Discussion Paper No.91
The effect of a partial relaxation of
the school-district system on land prices andacademic performance:
An empirical analysis in Japan
Yuta Kuroda

December, 2018

Data Science and Service Research
Discussion Paper

Center for Data Science and Service Research
Graduate School of Economic and Management
Tohoku University27-1 Kawauchi, Aobaku
Sendai 980-8576, JAPANThe effect of a partial relaxation of the school-district
system on land prices and academic performance:
An empirical analysis in Japan*
Yuta Kuroda †Abstract
This study investigates the extent to which gradual and partial relaxation of the school-district
system for public high schools impacts land prices and student academic achievement. In Matsue
City, Japan, students from outside the school district were strictly prohibited from enrolling in
general public schools. For many years, students who lived beyond the district boundary were unable
to attend the most academic high school that prepared students to apply to high-status universities. In
2008, however, the school-district system was eliminated in the top academic track, where students
were preparing to apply to prestigious universities. In the general track, up to 5% of all prospective
candidates were allowed to enroll from outside the school district. Since 2016, the percentage ofstudents allowed to enroll from outside the school district has risen to 20%. The present study
applies hedonic land-price models using the fixed-effect approach, together with panel data from2003 to 2018 and a regression discontinuity approach focusing on the boundary of the school district.
The findings show that relaxing the school-district system significantly reduced land-prices within
school districts with high-quality high schools. This suggests that relaxing the school-district system
may reduce the value attributed to living in a high-quality school district. The impact of this changeon the number of candidates successfully applying to universities is also analyzed. Although partial
relaxation of the school-district system will expand the disparity of the ratio of successful applicantsattending prestigious universities, the ratio of students attending national universities or all
universities does not change. This implies that a partial relaxation of the system may affect only
highly academic students.
Keywords: Land prices, School quality, School district, School choice
JEL classifications: R30, I20, I21, I28*
I would like to thank my advisor Kentaro Nakajima, as well as Midori Wakabayashi and Akira Hibiki.
All remaining errors are my own.
†
Graduate School of Economics and Management, Tohoku University, Sendai 980-8576, Japan. Email:kuroyu0725@gmail.com

1

1.

Introduction
There have been many discussions about abolishing the school-district system and introducing asystem of school choice; commentators have argued that adding market mechanisms to public
education will allow it to operate more efficiently (Ladd, 2002). Several studies have explored theeffect of changes in the school-district system on school-choice behavior, the educational market,
and academic performance (e.g., Belfield and Levin, 2002; Rouse and Barrow, 2009). In addition,
since public services, including public schools, are closely associated with residential-choice
behavior, there have been many studies on the relationship between the school-district system andthe property market (Black and Machin, 2011; Nguyen-Hoang and Yinger, 2011). However,relatively few studies have investigated the effect of changes to the school-district system on theproperty market.
Since abolishing the school-district system and introducing school choice reduces the importanceof living in school districts with high-quality schools, the relationship between school quality andproperty prices may weaken. Fack and Grenet (2010) have investigated the relationship between
school quality and house prices in the city of Paris. Their results suggest that an increase in one
standard deviation of public-school performance raises housing prices by 1.4 to 2.4%; this effectdecreases as more private schools become available in the neighborhood. Machin and Salvanes(2015) have shown that an abolition of the school-district system in Oslo County will reduce theimpact of school quality on housing prices by less than half. Chung (2015) has explored the impactof school-choice reforms on housing prices in the Seoul metropolitan area. He found that
school-choice systems reduce housing prices in high-quality school districts, relative to housing
prices in low-quality school districts.
These empirical studies show consistent results: relaxing the school-district system weakens therelationship between school quality and property prices, and reduces the value of property in school
districts with high-quality schools. However, since various types of school systems have been
analyzed, producing a range of different impacts, various areas and systems are necessary for policy
evaluation. Therefore, this study aims to investigate the effect of a partial relaxation of theschool-district system on the relationship between property prices and student academic
performance.
This study focuses on the school district of Matsue City, Japan, where the system was partiallyrelaxed, to explore the extent to which this change affected the relationship between school quality
and land price. Before 2008, Matsue City strictly prohibited students at general public schools
outside the city school district from enrolling in an academic high school that specialized in
preparing students for university. In 2008, however, the school-district system was eliminated forsome high-level pre-university courses. Even in general courses, approximately 5% of students could
be admitted from outside the school district. In 2016, the percentage of outside students was raised to2

20%. This institutional change may weaken the relationship between high-school quality and land
prices and may decrease the value of property in school districts with particularly good schools.
To estimate the impact of this change, I have drawn on hedonic models of land prices using the
fixed-effect approach and panel data from 2003 to 2018, as well as a regression discontinuity
approach focusing on the school-district boundary. Using land-price panel data and a controlled
fixed effect avoids the problem of unobserved confounding factors. Furthermore, following Black(1999), this study controls for unobserved neighborhood characteristics by comparing land near theboundary of the school district.
This study, in line with previous studies, shows that land prices decrease significantly when theschool-district system is relaxed. As a regression discontinuity design, focusing on the school-district
boundary, produces the same result, the main result is clearly robust. These findings also show thatpartially relaxing the school-district system will expand the disparity of the ratio of applicants
successfully applying to prestigious universities, but not of applicants to other universities, including
national universities. This implies that relaxing the school-district system may only affectparticularly good students because the change is partial.
In relation to previous research, this study makes several contributions to the literature. First, it
analyzes the impact on specific groups of students, focusing both on partial changes to theschool-district system and on the number of successful university candidates. If school-district
system is completely eliminated, many types of students are affected simultaneously. It is therefore
difficult to identify which types of students have improved their academic performance, even when
overall school performance has improved. In addition, previous studies have used variables based on
average school quality, such as test scores or deviation values; such studies are unable to pinpoint thetypes of students actually affected. To provide more precise data, the present study focuses on apartial relaxation of the school-district system, affecting a limited group of students. Thus, we areable to compare affected and unaffected students, deriving a more precise estimate of the impact ofrelaxing the school-district system. Furthermore, by using the ratio of successful candidates admitted
to each university from each high school as an index of high-school quality, the present study clearlyshows that only specific students have been affected.
Second, this study deals with various potential concerns by focusing on a city in which the school
district includes multiple public schools in a narrow area, with no high-quality private schools. In
cases where the school district is expansive or there are few modes of transportation, eliminating theschool-district system may have no effect on student behavior. When commuting is very expensive,
students may attend schools close to home, even if they are offered a choice in the matter. The
present study therefore focuses on a small school district with many different forms of transportation,
avoiding the need to address commuting costs and similar concerns. As there are no excellent private
schools, the effect of changing the school-district system is not diluted by private schools in the3

