Discussion Papers In Economics
And Business

Public nursery school costs and the effects of thefunding reforms in Japan
Miki MIYAKI

Discussion Paper 15-27

Graduate School of Economics andOsaka School of International Public Policy (OSIPP)
Osaka University, Toyonaka, Osaka 560-0043, JAPANPublic nursery school costs and the effects of thefunding reforms in Japan
Miki MIYAKI

Discussion Paper 15-27
October 2015

Graduate School of Economics andOsaka School of International Public Policy (OSIPP)
Osaka University, Toyonaka, Osaka 560-0043, JAPANPublic nursery school costs and the effects of thefunding reforms in Japan *Miki MIYAKI†

Abstract
With the social demand for childcare service diversifying and local governments
becoming increasingly cash-strapped, the proportion of public nursery schools in thechildcare sector has been shrinking in recent years. The funding reforms of 2004,
which abolished the national subsidy to public nursery schools, are seen as one of thetriggers of this phenomenon. Using a panel dataset of 983 municipalities across thenation, this paper investigates the impact of the reforms on the operating costs ofpublic nursery schools. We found that different municipalities responded differently tothe reforms. In areas with relatively large populations, fiscally stronger municipalities
were likely to spend less on public nursery schools in the wake of the reforms, whilemunicipalities in smaller cities spent more. Besides, municipalities that were notcompensated for the loss of the national subsidy reduced expenditures in large cities. In
small cities, on the other hand, such municipalities actually increased expenditures.

Keywords: operating cost, public nursery school, funding reform, difference-in-differences
approach

JEL Code: H71, H75, H77*

I would like to thank Dr. Anna Mignosa for her roles as discussant in the International Research
Forum at Kyoto Sangyo University and for many useful comments.
†
Corresponding author: Rikkyo University, College of Business, Tokyo, Japan, miyaki@rikkyo.ac.jp,
tel. +81 (0)3 3985 4094.

1. Introduction
Preschool education and childcare services in Japan mainly involve kindergartens(youchien) and nursery schools (hoikusyo). Unlike kindergartens that provide school
education (gakko kyoiku) to children aged 3 to 5, nursery schools offer care (hoiku) to
infants and young children up to the age of 5 who lack childcare at home. In this sense,childcare in nursery schools is distinct from the school education system in Japan.１
Following an increase in the number of nuclear family households and a rise in theemployment rate of women, the demand for childcare service, hoiku, is growing andbecoming more complex. In Japan, childcare is provided mainly by nursery schools,
both public and private. In recent years, however, private nursery schools, which offer
more flexibility, tight local government finances, and the high cost structure of publicnursery schools２ have led to a shrinking share of public nursery schools (Fig 1).
Public nursery schools in Japan are operated by the municipalities. Until the fiscal
year 2003, 50% of their operating costs were subsidized by the national government asa specific purpose grant. On April 1, 2004, however, this subsidy was abolished andmunicipalities were required to finance all costs from their general revenues. The main
purpose of this reform was to allow municipalities to become more autonomous so thatthey could improve their administrative efficiency and become more responsive to theincreasingly sophisticated demand for childcare. Although loss of the subsidy was tobe compensated with an increase in general revenues allocated through the system ofLocal Allocation Tax (LAT), the cost of running public nursery schools became more
dependent on each municipality’s fiscal situation. It is believed that this change in thefunding structure accelerated the spending cuts on and the privatization of publicnursery schools. For example, a survey by the Ministry of Health, Labor, and Welfare
found that nearly 40% of municipalities across the nation reduced their operational
budgets for public nursery schools in the 2004 budget (Takagi, 2005). According to the１

In recent years, kindergartens have been increasingly merged with nursery schools.
Hayashi (1996) examined cost differences between public and private nursery schools in 31
municipalities in Osaka prefecture. Owing to data availability constraints, he used the total cost of childwelfare services as a proxy for the cost of nursery schools. He reported that a one percent increase in theshare of public nurseries’ total capacity induces an increase in the cost of child welfare services per childof 7,868 Japanese yen. This is attributed to the additional allocation of staff in public nursery schools
and their higher cost because nursery staff tends to be older.
２

results of the survey ３ conducted by the Japan Childcare Association (JCA),
municipalities spent on average 2.4% less per enrolled child in a public nursery schoolin 2007 compared with 2003. In cities with populations of more than 300,000, thereduction was as high as 5% on average.
Many previous studies have attempted to evaluate the effects of such policychanges empirically. Ahlin and Mörk (2008) looked at whether the policies of
educational decentralization in Sweden caused any structural changes to local school
expenditures by simply applying interaction terms of localities’ fiscal characteristics
such as tax base and total intergovernmental grants and year dummies that took thevalue 1 after the reform. Applying the same estimation method, Kobayashi and
Hayashi (2011) examined how changes in funding affected municipal expenditures on
Japan’s School Expense Assistance (SEA) program. Using panel data for 2004 and2007 to examine the reforms of 2005, they showed that municipalities’ financial
strength affected SEA benefits per recipient more after the reforms. With recent
developments of quasi-experimental methods, such as the difference-in-differences(DID) approach, a number of studies have studied the effects of administrative or fiscal
reforms in various policy fields. In the field of education, for example, Wang et al.
(2011) investigated how provincial-level reforms affected local education expendituresin China. They concluded that counties that were subject to reforms tended to spend
less on public education than other counties. In the field of childcare services, Mörk etal. (2013) identified the causal effect of childcare costs on fertility rate, in the context
of exogenous variation in household user charges caused by Swedish childcare reforms.
In Japan, Ohtake and Sano (2009) and Hayashi and Kobayashi (2010) investigated theeffects of money transfers from government disbursement to LAT on compulsory
educational expenditures and on SEA benefits, respectively.
Although the negative impact of the reforms on public nursery schools seems
obvious, we have not found any empirical investigation so far. Did the reforms actuallytrigger municipalities’ lower spending on public nursery schools? If this were true,
which were the municipalities that were worst hit? This study investigates empirically３

The survey, conducted on April 1, 2007, targeted 807 municipalities across the country. The collection
ratio was 73.5%. (http://www.nippo.or.jp/news/pdfs/outline.pdf)

1

the impact of the change in funding scheme on the operational costs of public nurseryschools. More specifically, we investigate the impact of the change in fiscal strength on
the operational costs of public nursery schools before and after the reform. Second, we
examine the impact of the reform by focusing on the revenue compensation throughLAT being applied only to specific municipalities. We found that municipalities
responded differently to the reform according to the size of the cities in which they
operated. In areas with relatively large populations, fiscally stronger municipalities
were likely to spend less on public nursery schools in the wake of reforms, while
spending more in smaller cities. Besides, municipalities that were not compensated forthe loss of national subsidy reduced their expenditures in large cities. But in small
cities, such municipalities actually increased expenditures. After controlling for otherfactors, we did not find any significant impact of the reform on municipalities’
expenditures on public nursery schools in towns and villages.
This paper is organized as follows. Section 2 describes the funding system for
public nursery schools in Japan, before giving an overview of the 2004 reforms andtheir impact on the public nursery school sector. Section 3 details the study’s empiricalstrategy and the data. Section 4 presents the results of our estimation, and Section 5
concludes the paper.

