than doubled between 1997

and 2005. In Norway, the number of slots per child increased from 0.10 to 0.34 in the period
1975–1981.

9fered considerably across regions. We exploit this variation in the speed of childcare
expansion between regions to identify the causal effects. We extensively discuss ouridentification strategy in Section 5.3.

4

Data

4.1

Data Sources

Our main data source is the LSN21, which is a census of children born in the periods
January 10–17, 2001, July 10–17, 2001, and May 10–24, 2010. The first survey
was conducted when the children were 6 months old and subsequent questionnaires
were completed every year about 6 months after their birthdays. Surveys until thechildren were about 3½ years of age are currently available. The response rates
were high at 93.5 and 88.1 percent in the first survey years for those cohorts bornin 2001 and 2010, respectively. About 83 percent of respondents in the first survey
remained in the survey at age 3½ years. These response rates are higher than thosein the comparable National Longitudinal Survey of Children and Youth (NLSCY)
conducted in Canada5 .
We draw data on accredited childcare centers from the annual Report on Social
Welfare Administration and Services, which covers all provinces and major cities
where the population exceeds 200,000 persons. We define a region as either a major
city, or the set of all municipalities in a province except for the major cities. We
include 82 regions covered in the data in both 2002 and 2011, which consist of 45
provinces and 37 major cities. The provinces of Fukushima and Miyagi are omitted
owing to missing data because they were severely affected by the Great East Japan
Earthquake and the ensuing tsunami in 2011.
The child population is from the quinquennial census. For years when the census is not available, we estimate the child population using linear interpolation.
Other regional characteristics in 2000 are drawn from various sources. See the noteaccompanying Table 7 in Appendix A for details.
5 In the NLSCY in Canada, the response rate in the first cycle conducted in 1994/95 was 86.5

percent, and 67.8 percent of children in the original cohort responded in the third cycle conducted
in 1998/99.

10

4.2

Variable Definitions

4.2.1

Treatment Variable

We define treatment by childcare enrollment at age 2½ years because child development outcomes are only available for children aged 2½ and 3½ years. While we
do not control for treatment status at other ages, the treatment and control groups
exhibit very different childcare enrollment patterns over time. At 6 months of age,
few children are enrolled in childcare regardless of treatment status because many
Japanese mothers are entitled to job-protected leave until their child reaches 1 yearof age (see Asai (2015) and Yamaguchi (2016)). Many children begin attending
childcare from the age of 1½ years. At this age, about 68 percent of treated children are enrolled, but only about 2 percent of the untreated. At age 3½ years, about
88 percent of treated children continue to attend childcare, while only about 14
percent of the untreated are enrolled. These statistics indicate that enrollment is serially correlated and that the enrollment patterns over time are very different for thetreated and the untreated. The difference in enrollment patterns between the treatedand untreated would be smaller if treatment was defined by enrollment at ages otherthan 2½ years.
4.2.2

Child Outcomes

We construct measures according to child language development, aggression, andADHD symptoms. These measures are constructed from a set of questions that canbe answered with a simple yes or no. Parents or other adults such as grandparents
were eligible to respond, but in reality about 90% of the survey respondents weremothers.
We use the following three questions to measure the language development of
2½-year-old children: “Does your child say words such as ‘mom’?”, “Does yourchild put together two-word sentences?”, and “Does your child say his/her own
name?” These are commonly used by pediatricians to measure child development
and are included in the list of developmental milestones by the Centers for Disease
Control and Prevention in the US.
We measure ADHD symptoms for 3½-year-old children using five questions
11

comparable to those in the 5th edition of the Diagnostic and Statistical Manual(DSM), in the guidelines set by the American Psychiatric Association. The selected
questions are: “Does your child listen until the other person has finished speaking?”,“Does your child cut in line?”, “Does your child scream in public spaces (e.g. buses,
trains, and hospitals)?”, “Does your child have a short attention span?”, and “Is yourchild restless?” Similar measurements for ADHD are included in the NLSCY andanalyzed by Baker et al. (2008) and Currie, Stabile, and Jones (2014). Aggression
is another behavioral problem and is a part of disruptive behavior disorders, whichclosely resemble ADHD but are considered separate conditions by pediatricians.
We measure child aggression at age 3½ years using the following three questions:“Does your child break books and toys?”, “Is your child violent?”, and “Is yourchild short-tempered?”
We construct indices for child outcomes by standardizing the number of positive
responses in each category. These questions have some room for interpretation,and hence they are likely to be measured with error. We reduce the noise frommeasurement error by aggregating information from questions on the same theme.
If responses are consistent over a set of questions, we consider the indices to reflect
the child’s actual behavior. We also verify that the indices are not driven by the
response to a single question. As a result, all of our main results are relatively
robust to the removal of any one variable from the set of variables that measurechild development and behavior. Our main results are also unchanged if we use
principal component analysis to construct the indices.
We normalize child outcome measures so that the mean is zero and the standard
deviation is one. This normalization procedure is followed for outcomes for parents.
4.2.3

Outcomes for Parents

The index for parenting quality is constructed from responses to the question “Howdo you respond when your child behaves badly?” The five possible responses are:“Explain why your child should not do it”, “Just say ‘no’ without explanation”,“Ignore your child”, “Spank your child”, and “Confine your child in a place like acloset”. For each of these, the respondent is asked to choose between “Always”,“Sometimes”, and “Never”. These questions are asked when children are aged 3½
12

years.
We construct the parenting quality index by applying multiple correspondence
analysis. This is a dimension reduction technique similar to principal component
analysis and applicable to a set of ordered or categorical variables of the same substantive type. We summarize the main result and report the coordinates of eachpossible response in Table 9 in Appendix C. In the multiple correspondence analysis, the answers “Always explain”, “Never say just ‘no’ without explanation”, and“Never ignore the child” are regarded as indicators of high-quality parenting, while“Always confine the child in a place like a closet”, “Always spank the child”, and“Always ignore the child” are regarded as indicators of low-quality parenting.
Parental subjective wellbeing is measured by the question “What makes you
happy when you raise your child?” The following nine items are listed and the
respondents select all that apply: “Strengthened family ties”, “Interactions withchildren”, “Feeling that life is worthwhile”, “Children interacting with each other”,“Making more friends through raising the child”, “Learning from the child”, “Thechild making the whole family happier”, “Growth of the child”, and “Other”.
The survey also contains a few other variables relevant for determining thechild’s home environment. These ask whether parents know about good parenting practices, which is a self-reported binary response. The survey also requests the
respondent to provide the childcare and nonchildcare expenses for the child in thesurvey month. Any expenses for the siblings of the child in question are excluded.

4.3

Descriptive Statistics

Table 1 presents summary statistics for the uptake, family characteristics, and outcomes variables. The enrollment rate for 2½-year-old children in childcare is 0.314.
The mothers’ labor market participation rate is 0.372, higher than the enrollment
rate, suggesting that some mothers work using informal childcare arrangements.
The average age of mothers is 32.487 years, and fathers are about 2 years older.
About 5 percent of mothers and fathers have less than a high school education,
and about 35 percent graduated from high school without pursuing postsecondary
education. Postsecondary education levels differ substantially between mothers and13

Table 1: Summary Statistics

All

Uptake Variables
Childcare Enrollment
Market Work
Hours of Work Per Week
Mother’s Characteristics
Age
Less Than High School
High School
2-Yr College
4-Yr University or Higher
Father’s Characteristics
Age
Less Than High School
High School
2-Yr College
4-Yr University or Higher
Children’s CharacteristicsGirl
Low Birth WeightChild Outcomes
Language Development
Aggression
ADHD Symptoms
Parent’s Outcomes
Parenting Quality
Total Expenses
Childcare Expenses
Other Expenses
Lack of Parenting Knowledge
Subjective Well-Being

Nobs.

Mean

S.D.

Comparison by Treatment
p-value forTreated Untreated Difference

67913
67322
67322

0.314
0.374
12.5050.464
0.484
18.7051.000
0.873
31.317

0.000
0.146
3.899

0.000
0.000

67913
67913
67913
67913
67913

32.487
0.042
0.331
0.423
0.204

4.482
0.201
0.470
0.494
0.403

32.612
0.043
0.304
0.416
0.236

32.430
0.042
0.343
0.426
0.189

0.000
0.778
0.000
0.016
0.000

67913
67913
67913
67913
67913

34.482
0.069
0.350
0.169
0.412

5.485
0.254
0.477
0.375
0.492

34.508
0.084
0.365
0.183
0.369

34.470
0.062
0.343
0.163
0.432

0.404
0.000
0.000
0.000
0.000

67913
67913

0.483
0.088

0.500
0.283

0.472
0.087

0.488
0.088

0.000
0.812

67510
61304
59894

0.000
0.000
0.000

1.000
1.000
1.000

0.153
0.009
−0.035−0.070−0.004
0.016

0.000
0.152
0.000

62140
65999
67547
65740
67728
67873

0.000
2.602
0.904
1.677
0.089
0.000

1.000
2.853
1.676
2.198
0.284
1.000

0.010
4.431
2.708
1.714
0.084
0.027

−0.005
1.751
0.089
1.660
0.091
−0.012

0.091
0.000
0.000
0.003
0.002
0.000

Source: LSN21
Note: Children are in two-parent family. Child outcomes, parenting quality, and parent’s subjective
well-being are normalized so that the mean is zero and standard deviation is one. Child aggression,
ADHD symptoms, and parenting quality are measured when children are aged 3.5 year old. Other
variables are evaluated when children are 2.5 year old. Expenses in the survey month are measured
in 10,000 JPY (≈100 USD).