targeted area.
The structure of this paper is as follows: Section 2 reviews the previous literature and explains thecontribution made by this study. Section 3 provides institutional background and Section 4 describes
empirical strategies. Section 5 discusses the school-district system and the data. After Section 6presents the results and discussion, Section 7 concludes the paper.

2.

Literature review
Many studies have considered the effect of school quality on the property market; consistent

results have indicated that school quality has a significant positive effect on the price of housing
or land (Black and Machin, 2011; Nguyen-Hoang and Yinger, 2011). Consistent results havealso been obtained in Japan; in areas with school district systems, school quality is capitalized
by property prices (Kuroda, 2018; Yoshida, Zhang and Ushijima, 2008). Izumi (2010) has
investigated the Matsue City high-school district used in this study, showing that land prices in a
district with high-quality schools are high, even after controlling for fixed effects.
Among studies that have investigated the impact of eliminating school districts or introducing
a school-choice system on residential choice behavior or the property market, most studies havebeen theoretical, using a general equilibrium model (e.g., Nechyba, 2000; Ferreyra, 2007).

In

recent years, however, empirical research has also accumulated. Reback (2005) has explored thedeclining importance of the school district, following the adoption of a school-choice programin Minnesota. He has shown that residential property values increase in areas where students
can transfer to outside school districts, while significantly decreasing in school districts that
accept transfer students. Brunner, Cho, and Reback (2012) have also shown that introducing
school choice increases population density in previously low-quality areas and reduces
population density in previously high-quality areas. Chung (2015) has investigated the impact ofschool-choice reforms on housing prices in the Seoul metropolitan area, discovering that school
choice reduces house prices in high-quality school districts, relative to house prices in
low-quality school districts, by approximately 10-27%.
As these studies show, school choice reduces the importance of living in a high-quality school
district. Specifically, Machin and Salvanes (2015) have found that the effect of school quality on
housing prices dropped to less than half its previous level when the school-district system wasabolished in Oslo County. Schwartz, Voicu, and Horn (2014) have shown that introducing a
choice school reduces the value of schools capitalized to housing values to one-third of theprevious amount. Fack and Grenet (2010) have focused on private schools instead ofschool-choice system, using Parisian data. They have shown that increasing the standard
deviation of public-school quality increases house prices by 1.4–2.4%, although this effect4

decreases as more nearby private schools become available.
Some studies have analyzed the effect of school-choice system using Japanese data. Yoshida,
Zhang, and Ushijima (2008) have shown that introducing school choice weakens the effect ofschool quality on land prices. Nakamura (2009), Yoshida, Kogure, and Ushijima (2009) haveanalyzed the extent to which introducing public-school choice affects student sorting andacademic performance. Their study did not obtain consistent results on the effect ofschool-choice systems on academic ability; they also find that disparity between schools did not
expand. However, their results may have been weakened by the large number of private schoolsin Tokyo, a subject analyzed by previous Japanese studies.
The present study makes the following contributions to the literature. First, it analyzes theeffect of school choice on specific groups of students, using both partial changes to theschool-district system and the number of successful university candidates. If a school-district
system is completely eliminated, all the students are affected simultaneously, making it
impossible to tell which groups of students have improved in relation to the school’s overall
improved performance. To address this problem, the present study uses the partial relaxation of
the school-district system, which affects a limited number of students. In this study, only
academically advanced students had access to school choice; many average students were notaffected. Previous studies have used variables that measure overall school quality, such as testscores or deviation values; these cannot identify the types of students affected. By contrast, thisstudy uses the ratio of candidates who successfully apply to prestigious universities from eachhigh school as an index of high-school quality. It identifies different reactions in relation to thestudents’ level of academic achievement, distinguishing students who are affected andunaffected by the partial relaxation of the school-district system and enabling a precise estimateof the effect.
Second, the present study deals with various potential concerns by focusing on a city inwhich the school district includes multiple public schools in a narrow area, with no high-quality
private schools. In areas where the school district is expansive, or transportation is scarce,
eliminating the school-district system may have no effect on student behavior. When thecommuting costs are high, students may not attend schools which far from their home, even if
they are allowed to choose. This study therefore focuses on small school district with abundantmeans of transportation, eliminating the need to address commuting costs. Moreover, as thisarea has no private schools to compete with targeted public schools, they cannot weaken theschool-district system. Additionally, by focusing on public high schools, it is likewise
unnecessary to consider differences in school fees. It is therefore relatively straightforward to
estimate the impact of changing the school-district system.

5

3.