Fig 1: Ownership pattern of nursery schools and their enrolment
16,0001,600,000
1,400,000

14,0001,200,000
12,0001,000,000

10,000800,000
600,000

8,000

400,000
6,000

200,000

4,000

02000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013
public_enrolled children

private_enrolled children

public_facility

private_facility

Source: author (refer to the Survey of Social Welfare Institutions)22. Institutional background
2.1 The funding system for public nursery schools

The Child Welfare Act in 1947 mandates that municipalities (cities, towns, andvillages) are responsible for taking care of children who lack sufficient care in either
public or private nursery schools. The operating costs of public nursery schools
comprise mainly personnel, service, and administrative expenses, which are funded byboth the government and a household user charge. The total operational cost is
estimated based on the unit price (monthly operational cost per enrolled child), whichis legally defined by the national government. This depends on the age of the child (0–1, 2–3, and 4–5), the capacity of the nursery school, and the size of the municipality it
is located in. The national government also sets the user charge based on the child’sage, the number of enrolments, and household income. However, each municipality
can decide upon the actual user fee charged.
Fig 2 shows the funding system for public nursery schools. Until 2003, 50% of
the estimated total operating costs, after deducting the user charge, were taken care ofby the national government. The other half was funded by the prefecture (the local
government unit above the municipality) and by the municipality. Although the unit
price is supposedly set at a level that ensures universal and quality childcare across thecountry, the amount is seen as too low to fulfill the actual local demand for childcare.
As a result, there is a significant gap between the legally determined operating cost andthe actual cost. Kimura and Sugiyama (2009) pointed out the following specificproblems with the current national standard: the estimated minimum requirement ofnursery schools is too low; the estimated expense on nursery staff is much lower thanwhat is actually required; and, considering the growing demand for childcare services,
an opening time of eight hours per day is too short. Moreover, in most cases,
municipalities disregard the set user charge and determine their own charge forhouseholds in order to reduce the burden on households. All these factors resulted inmunicipalities having to spend more on public nursery schools to ensure quality
childcare by hiring better-paid staff or offering special care. This puts pressure on local
governments operating under tight budgets.

3

Fig 2: The funding system for public nursery schools(a) Legally defined operating cost
Total operating cost (national standard ) (A)Public expense (C) = (A) − (B)User charge

The national government

Prefecture

Municipality(national(C) × 50%

(C) × 25%(C) × 25%standard) (B)(b) Actual cost before the 2004 reform
Total operating cost (actual expense) (A)Public expense (C) = (A) − (B)Municipality’sUser charge (national standard) (B)The national

Prefecture

Municipality

additional

Municipality

Municipality

government (C) × 50%(C) × 25%(C) × 25%

expenseexemption

standard(c) Actual cost after the 2004 reform
Total operating cost (actual expense ) (A)Public expense (C) = (A) − (B)Compensation through Local Allocated Tax (LAT)

Municipality’suser charge (national standard) (B)additional

Municipality

Municipality

expenseexemption

standard

Source: Author

2.1 The 2004 reform
Until March 2004, 50% of the public expense was subsidized by the national
government as a specific purpose grant. On April 1, 2004, however, this national
government subsidy system was abolished in the wake of decentralization;
approximately 166 billion yen were moved from national to local tax revenues. At thesame time, prefectural disbursements were also abolished. As a result, municipalities
had to take responsibility for financing public nursery schools from their own budgets.
The burden on municipalities reached 332 billion yen in the fiscal year 2004 (Osawa,
2004; p.106). Fig 3 shows the trend in accumulated total operating costs for publicnursery schools by revenue source. It is clear that since the 2004 reforms, the share ofnational and prefectural disbursement decreased, while the share of general revenues
4increased significantly. Although the increase in general revenue was more significant
than the decrease in specific grant in the immediate aftermath of reforms, theproportion soon began to decline steadily. Yamamoto (2011), who analyzed thefinancial situation of public nursery schools in Fujinomiya city in Shizuoka prefecture,reported that cuts of personnel expense accounted for a large share of decrease ingeneral revenues after the reforms.

Fig 3: Trend in revenue sources of public nursery schools(billion, JPY)
900
800
700
600
500
400
300
200
100
0
2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013
national government and prefectural disbursement
user charge
miscellaneous earmarked revenue
general revenue

Source: Author (refer to the Survey of Local Finance Situation)

The losses of both the national government subsidy and prefectural contributions were
to be compensated by an increase in general revenues from the LAT system (Fig 2-(c)).
LAT, which is a general-purpose grant, is an allocation of money made by the national
government to each local government to guarantee standard and universal public
services regardless of disparities in local revenue sources. The amount of LAT isdetermined by the difference between local governments’ Basic Fiscal Needs (BFN)
and its Basic Fiscal Revenue (BFR). As long as BFN exceeds BFR (BFN/BFR>1), therelevant amount of LAT allocation is made. After the 2004 reforms, the operating costsof public nursery schools were also taken into account in the calculation of BFN.
Therefore, as Hayashi and Kobayashi (2010) pointed out, if the municipalities were
5