14

fathers. About 42 percent of mothers went to a 2-year college or equivalent, while
only about 20 percent graduated from a 4-year university education or higher. By
contrast, about 41 percent of fathers graduated from a 4-year university educationand only 17 percent attended a 2-year college or equivalent.
There are slightly more boys than girls in the sample, and about 9 percent of
the children had low birthweight as defined by the World Health Organization (lessthan 2,500 grams).
The total expenses for the surveyed children aged 2½ years are 26,020 JPY
per month. This comprises monthly childcare expenses of 9,040 JPY, and othermonthly expenses of 16,770 JPY. About 9 percent of mothers report that they do
not know about good parenting practices.
We compare the characteristics and outcomes of treated and untreated families.
The treatment in this study is enrollment at a childcare center at 2½ years of age.
Given the large sample size, most of the differences are statistically significant, even
if their magnitude is small.
Most treated mothers are in the labor market, as is expected, given the childcare eligibility rules. Treated mothers are better educated than untreated mothers,
but treated fathers are less educated than untreated fathers. Skilled mothers havestronger labor market attachment, but the wives of skilled men are less likely towork. We do not find large differences in children’s sex and birthweight by treatment status.
Treated children exhibit better language development and a lower frequency
of ADHD symptoms than untreated children. We find no statistically significant
difference in aggression. Treated parents report higher parenting quality, better
subjective wellbeing, and more parenting knowledge, although the differences areof small magnitudes. Expenses for surveyed children are greater for the treatmentgroup. Most of the difference is due to childcare expenses; the difference in otherexpenses is small by treatment status.
Understanding the counterfactual care mode is important for interpreting theeffects of center-based childcare, because the treatment effects are measured by thedeviations from outcomes under that mode (see Kline and Walters (2016)). Table
2 provides the distribution of childcare modes by the mothers’ labor market status.
15

Table 2: Childcare Mode by Mother’s Labor Market Status
Mother’s
Labor Market Status
Work
Home
Childcare Center 0.73
0.06
Grandparents
0.15
0.15
Sitters etc.
0.02
0.02
Parents Only
0.10
0.76
Source: LSN21
Note: All children are in two-parent family and 2.5 years old. The primary childcare mode is mutually exclusive and collectively exhaustive as defined by the following rule. If enrollment for achildcare center is reported, this is considered as the primary mode, because most enrolled children
attend full-time. If a child is cared by parents and grandparents only, the primary caregiver is grandparents. If any caregiver other than a childcare center and grandparents is reported, the primary
caregiver is a child sitter. If no caregiver except for parents is reported, parents are the primary
caregiver.

The share of center-based childcare is 73 percent for working mothers. While thisis the most common childcare mode for working mothers, many working mothers
use other childcare modes. The next most common childcare mode is informalcare by grandparents. Ten percent of working mothers do not report any nonparent
childcare mode. The use of babysitters and other informal childcare is very rare,accounting for only 2 percent of all childcare.
The use of center-based childcare is uncommon for stay-at-home mothers. Its
share of only 6 percent is reasonable because most parents need to satisfy the eligibility requirement by their usual work during the day. Most stay-at-home mothers do not report any nonparent childcare mode, and 15 percent use informal careby grandparents. The use of babysitters and other informal childcare is very rareamong stay-at-home mothers.

16

5

Econometric Methods

This section discusses our econometric methods. We first describe our specification for the IV regression, and then outline the MTE framework and the local IV
estimator. We discuss the validity of the instruments in the final subsection.

5.1

Instrumental Variable Regression

Our basic specification is based on IV regression. Define D as an indicator variable for childcare enrollment that takes a value of one if enrolled and zero if not.
Let Y be an outcome variable and X be a K-dimensional vector of exogenous variables including year and region dummies and family characteristics. The estimation
equation is given by

Y = βk=1 + βk=2 Xk=2 + · · · + βk=K Xk=K +τk=1 D + τk=2 D(Xk=2 − X̄k=2 ) + · · · + τk=K D(Xk=K − X̄k=K ) + ε.

(1)where ε is an error term that may be correlated with treatment status D, k indexes
the elements in the vectors, and X̄k is the mean of Xk . The coefficient for childcare
enrollment τk=1 is the average treatment effect (ATE), because we demean the covariates X. Demeaning does not affect the interpretation of the other coefficientsτk6=1 .
Childcare enrollment is determined by the following selection equation:
D = 1{Xγ + δ Z > V },(2)

where 1{·} is an indicator function that takes a value of one if the condition in the
curly brackets is satisfied and zero otherwise, Z is a vector of instrumental variables
excluded from the outcome equation (1), and V is a scalar of unobserved characteristics. Our instrument Z includes the childcare coverage rate, which is defined as
the number of childcare slots per child in a given region. The validity of the instrument is extensively discussed in Section 5.3. We also include the interactions of the17coverage rate and a subset of exogenous variables X in the instruments. We define
the propensity score for childcare enrollment such that P(X, Z) ≡ Pr(D = 1|X, Z).
We estimate equation (1) using instruments 1, P(X, Z), X, and P(X, Z) · (X − X̄)
instead of Z for the following reasons. First, this method produces the efficientIV estimator if the model for the propensity score is correctly specified. Second,
this method is consistent, even if the propensity score is misspecified. Wooldridge(2010) extensively discusses these issues. Third, the IV estimate can be interpreted
as a weighted average of the MTE with positive weights. We estimate the MTE by
the local IV estimator using the propensity score to explore the role of unobserved
heterogeneity. Our IV estimates can be interpreted in a unified framework when we
use the propensity score as an instrument.

5.2

Marginal Treatment EffectDefine j ∈ {0, 1} as an index of enrollment status for childcare such that j = 1
indicates being enrolled. A potential outcome Y j for enrollment status j is given by

Y j = Xβ j +U j
E(U j |X) = 0.

(3)
(4)

The enrollment status is determined by the selection equation (2) and can be
rewritten as
D = 1{Xγ + δ Z > V }(5)= 1{FV (Xγ + δ Z) > FV (V )}(6)= 1{P(Xγ + δ Z) > UD },(7)

where FV is the cumulative distribution function for V , P(·) is the propensity score,and UD is the quantile of V . We assume that (U j ,UD ) is independent of Z given
X. We refer to UD as the resistance to treatment, because a larger value of UD
keeps more families from treatment. This resistance to treatment summarizes all
unobserved factors that determine the selection into treatment.
18

The MTE is defined as
MT E(X = x, UD = uD ) = E(Y1 −Y0 |X = x, UD = uD ).

(8)This is interpreted as the gain from treatment for a family with observed characteristics X = x and unobserved resistance to treatment UD = u.
Heckman, Urzua, and Vytlacil (2006) show that the MTE can be estimated by
the local IV estimator. We assume that the MTE is additively separable into an
observed and an unobserved component,
MT E(X = x, UD = uD ) = x(β1 − β0 ) + E(U1 −U0 |UD = uD ).

(9)

The conditional mean outcome given the observed characteristics and the propensity score isE(Y |X = x, P(X, Z) = p) = xβ0 + x(β1 − β0 )p + K(p),(10)

where K(p) is a nonlinear function of the propensity score. The MTE for a familywith X = x and UD = p is given by the derivative of Equation (10) with respect tothe propensity score,∂ E(Y |X = x, P(X, Z) = p)∂p
∂ K(p).
= x(β1 − β0 ) +∂p

MT E(X = x, UD = p) =

(11)
(12)

How does the local IV estimator identify the MTE defined by unobserved characteristics uD ? When the propensity score is p, those with the unobserved characteristics uD < p are selected into treatment and those with uD = p are indifferent. If
we increase the propensity score by a small amount, those with uD = p are newly
induced into the treatment. We can see the treatment effects on these newly treatedpersons by the change in the outcome in Equation (10) in response to the marginalchange in p.
The unobserved resistance summarizes all the unobserved factors that determine

19

childcare enrollment. But what are they? Given the work requirement and the rationing rule that favors fulltime workers, the mother’s labor market attachment andskills are likely to be the main components of uD . In the case of excess demand, thelocal government ranks applications by how much parents work. Because skilled
mothers have a higher opportunity cost of staying at home, skilled mothers have a
low uD , so they are more likely to use childcare and work. By contrast, unskilled
mothers are likely to have a high uD .