Institutional backgrounds
This section explains the school-district system in the targeted area. Matsue City is a regional city

in southwest Japan. As of 2018, the population is approximately 200,000 and the density isapproximately 390 per square meters. There are three general high schools 1 in this city, which is
divided into Kita (north), Minami (south), and Higashi (east) high-school districts. High-school
enrolment has been determined by residential area. Following the establishment of the third highschool in 1983, this school-district system was fully maintained until 2008. With the merger ofmunicipalities around 2000, the authorities discussed a plan to reorganize or abolish theschool-district system. They agreed on a compromise plan: to partially relax the school-district
system. The system was abolished altogether for "science and mathematics courses." 2 In generalcourses, it was decided that 5% of students would come from outside the school district. There were
two mathematics and science courses, one each in the Kita and Minami High Schools; each class had
40 students. Approximately 900 students enroll in general courses at ordinary high schools. Of these,
5% come from outside the school district, corresponding to approximately 45 students. In fiscal 2016,
the ceiling for enrollment from outside the school district rose to 20%, equivalent to 180 students.
Figure 1 shows the school districts of general high schools in Matsue City. It is easy to commute
within this area; if the school district is abolished, will be possible for students from any area toattend any high school.
As this paper will describe later, students at Kita High School have particularly high-level
academic ability, while those at Higashi and Minami High Schools are roughly the same. For thisreason, the Kita high-school district may attract more demand than the other two school districts. In
Matsue City, some real-estate agencies advertise "Kita high-school district" as a strong point whenselling property. In the Kita district, property may have been more expensive than in the Minami andHigashi school districts. However, due to the relaxation of the school-district system, students
wishing to attend Kita High School do not necessarily have to live in the district. It follows,
therefore, that relaxing the school-district system is likely to reduce land prices in the Kita
high-school district.
In this case, since the school district system has been partially, rather than completely, relaxed, theeffect may be relatively weak. The students affected will have exceptional academic ability and may
also have high-income families with an academic background or enthusiasm for education. Yoshida,
Kogure, and Ushijima (2009) have suggested that households with stronger educational backgrounds
In Japan, general high schools aim to prepare students to go to college after graduation; their role differsfrom that of vocational (e.g., industrial, commercial, or agricultural) high schools, which try to help
students find employment after graduating.
2
Although this course focuses on science and mathematics, the students do not necessarily go toscience-based universities after graduating. In Japanese local cities, this course is recognized as helping
students attend prestigious universities.
16and higher incomes may exhibit more selective behavior in relation to education. If so, the impact onland prices will be significant, even in the case of a partial relaxation.

●: Kita High School◆: Minami High School▲: Higashi High School
Dark gray: Kita district
Light gray: Minami district
White: Higashi district

Figure 1. High School Districts in Matsue City

4.

Empirical strategies
The effect of school quality on land prices is estimated via a hedonic regression, using the fixed

effect approach on panel data from 2003 to 2018. The interaction terms, "school district dummy" and"institution change dummy" are regressed on land prices, with various control variables included.
The basic estimate equation is as follows:

7

2

2

2

𝑎𝑎=1

𝑎𝑎=1

𝑛𝑛=1

ln 𝑃𝑃𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖 = 𝛼𝛼 + � 𝛽𝛽1𝑎𝑎 𝑆𝑆𝑆𝑆ℎ𝑜𝑜𝑜𝑜𝑜𝑜𝑎𝑎 ∙ 𝐶𝐶ℎ𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑡𝑡 + � 𝛽𝛽2𝑎𝑎 𝑆𝑆𝑆𝑆ℎ𝑜𝑜𝑜𝑜𝑜𝑜𝑎𝑎 + � 𝛽𝛽3𝑛𝑛 𝐶𝐶ℎ𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑛𝑛𝑛𝑛 + 𝛾𝛾𝛾𝛾𝑖𝑖 + 𝛿𝛿𝑌𝑌𝑡𝑡
+ 𝜇𝜇𝜇𝜇𝑡𝑡𝑡𝑡 + 𝜀𝜀𝑖𝑖𝑖𝑖𝑖𝑖

where 𝑃𝑃𝑖𝑖𝑖𝑖𝑖𝑖 is the price of land i in high-school district a in year t. The vector 𝑋𝑋𝑖𝑖 includes

characteristics of the land, such as the distance from the nearest station or the floor-area ratio, or it
indicates land fixed effects that control time-invariant unobserved characteristics. Vector 𝑌𝑌𝑡𝑡

indicates the year fixed effects; 𝑊𝑊𝑡𝑡𝑡𝑡 controls the unique trend of central urban area j; 𝑆𝑆𝑆𝑆ℎ𝑜𝑜𝑜𝑜𝑜𝑜𝑎𝑎 is adummy variable representing the school districts of three public schools in Matsue City, andindicates 1 if the land located within the school district. Three high schools in Matsue City focus on
preparing students to apply to university. One of them (Kita High School) is excellent, while theother two (Higashi High School and Minami High School) are comparable and less academic. The
present study analyzes the coefficients of "Kita" and "Minami" high school dummies, with Higashi
High School as the baseline. 𝐶𝐶ℎ𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑛𝑛𝑛𝑛 is a dummy variable representing 1 after changing theschool-district system, n = 1 indicates change in 2008, n = 2 indicates change in 2016. In 2008,

school choice was added for one high-level pre-university course (the "science and mathematics
course." In the general course, 5% of all students could be enrolled from outside the school district

after 2008. This enrolment ceiling was raised to 20% in 2016. In other words, the coefficient 𝛽𝛽1𝑎𝑎 of
the interaction term shows how the price of land in each school district changed, following changes
to the school-district system.

The present study also applies a regression discontinuity design to deal with unobservablecharacteristics that cannot be controlled by land fixed effects. The vector of observed characteristics
is replaced with boundary dummies, which indicate land that shares a boundary and uses restricted
data. The sample was limited to land that was close to a boundary. Specifically, I created a subset ofapartments located within less than 1000 m, 750 m, and 500 m from the boundary of a school district.
Land within the same boundary is assumed to have similar characteristics, such as access to public
facilities and amenities. The boundary dummy variables control for any unobserved neighborhood
characteristics shared by land on either side of the boundary. They make it possible to compare landthat has very similar geographical information. Differences in the value of land are thought to becaused by having a choice of public high school.

5.