LAT recipients, the total revenue from the national government could be regarded as
the same both before and after the reforms. However, for non-LAT recipients, thereforms simply meant lower revenues.
Of course, there was no guarantee that LAT recipients would not reduce operating
costs since LAT is an unconditional revenue source. As mentioned in the Introduction,
Takagi (2005) and the JCA (2007) reported that many municipalities cut their operating
budgets for public nursery schools significantly after the reforms. Moreover, thenationwide survey conducted by the JCA in 2005 also reported different reactions inregions with municipalities of different sizes. Based on a grouping of six regions
according to their population size,４ the JCA reported that cost cutting was moresignificant in areas with relatively larger populations than in other areas with smaller
populations. Specifically, in those cities with the largest populations, the percentage of
public nursery schools which reported the cost cut of childcare services after thereform was the highest, reaching 47.6%, with 31.6% being the average; in places withthe smallest populations, such as towns and villages, up to 51.7% (48.3% on average)of public nursery schools said their budgets remained almost the same. Underpinningthese different reactions is the position that public nursery schools hold in different
areas. In urban areas, where the employment rate of women is high, demand forchildcare is likely to be greater and more complex than in rural areas. In response tothis demand, not only public but also the growth of private nursery schools has beensignificant. In rural areas, on the other hand, public nursery schools continue todominate. Thus, it is reasonable to expect that depending on the size of cities, local
governments would respond differently to the reforms.

４

The JCA (2005) investigated changes in the local financial situations of public nursery schools after
the reform. A questionnaire was submitted to 689 nursery schools, of which 291 were public schools and398 were private. The regions were divided into the following groups: special wards and designatedcities; prefectural capital cities; core cities with populations of more than 150,000; small cities withpopulations of more than 50,000 and less than 150,000; small cities with populations of less than
50,000; and towns and villages.

6

3. Test strategy and data
3.1 Empirical strategy
We use the panel dataset of a Japanese municipality to analyze the effects of the2004 reforms on municipalities’ expenditures on public nursery schools. Our main
purpose is to examine if there was any change in the impact of fiscal strength on publicnursery school expenditures before and after the reforms and to estimate the averagetreatment effect of the reform. Since the census survey we used is conducted every five
years, we can only use a five-year interval dataset. Besides, the number of enrolled
children after 2005 is not available from our data source.５ Hence, we use municipality
panel data with a five-year interval from the years 2000 and 2005. In addition, we
eliminate consolidated municipalities from our observations. Our final dataset isunbalanced panel data from 1,906 observations covering 983 municipalities in theyears 2000 and 2005. To answer the first question, we will estimate the following
empirical model based on the specification approach used by Ahlin and Mörk (2008)
and Kobayashi and Hayashi (2011).
𝑦𝑖𝑡 = 𝛼0 + (𝛽1 + 𝛽2 𝐷2005 )𝐹𝐼𝑆𝐶𝐴𝐿𝑖𝑡 + 𝛄𝐗 𝑖𝑡 + 𝑣𝑖 + 𝑣𝑡 + 𝜀𝑖𝑡(1)where 𝑦𝑖𝑡 denotes operating costs for public nursery schools per enrolled child inmunicipality i in year t. 𝐗 is the vector of independent variables that can influencecost. 𝑣𝑖 , 𝑣𝑡 , and 𝜀𝑖𝑡 are unobserved municipality-specific fixed effect, year effect, andan idiosyncratic error term (𝑖𝑖𝑑~𝑁(0,1)), respectively.
Since we want to focus on the change in the burden on municipalities, we deduct
the amount of user charges from total operating costs. 𝐷2005 is the dummy variable
that takes the value 1 in 2005. 𝐹𝐼𝑆𝐶𝐴𝐿𝑖𝑡 is the variable that indicates fiscal strength;
we use a fiscal capacity indicator (FCI) of municipality i. FCI is a simple three-year
average of the numbers derived by dividing BFR by BFN, which is used to calculate
ordinary LATs. In general, a higher FCI means stronger fiscal capacity. We focus on
the estimated parameter of the interaction term 𝛽2 that captures the change in the５

The data source is the Public Facilities Survey (Ministry of Internal Affairs and Communications[MIC]). For more details, refer to the description of data under Section 3.2.

7

effect of fiscal strength on childcare costs. In model (1), we assume we can capture theeffects of the reform by employing a year dummy variable that takes the value 1 in
2005. However, this method may fail to identify any possible macroeconomic
fluctuations during the period of analysis and the influence of funding reforms. To
overcome this problem and extract more accurately the effects of the reforms, we canutilize the empirical strategy suggested by Ohtake and Sano (2009) and Hayashi andKobayshi (2010).
Compensation for the fiscal loss through LAT can only be enjoyed by LAT
recipients that are “weak” enough to manage their finances based on their own local
tax revenues. Therefore, as Hayashi and Kobayashi (2010) pointed out, for non-LAT
recipients, this reform implies a rise in the real price of childcare service. Thus, as longas LAT compensates exactly the same amount as the loss caused by abolishing thenational grant, only non-LAT municipalities feel the impact of a decrease in revenue.
Therefore, by regarding non-LAT municipalities as a treatment group and LAT
municipalities as a control group, we can apply a DID approach to clarify the averagetreatment effect of the reform. We can interpret this effect as the income effect of LAT
compensation on public nursery school expenditures. Let subscripts 𝑎 and 𝑏 denote“after” and “before” reforms; the change in operating costs in the treated group (i.e.,
non-LAT municipalities) is measured by 𝐸𝑖 (𝑦1𝑎 − 𝑦1𝑏 |𝑑 = 1) and that for theuntreated group (i.e., LAT municipalities) by 𝐸𝑖 (𝑦1𝑎 − 𝑦1𝑏 |𝑑 = 0), where 𝑑 takes thevalue 1 for the treatment group and 0 otherwise. Then, if the parallel trend andcommon trend assumptions are valid, DID is a measure (𝑦1𝑎 − 𝑦1𝑏 |𝑑 = 1) −𝐸𝑖 (𝑦1𝑎 − 𝑦1𝑏 |𝑑 = 0) of the treatment effect.６
In this case, by defining the following regression model and obtaining the DID
estimator (𝛿), we estimate the average treatment effects of the reform.
∆𝑦𝑖 = 𝑐 + 𝛿𝑁𝐿𝐴𝑇𝑖 + ∑ 𝜃𝑘 ∆𝑋𝑘,𝑖 + 𝜖𝑖(2)where 𝑁𝐿𝐴𝑇𝑖 indicates the dummy variable, which takes the value 1 if themunicipality is a non-LAT recipient and 0 otherwise, in 2005. This model is defined as
６

For a more detailed discussion, see Cameron and Trivedi (2005, Chapter 25).

