Discussion Papers In Economics
And BusinessCan child benefits encourage parents’ attitudes toward achildrearing environment in Japan?
Effects of the expansions of the child benefit policy

Shinsuke Asakawa

Discussion Paper 19-04-Rev.2

April 2020

Graduate School of Economics
Osaka University, Toyonaka, Osaka 560-0043, JAPANNoname manuscript No.
(will be inserted by the editor)

Can child benefits encourage parents’ attitudes toward a childrearing environment
in Japan?
Effects of the expansions of the child benefit policy
Shinsuke Asakawa

Abstract This study clarifies the effects of the expansion of Japan’s child benefit policy (CB) on parental attitudes
toward childrearing environments in terms of culture, education, and childcare. A fixed-effect model was employedon data from Osaka University’s Preference Parameters Study, which provides large-scale longitudinal microdata covering not only specific individuals but also their families. Compared with the control group of parents
with only high-school children aged 16-18, the CB expansion, on average, increased the priority given to childcare
environments by one grade. The subsample analysis revealed that parents with only pre-school children (0-6 years
old) came to hope for a better childcare environment and those with only primary-school-aged children (7-12 years
old) demanded a better educational environment. Moreover, by dividing the respondents by whether the householdincome was above the mean, parents with higher household income levels were shown to increase the priority theygave to the educational environment in exchange for a decrease in the priority given to the childcare environment.
However, parents in the lower household income groups increased the priority they gave to the childcare environment. These results indicate that the unpredicted CB expansion led parents to react differently to the neighborhood
environment, depending on their child’s age and household income. Hence, the most important implication of thisstudy is that the government should carefully choose target households in light of policy objectives and not increase
the opportunity gaps between households when introducing new financial support policies.
Keywords child benefit · neighborhood environment · parental attitude · program evaluation · impact heterogeneity
JEL classification H52, I22, I24, I28, J13
Acknowledgments I acknowledge the advice from Masaru Sasaki, Fumio Ohtake, Masato Nishiwaki, Katsuya
Takii, Nobuyoshi Kikuchi, Hideo Akabayashi, Hidehiko Ichimura, Daiji Kawaguchi, Ryo Kambayashi, Yukinobu
Kitamura, Shinpei Sano, as well as the participants at the Asian and Australasian Society of Labour Economics(AASLE) 2018 Conference, Japanese Economic Association Fall Meeting 2018, East Asian Economic Association(EAEA) 16th International Convention, and 2019 Asia Meeting of the Econometric Society (2019 AMES). I usedmicrodata from the Preference Parameters Study of Osaka University’s 21st Century COE Program Behavioral
Macrodynamics Based on Surveys and Experiments and Global COE project Human Behavior and Socioeconomic Dynamics. I would also like to thank the project contributors―Yoshiro Tsutsui, Fumio Ohtake, andShinsuke Ikeda. I am solely responsible for any remaining errors.

1 Introduction
Based on the economic growth viewpoint, the income gap between the rich and poor households is widening
because rich households benefit the most from the recent economic development OECD (2015). The income gap
between generations can thus lead to an opportunity gap in accumulating human capital for subsequent generations.
The existing research shows that a higher socioeconomic status (SES) is associated with better educational andlabor marker outcomes (and vice versa), 1 and, as a result, intergenerational mobility remains challenging.2
Research Fellow, Japan Society for the Promotion of Science, Tokyo, Japan
Graduate School Student, Graduate School of Economics, Osaka University, Osaka, Japan
E-mail: u693464e@ecs.osaka-u.ac.jp
ORCID: 0000−0002−1756−0915
1 See Bradley and Corwyn (2002); Breen and Jonsson (2005); Conger, Conger and Martin (2010); Corak (2006); Hoff (2003); Kulic, Skopek,
Triventi and Blossfeld (2019)
2 See Black and Devereux (2011); Corak (2013); Solon (1999, 2002)

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Shinsuke Asakawa

However, there is a need to identify what drives the disparities in child outcomes: heredity or the environment? If the environment contributes to a child’s development, the social mobility of those with high ability canbe enhanced by providing financial and educational support to economically disadvantaged children. Therefore,
many researchers have tackled the nature versus nurture debate by applying various identification strategies (e.g.,
identical twins, adopted children, and genetic markers).3 While some studies point out significant correlations between pre- and post-birth factors, they conclude that not only pre-birth factors but also post-birth factors affect achild’s economic and educational outcomes. Moreover, Björklund and Jäntti (2012) show that the most important
post-birth factor for a child’s development is the family-specific background, defined as the experience shared withhis/her sibling (measured by the outcome correlation between a child and his/her sibling). However, more recent
studies reveal that the neighborhood environment also plays a vital role on a child’s outcomes. In sum, these studies
suggest that the parental attitudes toward their home and neighborhood environments can likely improve a child’s
outcomes by increasing the investment in the child’s rearing.
In the context of Japan, Matsuoka and Maeda (2015) conduct a review showing that parents in higher SES
households attribute higher importance to education, revealing that their attitudes also differ depending on theneighborhood environment (e.g., percentage of college graduates in the neighborhood). As a result, parents inhigher SES households tend to spend more on shadow education Matsuoka (2018). These studies suggest thatparents would enhance their attitudes toward not only education but also the home and neighborhood environments
if their household incomes were to exogenously increase. In this regard, an ideal exogenous variation―that is, theexpansion of CB in April 2010―can be used for causal inferences.
Since 1961, the Japanese government has been offering the CB to childrearing households, aimed at reducing the financial burden of increasing childcare costs. As childrearing expenditure kept increasing and household
structures kept changing, the government gradually and endogenously expanded the CB until March 2010. However, in August 2009, the Democratic Party in Japan (DPJ) overcame the long-lasting party in power at the time,that is, the Liberal Democratic Party (LDP). The DPJ campaigned for expanding social security services such as
childrearing, education, health care, and pensions. Although the electorate did not expect to defeat the LDP, theelection led to this unexpected regime change (see Noble (2010)). After April 2010, the DPJ increased the child’s
age threshold from 12 to 15 years and abolished the household income threshold. As a result, the number of recipients, as well as payments, increased; specifically, the monthly payment increased from JPY 5,000 per child (forfirst and second children) and JPY 10,000 per child (after the third child or for children under three years old) to
JPY 13,000 per child, regardless of age or birth order. Therefore, the nationwide and clear-cut policy changes in
the CB resulted in variations in the level of CB expansion among households. This exogenous variation can be
regarded as a quasi-natural experiment, with several previous studies estimating the impacts of the CB expansion
in April 2010 on various outcomes (See Bessho (2018); Naoi, Akabayashi, Nakamura, Nozaki, Sano, Senoh andShikishima (2017); Takeshita (2016)).
Specifically, Takeshita (2016) reveals that the CB expansion in April 2010 had no effect on targeted parents’attitudes toward their children’s education. Conversely, Naoi et al. (2017) shows that the policy change increased
the educational expenditure per child. Overall, these studies imply that parents improve not only their attitudes
toward education but also on the surrounding environment, including shadow education and extracurricular activities. Hence, this study tackles the following two research questions:
1. Did the CB expansion in April 2010 enhance parental attitudes toward the neighborhood environment contributing to child development?
2. Which households improved their attitudes toward the neighborhood environment based on the child’s age andhousehold income?
The definition of parental attitudes toward the neighborhood environment is thus crucial for this study. The
study uses a longitudinal questionnaire survey of 4,335 households selected randomly from a residential map
stratified by residential regions and city sizes. The main advantage of this survey is the information on the reasons
for which the respondents decide where to live. Out of 17 alternatives, the respondents can choose up to four reasons
and rank them in the order of their importance. To estimate the impacts of the CB expansion on parental attitudes
toward the neighborhood environment, this study quantifies respondents’ priority to cultural, educational, andchildcare environments, ranked as 4 for the first place, 3 for second, 2 for third, 1 for fourth, and 0 if not selected;
then, these values are used as dependent variables. Moreover, I control for several demographic characteristics.
3 Refer to the studies on identical twins Ashenfelter and Rouse (1998); Ashenfelter and Krueger (1994); Björklund and Jäntti (2012), adopted
children Björklund, Lindahl and Plug (2006); Plug and Vijverberg (2003); Sacerdote (2002), and genetic markers Lehrer and Ding (2017); Lin(2020); Papageorge and Thom (2019); Rauscher (2017); Thompson (2014)

Can child benefits encourage parents’ attitudes toward a childrearing environment in Japan?

3

Another important factor is the identification strategy. In comparing the outcomes of the pre- and post-treatment
periods, for example, a fixed effects model (FEM) or a difference-in-differences (DID) model can be used toestimate the average treatment effects. The estimators are biased, unless the time series variations in outcomes
satisfy the common trend assumption after all covariates are conditioned.4 . However, there exists the concern ofviolating the common shock assumption of the CB expansion by the introduction of a free high-school education
policy (FEP). Specifically, for the control group, parents with at least one high-school student were newly assigned
to the FEP at the same time as the CB expansion.5 The FEP removed the tuition fee at public high schools, making
the education virtually free. Additionally, the government offered an official grant, which was similar for all parents
with children studying in private high schools. Hence, the total FEP benefits are directly incorporated into the FEM
approach to eliminate omitted variable bias.
Parents with children that have not yet graduated from junior high school are assigned to the treatment group,and the ones with high-school-aged children (ages 16-18) form the control group. The main results, derived by
FEM, control for household-level (individual) fixed effects and reveal that the priority assigned by parents to thechildcare environment increased by around 0.08 after the CB expansion. 6
Next, the study classifies the treatment groups by the different effects of the CB expansion depending on thechild’s age: (1) parents with only pre-school-aged children (ages 0-6), (2) parents with only primary-school-aged
children (ages 7-12) that had been treated before the expansion, and (3) parents with only junior-high-school-aged
children (ages 13-15) that could receive benefits from fiscal year 2010. As a result, the CB expansion of April 2010
led parents with pre-school children (0-6) to increase the priority assigned to a childcare environment by around0.17. However, the parents with only primary-school-aged children (ages 7-12) did not improve their attitudes
toward the childcare environment but rather increased the priority to the educational environment by around 0.15.
The expansion in CB did not have any impact on the priority given to the living environment among parents with
only junior-high-school-aged children (ages 13-15).
Finally, by dividing respondents into groups based on being above or below the mean household income, thisstudy analyzes whether the impacts of the CB expansion differ by annual household income. This subsample
analysis reveals that parents whose household incomes are above the mean give higher priority to the educational
environment by about 0.16, and lower priority to the childcare environment by around 0.1. Moreover, these impacts
can only be seen for parents with junior-high-school-aged children. Respondents whose incomes are below themean household income, however, attribute a higher priority to the childcare environment. Therefore, the study
can conclude that the older children are and the higher the household income is, the more parents pay attention to
the educational environment. At the same time, the younger children are and the lower the household income is,the more parents pay attention to the childcare environment.
This study makes two important contributions to the literature. First, the study divides parental attitudes toward
the environment into three components―cultural, educational, and childcare environments. Previous researches
generally combined several neighborhood environment types into one indicator to evaluate the effects of a single
proxy variable for the quality of the neighborhood environment on child development. Another literature stream
on the impacts of CB expansion (Takeshita (2016)) and the effects of neighborhood SES and their educational
attainment (Matsuoka and Maeda (2015); Matsuoka (2018)) evaluates only parental attitudes toward education.
Therefore, this study adds to the literature by identifying which attitudes toward the childrearing environment are
affected by childrearing financial support from the government. The second contribution relates to identifying theheterogeneity of the CB expansion effects depending on the child’s age. In this regard, Lareau (2003) shows thathigh- and low-SES households have different strategies for child education; specifically, parents in higher- (lower) SES households provide structured (unstructured) education to their children. These findings imply that the CB
expansion has different impacts for higher- and lower-income households. Hence, this study also estimates the
subgroup treatment effects, divided by whether the household income is above the mean.
This remainder of the paper proceeds as follows. The backgrounds of the CB and FEP are explained in Section
2. Section 3 describes the empirical methods and procedures, and the data is then summarized in Section 4. The
results for the full sample and subsample analyses are shown in Section 5. Finally, the conclusions are presented
in Section 6.
4