Data
In this study, I mainly use land-price data and the high-school data. This section explains the data.
85.1. Land-price data
In this study, "published land prices" and "prefectural land-price research" are used to provide dataon land prices. These are appraisal evaluations carried out by real-estate appraisers; as such, they areobjective land assessments, independent of sellers and buyers. The same places are evaluatedannually and the data include various types of information, such as price-per-square meter, address,and distance from the nearest station. The values reported in published land prices reflect the valueof the land on January 1st each year, as estimated by two real estate appraisers. The values reported
in prefectural land-price research are estimated on July 1st each year by one real estate appraiser.
Although these are different investigations, they use the same the indicators and show almost thesame prices at the same points; for this reason, the present study combines these two data sources.
The land data derives from 2002–2018. There is a total of 160 observation points. However, thepanel data are unbalanced because they contain several defects. Summary statistics of land data areshown in Table 1, divided into all land and land currently used for residential purposes. 3 The "5%period dummy" is a dummy variable that ranges from 2008 to 2016 (from the first school-districtchange to the second). The "20% period dummy" is a dummy variable that corresponds to the time
period after the second school-district change in 2016. The dummy variable corresponding to theperiod after the first school-district change in 2008 is the "After 5% dummy.” For the landcharacteristics, this study uses the distance from the nearest station, acreage, an irregular shapedummy, road width, the building coverage ratio, and the floor-area ratio. Each piece of land has its
own use restrictions, known as "Land-Use Zones.” Dummy variables have been set for eachregulation. To address the concern that land prices may have different trends around the central
urban area and suburbs, I have set a "central urban dummy.” 4 Unobserved characteristics thatchange over time are controlled by using the interaction terms of the central urban dummy andyearly dummies. Finally, additional dummy variables indicate which school district the land islocated in. Matsue City is divided into three equal divisions by three school districts; there is no
extreme bias in the observation point. The Higashi school district provides the baseline. 5

The summary statistics involving samples limited to the school-district boundary are shown inAppendix A.
4
The definitions of “central urban” and “suburban” are based on the “Matsue City Central Area
Revitalization Basic Plan” published by Matsue City.
5
Summary statistics for residential land divided by school district are shown in Appendix B.
39

Table 1. Summary Statistics
Land price
5% period dummy
20% period dummy
After 5% dummyLand characteristics
Distance from nearest station (kilometer)
Acreage (square meter × 1/1000)
Irregular shape dummyRoad width (meter)
Building Coverage Ratio (%)
Floor-Area Ratio (‰)
Land use zones dummies
Category 1 residential zone dummy
Category 2 residential zone dummy
Category 1 low-rise residential zone dummy
Category 1 mid/high-rise residential zone dummy
Category 2 mid/high-rise residential zone dummyCommercial zone dummy
Neighborhood commercial zone dummy
Quasi-fire prevention zone dummy
Area characteristics
Central urban dummy
School district dummy
Kita dummy
Minami dummy
Higashi dummy
Number of observation points
Number of samples

Mean
60823
0.577
0.100
0.677

Full sample
S.D.
Min
44142
8200
0.494
0
0.300
0
0.468
0

Max
545000
1
1
1

Mean
53910
0.585
0.098
0.682

1.848
1.118
0.028
7.750
0.533
0.186

1.657
4.749
0.164
4.476
0.250
0.118

0
0.099
0
0.800
0
0

10.00040.0841
31.500
0.800
0.500

1.983
0.304
0.015
6.535
0.509
0.163

1.740
0.165
0.122
2.887
0.243
0.099

0
0.104
0
3.000
0
0

10.0001.710
122.000
0.800
0.500

0.230
0.065
0.094
0.060
0.075
0.123
0.048
0.125

0.421
0.247
0.292
0.237
0.263
0.329
0.214
0.331

0
0
0
0
0
0
0
0

1
1
1
1
1
1
1
1

0.289
0.074
0.119
0.079
0.080
0.055
0.021
0.056

0.453
0.263
0.323
0.271
0.272
0.228
0.143
0.229

0
0
0
0
0
0
0
0

1
1
1
1
1
1
1
1

0.227

0.419

0

1

0.176

0.381

0

1

0.351
0.316
0.334

0.477
0.465
0.472

0
0
0

1
1
1

0.315
0.361
0.324

0.465
0.481
0.468

0
0
0

1
1
1

160
1847

Residential land use
S.D.
Min
Max
29560
8200
223000
0.493
0
1
0.297
0
1
0.466
0
1

113
1384

5.2. School data
To create an academic-achievement index by high school, the present study uses data from the"Sunday Mainichi Bessatsu: Koukou no Jitsuryoku (Sunday Mainichi Extra Issue: High School
Performance)" issued by The Mainichi Newspapers Co., Ltd. This data set shows the total number ofsuccessful applicants from each high school who enroll in each university every year; 2003–2018
data have been used in this study. These data represent successful applicants, not a student who enterto school. If a student is accepted by several universities, he or she is counted more than once. Only
university-level information is listed; there is no information on major.
In This study, following Kondo (2014), I use the number of successful candidates in specificuniversity groups. This makes it possible to analyze the impact on a specific student, something thatcannot be done using a school-average index, such as the deviation value. In particular, I haveidentified high-ranking universities and the number of students accepted by each university. The
definition of this group is shown in Table 2; and the results by high school are shown in Table 3. As
mentioned above, Table 3 shows that Kita high-school students have a particularly high rate ofacceptance by high-ranking universities; Minami and Higashi have approximately the same lower
rate of acceptance.
10

Table 2. Results of the University Entrance Examination
Group

Group A
(most prestigious universities)

Group B(prestigious private universities)

Group C
(prestigious universities in the Kansai region)