8

follows and is based on Hayashi and Kobayashi (2010). Let 𝑀𝑖𝑡 indicate the amount ofgeneral transfers, including LAT grants of the ith municipality in year t. A series ofcontrol variables (𝑋ℎ𝑖 ) is the same as the ones we consider in equation (1). Then,
operating cost can be defined as the following linear response equation (3). Equation(2) is derived through the differentiation of equation (3).
𝑦𝑖𝑡 = 𝑎𝑡 + 𝜆𝑀𝑖𝑡 + ∑ 𝜃ℎ 𝑋ℎ𝑖𝑡 + 𝑣𝑖 + 𝑣𝑡 + 𝜀𝑖𝑡(3)

Taking the first difference of equation (3), we get equation (4) as given below:(𝑦𝑖,2005 − 𝑦𝑖,2000 )= (𝛼2005 − 𝛼2000 ) + 𝜆(𝑀𝑖,2005 − 𝑀𝑖,2000 )+ (∑ 𝜃ℎ 𝑋ℎ,𝑖,2005 − ∑ 𝜃ℎ 𝑋ℎ,𝑖,2000 ) + (𝜀𝑖,2005 − 𝜀𝑖,2000 )∆𝑦𝑖 = 𝑐 + 𝜆∆𝑀𝑖 + ∑ 𝜃𝑘 ∆𝑋𝑘,𝑖 + 𝜖𝑖

(4)Assuming we control for other factors in general revenues, ∆𝑀𝑖 will capture the netchange in total expense for public nursery schools after the reforms. So long as LAT
completely offsets the loss of national subsidy, ∆𝑀𝑖 will be equal to 0 formunicipalities with 𝑁𝐿𝐴𝑇𝑖 = 0 . Therefore, we can derive 𝜆∆𝑀𝑖 = 𝜆(𝑁𝐿𝐴𝑇𝑖 =1)∆𝑀𝑖 + 𝜆(𝑁𝐿𝐴𝑇𝑖 = 0)∆𝑀𝑖 = 𝜆𝑁𝐿𝐴𝑇𝑖 ∆𝑀𝑖 = 𝛿𝑁𝐿𝐴𝑇𝑖 and get 𝛿 = 𝜆∆𝑀𝑖 . If weassume operating cost is a normal good, 𝛿 will be interpreted as the negative income
effect since ∆𝑀𝑖 < 0 for municipalities with 𝑁𝐿𝐴𝐿𝑖 = 1.
Fig 4 represents reform of the funding system and the DID estimator (𝛿) is ourestimate. The operating and other costs per enrolled child are represented on thehorizontal and vertical axes, respectively. Assume that before the reforms,
municipalities select the optimization point a given the budget constraint line AB. After
reforms, the budget constraint line for non-LAT municipalities shifts from AB to AC;
therefore, the optimization point moves to b. On the other hand, inasmuch as LAT
perfectly offsets the national subsidy, LAT municipalities will optimize at point c with
the new budget constraint line ED. The DID estimator (𝛿) in equation (2) will capture
9the difference between the decrease in LAT municipalities (from a to c) and that innon-LAT municipalities (from a to b). 𝛿 will carry a negative sign if childcare service
is a normal good (the figure on the left side).
Fig 4: Impact of abolishing the national subsidy and compensation through LAT
Fig 4-1: When public childcare service is a normal goodFig 4-2: When public childcare service is an inferior good

Source: Author (refer to Ohtake and Sano (2009))
10

3.1 Data description
We define the dependent variable as the operating cost of public nursery schools perenrolled child in each municipality. The data is drawn from the Survey of Local
Finance Situation (Ministry of Internal Affairs and Communications [MIC]).
Information on the number of enrolled children and other details about nursery schools
is obtained from the Public Facilities Survey (Ministry of Internal Affairs andCommunications [MIC]). As for the independent variables, we first consider controlsthat reflect socioeconomic factors, such as population (POP), the share of preschool
population in total population (PRESCHOOL), and the share of taxpayers in total
population (TAXPAYER). Preschool population refers to the populations of children
under the age of 5. The share of taxpayers in total population is included as anindicator of wealth. PRESCHOOL is included to capture a municipality’s reaction topotential demand in the area. However, as Asada (2009) pointed out, this variable may
be potentially endogenous as a result of “Tiebout sorting.” That is, households withchildren under the age of 5 could move to an area where local childcare service is more
readily available. To avoid the possible bias of coefficients in the case of OLS
estimation, we run panel IV estimation additionally using the ratio of preschool
population five years ago and the employment rate of women five years ago as
instrumental variables. These variables are independent of the current level ofmunicipality expense on childcare. Based on the test result of endogeneity of
PRESCHOOL, we will decide which model to use. As stated in Section 2.1., childcare
costs largely depend on the age structure of enrolled children. However, owing to data
availability issues, we do not have the actual number of enrolled children by age ineach nursery school. Therefore, we approximate the age structure of actual enrolled
children by the population’s age structure in each municipality, assuming
municipalities offer childcare services to meet potential demand in the relevant area.
That is, we consider the share of infants aged 0, of children aged 1 to 2, and of children
aged 3 and above in the preschool population (R0, R1–2, and R3) to capture the effectsof the different age compositions on childcare costs. These age segments meet theactual classification that the national government uses to calculate the unit price ofchildcare in public nursery schools. We treat the share of children aged 4 to 5 in thepreschool population as a base group.
11