5.3

Threats to Identification

Our instrument is the childcare coverage rate, which is given by childcare slots
per child in a region. Because the exogenous variables include year and region
dummies as well as family characteristics, we account for time-constant differences
across regions and nationwide changes in economic conditions and policies. Hence,
this identification strategy is similar to the difference-in-differences approach. Notethat this identification strategy is more robust than the approach that employs spatial
variation, which is typically adopted in the MTE literature on returns to schooling.
Because we control for time and region fixed effects, the identifying variationin our approach is the regional variation in the growth of the childcare coveragerate. As discussed in Section 3.2, the financial condition of the local governmentand other regional factors are likely to affect the growth of the coverage rate. For
example, the Cabinet Office (2010) argues that three factors can slow rollout of thechildcare reform. First, the bureaucratic system may prevent local governmentsfrom acting in a timely manner. Second, some local governments do not havepermanent funds to subsidize childcare centers. Third, suitable land and qualified
childcare workers are scarce, particularly in major cities.
We assess how these factors and other regional characteristics affect the paceof childcare expansion by regressing the growth of the coverage rate from 2000 to2010 on the pre-reform regional characteristics. The covariates include the female
labor force participation rate, the total fertility rate, the financial capability index of
the local government, the land price, and the average female wage in 2000.
We summarize the main regression result here and provide an extensive anal-

20

ysis in Appendix A. We find that the female labor force participation rate in the
pre-reform period is positively correlated with the growth of the coverage rate,
which suggests that the government increased the coverage rate in regions where
the potential demand was high. The other factors do not have major effects on thegrowth of the coverage rate. This indicates that the growth of the coverage rate
is not completely random, and hence, accounting for potential policy endogeneity
is necessary for obtaining unbiased estimates. To address this issue, we include
interactions between the pre-reform regional characteristics and the year dummies
among our control variables.
There could be some concern that the quality of childcare deteriorated during
the childcare expansion. If the local governments traded off quality and quantity,
the childcare expansion would then have had a negative effect on child development, which biases our estimates. However, this is unlikely, because the quality
standard is legally set by the Child Welfare Act and is uniform across the country. Importantly, the national government did not change this regulation during thechildcare reform6 . Indeed, the regulation for quality control is partly responsible
for the slow progress of the childcare reform relative to other countries.
Another threat to the identification is selective migration. Popular opinion isthat obtaining a slot in an accredited childcare center is extremely difficult in Tokyoand that some parents even move to other districts just to access childcare. Using
the Employment Status Survey 20127 , we take a sample of mothers of children
under 6 years old and examine the reasons for their most recent move, and where
they moved from. We find that with respect to the reason “For childrearing andeducation”, 9.5 percent moved within the same city, 4.6 percent moved from anothercity in the same province, and 1.4 percent moved from another province. Because
we define a region in this study as either a major city or the rest of a province
excluding its major cities, at most 4.6 percent of the sample moved between regions
6 Local governments were originally not legally permitted to set a lower standard than the national
standard. However, the Comprehensive Regional Sovereignty Reform was legislated in April 2011,
which allowed local governments to set a lower standard. Nevertheless, the Ministry of Welfare,Labor and Health still publishes guidelines, and most local governments legislate their own standards
following these guidelines.
7 Conducted by the Statistics Bureau every 5 years and covering about 1 percent of the population.

21

for childcare purposes. As we show in Section 6.3, selective migration appears tohave little effect on the estimates.
Other issues that could affect our estimates include endogenous fertility, thepresence of siblings, the choice of control variables, and the assumptions of thefunctional form. These issues are discussed extensively in Section 6.3, but we find
our main results are largely unaffected.

6

Results

6.1

Childcare Enrollment

We identify the causal effects of childcare enrollment using regional variations in
the childcare coverage rate. This identifying variation is graphically presented in
Figure 1. We plot changes in the coverage rate over 2003–2012 on the horizontal
axis and childcare enrollment for children aged 2½ years during the same period on
the vertical axis for the 82 regions. The radii of the bubbles represent the numberof observations.
As shown, the coverage rate increased in all regions during this period, but themagnitude of the changes varied considerably from 0.02 to 0.23. Childcare enrollment also increased in all regions, and the growth ranged from 0.01 to 0.27. The
correlation coefficient is 0.62 and the standard error is 0.13, which is strongly significant. The graph provides prima facie evidence for the validity of our identification
strategy.
We estimate the probability of childcare enrollment or the propensity score using the logit model. The covariates include the coverage rate up to the third-orderpolynomial, the parents’ ages and education levels, the child’s sex and birthweight,
and dummies for year and region. The interactions between the coverage rate andparent and child characteristics are also included to allow for differential responses
to the coverage rate. In addition, to address the possible policy endogeneity, we
include interactions between the pre-reform regional characteristics in 2000 and theyear dummies. All the parameter estimates except for the region fixed effects are in
Table 10 in Appendix C.
22

Change in Enrollment Rate

0.2

0.1

0.0
0.05

0.10

0.15

0.20

Change in Coverage Rate

Figure 1: Growth of Coverage Rate and Enrollment Rate by Region
Source: LSN21, Census, and the annual Report on Social Welfare Administration and Services.
Note: Observations are for 2003-2012 and aggregated to the 82 regions. The radii of bubbles are thenumber of observations. The correlation coefficient is 0.62 and the standard error is 0.13.

We focus on the selected variables and report their average marginal effects inTable 3. The average marginal effect of the coverage rate is significantly positive at
0.795. The high statistical significance gives us confidence in the validity of our IV
regression, but a formal test for weak IV is in the following subsection.
The mother’s age has a significant positive effect on childcare enrollment. The
reference group for the mother’s education is mothers graduating from a 4-year
university education. The probability of childcare enrollment increases with themother’s education level, although the difference between high school graduates
and those who did not graduate from high school is small. Overall, childcare enrollment increases with the mother’s human capital. This is reasonable, because
mothers are effectively required to be working as a precondition for the use of accredited childcare, and labor force attachment is stronger for skilled women.
The father’s age has a significant negative effect on childcare enrollment. The
probability of childcare enrollment decreases with the father’s education level, although the difference between high school graduates and those with a 2-year college

23degree is small. The estimates indicate that childcare enrollment decreases with thefather’s human capital, which suggests that the father is the primary earner, andhence, his income has negative effects on both the mother’s labor supply and childcare enrollment.
Child characteristics do not have large effects on childcare enrollment. The reference group is normal-birthweight girls. The probability of childcare enrollment isonly about one percentage point higher for normal-birthweight boys. The childcare
enrollment rates of low-birthweight boys and girls are not significantly different
from those of normal-birthweight girls.

6.2

IV Estimates

Using the IV estimator in Section 5.1, we provide the estimated treatment effects
on several outcomes for both children and parents. The F-statistics for testing weak
instruments are well above 10 in all models, which implies that we can reject thenull hypothesis that the instruments are weak. All the reported standard errors are
clustered at the region level. For comparison purposes, we also report ordinary leastsquares (OLS) estimates in Tables 11 and 12 in Appendix C.
6.2.1

Maternal Labor Supply

Yamaguchi, Asai, and Kambayashi (2017) estimate the effects of childcare enrollment on various labor market outcomes for mothers. Because their specificationis slightly different from that in this analysis, we re-estimate the models for themother’s labor market participation and weekly hours of work. As shown in Table
4, the estimated ATEs on the mother’s labor market participation and weekly hours
of work are positive and significant at 0.690 and 32.128 hours, respectively.
The treatment effects vary by the mother’s education. In the following, we refer to mothers with less than a high school education as low-education mothers
and those with a 4-year university education or higher as high-education mothers.
The reference group is high-education mothers. The estimates show that the treatment effects are smaller for low-education mothers, and the difference for hours ofwork is statistically significant. We do not find statistically significant differences24

Table 3: First-Stage Regression: Average Marginal Effects on Childcare Enrollment
at Age 2.5

Ave. Marginal EffectRegionCoverage Rate

0.795
(0.136)

MotherAgeLess Than High School
High School
2-Yr College
FatherAgeLess Than High School
High School
2-Yr College
Child
Normal-Birth-Weight Boy
Low-Birth-Weight Boy
Normal-Birth-Weight Girl

0.001
(0.001)-0.098(0.010)-0.105(0.006)-0.072(0.005)-0.001(0.000)0.149(0.008)0.078(0.004)0.071(0.005)0.012(0.004)0.013(0.009)-0.013(0.008)Source: Authors’ calculation from LSN21.
Note: The propensity score is estimated by the logit model. Standard errors are in parenthesis. The
control variables include the coverage rate up to the third order polynomial, ages and educationof parents, sex and birth weight of children and dummies for year and region. We also include
interactions of pre-reform regional characteristics in 2000 and year dummies to account for the
possible policy endogeneity. The coverage rate is interacted with characteristics of parents andchildren. Parameter estimates for the logit model are reported in Table 10 in Appendix C.