See Ashenfelter and Card (1985); Card and Krueger (1994); Donald and Lang (2007)
For the CB and FEP, it is assumed that almost all parents with children aged 18 years or below were assigned to one of these policies after
fiscal year 2010, because the compulsory education requirements means that all children need to complete junior high-school by the age of 15,
while over 96% of them continue to high school in Japan (MEXT (2017)). This situation makes it possible to mitigate the selection problem.
Hence, these policy changes can be regarded as natural experiments for the targeted households.
6 The FEM approach may produce measurement errors because the priority of each living environment is based on subjective evaluations.
5

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Shinsuke Asakawa

2 Policy Background
2.1 Child benefit policy

Temporal Transition in Public Expenditure

40000

30000

variable
Total_Benefit
20000

Treasury_liability

10000

0
1970

1980

1990

2000

2010

Number of Children Receiving Child Benefit (Thousand)Annual Public Expenditure of Child Benefit (JPY 1 billion)

The CB was introduced in 1972 through the enforcement of the Child Allowance Act to prevent parents withmany children from slipping into poverty.7 Initially, the policy targeted parents whose annual household income,including tax, was less than JPY 2 million and who had more than three children. As in most countries that
implemented a CB, it started as a means-tested cash transfer policy. Detailed information about the historicalevolution of the CB from 1972 is shown in Figure 1 and Table 1.
Temporal Transition in Receipients

15000

10000

5000

1970

Year

1980

1990

2000

2010

Year

Fig. 1 Temporal Change of Child Benefit in Annual Public Expenditure and Recipients

After the many changes, both on child age and birth order and number of payments, all parents with at least
one child aged below 15 became eligible, regardless of the child’s birth order; additionally, the income thresholdhas been abolished since fiscal year 2010. Specifically, before fiscal year 2010, parents could receive JPY 5,000
per child for their first and second children and JPY 10,000 per child for their third child onward until the children
graduated from primary school at the age of 12. Furthermore, all parents were paid JPY 10,000 per child if the child
was less than 3 years old. However, these payments were offered only to those parents whose household income
was less than JPY 7.8 million including tax (JPY 8.6 million including tax for Employees’ Pension Subscriber).
After the CB expansion in April 2010, all parents received JPY 13,000 per child until the child turned 15 andgraduated from junior high school, without any conditions on household income. Hence, parents with children
aged between 13 and 15 could start receiving benefits from fiscal year 2010 onward after the CB expansion, andparents with children younger than 13 were also eligible and could be paid more than before. 8
However, in October 2011, due to budgetary deficit, the qualification for receiving the child benefits was again
revised based on the child’s age and birth order. Specifically, all parents could receive JPY 10,000 per child aged
under 15 years old. They were additionally paid JPY 5,000 if their child was less than 3 years old or if neither
the first nor the second child was between 3 and 12 years old; moreover, the income threshold was revised fromfiscal year 2012. These changes in the qualification criteria and amounts paid are shown in Table 1. Therefore, allrespondents with children aged 18 or younger received child benefits between April 2010 and October 2011.
In practice, the child benefits in Japan are transferred to households every four months, that is, in February,
June, and October. However, the data this study uses were gathered between January and March,9 so it is unclear
whether respondents answered after they had received child benefits based on the new criteria implemented in
October 2011.
7 The Ministry of Health, Labour and Welfare implemented the CB expansion. The policy’s jurisdiction was then transferred to the Cabinet
Office.
8 The existing tax credit for dependents was also reduced, according to the income tax for household income in April 2011 (in April 2012
for inhabitant tax). Regarding the policy, Bessho (2018) showed that parents reacted with a decrease in their labor supply. This means they
adjusted their labor supply to maintain the pretax total household income unchanged. Hence, the effects of the tax reform were removed bydirectly controlling for the total household income including tax.
9 For the 2009 survey, the respondents answered the questionnaires in February and March, and in January and February for the 2010 survey.
After 2011, the survey was sent to respondents and the responses were gathered between January and March.

Between 3 and 12

1st and After (Over 3 years old)

Less than 3 years old

3rd and After

Oct. 2011

5,000 JPY (1st and 2nd) and 10,000 JPY (3rd and After)↓ Gradually Update

-

-

-

-

780 (860) million JPY

780 (860) million JPY

780 (860) million JPY

596.3 (780) million JPY

15,000 JPY

15,000 JPY

10,000 JPY

13,000 JPY

10,000 JPY

5,000 JPY (1st and 2nd) and 10,000 JPY (3rd and After)

5,000 JPY (1st and 2nd) and 10,000 JPY (3rd and After)

5,000 JPY (1st and 2nd) and 10,000 JPY (3rd and After)

5,000 JPY (1st and 2nd) and 10,000 JPY (3rd and After)

5,000 JPY (1st and 2nd) and 10,000 JPY (3rd and After)↓ Gradually Update596.3 (780) million JPY

5,000 JPY

2,500 JPY

5,000 JPY

4,000 JPY

3,000 JPY

Monthly Payment from the Child Benefit (CB)340.6 (558.9) million JPY

340.6 (558.9) million JPY

415 million JPY

322 million JPY

200 million JPY

Threshold of Household Income

Note: JHS = junior high school, KG = kindergarten, PS = primary school. The thresholds for household income for employees’ pension subscribers are between parentheses since they were larger than that for the others.

Until JHS Graduation

1st and After

Until JHS Graduation-

Apr. 2010

New CB

2007

Until PS Graduation

Less than 3 years old

3rd and After

2006

Until 3rd Grade in PS

Until PS Graduation

3rd and After

2004

Until KG Graduation

Until KG Graduation

Until less than 3 years old

Until JHS Graduation

Until JHS Graduation

1st and After (Over 3 years old)

3rd and After

2001

Old CB

3rd and After

3rd and After

2000

Only 2nd1986

1rd and After

Until JHS Graduation

3rd and After

1975

1992

Until less than 2 years old

3rd and After

1974

Until JHS Graduation

3rd and After1972

Supply Period

Target Children

Enacted Year

Table 1 Temporal Transition on the Child Benefit Policy in Japan

Can child benefits encourage parents’ attitudes toward a childrearing environment in Japan?
5

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Shinsuke Asakawa

Here, the policy change of interest is the CB expansion of April 2010. Therefore, this study assumes parentschange their attitudes toward the childrearing environment based on the child benefit payment―JPY 13,000 per
child until age 15 and graduation from junior high school―and treats the rise in child benefits as an exogenous
shock, as in Naoi et al. (2017). In sum, this study estimates the impacts of the CB expansion by using the above
variation.

2.2 Free education policy for high school students
The Ministry of Education, Culture, Sports, Science and Technology (MEXT) has implemented a wide variety ofenrollment support aimed at reducing education expenses. For example, the FEP was one of the most importantand prevalent forms of official support to minimize the financial burden on households, regardless of their financial
condition MEXT (2009). This policy was called the Free Tuition at Public High Schools and the High School
Enrollment Support Fund, being approved on March 31, 2010, and enforced from April 1, 2010, when the DPJ
promoted the Public High-School Tuition Fee Gratuity and High School Support Grant System.
The FEP led to public high-school students being exempted from paying tuition fees. A similar grant based onhousehold income was also offered to all parents with children enrolled in private high schools. Specifically, allparents in Japan were exempt from paying JPY 118,800 per public high school student aged 16-18, while parents
with children in private high schools were compensated with JPY 236,000, JPY 178,200, and JPY 118,800 if theirhousehold income including tax was below JPY 2.5 million, between JPY 2.5 million and 3.5 million, and above
JPY 3.5 million, respectively. As per these policies, high schools received the money on behalf of the students andoffset their tuition fees to ensure smooth policy implementation. Therefore, parents were not directly paid, unlikein the case of the CB.
In fiscal year 2013, the amount paid to parents with a child enrolled in a private school was altered to JPY
247,500, JPY 198,000, JPY 148,500, and JPY 118,800 if the household income including tax was below JPY 2.5
million, between JPY 2.5 million and 3.5 million, between JPY 3.5 million and 5.9 million, and between JPY 5.9
million and JPY 9.1 million, respectively. That is, all households that earned less than JPY 9.1 million annually
could receive benefits through the FEP since fiscal year 2013. However, this study analyzes the impact of the CB
expansion in fiscal year 2010-2011 in terms of the parental attitudes toward the neighborhood environment. Hence,
only the benefits from FEP before fiscal year 2012 will be controlled for.

2.3 Monthly benefits from CB and FEP: Examples
The temporal transition of the monthly benefits from the CB and FEP between 2009 and 2013 is illustrated in Figure
2. In these examples, the total payment amounts from both policies are calculated for two children, because thesample for the primary analysis in this study only comprises parents with children under 18 and with two children
on average. Monthly payments over the survey period between January and March were used. Chronologicalvariations are shown in red (children under 3 years), blue (ages 3-12), green (ages 13-15), and orange (households
over income threshold) for CB, and gray (ages 16-18) for FEP.

5000 10000 15000 20000 25000 30000

Under Age 3
Age 4−12

Age 13−15
Over Income Threshold0

Amount of Subsidy (Per two children, JPY)

Child Benefit Schedule by Age Cohort

Feb.−Mar.2009(2009)

Jan.−Feb.2010(2010)

Jan.−Mar.2011(2011)

Jan.−Mar.2012(2012)

Timing of Survey (Sample Year)

Fig. 2 Temporal Variation of Monthly Child Benefit from fiscal 2009 to 2013

Jan.−Mar.2013(2013)

Age 16−18

Can child benefits encourage parents’ attitudes toward a childrearing environment in Japan?

7

3 Empirical Method and Procedure
The difficulties in inferring the policy impacts of the CB expansion are first presented. In Figure 3, the average
priorities of the parents with children aged 0-18, 0-6 (pre-school), 7-12 (elementary school), 13-15 (junior highschool), and 16-18 (high school) are represented by the black, red, blue, green, and gray lines, respectively. Here,for each priority, the time trends in the pre-treatment periods seem to differ between parents with and without
eligibility, as well as among eligible parents. This could be because the parents with children aged 0-12, as thetreatment group, had already received and continued to receive child benefits, unlike the parents with children inother age groups. The control group, comprising parents with a single child in high school, newly received benefits
from the FEP. This evidence breaks the common trend assumption. Therefore, the temporal trends are removed byusing a standard panel analysis, for example, pooled ordinary least squares (OLS) and FEM.