11

University
The University of Tokyo
Kyoto University
Osaka University
Nagoya University
Tohoku University
Hokkaido University
Kyushu University
Hitotsubashi University
Tokyo Institute of Technology
Waseda University
Keio University
Meiji University
Aoyama Gakuin University
Rikkyo University
Chuo University
Hosei University
Gakushuin University
Sophia University
Kansai University
Kwansei Gakuin University
Doshisha University
Ritsumeikan University

Table 3. Results of the University Entrance Examination
Kita high school
Number of graduates
Number of successful candidates at group A universities
Number of successful candidates at group B universities
Number of successful candidates at group C universities
Number of successful candidates at national universities
Number of successful candidates at private universities
Total number of successful candidates
Minami high school
Number of graduates
Number of successful candidates at group A universities
Number of successful candidates at group B universities
Number of successful candidates at group C universities
Number of successful candidates at national universities
Number of successful candidates at private universities
Total number of successful candidates
Higashi high school
Number of graduates
Number of successful candidates at group A universities
Number of successful candidates at group B universities
Number of successful candidates at group C universities
Number of successful candidates at national universities
Number of successful candidates at private universities
Total number of successful candidates
Year

6.

Mean

S.D.

Min

Max

325.81346.56317.62589.438213.750386.688600.438

30.70914.9796.726
23.68545.12862.49889.803

287
25
4
59
132
288
485

398
81
31
158
298
559
842

315.125
12.7507.813
50.063176.438
351.313527.750

28.2754.576
5.581
18.45428.11633.29551.886

275
6
2
26
95
300
443

368
20
21
85
232
439
671

247.750
8.625
6.563
28.125100.250
219.813320.063

31.717
5.476
4.107
9.232
39.35833.77745.984

188
1
1
16
10
170
180

303
16
17
47
167
290
380

16

Results and Discussions

6.1. Effect on land prices
Table 4 shows the estimated results using residential land. 6 Column (1) shows the estimatedresults without the dummy variables related to school-district-system changes; it simply indicates therelationship between school quality and land prices. The coefficient of the Kita dummy is positiveand significant, which means that a school district where the school caters to students of highacademic ability has high land prices. This result is consistent with Izumi (2010). The Minami
dummy also shows a positive and significant effect, but the coefficient is small. This result is
consistent with the fact that Minami High School is slightly better than Higashi High School.
Column (2) shows the result of using the After 5% dummy, showing the effect of the first change of
6
The results of all samples are shown in Appendix C. Since full samples contain industrial sites etc. thatare not related to school quality, these results are insignificant. In this study, therefore, land used forresidential is mainly used.

12

school-district system in 2008. Column (3) shows the result of using the 5% period dummy and the
20% period dummy, showing the effect of the two system changes in 2008 and in 2016. Columns (2)and (3) suggest that land prices in the Kita high-school district are high and unaffected by the systemchange. However, the fixed effects of the observation points are not controlled; thus, these results
may be biased by unobserved characteristics. For this reason, they have also been analyzed with theland fixed effect controlled. Column (4) shows the result of considering the first system change andcontrolling the land fixed effects: the intersection term of the Kita dummy and the After 5% dummyshow a significant and negative effect, suggesting that relaxing the school-district system decreased
the price of land in Kita high-school district. Column (5) incorporates changes to the school-district
system in 2008 and 2016, controlling for land fixed effects. This result indicates that land-pricedecreased significantly when the school-district system was relaxed by 5% in 2008; similarly, land
prices decreased again when the system was further relaxed. To summarize the above, high-quality
schools have the effect of raising land prices; the effect of school quality on land prices is reduced if
the-school district system is relaxed. This result is consistent with various previous studies (e.g.,
Chung, 2015; Machin and Salvanes, 2015; Schwartz, Voicu, and Horn, 2014).
As for the impact of the main results, the results in Table 4 suggest that the effect of a high-quality
school is reduced by about 3% after a change in the school-district system. Considering that land
prices in the Kita high-school district were originally 15% higher than other areas, this suggests thatthe impact of school quality on land prices declined by 20% when the school-district system wasrelaxed. This seems reasonable; Chung (2015) similarly suggested a decrease of 10–27%.
This result also suggests that even partially relaxing, rather than completely abolishing, school
districts can have a significant impact. There are several possible reasons. First, as previously
mentioned, people with high levels of academic ability are more likely to focus on school quality
and choosing the right school. Whether the school district is completely abolished or not, if certain
groups of people take selective actions, it follows that partially eliminating the school district will
have a significant effect. Second, because Kita High School has an excellent academic reputation,
sending more than 10% of students to prestigious universities every year, it is very expensive to livein Kita district; land prices in Kita high-school district may have increased excessively during theschool-district period. There were no other noteworthy amenities, such as a main station orcommercial area in the Kita high-school district; the main attraction was being able to go to Kita
High School. For this reason, relaxing the school-district system may have had a very large impact.

13

Table4. Baseline Results
Kita dummy
Minami dummy
After 5% (5% period) dummy(1).1482***(.0209).0540*(.0251)(2).1408***(.0325).0942*(.0422)-.0242(.0741).0121(.0385)-.0586(.0500)(3).1407***(.0325).0941*(.0423)-.0215(.0743).0042(.0394)-.0585(.0511)-.0295(.1067).0585(.0696)-.0586(.0848).0638***(.0080)-.2516***(.0663)-.1008(.0768).0364***(.0040).2955**(.1104)-.4683(.4587).8291***(.0480)1.196***(.0518).8126***(.0333).6930***(.0565)1.089***(.0522)1.100***(.1380).8283***(.0511)-.0945**(.0436).4985***(.0818).0634***(.0080)-.2531***(.0666)-.1055(.0772).0362***(.0040).2957**(.1109)-.4617(.4615).8293***(.0481)
1.192***(.0520).8119***(.0334).6931***(.0566)1.084***(.0525)1.099***(.1385).8291***(.0511)-.0952*(.0435).5147***(.0821).0633***(.0080)-.2529***(.0668)-.1055(.0771).0363***(.0040).2953**(.1110)-.4574(.4621).8291***(.0481)
1.195***(.0521).8123***(.0334).6929***(.0566)1.084***(.0526)
1.098***(.1387).8287***(.0511)-.0952*(.0436).5148***(.0821)