25

in treatment effects by the child’s sex or birthweight.
6.2.2

Child Development and Behavior

We next provide estimates for child development and behavior in Table 4. To facilitate interpretation, all of these outcome variables are normalized to have a mean ofzero and a standard deviation of one.
The ATE on language development is positive and significant. Language development is significantly slower for boys, particularly those with a low birthweight,
when not enrolled in childcare. However, childcare enrollment narrows the lag behind normal-birthweight girls. The estimated treatment effects are 0.217 and 0.774
standard deviations larger than those for normal-birthweight girls. We find no significant difference in treatment effects by the mother’s education.
The ATEs on children’s aggression and ADHD symptoms are insignificant, but
treatment seems effective for some groups. Without treatment, children of loweducation mothers show more aggressive behavior and ADHD symptoms than thechildren of high-education mothers. Boys and low-birthweight girls also behave
worse than normal-birthweight girls without treatment. Childcare enrollment tends
to reduce aggression and ADHD symptoms among these children, although someestimates are noisy. The results for the children of low-education mothers are mostconsistent: the differences from high-education mothers in the treatment effects are
statistically significant at –0.463 and –0.372 standard deviations for aggression and
ADHD symptoms, respectively. These estimates suggest that the children of loweducation mothers behave as well as the children of high-education mothers whenenrolled in childcare.
6.2.3

Parents’ Outcomes

To better understand the mechanism behind how childcare enrollment affects childoutcomes, we estimate the effects on the children’s home environment. Table 4
presents the IV estimates for the parenting quality index, which is normalized tohave a mean of zero and a standard deviation of one.
The ATE of childcare enrollment on parenting quality is nearly zero and in-26

27
15.390-0.031(0.021)-0.015(0.008)-0.021(0.019)-0.010(0.019)0.690(0.087)-0.082(0.065)0.014(0.021)-0.006(0.053)0.004(0.062)
15.547-1.390(0.922)-0.346(0.344)-0.057(0.741)0.069(0.898)32.128(3.173)-6.573(2.770)-0.633(1.017)-2.530(2.394)-0.398(2.845)Hours

15.522-0.009(0.055)-0.387(0.020)-0.922(0.089)-0.083(0.055)0.460(0.212)-0.096(0.159)0.217(0.059)0.774(0.265)-0.192(0.175)

Language

13.9000.329(0.069)0.341(0.017)0.392(0.054)0.166(0.056)0.095(0.215)-0.463(0.223)-0.161(0.051)
0.063(0.163)-0.354(0.201)Aggression

Child Outcomes

14.8680.234(0.065)0.238(0.020)0.441(0.079)0.117(0.064)-0.356(0.295)-0.372(0.180)-0.045(0.062)-0.307(0.208)-0.160(0.217)ADHD

14.799-0.505(0.091)-0.178(0.019)-0.245(0.077)-0.105(0.073)0.010(0.235)0.692(0.244)0.049(0.067)0.119(0.202)0.111(0.225)

Parenting
Quality

15.246-0.137(0.115)0.046(0.020)0.033(0.054)0.099(0.068)2.865(0.423)-0.780(0.291)-0.170(0.069)-0.086(0.145)-0.287(0.235)15.057-0.006(0.146)-0.061(0.063)-0.303(0.170)-0.063(0.109)1.320(0.559)0.614(0.387)0.013(0.174)1.016(0.579)0.387(0.386)15.4070.043(0.021)0.000(0.005)0.013(0.021)0.017(0.021)
0.036(0.064)-0.149(0.054)0.017(0.016)0.026(0.058)0.009(0.066)

Parents’ OutcomesInsufficient
NonParentingChildcare Childcare
Expenses Expenses Knowledge

Note: Parameter estimates for Equation (1). Standard errors are in parenthesis and clustered at the region level. The control variables include
ages and education of parents, sex and birth weight of children, and dummies for year and region. We also include interactions of the pre-reform
regional characteristics in 2000 and year dummies to address the possible policy endogeneity. Low-education mothers are those with less thanhigh school education. The reference group for mother’s education is mothers graduated from 4-year university or higher. NBW and LBW denotenormal birth weight and low birth weight, respectively. The reference group for child’s sex and birth weight is normal-birth-weight girls.

Test Statistic
Weak Instrument

Childcare × LBW GirlChildcare × LBW Boy

Childcare × NBW Boy

Childcare × Low-Edu.

Childcare

Low Birth Weight Girl

Low Birth Weight Boy

Normal Birth Weight Boy

Coefficients
Low-Education MotherWork

Mother’s
Labor Supply

Table 4: Effects of Childcare Enrollment on Mother’s Work and Child Outcomes

15.146-0.400(0.059)-0.040(0.021)-0.118(0.060)-0.117(0.067)-0.029(0.215)0.577(0.170)0.086(0.059)0.111(0.156)0.243(0.228)

Subjective
Well-Beingsignificant. However, the treatment effect for low-education mothers is 0.692 standard deviations greater than that for high-education mothers, and is statisticallysignificant. The parenting quality of low-education mothers is 0.505 standard deviations lower than that of high-education mothers without treatment, but childcare
enrollment improves it sufficiently that the gap disappears. Childcare enrollment
does not seem to significantly improve the parenting quality of mothers of boys
and low-birthweight children, even though their parenting quality is lower without
treatment.
Childcare enrollment also affects monetary expenses for children. Note thatthese monetary expenses are specifically for the surveyed child, and any expenses
for the child’s siblings are not included. The ATE on childcare expenses is positiveand significant at 28,650 JPY. The treatment effect for low-education mothers is
significantly lower, by 7,800 JPY, than that for high-education mothers, which isconsistent with the fact that a lower fee is charged for low-income families. The
magnitudes of the differences in treatment effects are not large for the other subgroups.
The ATE on nonchildcare expenses is positive and significant at 13,200 JPY,
which implies that childcare and other child-related services and goods are grosscomplements, rather than substitutes. The treatment effects for low-education mothers, boys, and low-birthweight children are greater than for their reference groups,
although the estimates are imprecise.
To see why the parenting quality of low-education mothers improves throughchildcare use, we estimate the treatment effects on those mothers who self-reportthat they do not have good parenting knowledge. The ATE is nearly zero and insignificant, but the treatment effects for low-education mothers are significantly
lower, by 14.9 percentage points, than for high-education mothers, which implies
that fewer low-education mothers report a lack of parenting knowledge once treated.
Childcare enrollment therefore seems to inform low-education mothers about better
parenting practices.
Finally, we estimate the effects on parents’ subjective wellbeing. The index isnormalized so that the mean is zero and the standard deviation is one. The ATE
is nearly zero and insignificant, but treatment benefits parents in some subgroups.
28

If their children are not enrolled in childcare, low-education mothers and parentsof boys and low-birthweight children report significantly lower wellbeing than thecorresponding reference groups. The treatment effects for low-education mothers
are 0.577 standard deviations higher than those for high-education mothers, and arestatistically significant. The treatment effects for boys and low-birthweight children are also greater than those for normal-birthweight girls, but not statisticallysignificant.