2009

2010

Treatment2012

Age 13−15
Age 16−18

2009

2010

Treatment

2012

0.4

Age 13−15
Age 16−18

0.2

0.3

Age 1−18
Age 1−6
Age 7−12

0.1

Average Priority of Childcare Environment

Age 1−18
Age 1−6
Age 7−12

Demand for Childcare Environment

0.0

0.4
0.3
0.2
0.1

Average Priority of Educational Environment

Age 13−15
Age 16−18

Survey Year

Demand for Educational Environment

0.0

0.4
0.3
0.2
0.1

Age 1−18
Age 1−6
Age 7−12

0.0

Average Priority of Cultural Environment

Demand for Cultural Environment

2009

Survey Year2010

Treatment

2012

Survey Year

Fig. 3 Temporal Transition in Original Average priorities to Each Living Environment

Here, the panel analysis to estimate the attitude function for newborns and each residential childrearing environment is explained. In panel analyses, researchers usually estimate various models, according to the numberof independent variables or types of identification strategies. After the empirical testing, the model that is mostsuitable in terms of both consistency and efficiency is selected. Accordingly, the model with the smallest variancein consistency is adopted here. The following discussion in this section is based on Croissant and Millo (2008);
Firebaugh, Warner and Massoglia (2013); Greene (2003); Wooldridge (2010).
Before describing the estimation methods, the dependent variables used in this study are explained. First, thenumber of newborns compared to the number of children in fiscal year 2009 is defined as the proxy of parental
demand for child quantity. For parental attitudes for living environments, the same proxy variables are used suchas the priority to cultural environment (Culture), educational environment (Education), and childcare environment(Childcare), respectively.
The standard pooled OLS is presented first. Let yit be a vector of dependent variables―the priority given to thecultural environment (Culture), educational environment (Education), and childcare environment (Childcare)―and X( it) be a vector of observable explanatory variables, where zi and xit are vectors of the time-invariant variablethrough the sample period and the time-variant variable among Xit , respectively. Moreover, the benefits from the
CB and FEP normalized to the post-treatment baseline (JPY 13,000 and JPY 11,880 per month) are controlledfor. This study uses 4,335 respondents and three periods―2009, 2010, and 2012―for the panel sample. For error
terms, let µi be an individual fixed effect (FE) of i ∈ {1, 2, ..., 4335}, vt be a time dummy of t ∈ {2009, 2010, 2012},and εit be other measurement errors from idiosyncratic effects. Based on the above notation, the pooled OLS canbe written as follows:
Pooled OLS
yit = ai zi + bit xit + α1CBit + α2 FEPit + µi + vt + εit= ai zi + bit xit + α1CBit + α2 FEPit + uit(1)

In Eq. (1), the consistent and efficient estimator of the impact of the CB expansion, represented by α1 , canbe derived by standard OLS but only if the orthogonal condition, that is, E(CBit , uit ) = 0, is satisfied, where uit
represents the sum of all error terms and is assumed to be independently and identically distributed (i.i.d.), withmean 0 and variance σu2 . This means that estimator α1 loses consistency if some error terms correlate with the8Shinsuke Asakawa

independent variables, including the CB expansion. Moreover, the estimators lose efficiency unless the error terms
causing heteroskedasticity are removed after the independent variables are controlled for. However, the pooled
OLS cannot specify which error terms generate heteroskedasticity and should be extracted, since Eq. (1) assumes
homoskedasticity for all error terms. The above problem is addressed by using FEM to remove the biases stemmingfrom time- or individual-specific heterogeneity.
Here, let µi and vt be individual and time FEs, respectively. As with the pooled OLS, uit includes all error
terms that are not estimated as constant terms. Additionally, let εit be the idiosyncratic effects not captured by
the explanatory variables and that vary with individuals and over time. This study formulates the following threemodels based on the constant term of only the time FE (i.e., time dummy), only individual FE, and none of them,with the first two being represented by FEM (Time) and FEM (Individual), and assuming that error term uit follows
IID (0, σu2 ), which is the same as in the pooled OLS.

FEM Time
yit = vt + ai zi + bit xit + α1CBit + α2 FEPit + µi + εit= vt + ai zi + bit xit + α1CBit + α2 FEPit + uit ,(2)

FEM Individual
yit = µi + ai zi + bit xit + α1CBit + α2 FEPit + vt + εit= µi + ai zi + bit xit + α1CBit + α2 FEPit + uit .

(3)

As a homoskedasticity test for the error terms is important for selecting the best model between the pooled
OLS and FEM, the following model selection procedure is carried out. In conducting a homoskedasticity test, thevalidity of the pooled OLS assumptions, where all error terms are i.i.d., is examined. If the null hypothesis is rejected, the FEM is adopted. However, which types of FE―time FE, individual FE, and idiosyncratic errors―create
heteroskedasticity is yet to be specified. Therefore, a homoskedasticity test is conducted, as proposed by Breusch
and Pagan (1979, 1980), who applied the Lagrangian multiplier test to specify the source of heteroskedasticity, andthen selected the best model in terms of efficiency from the pooled OLS and FEMs with individual and time FEs.
After ensuring homoskedasticity, more explanatory variables are added, in order, from the more recent variables. Here, it is assumed that the dependent variables have a strong connection with more recent household characteristics than older ones. Hence, the more recent ones are controlled for first and the others are added according
to their recency. Next, these models are compared from the viewpoint of the Akaike information criterion (AIC),
and the most efficient model is adopted. Finally, this study confirms the significance of the coefficients on the CB
and FEP―respectively α1 , α2 ― to classify whether the CB expansion (or the FEP) had causal impacts on theparental priorities to the childrearing environment when individual characteristics are controlled for.

4 DataThis study uses the Preference Parameters Study due to its requirement for large-scale microdata that cover not
only a specific individual but also the entire household including children, grandparents, spouses, and parents.10
The panel survey has been conducted annually since 2003 using a self-administered placement method against arandom sample of respondents aged 20-69. For waves 2004, 2006, and 2009, new samples were selected from the
Basic Resident Register (in 2003, 2004, and 2006) and a residential map (in 2009) stratified by residential regions
and city sizes; these were then combined with the data on previous respondents who continued to take the survey.
Specifically, respondents were asked to respond to the questionnaire during the subsequent survey until they nolonger answered the questionnaire or resampling did not occur. In 2006 and 2009, 2,000 and 6,000 individuals were
newly and randomly included as study subjects by two-stage stratified sampling, respectively. Of the participants
who responded in 2009, this study uses as a sample the 4,335 respondents who continued to respond until 2013.
This study mainly uses the data for 2009, 2010, and 2012 from the survey. However, for some time-invariant
characteristics―but those that were asked only in 2011 or 2013―the study also refers to the information on these10 This survey was conducted under the 21st Century Center of Excellence (COE) Program Behavioral Macro Dynamics Based on Surveys
and Experiments from 2003 to 2008 and Global COE Project Human Behavior and Socioeconomic Dynamics from 2008 to 2013 andfrom 2016 to 2018 at Osaka University. All survey questionnaires can be accessed at the following https://www.iser.osaka-u.ac.jp/
survey_data/survey_eng.html.

Can child benefits encourage parents’ attitudes toward a childrearing environment in Japan?

9

periods. In Japan, the fiscal year begins in April, and the survey was conducted from January to March, meaning
only the respondents from the 2012 survey can be regarded as the group assigned to the FEP and CB expansion.
The study assumes that more recent household characteristics correlate with the dependent variables, as discussed
in Section 3. Therefore, the proxy variables reflecting a household’s characteristics are chronologically preparedby each generation and explained in the following.11
First of all, the CB, which measures treatment impacts, are described. Before fiscal year 2010, only parents
with children below the primary school level and with annual household incomes below JPY 7.8 million were paid
JPY 5,000 per child for their first and second children and JPY 10,000 per child for every subsequent child or all
children under 3 years. the study refers to the existing policy as the old CB. After 2010, all parents with children
younger than 15 could be paid JPY 13,000 per month regardless of the household income. This revision is referred
to as the CB expansion. Here, the benefits from the monthly payment for an eligible child are normalized so thatthe total monthly benefits from the CB are divided by the amount of child benefits after fiscal year 2010 (JPY13,000) for each household and period. Each value is then assigned as CB payment for each household and period.
For example, 5/13 is assigned for parents with a treated child aged between 4-12 years before fiscal year 2010.
Hence, the magnitude of estimated coefficients can be interpreted as the effects when a parent with a treated child
newly comes to receive the expanded CB after April 2010.
Since the FEP is considered to only have an impact on the control group, the exempted tuition fee is evaluatedannually and added as an explanatory variable. Specifically, for parents with children in a public high school, theannual benefit was JPY 118,800 per child. For parents with children in private high schools, the annual compensation was JPY 236,000 if their household income with tax was below JPY 2.5 million, JPY 178,200 if the income
was between JPY 2.5 million and 3.5 million, and JPY 118,800 otherwise. Before the beginning of FEP, a valueof 0 was assigned to all parents, since there was no official support yet. Furthermore, the benefits from FEP forparents with children aged 16 are not available (NA) because detailed information about the school type of
their children can only be obtained for 2011 and cannot thus be specified for fiscal year 2012. This means that thisstudy can control for the impacts of FEP only for parents with children aged 17-18 that were already attending
high school before FEP started in fiscal year 2010.
This study controls the variables reflecting the purchasing power of each household. Initially, total householdincome is controlled based on the response to What is your annual household income including tax for theprevious year? JPY 0.8 million is assigned if income was below JPY 1 million, and JPY 1.5 million is assigned
if it was between JPY 1 and 2 million; then, the median of each section increased by JPY 2 million, and JPY
20 million was used for an income of JPY 20 million or more, as mentioned in Niimi (2016). Apart from thehousehold income variable, food, eating out, and total household expenditure excluding that on durable goods arealso controlled for. Here, all payment types are evaluated as JPY 10,000.
For the current parental characteristics, this study also uses (1) respondent characteristics variables by a dummythat takes 1 if the respondent is male, the number of desired children before the birth of the first child, and dummy
indicators of whether the respondent is living in a designated city and with at least a grandparent; (2) a specific areaof the education dummy, which takes 1 if the respondent lives in a prefecture with high educational intensity, suchas Tokyo, Osaka, Kyoto, and Hiroshima; (3) a Great East Japan Earthquake and radiation dummy, which takes
values from 1 to 4 based on the priority to move to another prefecture due to the earthquake itself or due to the fearof radiation effects.12 Moreover, to understand parental educational history, college graduate and high-school type
dummies (1 if they went to national or public schools and 0 if private) are used for both parents.
For the informal educational history of respondents with only pre-school or elementary school education, thisstudy uses their responses to Which of the following extracurricular activities did you engage in, including what
you learned from your parents? Please circle the applicable number. Information is thus gathered on whether they
took up swimming, ball games, fighting sports, instrument gymnastics, dance, singing, painting, calligraphy, igo,shogi, language school, print learning, higher cram school, supplementary cram school, or correspondence studies.
The corresponding dummy takes 1 if the respondent engaged in that activity.
The study also considers grandparental features. The proxy for the living standards of the grandparents when
the respondent was 15 takes values from 0 to 10 and is used for analysis. Moreover, for respondents and theirspouses, the dummy variables for whether their mothers had worked as regular or irregular workers when they11

A more detailed explanation of each variable is provided in Appendix Tables 1-4.
Using the same question on the priority in a living environment, the study attempts to control the impacts of the Great East Japan Earthquake
itself and the resulting radiation. These priorities are used for each alternative, Because there is no need to worry about earthquakes andBecause there is no need to worry about radiation, which were set only in 2012 and were newly added variables that are scored on a scaleof 4 to 0. These are similar to the attitude toward the neighborhood environment and are included in the current parental characteristics.
12

10

Shinsuke Asakawa

were 3, 7, and 15 are added to the model as independent variables.
The proxy variables capturing parental attitudes toward the living environment are as follows. The response toa double-layered question is used. The first question is If you can move another prefecture in Japan, do you hopethat? Please write ○ on one if you want and ○ on two if you do not want. and the second Why do you want tostay or move in such a prefecture? Please select four important reasons from the following alternative and circle ○and Write the ranking of 1 to 4 from the most important reasons in the four selected alternatives. 13 As such, therelevant dummy variable takes 1 for the desire of parents to move, and 0 otherwise. The indicators reflecting thepreference priority to each residential environment could be classified as Culture from the response to Because
the cultural environment is good (there are many institutions, such as music halls, theaters, libraries), Educationfrom Because the educational environment is good, and Childcare from Because there is an adequate numberof nursery schools and the childrearing environment is good. Similar to Contoyannis, Jones and Rice (2004), thisstudy uses a variable that takes 4 for the first place, 3 for second place, 2 for third place, 1 for fourth place, and 0
if not selected. The descriptive statistics are shown in Tables 2 and 3.