Next, we consider independent variables that relate to a nursery’s management. To
obtain the annual average wage of workers, we divide regular personnel expenses by
the total number of administrative and nursery staff after deducting temporary staff(WAGE).７ Higher wages would push up the operating costs. We also consider theratio of temporary staff in total staff (TEMPORARY STAFF). The more temporary
staff the municipality hires, the lower are its childcare costs. In addition, we take theeffects of economies of scale into account. Hence, we include the holding capacity of
public nursery schools (CAPSCHOOL). Lastly, we include the sufficiency rate of
public nursery schools (SUFFICIENCY). This indicator is calculated by taking theratio of total holding capacity of public nursery schools to the number of potential
applicants in each municipality. The number of potential applicants is surveyed by eachmunicipality’s ordinance and is obtained from the Public Facilities Survey. In urban
cities, a long waiting list for nursery school acceptance is considered a serious social
issue. However, in rural areas with a small and decreasing population, a high vacancy
rate, especially in public nursery schools, is common (Nobe, 2010). If the holding
capacity exceeds the number of potential applicants, municipalities may have
incentives to reduce public nursery school expenditures. Note that since all
independent and dependent variables are logarithmically transformed, the estimatedcoefficient indicates elasticity.
In our empirical analysis, we divide our observations into several categories basedon the size of municipalities to capture the difference in reactions. Although we refer to
the JCA’s classification standard, our dataset does not fit it exactly. We do not havedata for major cities and special wards; besides, many of the prefectural capital cities
that experienced consolidation are excluded from our observations. Given these issues,
we use the following three categories: core cities including some prefectural capitalcities (population >= 150,000); small cities (50,000 =< population < 150,000); andtowns and villages (population < 50,000).８ The sample sizes are 148, 340, and 1418,
respectively.
７

Although we can get details of staff numbers by occupation and employment status, the personnel
expense figure is not available.
８
According to the Local Autonomy Act, a city is defined as a municipality if it has a population of
more than 50,000. In fact, there are some parts of cities with populations of less than 50,000. Here we
include such cities in category 3 as well.

12Fig 5 show the distributional changes in operating costs per enrolled child in 2000
and 2005. Cases 1 to 4 show the observations of full sample, core cities, small cities,
and towns and villages, respectively. As seen from cases 1 and 4, municipalities thatspent relatively high amounts on childcare per child were concentrated in areas withsmall populations. There were seven towns and five villages that spent more than
2,500,000 yen in total. This may be because the number of enrolled children was too
small in these areas. To check the robustness of estimation, we regress same models
without observations whose dependent variable exceeds the range of three times aslarge as standard deviation.９ The mean values of childcare cost per enrolled child fell
from 831,000 yen in 2000 to 775,000 yen in 2005 (Case 1: all); from 1,208,000 to
1,089,000 yen (Case 2: core cities); from 981,000 to 857,000 yen (Case 3: small cities),and from 762,000 to 722,000 yen (Case 4: towns and villages), respectively. Although
in Case 1 (full observations) we see hardly any significant change in costs between thetwo periods, it is obvious that the density distribution has shifted to the left side inCase 2 and Case 3. This shift in distribution implies that the operating costs perenrolled child tended to decrease overall, especially in core and small cities. As for thechange in distributional variations, the values of standard deviation became smaller
during the reform period in every case–from 476,000 to 448,000 yen (Case 1), from
403,000 to 300,000 yen (Case 2), from 454,000 to 396,000 yen (Case 3), and from
464,000 to 455,000 yen (Case 4), respectively. Disparities in spending seem to besmaller during this period.

９

That is, the childcare cost per child was greater than 2,188,000 yen in the dataset of full observations.

13Fig 5: Distributional changes in operating costs per enrolled child

2000

2000

.001

2005

0

.0005

Density
0.0005

Density

2005

2000
4000
6000
childcare cost per-enrolled child(thousand).001

Case3: small cities
2000

500
1000
1500
2000
childcare cost per-enrolled child(thousand)Case4: towns and villages
2000

.001

2005

0

0

.0005

Density

2005

.0005

0

.0015

0

DensityCase2: core cties

.0015

.001

Case1: all

0

500
1000
1500
2000
2500
childcare cost per-enrolled child(thousand)

0

2000
4000
6000
childcare cost per-enrolled child(thousand)Note: The cost is not transformed into a logarithm. The cost covered by households in the form of
the user charge is exempted.
Source: Author

14

4. Results
Table 1 presents the estimation results of regression model (1) with full observations.
We ran pooled, random-effects, and fixed-effects models, and conducted the F-test, theBrush-Pagan LM test, and the Hausman test to specify a suitable model. A series ofhypothesis testing supported the choice of a fixed-effects model as most appropriate.
Since our main interest here is to examine the change in the effects of fiscal strength on
operating costs during the reforms, we focus on the estimated parameters of FCI (𝛽1)and its interaction with D2005 (𝛽2). Columns (3) and (4) show the estimation results byfixed-effects model, while column (4) shows the year effect. In both results, 𝛽1 isstatistically insignificant, implying that fiscal strength did not affect the per-child
operating cost. The estimated 𝛽2 in column (3) is positively significant at the 1% level,
implying that fiscally strong municipalities tended to increase per-child operating costs
after the reforms. However, once we take the year trend into consideration, thecoefficient is no longer significant. According to results of the likelihood ratio test, we
support the result of column (4) and conclude that there seems to be no structuralchange in the impact of fiscal strength on operating costs for public nursery schools.
Figures in column (5) show the result by IV model. Since we cannot reject the
hypothesis that PRESCHOOL is exogenous, the parameters estimated by fixed-effects
models are BLUE. Therefore, we will apply the fixed-effect model to the following
analysis.
Table 2 reports the results using the separated municipality scale samples. In thecase of core cities, municipal fiscal strength and per-child operating cost are positively
correlated (see columns (1) and (2)). This means the stronger the municipalities, themore they spend on public nursery schools. However, the estimated coefficient of theyear-interaction term (𝛽2) is negative and significant. That is, in relatively large cities,
fiscally stronger municipalities reduced operating cost more significantly after thereforms than the others. Next, in the case of small cities, there seems to be no
statistically significant correlation between fiscal strength and operating cost (seecolumns (3) and (4)). However, post-reforms, fiscally stronger municipalities werelikely to increase per-child operating costs. We confirm this tendency even after
controlling for year trends. Interestingly, the effects of fiscal strength on operating
costs for public nursery schools after the reform go the opposite way in core cities and15