6.3

Robustness

We now examine the robustness of our main results to alternative modeling assumptions. While there are many parameters in the model, we focus on just two
key parameters: the ATE and the difference in treatment effects between low- andhigh-education mothers. We address the following issues that may account for any
biased estimates.
6.3.1

Potential Threats for Identification

Endogenous Fertility Our instrument is the coverage rate or the number of childcare slots per child, which is a measure of childcare availability. If childcare availability influences the fertility rate, the coverage rate is also affected. To avoid thispotential problem from endogenous fertility, we estimate the model using an alternative measure of childcare availability, which is the number of childcare slots
per woman aged 20–44 years in the region. Because it is plausible that the female
population is more exogenous than the child population, the use of this alternative
instrument helps us understand the extent of bias in the main specification.
Selective Migration Another threat to the identification is selective migration forchildcare. Our estimates are upward biased if mothers who want to work moveto a region where childcare is more readily available. Evidence from the Employment Structure Survey 2012 indicates that among mothers of children under 6 yearsof age, only 1.4 percent move from another province “For childrearing and education”. Because interprovincial migration for childrearing and education is very
29

uncommon, the estimates based on the province-level variables are unlikely to bebiased by selective migration. We assess the extent of possible bias due to selective
migration by comparing our preferred estimates with those of an alternative modelin which the region-level variables are aggregated at the province level.
Siblings In our preferred specification, we do not control for the number of siblings, because it may be affected by the availability of childcare. However, childand parental outcomes may vary by the number of children in the household. In
addition, the municipal government prioritizes a family for childcare if an older
sibling is already enrolled in the same childcare center. Omitting the number ofsiblings therefore does not bias the results, but including it may lead to precise estimates as long as it is exogenous. To address this, we augment the main specification
by adding the numbers of younger and older siblings to the first- and second-stage
regressions.
Coverage Rate in Earlier Years Our treatment variable is an indicator for childcare enrollment at age 2½ years and instrumented by the coverage rate at that age.
However, 68 percent of treated children were already enrolled in childcare 1 year
before. This implies that the coverage rates in previous years may also be relevant for predicting childcare enrollment. Leaving out the coverage rates in earlier
years does not bias our estimates, but including them in the first-stage regression increases the efficiency of the estimator. We address this by estimating the propensity
score using the logit model that includes up to the third-order polynomials of thecoverage rates at ½ and 1½ years. All covariates in the benchmark specification are
included, and the specification for the second-stage regression remains identical.
Linear Probability Model in the First-Stage Regression Our preferred modelfor the propensity score is the logit model because it ensures the predicted scoreslie between zero and one. The propensity score must be correctly specified forconsistent estimation of the MTE by the local IV estimator. However, the propensity
score does not have to be correctly specified for the IV regression. Because the logit
model is a nonlinear estimator, our estimates may be driven by nonlinearity, rather
30

than variation in the data. To assess the consequences of the use of a nonlinear
model, we estimate the propensity score using the linear probability model andspecify it as an instrument in the second-stage regression.
6.3.2

Results

The estimates for the alternative models are reported in Table 5. The estimates forthe baseline specification are also reproduced for convenience. Most estimates forthe alternative models are very similar to those of the baseline model, but there
are some noticeable differences. When the variables are aggregated at the provincelevel to address selective migration (Model 3), the ATE on nonchildcare expenses
is small and insignificant. When the number of siblings is included (Model 4),
the ATEs on the mother’s participation and hours of work are implausibly greaterthan the benchmark estimates. Overall, our estimates are robust to the alternativemodeling assumptions.

6.4

Interpretation and Discussion

6.4.1

Child Outcomes

The estimates indicate that childcare enrollment on average improves the language
development of children. In addition, the treatment effects are stronger for boys,
particularly low-birthweight boys, than for normal-birthweight girls, such that they
perform almost equally well when enrolled in childcare.
Note that the language development index is constructed from questions forwhich the response would be positive for most children, e.g. 88 percent of children can say words such as “mom”, put together two-word sentences, and say theirown names. This suggests that our index is useful for detecting children with substantially slower language development, rather than ranking children. Hence, evenif childcare enrollment improves the language development of above-average children, we are unable to detect such effects owing to the nature of the index. In thissense, our index is similar to the German school readiness test, which 91 percent ofGerman children pass (see Cornelissen et al. (2015)).

31

32-0.082(0.065)-6.573(2.770)-0.096(0.159)-0.463(0.223)-0.372(0.180)0.692(0.244)-0.780(0.291)0.614(0.387)-0.149(0.054)0.577(0.170)0.690(0.087)32.128(3.173)0.460(0.212)0.095(0.215)-0.356(0.295)0.010(0.235)2.865(0.423)1.320(0.559)0.036(0.064)-0.029(0.215)-0.088(0.064)-6.502(2.721)-0.100(0.157)-0.462(0.224)-0.388(0.185)0.707(0.247)-0.859(0.332)0.591(0.400)-0.151(0.053)0.607(0.163)0.701(0.097)30.029(3.391)
0.405(0.227)0.012(0.209)-0.480(0.291)
0.153(0.223)3.496(0.466)1.692(0.629)0.042(0.066)-0.202(0.219)(2) Endogenous
Fertility-0.083(0.066)-6.690(2.906)-0.084(0.162)-0.484(0.210)-0.432(0.179)0.659(0.251)-0.748(0.267)0.601(0.407)-0.146(0.050)0.560(0.161)

0.773(0.062)28.095(2.347)0.229(0.111)
0.195(0.130)0.076(0.123)-0.157(0.167)2.233(0.274)0.206(0.330)0.007(0.036)0.122(0.129)(3) Selective
Migration-0.137(0.067)-9.718(2.878)0.003(0.137)-0.403(0.205)-0.313(0.166)0.636(0.209)-0.650(0.241)0.724(0.303)-0.102(0.051)0.544(0.158)1.114(0.098)48.286(3.892)0.424(0.180)0.041(0.222)-0.245(0.267)-0.209(0.179)2.034(0.322)0.848(0.431)
0.008(0.068)-0.092(0.214)(4) Siblings-0.084(0.064)-6.637(2.706)-0.074(0.153)-0.459(0.218)-0.367(0.179)0.692(0.242)-0.772(0.293)0.614(0.382)-0.147(0.054)0.571(0.166)0.676(0.083)29.881(3.509)0.293(0.199)0.015(0.211)-0.349(0.292)-0.046(0.222)2.902(0.407)1.213(0.544)0.027(0.063)-0.058(0.213)(5) Lagged
Cov. Rate-0.087(0.065)-6.414(2.717)-0.097(0.155)-0.420(0.227)-0.370(0.179)0.725(0.252)-0.853(0.364)0.594(0.372)-0.153(0.055)0.616(0.169)0.660(0.108)25.966(3.783)0.331(0.232)0.053(0.240)-0.250(0.301)0.280(0.294)3.840(0.535)1.265(0.654)0.004(0.063)-0.115(0.214)(6) Linear
in 1st Stage

Note: See note for Table 4 for the specification of the benchmark model. Standard errors are in parenthesis and clustered at the region level. In model (2), the instrument is thenumber of childcare spots per women aged 20-44. In model (3), regional variables are aggregated to the province level. In model (4), the numbers of younger and older siblings areincluded. In model (5), the coverage rates at age 0.5 and 1.5 are also included in the set of instruments. In model (6), the linear probability model instead of the logit model is used in
the first stage.

Well-Being

Lacking Parenting Knowledge

Other Expense

Childcare Expense

Parenting Quality

ADHD Symptoms

Aggression

Mental Development

Hours

Diff. in ATE for Low-Educ. Mothers
Participation

Well-Being

Lacking Parenting Knowledge

Other Expense

Childcare Expense

Parenting Quality

ADHD Symptoms

Aggression

Mental Development

Hours

Ave. Treatment EffectParticipation(1) Benchmark

Table 5: Robustness Checks

Our estimates indicate that the ATEs on child aggression and ADHD symptoms
are almost zero. However, childcare enrollment significantly improves the behaviorof children in some subgroups. Children of low-education mothers behave worse
than those of high-education mothers when not enrolled in childcare, but they behave equally well when enrolled in childcare. This result is robust to alternativemodeling assumptions. Some of the estimated effects on the behavior of boys andlow-birthweight girls are positive, but they are not consistently significant.
The indices for children’s behavior are less objective than the language development index, and we cannot rule out the possibility that outcomes are measured witherror. We attempt to minimize the role of measurement errors by using aggregatedindices, rather than relying on a single variable. Note that this measurement error
issue is not unique to our study; it is also found in previous work by Baker et al.
(2008) and Currie et al. (2014) using the NLSCY.
6.4.2

Why Do Child Outcomes Improve?

Why does childcare enrollment improve language development and the behavior ofsome children? There are direct and indirect paths. One is that the learning environment at a childcare center may be of better quality than the home environment ofsome children. If so, spending more time at a childcare center can directly improvethese outcomes. In addition, childcare enrollment may improve child outcomes indirectly through encouraging a better home environment. Our estimates indicatethat childcare enrollment improves parenting quality, nonchildcare expenses, parenting knowledge, and the wellbeing of low-education mothers, which may eventually affect their children.
Although few economics studies consider this indirect path, there is theory andempirical evidence from pediatrics and developmental psychology of the effects ofparenting practices on child behavioral outcomes (see Gershoff (2002) and Deault(2010) for surveys). While many studies adopt a correlational or longitudinal design, Shaw et al. (2006), Gardner et al. (2007), and Brotman et al. (2011) establish a
causal effect from randomized controlled trials that are designed to promote effective parenting practices. These studies find that the interventions improve parenting
quality and the behavior of children from disadvantaged families.
33

Although our results are consistent with these findings from the field of developmental psychology, we do not rule out the possibility that better child development and behavior also improve parenting quality. Indeed, Deault (2010) reviews
empirical studies on the association between ADHD and parenting practices andfinds that poor child behavior can lead to poor parent–child interaction and harmto the mother’s mental health. Nevertheless, our estimates indicate that childcare
enrollment improves the parenting knowledge of low-education mothers, which isunlikely to be driven by any change in child behavior. Overall, our analysis suggeststhat educating not only children but also their parents can increase the effectivenessof a childcare program.