5 Results
5.1 Balance test between treated and control groups in the pre-treatment period
This study cannot estimate the true effects of the CB expansion if the dependent variables in the pre-treatment
period are potentially different between the treated and control groups. Therefore, before the main analysis, abalance test is conducted for the dependent variables between the groups to confirm whether each one has astatistically significant difference in the pre-treatment period (potential difference). Then, each estimator of thepolicy impact of the CB expansion is compared to the potential differences between groups. As such, an estimatedimpact is justifiable if the estimated effect of the CB expansion is far enough from the potential difference.
The Welch’s two-sample t-test is then implemented for a robustness check, even if the true variances differ
between groups. This is because not only the number of respondents but also their characteristics might differ
among the parents classified by their children’s age cohort; moreover, parents might move to a higher age cohort as
their children grow or move down if they have newborns within the sample period. The results of the balance testare provided in Table 4. Columns 1-5 represent the mean of each variable calculated by the four types of treatments
on parents whose children were aged 0-15 (full sample), 0-6 (pre-school), 7-12 (primary school), and 13-15 (juniorhigh school), and the control group. The p-values of Welch’s two-sample t-test between each of the four types oftreatment and control groups are shown in columns 6-9.
Initially, the p-test was conducted on the pre-treatment difference of the proxies for the living environment
between subgroups, divided by the child’s school attainment. Table 4, column 6 confirms that the average attitude
toward Childcare can potentially differ. After dividing the treated parents into three subgroups, only the treatedparents with at least one primary-school-aged child (ages 7-12) potentially preferred Education more than thecontrol group at the 10% significance level, as per Table 4, column 8. Furthermore, the potential differences in thefull sample are generated by the parents with pre-school-aged children (ages 0-6), as per Table 4, column 7.
Prior knowledge reveals the potential differences between the three treatment groups, that is, parents withpre-school-aged children, primary-school-aged children, and junior-high-school-aged children, and the referencegroup. Hence, this study will compare the estimated average treatment effects of the CB expansion with the potential differences between the treated and control groups to confirm whether the CB expansion, rather than thepotential differences between the groups, generated the differences in the estimations of the dependent variables.

13

The questionnaire is presented in Appendix Table 1.

Can child benefits encourage parents’ attitudes toward a childrearing environment in Japan?

11

Table 2 Basic Statistics : Full Sample (Age 0-18)

StatisticN

Mean

St. Dev.

Min

Max

Priority to Living Environment
Priority to cultural environment
Priority to educational environment
Priority to childcare environment

3,667
3,619
3,667

0.150
0.402
0.171

0.606
0.960
0.648

0
0
0

4
4
4

Parental Characteristics (Current)
Free education
Child benefit
Total household income
Total expenditure per month
Food expenditure per month
Eating-outside expenditure per month
Having a desire to move another prefecture
Living in a cabinet designed city
Male
Number of children
Number of desired children
Living with at least a grandparent
Special area for education
The great east japan earthquake

3,708
3,524
3,391
3,097
3,437
3,364
3,676
3,708
3,708
3,620
3,488
2,833
2,833
2,833

0.011
0.796
642.557
20.516
5.807
1.374
0.141
0.270
0.449
1.999
2.258
0.213
0.214
0.023

0.107
0.808
330.769
11.917
3.369
1.296
0.348
0.444
0.497
0.767
0.679
0.409
0.410
0.229

0.000
0.000
80
1
0
0
0
0
0
0
0
0
0
0

1.500
5.000
2,000
150
60
18
1
1
1
7
8
1
1
4

Parental Formal Education History
University graduation
University graduation(spouse)
High school typeHigh school type(spouse)

3,362
2,996
3,541
3,541

0.518
0.490
0.754
0.783

0.500
0.500
0.431
0.412

0
0
0
0

1
1
1
1

Parental Extracurricular History (preschool)
Swimming
Ball
Fighting
Gym
Dance
Music
Sing
Art
Penmanship
Igo
Shogi
Language school
Print learning
Higher cram school
Lower cram school
Corresponding learning

3,416
3,416
3,416
3,416
3,416
3,416
3,416
3,416
3,416
3,416
3,416
3,416
3,416
3,416
3,416
3,416

0.061
0.022
0.010
0.011
0.021
0.189
0.004
0.032
0.070
0.001
0.011
0.020
0.020
0.005
0.002
0.011

0.240
0.146
0.101
0.106
0.144
0.392
0.059
0.176
0.255
0.038
0.105
0.139
0.140
0.070
0.048
0.102

0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0

1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1

12

Shinsuke Asakawa

Table 3 Basic Statistics : Full Sample (Age 0-18)

StatisticN

Mean

St. Dev.

Min

Max

Parental Extracurricular History (primary school)
Swimming (primary)Ball (primary)Fighting (primary)Gym (primary)Dance (primary)Music (primary)Sing (primary)
Art (primary)Penmanship (primary)
Igo (primary)
Shogi (primary)
Language school (primary)Print learning (primary)Higher cram school (primary)Lower cram school (primary)Corresponding learning (primary)

3,416
3,416
3,416
3,416
3,416
3,416
3,416
3,416
3,416
3,416
3,416
3,416
3,416
3,416
3,416
3,416

0.184
0.254
0.097
0.030
0.029
0.298
0.026
0.058
0.551
0.008
0.033
0.081
0.184
0.121
0.157
0.233

0.387
0.435
0.297
0.169
0.168
0.457
0.160
0.233
0.497
0.090
0.178
0.273
0.388
0.326
0.364
0.423

0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0

1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1

Characteristics of Grandparents
Living Standard at 15 years old
Mother’s labor status (full time [FT]) at age 3
Mother’s labor status (part time [PT]) at age 3
Mother’s labor status (FT) at age 7
Mother’s labor status (PT) at age 7
Mother’s labor status (FT) at age 15
Mother’s labor status (PT) at age 15
Mother’s labor status (spouse, FT) at age 3
Mother’s labor status (spouse, PT) at age 3
Mother’s labor status (spouse, FT) at age 7
Mother’s labor status (spouse, PT) at age 7
Mother’s labor status (spouse, FT) at age 15
Mother’s labor status (spouse, PT) at age 15

3,669
3,541
3,541
3,541
3,541
3,541
3,541
3,541
3,541
3,541
3,541
3,541
3,541

5.164
0.302
0.168
0.338
0.272
0.396
0.370
0.269
0.165
0.299
0.238
0.339
0.303

1.796
0.459
0.374
0.473
0.445
0.489
0.483
0.443
0.371
0.458
0.426
0.474
0.460

0
0
0
0
0
0
0
0
0
0
0
0
0

10
1
1
1
1
1
1
1
1
1
1
1
1

0.23880.1409Priority to educational environment

Priority to childcare environment

Control

the t-test between the treatment and control groups. ∗ p<0.1; ∗∗ p<0.05; ∗∗∗ p<0.01and

0.08450.07870.16610.0084∗0.0351∗∗indicate the p-value of

0.875

0.5986
0.47160.83580.40190.0911∗0.82830.75030.39290.51580.0527∗(4)−(5)0.9568(3)−(5)Difference Between

0.77540.5047show the mean values of three treatment groups and a control group in both the pre- and post- treatment periods.

Treatment

0.10410.1966

Note: The columns

0.2046

0.25930.16670.18130.98147.427e-05∗∗∗

0.97760.30340.0002∗∗∗0.9654(2)−(5)

Subsample

Difference between

0.14940.8439Priority to childcare environment

0.12960.07960.20480.1207(1)−(5)Full Sample

0.13080.13990.07630.21240.1322(5)

Age 16−18

0.20090.13030.09230.25130.1198(4)

Age 13−15

Priority to educational environment

0.13070.14740.2291

0.12(3)

Age 7−12

Control

Priority to cultural environment

Post-Treatment

0.124

(2)

(1)

Priority to cultural environment

Pre-Treatment

Age 0−6

Age 0−15

Treatment

Table 4 Balance Test in Dependent Variables Between Treatment and Control Groups: priority to Each Environment

Can child benefits encourage parents’ attitudes toward a childrearing environment in Japan?
13

14

Shinsuke Asakawa

5.2 Full sample analysis
If the scale of empirical formalization is less than that of a real model, that is, some important explanatory variables
are included in the error term, efficiency decreases sharply, although the consistency of estimators is satisfied. In
this case, adding more explanatory variables leads to more efficient estimators, thereby thoroughly mitigating
omitted variable biases. However, if an excessive formulation occurs and strong multicollinearity arises due to theaddition of new explanatory variables, these variables may correlate with the error terms, and the estimators lose
consistency. Hence, this study selects independent variables that can be incorporated in the estimation equations
from the viewpoint of the scale of the estimation model.
Specifically, excessive- and under-formalization is tested for according to the following procedure. First, current parental characteristics (see Section 4) are considered the primary independent variables and regressed by eachdependent variable. Second, adding the other characteristics in Section 4 to these primary variables in chronologicalorder, the robustness in terms of efficiency and heteroskedasticity is tested by comparing the AIC and implementing the Breusch-Pagan (BP) test, respectively. Considering the results of both tests, the model with the largest
sample observations without heteroskedasticity is used.
For parents with children aged below 18, that is, the full sample, the more recent variables are added in chronological order to the covariates and regressed by each priority. The results are presented in Tables 5. Specifically,
as a basic model, the estimation model is defined with only the current parental characteristics that are controlled
for; the corresponding estimators of the CB and FEP are shown in column 1. Next, the estimators with additional
covariates related to parental educational history (i.e., whether the respondent graduated from university or highschool) are shown in column 2. The out-of-school activity history of the respondents is considered as extracurricular activities in pre- and primary schools, and these are added to the previous equation; the results are shown incolumn 3. Finally, the subjective living standard when respondents were 15 and the maternal employment statuswhen the child was 3, 7, and 15 for both the respondent and the spouse are included as explanatory variables.
Column 4 presents these estimations for the CB and FEP.
Initially, the heteroskedasticity problem is discussed. From the BP test for each dependent variable, the homoskedasticity hypothesis is accepted only in the model including individual FEs, except in the priority to childcare environment, Table 5 column 1. Therefore, in columns 2-4, the FEM (individual FE) is adopted as the main
empirical method. Next, efficiency is discussed. From the AIC viewpoint, the model in column 4 is more efficientthan the other models for all dependent variable types. Here, the models with the most efficient estimators arehighlighted in blue in Table 5. To deal with the multicollinearity problem, the study regards the model estimated incolumn 2 as the main estimation model. However, the models in columns 3 and 4 also support the null hypothesisof homoskedasticity, and, therefore, both models are used for robustness checks.
Next, the results in Table 5 are reviewed. These tables show that the CB expansion has significant positive
impacts on Childcare (0.08 0.084). The magnitude of this coefficient denotes that parents with children aged
less than 15 increase the priority assigned to a childcare environment by around 0.08 on average due to the CB
expansion. Considering the pre-treatment differences, it can be concluded that the CB expansion weakly enhancesChildcare because the pre-difference for Childcare is 0.0613.

5.3 Subsample analysis
These overall results could be offset by the heterogeneity in policy impacts between parent cohorts. Hence, parents
are divided according to their children’s age and the treatment effects compared for each subsample, that is, parents
with children aged 0-6, 7-12, and 13-15. These results are shown in Table 6, columns 2-4.
From column 2, the parents with pre-school-aged children enhance their attitudes only for Childcare via theCB expansion as well as the above estimations; however, the coefficient (0.171) is larger than that for the fullsample. Moreover, the estimated coefficient is sufficiently large to conclude that the CB expansion has a significant
positive impact on Childcare, compared to the potential difference for Childcare (0.0678). This result suggests that
estimating the overall treatment effect of the CB expansion conceals the impact on a specific group regarding thebenefits from the CB expansion. However, in column 3, the results for parents with only primary-school-aged
children show that the treated parents increased their attitude toward Education by around 0.15, while no impact
is confirmed on the other attitudes. Since this coefficient is significantly larger than the pre-treatment difference of
Education (0.0465), the impact is significant. Finally, the treatment group of parents with junior-high-school-aged
children does not have any impact, as seen in column 4 of the same table.

Can child benefits encourage parents’ attitudes toward a childrearing environment in Japan?