small cities. After the reform, the “richer” municipalities in core cities spent less on
public nursery schools; on the other hand, the “richer” municipalities in small cities
spent more. This may be because public nursery schools play a much more prominentrole in small cities than in bigger ones. In core cities, municipalities are more likely tospend less on costly public facilities so that they can boost substitute childcare services
offered by the private sector or other parties. They may even have more incentives toadvance the privatization of public facilities. In contrast, public nursery schools are anecessity in small cities, which makes municipalities respond to resident demands
through public facilities. Lastly, columns (5) and (6) show the case of towns andvillages. Although the estimated coefficients (𝛽2) indicate a positive effect, similar tothat for small cities, it is no longer statistically significant once we consider the year
effect.
We now look at some of the control variables from Table.2. Except for the case ofsmall cities, the ratio of preschool population to total population (PRESCHOOL) is
statistically negative and significant with a small p-value, which implies thatdiseconomies of scale due to the decline in young child-population pushes up theoperating costs. After controlling for time trend, a 1% increase in preschool children
old enough to receive childcare service decreases childcare cost by approximately
0.6%. The coefficients of R1-2 are positive in all the cases implying that the high shareof one- and two-year-old children pushes up the operating costs for public nurseryschools. However, it is only significant in towns and villages. When we control foryear effects, the coefficients of R0 and R3 are negative and statistically significant onlyin core cities. Since the cost of childcare is supposed to be inversely proportional to achild’s age, this result is rather unexpected. The average wage is significant andpositively associated with the dependent variable, as expected. In core and small cities,
the temporary staff ratio has a negative and significant effect on childcare cost. The
impact is more significant in small cities, with a 1% increase in the ratio triggering anapproximate 0.45% decrease in childcare cost per enrolled child. The coefficient of
CAPSCHOOL is negative and significant at almost 5% in core cities, and towns andvillages, indicating the existence of economies of scale. The estimated coefficient of
SUFFICIENCY is negatively significant only in small cities. In small cities where
public nursery schools’ sufficiency rate is higher, the municipality is likely to reduce16spending so that they can bridge the gap between supply and demand.
Table 3 presents the results of the DID estimation. The first column in eachsample category considers the case with only the 𝑁𝐿𝐴𝑇 dummy, whereas the second
column reports the case with control variables. First, in the case of core cities, theestimated 𝛿 is negative and statistically significant (see columns (1) and (2)).
Municipalities with no compensation through general resource revenue decrease
per-enrolled-child operating cost by about 0.07% on average after controlling for otherfactors. This result admits the positive income effect of the compensation. Thus, incore cities, childcare service in public nursery schools can be regarded as a normal
good. In the case of small cities, the result is the exact opposite of what happens in thecore cities. The estimated coefficient of 𝛿 is positive and significant at 5%, as column(4) reports. In small cities, municipalities with no compensation through LAT increase
per-enrolled-child operating cost by about 0.09% on average. That is, childcare service
in public nursery schools can be considered an inferior good for municipalities in small
cities. Columns (5) and (6) show the result for towns and villages; the estimatedcoefficient of 𝛿 is positive and implies the characteristics of an inferior good.
However, it is insignificant and we fail to capture the average treatment effect of thereform. As for the other control variables, the significance and sign of coefficients are
similar to the estimation results presented in Table 2.

17

Table1: Estimation results (full observations)
Dependen Var.: per-child
operating cost for publicnursery school

Pooled

RE

FE

FE

FEIV

(1)

(2)

(3)

(4)

(5)

FCI*D2005

0.08***(0.024)0.035(0.034)0.120(0.090)0.985***(0.233)0.056(0.118)0.415***(0.068)0.100***(0.015)0.514***(0.109)0.626***(0.027)-0.238***(0.058)-0.387***(0.039)-0.056***(0.021)

0.068***(0.014)-0.011(0.039)0.118*(0.064)0.746***(0.166)-0.069(0.085)-0.410***(0.072)0.118***(0.018)-0.411***(0.128)0.512***(0.026)-0.192***(0.059)-0.350***(0.043)-0.036(0.023)0.0672***(0.026)-0.110(0.128)0.081(0.073)0.600***(0.195)-0.128(0.102)-0.383**(0.173)-0.763**(0.338)0.401(0.260)0.317***(0.106)-0.080(0.085)-0.208**(0.088)0.034(0.051)

0.022(0.033)0.045(0.114)-0.030(0.087)0.673***(0.197)-0.075(0.106)-0.950***(0.308)-0.212(0.457)0.263(0.246)
0.316***(0.107)-0.064(0.085)-0.161*(0.087)-0.019(0.052)-0.119***(0.036)

1.092**(0.497)
1,906
0.330
no
983

1.418***(0.479)
1,906
0.126
yes
983

0.025
(0.032)0.034(0.116)0.036(0.075)0.591***(0.187)-0.108(0.103)-0.587***(0.202)-0.426(0.394)0.235(0.245)0.316***(0.108)-0.074(0.084)-0.174**(0.088)0.005(0.052)-0.0878***(0.029)7.618*(4.404)
1,906
0.159
yes
983

FCI
R0
R1-2
R3
PRESCHOOLPOP
TAXPAYERWAGE
TEMPORARY STAFFCAPSCHOOL
SUFFICIENCY
year effect
Constant

11.77***(3.700)
1,906
0.149
yes
983
F(982, 911) = 6.28***
chibar2(1) = 432.55***
chi2(12)=86.59***

Observations
R-squared
Municipality fixed-effects
Number of municipality
F test that all Ui=0
Breusch and Pagan LM test
Hausman test
likelihood ratio-test(specification model(3) or (4))
F statistic(first stage)
Partial R-squared
Kleibergen-Paap rk LM statistic
Hansen J statistic (p-value)
Tests of endogeneity of:
PRESCHOOL Durbin-Wuhausman test
Robust standard errors in parentheses*** p<0.01, ** p<0.05, * p<0.1
All independent and dependent variables are logarithmically transformed.

Source: Author

18

1,844
0.152
yes
922

LR chi2(1)=22.47***
61.00
0.313
49.25***
0.812
1.173 Chi-sq(1)Table 2: Estimation results (observations by municipality scale)

Dependen Var.: per-child
operating cost for publicnursery school

FCI*D2005
FCI
R0
R1-2
R3
PRESCHOOLPOP
TAXPAYERWAGE
TEMPORARY STAFFCAPSCHOOL
SUFFICIENCYCore cities

Observations
R-squared (within)
Municipality fixed-effects
Number of municipality
likelihood ratio-test

Towns and Villages

FE

FE

FE

FE

FE

FE

(1)

(2)

(3)

(4)

(5)

(6)