7

Marginal Treatment Effects

7.1

Local Instrumental Variable Estimates

We estimate the MTE, which varies by unobserved resistance to treatment. Given
the work requirement and the rationing rule that favors fulltime workers, the unobserved resistance is likely to represent the mother’s labor market attachment andskills. Namely, those with weak resistance to treatment are likely to be skilled,
while those with strong resistance to treatment are likely to be unskilled. The MTE
estimates enable us to understand how treatment effects vary by such unobserved
characteristics.
Figure 2 depicts how the MTE changes with unobserved resistance to treatment
uD . The three panels at the top present the MTE curves for child development andbehavioral outcomes. Note that negative values for treatment effects on aggression
and ADHD symptoms imply that treatment improves children’s behavior (i.e. lessbad behavior). For all of the three child outcomes, children from families with aweak resistance to treatment are not significantly affected by childcare enrollment.
By contrast, childcare enrollment improves the outcomes of children from families
with a strong resistance to treatment. These results are in line with the finding
about children of low-education mothers, because mothers with a strong resistanceto treatment are likely to be unskilled.
34

The two panels in the middle plot the MTE curves for expenses for the surveyedchild. The MTE on childcare expenses is positive and significant and decreases withunobserved resistance. This is reasonable given that the childcare fees increase withhousehold income and that mothers with a high resistance are likely to be unskilled.
The MTE on expenses other than childcare is upward sloping, and is positive andsignificant for higher values of unobserved resistance. These results again suggestthat childcare and other goods and services for the child are gross complements.
The bottom three panels illustrate the MTE curves for parenting quality, parenting knowledge, and subjective wellbeing. The estimates are noisy and statisticallyinsignificant. We cannot observe a systematic relationship between unobserved resistance and these parental outcomes.
We also calculate the ATE, treatment effect on the treated (TT), and treatment
effect on the untreated (TUT) by taking the relevant weighted averages of the MTE.
The estimates for the aggregate treatment parameters and the corresponding weights
are shown in Table 8 and Figure 3 in Appendix B, respectively. The ATEs are
similar to those estimated by the IV regression. For outcomes for which the MTE
curve is upward sloping, the TT is smaller than the TUT, and vice versa when the
MTE curve is downward sloping.

7.2

Counterfactual Policy Simulations

To evaluate the effects of a further childcare expansion, we conduct counterfactual simulations in which the coverage rate is raised by 0.1, 0.2, or 0.3. Table 6
summarizes the simulation results. Given the current coverage rate, the childcare
enrollment rate is 0.392. This increases to 0.480 when the coverage rate is raised
by 0.1. When the coverage rate is raised by 0.3, the enrollment rate increases to0.649, which is about the same as the enrollment rate for children aged 0–2 years
in formal childcare in Denmark, which has the highest childcare enrollment rate in
the OECD.
We calculate the policy-relevant treatment effects on three child outcomes. The
policy-relevant treatment effects are average effects for those newly induced into
treatment by the policy change. As shown in Appendix B, the policy-relevant treat-35

Language Development

Aggression

ADHD Symptoms

1.0

0.5

0.0

Marginal Treatment EffectMarginal Treatment EffectMarginal Treatment Effect

1.5
1.0
0.5
0.0
−0.5
−1.0

1

0

−1

0.00 0.25 0.50 0.75 1.00

0.00 0.25 0.50 0.75 1.00

0.00 0.25 0.50 0.75 1.00

ud (Resistance to Treatment)ud (Resistance to Treatment)ud (Resistance to Treatment)Childcare Expenses

Other Expenses for Child
6
Marginal Treatment EffectMarginal Treatment Effect

6
4
2
0
−2

4
2
0
−2

0.00 0.25 0.50 0.75 1.00

0.00 0.25 0.50 0.75 1.00

ud (Resistance to Treatment)ud (Resistance to Treatment)

Parenting Quality

Little Knowledge

Well−Being

0.5

0.0

−0.5

Marginal Treatment EffectMarginal Treatment EffectMarginal Treatment Effect

0.3
0.2
0.1
0.0
−0.1

0.5

0.0

−0.5

−0.2
0.00 0.25 0.50 0.75 1.00

0.00 0.25 0.50 0.75 1.00

0.00 0.25 0.50 0.75 1.00

ud (Resistance to Treatment)ud (Resistance to Treatment)ud (Resistance to Treatment)

Figure 2: Marginal Treatment Effect Curves
Note: The marginal treatment effect is graphically presented with the 90% confidence interval. The
standard errors are clustered at the region level. The MTE curve is based on the estimated outcome
equation (10) and evaluated at the mean of all covariates except for the propensity score.

36

Table 6: Counterfactual Policy Simulations

Raise Coverage Rate by 0.1
Raise Coverage Rate by 0.2
Raise Coverage Rate by 0.3

Policy-Relevant Treatment EffectMentalDevelopment Aggression ADHD
0.514
0.099
-0.258(0.191)(0.209)(0.301)
0.535
0.043-0.324(0.184)(0.201)(0.292)0.554-0.009-0.385(0.181)(0.197)(0.286)

Propensity Score
New
Baseline Policy
0.392
0.480
(0.012)(0.018)0.392
0.568(0.012)(0.029)0.392
0.649(0.012)(0.039)Note: Simulations are based on the estimated MTE. Standard errors are in parenthesis and clusteredat the region-level. Weights for the policy-relevant treatment effect are provided in Appendix B.

ment effects are given by the weighted average of the MTE. The estimates indicatethat as the coverage rate increases, the policy-relevant treatment effects become increasingly strong in the direction that improves child outcomes. The MTE curves
for child outcomes show that the MTE is stronger for children of parents with astronger resistance to treatment, that is, those who are less likely to use childcare.
As the childcare coverage rate increases, these children are gradually enrolled inchildcare and improve their behavior.
The MTE estimates indicate that children who would most benefit from childcare enrollment are less likely to be enrolled. These children are gradually induced
into treatment as childcare reforms progress; however, their enrollment levels are
slow to respond to the expanded supply. Our analysis therefore suggests that increasing the supply of childcare may not produce sufficiently fast results among the
population segment that would benefit most from formal childcare, and that otherpolicy measures are also necessary to bring these children into formal childcare.
The efficacy of the program could be improved by targeting children from disadvantaged families.

37

8Conclusion

We estimate the effects of childcare enrollment on the outcomes of children andparents. Our estimates indicate that childcare enrollment improves language development and reduces aggression and ADHD symptoms in some children. These children show slower language development and more aggression and ADHD symptoms than other children when they are not enrolled in childcare. However, childcare enrollment helps them catch up and provides a level playing field.
The estimates indicate that childcare enrollment is particularly effective for improving the behavior of children of low-education mothers. Part of the strong positive effect may be indirectly brought about through better parenting quality andgreater monetary investment in children. Although we cannot exclude the possible
effects of child behavior on parenting quality, evidence suggests that childcare enrollment informs low-education mothers about good parenting practices. Promoting
positive parental involvement therefore might further improve the effectiveness ofa childcare program.
The MTE framework enables us to identify the treatment effects varying by
the unobserved propensity to use childcare. The estimates indicate that childcare
enrollment is effective for some children, but their mothers are less likely to use
childcare. Because the rationing rule ranks childcare applications by how muchthe parents work, the mothers of nonparticipants are more likely to have weak labor
force attachment and low skills. This implies that the rationing rule may prevent disadvantaged children from being enrolled in childcare. Although the rationing rule
is different in other countries, tax deductions for childcare are commonly available
in most. Such deductions are also likely to lead to negative selection into treatment,
similar to the pattern found in this analysis, because they lower the effective priceof childcare only when both parents work and pay significant tax. Our analysis
suggests that childcare and other related social programs need to be carefully designed to ensure that this public service is delivered to children from disadvantaged
families.
In terms of limitations, our measures of child outcomes and parenting quality
are based on simple yes/no answers to questions that may leave room for interpre-38

tation in some cases, so we are unable to exclude the possibility that outcomes aremeasured with significant errors. Another limitation is that our outcome measures
are contemporaneous with or 1 year after childcare enrollment, and the long-term
outcomes of childcare enrollment remain largely unknown. The positive effects
of childcare may either dissipate over time or persist into adulthood, as shown byexisting studies. These important issues are left for future research.