15

Table 5 Fixed Effect Model (FEM) with Individual Fixed Effects (FE): Priority to “Cultural Environment”, “Educational Environment’,“Childcare Environment’

Dependent variable : Priority to Cultural Environment
(1)
(2)
(3)
(4)
0.017
0.014
0.018
0.018
(0.018)(0.021)(0.021)(0.021)ChildBenefit
Mean Difference

0.0033Current parental characteristics
Parental formal education history
Parental informal education history
Grandparental characteristics
Observations
Adjusted R2
F Statistic
AIC
BP Test (Pooled OLS)BP Test (Time FE)
BP Test (Individual FE)

○

○
○

3,006
0.083
19.252∗∗∗8110
2.2e-16∗∗∗
2.2e-16∗∗∗0.2631

2,451
0.087
13.340∗∗∗6781.142.2e-16∗∗∗
2.2e-16∗∗∗0.2641

○
○
○
○
2,373
0.094
8.114∗∗∗
6591.66
2.2e-16∗∗∗
2.2e-16∗∗∗0.2784

○
○
○
○
2,364
0.102
6.610∗∗∗
6566.892.2e-16∗∗∗
2.2e-16∗∗∗0.2924

Note: The Breusch-Pagan test is used to confirm whether to assume the homoskedasticity for all specification types. The Pvalues of BP test are proposed at the bottom of this table. Mean Difference represents the absolute mean differences between
groups before 2010, i.e. E[CultureTreated,Be f ore2010 ]−E[CultureControl,Be f ore2010 ]. ∗ p<0.1; ∗∗ p<0.05; ∗∗∗ p<0.01

Dependent variable : Priority to Educational Environment
(1)
(2)
(3)
(4)
0.029
−0.0060.002−0.004(0.028)(0.032)(0.033)(0.034)

ChildBenefit

0.034∗∗∗

Mean Difference
Current parental characteristics
Parental formal education history
Parental informal education history
Grandparental characteristics
Observations
Adjusted R2
F Statistic
AIC
BP Test (Pooled OLS)BP Test (Time FE)
BP Test (Individual FE)

○

○
○

2,973
0.022
5.517∗∗∗
5661.04
2.2e-16∗∗∗
2.2e-16∗∗∗
0.48362,429
0.026
4.486∗∗∗
4650.992.2e-16∗∗∗
2.2e-16∗∗∗0.4879

○
○
○
○
2,351
0.026
2.832∗∗∗
4531.45
2.2e-16∗∗∗
2.2e-16∗∗∗
0.5835

○
○
○
○
2,341
0.028
2.428∗∗∗
4516.62
2.2e-16∗∗∗
2.2e-16∗∗∗0.5458

Note: The Breusch-Pagan test is used to confirm whether to assume the homoskedasticity for all specification types. The Pvalues of BP test are proposed at the bottom of this table. Mean Difference represents the absolute mean differences between
groups before 2010, i.e. E[EducationTreated,Be f ore2010 ]−E[CultureEducation,Be f ore2010 ]. ∗ p<0.1; ∗∗ p<0.05; ∗∗∗ p<0.01ChildBenefit

Dependent variable : Priority to Childcare Environment
(1)
(2)
(3)
(4)
0.090∗∗∗
0.080∗∗∗
0.081∗∗∗
0.084∗∗∗(0.022)(0.022)(0.023)(0.018)0.0613∗∗∗

Mean Difference
Current parental characteristics
Parental formal education history
Parental informal education history
Grandparental characteristics
Observations
Adjusted R2
F Statistic
AIC
BP Test (Pooled OLS)BP Test (Time FE)
BP Test (Individual FE)

○

○
○

3,006
0.013
3.729∗∗∗
5783.235.3714e-11∗∗∗4.2539e-11∗∗∗0.0129∗∗

2,456
0.015
3.135∗∗∗
4911.91
1.2534e-09∗∗∗
1.6437e-10∗∗∗0.4711

○
○
○
○
2,378
0.015
2.083∗∗∗
4765.91
1.5497e-09∗∗∗
2.1161e-10∗∗∗0.4652

○
○
○
○
2,368
0.016
1.837∗∗∗
4765.97
1.4690e-08∗∗∗
2.1670e-09∗∗∗
0.4727

Note: The Breusch-Pagan test is used to confirm whether to assume the homoskedasticity for all specification types. The P-values of BP
test are proposed at the bottom of this table. Mean Difference represents the absolute mean differences between groups before 2010, i.e.
E[ChildcareTreated,Be f ore2010 ]−E[ChildcareControl,Be f ore2010 ]. ∗ p<0.1; ∗∗ p<0.05; ∗∗∗ p<0.0116

Shinsuke Asakawa

Table 6 Fixed Effect Model (FEM) with Individual Fixed Effects (FE) (full sample and subsample): Comparison of the Results of the Priority
to Cultural, Educational, and Childcare Environment depending on the child’s age

Dependent variable: Priority to Cultural Environment
Full Sample

Subsample

Age 0−15

Age 0−6(Preschool)Age 7−12(Primary school)Age 13−15(Junior High school)

ChildBenefit

0.014(0.021)−0.071(0.043)0.007(0.038)0.011(0.075)

Mean Difference

0.0033−0.0007−0.0009

0.0115Observations
F Statistic

2,451
13.340∗∗∗673
4.632∗∗∗

817
4.043∗∗∗

633
4.507∗∗∗BP Test (Pooled OLS)BP Test (Time FE)
BP Test (Individual FE)

2.2e-16∗∗∗
2.2e-16∗∗∗0.26410.0090∗∗0.0041∗∗∗0.27562.6314e-07∗∗
6.8898e-08∗∗∗
0.2722

4.2183e-06∗∗∗
8.1396e-07∗∗∗0.5132

Note: All columns are estimated by Model (2) which has the largest observations with BP test passed. Mean Difference represents the Absolute Difference between groups before 2010, i.e. E[CultureTreated,Be f ore2010 ]−E[CultureControl,Be f ore2010 ]. ∗ p<0.1; ∗∗ p<0.05; ∗∗∗ p<0.01

Dependent variable: Priority to Educational Environment
Full Sample

Subsample

Age 0−15

Age 0−6(Preschool)Age 7−12(Primary school)Age 13−15(Junior High school)

ChildBenefit−0.006(0.032)0.043(0.060)0.151∗∗(0.061)

0.071(0.109)Mean Difference

0.034∗∗∗

0.02440.0465∗0.0077

Observations
F Statistic

2,429
4.737∗∗∗

674
2.224∗∗∗

807
2.418∗∗∗

626
2.630∗∗∗BP Test (Pooled OLS)BP Test (Time FE)
BP Test (Individual FE)

2.2e-16∗∗∗
2.2e-16∗∗∗0.48793.8878e-05∗∗∗
8.9901e-06∗∗∗
0.44047.0956e-05∗∗∗
1.2945e-05∗∗∗
0.76630.0002∗∗∗
3.9731e-05∗∗∗
0.4734

Note: All columns are estimated by Model (2) which has the largest observations with BP test passed. Mean Difference represents the Absolute
Difference between groups before 2010, i.e. E[EducationTreated,Be f ore2010 ]−E[EducationControl,Be f ore2010 ]. ∗ p<0.1; ∗∗ p<0.05; ∗∗∗ p<0.01

Dependent variable: Priority to Childcare Environment
Full Sample

Subsample

Age 0−15

Age 0−6(Preschool)Age 7−12(Primary school)Age 13−15(Junior High school)

ChildBenefit

0.080∗∗∗(0.022)0.171∗∗∗(0.054)−0.005(0.029)−0.068(0.043)

Mean Difference

0.0613∗∗∗0.0678∗∗∗0.0127−0.0032

Observations
F Statistic

2,456
3.147∗∗∗

673
2.165∗∗∗

823
1.319

640
1.380

1.1903e-09∗∗∗
1.8885e-10∗∗∗0.47110.65580.47880.55870.34240.39490.23350.0674∗0.1045
0.0968∗

BP Test (Pooled OLS)BP Test (Time FE)
BP Test (Individual FE)

Note: All columns are estimated by Model (2) which has the largest observations with BP test passed. Mean Difference represents the Absolute
Difference between groups before 2010, i.e. E[ChildcareTreated,Be f ore2010 ]−E[ChildcareControl,Be f ore2010 ]. ∗ p<0.1; ∗∗ p<0.05; ∗∗∗ p<0.01

Can child benefits encourage parents’ attitudes toward a childrearing environment in Japan?

17

Furthermore, this study divides respondents into above and below the mean in terms of household income,and it clarifies whether impacts of the CB expansion differ between higher- and lower-income households (Table
7).14 Table 7 shows that, in the higher-income households, only parents with elementary-school-aged children givehigher priority to educational environment by about 0.16 and lower priority to childcare environment by 0.1. For
respondents below the mean household income, parents with pre-school students give higher priority to childcare
environment by about 0.28.
Overall, the CB expansion prompted the parents with children aged 0-6 only to enhance their attitude toward
the childcare environment and the number of children. However, parents with children aged 7-12 were induced toenhance their attitudes toward a better educational environment. There was no significant impact on attitudes for theparents with children aged 13-15 only. Moreover, by dividing parents into higher- and lower-income households,
this study concludes that the CB expansion led higher-income households with older children to pay attention to
the educational environment; at the same time, lower income households with younger children increased the levelof attention paid to childcare environment. These results suggest that the CB expansion had heterogeneous impacts
on parents, according to the children’s age and household income.

6 Concluding remarks
This study verified the policy impacts of the CB expansion after fiscal year 2010 on parental attitudes toward theneighborhood environment by using proxy variables such as the priorities to cultural, educational, and childcare
environments. The policy impacts were examined by FEM, including individual FE.
Before the main analysis, a BP homoskedasticity test was conducted. The results showed that heteroskedasticity
was confirmed unless the individual FE was controlled for. Hence, this study adopted the FEM with individual FE
as the main estimation method. The results estimated by the above model showed that the CB expansion led tothe parents with children aged 15 years or less to give higher priority to the childcare environment by around
8%. These results were robust even when the potential differences between the treated and control groups wereconsidered.
In the subsample analysis, depending on the children’s age, parents with only pre-school children gave higher
priority to childcare environment by around 0.17. However, among the parents with children in primary school
only, the priority to educational environment was increased by around 0.15. These coefficients are sufficiently
large compared with the potential differences between the treated and control groups. Moreover, no impact was
confirmed on the priority to childrearing environment by parents with only junior-high-school-aged children (ages13-15). Therefore, I can conclude that the CB expansion in Japan had different impacts on parents with children
aged 12 or younger according to the child’s education stage. Moreover, dividing parents by household income at
the boundary of the sample mean showed that those in higher-income households increased their priorities toward
the educational environment and decreased them toward the childcare environment. However, parents in lowerincome households only increased their priorities toward childcare environment. These results are consistent withthe findings of Lareau (2003); higher- (lower-) SES households give structured (unstructured) education to theirchildren.
However, using the priorities to three environments as the proxies of parental attitudes toward the neighborhood
environment could generate a new source of bias, such as a correlation between dependent variables, that is,simultaneous dependence. For example, parents may assign higher priorities to some living environments after
the birth of a child, since these alternatives are adjacent in the survey. Therefore, the robustness of the estimation
results is confirmed by clarifying whether the dependent variables mutually correlate. Moreover, simultaneous
equation model (SEM) mitigates possible simultaneous dependencies. A more detailed discussion is presented inAppendices A-C.
Because of the current data limitations, this study can be extended in the future as follows. In general, anintertemporal correlation, in which the decisions made in one period affect the ones in the following periods, is amajor concern in decision-making processes under standard structural models, such as in Rust (1987). By contrast,
this research assumes intertemporal independence for decision making, where the introduction of FEP and the CB
expansion are regarded as exogenous shocks for both the treatment and control groups. If parents treated on orafter fiscal year 2010 could predict the introduction of FEP and CB expansion and could change their decisions inadvance, the results of this study would lose consistency due to the omitted serial correlation for decision making.
14 Some results cannot pass the F-test because split samples are not enough to test the significance of the parameters of interest. Therefore, I
only report results that can pass the F-test in the following.