-0.164**(0.082)0.377(0.361)-0.290(0.488)1.328(1.168)-1.811***(0.614)0.163(0.432)-0.975(0.767)0.569(1.029)0.747***(0.034)-0.159(0.103)-0.501*(0.269)0.095(0.063)-0.251***(0.079)0.733***(0.272)-0.871*(0.453)0.665(0.813)-1.676***(0.461)-0.661**(0.323)0.579(0.587)-0.217(0.960)0.773***(0.033)-0.165*(0.091)-0.479**(0.228)0.021(0.050)-0.201***(0.042)-10.980(8.004)
148
0.938
yes
81
LR chi2(1)=45.21***0.169***(0.055)-0.104(0.224)0.519*(0.272)0.845(0.706)-0.476(0.388)-0.011(0.250)-0.628(0.595)0.857(0.749)0.403***(0.143)-0.467***(0.155)-0.157(0.173)-0.162**(0.081)0.107*(0.064)0.119(0.231)0.296(0.309)0.742(0.691)-0.400(0.377)-0.319(0.292)-0.212(0.654)0.557(0.744)
0.393**(0.154)-0.458***(0.163)-0.108(0.171)-0.188**(0.078)-0.0924*(0.053)
6.053(8.231)
340
0.452
yes
175
LR chi2(1)=7.13***

0.0628**(0.028)-0.159(0.145)0.035(0.075)0.524***(0.192)-0.133(0.103)-0.518***(0.199)-0.524(0.391)
0.339(0.267)0.256**(0.117)-0.005(0.084)-0.211**(0.092)0.031(0.058)0.041(0.038)-0.081(0.136)0.021(0.077)0.531***(0.190)-0.123(0.106)-0.588***(0.218)-0.438(0.421)0.270(0.257)0.258**(0.117)-0.002(0.084)-0.196**(0.093)0.022(0.059)-0.047(0.040)7.769*(4.395)
1,418
0.12
yes
734
LR chi2(1)= 3.14*

year effect
Constant

Small cities

13.130(9.168)
148
0.916
yes
81

12.30*(7.284)
340
0.44
yes
175

Robust standard errors in parentheses*** p<0.01, ** p<0.05, * p<0.1
All independent and dependent variables are logarithmically transformed.

Source: Author

198.839**(4.033)
1,418
0.118
yes
734

Table 3: Estimation results (DID estimation)

Dependen Var.: perchild operating cost for
public nursery school
non-LAT municipality
⊿FCI
⊿R0
⊿R1-2
⊿R3
⊿PRESCHOOL⊿POP⊿TAXPAYER⊿WAGE⊿TEMPORARY STAFF⊿CAPSCHOOL⊿SUFICIENCY
Constant

Core cities

Small cities

Towns and Villages

(1)

(2)

(3)

(4)

(5)

(6)

-0.1199*(0.072)-0.0730**(0.031)
0.692**(0.267)-0.903**(0.424)0.881(0.796)-1.677***(0.479)-0.789**(0.323)0.263(0.600)-0.718(0.904)0.782***(0.029)-0.129(0.079)-0.522***(0.195)0.017(0.044)-0.125**(0.052)
69
0.91

0.073
(0.054)

0.0937**(0.047)0.090(0.227)0.289(0.305)0.689(0.689)-0.400(0.373)-0.268(0.288)-0.178(0.634)0.540(0.733)0.374**(0.146)-0.437***(0.154)-0.132(0.176)-0.180**(0.073)-0.131***(0.046)
168
0.233

0.058
(0.088)0.111(0.089)-0.118(0.129)0.019(0.077)0.527***(0.187)-0.127(0.107)-0.609***(0.215)-0.301(0.343)0.328(0.249)0.258**(0.118)-0.016(0.083)-0.192**(0.093)0.018(0.059)-0.0881***(0.029)685
0.103-0.051***-0.072***(0.022)(0.020)Observations
69
171
R-squared
0.025
0.013
Robust standard errors in parentheses*** p<0.01, ** p<0.05, * p<0.1
All independent and dependent variables are logarithmically transformed.

Source: Author

20-0.060***(0.021)744
0.001

Table 4: Descriptive statistics
Variable

Mean

Std. Dev.

Min

Max

Mean

Std. Dev.

Min

Max

Mean

Std. Dev.

Min

Max

Mean

Std. Dev.