YES
YES

YES
YES

YES
YES

1336
0.76851333
0.76841330
0.7680Kita dummy × After 5% (5% period) dummy
Minami dummy × After 5% (5% period) dummy
20% period dummy
Kita dummy × 20% period dummy
Minami dummy × 20% period dummyLand characteristics
Distance from nearest station (kilometer)
Acreage (square meter × 1/1000)
Irregular shape dummyRoad width (meter)
Building Coverage Ratio (% × 100)
Floor-Area Ratio (% × 1000)
Category 1 residential dummy
Category 2 residential dummy
Category 1 low-rise dummy
Category 1 mid/high-rise dummy
Category 2 mid/high-rise dummyCommercial dummy
Neighborhood commercial dummy
Quasi-fire prevention dummyCentral urban dummyControl variables
Year fixed effects
Central urban trends
Land fixed effects
N
Adjusted R2※*, ** and *** indicate statistical significance at 5%, 1%, 0.1%, respectively.
※Standard errors are adjusted for clustering at the area level.

14

(4)

(5)

.0044
(.0089)-.0302***(.0089)-.0020(.0080).0046(.0089)-.0295***(.0089)-.0021(.0079).0135(.0165)-.0352*(.0155)-.0012(.0145)

YES
YES
YES
1238
0.9948

YES
YES
YES
1235
0.9948

Table 5 shows the estimated results when the sample is restricted to the boundary of the school
district and the individual fixed effect is controlled. 7 Columns (1) and (2) show the results estimatedusing samples within 1000 meters of the school-district boundary. Like the baseline results, land
prices near Kita High School decreased by about 3.5%, due to the relaxation of the school-district
system. Although the effect of the second change was not significant, it had a negative effect on land
prices in Kita high-school district. Columns (3) and (4) show the estimated results using samples
within 750 meters of the school-district boundary. These also show that land prices in Kita
high-school district are decreasing, due to the relaxation of the school-district system; the coefficientis about 5.5%. In Column (4), the coefficient of the interaction terms, Kita dummy and 20% period
dummy, is significantly negative; the impact is larger than after the first change of the school-district
system. This suggests that the 5% relaxation of the school district decreased land prices in the Kita
high-school district, and land prices decreased additionally when the system was further relaxed.
Columns (5) and (6) show the estimated results using samples within 500 meters of theschool-district boundary. This result is consistent with the other results, suggesting that land prices inKita high-school district decreased by about 5% when the school-district system was first relaxed;
they decreased by an additional 2% after the second change. In summary, relaxing the school-district
system significantly reduced land prices in Kita high-school district, even after controlling forunobserved characteristics by focusing on the school-district boundaries. This indicates that thevalue of property around Kita High School has decreased, due to the relaxation of the school-district
system. The results also suggest that even partial relaxation will have a significant impact on land
prices.

7
The results estimated using the school-district boundary dummy, without controlling for the land fixed
effect, are shown in Appendix D. The intersection terms between the Kita dummy and the system change
dummy, the main variables, are not significant but show a consistent negative effect. This result is thesame as the fixed effect result.

15

Table 5. Results Focusing on School-District Boundaries
After 5% (5% period) dummy
Kita dummy × After 5% (5% period) dummy
Minami dummy × After 5% (5% period) dummy
20% period dummy
Kita dummy × 20% period dummy
Minami dummy × 20% period dummyControl variables
Year fixed effects
Central urban trends
Land fixed effects
N
Adjusted R21000 meters
(1)
(2)
.0080
.0084
(.0099)(.0099)-.0352**-.0357**(.0118)(.0118).0057.0056(.0092)(.0092).0250(.0189)-.0399(.0207).0106(.0164)750 meters
(3)
(4)
.0166
.0165
(.0129)(.0127)-.0575***-.0560***(.0144)(.0144).0089.0085(.0118)(.0118).0381(.0256)-.0698*(.0271).0148(.0211)500 meters
(5)
(6)
.0048
.0048
(.0114)(.0114)-.0520**-.0493**(.0186)(.0187)-.0073-.0065(.0132)(.0134).0384(.0262)-.0735*(.0344).0106(.0221)

YES
YES
YES
522
0.9844

YES
YES
YES
389
0.9840

YES
YES
YES
270
0.9798

YES
YES
YES
519
0.9843

YES
YES
YES
386
0.9840

YES
YES
YES
267
0.9797※*, ** and *** indicate statistical significance at 5%, 1%, 0.1%, respectively.
※Standard errors are adjusted for clustering at the area level.

6.2. Effect on enrollment rate
The results in the previous section show that relaxing the school-district system significantly
reduces land prices. In this section, I analyze the extent to which high-school students’ results on
their university examinations were affected by a partial relaxation of the school-district system.
Since the first change took place in 2008, the students affected by that change took their
examinations in 2011. As no data are yet available on the students who enrolled after the second
change in 2016, this study analyzes only the impact of the first change.
Figure 2 shows the passing rate for each of the three schools, for students applying to alluniversities. Figure 2 shows that Kita and Minami High Schools experienced no significant changes
before and after the school-district system relaxation. Although the Higashi High School passing rate
gradually improved, this trend seems unrelated to the change of school-district system.
Figure 3 shows the passing rate of national universities. 8 At Kita and Minami High Schools, there
was no major difference before and after the system change; in Higashi High School, the passing
rate declined after the system change. This suggests that students hoping to go to national
universities may have clustered at better high schools by relaxation of the school district system.
Figure 4 shows the passing rate for Group B and C universities. According to this graph, theIn 2006, Higashi High School had an extremely small number of successful candidates, compared toother years (whereas the average number of successful candidates was about 106, in 2006, there were
only 10). This figure has been excluded from the analysis as an outlier, given the possibility of
typographical error.