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41

A

Determinants of the Growth the Supply of Childcare (For Online Publication)

Cabinet Office (2010) argues that three factors slow down the rollout of the childcare reform. First, the bureaucratic system prevents them from acting timely. Second, some local governments do not have permanent funds to subsidize childcare
centers. Third, land and qualified childcare workers are scarce particularly in major
cities.
We assess how these factors and other regional characteristics affect the pace ofchildcare expansion. Following the literature, we measure the supply of childcare
relative to potential demand by the coverage rate defined by the number of spots per
children aged 0-5 in a given region. By construction, the coverage rate increases
when the number of spots increases, the number of children decreases, or both
happen. Let Ct and Nt be the numbers of spots and children, respectively. Thechange of the coverage rate from t + 1 to t can be decomposed as
Ct+1 Ct−
Nt+1 Nt= Ct+1 −Ct
Ct+1 Ct+1
−
−
.
Nt
Nt
Nt+1

(13)

We refer the first term on the right hand side as the supply factor and the second
term as the population factor. The first term measures the growth of the number ofspots per children in the base year t, and hence, this term isolates the effect of achange in the supply of childcare. The second term measures the effect of a change
in child population. With a fixed number of childcare spots, fewer children implies
a higher coverage rate.
We regress the supply and population factors as well as the change of the coverage rate from 2000 to 2010 on regional characteristics for 82 regions used in our
main analysis.8 Given the objectives of the childcare reforms and the argument
above, we include the female labor force participation rate, the total fertility rate,
the financial capability index of the local government, land price, and average female wage in 2000. These factors are likely to influence the decisions on childcare
supply. Although these factors may not causally affect the population size of chil8 See Section 4 for our definition of region and selection criteria.

42dren, they may be correlated. Note that the population size of children is indirectly
affected by the population size of young adults. Young adults tend to move fromrural regions or smaller cities to major cities for school and/or work,9 which may
eventually affect the size of child population.
Table 7 reports the regression results. The first column shows determinants ofthe supply of childcare. The female labor force participation rate in 2000 is negatively correlated with the growth of the supply of childcare. This implies that the
supply of childcare increased in the regions where the female labor force participation rate was low, which is consistent with the objective of the childcare reform.
The effect of total fertility rate is small and insignificant. As expected, the effect offinancial capability of local governments is positive, while the effects of land prices
and wages of female workers are negative but insignificant.
The second column shows how changes in child population are correlated with
regional characteristics in 2000. Note that a positive coefficient implies child population increases with the variable of interest. The female labor force participation
rate is negatively correlated with the growth of child population. Given the conflict
between work and family life, a higher labor force participation rate may lead toa lower fertility rate. The financial capability index is positively correlated withthe growth of child population. This may reflect the geographic mobility of young
adults to large cities, because the financial status of major cities are generally better
than that of smaller cities. We also note that a better financial status may increase
the fertility rate by providing a better support for young families.
The third column shows determinants of changes in the coverage rate. The coefficient of each variable is given by subtracting the corresponding coefficient for the
population factor from that for the supply factor (see Equation 13). Because the supply and population factors offset each other, the coefficients are small, although thecoefficient for the female labor force participation rate is marginally significant at
the 10% level. Although the supply of childcare or child population is not random,
the change of the coverage rate is only weakly correlated with regional character9 According to School Basic Survey 2011 conducted by the Ministry of Education, about a halfof high-school graduates in Aomori, Iwate, and Akita (smaller provinces in the North East) find ajob outside their home provinces, while only 11.9% of high-school graduates in Tokyo do so.

43Table 7: Determinants of the Growth of Childcare Coverage Rate in 2000-2010

Female Labor Force Participation Rate
Total Fertility Rate
Financial Capability Index
Log Land Price
Log Average Female Wage
Num. obs.

Change in
Supply−0.557(0.190)0.154(0.106)0.157(0.074)−0.019(0.032)−0.193(0.210)80

Change inChild Population−0.743(0.156)0.156(0.087)0.220(0.061)−0.028(0.026)−0.132(0.173)80

Change inCoverage Rate
0.186
(0.089)−0.001(0.050)−0.062(0.035)0.009(0.015)−0.060(0.099)80

Sources: Standard errors are in parenthesis. All explanatory variables are measured in 2000 unlessotherwise noted. Labor force participation rate for women aged 20-64 is from the Census. The
total fertility rate is from Vital Statistics. The financial capability index is from Table for Financial
Capability Indices of Prefectures constructed by Ministry of Internal Affairs and Communications.
The land price is the average land price per square meter in residential areas, which is taken from
Survey on Land Price of Prefectures by Ministry of Land, Infrastructure, Transport and Tourism.
The mean female wage is calculated by dividing scheduled cash earnings by scheduled hours ofwork, which are from Basic Survey of Wage Structure 2001. For data consistency, we omit City of
Yokosuka and non-major cities in the Province of Kanagawa, although they are included in the main
analysis.

istics. In the following, we account for a possible policy endogeneity by including
the interaction of these regional characteristics and year dummies, although doing
so has little influence on our results.

B

Treatment Parameters (For Online Publication)

B.1

Weights for Aggregate Treatment Parameters

We calculate treatment parameters following the method outlined by Cornelissen
et al. (2015); Cornelissen, Dustmann, Raute, and Schönberg (2016). Let xi and pi bea vector of control variables and the propensity score for family i. The unobserved0
component of the MTE is denoted by K (uD ). The sample mean of the propensity
44

score is p̄ = 1/N ∑N
i=1 pi . The ATE, TT, and TUT are given by
ˆ 1
0
1 N
K (u)duATE =xi (β1 − β0 ) +
∑
N i=1
0
ˆ 1
N
0
1/N ∑N1pi
i=1 I(pi > u)
TT =
K
(u)
·
x
(β
−
β
)
+
du
i 1
0
∑
N i=1 p̄p̄0
ˆ 1
0
1/N ∑N1 N 1 − pi
i=1 I(pi ≤ u)
K (u) ·TUT =xi (β1 − β0 ) +du.
∑
N i=1 1 − p̄
1 − p̄
0
The integral can be easily calculated by discretizing the grid for uD .
Denote the propensity score under the baseline policy by pi and the propensity
0
score under the alternative policy by pi . The sample means of the propensity scores
0
under these two policies are denoted by p̄ and p̄ . The policy-relevant treatment
effect (PRTE) is given by
ˆ 1
0
0
N
0
1/N ∑N
1 N pi − pi
i=1 I(pi > u) − 1/N ∑i=1 I(pi > u)
PRTE =
x
(β
−
β
)
+
K
(u)
·
du.
0
∑ p̄0 − p̄ i 1 0 0
N i=1p̄ − p̄B.2

Estimates for Aggregate Treatment Parameters

The conventional treatment effect parameters can be calculated by aggregating MTEwith proper weights. The weights for ATE is uniform, and those for TT and TUT
are graphically presented in Figure 3. For TT, individuals with lower values of unobserved resistance are given more weights, while for TUT, individuals with higher
values of unobserved resistance are given more weights.
Table 8 reports ATE, TT, TUT, and the difference between TT and TUT. ATEs
are similar to our baseline IV estimates (see Table 5). TUTs tend to be stronger(or “better”) than TT on mother’s labor supply and child outcomes. For childcare
expenses, TT is significantly larger than TUT, while TUT is significantly larger thanTT for other expenses than childcare. No significant differences are found for otheroutcomes.

45

Table 8: Aggregate Treatment Effect Parameters

ATE
Market Participation
0.591(0.092)
Hours Worked
27.398(3.814)
Language Development
0.546(0.191)Aggression
0.002(0.235)ADHD-0.375(0.282)
Parenting Quality
0.046(0.221)Childcare Expenses
2.311(0.427)
Other Expenses
1.168(0.516)Lack of Parenting Knowledge 0.039(0.070)Subjective Well-Being
0.022(0.195)TT
0.546(0.118)17.728(5.169)0.383(0.277)0.440(0.339)0.139(0.410)0.139(0.337)3.395(0.646)0.188(0.675)-0.011(0.094)0.178(0.258)

TUT TT - TUT
0.611-0.065(0.104) (0.125)
31.876 -14.148(4.303) (5.398)
0.622-0.239(0.214) (0.297)-0.198 0.638(0.243) (0.317)-0.609 0.748(0.270) (0.336)
0.003
0.136
(0.224) (0.315)
1.811
1.584
(0.424) (0.588)
1.621-1.433(0.562) (0.635)
0.062-0.073(0.074) (0.086)-0.050 0.228(0.216) (0.262)

Note: Standard errors are clustered at the region level. ATE is the average treatment effect, TT is
the treatment effect on the treated, and TUT is the treatment effect on the untreated.