18

Shinsuke Asakawa

Table 7 Fixed Effect Model (FEM) with Individual Fixed Effects (FE) (full sample and subsample): Comparison of the Results of the Priority
to Cultural, Educational, and Childcare Environment between High-Income and Low-Income Households

Dependent variable: Priority to Cultural Environment
Full Sample

Subsample

Age 0−15

Age 0−6(Preschool)Age 7−12(Primary School)Age 13−15(Junior High School)

ChildBenefit(High Income HH)

0.003(0.033)−0.127(0.082)0.004(0.062)−0.079(0.105)Observations
F Statistic

1,229
9.375∗∗∗

289
2.469∗∗∗

395
2.201∗∗∗

340
2.822∗∗∗

ChildBenefit(Low Income HH)

0.040(0.027)−0.020(0.052)0.039(0.052)0.148(0.112)

Observations
F Statistic

1,222
4.236∗∗∗

384
3.014∗∗∗

422
3.135∗∗∗

293
2.361∗∗∗

Dependent variable: Priority to Educational Environment
Full Sample

Subsample

Age 0−15

Age 0−6(Preschool)Age 7−12(Primary School)Age 13−15(Junior High School)

ChildBenefit(High Income HH)

0.016(0.050)0.048(0.116)0.158∗(0.095)0.104(0.149)Observations
F Statistic

1,208
3.027∗∗∗

288
1.882∗∗

384
1.847∗∗

335
2.414∗∗∗

ChildBenefit(Low Income HH)−0.023(0.043)0.039(0.070)0.127(0.087)0.062(0.168)Observations
F Statistic

1,221
2.385∗∗∗

386
1.283

423
1.514∗

291
0.967

Dependent variable: Priority to Childcare Environment
Full Sample

Subsample

Age 0−15

Age 0−6(Preschool)Age 7−12(Primary School)Age 13−15(Junior High School)

ChildBenefit(High Income HH)

0.019(0.028)−0.062(0.076)−0.103∗∗(0.044)−0.088(0.065)Observations
F Statistic

1,235
2.199∗∗∗

292
1.452

399
1.965∗∗∗

346
1.392

ChildBenefit(Low Income HH)

0.152∗∗∗(0.034)0.284∗∗∗(0.079)0.086∗∗(0.043)−0.029(0.052)

Observations
F Statistic

1,221
2.005∗∗∗

381
1.911∗∗

424
0.956

294
0.985

Note: All columns are estimated by Model (2), which has the largest number of observations that have passed the Breusch-Pagan (BP)test. High- (Low-) Income HH
refers to those households whose income is above (below) the mean of each subgroup. ∗ p<0.1;∗∗ p<0.05; ∗∗∗ p<0.01

Can child benefits encourage parents’ attitudes toward a childrearing environment in Japan?

19

In this regard, it is necessary to verify the validity of the assumptions by using longer sample periods.
Overall, the universal and unconditional cash transfers via the CB expansion had a significant impact on treatedparents with children younger than 13 years old. However, the effects on the attitude toward the childrearing environment qualitatively differed according to the children’s age. This result implies that, based on not only thechildren’s age but also household income, parents had different reactions to the sudden income gain from the
CB expansion. In this case, the child benefit amount for lower-income households with pre-school-aged children
should increase when the government hopes to enhance parental attitudes toward environments with rich childcare facilities. However, the government should provide more child benefits for higher-income households withprimary-school-aged children to encourage parents to attach a higher priority to the educational environment; inany case, this policy can address the gaps in the opportunities for outside-school education between higher- andlower-income households. Both policies are compatible if the government secures a sufficient budget for childrearing households. Hence, the government should distribute the budget so the opportunity gaps do not widen,
depending on the SES.
In conclusion, the results have an important policy implication: financial support from the government without
any conditions, such as child benefit, has heterogeneous impacts on the parental attitudes toward the neighborhood
environment according to the children’s age and household income; as such, the government should carefully
choose the target households to achieve its policy goals.

Appendix
Appendix A: Definition of attitude considering the number of children
How did the CB expansion affect the birthrate? Unfortunately, the utilized survey does not include a question onwhether the respondent has recently given birth; however, it collected information on the number of children in ahousehold. Therefore, the approximate number of newborns can be derived for each respondent by the difference inthe number of children within the same household between successive surveys. However, the number of newborns
in fiscal year 2009 cannot be considered because the analyzed data were first collected in fiscal year 2008. Hence,
the number of newborns in the pre-treatment period (based on the differences between fiscal years 2010 and 2009)and post-treatment period (based on the differences between fiscal years 2012 and 2011) are calculated, and thenumber of newborns in fiscal year 2009 is considered as not available or NA. This means I do not have enoughinformation to verify whether the trend holds because the survey covers only a two-year sample period before
the CB expansion. Hence, I present the results on newborns as supportive evidence. The summary statistics areprovided in Appendix Table 5.

Appendix B: Validity test for the dependent variables
The study confirms and discusses the dependent variables defined in the previous sections. To this end, thesevariables are compared with the variables that capture the level of extra income spent on children, that is, numberof children, total household income, total household expenditure excluding that on durable goods, and expenditure
on food and eating out. Appendix Table 6 shows the differences in the p-values of the Pearson’s correlation tests
between proxies and control variables.
Row 1 in Table 4 shows that all dependent variables are significantly correlated with the actual number ofchildren. However, only the attitude toward the cultural environment (Culture) has a negative correlation with thenumber of newborns (Newborn), while the attitudes toward the other environments are positively correlated withthis variable. These results suggest thatCulture is a proxy variable qualitatively different from the others.
Household income and total monthly expenditure have coefficients similar to those of the dependent variables(Appendix Table 6, rows 2 and 3). These results imply that higher-income households prefer Culture and Education and are less likely to have newborn children, thus expressing a preference for Childcare, while the reverse
is true for lower-income households. Further, the expenditure on food and eating out is positively correlated with
Culture and negatively correlated with Childcare. Overall, the proxies show significant correlations with the necessary components for childrearing and exhibit mutually different features. Therefore, the validity of the dependent
variables used is confirmed.

20

Shinsuke Asakawa

Appendix C: Robustness test
To test the robustness of the results, omitted variable biases (except for FEP) are addressed through the unobserved
effects between the number of newborns and each factor on the attitudes toward the neighborhood environment.
Biases, if any, would offset the causal income effects of the CB expansion on child education as a substitution
effect.
Specifically, the parameters that directly capture the existence of newborns and the attitudes toward the neighborhood environment might vary over time if the degree of the correlations between attitudes are altered by omitted
exogenous shocks, for example, the accessibility expansion to nursery schools (see Asai, Kambayashi and Yamaguchi (2015); Fukai (2017)) and the Great East Japan Earthquake (Hanaoka, Shigeoka and Watanabe (2018)).
These omitted variable biases cannot be directly tested and controlled for because the available resources for children vary by household. Moreover, survey questionnaires, in general, cannot capture them completely. As such,
efficient estimators cannot be acquired without controlling for simultaneous dependencies not only within attitudes but also between one of the attitudes and newborns if statistically significant; these correlation coefficientsvary over time, particularly between the pre- and post-treatment periods. Specifically, by applying the SEM to thestandard FEM, the omitted variable bias caused by the correlations between dependent variables―simultaneous
dependencies―can be mitigated.
In addition to the standard panel analysis in Section 3, the substitutive effects of omitted fluctuations are removed by directly controlling for simultaneous dependencies. As such, the spatial auto-regressive model is introduced in addition to the standard panel analysis. This method can control for the simultaneous spatial dependencies
captured by spatial weight matrix W in Greene (2003) or LeSage and Pace (2009). Using the same notation as in
Section 3, the spatial auto-regressive model with temporal variation can be expressed as:
Spatial Auto-Regressive Model (with Temporal Variation)
yit = χ Wyit + Xit βi + εit ,(4)(Iit − χ W )yit = E(yit ) = Xit βi + εit ,(5)yit = (Iit − χ W )−1 Xit βi + (Iit − χ W )−1 εit .

(6)

Based on Eqs. (4)-(6), simultaneous dependencies can be removed. χ captures the parameters measuring thestrength of simultaneous dependencies and β represents the correlations between the dependent and independent
variables. Moreover, W reflects the weight of simultaneous dependencies, which take 1 for other priorities to a
respondent and 0 for all priorities between respondents; the weight is exogenously given for each period, as in
the literature (see Greene (2003); LeSage and Pace (2009)). Based on the above assumptions, this study allows
only intertemporal weight fluctuations, not from past variables but from exogenous shocks, regardless of whether
these are observable or not. Therefore, Eq. (4) is an analogy for the typical SEM. Hence, the temporal spatial
auto-regressive model can be regarded as an SEM. Overall, the consistent estimator of (Iit − χ W )−1 Xit β̂i can be
estimated by Eq. (6) if the value of (Iit − χ W )−1 ε̂it approximately converges to zero.
As in Section 3, SEM can be defined by applying the same notation as in Eqs. (2) and (3). Similar to LeSage
and Pace (2009), this study explicitly assumes the structure of the correlation between the dependent variables
Newbornit , Cultureit , Educationit , and Childcareit , which are represented by ysit , s∈ {1, 2, 3, 4}. Assuming thatthe error terms between equations are εmit and εn(̸=m)it , where m, n ∈ 1, 2, 3, 4, correlation coefficients rhomn are
directly estimated and simultaneous dependencies are controlled for. Regarding the error terms, it is assumed thatthey are 0 not only between respondents but also across time. This is equivalent to assuming that, except for theserial correlations, all intertemporal variations in the correlation coefficients are generated by omitted variables.
Therefore, the SEM can be formulated as follows:
Simultaneous Equation Model (SEM) 
Newbornit
α11CBit + α21 FEit + δ1 y2it + δ2 y3it + δ3 y4it + θ1 Xit + ε1it (ε2it , ε3it , ε4it ) Cultureit   α12CBit + α22 FEit + δ4 y1it + δ5 y3it + δ6 y4it + θ2 Xit + ε2it (ε1it , ε3it , ε4it )   Educationit  =  α13CBit + α23 FEit + δ7 y1it + δ8 y3it + δ9 y4it + θ3 Xit + ε3it (ε1it , ε2it , ε4it ) Childcareit
α14CBit + α24 FEit + δ10 y2it + δ11 y3it + δ12 y4it + θ4 Xit + ε4it (ε1it , ε2it , ε3it )(7)

Are the estimated treatment effects of the CB expansion biased by omitted variables? To answer this question,
SEM is used to assess whether the correlation degrees between dependent variables―that is, simultaneous dependencies―are significant in the above FEM (individual FE) results and whether they vary over time.

Can child benefits encourage parents’ attitudes toward a childrearing environment in Japan?