816

passing rates for Minami and Higashi High School, in relation to prestigious private universities,
decreased after the school-district system was relaxed. This result suggests that excellent students
aspiring to attend prestigious universities may have gathered at Kita High School.
Figure 5 shows the passing rate for Group A (the most prestigious universities). Before relaxing
the school-district system, Kita High School had a stable passing rate of about 15%; Minami andHigashi High Schools were stable at about 5%. However, the passing rate of Minami and Higashi
High School fell dramatically after the school-district system was relaxed. At Higashi High School,
the passing rate fell to 1% or less. This suggests that excellent students who lived in Higashi district
may have chosen Kita High School or Minami High School. However, Figure 5 shows an overall
downward trend, suggesting that the school-district change may have negatively affected the wholearea. To analyze the effect of the school-district change on student performance, these results need tobe compared with those of other cities, that did not undergo a system change.
In summary, partially relaxing the school district does not affect student achievement in all
university entrance examinations; however it may have a strong effect on the higher academic tiers.
It should be noted that some students in Japanese local cities choose to study medicine at local
universities rather more difficult, high-status universities. Since the data used in this study do notinclude information on faculties, this is a limitation of the present study.

17

3
2.5
2
1.5
1
0.5
0

2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018
Kita

Minami

HigashiFigure 2. Ratio of Successful Applicants to All Universities

0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0

2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018
Kita

Minami

HigashiFigure 3. Ratio of Successful Applicants to National Universities

18

0.5
0.45
0.4
0.35
0.3
0.25
0.2
0.15
0.1
0.05
0

2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018
Kita

Minami

HigashiFigure 4. Ratio of Successful Applicants to Group B and C Universities

0.25

0.2

0.15

0.1

0.05

0

2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018
Kita

Minami

HigashiFigure 5. Ratio of Successful Applicants to Group A Universities

19

7.

Conclusion
This study explores the impact of the school-district system on the relationship between school

quality and land prices. The data were drawn from Matsue City, Japan, where the school-district
system of public high schools was partly relaxed. The findings show that relaxing the school-district
system significantly reduces land prices in high-level school districts. Using a regression
discontinuity design to focus on school-district boundaries produces the same result. This suggests
that, as the school-district system is relaxed, families no longer find it necessary to live in a specificschool district, weakening the relationship between school quality and land prices.
A partial relaxation of the school-district system also increases the disparity of the ratio of
successful applicants to prestigious universities; however, there is no large change in the case of
applicants to national or other universities. This implies that relaxing the school-district system may
have an impact only on particularly academic students if the change is partial. However, it isnecessary to analyze areas without a school-district system to discover whether this effect is actuallycaused by the change of school-district systems.

20References
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mochiita kenshou (School quality and land price: Analysis using land price data of AdachiWard). Quarterly Journal of Housing and Land Economics, 68, 10-18.

22Appendix A. Summary Statistics of Samples at the School-District BoundaryLand price
5% period dummy
20% period dummy
After 5% dummyLand characteristics
Distance from nearest station (kilometer)
Acreage (square meter × 1/1000)
Irregular shape dummyRoad width (meter)
Building Coverage Ratio (%)
Floor-Area Ratio (‰)
Land use zones dummies
Category 1 residential zone dummy
Category 2 residential zone dummy
Category 1 low-rise residential zone dummy
Category 1 mid/high-rise residential zone dummy
Category 2 mid/high-rise residential zone dummyCommercial zone dummy
Neighborhood commercial zone dummy
Quasi-fire prevention zone dummy
Area characteristics
Central urban dummy
School district dummy
Kita dummy
Minami dummy
Higashi dummy
Number of observation points
Number of samples

Mean
75280
0.558
0.082
0.640

1000m
S.D.
Min
25217
11300
0.497
0
0.274
0
0.480
0

Max
223000
1
1
1

Mean
77959
0.560
0.081
0.642

750m
S.D.
Min
26486
11300
0.497
0
0.274
0
0.480
0

Max
223000
1
1
1

Mean
79904
0.560
0.083
0.642

500m
S.D.
Min
26548
11300
0.497
0
0.276
0
0.480
0

Max
223000
1
1
1

1.994
0.300

1.207
0.187

0.100
0.111

6.900
1.710

1.879
0.302

1.054
0.204

0.100
0.111

6.900
1.710

1.859
0.294

0.892
0.239

0.650
0.111

6.900
1.710

6.926
0.610
0.204

3.479
0.107
0.079

3.400
0
0.060

22.000
0.800
0.500

7.300
0.613
0.215

3.764
0.109
0.084

3.500
0
0.080

22.000
0.800
0.500

7.409
0.611
0.220

4.226
0.117
0.082

3.500
0
0.080

22.000
0.800
0.500

0.357
0.145
0.127
0.107
0.107
0.080
0.048
0.128

0.479
0.352
0.333
0.309
0.309
0.272
0.215
0.335

0
0
0
0
0
0
0
0

1
1
1
1
1
1
1
1

0.365
0.156
0.070
0.106
0.123
0.106
0.035
0.141

0.482
0.363
0.256
0.308
0.329
0.308
0.184
0.348

0
0
0
0
0
0
0
0

1
1
1
1
1
1
1
1

0.459
0.168
0.049
0.049
0.122
0.098
0.049
0.147

0.499
0.375
0.216
0.216
0.328
0.298
0.216
0.354

0
0
0
0
0
0
0
0

1
1
1
1
1
1
1
1

0.407

0.492

0

1

0.437

0.497

0

1

0.462

0.499

0

1

0.367
0.233
0.400

0.482
0.423
0.490

0
0
0

1
1
1

0.332
0.264
0.404

0.471
0.441
0.491

0
0
0

1
1
1

0.315
0.269
0.416

0.465
0.444
0.494

0
0
0

1
1
1

NA

45
600

23

NA

33
455

NA

24
327

Appendix B. Summary Statistics for Each School District
Land price
5% period dummy
20% period dummy
After 5% dummyLand characteristics
Distance from nearest station (kilometer)
Acreage (square meter × 1/1000)
Irregular shape dummyRoad width (meter)
Building Coverage Ratio (%)
Floor-Area Ratio (‰)
Land use zones dummies
Category 1 residential zone dummy
Category 2 residential zone dummy
Category 1 low-rise residential zone dummy
Category 1 mid/high-rise residential zone dummy
Category 2 mid/high-rise residential zone dummyCommercial zone dummy
Neighborhood commercial zone dummy
Quasi-fire prevention zone dummy
Area characteristics
Central urban dummy
Number of observation points
Number of samples