46

Weight

0.03

0.02
TT
TUT
0.01

0.00
0.00

0.25

0.50

0.75

1.00

ud (Resistance to Treatment)

Figure 3: Weights for TT and TUT

C

Additional Tables and Figures (For Online Publication)
Table 9: Coordinates of Each Response

AlwaysSometimes
Never

Explain
0.358−1.802−1.828

Just No−2.1600.416
1.489

Ignore−3.422−0.165
1.271

Spank−4.480−0.8500.471

Confine−7.311−1.592
0.438

Source: LSN21 and authors’ calculation.
Note: The coordinates of each response in the multiple correspondence analysis are reported. The
number in the cell indicates how each item increases/decreases the parenting quality index.

47

27

73
24

Percent

Percent

60

22

20

13
10

10

40

20

18

4
0

0

<7

7−8

−11

8−9 9−10 10

−12

11

> 12

0

1

2

1

2

Hours Per Day

3

3

3

4

0
5

6

Days Per Week

Figure 4: Distributions of Hours and Days in Childcare Center
Source: LSN21.
Note: All children are in two-parent family and at age 2.5.

●

% Enrolled in Childcare

100

100

88
●

68

75

●
● Treateda50

a Untreated
25

0

10

14

●

0

2

0.5

1.5

0
2.5

3.5

Age

Figure 5: Childcare Enrollment Over Time
Source: LSN21
Note: Treated children are those enrolled in childcare at age 2.5. Untreated children are those notenrolled.

48

7

Table 10: Parameter Estimates for Selection Equation

Coverage Rate and Intercept
Intercept
Coverage Rate
Coverage Rate Squared
MotherAge
Age-Sq.
Less Than HS
HS
2-Yr College
Cov. Rate × Age
Cov. Rate × Less Than HS
Cov. Rate × HS
Cov. Rate × 2-Yr College
Cov. Rate Sq. × Less Than HS
Cov. Rate Sq. × HS
Cov. Rate Sq. × 2-Yr College
FatherAge
Age-Sq.
Less Than HS
HS
2-Yr College
Cov. Rate × Age
Cov. Rate × Less Than HS
Cov. Rate × HS
Cov. Rate × 2-Yr College
Child
Born in July 2001
Born in 2010
Normal-Birth-Weight Boy
Low-Birth-Weight Boy
Low-Birth-Weight Girl
Cov. Rate Sq. × Normal-Birth-Weight Boy
Cov. Rate Sq. × Low-Birth-Weight Boy
Cov. Rate Sq. × Low-Birth-Weight Girl
RegionBorn in 2010 × Female LFP Rate
Born in 2010 × Fertility Rate
Born in 2010 × Financial Status
Born in 2010 × Log Land Price
Born in 2010 × Log Mean Female Wage

49

Estimate

Std. Error-0.328
2.179
0.954

0.530
1.786
1.710

-0.0060.046-1.466-1.240-0.835-0.052
4.821
3.008
2.114
-4.924-2.163-1.636

0.029
0.042
0.472
0.201
0.198
0.024
2.546
1.079
1.105
3.205
1.249
1.383

-0.078
0.077
0.803
0.372
0.216
0.054
-0.251
0.081
0.450

0.020
0.026
0.123
0.061
0.086
0.015
0.372
0.177
0.264

-0.082-0.338
0.081
0.248
0.122
-0.057-0.559-0.565

0.025
1.082
0.041
0.111
0.118
0.122
0.325
0.349

0.102
-0.219-0.149
0.076
0.293

0.767
0.380
0.294
0.095
0.619

50

-0.018(0.008)-0.010(0.004)-0.022(0.008)-0.008(0.008)0.724(0.012)-0.105(0.016)-0.002(0.005)-0.005(0.016)0.001(0.013)15.3901.783-0.031(0.021)-0.015(0.008)-0.021(0.019)-0.010(0.019)0.690(0.087)-0.082(0.065)0.014(0.021)-0.006(0.053)0.004(0.062)

Note: Parameter estimates for Equation (1). See note for Table 4.

Test Statistics
Weak Instrument
Wu-HausmanChildcare × LBW GirlChildcare × LBW Boy

Childcare × NBW Boy

Childcare × Low-Edu.

Childcare

Low Birth Weight Girl

Low Birth Weight Boy

Normal Birth Weight Boy

Coefficients
Low-Education MotherParticipation
OLS
IV-1.202(0.352)-0.279(0.118)-0.552(0.270)-0.209(0.285)27.428(0.385)-8.437(0.625)-0.654(0.212)-0.871(0.716)0.348(0.598)15.5471.286-1.390(0.922)-0.346(0.344)-0.057(0.741)0.069(0.898)32.128(3.173)-6.573(2.770)-0.633(1.017)-2.530(2.394)-0.398(2.845)

Hours of Work
OLS
IV
-0.081(0.029)-0.375(0.010)-0.781(0.034)-0.172(0.024)0.115(0.007)0.008(0.036)0.192(0.016)0.357(0.050)0.091(0.034)15.5222.278-0.009(0.055)-0.387(0.020)-0.922(0.089)-0.083(0.055)0.460(0.212)-0.096(0.159)0.217(0.059)0.774(0.265)-0.192(0.175)

Language
Development
OLS
IV
0.199
(0.039)0.296(0.010)0.410(0.024)0.072(0.017)0.016(0.015)-0.043(0.066)-0.014(0.018)0.009(0.043)-0.035(0.036)13.9002.374

0.329(0.069)0.341(0.017)0.392(0.054)0.166(0.056)0.095(0.215)-0.463(0.223)-0.161(0.051)
0.063(0.163)-0.354(0.201)Aggression
OLS
IV

0.170
(0.031)0.218(0.009)0.350(0.030)0.080(0.020)-0.037(0.016)-0.044(0.056)0.002(0.016)-0.026(0.052)-0.018(0.041)14.8681.158

0.234(0.065)0.238(0.020)0.441(0.079)0.117(0.064)-0.356(0.295)-0.372(0.180)-0.045(0.062)-0.307(0.208)-0.160(0.217)

ADHD
OLS
IV

Table 11: Effects of Childcare Enrollment on Mother’s Work and Child Outcomes

51-0.335(0.031)-0.169(0.010)-0.221(0.032)-0.085(0.028)-0.018(0.014)0.134(0.042)0.020(0.019)0.038(0.035)0.042(0.044)14.7991.604-0.505(0.091)-0.178(0.019)-0.245(0.077)-0.105(0.073)0.010(0.235)0.692(0.244)0.049(0.067)0.119(0.202)0.111(0.225)

Note: Parameter estimates for Equation (1). See note for Table 4.

Test Statistics
Weak Instrument
Wu-HausmanChildcare × LBW GirlChildcare × LBW Boy

Childcare × NBW Boy

Childcare × Low-Edu.

Childcare

Low Birth Weight Girl

Low Birth Weight Boy

Normal Birth Weight Boy

Coefficients
Low-Education Mother

Parenting Quality
OLS
IV
0.111
(0.022)0.001(0.006)0.032(0.019)0.015(0.018)2.646(0.052)-1.570(0.068)-0.014(0.024)-0.079(0.092)-0.009(0.064)15.2466.369-0.137(0.115)0.046(0.020)0.033(0.054)0.099(0.068)2.865(0.423)-0.780(0.291)-0.170(0.069)-0.086(0.145)-0.287(0.235)

Childcare Expenses
for the Child
OLS
IV
0.029
(0.057)-0.061(0.023)0.001(0.053)-0.007(0.043)-0.016(0.033)0.094(0.084)0.066(0.035)0.149(0.175)0.141(0.080)15.0572.581-0.006(0.146)-0.061(0.063)-0.303(0.170)-0.063(0.109)1.320(0.559)0.614(0.387)0.013(0.174)1.016(0.579)0.387(0.386)

Other Expenses
for the Child
OLS
IV-0.010(0.009)0.007(0.003)0.027(0.007)0.025(0.008)-0.006(0.003)-0.005(0.012)-0.004(0.005)-0.014(0.013)-0.019(0.013)15.4071.620

0.043(0.021)0.000(0.005)0.013(0.021)0.017(0.021)
0.036(0.064)-0.149(0.054)0.017(0.016)0.026(0.058)0.009(0.066)Lack ofParenting Knowledge
OLS
IV

Table 12: Effects of Childcare Enrollment on Parent’s Outcomes-0.212(0.031)-0.013(0.009)-0.098(0.029)-0.059(0.019)0.006(0.014)-0.016(0.046)0.001(0.016)0.042(0.044)0.050(0.046)15.1462.670-0.400(0.059)-0.040(0.021)-0.118(0.060)-0.117(0.067)-0.029(0.215)0.577(0.170)0.086(0.059)0.111(0.156)0.243(0.228)

Subjective
Well-Being
OLS
IV