21

Here, the correlation coefficients between the residuals estimated by FEM (individual FE) are tested using Pearson’s correlation coefficient test. The results are shown in Appendix Table 7, columns 1-3. Cultureit
and Educationit have a positive correlation coefficient, significant throughout the sample period. Furthermore,Childcareit has a significant positive correlation with both Educationit and Newbornit but only in fiscal year 2012.
Welch’s t-test clarifies whether these simultaneous dependencies vary over time. From the p-values of the test in
Appendix Table 7, columns 4-6, no serial correlation in the correlation coefficients between proxies can be confirmed. From the above discussion, using SEM improves efficiency by mitigating simultaneous dependencies.
Next, the SEM results are presented. The estimators of the degree of simultaneous dependencies represented byδ1 12 , and the policy impacts of the CB expansion and FEP introduction, respectively represented by α1 and α2 areshown in Appendix 8 -11. For the full sample, the CB expansion had a positive impact on Newbornit (0.0505) at the
1% significance level and Childcareit (0.0314) at the 10% level (see Appendix Tables 8 -11, column 2). However,the SEM shows reverse correlations between Newborn and Childcare, so that the coefficient increases (decreases)
for Newbornit (for Childcareit ) compared with the FEM results. When considering the pre-treatment difference inChildcareit and Newbornit between the treated (0.0833) and control groups (0.0121), it can be concluded that the
CB expansion had a significant positive impact on Newbornit only.
However, there are concerns that these overall results are offset by the heterogeneity in policy impacts within
the parents’ cohort. Hence, parents are grouped according to their children’s ages and the treatment effects ofeach resulting subsample are compared, that is, among parents with children aged 0-6, 7-12, and 13-15. The
results are shown in columns 3-5, respectively. Column 3 indicates that parents with pre-school children show
increases in the parental attitudes for Childcareit and Newbornit only due to the CB expansion, as well as theabove estimations; however, both coefficients are larger than that for the full sample. Moreover, the estimatedcoefficients are sufficiently large to conclude that the CB expansion had a significant positive impact on Childcareit
and Newbornit compared with the potential difference (0.0678 and 0.0237). This suggests the possibility that
estimating the overall treatment effect of the CB expansion conceals the impact on a specific group among all
samples that benefitted from the CB expansion. Moreover, deeper insights about the differences in the proxies ofChildcareit and Newbornit are obtained. In terms of treatment effects, a parent in this subgroup is more likely
to give birth (0.1753) than enhance his/her attitude toward the childcare environment (0.1095). This implies thatparents do not always take a keen interest in the childcare environment, even if they have more children. However,column 4, which shows the results for parents with primary-school-aged children only, indicates that the treatedparents improved their attitude toward Education by around 8.6%, while no impact was confirmed for the otherattitudes. Since this coefficient is larger than the pre-treatment difference (0.0465), this impact is sufficiently large.
Finally, there is no impact on the treatment group of parents with junior-high-school-aged children only, as seenfrom column 5.
The analysis proceeded from FEM to SEM. As a result of the correlation test using the residuals estimatedby FEM (individual FE), the attitudes toward the cultural and educational environments were shown to have had
positive and significant correlations throughout the sample periods, and the attitude toward childcare was significantly correlated with the attitude toward the educational environment and number of newborns, but only in fiscal
year 2012. These results confirm that exploiting the SEM mitigates the omitted variable biases stemming from thecorrelations between the dependent variables.

Appendix D: Appendix Figures and Tables

Appendix Table 1 Questionnaires for Dependent Variables

Questionnaires
Questionnaire 1

If you can move another prefecture in Japan, do you hope that?
Please write ○ on one if you want and ○ on two if you do not want.

Questionnaire 2”Why do you want to stay or move in such a prefecture?
Please select four important reasons from the following alternative and circle ○ andWrite the ranking of 1 to 4 from the most important reasons in the four selected alternatives.”

The order which takes 4 to 1 where the household prefers a cultural environment with the rank of 1 to 4 in Questionnaire 2
The order which takes 4 to 1 where the household prefers an educational environment with the rank of 1 to 4 in Questionnaire 2
The order which takes 4 to 1 where the household prefers a childcare environment with the rank of 1 to 4 in Questionnaire 2

Annual payments from FEP of the household / Normal annual amount of FEP per child (118,800 JPN)
Monthly payments from CB of the household / Normal monthly amount of CB per child (13,000 JPN)
0.8 million JPY if the household income is less than 1 million yen
1.5 million JPY if the household income places between 1 and 2 million JPY... Medians in each income band climbing up by 2 million JPY if the household income places between 2 and 20 million JPY
20 million JPY if a household income is over 20 million JPY
Total Expenditure per month evaluated by ten thousand JPY
Food Expenditure per month evaluated by ten thousand JPY
Eating-Outside Expenditure per month evaluated by ten thousand JPY
Dummy variable which takes 1 if the household hopes to move another prefecture
Dummy variable which takes 1 if the household is living in a cabinet designed city (20 cities in Japan)
Dummy variable which takes 1 if the respondent is male
Number of Children in each year
Number of Desired Children in 2009
Dummy variable which takes 1 if the household is living with their parent
Dummy variable which takes 1 if the household is living in Tokyo, Osaka, Kyoto and Hiroshima which prefectures have provided
any financial supports for parents with a primary or junior high school student before CB expansion started..
The order which takes 4 to 1 where the household hopes to move another prefecture due to the earthquake itself
or the fear of radiation damage in Questionnaire 2

Priority to educational environment

Priority to childcare environment

Parental Characteristics (Current)
Free education

Child Benefit

Total household income

Total expenditure per month

Food expenditure per month

Eating-outside expenditure per month

Having a desire to move another prefecture

Living in a cabinet designed city

Male

Number of children

Number of desired children

Living with at least a grandparent

Special area for education

The great east Japan earthquake

Definition

Priority to cultural environment

Priority to Living Environment

Variable

Appendix Table 2 Definitions for Each Variable

22
Shinsuke Asakawa

Dummy variable which takes 1 if the respondent graduated an university
Dummy variable which takes 1 if the spouse of the respondent graduated an university
Dummy variable which takes 1 (0) if the respondent graduated a public high school (a private high school)
Dummy variable which takes 1 (0) if the spouse of the respondent graduated a public high school (a private high school)

Dummy variable which takes 1 if the respondent experienced swimming activity in his/her preschool days
Dummy variable which takes 1 if the respondent experienced a ball activity (e.g., baseball or soccer) in his/her preschool days
Dummy variable which takes 1 if the respondent experienced a fighting activity (e.g., martial arts) in his/her preschool days
Dummy variable which takes 1 if the respondent experienced an instrument gymnastics in his/her preschool days
Dummy variable which takes 1 if the respondent experienced dance activity in his/her preschool days
Dummy variable which takes 1 if the respondent experienced a music activity in his/her preschool days
Dummy variable which takes 1 if the respondent experienced a singing in his/her preschool days
Dummy variable which takes 1 if the respondent experienced a painting in his/her preschool days
Dummy variable which takes 1 if the respondent experienced a calligraphy in his/her preschool days
Dummy variable which takes 1 if the respondent experienced an igo activity in his/her preschool days
Dummy variable which takes 1 if the respondent experienced a shogi activity in his/her preschool days
Dummy variable which takes 1 if the respondent went to a language school in his/her preschool days
Dummy variable which takes 1 if the respondent learned by using print materials in his/her preschool days
Dummy variable which takes 1 if the respondent went to higher cram school in his/her preschool days
Dummy variable which takes 1 if the respondent went to lower cram school in his/her preschool days
Dummy variable which takes 1 if the respondent utilized a corresponding learning service in his/her preschool days

Dummy variable which takes 1 if the respondent experienced EXPERIENCE in his/her primary school days

Parental Formal Education History
University graduation
University graduation(spouse)
High school typeHigh school type (spouse)

Parental Extracurricular History (preschool)
Swimming
Ball
Fighting
Gym
Dance
Music
Sing
Art
Penmanship
Igo
Shogi
Language school
Print learning
Higher cram school
Lower cram school
Corresponding learning

Parental Extracurricular History (primary school)
EXPERIENCE (primary)Note: EXPERIENCE means the same kind of variable as Parental Extracurricular History (preschool).

Definition

Variable

Appendix Table 3 Definitions for each variable (cont’d)

Can child benefits encourage parents’ attitudes toward a childrearing environment in Japan?
23

Definition

The order of the respondent’s living standard which takes 0 (min) to 10 (max) when the respondent was 15 years old
Dummy variable which takes 1 if the respondent’s mother worked full time when the respondent was 3 years old
Dummy variable which takes 1 if the respondent’s mother worked part time when the respondent was 3 years old
Dummy variable which takes 1 if the respondent’s mother worked full time when the respondent was 7 years old
Dummy variable which takes 1 if the respondent’s mother worked part time when the respondent was 7 years old
Dummy variable which takes 1 if the respondent’s mother worked full time when the respondent was 15 years old
Dummy variable which takes 1 if the respondent’s mother worked part time when the respondent was 15 years old
Dummy variable which takes 1 if the spouse’s mother worked full time when the spouse was 3 years old
Dummy variable which takes 1 if the spouse’s mother worked part time when the spouse was 3 years old
Dummy variable which takes 1 if the spouse’s mother worked full time when the spouse was 7 years old
Dummy variable which takes 1 if the spouse’s mother worked part time when the spouse was 7 years old
Dummy variable which takes 1 if the spouse’s mother worked full time when the spouse was 15 years old
Dummy variable which takes 1 if the spouse’s mother worked part time when the spouse was 15 years old

Variable

Characteristics of Grandparents
Living Standard at 15 years old

Mother’s labor status (Full time[FT]) at age 3

Mother’s labor status (Part time[PT]) at age 3

Mother’s labor status (FT) at age 7

Mother’s labor status (PT) at age 7

Mother’s labor status (FT) at age 15

Mother’s labor status (PT) at age 15

Mother’s labor status (spouse, FT) at age 3

Mother’s labor status (spouse, PT) at age 3

Mother’s labor status (spouse, FT) at age 7

Mother’s labor status (spouse, PT) at age 7

Mother’s labor status (spouse, FT) at age 15

Mother’s labor status (spouse, PT) at age 15

Appendix Table 4 Definitions for each variable (cont’d)

24
Shinsuke Asakawa

Can child benefits encourage parents’ attitudes toward a childrearing environment in Japan?

25

Appendix Table 5 Basic Statistics : Full Sample (Age 0-18)

Statistic
Number of Newborn

N

Mean

St. Dev.

Min

Max

2,212

0.052

0.236

0.000

3.000

Note: Number of Newborn was defined as the difference in the number of children between 2009-10
and between 2011-12. We assigned NA for Newborn in 2009 since we have no data before 2008

Appendix Table 6 The Validity Test for Each Proxy Variables

The correlation coefficient

Number of children
Household income
Total expenditure
Food expenditure
Eating-outside expenditure

CultureEducation

Childcare

Newborn−0.0643∗∗∗0.0603∗∗∗0.0661∗∗∗0.0309∗∗∗0.0398∗∗∗0.0539∗∗∗0.0728∗∗∗0.0549∗∗∗−0.00140.00720.0362∗∗∗−0.0196∗−0.007−0.0227∗−0.0235∗∗0.2007∗∗∗−0.0700∗∗∗−0.0450∗∗−0.0312−0.0320

Note: The significance of each coefficient is tested by Pearson’s Product Moment Correlation Coefficient. Newborn, theproxy of the parental demand for child’s quantity, is used for the analysis in the Appendix. ∗ p<0.1; ∗∗ p<0.05; ∗∗∗ p<0.01ρ23,2010 : 0.0085
ρ14,2010 : 0.0254
ρ14,2010 : 0.0360
ρ24,2010 : −0.0144

ρ23,2009 : 0.0452

ρ14,2009 : -ρ14,2009 : -ρ24,2009 : -Education - Childcare

Culture - Newborn

Education - Newborn

Childcare - Newborn-

ρ14,2012 : −0.0465
ρ24,2012 : 0.0744∗∗

ρ14,2012 : −0.0377-

0.5037

ρ23,2012

0.369: 0.1017∗∗∗0.4727

2009−2010

ρ13,2012 : −0.0425

ρ12,2012: 0.0772∗∗

2012

0.10790.16240.26810.18230.3631

0.9522

2010−2012---

0.44660.994

0.50652009−2012

P value of Difference Between

Education, Childcare, and Newborn, respectively. ∗ p<0.1; ∗∗ p<0.05; ∗∗∗ p<0.01

Note: ρmn,Year (where, m(̸= n) ∈ [1, 2, 3, 4], Year ∈ [2009, 2010, 2012] ) means a correlation coefficient between m and n. Each number of 1 to 4 is equivalent to the order of Culture,ρ13,2010 : −0.0139

ρ13,2009 : −0.0423

Culture - Childcare

ρ12,2010

ρ12,2009: 0.0730∗∗

2010

Culture - Education: 0.1239∗∗∗

2009

Correlation Coefficients Between Dependent Variables in each periods

Appendix Table 7 Correlation Coefficients Between Dependent Variables in each periods

26
Shinsuke Asakawa

Can child benefits encourage parents’ attitudes toward a childrearing environment in Japan?