Min

Max

per-child operating costfor public nursery school

6.55

0.64

0.74

8.69

6.98

0.40

4.71

7.69

6.71

0.55

3.81

7.74

6.47

0.66

0.74

8.69

FCI
R0
R1-2
R3
PRESCHOOLPOP
TAXPAYERWAGE
TEMPORARY STAFFCAPSCHOOL
SUFFICIENCY

-0.86
-1.87
-0.74
-1.77
-2.98
9.87
-1.00
8.81
0.07
4.32
-0.35

0.69
0.16
0.07
0.11
0.22
1.39
0.15
0.46
0.21
0.41
0.67

-2.81
-3.26
-1.31
-2.81
-4.19
5.31
-1.73
4.17
0.00
2.71
-3.30

0.91
-1.17
-0.41
-1.10
-2.40
15.09
-0.61
11.32
1.84
5.63
1.91

-0.11
-1.81
-0.71
-1.78
-2.88
12.56
-0.88
8.93
0.07
4.57
-0.83

0.20
0.06
0.03
0.02
0.11
0.61
0.07
0.30
0.18
0.17
0.59

-0.76
-1.96
-0.77
-1.83
-3.18
11.92
-1.22
5.84
0.00
4.14
-2.78

0.50
-1.65
-0.63
-1.72
-2.56
15.09
-0.76
9.41
0.91
4.92
1.91

-0.28
-1.83
-0.72
-1.78
-2.87
11.29
-0.91
8.86
0.05
4.55
-0.68

0.28
0.08
0.04
0.04
0.15
0.31
0.10
0.43
0.19
0.25
0.63

-1.02
-2.10
-0.82
-1.89
-3.27
10.82
-1.36
4.93
0.00
3.40
-2.78

0.54
-1.67
-0.60
-1.65
-2.41
11.92
-0.75
11.21
1.84
5.25
0.45

-1.08
-1.89
-0.75
-1.77
-3.02
9.25
-1.03
8.79
0.08
4.24
-0.22

0.65
0.17
0.07
0.13
0.23
0.95
0.16
0.47
0.22
0.42
0.64

-2.81
-3.26
-1.31
-2.81
-4.19
5.31
-1.73
4.17
0.00
2.71
-3.30

0.91
-1.17
-0.41
-1.10
-2.40
10.82
-0.61
11.32
1.79
5.63
1.67

1906

148

ALL
Std. Dev.
Min

VariableMean⊿per-child operating costfor public nursery school

-0.08

0.41

non-LAT municipality
⊿FCI
⊿R0
⊿R1-2
⊿R3
⊿PRESCH⊿POP⊿TAXPAYER⊿WAGE⊿TEMPORARY STAFF⊿CAPSCHOOL⊿SUFFICIENCY

0.11
0.11
-0.07
-0.02
0.02
-0.07
-0.02
-0.02
0.02
0.03
0.03
-0.07

0.32
0.11
0.21
0.09
0.15
0.10
0.05
0.05
0.36
0.20
0.17
0.32
922

340

Core cities
Std. Dev.
Min

Max

Mean

-3.91

2.97

-0.09

0.36

0.00
-0.28
-1.75
-0.68
-1.02
-0.82
-0.40
-0.33
-2.48
-1.84
-0.69
-1.72

1.00
0.98
1.38
0.46
0.72
0.61
0.26
0.23
3.49
1.63
1.16
2.01

0.36
0.03
-0.09
-0.04
0.02
-0.05
0.02
-0.01
0.02
0.01
0.02
-0.11

0.48
0.07
0.04
0.02
0.03
0.05
0.03
0.02
0.44
0.15
0.06
0.28

Small cities
Std. Dev.
Min

Max

Mean

-0.70

2.56

-0.14

0.23

0.00
-0.12
-0.18
-0.09
-0.07
-0.15
-0.04
-0.05
-0.33
-0.46
-0.22
-0.81

1.00
0.19
0.02
0.02
0.08
0.10
0.16
0.04
3.49
0.69
0.15
1.89

0.19
0.06
-0.08
-0.03
0.02
-0.06
0.01
-0.01
-0.02
-0.01
0.04
-0.10

0.39
0.07
0.07
0.03
0.05
0.06
0.03
0.03
0.27
0.20
0.10
0.23

69

168

Source: Author
21

1418
Towns and Villages
Std. Dev.
Min

Max

Mean

-0.76

0.95

-0.06

0.45

-3.91

2.97

0.00
-0.25
-0.27
-0.13
-0.14
-0.33
-0.06
-0.09
-2.24
-1.84
-0.30
-1.59

1.00
0.23
0.10
0.05
0.13
0.09
0.11
0.06
1.16
0.51
0.59
1.62

0.07
0.13
-0.07
-0.02
0.03
-0.07
-0.03
-0.03
0.03
0.04
0.03
-0.06

0.26
0.11
0.24
0.10
0.17
0.12
0.05
0.06
0.36
0.20
0.19
0.34

0.00
-0.28
-1.75
-0.68
-1.02
-0.82
-0.40
-0.33
-2.48
-1.40
-0.69
-1.72

1.00
0.98
1.38
0.46
0.72
0.61
0.26
0.23
3.37
1.63
1.16
2.01

685

Max

5. Conclusion
In the early 2000s, a series of structural reforms were initiated to give local
governments more fiscal autonomy through decentralization. The 2004 change in thefunding system for public nursery schools was one such initiative. This reform
abolished the national subsidy for public nursery schools and compensated this loss
with the LAT. Several studies have claimed that this change led to local governments
spending less on public nursery schools, given their increasing autonomy. However,few studies examined this impact empirically. Using a panel dataset for municipalities
across Japan for the years 2000 and 2005, we investigated the structural change in theimpact of fiscal strength on operating costs for public nursery schools before and after
the reforms and the average treatment effect of the reforms given that the revenue
compensation through LAT was applied only to specific municipalities.
We found that large and small cities were likely to respond differently to the reforms.
As to the first question, in areas with relatively large populations, such as core cities,
fiscally stronger municipalities were likely to spend less on public nursery schools
after the reforms. The share of private nursery schools, which are more flexible inmeeting a growing demand, was higher in urban than in rural areas. Fiscally stronger
municipalities tended to shrink the size of the public sector to shift the budget
allocation from the public to the private sector or advance the privatization of public
facilities. On the other hand, as public nursery schools play the role of a necessary
good in small cities, fiscally stronger municipalities enriched the public sector when
they had greater fiscal discretion. We did not observe any change in the impact of fiscal
strength on public nursery school expenditures in towns and villages before and after
the reforms. We utilized a DID approach to answer the second question since
compensation for the loss of national subsidy was only applicable to LAT
municipalities. We found that in core cities, municipalities that did not receive revenue
compensation from LAT and faced a rise in the real price of public childcare services,
decreased expenditures on public nursery schools. Thus, in these areas, childcare in
public nursery schools can be regarded as a normal good. We found the opposite resultin small cities, regardless of the rise in the real price of public childcare service.
Municipalities tended to increase expenditures, implying that childcare service inpublic nursery schools is an inferior good.
22

Our paper shows how local governments reacted when they had more autonomy
when dealing with public nursery school management. Their different responses,
depending on the size of cities or fiscal capacity, reflect the differences in the role andpositioning of public nursery schools in each region. Policymakers should pay more
attention to such complex information to determine the best childcare policy. Further
research is necessary for an updated picture of the current situation with regard to
public nursery schools, examining not only the immediate impact but also thelong-term influence of the reforms. Applying other indicators such as the progress ofprivatization may also be useful to provide deeper insights.

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no uneikanri ni kansuru cyousa kenkyu houkokusyo” [Report on the legal change
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Appendix
The variables used in this paper are defined as follows
Per enrolled child operating cost (y):Cost per child attending public nursery schools in the municipality, excluding a user
charge burdened by households.


FCI:

Fiscal capacity indicator in the municipality


R0:

The share of infants aged 0 in the preschool population in the municipality


R1-2:

The share of children aged 1 and 2 in the preschool population in the municipality
R3:

The share of children aged 3 in the preschool population in the municipality


PRESCHOOL:

The share of children under the age of 5 in total population in the municipality
POP:

Population in the municipality


TAXPAYER:

The share of taxpayers in total population in the municipality


WAGE:

The regular personnel expense divided by the total number of administrative andnursery staff after deducting temporary staffTEMPORARY STAFF:The share of temporary hired staff in total staff

CAPSCHOOL:

The holding capacity of public nursery schools in the municipality


SUFFICIENCY:

The ratio of total holding capacity of public nursery schools to the number of potential
applicants in the municipality

25