Mean
66332
0.562
0.094
0.656

Kita school district
S.D.
Min
32172
11200
0.497
0
0.292
0
0.476
0

Max
223000
1
1
1

2.024
0.310
0.032
7.085
0.536
0.180

1.681
0.137
0.176
2.911
0.233
0.115

0.000
0.111
0
3.400
0
0

5.700
0.856
1
17.000
0.800
0.500

0.128
0.071
0.165
0.147
0.110
0.110
0.030
0.140

0.335
0.257
0.372
0.354
0.313
0.313
0.170
0.347

0
0
0
0
0
0
0
0

1
1
1
1
1
1
1
1

0.004

0.485

0

1

33
436

Minami school district
Mean
S.D.
Min
Max
46399
21344
11300 120000
0.614
0.487
0
1
0.102
0.303
0
1
0.716
0.451
0
1

Higashi school district
Mean
S.D.
Min
Max
50203
30989
8200
150000
0.574
0.495
0
1
0.096
0.295
0
1
0.670
0.471
0
1

1.854
0.287

1.513
0.128

5.927
0.550
0.178

2.086
0.318
0.016
6.678
0.437
0.131

2.009
0.217
0.124
3.368
0.278
0.089

0.000
0.150
0
3.000
0
0

10.0001.710
122.000
0.800
0.400

0.418
0.032
0.104
0.060
0.094
0.056

0.301
0.125
0.089
0.036
0.036

0.459
0.331
0.285
0.186
0.186

0
0
0
0
0

1
1
1
1
1

0.036
0.036

0.186
0.186

0
0

1
1

0.179

0.383

0

1

0.000
0.104

7.200
0.712

2.215
0.200
0.086

3.500
0
0

15.100
0.800
0.400

0.494
0.176
0.306
0.238
0.292
0.230

0
0
0
0
0
0

1
1
1
1
1
1

NA

NA
NA
NA
43
501

24

NA

37
448

Appendix C. Full sample results
Kita dummy
Minami dummy
After 5% (5% period) dummy(1).0702**(.0227)-.0369(.0290)(2).0553(.0379)-.0200(.0501)-.1302(.0763).0251(.0443)-.0237(.0576)(3).0554(.0380)-.0194(.0502)-.1287(.0766).0178(.0453)-.0233(.0588)-.2496*(.1116).0753(.0775)-.0347(.0912)

YES
YES
YES

YES
YES
YES

YES
YES
YES

1784
0.7363

1781
0.73651778
0.7365

Kita dummy × After 5% (5% period) dummy
Minami dummy × After 5% (5% period) dummy
20% period dummy
Kita dummy × 20% period dummy
Minami dummy × 20% period dummyControl variables
Year fixed effects
Central urban trends
Land characteristics
Area fixed effects
N
Adjusted R2※*, ** and *** indicate statistical significance at 5%, 1%, 0.1%, respectively.
※Standard errors are adjusted for clustering at the area level.

25

(4)

(5)

.0017
(.0099)-.0171(.0107).0152(.0088).0029(.0099)-.0179(.0106).0142(.0088).0086(.0175)-.0119(.0172).0234(.0148)

YES
YES

YES
YES

YES
1654
0.9936

YES
1651
0.9936

Appendix D. Results using School-District Boundaries
Kita dummy
Minami dummy
After 5% (5% period) dummy
Kita dummy × After 5% (5% period) dummy
Minami dummy × After 5% (5% period) dummy
20% period dummy
Kita dummy × 20% period dummy
Minami dummy × 20% period dummyControl variables
Year fixed effects
Central urban trends
Boundary fixed effects
Land characteristics
N
Adjusted R21000 meters
(1)
(2)
.0001
.0004
(.0206)(.0207)-.0615-.0610(.0386)(.0387).0885*.0887*(.0397)(.0400)-.0397-.0401(.0241)(.0245)-.0314-.0237(.0290)(.0296).1621**(.0596)-.0413(.0429)-.0726(.0603)750 meters(3)(4).0640**.0641**(.0209)(.0210)-.4276***-.4268***(.0351)(.0355).0603*.0603*(.0276)(.0278)-.0513*-.0514*(.0200)(.0201)-.0106-.0074(.0241)(.0246).0993(.0515)-.0555(.0421)-.0294(.0498)500 meters(5)(6)-.0524*-.0518*(.0259)(.0260)-.3356***-.3346***(.0315)(.0321).0541.0546(.0347)(.0350)-.0363-.0365(.0220)(.0222)-.0680*-.0662*(.0296)(.0301).1160*(.0541)-.0328(.0487)-.0918(.0476)

YES
YES
YES
YES
548
0.8926

YES
YES
YES
YES
403
0.9456

YES
YES
YES
YES
275
0.9493

YES
YES
YES
YES
545
0.8926※*, ** and *** indicate statistical significance at 5%, 1%, 0.1%, respectively.
※Standard errors are adjusted for clustering at the area level.

26

YES
YES
YES
YES
400
0.9454

YES
YES
YES
YES
272
0.9492