27

Appendix Table 8 SEM Estimation : Demand for “Number of Newborn”

FEM (Individual FE)

Simultaneous Equation Model

Full Sample

Full Sample

Subsample

Age 0−15

Age 0−15

Age 0−6

Age 7−12

Age 13−15

ChildBenefit
FreeEducation

0.048∗∗∗−0.0940.0505∗∗∗−0.0452

0.1753∗∗∗−0.06420.0042−0.0161−0.0007−0.0409

Mean Difference

0.01210.01210.0237∗∗−0.0059−0.0009

Observations
Adjusted R2

2,734
0.022

1809
0.0449519
0.1258693−0.0069894
0.2239Note:All groups are compared with the control group (Age 16-18) in Model (4).Mean Difference represents the Absolute Difference
between groups before 2010, i.e. E[NewbornTreated,Be f ore2010 ]−E[NewbornControl,Be f ore2010 ]. ∗ p<0.1; ∗∗ p<0.05; ∗∗∗ p<0.01

Appendix Table 9 SEM Estimation : Priority to “Cultural Environment”

FEM (Individual FE)

Simultaneous Equation Model

Full Sample

Full Sample

Subsample

Age 0−15

Age 0−15

Age 0−6

Age 7−12

Age 13−15

ChildBenefit
FreeEducation−0.0080.084

0.00670.07950.06920.18980.01940.23930.00840.0410Mean Difference

0.01150.0115−0.0007−0.0009

0.0115Observations
Adjusted R2

2,726
0.119

1809
0.1002519
0.0804693
0.0601894
0.0706

Note:All groups are compared with the control group (Age 16-18) in Model (4).Mean Difference represents the Absolute Difference
between groups before 2010, i.e. E[CultureTreated,Be f ore2010 ]−E[CultureControl,Be f ore2010 ]. ∗ p<0.1; ∗∗ p<0.05; ∗∗∗ p<0.01

Appendix Table 10 SEM Estimation : Priority to “Educational Environment’FEM (Individual FE)

Simultaneous Equation Model

Full Sample

Full Sample

Subsample

Age 0−15

Age 0−15

Age 0−6

Age 7−12

Age 13−15

ChildBenefit
FreeEducation−0.0080.083−0.03540.04920.04410.28570.0867∗−0.0400

0.06670.1428Mean Difference

0.165∗∗∗

0.165∗∗∗

0.02440.0465∗0.0077

Observations
Adjusted R2

2,694
0.061

1809
0.0124519
0.0348693
0.0118894
0.0340Note:All groups are compared with the control group (Age 16-18) in Model (4).Mean Difference represents the Absolute Difference
between groups before 2010, i.e. E[EducationTreated,Be f ore2010 ]−E[EducationControl,Be f ore2010 ]. ∗ p<0.1; ∗∗ p<0.05; ∗∗∗ p<0.0128

Shinsuke Asakawa

Appendix Table 11 SEM Estimation : Priority to “Childcare Environment”

FEM (Individual FE)

Simultaneous Equation Model

Full Sample

Full Sample

Age 0−15

Age 0−15

Age 0−6

Age 7−12

Age 13−15

ChildBenefit
FreeEducation

0.071∗∗∗−0.0940.0314∗−0.13220.1095∗∗−0.1281−0.0038−0.0471−0.0166−0.0574

Mean Difference

0.0833∗∗∗0.0833∗∗∗0.0678∗∗∗0.0127−0.0032

2,734
0.022

1809
-3.6e-05

519-0.0041693−0.0137894−0.0079

Observations
Adjusted R2

Subsample

Note:All groups are compared with the control group (Age 16-18) in Model (4).Mean Difference represents the Absolute Difference
between groups before 2010, i.e. E[ChildcareTreated,Be f ore2010 ]−E[ChildcareControl,Be f ore2010 ]. ∗ p<0.1; ∗∗ p<0.05; ∗∗∗ p<0.01

References
Asai, Yukiko, Ryo Kambayashi, and Shintaro Yamaguchi, “Childcare availability, household structure, andmaternal employment,” Journal of the Japanese and International Economies, dec 2015, 38, 172–192.
Ashenfelter, O. and C. Rouse, “Income, Schooling, and Ability: Evidence from a New Sample of Identical Twins,”
The Quarterly Journal of Economics, feb 1998, 113 (1), 253–284.
Ashenfelter, Orley and Alan Krueger, “Estimates of the Economic Return to Schooling from a New Sample of
Twins,” The American Economic Review, 1994, 84, 1157–1173.
and David Card, “Using the Longitudinal Structure of Earnings to Estimate the Effect of Training Programs,”
Technical Report 4 1985.
Bessho, Shunichiro, “Child Benefit, Tax Allowances and Behavioural Responses: The Case of Japanese Reform,
2010–2011,” Japanese Economic Review, 2018, 69 (4), 478–501.
Björklund, Anders and Markus Jäntti, “How important is family background for labor-economic outcomes?,”Labour Economics, 2012, 19 (4), 465–474.
, Mikael Lindahl, and Erik Plug, “The origins of intergenerational associations: Lessons from Swedish adoption data,” 2006.
Black, Sandra E. and Paul J. Devereux, Recent developments in intergenerational mobility, Vol. 4, Elsevier, jan
2011.
Bradley, Robert H. and Robert F. Corwyn, “Socioeconomic Status and Child Development,” Annual Review of
Psychology, feb 2002, 53 (1), 371–399.
Breen, Richard and Jan O. Jonsson, “Inequality of Opportunity in Comparative Perspective: Recent Researchon Educational Attainment and Social Mobility,” Annual Review of Sociology, aug 2005, 31 (1), 223–243.
Breusch, T. S. and A. R. Pagan, “A Simple Test for Heteroscedasticity and Random Coefficient Variation,”
Econometrica, sep 1979, 47 (5), 1287.
and , “The Lagrange Multiplier Test and its Applications to Model Specification in Econometrics,” The
Review of Economic Studies, jan 1980, 47 (1), 239.
Card, D. and A. B. Krueger, “Minimum wages and employment: a case study of the fast-food industry in New
Jersey and Pennsylvania,” Technical Report 4, National Bureau of Economic Research, Cambridge, MA oct
1994.
Conger, Rand D., Katherine J Conger, and Monica J Martin, “Socioeconomic status, family processes,andindividual development,” 2010.
Contoyannis, Paul, Andrew M. Jones, and Nigel Rice, “The dynamics of health in the British Household Panel
Survey,” Journal of Applied Econometrics, jul 2004, 19 (4), 473–503.
Corak, Miles, “Do Poor Children Become Poor Adults? Lessons from a Cross-Country Comparison of Generational Earnings Mobility,” 2006.
, “Income inequality, equality of opportunity, and intergenerational mobility,” Journal of Economic Perspectives,
2013, 27 (3), 79–102.
Croissant, Yves and Giovanni Millo, “Panel data econometrics in R: The plm package,” Journal of statisticalsoftware, 2008.
Donald, Stephen G. and Kevin Lang, “Inference with difference-in-differences and other panel data,” Technical
Report 2 2007.
Firebaugh, Glenn, Cody Warner, and Michael Massoglia, “Fixed Effects, Random Effects, and Hybrid Models
for Causal Analysis,” in “in” 2013, pp. 113–132.
Fukai, Taiyo, “Childcare availability and fertility: Evidence from municipalities in Japan,” Journal of the JapaneseCan child benefits encourage parents’ attitudes toward a childrearing environment in Japan?

29

and International Economies, mar 2017, 43, 1–18.
Greene, William H, “Econometric Analysis. 5th Edition,” 2003.
Hanaoka, Chie, Hitoshi Shigeoka, and Yasutora Watanabe, “Do Risk Preferences Change? Evidence from theGreat East Japan Earthquake,” American Economic Journal: Applied Economics, apr 2018, 10 (2), 298–330.
Hoff, Erika, “The Specificity of Environmental Influence: Socioeconomic Status Affects Early Vocabulary Development Via Maternal Speech,” Technical Report 5 2003.
Kulic, Nevena, Jan Skopek, Moris Triventi, and Hans-Peter Blossfeld, “Social Background and Children’s
Cognitive Skills: The Role of Early Childhood Education and Care in a Cross-National Perspective,” Annual
Review of Sociology, jul 2019, 45 (1), 557–579.
Lareau, Annette, Unequal Childhoods: Class, Race, and Family Life 2003.
Lehrer, Steven F and Weili Ding, “Are genetic markers of interest for economic research?,” 2017.
LeSage, James and Robert Kelley Pace, Introduction to Spatial Econometrics (Statistics: A Series of Textbooks
and Monographs) 2009.
Lin, Jung, “The social and genetic inheritance of educational attainment: Genes, parental education, and educational expansion,” Social Science Research, 2020, 86, 102387.
Matsuoka, Ryoji, “Inequality in shadow education participation in an egalitarian compulsory education system,”Comparative Education Review, 2018, 62 (4), 565–586.
and Tadahiko Maeda, “Attitudes Toward Education As Influenced by Neighborhood Socioeconomic Characteristics: An Application of Multilevel Structural Equation Modeling,” Behaviormetrika, 2015, 42 (1), 19–35.
MEXT, “White Paper on Education , Culture , Sports , Science and Technology,” 2009.
, “White Paper on Education , Culture , Sports , Science and Technology,” 2017.
Naoi, Michio, Hideo Akabayashi, Ryosuke Nakamura, Kayo Nozaki, Shinpei Sano, Wataru Senoh, andChizuru Shikishima, “Causal Effects of Family Income on Child Outcomes and Educational Spending: Evidence from a Child Allowance Policy Reform in Japan,” Keio-IES Discussion Paper Series, nov 2017.
Niimi, Yoko, “The Costs of informal care: an analysis of the impact of elderly care on caregivers’ subjective
well-being in Japan,” Review of Economics of the Household, dec 2016, 14 (4), 779–810.
Noble, Gregory W., “The Decline of Particularism in Japanese Politics,” Journal of East Asian Studies, aug 2010,
10 (2), 239–274.
OECD, In It Together: Why Less Inequality Benefits All, Vol. 84, OECD, may 2015.
Papageorge, Nicholas W and Kevin Thom, “Genes, Education, and Labor Market Outcomes: Evidence from the
Health and Retirement Study,” Journal of the European Economic Association, 2019.
Plug, Erik and Wim Vijverberg, “Schooling, Family background, and adoption: Is it nature or is it nurture?,”Journal of Political Economy, jun 2003, 111 (3), 611–641.
Rauscher, Emily, “Plastic and immobile: Unequal intergenerational mobility by genetic sensitivity score withinsibling pairs,” Social Science Research, 2017, 65, 112–129.
Rust, John, “Optimal Replacement of GMC Bus Engines: An Empirical Model of Harold Zurcher,” Econometrica,
sep 1987, 55 (5), 999.
Sacerdote, Bruce, “The Nature and Nurture of Economic Outcomes,” American Economic Review, may 2002, 92(2), 344–348.
Solon, Gary, “Intergenerational mobility in the labor market,” in “Handbook of Labor Economics,” Vol. 3 PART,
Elsevier B.V., jan 1999, pp. 1761–1800.
, “Cross-country differences in intergenerational earnings mobility,” Technical Report 3 2002.
Takeshita, Ryo, “The effects of child allowance and free education in high school on the decision making of theparents about investments in education,” The Bulletin of the Faculty of Commerce, Meiji University, 2016, 98(2),117–133.
Thompson, Owen, “Economic background and educational attainment: The role of gene-environment interactions,” Journal of Human Resources, mar 2014, 49 (2), 263–294.
Wooldridge, “Econometric analysis of cross section and panel data.,” Technical Report 2010.